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TUBERCULOSIS CONTROL:

FINANCIAL EVALUATION FOR THE 2001 – 2005 PERIOD

IN LOW AND LOWER MIDDLE-INCOME COUNTRIES

ECONOMIC ANNEX TO THE

GLOBAL PLAN TO STOP TB

OLIVIER APPAIX

BOSTON, JUNE 2003

Partners in Health

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TABLE OF CONTENTS

LIST OF TABLES AND BOXES.......................................................................................................................4

LIST OF ABBREVIATIONS AND DEFINITIONS ........................................................................................6

ACKNOWLEDGEMENTS.................................................................................................................................7

FOREWORD........................................................................................................................................................9 1. OBJECTIVES AND FRAMEWORK................................................................................................................10

1.1. Covering the Major Aspects of TB Control .............................................................................10 1.1.1. Implementation and expansion of DOTS .....................................................................................10 1.1.2. Prophylaxis .................................................................................................................................11 1.1.3. Treatment of multi-drug resistant tuberculosis (MDR-TB) ...........................................................11 1.1.4. Research and development efforts ...............................................................................................11 1.1.5. Use of health-care services ..........................................................................................................11 1.1.6. Other aspects of TB control in the 2001 – 2005 period ...............................................................11

1.2. Limitations to the Evaluation Process ......................................................................................11 1.2.1. Uncertainty of data and results...................................................................................................11 1.2.2. Complexities of estimating future TB caseloads............................................................................12 1.2.3. Chemoprophylaxis for TB-HIV co-infected people. .....................................................................12 1.2.4. Treatment of MDR-TB is gaining momentum, but the full economic panorama of DOTS-Plus

intervention has just started to be investigated. ..........................................................................12 1.2.5. Health-care services are usually not fully dedicated to one specific ailment such as TB.............12 1.2.6. Variation in local conditions and use of resources makes several interpretations of input data

and results possible.....................................................................................................................13 1.2.7. Research and development activities are not easy to evaluate precisely. ....................................13 1.2.8. Financial gaps ............................................................................................................................13

2. PRINCIPLES AND METHODOLOGY .............................................................................................................14 2.1. Economic and Financial Aspects of TB Control Covered by the Study.................................14 2.2. Organization of the Financial Evaluation..................................................................................16

2.2.1. Costs classification for financial evaluation ...............................................................................16 2.2.2. Costing methodology..................................................................................................................20 2.2.3. Costing model .............................................................................................................................21

2.3. Specific Assumptions Built Into the Model .............................................................................24 2.3.1. Epidemiology ..............................................................................................................................24 2.3.2. DOTS coverage and case detection rates....................................................................................26 2.3.3. Treatment success rates...............................................................................................................27 2.3.4. Evaluation of the number of cases detected and treated .............................................................30 2.3.5. Evaluating the costs of drugs and laboratory supplies ...............................................................36 2.3.6. Evaluating other program and intervention costs .......................................................................39 2.3.7. Evaluating the cost of health-care services .................................................................................40 2.3.8. Extrapolations.............................................................................................................................41

2.4. Cost Annualization......................................................................................................................46 2.5. Price Contingencies ...................................................................................................................46 2.6. Ranges and Approximations .......................................................................................................46 2.7. Financial Gaps .............................................................................................................................47

3. DATA ......................................................................................................................................................49 4. RESULTS ..................................................................................................................................................52

4.1. Epidemiological Projections .....................................................................................................53 4.2. Total Financial Requirements for TB Control during the 2001 – 2005 Period in Low- and

Lower Middle-Income Countries..............................................................................................63 4.3. Costs to National TB Control Programs (First-Line Treatments and Retreatments)...........71 4.4. Costs of the Use of Health-Care Services................................................................................80 4.5. Costs Associated with TB-HIV Chemoprophylaxis.................................................................83 4.6. Costs Associated with the Treatment of MDR-TB ..................................................................87 4.7. Financial Requirements for Research and Development Efforts—Chapter 5 of the Global

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Plan to Stop TB...........................................................................................................................91 5. FUTURE DEVELOPMENTS OF THIS WORK..................................................................................................93

APPENDIX A: SOCIOECONOMIC PANORAMA OF TUBERCULOSIS ..............................................................95 APPENDIX B: COMPOSITION OF REGIONAL CLUSTERS (LOW AND MIDDLE-INCOME COUNTRIES)...........99 APPENDIX C: DATA USED IN THE MODEL, BY TYPE, SOURCE, AND COUNTRY...................................... 101

Appendix C1: Data Sources ....................................................................................................... 101 Appendix C2: Cost Database for First-Line Treatments and Programs ............................ 104 Appendix C3: Inputs Used in the Evaluation of Costs Associated with the Use of

Health-Care Services, MDR-TB Treatment, and TB-HIV Co-Infection Prophylactic Intervention.................................................................................. 109

Appendix C4: Economic, Demographic, and Epidemiological Data ................................... 114 Appendix C5. Epidemiological Data Associated with MDR-TB and TB-HIV Co-Infection119

APPENDIX D: RESULTS (OUTPUT OF THE FINANCIAL MODEL)............................................................. 124 Appendix D1. Results for NTP Budgets and Health-Care Services...................................... 124 Appendix D2. Results for TB-HIV Co-Infection Prophylaxis and MDR-TB Treatment...... 130

APPENDIX E: BUDGETS OF WHO’S TB WORKING GROUPS AND TB PARTNERSHIP ............................. 135 ENDNOTES ............................................................................................................................................ 136

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LIST OF TABLES, FIGURES, AND BOXES Table 1. Classification of Costs and Cost Drivers...................................................................................................... 17 Table 2. Situation of TB Control by Regional Cluster ............................................................................................... 28 Table 3. Situation of TB control in the 22 High-Burden Countries .......................................................................... 29 Table 4. Status of Case Detection Rates in Low and Lower Middle-Income Countries (LLMIC), 1999 ............. 31 Table 5. Scenario for the Expansion of DOTS-Plus or DOTS-Plus-Equivalent Programs ..................................... 33 Table 6. Scenario for TB-HIV Co-infection Case Detection for the 2001 – 2005 period..................................... 36 Table 7. Annualization of the Scenario for TB-HIV Co-infection Case Detection................................................. 36 Table 8. Regional Clusters............................................................................................................................................ 43 Table 9a Worldwide Caseload Estimates (2001–2005) by Cluster: All TB Cases and First-Line Strategies

(Including DOTS) and MDR-TB Treatments ................................................................................................ 58 Table 9b. Worldwide Caseload Estimates (2001–2005) by Cluster: TB-HIV Co-Infection.................................... 59 Table 10. Transformation of the TB-Control Panorama Between 2000 and 2005 in Low and Lower Middle-

Income Countries.......................................................................................................................................................... 60

Table 11 Transformation of the TB-Control Panorama Between 2000 and 2005 in Low and Lower Middle-Income

Countries (LLMIC) ......................................................................................................................................... 61 Table 12. TB-control Financial Panorama during the 2001–2005 Period in Low and Lower Middle-Income

Countries.......................................................................................................................................................... 66 Table 13. Annualization of Projected TB-Control Costs during the 2001–2005 Period ......................................... 67 Table 14. Summary of Costs for 114 Countries by TB-Control Activity, as Shown in the GPSTB......................... 68 Table 15. Global Results: Cost Estimates by Regional Cluster for National Programs and Interventions of TB-

Control, 2001–2005....................................................................................................................................... 70 Table 16. Total Financial Requirements for DOTS Implementation and Expansion, and for the Running of

National Tuberculosis Control Programs (2001–2005), in Low and Middle-Income Countries: General Results ............................................................................................................................................... 75

Table 17. Annualization of Financial Requirements for DOTS Implementation and Expansion, and for Running of NTPs (2001–2005) in Low and Lower Middle-Income Countries ...................................................... 75 Table 18. Unit Costs of First-Line Treatments, Low and Lower Middle-Income Countries, 2001–2005 ............. 78 Table 19. Total Costs to National TB Control Programs, Low and Lower Middle-Income Countries, 2001–2005

79 Table 20. Costs to Health-Care Services of TB Case Management for the 114 Countries Included in the GPSTB (by Regional Cluster), 2001–2005 ............................................................................................................... 82 Table 21. Scenario of Detection and Treatment for Cases of TB-HIV Co-Infection in the 2001–2005 Period in

Low and Lower Middle-Income Countries................................................................................................... 84 Table 22 Intervention Costs for Chemoprophylaxis of TB-HIV Co-Infected Patients in the 2001–2005 Period in

Low and Lower Middle-Income Countries................................................................................................... 86 Table 23. Total Costs for the Control of TB-HIV Co-Infection in the 2001–2005 Period in Low and Lower

Middle- Income Countries ............................................................................................................................. 86 Table 24. Scenario of Detection and Treatment Rates for MDR-TB Cases in the 2001–2005 Period in Low and

Lower Middle-Income Countries .................................................................................................................. 88 Table 25. Intervention Costs for MDR-TB Control in the 2001–2005 Period in Low and Lower Middle-Income

Countries.......................................................................................................................................................... 89 Table 26. Total Costs for MDR-TB Control in the 2001–2005 Period in Low and Middle-Income Countries .... 90 Table 27. Annual Estimates of MDR-TB Costs during the 2001–2005 Period for 114 Low and Lower Middle

Income Countries............................................................................................................................................ 91 Table 28. Financial Panorama of Research and Development for TB control (2001–2005) ................................... 92 Figure 1. Diagram of the Costing Model for TB-Control Costs over the 2001–2005 period .................................22 Figure 2. TB Incidence and Case Detection—All Clusters (2001–2005, Cumulative)........................................... 57 Figure 3. TB-Control Costs per Main Area of Intervention (GPSTB numbers)........................................................ 69 Figure 4. Financial Shortfall Currently Identified (GPSTB numbers)....................................................................... 69

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Box 1. The “Inventory Effect”............................................................................................................................... 38 Box 2. Non-Traded Goods and Services................................................................................................................ 45 Box 3: Links with Latest Estimates from the Global DOTS Expansion Plan (GDEP).......................................... 74 Box 4: Procurement Strategies............................................................................................................................... 76

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LIST OF ABBREVIATIONS AND DEFINITIONS

AIDS Acquired Immunodeficiency Syndrome BCG Bacille Calmette-Guérin (TB vaccine given to very young children) CDR Case Detection Rate DOTS Directly Observed Treatment—Short course (program and protocol recommended by

WHO for the management and treatment of Mycobacterium tuberculosis infections) DOTS-Plus DOTS protocol specific to multidrug-resistant TB strains EPI Expanded Program on Immunization FSTI Fonds de Solidarité Thérapeutique Internationale GDEP Global DOTS Expansion Plan GDF Global Drug Fund GNP Gross National Product (valuation of an economy’s size—the added value produced by an

economy) GPSTB Global Plan to Stop TB HBC High Burden Countries (22 countries whose combined TB case load makes up 80% of the

total number of new TB cases annually, worldwide) HCS Health Care Services HIV Human Immunodeficiency Virus HIV+ Infected with the HIV virus (“HIV-positive”) IEC Information, Education, and Communication. An NTP tool to enhance TB awareness

among the general public INH Isoniazid (one of the most common anti-bacterial drugs used to fight MTB) LLMIC Low and Lower Middle-Income Countries (all 114 countries covered by the GPSTB,

including Botswana—which, technically, is a high middle-income country) MDR-TB Multidrug-Resistant Tuberculosis (defined as resistance to at least rifampicin and isoniazid) MSF Médecins Sans Frontières (Doctors without Borders) MTB Mycobacterium tuberculosis (the bacterium responsible for TB infection) NGO Nongovernmental Organization NTP National Tuberculosis Control Program PPP Purchase Power Parity (one of two main methods to evaluate and compare GNPs. The use

of PPP attenuates the distortion introduced by exchange rates, which are subject to fluctuations and a variety of policy-driven manipulations.)

ProTEST An integrated program to tackle both HIV and TB. ProTEST promotes HIV and TB testing, counseling, prevention, and treatment.

R&D Research and Development SSA Sub-Saharan Africa (here SSA excludes the middle income countries from Austral Africa,

which are clustered in another, specific cluster) SS+ “Smear-positive” tuberculosis (the infectious form of tuberculosis) SS- “Smear-negative” tuberculosis (not infectious) STB The World Health Organization’s Stop TB Initiative TB Tuberculosis UNAIDS United Nations Program on AIDS WG Working Group (refers to international working groups set up by WHO for each of the

main areas of TB control) WHO World Health Organization

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Acknowledgements This work is part of the Global Plan to Stop TB (GPSTB), officially launched in October 2001. It would not have been possible without the technical support of the World Health Organization’s (WHO’s) Stop TB initiative (STB) and the financial, technical, and moral support of Partners In Health and the Open Society Institute, which financed the project. Though only one author is listed on the cover of this document, no one should believe that the document itself is the work of only one person. This is the result of a broad collaborative network at the hub of which I found myself, as the economist in charge of the evaluations presented hereafter. It is therefore natural to pay a grateful tribute to all those who helped me in a variety of ways to navigate through sometimes uncharted territories and to achieve this complex but exciting mission. I am particularly grateful to Dr. Jim Yong Kim (Partners In Health and Harvard Medical School’s Department of Social Medicine), who granted me his full confidence and essential help as well as technical advice throughout this journey. Drs. Ian Smith and Katherine Floyd of WHO’s Stop TB initiative were instrumental in providing documentation, recommendations, and technical advice. I worked closely with Katherine Floyd, in particular, in order to coordinate our parallel efforts regarding the evaluation of DOTS expansion activities. Drs. Maarten Bosman, Leopold Blanc, Salah-Eddine Ottmani, Marcos Espinal, Malgosia Grzemska, and Christopher Dye of WHO’s headquarters in Geneva were very useful sources of data and documentation. Dr. Paul Nunn of WHO also provided much needed help in the area of Research and Development, and most of what is said here about this essential aspect of TB control derives from what he communicated to me and from his own evaluations (see Chapter 5 of the GPSTB). Estimates for this very important aspect of TB control were reviewed with Drs. Giorgio Roscigno (WHO’s Working Group on TB Drug Development), Mark Perkins (WHO’s Working Group on New TB Diagnostics) and Anne Ginsburg (WHO’s Working Group on TB Vaccine Development). The following evaluations would not have been possible without the tremendous amount of work done by the National TB Control Programs (NTPs), especially those of the “High-Burden Countries,” whose national coordinators I had a chance to meet at a conference in Cairo in November 2000. Direct interviews as well as continuous exchanges with a number of these national coordinators were instrumental in the collection of data and the general understanding of TB control at a national level. They include Drs. Ruffino Netto (Brazil), Henriette Wembanyama (Democratic Republic of Congo), Jaime Lagahid (the Philippines), Pedro Guillermo Suárez (Peru), Alexei Karpeev and Sergei Borisov (Russian Federation), G.R. Khatri (India), Belayet Hossain (Bangladesh), Gul Zareen (Afghanistan), Touch Sareth, Sivanna Tieng, and Bak Khim Team (Cambodia), Jan van den Hombergh (assessor to the Ethiopian program), and Holger Sawert (from WHO, but with Thailand’s Ministry of Health). Cuba’s experience four decades ago was also used to detail this report, thanks to the help of Dr. Edilberto González Ochoa. Thanks should also be extended to national coordinators of the People’s Republic of China, Indonesia, Vietnam, Tanzania, Kenya, Uganda, Zimbabwe, Myanmar, and Nigeria, who provided much needed data and information. I am extremely thankful also to Drs. Diana Weil and Christy Hanson of the World Bank/WHO joint office; they provided me with data, documents, and very useful suggestions and information. Dr. José Ramón Cruz of the Pan American Health Organization (PAHO) was a very kind and useful source of information and direct insights into TB control; he was always available to help and share his experience in Nicaragua and his knowledge of Latin American NTPs in general. Dr. Daniel Chin from the WHO’s office in Beijing, Dr. Dong Il Ahn of WHO’s office in Manila, and Dr. Thomas Frieden in New Delhi also kindly answered my many questions regarding TB control in their areas of competence.

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I must also thank very much Paul Zintl for the extremely useful discussions we had on the shaping of this report. Paul Zintl is the main redactor of the executive summary of this report that is included in the GPSTB’s annex. Warm thanks also go to Rajeesh Gupta and Kedar Mate for their congenial and efficient support and help in general. Last but not least, is the mention of the constant, constructive, and thoughtful support of Dr. Arachu Castro.

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Foreword This document is the economic annex to the Global Plan to Stop Tuberculosis (GPSTB) launched in October 2001, and published in its final version in June 2002.* The main objective of this document is to define the challenges, objectives, and financial needs of TB control in the 2001 – 2005 period. It details methods and assumptions used to evaluate the financial cost of TB control in low and lower middle-income countries for the 2001 – 2005 period, as reported in the GPSTB. It also details, by activity, region, and year, anticipated costs for all the main aspects of TB control encompassed by the GPSTB. Costs estimates associated with the activities of WHO’s working groups and the Stop TB Partnership Secretariat have been included in this study. The total cost estimate here therefore corresponds exactly to that of the GPSTB. However, these partnership costs (U.S. $340 million over five years) have not been analyzed or evaluated here. This economic annex also evaluates the gap between total estimated costs and currently known financial commitments for the five-year period. This analysis provides TB-control cost estimates for all low and lower middle-income countries (defined as countries having a GNP/capita lower than U.S. $3,000 as of 1999); for Botswana (GNP/capita of U.S. $3,240); and for all 22 High-Burden Countries (HBCs), regardless of their GNP level. In some cases, cost estimates are given in ranges (in the cost of controlling multidrug-resistant tuberculosis, or MDR-TB, for example). Explanations of the ranges and how they were calculated are provided here; however, for presentation in the GPSTB these ranges were averaged to a midpoint, in order to simplify the presentation. On a technical note, footnotes here are marked by Roman numerals (I, II, III, IV, etc.) and appear at the bottom of the respective page (for those printing this document), while referenced bibliography or citations are listed at the end of the document in the Endnotes section, and are marked by Arabic numerals (1, 2, 3, etc.). The intent of this study is to inform and support decision-makers, economists, researchers, and students of TB control, and to provide a foundation for further study and analysis. The study draws upon knowledge of TB-control strategy and progress through September 2001, yet in some cases includes data available as late as March 2002. It draws on a great variety of documents (some unpublished), but does not present any original data itself. Where data are unavailable or scant, extrapolations were used (as explained hereafter), to produce a reasonably complete and accurate estimate of the cost of TB control based on knowledge now available. Appendix C provides the databases on which these cost estimates were built. * Partners In Health and World Health Organization (WHO), Global Plan to Stop TB, 2002.

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1. Objectives and Framework

1.1. Covering the Major Aspects of TB Control This document estimates the financial requirements for all aspects of tuberculosis (TB) control in the period 2001 – 2005, for low and middle-income countries,I categorized broadly into the six components presented in the following sections. The cost analysis focuses on the World Health Organization’s (WHO) goals for tuberculosis (TB) control set for 2005:

• 70% detection rate (minimum) for smear-positive cases; and • 85% cure rate (minimum) for smear-positive cases.

Specifically, the aim of this document is to estimate the gap that exists between the global financial needs if WHO’s goals are to be achieved, and the committed funds for this five-year period.

1.1.1. Implementation and expansion of DOTS Directly Observed Treatment—Short Course (DOTS) is the WHO-recommended system for detection, treatment, and monitoring of patients with TB. It is based on five major components:II

• Government commitment to sustained TB-control activities; • Case detection by sputum smear microscopy for all symptomatic patients reporting to health

service providers; • Standardized treatment regimen of six to eight months for at least all confirmed smear-positive

cases, with directly observed treatment (DOT) for at least the initial two months; • A regular, uninterrupted supply of all essential anti-TB drugs; • A standardized recording and reporting system to assess treatment results for each patient and

the TB control program overall.

An implicit goal—though not an official objective—of WHO’s recommended TB-control strategy in TB high-burdened countries is to make DOTS available to 100% of the population in those countries. DOTS coverage is, actually, one of the main criteria used in monitoring the advancement of TB control, both in international statistics (for example, those cited by WHO and the World Bank) as well as in National Tuberculosis Control Plans (NTPs). Consequently, this study, like many NTPs, estimates costs for achieving 100% coverage by 2005 for all countries for which specific coverage goals have not been set. Where countries have used a lower coverage target in detailed multi-year plans and budgets, those national targets were used instead.

I According to the classification used by the World Bank, low-income countries have a GNP/capita (translated into U.S. dollars using official exchange rates) lower than U.S. $755 as of 1999. Middle income countries (with a GNP/capita from U.S. $756 to U.S. $9,265) are subdivided into two groups: lower middle-income (U.S. $756 to U.S. $2,995) and upper middle-income countries (U.S. $2,996 to U.S. $9,265). This subdivi sion is also used in this work to compute results for countries least capable of affording TB control on a large scale (that is, lower-income and lower middle-income countries). II World Health Organization: What is DOTS? A Guide to Understanding the WHO-Recommended TB Control Strategy Known as DOTS. WHO, Geneva, 1999.

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1.1.2. Prophylaxis Prevention strategies are a major component of the global fight against TB. Although a variety of prevention strategies are available, only one is used on a large scale: childhood vaccination with BCG. However, prophylactic interventions meant to reduce the risk of people infected by both HIV and mycobacterium tuberculosis (MTB) from contracting active TB are likely to be used on a much wider scale, as HIV/AIDS is believed to be fueling an increase of TB incidence. As a result, projected increases in the use of a second prevention measure—isoniazid chemoprophylaxis—are built into the following cost estimates for countries widely affected by HIV/AIDS.1

1.1.3. Treatment of multi-drug resistant tuberculosis (MDR-TB) The WHO now recommends DOTS-Plus programs to respond to MDR-TB in countries that have well-established basic DOTS programs. So far, only Peru has included DOTS-Plus as a significant component of its NTP, but six other pilot projects are underway in other countries to evaluate the effectiveness of DOTS-Plus: Latvia, Estonia, Philippines, and three oblasts of the Russian Federation (Tomsk, Kemerovo, and Orel). If results from DOTS-Plus pilot projects continue to be promising, such programs will increasingly be adopted in countries with high levels of TB drug resistance.

1.1.4. Research and development efforts R & D costs aimed at developing new diagnostic, therapeutic, and prophylactic tools for TB control are included in this analysis, as are costs for operational and health policy research.

1.1.5. Use of health-care services The costs to the general health-care system for delivery of TB-control services are usually not included in NTP budgets. Yet, they are a very important aspect of TB control, as TB care is delivered via general ambulatory care clinics and in general-purpose hospitals. In two major countries, however—Russia and China—there are networks of dedicated TB clinics, dispensaries, and sanatoriums for TB control. The costs associated with these health-care systems are difficult to evaluate with precision, but estimates have been made, and are included in this study.

1.1.6. Other aspects of TB control in the 2001 – 2005 period Some other miscellaneous and administrative costs of TB control are also estimated in this study—such as the administrative costs of STOP TB working groups (established by the Stop TB Partnership), costs for advocacy, and resource development.

1.2. Limitations to the Evaluation Process

There are many difficulties in evaluating the financial burden of TB control that limit the precision of the analysis. The following limitations are noted here, and are further discussed in Section 2.3. 1.2.1. Uncertainty of data and results. In the absence of hard data, gross assumptions are at times necessary. For example, DOTS coverage and TB or HIV incidence rates are often unavailable for particular countries or areas. In such cases, values have to be estimated through extrapolation from time-limited and partial data. Individual country data may at times be difficult to use because its presentation is unclear or lacks details. For example, the type and cost of additional capital investment required to upgrade the level of coverage of TB control activities (particularly,

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DOTS) may be discussed in detail in one country’s plan for the 2001 – 2005 period, but not in another. Uncertainty is also introduced where cost levels have been applied from one country to others in which the situation may be very different. The same type and cost of investment is not necessarily needed by countries with similar populations and caseloads, because their current levels of infrastructure, case management protocols, and capacity to provide the required additional investment may differ; the total estimated cost for countries with similar TB burdens may therefore vary quite significantly. The comprehensive work done by WHO regarding the 22 high-burden countries (HBCs)III permits some limits to be placed on this uncertainty.2 1.2.2. Complexities of estimating future TB caseloads. Uncertainties in some of the epidemiological data and the uncertain impact of powerful contributing factors (such as the extent and efficiency of TB-control activities, and the impact of HIV/AIDS on the spread of MDR-TB) complicate projections of future TB caseloads. However, NTPs have based their own budgets on a fixed number of projected cases, while WHO releases caseload numbers and data on the present state of the disease that exclude ranges.3 Assumptions used to build the present evaluation are discussed in Section 2.3.1. 1.2.3. Chemoprophylaxis for TB-HIV co-infected people. Because it is difficult to estimate the rate of TB-HIV co-infection, detection, and treatment, it is all the more difficult to project the need for chemoprophylaxis for co-infected patients. Similarly, more needs to be known about implementing and scaling up interventions targeted to co-infected patients. Despite these uncertainties, the estimates here attempt to reflect what would be needed to tackle the mounting impact of the HIV/AIDS pandemic on the incidence of TB. 1.2.4. Treatment of MDR-TB is gaining momentum, but the full economic panorama of DOTS-Plus intervention has just started to be investigated. Given the relative complexity of diagnosing and treating patients with multidrug-resistant strains of TB and the still brief experience of DOTS-Plus programs, a general lack of published data on resources and associated costs needed to carry out this specific intervention exists. Two reports on the national program in Peru4 are the first published material documenting costs associated with MDR-TB treatment. However, much more needs to be done. Efforts are under way that should soon further document the costs of interventions aimed at MDR-TB. Another factor contributing to uncertainty is the lack of hard data regarding the magnitude of the MDR-TB epidemic. Reports are now available that provide data for a number of settings throughout the world,5 and scenarios regarding the magnitude of the epidemic are being processed;6 however, at the time of the first edition of The Global Plan to Stop TB (GPSTB), these scenarios leave room for speculation, especially as to the number of cases likely to occur and to be detected in the future. 1.2.5. Health-care services are usually not fully dedicated to one specific ailment such as TB. In only a few countries such as China and Russia do dedicated networks of TB dispensaries, clinics, and sanatoriums exist. In such cases it is relatively easy to estimate these systems’ full annual cost. For other countries, estimates have to be based on the division of total health-care services costs. Moreover, data related to the use of health care services are scarce, making it difficult to evaluate the level of service utilization in many countries and the capacity of these services to absorb more TB cases. III The high-burden countries account for 80% of all annual new TB cases worldwide. There are currently (2001) 23 HBCs, listed in decreasing order of number of cases: India, China, Indonesia, Nigeria, Bangladesh, Pakistan, Philippines, Ethiopia, South Africa, Russian Federation, DR Congo, Vietnam, Kenya, Brazil, Tanzania, Thailand, Mozambique, Myanmar, Uganda, Afghanistan, Zimbabwe, Cambodia, Peru. (WHO Report 2001, Global Tuberculosis Control). Peru was on the list in 2000, while Mozambique was not. This economic evaluation and the Global Plan to Stop TB are based on the 2000 list, or 22 countries.

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1.2.6. Variation in local conditions and use of resources makes several interpretations of input data and results possible. Health-care services and incentive policies for patients and caregivers vary widely. In Russia, for example, the standard treatment is inpatient care for all detected TB cases during the entire period of treatment,7 whereas treatment is conducted almost entirely through ambulatory care in most of Africa and the Philippines.IV In Peru, food is provided to patients who are poor (68% of families),8 because it improves the patients’ condition and treatment compliance could otherwise be jeopardized. In Brazil, incentives are paid to municipalities in proportion to notification (report of new cases to the NTP) and successful treatment on the part of local health systems (the incentive is higher for treatment of smear-positive cases through DOTS). In many countries, no incentives are used, although quasi-incentives might be identified in some. In Nicaragua, for example,V poor patients are hospitalized longer than clinically necessary to ensure that they get food and proper attention to complete their treatment. 1.2.7. Research and development activities are not easy to evaluate precisely. R & D relies on a multiplicity of initiatives in both the public and private sectors. Data for the latter are not always readily available. Moreover, these are long-term efforts, which span more than five years. It is estimated, for example, that the development of a new vaccine may require up to U.S. $1 billion over 15 years; that of an antibiotic, U.S. $300 million over 8 to 20 years.9 This complicates the identification of how much funding will be needed during the 2001 – 2005 period. Chapters 5 and 7 of the GPSTB (as well as Annex 1) identify areas of research that may contribute to global TB control, as well as likely financial requirements. Work is still in progress on a more refined assessment of the total financial needs regarding R & D. 1.2.8. Financial gaps are also difficult to evaluate, because of the relative lack of information regarding currently available and committed funds. So far, only first-line treatments and program costs of NTPs from a number of HBCs have been documented in the recent WHO publication, Global DOTS Expansion Plan (GDEP)—Progress in TB Control in High Burden Countries: One Year after Amsterdam.10 The GDEP does not include the evaluation of TB-HIV co-infection and MDR-TB-related interventions, nor are research and development efforts or the full extent of health-care services’ use included. Box 3 analyses the links between the present evaluation and that of the GDEP, and reconciles costs resulting from both exercises. Financial gap estimates for NTP first-line strategies and the combined use of health-care services have been extrapolated from the GDEP.

As a result of these uncertainties, the financial evaluation here is presented as ranges of estimates. Lower and higher values reflect a spectrum of possible scenarios and a range of cost inputs.

IV Dr. Lagahid, National TB Control Programme coordinator, Philippines. (Personal communication, November 2000.) V Dr. José Ramón Cruz, Pan American Health Organization. (Personal communication, 2001.)

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2. Principles and Methodology

2.1. Economic and Financial Aspects of TB Control Covered by the Study To design the financial evaluation process by which this study is conducted it is first necessary to define the resources included and type of costs by identifying and selecting the socioeconomic aspects of the disease that can and will be financially evaluated. The GPSTB has been designed to support National TB Control Programs as well as other major activities of TB control worldwide in order to strengthen and expand the reach and efficiency of these programs and activities. It intends to identify direct financial needs and shortfalls of all these programs and activities, in order to bridge existing and potential gaps. Needs—and related gaps—are set against coverage and detection objectives set by WHO in accordance with national programs. For this reason, the present analysis, which serves as the basis for economic and financial information of the GPSTB, reflects this general framework. It focuses on “direct costs” for controlling TB. That is, it captures provider costs in the health-care system, such as costs of visits to health centers and ambulatory treatment, as well as costs of inpatient care. It also includes specific nonmedical costs directly generated by the existence of the disease, such as operational research, program management, and surveillance. Costs associated with prevention or prophylactic activities related to TB have also been evaluated. Other aspects of TB control covered by the financial evaluation include R & D efforts to expand and improve TB control, as well as international procurement strategies to increase availability and lower the costs of drugs. Appendix A discusses the full socioeconomic panorama of TB: it covers all activities involved in TB control, as well as the socioeconomic consequences of the disease. It also presents the cost inputs associated with TB-control activities, identifying those covered by the present evaluation and those left out. The latter essentially include patient and indirect costs. (The first category covers expenses incurred to patient’s families by the disease, beyond what they would normally incur otherwise in the areas of transportation, extra food, and drugs not covered by the NTP. The second category includes lost income for households affected by TB, as well as production, in terms of GNP, lost to society.) The evaluation here does not cover costs of resources that may be used by programs but which are not compensated monetarily, as they do not appear in budgets or economic data used in the study. In other words, the present evaluation does not compute economic costs in the sense generally used in the economic literature, but rather financial costs. The analysis concentrates first on direct costs associated with the treatment of patients:

• Costs to NTPs, which provide essential TB-control inputs (such as drugs, diagnosis

supplies, and training) for first-line treatments as well as second-line regimens. This includes DOTS and other first-line strategies financed by other NTPs or other care providers, as well as DOTS-Plus programs for multidrug-resistant TB.

• Costs to general health-care services (for their share of care provided to TB patients, both first and second-line).

• Costs of programs aimed at tackling the problem of TB and HIV co-infection, because of the intimate and vicious relationship between the two infections.

Treatment costs are not entirely covered by a direct or health-system costing approach. Costs borne by patients—out-of-pocket expenses as well as income lost due to the disease—should also be accounted for when a full evaluation of the cost of illness is attempted (see Appendix A for further discussion). However, these aspects of the costs have been left out, as national TB control programs do not directly monitor them. Yet, in a few cases, direct contributions by NTPs to living expenses, either financially or in kind, have been

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included, as they are part of their strategy to fight the disease. These costs are classified as incentives because their primary goal is to help the patient sustain the relatively long treatment. These incentives are especially important for poor patients, who cannot afford enough drugs and food. In Peru, for instance, food packages are provided to a majority of TB patients at an annual cost of U.S. $4 million, or almost half the cost of the entire program’s direct costs.11 In Nicaragua, poor patients can be kept in inpatient services for up to three months, to assure that they have enough food and support to maintain the regimen.VI These incentives may significantly impact the effectiveness of the program, as they support patients who might otherwise abandon treatment. However, we still lack evidence of the magnitude of such impact. Nevertheless, whenever these costs were reported, they have been included in this evaluation. Another important element of the economic panorama of TB control is research and development of new tools for detection, diagnosis, prophylaxis, treatment, and control. Diagnostic, treatment, and policy tools used every day to control TB include a share of the R & D costs necessary to create them, which contribute to the direct cost. But this contribution varies over time as prevention, detection, diagnosis, and treatment have an epidemiological and, ultimately, an economic impact, as demonstrated by countries where successful programs have been in place for decades. Incidence rates do decrease, and so most probably do infection prevalence rates, as decreased rates in industrialized countries tend to show.VII Consequently, the overall economic cost of the disease decreases, as a percentage of GNP and in terms of suffering and lost income for individuals. Thus, despite the long-term impact of these investments, and the fact that a measurable return in terms of disease control cannot be expected within the time frame of this exercise, R & D costs must be included here. Similarly, efforts at lowering costs and improving the efficiency and the effectiveness of programs and tools should be considered as well. Initiatives to increase access to drugs, such as the Fonds de Solidarité Thérapeutique Internationale (FSTI),VIII the United Nations’ Global Fund to Fight HIV/AIDS, Malaria and Tuberculosis, and WHO’s Global Drug Fund (GDF) for tuberculosis, contribute to more successful outcomes at lower unit costs. However, the impact of these initiatives on direct costs of TB control has to be restricted to their administrative costs. Indeed, these funds will purchase drugs on behalf of national programs and, possibly, nongovernmental organizations (NGOs) involved in TB control. Médecins Sans Frontières (MSF), for example, is actively involved in efforts to lower drug costs, as well as to widen and secure drug procurement. The GDF is the only initiative of this sort strictly dedicated to TB drugs. Others, such as the FSTI and the Global Fund, cover more conditions, particularly HIV/AIDS. In the present study, however, administrative costs of procurement strategies are included in direct NTP costs, as such costs for international initiatives are very small. The costs of the GDF for the five-year period covered here are estimated at around U.S. $8 million, which will serve to channel approximately U.S. $250 million worth of drugs to NTPs. Administrative financial needs for the GDF are already covered for the 2001 – 2005 period by a grant from the Canadian international cooperation agency (CIDA).

VI Dr. Jose Ramon Cruz, Pan American Health Organization. (Personal communication, 2001.) VII Infection prevalence rates define the proportion of a given population that carries MTB. The worldwide rate is currently about 32%. (C. Dye. et al., “Global Burden of Tuberculosis [estimated incidence, prevalence and mortality by country.]” Journal of the American Medical Association, Vol. 282, n°7, August 18, 1999). This means that one third of the human population is infected. However, this rate varies from 15% in western Europe to 44% in southeast Asia. VIII The Fonds de Solidarité Thérapeutique International was launched with the sponsorship of the French government in 1997, in association with the United Nations Program on Aids (UNAIDS) and other agencies. It is currently serving programs in five countries (Bénin, Côte d’Ivoire, Morocco, Sénégal, and South Africa). It covers drugs and hospitalisation costs for AIDS and opportunistic infections, including TB. More than 10 countries have so far requested help from this fund. Price reductions of up to 70% had already been obtained from pharmaceutical companies back in 1999. The current annual financial commitment is U.S. $2 million, with French and Korean funds participating. (Catherine Bilger, Secretariat of Health. Personal communication, June 2001).

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Reflecting the growing international cooperation dedicated to TB control, the present analysis also includes the projected costs of the Partnership to Stop TB, lead by WHO. This initiative intends to help coordinate a variety of activities aimed at helping NTPs strengthen their efforts, as well as directing more resources toward research and development of new TB-control tools.

2.2. Organization of the Financial Evaluation

2.2.1. Costs classification for financial evaluation For this study, recurring costs have been distinguished from nonrecurring or initial costs in specific countries. This has allowed for the use of unit cost data available from countries to extrapolate costs for neighboring countries that are without data. Classifying costs by type of input is useful and necessary, as each cost item “behaves” differently.IX Variable costs, such as those for drugs or diagnostic supplies (which amount to a fixed amount per patient), can be projected based on estimated numbers of patients. Yet other aspects of DOTS program expansion are correlated with the size of the population covered, as in the case of laboratory costs. (The WHO recommends, for example, that one microscope be in place for every 100,000 inhabitants.) Therefore, two specific unit costs can be calculated in this study, one based on the number of cases treated, one based on the population covered. Some costs, however, do not depend simply on the number of cases or the number of people covered by the program. This is true for costs of national and provincial administration of TB control programs, national and regional reference laboratories, and operational research efforts. These inputs depend in part on the scope and quality of national TB control programs already existing in any given country. On the other hand, other costs are probably driven by two or more factors. The intensity of supervision and the associated costs seem to depend on the severity of the disease within a given population (expressed by the incidence rate) as well as by the number of administrative health units covering the country. The latter largely relates to the size of the population and the geographic size of the country. In this study, the principal factors or “drivers” of costs relating to TB-control are:

• The number of patients to detect/treat. This number directly drives total costs for inputs such as drugs, diagnostic supplies, incentives for case detection and adherence to treatments, and use of health-care services.

• The size of the population to cover. The population size drives the total cost of implementating a laboratory network and expanding a program—notably when regional or provincial satellite sites are opened to expand coverage to a larger proportion of the population—as well as expanding communication and health education campaigns.

• The previous existence of a dedicated program, whether nationally or locally. The existence of such a program is dependent on the severity of the problem—that is, the number of cases per 100,000 population, or incidence. The severity in turn drives the size of administrative costs.

• The geographic size of the country. This also determines the magnitude of certain costs, such as training. (Transportation costs for trainees is higher when a country is large, and may require air transportation.)

• The dimension/severity of the HIV/AIDS epidemic in the country. Tuberculosis is the number-one opportunistic infection in people infected with HIV; a serious HIV problem will therefore

IX This is also demonstrated by the fact that each cost element is “driven” (or its level determined) by a different factor. Among the “cost drivers” are: the size of the population covered by the program, the number of patients treated, the number of training sessions or of trainees, the number of hospital beds or bed-days, the number of administrative or health units (districts, provinces, health centers), and so forth.

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require specific programs and interventions to tackle dually infected cases. For instance, prophylactic measures will be needed to prevent the onset of TB in HIV-infected persons.

• The dimension/severity of the MDR-TB epidemic. As with the case of HIV, a significant prevalence of MDR-TB will induce the establishment of specific programs—such as DOTS-Plus—to respond to that particular aspect of TB control, which DOTS alone cannot address.

• Treatment protocols. In each country, treatment practices vary, with some countries relying heavily on hospitalization and inpatient care, and some not. Even with DOTS being implemented on a mostly ambulatory basis, the number and types of visits to ensure that patients are following the program can vary: patients can be visited by health workers, or go to health centers. Home visits can also be performed by relatives or neighbors.

Costs have also been categorized here according to origin. Costs corresponding to local resources on one hand and imported resources on the other cannot be used in the same manner, especially when unit costs are used for extrapolation. (See Section 2.3.8. for discussion of extrapolation techniques used in this study.) In order to classify costs and determine how they should be used in the evaluation process, detailed national program budgets have been analyzed according to the principles discussed previously, and are further defined in Table 1. Detailed budgets from 13 of the 22 HBC have been usedX and complemented with questionnaires and personal communications with most national program coordinators from the 22 HBC, as well as by WHO officials in Geneva and regional headquarters. Appendix C displays the cost inputs available by source and by country; Section 3 further discusses the availability and use of data. Cost inputs have been classified into two main groups. Nonrecurring costs include all one-time or initial investment expenditures (that is, all costs related with the launch or expansion of national DOTS programs or of other programs covered by this study). Recurring costs include expenditures that occur on a regular basis, as long as there are TB patients. Table 1 presents the classification of cost inputs and the drivers (or cost factors) that have been chosen to conduct the evaluation of program costs.

Table 1. Classification of Costs and Cost Drivers

Cost input Nature of cost Origin of Cost Treatment of cost in the evaluation process

• Buildings Initial (one-shot investment over a 5-year period)

Local cost Based on budget information for individual inputs. Unit costs are calculated on the basis of the population covered by the equipment needed.

• Microscopes Initial (but also includes spare parts)

Mostly imported input in low and lower middle- income countries

Unit costs are based on cost per microscope—which varies with the region—divided by 100,000 population. (WHO recommendation for laboratory coverage: 1 microscope for every 100,000 inhabitants; but coverage varies from country to country, according to national TB-control plans.)

• Other equipment (computers, printers, X-ray machines, and vehicles for supervision)

Initial (one-shot investment over a 5-year period)

Same as for microscopes

Based on budget information for individual inputs. Unit costs are calculated on the basis of the population covered by the equipment needed.

X The 13 detailed budgets most useful for the present evaluation are from (in alphabetical order): Afghanistan, Cambodia, China (People’s Republic of), Congo (Democratic Republic of), Ethiopia, India, Myanmar, Nigeria, Peru, Philippines, Tanzania, Thailand, and Vietnam. See Section 3 and Appendix C for more details on data sources.

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• Initial training Initial (considered one-shot cost by most plans over the 5-year period)

Partly imported cost (but normally accounted for by providing agency—WHO essentially). Local costs for continuation phase, transportation, hotels, and other expenses.

Unit costs were calculated per inhabitant of the population covered by the program.

• Drugs (DOTS and non-DOTS regimens, non-TB drugs, and prophylactic regimens)

Recurrent: variable

Mostly imported costs in low-income countries. A few large countries, however, have some local production.

Cost is per patient for full course. Total amount depends on the number of patients treated (in addition to a stock provision for the 5-year period).

• Diagnostic supplies (reagents, slides, X-ray films, and salaries, when specifically included in NTPs’ budgets)XI

Recurrent: variable

Same as for drugs Cost is per patient for each smear test and X-ray conducted. Number of smear tests depends on whether a given patient’s TB is smear-positive (in which case about 30 tests are conducted in all, on average) or smear-negative (3 tests only).

• Recurrent training (includes salaries that would not already be included in administration and education material)

Recurrent: fixed

Local costs Regular training sessions need to be conducted for all operational staff (including microscopists and health workers) in order to maintain or upgrade their knowledge. Unit cost is based on population covered by the program (cost per capita).

• Administrative expenses (salaries, paper, toner, telephone, and so forth)

Recurrent: fixed

Local costs Some NTPs provide total costs. Programs expansion is based both on the magnitude of the TB problem (number of cases) and population coverage (addition of new provinces, and other administrative units, which may imply additional administrative costs).

• Incentives provided for case detection and appropriate treatment

Recurrent: variable

Local costs Incentives can be either of a financial nature, given to the providers of TB case management (supply-driven), or of various forms given to patients to encourage adherence to treatment protocol (demand-driven). Total cost depends on total number of cases for which incentives are paid or given out.

• Information, education, and communication tools and documents

Recurrent: fixed

Local costs Health education campaigns and communication about the disease to foster case referral. Unit cost has been based on the population covered by the program (cost per capita). A per case unit cost would probably have yielded similar total estimates.

XI Culture and drug sensitivity tests are not part of the DOTS strategy. However, such tests are routine in many countries—mostly resource rich countries where the caseload is minor. It is assumed here that the financial burden incurred by these tests is borne by national health budgets or insurance plans. Such tests are also routine in the countries of the former Soviet Union. To be coherent with the NTPs of these countries, it has been assumed that culture and sensitivity tests were indeed part of the normal intervention; consequently the costs for such tests have been accounted in the present evaluation.

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• Operational R & D recurrent expenses (including salaries)

Recurrent: fixed

Mostly local costs This activity includes epidemiological surveys and studies of cost-effectiveness to identify better ways to deliver care. Unit cost has been based on population covered by the program (cost per capita).

• Ambulatory visits (includes staff cost, utilities, depreciation of equipment and buildings, and overhead)

Recurrent: treated as both fixed and variable in two separate scenarios

Local costs Costs were available per patient from cost studies. See following discussion on the specific use of these unit costs. Total costs were computed on a per case basis.

• Hospitalization—inpatient care (includes staff cost, utilities, depreciation of equipment and buildings, and overhead)

Recurrent: treated as both fixed and variable in two separate scenarios

Local costs Costs were available per patient from cost studies. See following discussion on the specific use of these unit costs. Total costs were computed on a per-case basis.

The cost estimates for initiatives to combat MDR-TB and TB-HIV co-infection are particularly difficult to calculate, as few of these pilot programs exist, and little information is available regarding their costs. Using two reports from Peru, the itemized cost estimates for MDR-TB could be broken down into drugs (the largest single item), several specific diagnostic tests (tuberculin test, X-rays, drug-sensitivity tests, and cultures) as well as both general program costs (mainly covering administration, monitoring, and transportation) and health-care services for hospitalization (see Appendices C2 and C3 for details). Some indication of costs for the initial implementation of an NTP DOTS-Plus program were suggested by the data from PeruXII; however these costs did not seem to cover all aspects of setting up totally new programs, a process which is now under way in 6 other settings, as previously mentioned, and which will be replicated elsewhere. Consequently, a 10% provision for additional costs was added to the program costs, in order to partially cover these up-front implementation costs. Future economic evaluation of these programs will require detailed documentation of these costs. As a consequence of the scarcity of data and the difficulties in analyzing the behavior of costs in those programs, unit costs are all estimated on a per patient basis. A variation in the number of patients detected and treated generates a proportionate modification of total costs. The situation is similar for interventions and programs targeting TB-HIV co-infection. Some data from prophylactic interventions in sub-Saharan Africa and very aggregate economic information from early ProTEST projects were not enough to break down costs estimates into more than three items: chemoprophylaxis (consisting of a 6-month course of isionazid), the costs of HIV testing and counseling (including these interventions’ share of program costs), and the costs of TB screening and testing (also including their share of program costs). Much more documentation is needed in order to refine this breakdown and the financial estimates. Health-care services costs were computed on a country-by-country basis by estimating the unit costs of clinic visits and hospital bed-days from published sources (see Section 3 and Appendix C3). These unit costs were applied to the estimated average numbers of visits and bed-days that TB patients typically have to undergo during their treatment. For the Russian Federation, information on the estimated total annual cost of

XII Note that until recently Peru had two distinct second-line programs . One, based on a standardized treatment, was run by the NTP, while another, concentrated in the poor suburbs of Lima and run by an NGO (Socios en Salud—Partners in Health), is based on individualized regimens. These two programs are now being merged into one. Both can be considered as the first two DOTS-Plus pilot projects.

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the dedicated network of sanatoriums and TB dispensaries was directly inserted in the lower end of the range of total costs,XIII while the upper end was evaluated proportionally on the basis of the projected number of cases for the 2001 – 2005 period. The resulting total costs reflect the economic burden of TB on health-care services. However, it is not yet possible to infer how much more funding will be needed to upgrade these services in order to address the expected growth in cases detected and treated. More information needs to be known about the actual capacity and future requirement of health- care services to do so. Crude as it is, this methodology has additional limitations that should be noted here. Published information used for this evaluation often lacked adequate notation of assumptions used to estimate costs, and/or how base data were compiled. This is particularly the case with data regarding the cost-effectiveness of interventions, and the costs of health-care services in general. Depreciation methods, for example, were usually not explained, nor were methodologies for allocating the cost of health systems’ shared resources, such as administration, logistics, transportation, utilities, and other resources shared by different clinical and laboratory services within institutions. It would have been a daunting task to track every single study and investigate all their assumptions. The lack of adequate notations of published studies impeded the necessary process of harmonizing those assumptions and the possibility of calculating costs on one and the same basis. (This mostly applies to the costs of health-care services.)

2.2.2. Costing methodology Given the scope of the Global Plan to Stop TB (which includes both direct NTP costs and general health-care services costs, as well as other aspects of TB control not carried out by NTP or national health systems) the current study had to aggregate two forms of costing: budgetary and financial. Budgetary costing: Budgetary costing is a cash-based approach to financial evaluation. It has been used in this study to estimate costs for DOTS expansion, direct (or “vertical”) NTP costs, MDR-TB treatment, TB-HIV co-infection prophylaxis, and R & D efforts. Because the aim of the Global Plan to Stop TB is to raise awareness and resources for TB control, this study attempts to assess financial resources that NTPs and other institutions will have to mobilize in order to control the disease. Consequently, for these aspects of TB control, capital investments have been treated as expenses at their full procurement cost, and no depreciation expense has been calculated. This reflects the presentation of costs used by NTPs in their multiyear plans. This budgetary costing methodology has not accounted for the value of nonmonetary inputs such as voluntary work, which can be considerable. In DOTS, for example, observation of treatment is usually performed either by relatives, community health workers, or by volunteer personnel at health centers. Financial costing: The evaluation of the cost associated with the use of health-care services required a different approach. The important question for evaluating the capacity of countries to control significant TB epidemics is whether the country’s health-care services have adequate capacity to handle the expected additional volume of cases as TB detection improves: that is, what is the available capacity for clinic visits, and ambulatory and inpatient care? How much would it cost to upgrade this capacity, when necessary? It is very difficult, country by country, to identify where capacity is large enough to absorb the additional burden of patients. It is possible, however, to approximate the cost of using these services. Such an approximation can help indicate additional costs (both marginal and incrementalXIV) in cases where health-care systems do

XIII Data were provided by the Global DOTS Expansion Plan, which provides an estimate of the cost of health-care services for TB in countries such as China and the Russian Federation. XIV “Marginal” refers to the cost of attending one more patient, while “incremental” refers to the cost of expanding a given program, or adding a large number of patients, or adding a new program. (The cost of implementing DOTS-Plus for MDR-TB control would be an incremental cost for the whole of TB control in one given country or

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not currently have the capacity to care for more patients. Financial costing differs from budgetary costing in that it accounts for depreciation (in which capital costs are spread over their economically useful life). The assumption then is made that the marginal budgetary cost of upgrading health-care services will be approximately equivalent to the marginal financial cost thus calculated.XV The actual costing is based on financial unit costs of outpatient visits and bed-days wherever available, as reflected in Appendix C3. Assumptions for the number of bed-days and outpatient visits were discussed with a number of WHO experts and NTP coordinators. When estimates were missing, those available were used for all neighboring countries within the same cluster. No specific evaluation was conducted for costs of surgery. (Including surgery costs would have increased total costs—but probably only slightly.) The inavailability of reliable data is a limiting factor for the accurate inclusion of that respective cost in the estimates. In the case of MDR-TB and TB-HIV co-infection interventions, no investment cost has yet been evaluated. Costing has focused on recurrent consumption (diagnostic tests, drugs, and other treatment costs) as well as on apportioning dedicated health-care providers’ salary costs (excluding hospital costs, which are already accounted for by the “health-care services” costs section). Further work on these interventions may identify needs for specific investments.

2.2.3. Costing model Using the approach previously described, the current study relies on a financial model to project the costs of NTPs and other TB-related health interventions and programs throughout the 2001 – 2005 period. Miscellaneous costs (fundamental research, working groups’ and the budgets for the Partnership to Stop TB) have been treated separately, as their evaluation does not entail the same level of complexity. (That evaluation was conducted separately.) These costs were merely added to the costs of TB, TB-HIV co-infection, and MDR-TB programs computed here, according to the area of TB control they corresponded to, when needed. The financial model was created in an Excel™ spreadsheet structured as a set of data fields interacting sequentially to produce unit and total costs to simulate the total budgetary needs of NTPs in the 2001 – 2005 period. The model elaborates total costs on the basis of unit costs and direct total inputs (according to availability of data and types of inputs) country by country. Extrapolation techniques are used when data are lacking (see Section 2.3.8). Structure of the spreadsheet is summarized in Figure 1. Fundamental R & D costs have been organized in a summary table with data extracted principally from Chapter 5 of the Global Plan to Stop TB. The table is included in the GPSTB’s executive summary, and in Section 4 here. Total financial needs for the GDF, estimated at U. S. $50 million a year12 (with almost all of this dedicated to the purchase of drugs for the poorest countries) are not shown, as such drug needs are already accounted for by the NTPs’ 2001 – 2005 budgets. The model used for the evaluation of the costs of DOTS implementation and expansion, as well as the running costs of the NTPs (including second-line and prophylactic strategies), is made up of seven main parts, organized as shown in Figure 1.

territory.) XV In this case, it is assumed that the cost of equipment, which is fully accounted for by annual budgets but is accounted for only in part by annual economic costs as explained in this section, can still be appropriately estimated by multiplying the number of additional cases attended in one year by the unit cost of attending one patient that includes only a portion of equipment cost spread over more than one year (up to various decades for a building, for example).

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Figure 1 Diagram of the Costing Model for TB-Control Costs during the 2001 – 2005 Period X X X = = = X X X = = =

A. Demographic and Economic Data Population size, population, birth rates, GNP/capita (in U.S. dollars, using both the exchange rate and purchase power parity methods).

B. Epidemiological Data Incidence and notification rates; proportion of all notified cases managed through DOTS; estimates of rates of smear-positive detection; proportion of MDR-TB; and proportion of HIV-TB co-infection.

C. Program Data With population coverage and detection rates of smear-positive cases (actual rates used for 1999 and 2000, and objectives used for 2005), computation of gaps between actual coverage and detection rates and objectives are made.

E. Cost Database for Each Country All data that have been collected, organized country-by-country, and by item (as shown in Appendix C).

U1. Unit Costs Database (per case treated): U1 = E / N2 Computations made on the basis of the data available from all sources and extrapolated to all countries where no data are available (see Section 2.3.8); calculations are per patient treated for patient-based inputs (see Box T1 hereafter for details).

N1. Number of Cases: N1 = A x B Using data from the first two blocks, estimates of the number of new TB cases occurring over the 2001 – 2005 period (year by year) are calculated as described in Section 2.3.1. For TB-HIV co-infections, an estimate of the stock of co-infections as of 2000 is calculated, and then added to the estimate of new

D. Prophylaxis and Treatment Protocols Data on the number of visits and bed-days for DOTS and other interventions used in TB control.

N2. Projection of the Number of Cases Treated over the Period: N2 = N1 x C Using data from the first two blocks, estimates of the number of new cases occurring over the 2001 – 2005 period (year by year) are calculated as described in Section 2.3.4. For TB-HIV co-infections, an estimate of the stock of co-infections as of 2000 is calculated, and then added to the estimate of new cases over the 5-year period.

U2. Unit Costs Database (per inhabitant covered): U2 = E / A (population data only) Computations made on the basis of the data available from all sources and extrapolated to all countries where no data are available (see Section 2.3.8); calculations are per inhabitant for population-based inputs of the programs (see Box T2 hereafter for details).

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T. Total Costs (Incremental and Total Budgetary): T = T1 + T2 Results were grouped by main TB-control cost categories:

• Initial DOTS expansion costs (costs of implementing and expanding DOTS programs) • Recurrent DOTS and other first-line programs costs • Health-care system costs associated with first-line strategies (ambulatory visits and

hospitalization) • MDR-TB costs (recurrent costs only for drugs, programs costs, health-care system costs) • TB/HIV costs (ProTEST and preventive therapy for TB)

T1. Recurrent Treatment and Program Costs based On the Number of Patients Covered: T1 = U1 x D x N2 Calculations are per patient treated for patient-based inputs. Direct inputs where made when itemized costs were available and usable from national programs. Results are calculated for each year across the 2001 – 2005 period. Inputs evaluated following this method are:

• Drugs (for DOTS, MDR-TB, and TB-HIV co-infection prophylaxis)

• Reagents and other diagnostic supplies (for smear tests as well as X-rays, when done on a regular basis)

• Hospital- and sanatorium-specific costs (bed-days)

• Visits to health centres (or to patients’ homes, when not performed on a voluntary basis)

• Continuous training (DOTS) • NTPs’ management (includes monitoring,

supervision, and reporting) • Incentives • Miscellaneous (includes operational research

and surveillance) • TB-HIV co-infection prophylaxis-associated

non-drug inputs • MDR-TB-associated non-TB drug inputs • Incentives • TB-HIV co-infection prophylaxis program

(non-drug costs) • ProTEST program costs • MDR-TB program (non-drug costs)

T2. Implementation and Expansion (Program) Costs (Excluding Treatment Costs) Based On the Size of the Population Covered: T2 = U2 x N2 Calculations are per additional inhabitant covered by the program (DOTS only). Direct inputs where made when itemised costs were available and usable from national programs. Results are calculated for each year across the 2001 – 2005 period. Inputs evaluated following this method are:

• Microscopes • Other equipment • Initial training • Other up-front costs

Total Unit Costs (per treated case, per cured case): T / N2 (and (T / N2) x cure rates) Division of total costs per number of cases treated (and cure rates).

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2.3. Specific Assumptions Built Into the Model The present model assumes that all patients are managed according to NTPs’ rules. This does not mean that NTPs actually treat patients. They usually don’t. But they provide essential inputs (drugs, reagents, training, monitoring), instrumental to the management of TB patients. Nor does the model mean either that publicly run health-care facilities or practitioners take care of all patients. In fact, a very large number of patients are managed by other programs and care providers. For example, in South Africa, 50% of health-care expenditure takes place in the private sector. Even if that percentage is not a direct indicator of what the proportion of TB patients who go to private practices is, it still indicates the importance of private practitioners in the delivery of care.13 These practitioners can also include nongovernmental organizations (NGOs), such as the BRAC (originally known as the “Bangladesh Rural Advancement Committee”) in Bangladesh and Fondation Damien in the Democratic Republic of Congo (formerly Zaïre). In India, approximately 80% of all qualified doctors, 75% of dispensaries, and 60% of hospitals are in the private sector.14 The public sector in India accounts for only 25% of health-care expenditure, and the exact proportion of TB patients who are actually treated by the private sector is not known. Yet, according to WHO data, the total case detection rate is relatively good in India, as it reached 66.5% of the total estimated incidence in 1999.15 This rate is not as high, however, for smear-positive TB cases, only 42% of which were detected that same year.XVI Because most TB patients refer themselves to private providers of care first,16 a large proportion of patients are therefore subsequently reported to the NTP, while a number of patients are subsequently also taken care of by public health-care providers, or by those private organizations that collaborate with the NTP. This underlines the necessity for all TB-care providers to collaborate with each other, a necessity emphasized by WHO policies.17 As a consequence, the present model includes all providers, postulating that whatever the provider, TB patients are cared for with appropriate drugs. This is not actually true everywhere; but it is assumed that, provided that a patient is notified (notifications being the basis for the calculation of case detection rates), he or she is then receiving care, the financing of which needs to be accounted for by the GPSTB. Some of that financing might not be accounted for by the NTP itself or channeled through it; but even so, it remains a critical component of TB-control nonetheless. Private providers may have very different costs at the moment (for example, Fondation Damien in DR Congo pays much higher salaries to regional coordinators who would otherwise leave the programXVII); however, the present study relies mostly on publicly funded programs for its cost database.

2.3.1. Epidemiology The epidemiology of tuberculosis is enormously complicated by the impact of HIV/AIDS, and by the lesser, but important, impact of TB drug resistance. Owing to the complexity and uncertainty of the interaction of these factors on TB transmission, this plan assumes that incidence rates and the proportion of smear-positive (SS+) cases will remain constant throughout the 2001 – 2005 period, at 1999 levels.XVIII Flat incidence rate projections will, however, result in increased projections of global incidence, due to

XVI Significantly higher total detection rates (that is, the combination of SS+ and SS- cases) is a pattern found only in the Indian sub-continent and eastern Europe, and the nations from the former Soviet Union. Vietnam presents an opposite—though common—pattern, with 60% total CDR and 80% for SS+. XVII Dr. Henriette Wembanyama, national coordinator of the NTP, DR of the Congo. (Personal communication.) XVIII C. Dye et al., “ Global Burden of Tuberculosis (estimated incidence, prevalence and mortality by country),” Journal of the American Medical Association, Vol. 282, n°7, August 18, 1999, and WHO, Global Tuberculosis Report 2001. (World Health Organization, Global Tuberculosis Control, Report 2001, WHO, Communicable Diseases, WHO/CDS/TB/2001.287, Geneva, 2001.)

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demographic expansion in countries with the highest incidence rates. Recent data show that TB rates are increasing rapidly in countries where TB and HIV/AIDS are both highly prevalent, as in southern Africa.18 It is also known that the implementation and expansion of DOTS and DOTS-Plus programs, as well as of prophylactic treatments, will help reduce the incidence of TB. Cuba, and more recently Peru, have been clear examples of the benefits of a large, well-organized program. In Cuba,19 the number of reported cases decreased by 94.6% between 1965 and 1996 (4% per year on average). This was achieved through the integration of the TB-control strategy with the general health-care system; the strengthening of primary health-care services; and the introduction of DOTS in 1970. The eradication of TB now seems possible in Cuba. Peru, which has just dropped to the bottom of the list of the HBCs, has also been able to curb the incidence rate of TB, which has been decreasing by 4% annually on average, after a peak in 1992.20 However, the impact of the HIV/AIDS epidemic on TB transmission and morbidity creates a significant uncertainty for the epidemiology of TB. Recent reports from UNAIDS show that HIV/AIDS cases have increased 30% from 1998 to 1999 in eastern Europe alone, probably due to of an actual increase in the incidence rate—and to more accurate detection and reporting.21 Recent estimates of incidence rates of TB in sub-Saharan Africa show a steep increase from 1998 to 2000 (on the order of 25% in South Africa and 40% in Botswana).22 The rise of MDR-TB cases creates another uncertainty for the epidemiology of TB. Recent estimates of the proportion of TB cases that are multidrug-resistant are mainly available from surveys regrouped in a joint program on anti-tuberculosis drug resistance surveillance sponsored by WHO and the International Union Against Tuberculosis and Lung Disease (IUATLD). In very large countries, such as China, India, and Russia, where MDR-TB is a clear threat to TB control as a whole, proportions of MDR-TB are available only for a few provinces or regions. Rates vary significantly between regions. In Henan province, with 92 million inhabitants in 1996 and approximately 92,000 TB cases annually, the proportion found was 15.1%, while it was 4.3% in Guandong province (70 million inhabitants in 1998, and approximately 37,000 cases annually). To estimate the proportion of all TB cases in the 2001 – 2005 period that are probably multidrug-resistant, two scenarios have been constructed. One is based on the data proposed in Dye et al. 23 as well as that in Espinal et al.24 The other combines data published by WHO25 and estimates provided by Mercedes Becerra26 for countries not covered by published studies. In this second scenario, for large countries with data for a set of areas (provinces in China, for example), average prevalence was estimated from available data in order to extrapolate prevalence to the whole country. For example, the proportion of MDR-TB cases estimated for China was 9.8% (while it is 5.1% in Dye et al.’s estimates); it was 7.1% for India (versus 3.4% in Dye et al.) and 2% for Pakistan (versus 9.6%). All data and assumptions are displayed in Appendix C5. Altogether, the first scenario provides a more conservative estimate of the proportion of MDR-TB cases (3.2% of all cases), while the second scenario depicts a more prevalent MDR-TB (4.6% of all cases). The evaluation of costs associated with co-infection by MTB and HIV also requires the identification of the likely total number of cases detected and appropriately dealt with in the 2001 – 2005 period. Prophylactic measures known to prevent the onset of TB in HIV-infected persons are the intervention selected here. Epidemiological data associated with TB-HIV co-infections are not readily available at the moment; they have to be derived from epidemiological models. Co-infection rates can be calculated by multiplying HIV infection rates (principally available for adults 15 to 49 years of age) by the rate of infection of the population by mycobacterium tuberculosis (MTB). The latter is estimated to be equal to approximately one-third of the total human population, with regional variations ranging from approximately 15% of the population of high-income countries (and even less in Cuba and Jamaica) to approximately 45% in southern Asia. However, this multiplication method might prove unsatisfactory, as it has been shown in a number of cases that the proportion of HIV-positive persons among TB patients may be as high as 50% in southern Africa.27 Moreover, screening and testing for both HIV and TB account for most of the costs associated with TB-HIV co-infection prophylaxis; the cost of the prophylactic regimen itself is very small. In the

26

absence of more detailed data at the moment, it seemed to us that the number of HIV-positive people factored by a detection rate would be a sufficient indicator to evaluate the number of cases starting the prophylactic regimen. Drug costs were, nevertheless, calculated separately, to take into account the fact that only those HIV-positive persons testing positive for MTB infection (but not diseased with TB) would be put on preventive therapy.

2.3.2. DOTS coverage and case detection rates As noted previously, this study uses rates for DOTS population coverage and case detection for 2000 (for all high burden countries) and 1999 (all other countries) as reported in WHO’s Global Reports 2001 and 200228 and incidence estimates by Dye29 included therein. Combined with population size, these indicators contribute to the calculation of case loads and the rising proportion of cases detected that will be managed through DOTS during the five-year period covered here. The increase in the number of cases detected and treated combined with the expansion of the population coverage by DOTS drives the cost estimates required to reach WHO goals by 2005. Based on the objectives set by WHO for TB control in 2005, the present model assumes that national programs that have not yet reached those goals will do so gradually over the coming years. If a country’s DOTS coverage rate is 50% in 2000, and the objective is 100% in 2005, it is assumed that another 10% of TB patients will be added to the DOTS coverage each year, from 2001 until 2005. Similarly, case detection rates are assumed to increase progressively from their 2000 or 1999 level to 70%. For countries that have already achieved or surpassed these WHO objectives, the model assumes no further gain in the future. Though simple in principle, these assumptions are not straightforwardly applicable in the model. Section 2.3.4. discusses the relatively complex application of these assumptions for computing the expected number of cases detected. It also has to be noted that improving case detection becomes increasingly difficult at high detection levels. The marginal cost of detecting additional cases rises, because the remaining undetected cases are among the least accessible populations. In Vietnam, for example, where the DOTS coverage rate is now very high (99%), the five-year plan aims at getting access to homeless people and to residents of mountainous areas where DOTS is not yet available. The model, however, does not attempt to quantify increased marginal costs for improving case detection or population coverage at high levels. Consequently, cost data from budgets were used to compute unit costs (as described in Table 1) and were used uniformly for all cases covered. A few countries have planned to reach WHO goals before 2005. Vietnam and Cambodia, for example, plan to cover their entire population as early as 2002 and 2004, respectively. When such situations exist, the model is adapted on a case-by-case basis in order to reflect the circumstances in each country. Countries with no multiyear TB-control plan providing a timetable and case detection objectives are assumed to gradually reach WHO’s goals in 2005. Tables 2 and 3 present the status of detection rates and population coverage by DOTS as of 1999, based on WHO’s TB control Global Reports for 2000 and 2001.30 Many countries record numbers of cases reported and incidence rates, with 127 countries implementing DOTS as of 1999 (out of 211 worldwide).31 DOTS-Plus specific coverage rates (for MDR-TB cases) have not yet been estimated. However, the financial evaluation requires the simulation of the number of likely detectable cases that could be appropriately treated in the coming years. Therefore, such simulation has been elaborated. The methodology is very simplistic at the moment (see Table 5). It assumes that by 2005, 80% of all detected TB cases proving to be multidrug-resistant will be appropriately managed by DOTS-Plus programs, compared to an estimated 5% in 2001.

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The same type of problem characterizes the TB-HIV co-infection situation. In the current absence of detection rates and large-scale programs, a scenario has been elaborated, and is discussed in detail in Section 2.3.4. and summarized in Tables 6 and 7.

2.3.3. Treatment success rates Treatment success rates (cure rates added to treatment completion ) are part of the core objectives set by WHO for TB control. The target for treatment success rates has been set at 85% for smear-positive cases managed by DOTS programs in 2005. According to data for 1999, success rates were roughly 80% for SS+ cases in DOTS programs globally (with 4% of treatment outcomes not evaluated), and 27.6% for cases dealt with by other programs (59% of treatment outcomes not evaluated).32 These indicators were respectively 81% for SS+ cases under DOTS and 37% for SS+ in other programs for 1998.33 The rate was 78% for SS+ cases in DOTS programs in the previous year. This information shows significant variations in treatment outcomes, especially among non-DOTS programs, and may be an indicator of poor knowledge of what actually takes place in non-DOTS programs, as the very high level of nonevaluated cases shows (greater than 50% in all these years). Treatment success rates do play a role in the pace of decline of the disease and the cost of treating patients. Patients who fail to be cured by treatment, particularly first-line regimens, are still infectious (if the patient was originally smear-positive), and are normally given a retreatment regimen (or second-line treatment). The cost of that retreatment is significantly higher than first-line regimens. In Ukraine for instance, second-line drug regimens cost U.S. $4,778 per patient in 2000, compared to $53 per patient for a standard first-line regimen.XIX Also, failed first-line treatments may trigger or worsen drug-resistance,34 which leads to ever more costly interventions. As DOTS programs expand and strengthen and failure rates for first-line treatments consequently decrease, so too will the overall cost of managing the disease. However, factoring this into the costing model is quite complex and highly hypothetical, especially in the absence of hard evidence regarding the magnitude of cost reductions due to the decrease of failure rates. Moreover, when this study was undertaken country data on the differentiated unit costs of treatment protocols (first-line treatment versus second-line treatment in particular) were not available; only aggregated data were. Projections of reduction in treatment failure rates due to the expansion and improvement of DOTS were not estimated. Also missing was a clear relationship between treatment failure for smear-positive cases under DOTSXX and the number of retreatment cases. A number of failed treatments may indeed not be followed by automatic retreatment, for a variety of reasons; but this was not evaluated in the documents available. Cases handled by non-DOTS programs that fail treatment seem much more numerous, proportionally; but we also lack information as to what happens to these cases after failure, as shown by the high rates of cases “not evaluated” under non-DOTS programs. The present plan includes all cases treated, covered by DOTS or not. For all the reasons mentioned previously, using the 85% treatment success target (for SS+ cases in DOTS programs only) in this study proved very difficult and not appropriate. Doing so would have required the projection of treatment success rates throughout the five-year period covered and the construction of scenarios of the economic effects of reduced treatment failure rates. One cannot discard, however, the positive economic consequence on the average treatment cost of raising treatment success rates; this should be further investigated and accounted for in future economic evaluation of TB-control programs. At least limited evaluation of the impact, within DOTS programs, of higher treatment success rates could be included in future estimates. It should be noted that the global success rate for SS+ cases was estimated at

XIX Data are from the Ukranian DOTS program and were provided by Dr. Ian Smith of the Stop TB program at WHO headquarters in Geneva. XX WHO reports on treatment outcomes for new SS+ cases under DOTS and non-DOTS programs, as well as retreatment outcomes under DOTS only. No data are available for smear-negative cases at all.

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80.2% for the 1999 cohort.35 But raising treatment success rates may entail a cost to those programs as well, a circumstance that has not been specifically documented by national plans available for this study.

Table 2. Situation of TB Control by Regional Cluster(a) (Epidemiology of TB, Detection Rates, and DOTS Coverage—1999)

Average

estimated incidence

rate of TB(b) (1)

Average estimated

SS+ incidence

rate(b) (2)

Total notification

rate(b) (3)

Total case

detection rate (%) (3/1)

SS+ detection

rate (%)

DOTS coverage

as of 1999 (% of

population)

% of detected

cases covered by DOTS

Austral Africa (AA) 494 200 306 62.0 93.4 65.0 73.1 Sub-Saharan Africa (excluding AA) 326 136 100 30.7 42.0 62.9 88.6 Middle East and northern Africa 60 28 42 69.8 53.8 32.7 31.7 Western Europe and high-income Mediterranean

19 6 11 58.5 55.2 11.0 1.7

Eastern Europe and former Soviet Union

92 39 74 80.7 46.2 10.7 16.1

North America 6 3 5.4 89.8 81.7 89.8 73.9 Urbanized Southern Cone (USC) (South America)

64 29 43 66.8 76.1 26.6 10.0

Latin America (excluding USC) and the Caribbean

85 38 42 49.6 69.8 58.9 73.7

Indian sub-continent 188 84 109 57.8 36.7 22.0 16.8 China, Indochina, and Miscellaneous Asia

119 53 41 34.4 37.9 64.1 84.7

Industrial eastern Asia 43 19 49 113.6 (c) 65.2 23.0 13.3 Southeast Asia 251 114 73 29.0 29.2 69.1 48.5 Pacific 13 7 11 86.3 45.4 34.1 16.2

Total world

141

62

64

45.5

39.3

44.1

43.6

Total low and lower middle- income countries

171

75

76

44.6

41.1

44.8

45.7

Total 22 HBC

178

79

79

44.6

39.0

46.3

44.5

(a) For the definition of “cluster” see section 2.3.8. For the list of countries by cluster, see Appendix B. (b) For every 100,000 inhabitants. (c) The case detection rate is higher than 100% in industrial eastern Asia because the number of cases notified in

1999 exceeded the total number of new cases predicted by the incidence rate. This might be because that incidence rate (which is an estimate) is set too low, or because an exceptionally high number of cases were registered that year.

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Table 3. Situation of TB control in the 22 High-Burden Countries(a) (Epidemiology of TB, Detection Rates, and DOTS coverage—1999

Proportion (%) of

MDR-TB (among new TB cases)

Total population (in 000s)

(1)

Estimated number of new TB

cases

(2)

Average estimated incidence

rate of TB(b)

(3)=(2)/(1)

Average estimated

SS+ incidence

rate(b) (4)

Total notifi-cation rate(b)

(5)

Total case detection

rate (%)

(5/3)

SS+ detection

rate (%)

DOTS coverage

(% of population)

% of all detected

cases covered

by DOTS

% of new SS+

cases managed by DOTS Scenario 1(c)

Scenario 2(c)

% of HIV infection among

TB cases(d)

India 986,600 1,826,046 185 83 123 66.5 42.0 14.0 9.8 6.4 7.1 3.4 3 China 1,254,100 1,286,729 103 46 36 35.4 36.0 64.0 75.2 32.0 9.8 5.1 0 Indonesia 211,800 597,302 282 127 33 11.7 12.2 90.0 100.0 19.0 1.0 0.7 1 Nigeria 113,800 341,984 301 130 22 7.3 11.7 45.0 100.0 11.7 1.0 1.7 14 Bangladesh 125,700 303,040 241 108 62 25.7 27.5 90.0 89.9 25.0 2.0 1.4 0 Pakistan 146,500 258,930 177 79 61 34.2 5.2 8.0 22.3 1.9 2.0 9.6 1 Philippines 74,700 222,164 297 141 196 65.9 70.0 43.0 21.8 20.0 1.0 3.2 0 Ethiopia 59,700 222,798 373 157 118 31.6 28.0 63.0 100.0 22.0 1.0 2.3 30 South Africa 42,600 210,780 495 201 323 65.3 97.0 66.0 70.0 68.0 2.5 1.5 45 Russian Federation 146,512 179,879 123 55 91 74.1 27.0 5.0 2.8 1.6 13.0 6.0 1 Congo DR 50,500 151,838 301 130 118 39.2 60.7 62.0 100.0 46.0 1.0 1.5 25 Vietnam 79,500 150,257 189 85 113 59.6 80.0 99.0 99.5 80.4 2.3 2.3 1 Kenya 29,410 122,501 417 173 194 46.6 68.1 100.0 100.0 53.0 1.0 0.0 40 Brazil 168,066 118,286 70 31 47 66.8 79.0 7.0 4.9 4.0 1.3 0.9 5 Tanzania UR 32,923 111,994 340 145 160 47.0 58.0 100.0 100.0 51.0 1.0 1.2 37 Thailand 61,691 86,754 141 62 48 34.1 21.3 59.0 100.0 40.0 5.0 2.1 10 Myanmar 48,100 81,269 169 76 33 19.6 27.7 64.0 100.0 33.0 1.0 1.5 5 Uganda 22,800 78,160 343 146 166 48.4 66.7 100.0 100.0 59.0 0.8 0.5 50 Afghanistan 25,869 84,076 325 146 15 4.6 5.4 14.0 100.0 5.2 1.0 7.3 5 Zimbabwe 11,904 66,853 562 226 435 77.5 59.1 12.0 100.0 55.0 2.4 1.9 65 Cambodia 11,757 65,894 560 251 176 31.4 53.6 65.0 100.0 57.0 1.0 4.2 3 Peru 26,600 60,680 228 102 160 70.1 94.1 100.0 100.0 95.0 4.3 3.1 2 Total 22 HBC 3,731,132 6,628,216 176 79 79 44.6 39.0 46.3 44.5 22.3 5.3 3.3 n/a

Total World 5,979,881 8,434,598 171 75 76 44.6 41.1 44.8 45.7 23.2 4.8 3.4 12

22 HBC as % of world total

62.4% 78.6% n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a

(a) Source: World Bank, 2001 (for population growth); WHO, 2001 (unless specified). (b) For every 100,000 inhabitants. (c) Scenario 1 is based on data provided by Mercedes Becerra, the WHO, and the IUATLD. Scenario 2 is based on data provided by Christopher Dye of WHO. (d) Proportions provided by Christopher Dye .

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2.3.4. Evaluation of the number of cases detected and treated The computation of the projected number of new cases of TB throughout the 2001 – 2005 period covered by the Global Plan to Stop TB (including multidrug-resistant cases and dually infected TB-HIV cases) is instrumental to the current study, and was the first step taken in the economic evaluation process. Projecting the number of cases, country by country, is a complex task that combines the epidemiological estimates discussed in Section 2.3.1 and the case detection rates (and DOTS coverage rates for cases dealt with by DOTS programs) discussed in Section 2.3.2. Multiyear plans from some of the 22 high-burden countries—which account for 80% of all new cases worldwide—did provide some estimates of cases; but few of these plans detailed those projections. For example, the TB-control plan for India, which had 33% of all cases reported worldwide in 1999, did not clearly project the number of cases to be detected and treated during 2001 – 2005. In cases where national plans did provide epidemiological projections (as in the case of the Democratic Republic of Congo), the plans did not adequately explain the underlying methodological assumptions, nor the link between their projections and the costs built into the plans. Because costs are so clearly tied to epidemiology and case detection and treatment, this made it necessary to compute epidemiological projections for all countries covered in this study, including all the 22 HBCs. The estimated number of TB cases to be treated in the 2001 – 2005 period in each country has been calculated using the estimated incidence as provided by WHO statistics36 (based on the number of new cases likely to occur each year) and the case detection rate (CDR), which presumes the WHO objective of 70% case detection for smear-positive TB (SS+) cases by 2005.XXI Detection rates for each country for 1999 and 2000 (as published by WHO) were used as a starting point for the period.

Case Detection Rate (CDR) = Notifications (number of cases reported)/Estimated Incidence (number of new cases)

SS+ Detection Rate = SS+ Notifications (number of cases of infectious TB notified)/ Estimated SS+ Incidence (number of new cases of SS+ TB)

Where no data were available for individual countries, case detection rates were estimated using statistical averages of data from neighboring countries. Gambia, for example, had no reported case notification rate, so a rounded rate from neighboring Senegal was used, with no discrimination between SS- and SS+ cases. (Senegal has a 61% detection rate for SS+ TB cases; a rate of 60% was applied to the Gambia.) Case detection rates vary widely in lower and lower-middle income countries. Seventeen of these countries had achieved or surpassed the 70% case detection rate target in 1999 (for all TB cases, as well as for SS+ cases; see Table 4) but these countries accounted for only 3% of the estimated number of new cases that year. As shown in Table 4, 68 countries accounting for 83% of all TB cases were well below the 70% target for total TB and SS+ case detection rates. These countries are those concentrated in sub-Saharan Africa, the Indian sub-continent, and southeast Asia, while also including China, Indonesia, Cambodia, and Myanmar. XXI The detection rate is derived by dividing the number of case notifications by the estimated incidence in a given country.

31

Table 4. Status of Case Detection Rates in Low and Lower Middle-Income Countries (LLMIC), 1999 Categories of CDR status Number of

countries in each category

Countries from the 22 HBC list Percentage of all new cases in all 114 LLMIC

in each category Total CDR and SS+ CDR = 70% 17 Peru 3% Total CDR and SS+ CDR < 70% 68 Congo DR, Ethiopia, Kenya,

Nigeria, Uganda, Tanzania, Bangladesh, India, Pakistan, Afghanistan, Cambodia, China, Myanmar, Indonesia, Thailand

83%

Total CDR = 70% and SS+ CDR < 70%

16 Zimbabwe, Russian Federation 4%

Total CDR < 70% and SS+ CDR = 70%

13 South Africa, Brazil, Vietnam, Philippines

10%

TOTAL 114 100% Adding to the possible confusion of CDR status is the fact that, in a number of countries, at some point in time the number of cases reported may be higher than what would be predicted by the incidence rate. This was, for example, the case in South Africa in 1998. There were 82,293 infectious (smear-positive) cases detected that year, when predictions were for 69,800 new SS+ cases, based on estimated incidence rates. Kazakhstan and Costa Rica were another two examples, where total CDRs were at 118.5 and 106.6% respectively in 2000. Erroneous or underestimated incidence rates may also explain these differences. In the case of South Africa, the impact of the HIV/AIDS pandemic may be cited as a complicating factor.XXII Also, successful detection programs may at some point suddenly push the detection rate upward, while the incidence remains more or less constant. However, other inconsistencies are somewhat more confusing, as in the case of the Russian Federation. There, the level of total case detection was 74% in 1999, compared to only 30% for smear-positive cases. When these two rates are juxtaposed with the proportion of SS+ cases estimated in the total TB load, the resulting number of detected smear-negative cases that can be inferred is far higher than what the incidence rate would predict. Moreover, according to reports by WHO, these rates have been changing dramatically in recent years; SS+ case detection rates were greater than 60% until 1998. Where case detection rates (both total and SS+) already reach or exceed the 70% target, we were not in a position to further project the evolution of detection rates throughout the 2001 – 2005 period, unless such rates were specifically stated by available multiyear plans. Botswana, for example, has already reported case detection rates of 73% for all cases and 91% for SS+ cases, but in the absence of projections, the model had to build its own assumptions, as described later in this section. Whenever available (which was rarely the case) country estimates were used when the variation of detection rates was stated. In all cases, however, data available from NTP plans were compared to the results of the model. Main assumptions built into the computation of cases Reflecting this great variety of CDR situations and coping with all possible inconsistencies in available data in one model is a complex task. In order to at least reflect the major features of the state of these situations, the current model assumes the following:

XXII In a number of countries, notably in southern sub-Saharan Africa, too low-estimated incidence rates, as well as the combination of HIV and other factors, can explain the uncertainty surrounding CDRs.

32

• Incidence rates remain flat across the 2001 – 2005 period. • Every country achieves the 70% WHO objective as of 2005, and calculates the costs of doing so

by gradually bringing up the current rates to 70%. • For countries that have already achieved CDRs of 70% or more (both total and SS+), current

levels of rates are maintained throughout the period. Where active CDR policies and investments will be put in place this might underestimate the number of cases potentially detected.

• For countries where the SS+ CDR is lower than 70%, the model assumes that bringing up the CDR for smear-positive cases will require the increase of the case detection rate for all forms of TB. Total CDR in these cases is gradually increased by adding one-fifth of the difference between the 2000 SS+ CDR level and the 70% target. When SS+ CDRs are relatively similar to total CDRs, this method is unproblematic. But where there is a significant gap between the reported SS+ detection rate and the total TB detection rate—as noted for the Russian Federation—the model projects case numbers that exceed those predicted by the reported 1999 or 2000 incidence rates. However, great differences and variations in detection rates also reflect a great uncertainty in currently reported data, and indicate the need for caution in the use of these reports.

• For countries where the SS+ CDR is at or above the target level but the total CDR is below 70% (as is the case of VietnamXXIII), the model assumes that total CDR will rise to 70%. It eliminates one fifth of the differential every year. Because it also assumes a constant ratio of infectious cases to total new cases of TB, the model therefore mechanically projects an increasing SS+ detection rate.

Outcomes of the model described previously were compared to data available from multiyear plans available from some of the 22 high-burden countries. There were no significant discrepancies in the total number of cases likely to be detected and treated during 2001 – 2005. Simulations were also conducted by comparing results from the application of each of the various sub-models previously described to all countries included in the present study. This allowed the study to frame projected numbers of cases within a probable range. No major variations in the results that would have invalidated the assumptions were found, except in two cases: India and Pakistan. Simulations yielded numbers ranging from 6.3 to 8 million cases detected during the five-year period in India. For Pakistan, the number ranged from 580,000 to 1,045,000 cases. No discrepancies of such magnitude were found for other countries (except for very small countries with similar epidemiological and TB-control profiles). The reason for the amplitude of the range of results for these two large countries is the fact that they both have much lower SS+ case detection rates than total case detection rates. (The case of the Russian Federation has already been discussed). India had, in 2000, a SS+ case detection rate of 42% and a total case detection rate of 60%. For Pakistan, the difference was even greater: 5.2% for SS+ cases and 34.2% for all cases. The model uses one rate or the other (as both rates are below target) and brings the chosen rate up to target (70%) in 2005 by adding one fifth of the difference each year to the total case detection rate. Using the lower SS+ detection rate creates a higher proportion added each year (one fifth of a greater difference). Hence the doubling of cases in Pakistan between one simulation and the other. In the absence of data for case projections from these countries at the time of the computation of the epidemiological and detection projections for the 2001 – 2005 period, manual adjustments were made for India and Pakistan that assumed mid-points in the range of results obtained. For India the mid-point introduced in the model was 7.1 million cases detected and treated; for Pakistan, it was 750,000. These assumptions are certainly arguable, and reflect the relative uncertainty that surrounds the epidemiology of TB and the case detection rates in these countries.

XXIII Vietnam has an 80% SS+ detection rate (well above the 70% target), but an overall detection rate of only 60%. The model assumes that Vietnam will bring its overall detection rate to 70%, and calculates the cost of treating the resulting number of cases.

33

The case of China also needs to be mentioned here. Because China’s NTP treats very few SS-negative cases notified, it reports far fewer total cases than notification rates would suggest. Other programs and private health-care providers manage SS- cases not managed by the national TB control program. However, the financial model here assumes that all cases notified are treated, following the NTP’s protocols and financial arrangements. In another example, Brazil still has a very low rate of DOTS coverage (7% in 2000), but the total detection rate is high (68% in 2000) and a federal program provides TB drugs to all municipalities. Approximately 30 countries did not report notification rates in 1999.37 For these, estimates have been made using data from neighboring countries within the same cluster (see Section 2.3.8). Apart from Zambia, these were countries with very small populations. Regarding the proportion of TB cases notified from 2001 to 2005 that will be managed by DOTS, the current model based its computation on proportions reported by WHO for 2000, and assumed an objective of 100% in 2005. The baseline for that coverage was assumed to be the 2000 population covered by DOTS (as reported by WHO). It was therefore assumed that if X% of the population is covered by DOTS, then X% of cases detected will be managed by DOTS. In practice, if the proportion of all cases notified covered by DOTS was 40% in 1999, the proportion over the five-year period will be 70% (40% + ½ (100% - 40%)). This does not reflect the precise reality of the situation, as cases notified (which serve as the basis for the computation of detection rates) may be concentrated in areas covered by DOTS. Regarding MDR-TB cases, the situation is somewhat peculiar, as DOTS-Plus programs still have to be set up in most countries. (Peru is an exception among the 22 HBC, with a 100% DOTS-Plus coverage rate and approximately 450 patients being treated in 1999.38) It is therefore unlikely that all potentially detectable MDR-TB cases can be identified and treated. In the Russian Federation, for example, where the magnitude of the MDR-TB epidemic is very large (with a prevalence ranging from 6% to 13% of the source estimates—see Appendix C5), plans for the treatment of MDR-TB patients are now being implemented.XXIV However, these plans suggest that only a fraction of the patients are currently planned to receive treatment (approximately 20 to 45% of all cases potentially detected, according to the prevalence scenario used). In many countries with large pools of MDR-TB cases, no such plans are even being implemented. It has been estimated that only about 7,000 cases of MDR-TB were actually being treated as of 2000.39 All in all, this would mean approximately 2 – 3% of all new annual cases of MDR-TB, and 3.5 – 5% of TB cases, are currently being detected. All this suggests caution be taken in the projection of the number of TB cases that could be detected and treated as MDR-TB in the 2001 – 2005 period. However, the Global Plan to Stop TB needs to base its assumptions and corresponding costs on ambitious goals, if TB control targets are to be met and MDR-TB is to be tackled efficiently and efficaciously. Consequently, the model assumes that only a portion of all MDR-TB cases detected each year will be managed as such. The proportion set for each year (Table 5) assumes that DOTS-Plus programs and other large-scale MDR-TB-specific treatment programs will spread progressively as awareness of the problem expands and WHO-recommended DOTS-Plus protocols are used. The assumption is that, reaching 2005, 80% of all new MDR-TB cases detected will be treated according to MDR-TB-specific programs (DOTS-Plus or DOTS-Plus-equivalent). The resulting five-year proportion of 40% might be optimistic and is definitely arguable. A range—equally arguable—could have been used. But this would have further complicated assumptions and added more ranges to those already defined. The scenario of MDR-TB case detection and treatment used in the present evaluation (shown in Table 5) has been elaborated on a global scale and applied uniformly to each country in the absence of DOTS-Plus or DOTS-Plus-equivalent national programs, with the exception of Peru.

Table 5. Scenario for the Expansion of DOTS-Plus or DOTS-Plus-Equivalent Programs (Proportion of MDR-TB Cases Detected that Will Be Treated by Such Programs over the 2001 – 2005 Period)

XXIV Several programs are covering prisons and civilian populations.

34

2001

2002

2003

2004

2005

Global proportion (2001 – 2005)

Proportion of MDR-TB cases detected covered by DOTS-Plus programs or equivalent

5%

15%

30%

50%

80%

40%*

*The global proportion is the result of dividing the total number of MDR-TB cases assumed to be treated by DOTS-Plus or equivalent programs by the total number of MDR-TB cases detected. The total number of cases of MDR-TB treated by such programs results from multiplying each annual percentage indicated in the table by the total number of TB cases detected year-by-year, as estimated according to principles described earlier. The proportions given in Table 5 may appear optimistic to some, as DOTS-Plus programs are still in the design process. However, the intent is to simulate the kind of effort that would be needed to achieve difficult yet necessary objectives by 2005. This provides a basis for further discussion of program expansion and encouragement for more research on how to achieve more extensive TB control, including control of MDR-TB. Such efforts will, in turn, yield better projections of case detection and treatment rates that will help refine the cost estimates. The situation regarding TB-HIV co-infected persons is somewhat similar, though even more complex. UNAIDS and other sources do not provide case detection rates for low- and middle-income countries. Too little is known and has been done yet—on a broad scale—that could allow for sophisticated and solid assumptions about how many cases are likely to be detected and preventively treated in the next few years. Given the geographic and socioeconomic concentration of the problem (mostly in sub-Saharan Africa and south Asia) in areas where health-care infrastructure is often lacking and where only a small fraction of HIV-positive persons know about their condition, it is clear that even with a rapid expansion of existing interventions and programs, only a relatively small proportion of patients will receive appropriate treatment in the near future. Yet massive interventions and programs targeting the interaction between HIV/AIDS and TB are urgently needed; they will also have to be implemented also in areas where HIV is a mounting or already significant problem, such as Haiti, the Dominican Republic, Russia, China, and India. Recent reports on the rapid increase of HIV/AIDS infection40 in these areas, coupled with the serious problem of MDR-TB, are strong incentives for action. As a consequence, the GPSTB had to reflect the dimension of the problem and evaluate how much funding would be needed if at least a significant portion of all potentially detectable cases were to be duly treated. To do so, a simulation has been built using both the current status and the planned expansion of ProTEST programs41 (see GPSTB, page 67, for a description of current programs). ProTEST aims at helping people get HIV testing, counseling, and medical care, as well as TB testing and prevention (when not active) or referral to the host country’s NTP (in cases of active TB). Patients voluntarily refer themselves to the ProTEST centers. ProTEST was started in 1999 in South Africa, where it is planned to cover the whole country in 2005.42 It has also already started in Malawi, Uganda, and Zambia. Another eight sub-Saharan countries are included in the next wave of expansion (namely Congo DR, Côte d’Ivoire, Ethiopia, Kenya, Mozambique, Rwanda, Senegal, and Tanzania).43 The economic evaluation for the GPSTB focuses on those 12 African countries. However, other countries where TB and HIV are very significant problems might also start similar programs. A simulation was therefore conducted, assuming that implementation and expansion of ProTEST or ProTEST-like programs follows for a number of other countries where HIV and TB are a significant combined problem (including China, India, Russia, Brazil, Thailand, Cambodia, Vietnam, Myanmar, Haiti, the Dominican Republic, and all other countries of sub-Saharan Africa). In all, another 45 countries have been included in a higher-end of estimates for which the baseline estimate assumes that 5% of the adult population will refer itself to TB-HIV co-infection programs (either ProTEST or ProTEST-like) over the 2001 – 2005 period (see Table 6).

35

Because it is now very difficult to forecast how many potentially co-infected persons will be detected and appropriately treated over the 2001 – 2005 period, case detection rates are pure assumptions. They were estimated for the five-year period in collaboration with WHO experts from the Stop TB team who are working on TB-HIV co-infection and ProTEST programs.XXV The scenario assumes a rapid yet progressive expansion in the rate of detection of cases, due to an increasing efficacy. (This assumption is incorporated into the scenario by increasing first-year detection rates proportionately as years go by). See Tables 6 and 7. Further understanding of these programs, especially of their viable possibilities for reaching people and actually detecting TB-HIV co-infections, will allow the building of more refined and reliable scenarios. Estimates of the number of people who could refer themselves to the ProTEST programs and who could be tested, counseled, and given therapy to prevent the onset of active TB were calculated according to the following algorithm:

Population of the given country (for each year between 2001 and 2005) X

50% (that is, the approximate proportion of the total population made up of adults 15 to 49 years of age in most low and middle-income countries)

X the estimated proportion of the adult population likely to refer itself to Pro-TEST or Pro-TEST-like

programs X

the prevalence rate of HIV infection among adults in the given country The prevalence rate of TB infection among HIV-positive persons can be inferred either from the infection rate within the total population (which estimates, country by country, were provided by WHO), or from surveys of HIV-positive persons who have been tested for TB. Reports from early ProTEST projects seem to indicate that the proportion of people with HIV who are also infected with MTB is higher than the infection rate estimated for the general population. In Zambia, rates of at least 50% have been observed,44 while the general rate of MTB infection in the general population is estimated at 36%.45 In South Africa, similar proportions have been observed. Consequently, two scenarios have been constructed from these data. One applies to HIV-positive persons the same rate of infection by MTB as estimated in the general population; the other uses a flat 50% rate (as a high-end scenario). Simulations conducted with experts at WHO actually used the second infection rate and served for the numbers eventually reported in the GPSTB. Unfortunately, very little cost data can be directly related to the number of cases identified. Cost data that could be used for the economic evaluation of programs tackling dual infection are available only from a Pro-TEST program in Zambia; this data can—with a lot of uncertainty—be supplemented with a preventive therapy program using a 6-month regimen of isionazid in Uganda. Cost data from Zambia, at the moment, are only available on a per HIV-positive patient basis. The coverage of cost data from Uganda also needs further investigation. Further work on these estimates will therefore need to use more detailed cost data in order to refine the unit cost database, which, in turn, will allow better use of epidemiological estimates. Those estimates also will need to be revised as soon as new reports on the implementation and upscaling of ProTEST programs and prophylactic strategies become available.

XXV Drs. Philippe Onyebujoh (WHO-ProTEST coordinator) and Paul Nunn provided reports on the current status and proximate future of ProTEST projects and helped estimate likely case detection rates for countries included in the ProTEST program.

36

After discussions with WHO experts, the following proportions of self-reference to ProTEST programs were estimated over the five-year period covered by the present evaluation (Table 6):

Table 6. Scenario for TB-HIV Co-infection Case Detection for the 2001 – 2005 period

Malawi, South Africa, Uganda, Zambia (where ProTEST programs have already started): 20% self-referral of the adult population.

For Malawi, for example, a 20% self-referral rate would mean approximately 184,000 HIV-positive persons being detected over the five -year period, out of a total 1.15 million adults 15 to 49 years of age being screened (20% of the total adult population). Early reports of the Malawian projects show that 8% of the adult population catchment areas of the first ProTEST projects (more than 18,000 people) had already been tested in 17 months from February 2000 to July 2001.46

Ethiopia, Kenya, Mozambique, and Tanzania (where ProTEST programs are about to be started): 20% self-referral of the adult population.

Congo DR, Côte d’Ivoire, Rwanda, and Senegal (where ProTEST programs are scheduled to start in the near future):

10% self-referral of the adult population. All other low and lower middle-income countries (where HIV/AIDS and TB are combining to be a large threat

to the health of the population, especially China, India, Russia, Brazil, Thailand, Cambodia, Vietnam, Myanmar, Haiti, the Dominican Republic, and all other countries of sub-Saharan Africa. The assumption is

that ProTEST or ProTEST-equivalent programs will be available in the near future in these countries): 5% self-referral of the adult population.

Table 7. Annualization of the Scenario for TB-HIV Co-infection Case Detection (Percentage of the Adult Population Going Through ProTEST Programs Each Year)

2001 2002 2003 2004 2005 Total

2001 – 2005

Malawi, South Africa, Uganda, Zambia

0.5% 1.5% 3.0% 5.0% 10.0% 20.0%

Ethiopia, Kenya, Mozambique, Tanzania

0.0% 2.0% 3.0% 5.0% 10.0% 20.0%

Congo DR, Côte d’Ivoire, Rwanda, Senegal

0.0% 1.0% 2.0% 3.0% 4.0% 10.0%

Other low and lower middle-income countries

0.0% 0.0% 0.5% 1.5% 3.0% 5.0%

2.3.5. Evaluating the costs of drugs and laboratory supplies Drug and laboratory supply costs are variable costsXXVI computed by multiplying the unit average cost of a regimen or protocol by the number of cases detected, which roughly corresponds to the number of cases

XXVI Variable costs are proportionate to the volume of operation, while fixed costs are not modified by a change in that volume (until a certain point). Incentives, also proportionate to the number of cases, are also variable costs. Other program costs (administration, communication activities, monitoring, supervision, surveillance, operational research) may be considered to be principally fixed costs, but their total level is modified by the expansion of the program, as discussed in Section 2.2.1.

37

treated. It has been assumed in the present evaluation that the total cost of treating one patient is fully incurred in the year in which the case is treated, even though this is not practically the case (see Box 1). The selection of treatment regimens is an important determinant of drug costs, and a great variety of protocols are in use around the world. DOTS regimens are standardized, but most TB patients in the world still do not have access to DOTS. Cost calculations in this model are based on regimens now in use by national TB programs. Thus, the model assumes lengthy regimens of more than 6 to 8 months in countries in eastern Europe and the former Soviet Union, even though such regimens have been shown to be less cost-effective than ambulatory DOTS. 47,48 Where national TB control plans anticipate expanding the use of standardized DOTS regimens, the model assumes that they succeed in doing so over the next several years, and reflects this change in the cost calculations. National programs and other sources provided average unit costs of drugs (and other recurrent inputs for diagnosis and treatment) that apply to the entire treatment. Because the model does not differentiate between treatments of forms of TB other than MDR-TB, these unit costs include all categories (including retreatment cases, which are far more expensive than regular ones but far less numerous). Smear-positive and smear-negative cases also require different regimens entailing different costs. Data provided by WHO show that on average regimens for SS+ and SS- cases can cost $43 and $25 per patient respectively in sub-Saharan Africa, but cost the same in China ($18), Latin America ($55), or in the former Soviet Union ($83). Retreatment cases cost roughly twice as much. However, most national plans available for this study did not differentiate budget figures between such categories, and just provided a total cost or a total unit cost. These costs were used as illustrated in Appendix C2. Direct inputs of the total cost were also used for a few countries for which total drug and diagnosis costs were provided, when the model predicted a significantly different number. This was notably the case for countries that have anticipated reaching WHO goals earlier than 2005. Different drug regimens might be used for the same category of patients within the same country while a variety of programs—not all compliant with DOTS—might be run by the government, NGOs, and independent practitioners. However, unit costs of drug regimens reflect those of national programs for which information was available (principally countries implementing or pledging to implement DOTS). Unit costs provided by or calculated from national TB control plans have been inflated by 10 to 20% on average (depending on the provisions already included by the plans’ budgets) to account for losses and storage costs; for drugs used for adverse effects of anti-TB drugs49; and for building a security stock, as recommended by WHO (normally a 1.5-year stock). Costs for most anti-TB drugs have been declining. However, NTPs had not anticipated all price reductions in the budgetary data used in the present analysis. Nevertheless, average unit cost for first-line drugs in the case of India is $14.4 in the present evaluation (retreatments included), which translates into $12 without the security stock and other provisions cited previously. This cost is close to the funding now available in some procurement strategies. (See Appendix C2 for details on drug costs assumptions). 57 Drug costs for the formerly socialist economies of eastern Europe and the former Soviet Union remain quite high (around $60 per case on average) and might drop significantly in the future. These potential reductions have not yet been evaluated; any decision to lower these costs will principally lie with the national programs. Future editions of the GPSTB will reflect any change in those costs.

Box 1: The “Inventory Effect” The actual number of cases treated in a given year will never exactly correspond to the number of cases detected that same year. Nor will the cost of drugs and laboratory supplies be exactly the same as the unit costs of drugs and supplies multiplied by the number of cases detected. Instead, a number of cases from the previous year will still be treated in the given year. However, in economic terms, the former form of evaluation is a relatively good approximation. For example, if 100 new cases are detected during year 2003 and the average treatment time is six months, half of the patients (100/2, or 50) will not have completed treatment at the end of the year—only half of it on average for these other 50 cases—and therefore will be added to the new cases detected in the year 2004. In financial terms, it is as though one quarter of the total cost of drugs used for the whole of the 100 patients was actually incurred in year 2004I. However, in year 2004, the same phenomenon occurs with the same numbers, if

38

Adverse effects of drugs may induce changes in drug regimens involving a single drug or entire regimens, as well as an addition of drugs to manage side effects.50 The model does not attempt to quantify the cost of these variations in, or additions to, drug regimens for patients with drug-sensitive TB. With MDR-TB, which requires more powerful drugs, the cost of drugs to manage side effects has been accounted for, based on what is done in a specific DOTS-Plus program in Peru.XXVII The intervention selected for the prophylactic treatment of MTB-HIV co-infection is a six-month isoniazid regimen that has proven to be highly cost-effective.51 The cost of this regimen is currently accessible, at $1.6752 (this does not include costs of procurement, storage, and losses). However, the cost of the isoniazid regimen is currently $6.84 in Morocco,53 and is also probably higher in eastern Europe and Latin America, where drug prices are high. A number of other prophylactic strategies are available, but at a higher costXXVIII; these include a rifampin-pyrazinamide regimen twice weekly for two months, as well as an isoniazid-rifampin regimen daily for three months. The use of co-trimoxazole, a well-known antibiotic recommended as a first-line drug against bacterial pneumonia, could also be included in the estimates, although co-trimoxazole does not combat TB specifically.54 Indeed, it is regularly used in some countries55 to prevent the onset of bacterial infections, principally in HIV-positive patients; some of the first ProTEST projects have included it.56 However, its practical application to the chemoprophylaxis of TB-HIV co-infected patients still has to be clarified. Consequently, the costs of such specific interventions have not been assessed. Future information will be integrated in proportion to the role that this aspect of TB control plays in national programs. The cost of diagnostic supplies are estimated by multiplying unit costs of reagents and other smear test or X-ray supplies by the average number of sputum-smear tests (or X-rays) done for each case. The latter is normally provided by NTPs with their budget projections. When not specified, it is assumed that 10 smear tests (with examination of three sputa each time) are needed to detect one smear-positive case. Consequently, given the fact that smear tests are conducted at least three more times during treatment, an average of 33 sputum examinations will be conducted for smear-positive cases and zero for smear-negative cases.

XXVII Data provided by Socios En Salud, Lima, Peru in 2001. Data cover patients from 1996 to 2000. XXVIII According to data used in the study by Bell et al. (Bell JC., Rose DN., Sacks HS., Tuberculosis Therapy for HIV-infected People in sub-Saharan Africa is Cost-effective, AIDS, 13:1549 – 1556, 1999), the health-care cost of preventive therapy using isoniazid daily for six months was $22.95, (not including the use for treatment of secondary effects), compared with $36.67 for isoniazid in combination with rifampicin daily for three months, and $44 in the case of rifampicin in combination with pirazinamide. These costs include diagnosis, chemoprophylaxis, and salaries of the medical and program administration staff.

39

Regarding MDR-TB, in the absence of hard data on the cost of protocols that are significantly more complex than regular smear tests, diagnostic costs are not yet fully covered by the present model. The selection of protocols—whether standardized or individualized—is also an important factor in the design of the diagnostic and treatment protocols and drug regimens. In the absence of nationwide documented DOTS-Plus or equivalent programs, (with the exception of some Peruvian data not sufficient for specific extrapolation) the evaluation is complicated. Consequently, a provision to cover these costs has been added to other costs associated with MDR-TB treatment, as discussed in the following section.

2.3.6. Evaluating other program and intervention costs Other program and/or intervention costs principally cover factors not directly linked to the number of TB cases treated. These factors mostly result in “fixed” costs; that is, costs for which the total is not directly related to the number of patients treated. These costs may vary in proportion to the number of administrative units covered by the program or the percentage of the population that has access to it: salaries, transportation, and other recurrent expenses of the program and/or intervention activities such as administration, monitoring, training, supervision, and operational research are representative of such costs. On the other hand, financial and nonfinancial incentives behave as variable expenses, as they are usually related to the number of cases treated. In Brazil, for example, a fixed sum is paid to municipalities for each case detected.57 The amount is higher for infectious cases detected and successfully managed by a DOTS program. The model uses the input breakdown shown in Section 2.2.1. When data were missing in the national plan or when there was no plan at all (as was the case in a few of the 22 HBCs and all other countries), extrapolation techniques were used to develop inputs for all countries, as detailed in Section 2.3.8. No extrapolations of incentives costs have been conducted, as these correspond to policies specific to each country. Most national TB programs for which multiyear plans were available provide well-specified and adequately detailed treatment of these other program costs, apart from costs for interventions to fight MDR-TB and TB-HIV co-infection. As noted previously, little data are presently available for these two important aspects of TB control. For MDR-TB treatment, data were available only from Peru and South Africa (see Section 3). Breakdown of program costs other than drug and diagnostic costs were available only from a Peruvian program,58 including costs for program administration at central and local levels; training (initial and routine); supervision; incentives (food parcels); and ambulatory visits. Incentives included in the Peruvian model were not replicated in other countries’ estimates, because such incentives correspond to local adaptations of generic programs. As the present model can extrapolate only from one or two sources, the accuracy of the present evaluation is necessarily limited. Specific costs associated with the diagnosis of MDR-TB still have to be fully documented, thereby increasing the range of costs associated with MDR-TB that have probably been undervalued. Given the scarcity of hard data, and drawing on the analysis of DOTS expansion programs, the unit cost of resources associated with the management of MDR-TB (hospitalization costs included) has been inflated by 10% to attempt to capture at least in part up-front costs that seem not fully integrated into the available data.XXIX This will obviously have to be reevaluated as new information is available on DOTS-Plus programs. Similar comments apply to the prophylactic intervention chosen for the evaluation of TB-HIV co-infection strategies. The data available (see Section 3) provided limited detail regarding non-drug costs, and certainly omitted some very important aspects of such interventions. Indeed, in order to be implemented and widely

XXIX In other words, an input that cost $100 was evaluated at $110 in the model.

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used, these interventions require initial training, equipment (for example, for screening and testing), specific administration, supervision, monitoring, communication to the general public to publicize the programs and other activities probably not fully contemplated here. As with MDR-TB, the present model bases its calculations on limited available data (principally from Uganda), which covered some screening costs for MDR-TB apart from drug costs (the latter including drugs for side effects in 6% of cases59) and diagnostic supplies costs. The unit costs of the non-drug costs associated with the preventive therapy were therefore inflated by 50% to account for implementation and upscale costs not contemplated in the available data, and for possible understatement of the recurrent program costs associated with the lack of methodological material and data. TB-HIV co-infection programs and interventions couple activities associated with the management of both infections. In most places, HIV/AIDS programs are likely to be in charge of the HIV-related activities, whereas NTPs take care of TB-related activities. But with integrated programs such as ProTEST, drawing a line between budget responsibilities is not easy. Consequently, the range of possible cost estimates is huge, especially given the high price tag of HIV-related activities such as testing, counseling, and treatment. The present work may underestimate the full program costs, notably due to the present, almost total absence of usable data. Finally, ProTEST or equivalent programs and interventions require specific implementation activities (such as training and equipment acquisition) not fully contemplated here, owing to the current scarcity of data. A unit cost per HIV-positive person being detected by a ProTEST project in Zambia supplemented some important aspects of these programs; but with no detail of the costs nor the methodology used to assess them, the accuracy of the present evaluation is, de facto, quite limited. Revisions to TB-HIV co-infection cost estimates will need to be conducted as more data become available concerning both preventive strategies and ProTEST programs or equivalent. In conclusion, the unit costs used in the estimates for MDR-TB and TB-HIV co-infection interventions could be significantly understated, even with provisions added to non-drug costs. It is possible, on the other hand, that the estimated numbers of cases detected and treated appropriately may be too high, especially considering the present limited extent of the base-programs from which they are derived. In the case of MDR-TB treatment, it is assumed that DOTS-Plus programs now being implemented will progressively be up-scaled and will cover most of the “hot spots”XXX by 2005, leading to an estimated 40% of all cases being appropriately treated during the 2001 – 2005 period. In the case of TB-HIV co-infection, assumptions included in the scenario described in Section 2.3.4 (Tables 6 and 7) yield a 10 to 12% case detection rate worldwide, with 86 to 88% of the total number of cases concentrated in sub-Saharan Africa. These proportions might be seen as too high, but one objective of the Global Plan to Stop TB is to assess what the cost would be to achieve ambitious yet necessary goals, such as those set for DOTS expansion. This philosophy has driven the estimates for MDR-TB and TB-HIV co-infection-specific interventions and programs as expressed in Section 2.3.4.

2.3.7. Evaluating the cost of health-care services As anticipated in Section 2, it has been assumedXXXI that in the long run the unit cost of a bed-day or a visit to a primary health center—including a share of all common activities within the premises and a share of costs for equipment and building depreciation—is a good proxy for resources used per case handled. Statistics on capacity use of health-care systems should be introduced for each country to better evaluate whether the incremental use of these services will necessitate more investment. However, TB is not the only

XXX Areas where the proportion of all TB cases that are multidrug-resistant equals or exceeds 5%. XXXI After discussion and in accordance with Dr. Katherine Floyd of Stop TB, WHO, who has prepared the economic evaluations for the Global DOTS Expansion Plan (World Health Organization, Global DOTS Expansion Plan. Progress in TB control in High-burden Countries: One year after Amsterdam, WHO/CDS/STB/2001.11, World Health Organization, Geneva, 2001).

41

condition requiring resources from health-care systems. Therefore, such evaluations should, ideally, be included in a full assessment of health-care facilities’ capacity with regard to all needs. At the moment, the model assumes that each additional case of TB requires the same marginal resources as those required by managing one patient in the present configuration of the health-care system. This assumption probably exaggerates the real cost. For example, in Russia and South Africa, interning patients for all or part of their TB treatment is routine. But ambulatory care with DOTS can yield cure rates similar to those in Russia and South Africa for a much lower unit cost,60,61 as the evolution of TB control in Cuba has shown. (The network of Cuban sanatoria was closed, and ambulatory care became routine after the introduction of DOTS (“TADO” in Cuba) as early as 1970.62) But implementing new protocols is a complex process requiring a strong political and social commitment, as well as substantial initial investments (particularly in training and health education). To reflect differing scenarios, costs of health-care services are presented as a range. The lower end of the range assumes that the present capacity of all health-care systems is sufficient to absorb all additional cases detected in the five-year period in countries included in the analysis. The upper end estimates the total cost incurred if all systems have to be upgraded in proportion to the increase in the number of patients. The model currently does not differentiate case management by category (smear-positive or smear-negative; first-line or second-line regimen; new, relapse, or retreatment). This is due to the complexity of disaggregating data for many countries, and the current unavailability of disaggregated data as well. The only exception is the inclusion of the Peruvian data regarding the management of second-line patients affected by MDR-TB, for which some data were available regarding hospitalization and ambulatory care. Data from Peru were extrapolated to all countries.

2.3.8. Extrapolations As discussed in Section 3, only limited data are available for most low- and middle-income countries. Most data come from the NTPs of HBCs that have prepared TB-control plans, and from a few other selected countries. These sources were used as a foundation for extrapolating worldwide costs for TB control. However, a few words of caution regarding these extrapolations are required to outline their limitations. First, countries vary widely in regard to their economic structure and sophistication. Consequently, the cost structure of health services and the relative weight of each input in the total cost of health services and the relative weight of specific programs vary. Second, epidemiological situations differ greatly between countries. Incidence rates can vary among regions in the world, as well as among countries on the same continent. For example, the incidence rate for TB is more than four times higher in sub-Saharan Africa than in northern Africa and the Middle East (see Table 2). Sri Lanka (19 million inhabitants) has 60 new cases for every 100,000 inhabitants each year, which is one-third to one-fourth the rate of the rest of the Indian sub-continent (which consists of approximately 1.3 billion people). However, Sri Lanka has been clustered with the other nations of the sub-continent due to its relatively comparable economic structure and the small proportion of its population within the cluster. Thirteen clusters have been designed to group countries according to their socioeconomic structure or recent historical profiles and epidemiological characteristics. Table 2 lists these clusters and a few important indicators that characterize them. (The countries in each cluster are listed in Appendix B.) Four clusters corresponding to the most advanced industrialized regions have been entirely excluded from the economic evaluation process. However, they were used to estimate the total number of TB cases likely to occur in the 2001 – 2005 period. The nine clusters representing low- and middle-income countries include 114 countries (see Appendix B).

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One goal of the present study was to gather as much available data as possible for each cluster, in order to conduct separate extrapolations within each cluster. Of the nine clusters used for calculations, only one—the Middle East and northern Africa—does not include one of the 22 HBCs. However, a number of studies conducted in Morocco,63 Egypt,64 and Syria,65 were used to help work out financial estimates for this region. Sub-Saharan Africa has been divided into two sub-groups, because several of the countries located in the southern part of the continent have a higher income per capita, reflecting more complex and relatively industrialized economies. In the case of sub-Saharan Africa and “less industrialized” Latin American and Caribbean clusters, the clusters have been divided into sub-clusters whenever there was enough information to do so. For example, with eight HBCs belonging to the sub-Saharan Africa cluster—thus providing a relative wealth of data for a very big cluster—it was possible to create sub-clusters. This was done input by input, according to the availability of data and the depth of the data’s detail. Examples are given in Section 3. The most detailed data available regarding the cost of implementing and expanding DOTS programs in the largest number of countries possible were collected. This principally covers the 22 HBCs surveyed by WHO in 2000. Estimates resulting from this survey have recently been published in the GDEP.66 Extrapolations were conducted on the basis of the data obtained from neighboring countries with similar economic and epidemiological patterns.

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Table 8. Regional Clusters

(All Countries, Regardless of Their Inclusion or Exclusion in the Financial Estimates)

Average GNP/Cap (in 1999 $)(a)

Number of countries & territories

Number of HBCs in

the cluster

Total mid-1999 population

(in millions)(a) At exch.

rates At PPP(b)

Average estimated incidence

rate of TB(c)

Austral Africa (AA) 7 1 50 3,000 7,800 494 Sub-Saharan Africa (excluding AA) 41 7 582 290 900 326 Middle East and Northern Africa 19 0 367 2,300 4,600 60 Western Europe and High-Income Mediterranean(d)

25 0 395 22,200 21,100 19


27 1 413 2,150 5,650 92

North America(d) 4 0 304 29,450 29,900 6 Urbanized Southern Cone (USC) (South America)

4 1 223 5,000 7,300 64

Latin America (excluding USC) and Caribbean

36 1 285 2,900 5,200 85

Indian sub-continent 7 3 1,303 450 2,050 188 China, Indochina, and Misc. Asia 8 5 1,450 700 3,000 119 Industrial Eastern Asia(d) 6 0 206 25,750 21,500 43 Southeast Asia 6 3 376 1,100 3,600 251 Pacific(d) 22 0 26 17,250 19,600 13

Totals 212 22 5,980 4,810 6,385 141

Total Low & Lower Middle-Income Countries(e)

114 22 4,727 915 2,890 171

Total 22 HBC 22 22 3,731 874 2,886 178

(a) Source: World Bank, World Development Indicators 2000. (b) “PPP” stands for “purchasing power parity,” which is a correction applied to conventional GNP/capita that are

first translated into one international currency (usually the U.S. dollar in World Bank, IMF, and U.N. agency documents) and then into “international dollars,” and which takes into account the fact that exchange rates introduce a certain distortion between the real purchasing power of one dollar in any given country and the purchasing power reflected by the exchange rate-based figures. As a result of the correction, most nonindustrial countries see their purchasing power-adjusted GNP inflate; higher-income countries typically see their purchasing power-adjusted GNP decrease.

(c) On the basis of data provided by WHO’s TB Control Global Report 2001 (estimated incidence rates as of 1999).

(d) These clusters have been excluded from the economic/financial evaluations. Their incidence rate is low and their public health resources are large enough to tackle the situation without external help.

(e) According to the World Bank (Development Indicators, 2000) low and lower middle-income countries include all countries in which GNP/capita is lower than $2,996 (calculated with exchange rates), as of 1999.

Recurrent program costs (including costs for administration, monitoring, supervision, operational research, training, and surveillance) consist of salaries, transportation costs, utilities, and miscellaneous overhead expenses, which are mainly non-traded costs (see Box 2). They also include maintenance and replacement

44

of equipment (including vehicles, facilities, and computers). Evaluating these costs requires a provision for replacement costs. Because most equipment items in the low-income countries covered by this study are imported, the share of these recurrent program costs (approximately half of the total in Tanzania, for example) should be considered as an imported cost. The remaining part of the cost corresponds mostly to local inputs, the level of which depends on the number of TB cases and the geographical and administrative characteristics of the country. The extrapolation exercise for administration costs took into account the distinction in the nature of resources between local and imported. For example, using data provided by the Tanzanian plan,67 administrative and related unit costs were computed for both the local and the imported part of the total cost of administrative activities in Tanzania from 2000 to 2004. The local component was evaluated on the basis of a unit cost per patient (therefore, total cost depends on the number of patients treated) and adapted to local costs of neighboring countries using PPP GNP/capita. The imported component was also based on the number of patients but was left unchanged in unit dollar value because prices are not likely to change significantly from one country to another within the same cluster. Theoretically, another cost driver for recurrent program costs is the very existence of a country or a program. But unavailability of minimal cost studies regarding this aspect of program costs impeded more refined evaluation. The extrapolation technique previously described allowed for the creation of program costs wherever TB occurred in low- and middle-income countries. The geographical expansion of any given program affects equipment costsXXXII as well as training and administration costs (especially when there is initially no program whatsoever). Indeed, these costs are more related to the extent of coverage than to the number of cases treated. Nevertheless, a small number of cases do not require the same amount of infrastructure and program administration as does a large number. So, to some extent, these fixed costs are also related to the size of the patient pool. In the case of training costs, the size of the country and of its population influence the amount of the expense. Training in a large country can require air transportation for attendees, whereas in smaller countries auto or train suffice. Consequently, the unit cost of a training session is higher per participant in a large country than in a smaller one of comparable per capita income and economic structure. This was determined by discussions with an expert of the Pan-American Health Organization (PAHO) who had experience in Latin America and who provided costs and cost estimates of training sessions for small countries such as Nicaragua, as well as large countries such as Argentina.XXXIII

XXXII Data regarding the density of the laboratory network throughout Africa has been adapted from available data. The Nigerian plan, for example, anticipates that 2.3 microscopes will be available for every 100,000 inhabitants. The Ethiopian plan assumes that density will be 1.5 microscopes per 100,000. This latter proportion was used throughout the sub-Saharan African and Austral-African clusters, except for Nigeria, where the proportion is set at 2.3 microscopes per 100,000, even though WHO recommends the density should be 1 microscope for every 100,000 inhabitants. XXXIII Discussion with Dr. José-Ramón Cruz of PAHO (based in Washington, D.C.), and data from Nicaragua and Argentina.

45

Box 2: Non-Traded Goods and Services A study focusing on the relationship between the salaries of health-care workers and the GNP/capita expressed in international dollars (that is, the GNP expressed in terms of “PPP” or “purchasing power” parity) shows that there is a significant correlation. (Appaix O., Correlation between GNP/capita and Salaries in the Health Care Sector. 1998, unpublished). The general conclusion of the study is that the lower the GNP/capita, the higher the ratio between the annual salary of health-care workers and the GNP/capita of the country they work in. Based on 1995 data from the International Labor Organization, for the poorest of sub-Saharan African countries, the ratio of salary cost to GNP/capita was as high as 8 for auxiliary nurses and 9 for general physicians. For market-based, highly industrialized countries, these ratios were approximately 1 and 2 respectively. However, due to the specific socioeconomic history of these countries, former socialist economies do not follow the trend. The ratios found for these latter countries are not predictable by the regression model (which required the use of a dummy variable). Indeed, while being middle income countries, they have ratios comparable to those of established and wealthy market-based economies. This means, for example, that, relative to the local GNP/capita, the salary of a nurse in the Russian Federation is comparable to that of a nurse in France, but much lower than that of a nurse in Tanzania, even though it is much higher in absolute dollar terms.

G e n e r a l P h y s i c i a n - 1 9 9 5

0 . 0 0 %

1 0 0 . 0 0 %

2 0 0 . 0 0 %

3 0 0 . 0 0 %

4 0 0 . 0 0 %

5 0 0 . 0 0 %

6 0 0 . 0 0 %

7 0 0 . 0 0 %

8 0 0 . 0 0 %

9 0 0 . 0 0 %

1 0 0 0 . 0 0 %

0 2 , 5 0 0 5 , 0 0 0 7 , 5 0 0 1 0 , 0 0 0 1 2 , 5 0 0 1 5 , 0 0 0 1 7 , 5 0 0 2 0 , 0 0 0 2 2 , 5 0 0 2 5 , 0 0 0 2 7 , 5 0 0 3 0 , 0 0 0 3 2 , 5 0 0 3 5 , 0 0 0

G N P / C a p i t a ( 1 9 9 5 d o l l a r a m o u n t s )

% o

f G

DP

/Cap

ita (

in lo

cal c

urre

ncie

s)

H i g h - i n c o m e c o u n t r i e sS o u t h K o r e a

S u b - S a h a r a n c o u n t r i e s

F o r m e r l y s o c i a l i s t e c o n o m i e s

C o s t a R i c a

D o m i n i c a n R e p u b l i c

A u x i l i a r y N u r s e - 1 9 9 5

0 . 0 0 %

1 0 0 . 0 0 %

2 0 0 . 0 0 %

3 0 0 . 0 0 %

4 0 0 . 0 0 %

5 0 0 . 0 0 %

6 0 0 . 0 0 %

0 2 , 5 0 0 5 , 0 0 0 7 , 5 0 0 1 0 , 0 0 0 1 2 , 5 0 0 1 5 , 0 0 0 1 7 , 5 0 0 2 0 , 0 0 0 2 2 , 5 0 0 2 5 , 0 0 0 2 7 , 5 0 0 3 0 , 0 0 0 3 2 , 5 0 0 3 5 , 0 0 0

G N P / C a p i t a ( 1 9 9 5 d o l l a r a m o u n t s )

% o

f GD

P/C

apit

a (i

n lo

cal c

urr

enci

es)

46

2.4. Cost Annualization The Global Plan to Stop TB concentrates on the effort required over the 2001 – 2005 period to reach WHO goals for TB control. Consequently, costs cover this same period. A number of national programs, however, have five-year plans that span different periods of time. For example, Thailand (which belongs to the high-burden group) did not have a five-year plan including 2005 at the time of the completion of this report, but had a 1997 – 2001 plan. Other countries have TB-control plans that cover the 2000 – 2004 period, as in the case of Vietnam and Myanmar. Data from these plans have been adapted in order to match the time frame used here. Year 2000 costs have been removed and extrapolations to 2005 of 2004 recurrent costs have been conducted. In some but not all cases, national plans detail the financial requirement year by year. It would be better for financial planning reasons to do this, and the GPSTB does attempt to annualize costs to reflect the phasing in of resources required by the plan, especially as implementation and expansion costs are likely to be concentrated in the first two to three years. Recurrent costs will increase as more cases are detected, due to better coverage and higher detection rates. Efforts to reduce prices, as in the case of drugs, as well as increased productivity due to lessons learned from larger programs will contribute to unit cost reductions. However, expanding programs to remote areas will most likely pull marginal unit costs up. In India, for example, the national program needs to reach populations in remote and mountainous areas where the cost of access will be higher than in more accessibly areas.68 In Vietnam, which already covers most of its population, the NTP now plans to reach homeless people and populations in mountainous areas not yet covered.69 Annual estimates have been computed on the basis of available data for the HBCs combined with the annualization of the number of TB cases detected, based on information in Sections 2.3.1. and 2.3.2.

2.5. Price Contingencies Although inflation is another important aspect of financial planning, present valuations have been used because the objective here is to identify the global financial requirement over the next five years only. Consequently, costs are evaluated in 2000 U.S. dollars. Whenever data were given year by year and inflation rates had been taken into account and specified, costs were deflated to bring them back to 2000 values. This can be justified because most current budgets are based on known costs as of 2000. These budgets do not anticipate possible—and probable—price adjustments, particularly in the case of drug prices. Thanks to international procurement strategies—the GDF and other initiatives—prices are likely to decrease further. However, without precise information regarding future developments, costs computed here reflect the level of prices quoted in national plans or other sources, mostly as they are currently incurred or as they have been recently pledged by drug manufacturers. However, given the recent history of steep price declines in second-line drugs, a further and modest 20% decrease for expensive drugs has been anticipated throughout the plan period. As new information concerning drug prices, the use of general health services, and the cost of diagnostic consumables appears in the future, total costs may be adjusted. Annual estimates could also be adjusted using more refined projections for case detection levels, prophylactic strategies, MDR-TB treatments and TB-HIV co-infection-specific programs.

2.6. Ranges and Approximations

47

In some cases, the level of uncertainty (either in basic data or in the evaluation process) was high enough to require the computation of a range of estimates instead of one single result. Notably, investment to upgrade health-care services and the TB-specific infrastructure may vary widely from country to country. Moreover, as noted in Section 1.2., some aspects of TB control are relatively new and require more investigation to further understand their full economic characteristics and assess their financial burden. This is the case for the treatment of MDR-TB patients enrolled in DOTS-Plus programs, as well as for prophylaxis of TB-HIV co-infected patients. In the case of MDR-TB, the two epidemiological scenarios described in Section 2.3.4 yield two different sets of costs. Also, some cost inputs are subject to change and are currently highly volatile. This is the case for second-line drugs. As the search for cheaper drugs goes on, new information that progressively changes the financial panorama of TB control is constantly made available. Consequently, upper and lower ends of estimates have to be calculated. Scenarios can also be constructed from available information to simulate what costs would be in a number of different circumstances. Regarding TB-HIV co-infection prophylactic treatments, two scenarios have been evaluated according to the extent to which specific programs and interventions (whether ProTEST or the equivalent) would be implemented. One covers only 12 sub-Saharan countries where ProTEST has already been started or is to be started in the near future; the other estimates the resources needed to cover countries where HIV infection affects at least 1% of the adult population, or where HIV is a rapidly mounting problem, as in Brazil, Russia, India, and China. (Peru is also included in this group, as it already uses preventive therapy as part of its TB-control strategy.) The one-percent threshold was chosen because TB-HIV co-infection programs are expensive programs that may be easier—at least in the early phases—to design and implement wherever co-infection is highly prevalent. For the costs of health-care services, two scenarios have also been evaluated. The lower-end scenario suggests that no additional cost to health-care systems would be incurred by the additional caseload created by higher case detection rates. The upper-end scenario suggests that all health-care services already work at full capacity, and that every additional case will result in a proportionate incremental cost, equivalent to the unit cost of treating one patient in the present configuration. Assumptions regarding the unit costs used are discussed in Sections 2.2.1, 2.3.5., and 2.3.6. The present model assumes that all patients treated through a given DOTS program require the same number of visits (specific to each country, as shown in Appendix C3) and that non-DOTS patients visit a health center an average of two times. As for hospitalizations, it is assumed that a portion of all patients will be admitted to a hospital regardless of which program addresses their condition. It is also assumed that hospitalization practices (as evaluated country by country) will not be modified throughout the five-year period, even though DOTS is recommended to be administered on an ambulatory basis. For DOTS programs and other first-line strategies, no range has been computed, as most of the NTPs that provided information did not calculate ranges. See Section 3. Although ranges of financial and economic estimates are discussed in Section 4, only one estimate per category of TB control has been reported in the presentation of costs included in the GPSTB’s main document. This decision was made in order to clarify the presentation and focus on a “best estimate” approach. However, the present document, which serves as an appendix to the GPSTB, needs to evaluate and discuss the range of possibilities, given the variety of situations and scenarios covered by TB control. Costs of health-care services, as well as costs associated with MDR-TB, still require the discussion of such ranges at the present level of knowledge.

2.7. Financial Gaps

48

One of the main objectives of the GPSTB is to identify the financial shortfalls of current TB-control activities and help raise funds to bridge those gaps and allow NTPs to reach their objectives. However, NTP documents available for the elaboration of these estimates (that is, for DOTS and other first-line strategies) do not identify such gaps. As a consequence, WHO conducted surveys of the financing status of the TB-control efforts in all high-burden countries. Results of these surveys were discussed with WHO officials, and are used in the present evaluations. The same proportion of identified or likely gaps in the total requirements were then extrapolated to all other low and middle-income countries included in the estimates. One problem with those gap estimates is the fact that in many cases—especially in the first versions of those estimates—unknown allocations or even the likelihood of the host country being able to obtain the funds was very common, leaving much room for speculation regarding the eventual materialization of the expected funds. As a consequence, first estimates—delineated in the early version of the GPSTB and of the Economic Annex released in October 2001—were very conservative, virtually eliminating all funds for which a final destination was unknown. Further analysis conducted by WHO and published recently70 allowed a more refined utilization of the data as more became known of actual financial commitments and pledges to the national programs of the 22 HBCs. This helped reduce the estimated financial gap for NTPs considerably. Regarding MDR-TB and TB-HIV co-infection control strategies, however, no hard data are currently available regarding funding requirements and shortfalls. As a consequence, the evaluation of likely financial gaps for these categories of TB control was based on the following interpretation of the current status of TB control: because those strategies are only now being developed on a wider scale and were not integrated into NTP estimates—with the exception of Peru—it is probable that most of the costs associated with those strategies will require new funding, and that most of the costs identified are not currently covered by national funding schemes. This underfunding is also due to the size of the financial requirement, and leads to a very gross approximation of what financial gaps are likely to be for those categories of TB control. The concentration of the costs in certain countries (for example, South Africa, in regard to costs associated with TB-HIV co-infection control strategies) also drove the evaluation process. As MDR-TB is a problem concentrated mostly in low-income countries—with the exception of the Russian Federation and Peru—an estimated 80% of the costs projected for all countries to tackle MDR-TB during the five-year period will not, at present, be matched by nationally available funds. Regarding TB-HIV co-infection control strategies, cases are principally concentrated in sub-Saharan Africa, with South Africa being the country most affected by the dual infection. (Being a higher middle-income country, South Africa is characterized by a high cost structure, though it is included in the estimates because it is also a high-burden country). Consequently, South Africa constitutes a high proportion of the global estimate for TB-HIV co-infection control strategies. It was estimated that this country would be more resourceful in facing the expansion of such strategies. Commitment of national funds from the Department of Health was recently renewed for the ProTEST pilot projects, while plans have already been designed to cover the entire country with ProTEST programs by 2005. External funds from the Belgian government have been committed to participate in this expansion over the next five years.71 Overall, as discussed in Section 4.5., it was estimated that approximately 50% of the total requirement to fund TB-HIV co-infection control strategies will still be missing after national funding and early external commitments are known. Overall, funding gaps are relatively well known for DOTS expansion and running plans, while their evaluation is still totally preliminary and even arbitrary for other aspects of TB control. Much more information needs to be known in order to refine these estimates. The current development of MDR-TB and TB-HIV co-infection control strategies, along with the development of financial plans and budgets, will provide much-needed information on these critical aspects of TB control as a whole.

49

3. Data Data sources by country are detailed in Appendix C1. Appendices C2 through C5 display basic data used in the costing process by country. Numerous exchanges through e-mails, telephone, fax, and direct conversations (in particular at the November 2000 meeting of the 22 HBC national TB control coordinators in Cairo, and at the Paris meetings of the WHO Working Group on DOTS Expansion and the annual IUATLD conference) have taken place, in order to refine the data and double-check a number of figures and assumptions. The following areas were examined to derive information for this study:

• Epidemiology: Incidence and notification rates were extracted from WHO’s Global Tuberculosis Control Report 200072 and Christopher Dye’s “Global Burden of Tuberculosis,” published in the Journal of the American Medical Association in 1999.73 A number of national plans or review documents used to construct estimates in the present study did not always clearly specify the number of cases to be detected and treated. This was particularly the case for Nigeria74 and Tanzania.75 Even for countries such as India76 and China,77 numbers of cases were at times not clearly linked with budgetary estimates. The evaluation model proved to be reasonably accurate in estimating cases for the five-year period when its estimations were compared with the actual number of cases for countries in which such information was available. When the difference between the model’s prediction and the number provided by the national plan or the review document was significant (that is, more than about 10%), drug and diagnosis costs evaluated by the NTP were directly inserted and not computed. Proportions of MDR-TB cases were extracted from three main sourcesXXXIV from which two scenarios were built to evaluate the number of patients likely to be detected and treated (see Section 2.3.1). Data from WHO (Christopher Dye) and UNAIDS78 were used to evaluate the number of TB-HIV co-infected patients who were likely to be candidates for prophylactic treatments.

• Demography: Population levels and their increase rates were taken from the World Bank’s World Development Indicators 200079 and the Institut National d’Etudes Démographiques’s database 2000 (downloaded from http://www.ined.fr). All basic demographic data correspond to 1999. Population changes during the 2001 – 2005 period were calculated from the 1999 population figures multiplied by the increase (or decrease) rates, assuming no change in the latter throughout the 2001 – 2005 period.

• Drugs: Unit costs were available for a number of countries covering each of the nine clusters used for costing. When costs were available for several countries in a given cluster (as in the case of sub-Saharan African), unit costs were used for all neighboring countries. For example, costs available for Ethiopia have also been used for Sudan, Somalia, Eritrea, and Djibouti, XXXV whereas cost data for Tanzania have been used for the southeastern part of Africa. Cost data from the Nigerian plan were used for the surrounding countries and western Africa, and that of Congo (DR) for the other Congo, Cameroon, and the Guineas, as well as for Rwanda and Burundi. It is assumed here that the cost data provided by national programs hold steady for the next five years, even though, in some cases, the data are already changing. Importantly, unit costs used for first-line and

XXXIV These are: Espinal et al., Global Trends in Resistance to Antituberculosis Drugs, New England Journal of Medicine, Vol.344, n°17, April 26, 2001; World Health Organization, Anti-Tuberculosis Drug Resistance in the World, Report n°2, Prevalence and Trends, the WHO/IUATLD global project on anti-tuberculosis drug resistance surveillance, WHO/CDS/TB/2000.278, Geneva, 2000; and Dr. Mercedes Becerra of Harvard Medical School and Partners In Health (2000), who provided estimates for countries with no data. XXXV These countries, together with Kenya, actually participate in a joint program (the Horn of Africa Tuberculosis Control Initiative) aiming at the standardization of TB-control strategies in their border areas.

50

retreatment regimens in the model have been applied to all forms of TB (except forms of multidrug-resistant TB, which are treated specifically). Actual treatment costs can vary according to the type treated: smear-positive, smear-negative, or retreatment. In sub-Saharan Africa, for example, the average cost of the regimen for a new SS+ case was estimated at $43, while it was $25 for a new smear-negative case and $86 for a retreatment case.80 However, because the model does not discriminate between forms in the process of costing treatment, an average treatment cost was used for each country, based on data provided by the NTPs. On average, the unit cost of the treatment regimen was, for example, set at $52 in South Africa, $38 in Botswana, $41 in Côte d’Ivoire, and $30 in the DR of Congo. These costs were used for neighboring countries of sub-clusters in the part of the world that was in question; in doing so, a 20% provision to cover losses, storage costs, and the creation of security stocks was integrated into the figures.

• Reagents and other diagnostic supplies: Unit costs have been calculated for consumables only, and therefore do not include depreciation of equipment. Beyond the very local cost of sputum smear microscopy, the unit cost of diagnosis is profoundly altered by the use or nonuse of X-rays and cultures. The latter are more commonly used among industrialized countries, including those of the former socialist bloc. Consequently, these techniques have been assumed to be used throughout eastern Europe and the former Soviet Union. When only sputum smear is conducted, the total unit cost averages $3.60 in the poorest countries, such as most of sub-Saharan Africa and the Indian sub-continent.81 With the inclusion of X-rays and cultures, the total unit cost jumps to $39 in Ukraine (with salary costs included). Approximately 33 sputum smear tests are conducted for each smear-positive case detected (see Section 2.3.5). Smear-negative cases do not require further smear tests. The model therefore differentiates smear-positive and smear-negative cases in the computation of diagnostic costs. When NTPs provided data, those data were also used for the other countries in the same cluster (or sub-cluster in sub-Saharan Africa). In some cases, however, due to the complete absence of data, inputs used the $3.60 estimated average (particularly in the case of Middle Eastern and Northern African countries). • Training, administration, monitoring, communication, incentives, and operational research: Data were taken from national TB control plans or from direct exchanges with national program officials and extrapolated to nearby countries of the same cluster or sub-cluster, as described in Section 2.3.6 and 2.3.8.

• Costs associated with MDR-TB treatment: There were very few sources for data regarding costs associated with MDR-TB treatment, as there is still very little published information about the economic dimensions of MDR-TB. Drug costs were available for Peru, Russia, and Brazil. Sources from Peru82 were used to evaluate costs associated with the disease, other than second-line drug costs. Diagnostic test costs were also available for South Africa (see Appendix C3). Extrapolation methods described in Section 2.3.8 were used to cover all countries for which estimates had to be calculated. However, the use of the little data available and the extrapolation techniques are obviously insufficient to fully understand and account for the magnitude of the economic impact of MDR-TB, particularly where direct costs are concerned. Drug costs are believed to represent the biggest share of the total cost of treating MDR-TB (probably around 40 – 50%), but specific DOTS-Plus program costs will have to be evaluated. Specific training, monitoring, and supervision or surveillance costs were available only for a national program in Peru, with no indication of specific implementation costs of MDR-TB control strategies. A 10% provision was therefore added to unit costs.

• TB-HIV co-infection prophylaxis (6-month course of isoniazid): Cost data regarding preventive therapy were principally provided by an article on the cost-effectiveness of prophylactic interventions in Uganda,83 as well as by a report from Peru.84 These data obviously are quite insufficient to build strong estimates. A 50% provision for implementation and expansion costs was

51

added to the unit costs available. Extrapolation methods described in Section 2.3.8 were used. Preliminary data regarding ProTEST program costs were presented recently (November 2001).85 The data cover a pilot project in Zambia, but still need further analysis. Despite efforts to obtain more details concerning the methodology and cost inputs used, these critical aspects of the evaluation process were not available. No other provision was added to unit costs already available for ProTEST programs, as a 50% provision has already been accounted for in the program-specific costs of the preventive therapy.

• Health-care services: Data on the costs of bed-days and visits to health centers (or visits of health workers to patients) were taken from a variety of sources. For Africa, a document provided by Katherine Floyd of WHO provided much-needed information for a number of African countries (see Appendix C3). For other countries, reports and communications with experts and program coordinators from Peru (op cit.), Russia, China, the Philippines, Cambodia, and Vietnam were used (see Appendix C3 for details). Two cost-effectiveness studies on the programs in Syria86 and Egypt87 also provided costs of bed-days and visits. A cost-saving analysis on the program in Poland88 provided similar sets of data. Numbers of bed-days spent on average by patients (and proportions of patients hospitalized) were taken from a survey conducted among program coordinators of the 22 HBCs (see Appendix C3). Direct interviews and follow-up by fax and electronic mail were used to collect data. The survey was conducted by three economists: Katherine Floyd and Chika Hayashi, from WHO Geneva, and Olivier Appaix. An estimate of the total cost of health-care services in the treatment of TB in the Russian Federation was discussed with Katherine Floyd of WHO and reflects data published in the Global DOTS Expansion Plan.89 • R & D: Evaluation of efforts to develop new diagnostic, therapeutic, prophylactic, and policy tools was essentially based on data provided in Chapter 5 of the GPSTB, as well as through discussions with experts (including Paul Nunn, coordinator of Chapter 5, Giorgio Roscigno, director of the WHO’s Working Group on TB Drug Development, and Bernard Pécoul of MSF).

• Financial gaps: Financial gaps were usually not reported in documentation available from the NTPs, nor in the data discussed directly with NTP managers. However, the World Health Organization did conduct surveys of which funds already were known to be committed, and which sources those funds will be coming from. Results of these surveys were communicated to the author of the present evaluations and discussed with Katherine Floyd of WHO and Paul Zintl of Partners In Health, both of whom helped to interpret and use them. The financial gaps were first published in the Global DOTS Expansion Plan (GDEP) in 2001,90 and were later published—with more details and greater accuracy—in the Global Tuberculosis Control Report of 2002.91 The almost total absence of national plans to implement and upgrade MDR-TB and TB-HIV co-infection control strategies also meant that no hard data were available for the evaluation of financial gaps regarding these aspects of TB control. Very preliminary information pertaining to the present status of preventive therapy interventions in addition to the status of TB-HIV co-infection control strategies (namely, ProTEST programs) was available from an unpublished report on the present situation of early ProTEST programs in Zambia, Malawi, and South Africa.92 However, too little is currently known of the funding requirements and shortfalls to allow for the construction of realistic estimates of the latter. Section 2.7 describes the very preliminary strategy used to evaluate these gaps.

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4. Results

Technical Note: Following the breakdown of major aspects of TB control discussed in the first section of this document, financial results here are classified into six main categories: • Direct costs of National TB Control Programs (NTPs), excluding those costs associated with running

dedicated networks of TB clinics, dispensaries, or sanatoriums (specifically, in China and Russia), and including costs associated with investments needed to implement and expand DOTS programs to achieve WHO goals for the year 2005.

• Costs for the use of health-care services for outpatient visits as well as for inpatient care, beyond the provision of drugs and diagnostic tests. (This category includes costs associated with dedicated TB clinics, dispensaries, and sanatoriums.)

• TB-HIV co-infection control strategies costs. • Costs for the management of MDR-TB cases (for the partial coverage of future DOTS programs). • Research and Development costs, to design new therapeutic, prophylactic, diagnostic, and policy tools to

combat TB (excluding operational R & D, which is specifically budgeted by NTPs). • Costs associated with the activities of WHO’s working groups (which address all major aspects of TB control)

and the Global Partnership to Stop TB. To reflect the presentation of costs in the GPSTB, the last category was also consolidated into each area of TB control evaluated here. Epidemiological projections and costs were computed on a country by country basis. Although results may differ from either the NTPs’ own evaluations or future evaluations, overall evaluations are consistent with country information; when discrepancies were not justifiable, direct adjustments were made to reflect the NTPs’ data. National data for first-line strategies are also available in the Global DOTS Expansion Plan and WHO’s Global Tuberculosis Control Report for 2002, the total evaluations of which are in line with those included in this document, as explicated in Section 4.3 (see Box 3 in particular). Individual country results appear in Appendix D. Epidemiological results include projections for the number of TB cases worldwide, while financial evaluations were conducted for low and lower middle-income countries only (114 countries in all). Though technically an upper middle-income country (GNP/capita of $3,240 in 1999), Botswana was included in the financial estimates due to the very heavy burden of TB and HIV/AIDS in that country.

53

4.1. Epidemiological Projections Using the assumptions stated in Section 2.3.1., the present model estimates that approximately 44.6 million new cases of TB will occur between 2001 and 2005 worldwide (Table 9a). Of these cases, 27.4 million, or 62% of the total new caseload, would be detected and treated, compared with 44% in 199993 and only 42% in 2000.94 All low- and middle-income countries (147 countries) would account for approximately 43 million new cases (or 98% of the total new caseload); 26.5 million, or 61%, of these cases would be detected and treated. The 22 HBCs would account for 35 million new cases (or 78.4% of all new cases), out of which 21.4 million (61.2%) are likely to be detected and treated. The total for the 22 HBCs and low and lower-middle income countries covered by the GPSTB (the 114 countries covered by the financial evaluation) is 42.9 million new cases over the five-year period, out of which 26 million would be detected and treated, or 60.7% of the total caseload. All these numbers assume that the goals set by WHO for 2005 are achieved. Table 9a summarizes the epidemiological projections used in the financial evaluation. The approximation of 26 million detected and treated cases in low and lower middle-income countries is quite complex in its construction and certainly will need to be adjusted as new data become available in the near future. As explained in Section 2.3.1, the model uses relatively straightforward methods to assess the number of cases and makes basic assumptions, such as flat incidence rates and constant population increase rates throughout the five-year period. Projections used by WHO estimate that TB incidence worldwide might increase 3% every year until 2005 (or 10% in African countries most affected by HIV/AIDS), resulting in 10.2 million new cases in 2005, in the worst-case scenario.95 This would imply approximately 46.5 million new TB cases worldwide between 2001 and 2005, compared with the 44.6 million new TB cases estimated here. This lower number, which serves as a basis for projections of case detection in this study, could be considered a less pessimistic scenario. An even less pessimistic scenario would arise if, for example, the annual number of new TB cases was left unchanged throughout the five years of the projections, remaining equal to the 1999 estimate of 8.4 million. The total new caseload for the 2001 – 2005 period would then be 42 million. Consequently, a total new caseload of 44.6 million worldwide, with 27.4 million cases detected and treated, seems to fall into the likely range of possibilities, and to anticipate the effects of the expansion and tightening of TB control. However, these calculations will have to be refined, as new incidence and notification data for 2000 and 2001 are now becoming known. Recent data also show that incidence rates rise quickly in some regions of the world, particularly those heavily affected by the HIV/AIDS pandemic.96

The Indian sub-continent alone could account for 29%, or 13 million, of all new TB cases over the five-year period (Table 9a). The case detection rate in this region of the world is currently 57.8% for all types of TB, and only 36.7% for SS+ cases. It is expected that, overall, 8.9 million cases will be detectedXXXVI in the

XXXVI The estimation of the number of cases detected and treated throughout the Indian sub-continent over the 2001 – 2005 period is a particularly complicated task. Total case detection rates are usually higher there than for smear-positive cases alone, forcing one to adapt the model to this specific situation. In India (which had 33% of all cases notified worldwide in 1999, according to WHO data from the Global TB Control Report 2001), the total case detection rate was already estimated at 66.5% as of 1999, while it was only 42.0% for smear-positive cases. DOTS coverage in India is currently very low (14% in 1999). So it is likely that expanded coverage wi ll significantly increase not only the detection rate of SS+ cases—which is one of the major objectives of DOTS—but also the total detection rate. Indeed, to detect more smear-positive cases, more of all types of TB cases need to be identified. So, in the case of India and Pakistan (total case detection rate = 34.2%, SS+ detection rate = 5.2%, DOTS coverage = 8%), after running a set of four different simulations, the estimated number of cases detected in the 2001 – 2005 period was adjusted to a mid-point between the results of the simulations. These results were based on two main scenarios that were progressively refined in order to take the specific situation of these countries into account: one scenario was based on the SS+ case detection rate (and the 70% WHO objective) and one was based on the total case detection rate. For India, the simulations yielded a detected load ranging from 6.3

54

Indian sub-continent during the 2001 – 2005 period, or 34% of all detections in low and lower middle-income countries. Combined, India and China will account for 16.2 million new TB cases, or 38% of the total for low and lower middle-income countries. Two thirds of these, or 10.8 million cases, are likely to be detected and treated. The whole of sub-Saharan Africa (two clusters) will detect 50.3% of all cases, or 5.9 million, out of 11.6 million new occurrences. Currently, the detection rate is 33%. Table 9a summarizes expected case detection rates by regional cluster; Appendix C4 presents epidemiological estimates by country. It is estimated that DOTS programs throughout the five-year period will manage 83% of the low and lower middle-income countries’ 26 million cases, or approximately 21.5 million cases. Should DOTS expansion fail, only about 19.1 million cases would be detected among these countries (16 million among the 22 HBCs), of which only 46% (or about 8.7 million cases) would be managed under the DOTS programs. This means that if WHO goals are achieved by 2005, an additional 12.8 million cases will be managed through DOTS in low and lower middle-income countries. Tables 10 and 11 summarize the transformation of the TB-control panorama over the five-year period and year by year, with and without further efforts to strengthen TB control. Based on these evaluations, the additional number of cases detected among low and lower middle-income countries (when compared to a situation in which no further improvements in case detection rates are made after 2000) would be approximately 6.9 million, out of which 3.5 million will be smear-positive. This means that 460,000 new cases of TB should additionally be detected each year until 2005. Subtracting the 6.9 million additional cases detected and treated from the 12.8 million more cases managed through DOTS in all low and lower middle-income countries (Table 10), while assuming that most of the former will be detected and treated as a result of the expansion of DOTS programs, we can estimate that DOTS will claim approximately 5.9 million of the cases that would otherwise be treated by other programs were DOTS expansion not to take place during the five-year period. In other words, DOTS will claim “market share” within the current case pool (5.9 million cases, out of an estimated 9 million cases were no improvement in DOTS expansion to take place), and will also claim most of the additional detected and treated load of 6.9 million cases (Tables 10 and 11). In all, improved DOTS coverage (assumed to reach 100% in 2005) and increased case detection (reaching at least 70% in 2005) will add 850,000 cases to DOTS programs every year; out of these 850,000 cases 350,000 will be smear-positive. This is in line with the minimum of 300,000 new smear-positive cases XXXVII that WHO recommends be added to DOTS programs yearly.97 Regarding treatment outcomes, assuming that cure rates for patients managed by DOTS will be 85%, and assuming that only 60% of those treated by other programs are effectively cured,XXXVIII altogether an additional 5.3 million people will be cured if WHO goals are reached by 2005 (Table 10). The figures used in these assumptions are gross estimates that should be refined with more detailed, country by country analysis; however, they indicate the magnitude of impact on human life the completion of the GPSTB can bring about.98 On the basis of the two epidemiological scenarios described in Section 2.3.1, the number of MDR-TB cases likely to occur during the 2001 – 2005 period ranges from 1.4 to 1.9 million (Table 9a). Of these, 880,000 to 1.24 million could be detected, or 63% to 66% of all cases. Eighty to ninety percent of new cases will occur among the 22 HBCs, and 36 to 43% will occur on the Indian sub-continent alone. Unfortunately, it is quite unlikely that all of the potentially detectable cases will be managed appropriately. Where MDR-TB is

to 8.0 million cases, while it was 0.7 to 1.05 million cases for Pakistan. The numbers eventually introduced into the model were 7.1 and 0.75 million, respectively. XXXVII WHO, Global Tuberculosis Control, Report 2001, p. 33. XXXVIII Derived from cure-rate statistics provided by the WHO’s Global Tuberculosis Control, Report 2001.

55

prevalent DOTS-Plus programs (particularly in PeruXXXIX) are now being designed that will serve as a basis for extended programs. It has been assumed here that only 60% of all MDR-TB cases detected will actually be treated as such throughout the period. Even this might be optimistic. As for TB-HIV co-infection, the current model estimates that approximately 3.3 million new cases will occur worldwide in the 2001 – 2005 period. As shown in Table 9b, 2.8 million of these new cases will be concentrated either in sub-Saharan Africa (2.3 million) or in Austral Africa (0.5 million), where the HIV/AIDS epidemic is particularly severe. These new co-infections will inflate the current stock of co-infected persons, which the model estimates at 12.1 to 16.5 million cases in 2000 (9 to 12.6 million of which are in Africa). The range for the estimated number of co-infected people is the result of two scenarios: one in which the estimated rate of MTB infection in the general population is applied to new HIV infections, and the other (used for GPSTB estimates) that assumes that 50% of those already living with HIV are also infected with MTB. (The latter rate was suggested, for example, by early ProTEST reports from sub-Saharan Africa.99) As a result, a total of roughly 15.6 to 19.9 million people are potential candidates for the prophylactic treatment that has been selected to calculate TB-HIV co-infection costs throughout the 2001 – 2005 period. Between 7.9 and 10.4 million of the dual infections will be concentrated in the 12 countries where ProTEST programs have already been (or will soon be) started. The range of results stems from the fact that two hypotheses were used to estimate the prevalence of m. tuberculosis infection among HIV-positive patients (see Section 2.3.4). Unfortunately, these numbers will probably have to be revised significantly upward in light of the latest estimates available regarding the HIV/AIDS epidemic, which show a rapid extension of this infectious disease beyond its main location in sub-Saharan Africa, now affecting countries throughout eastern Europe, the former Soviet Union, India, China, and Haiti. (In sub-Saharan Africa, adult HIV infection rates are estimated to be greater than 20 and even 30% in several countries in the southern part of the continent.100) Using the detection scenario described in Section 2.3.4 and applying it to all 57 countries included in the high-end scenario, the model shows that approximately 1.6 to 2.2 million cases of co-infection (or roughly 10 to 11% of the total) will be detectable and appropriately treated if TB-HIV co-infection strategies are rapidly up-scaled and widely available by 2005 (as is planned in South Africa with the ProTEST program, for example). This may look like a modest goal, but we are confronting a huge problem with very new approaches that were only recently started in a very limited number of settings. Moreover, detecting 1.6 to 2.2 million co-infection cases in those 57 countries means detecting 4.4 million HIV-positive persons out of approximately 108 million people passing through programs providing both HIV counseling and testing as well as TB preventive therapy (Table 9b). These figures envision very large numbers of people from China (32 million “clients” will have to pass through the program in order to detect 224,000 HIV-positive cases and a corresponding 80,000 to 112,000 dually infected persons), as well as India (25.6 million “clients,” 180,000 HIV-positive patients, 79,000 to 90,000 dually infected patients). These numbers will probably prove to be inflated, even if a rapid implementation and upscaling of such programs occurs. Indeed, infection rates used to compute these estimates are probably misleading, as there are significant concentrations of cases in certain provinces (as currently available data from China tend to indicate); thus, preventive therapy programs will most likely target these areas first, and may detect large numbers of co-infected persons, resulting in far fewer clients referring themselves to the programs. On the other hand, recent data also indicate rapid increases in the expansion of the HIV/AIDS epidemic in some parts of the world, such as China. All in all, the present simulation indicates how large the challenge is. If restricted to only the twelve countries where ProTEST programs have already started (or are soon going to be implemented), the number of adults 15 to 49 years of age who will pass through the programs is estimated at 28 million, resulting in the detection of 3.4 million HIV-positive persons (or 12% of the total number of people screened and tested), of whom 1.2 to 1.7 million (or 35 – 50%) will be dually infected. XXXIX Two programs are addressing MDR-TB in Peru: one is part of the Peruvian NTP per se, the other is run by Socios En Salud, an NGO associated with Partners In Health, based in Boston.

56

Clearly, scenarios associated with TB-HIV co-infection control strategies will have to be adjusted as new data regarding programs, their objectives, and their means and achievements, become available.

Figure 2: TB Incidence and Case Detection—All Clusters (2001 – 2005, Cumulative)

Chart explanation. The number of countries included in the epidemiological projections is given in parenthesis). See also Appendix B for details. AA = Austral Africa (7) SSA = Sub-Saharan Africa, excluding the 6 countries of Austral Africa (41) ME-NA = Middle East and northern Africa (19) WE = Western Europe and high-income Mediterranean (25) EE + ex-SU = Eastern Europe and the former Soviet Union (27) NAM = North America (4) USC = Urbanized South Cone (4) LAM+CAR = Latin America (excluding USC) and the Caribbean (29) ISC = Indian sub-continent (7) CIMA = China, Indochina, and Miscellaneous Asia (8) SEA = Southeast Asia (5) P = Pacific (22)

T B I n c i d e n c e a n d C a s e d e t e c t i o n - - a l l c l u s t e r s ( 2 0 0 1 - - 2 0 0 5 , C u m u l a t i v e )

0

1,000

2 , 0 0 0

3 , 0 0 0

4 , 0 0 0

5 , 0 0 0

6 , 0 0 0

7 , 0 0 0

8 , 0 0 0

9 , 0 0 0

10 ,000

11,000

12 ,000

13 ,000

14 ,000

A A ME-NA EE+ex-SU USC ISC IEA P

Nu

mb

er (

in t

ho

usa

nd

s) o

f ca

ses

Number o fnew TBcases

TB casesdetectedand treated

Casesmanaged byDOTS

58

Table 9a. Worldwide Caseload Estimates (2001 – 2005) by Cluster: All TB Cases and First-Line Strategies (Including DOTS) and MDR-TB Treatments

(in thousands of cases)

MDR-TB as a % of all cases detected

(5)

Total number of new MDR-TB cases

(5) x (1)/100

Number of MDR-TB cases

detected (6)

Number of new

TB cases (all forms)

(1)

Total number of cases detected & treated

(2)

Detection rate

(in %)

(2) / (1)

Incremental number of

cases detected(a)

(3)

Number of cases managed through DOTS

(4)

DOTS as a % of all cases

detected (4) / (2)

Scenario 1(b)

Scenario 2(c)

Scenario 1(b)

Scenario 2c)

Scenario 1(b)

Scenario 2(c)

Austral Africa (AA) 1,295 891 68.8% 43 750 84.2 1.5 2.3 19 30 13 20

Sub-Saharan Africa (excluding AA)

10,377 4,987 48.1% 1,769 4,677 93.8 2.1 1.2 219 132 105 62

Middle East and northern Africa

1,178 972 82.5% 155 639 65.7 4.2 2.6 52 32 41 25

Western Europe(e) 377 316 83.7% 61 158 50.0 0.7 0.1 n/a n/a 2 0


1,883 1,829 97.1% 310 1,046 57.2 5.4 8.4 99 153 100 153

North America(e) 94 75 80.1% 0 38 50.0 1.2 1.4 n/a n/a 1 1

Urbanized South Cone (South America) (USC)

748 549 73.4% 49 301 54.8 1.4 2.2 6 8 8 12

Latin America (excluding USC) and Caribbean

1,286 758 58.9% 123 655 86.4 2.7 3.1 30 27 20 24

Indian sub-continent 12,985 8,909 68.6% 1,423 5,347 60.0 3.7 6.0 488 748 386 539

China, Indochina, and Miscellaneous (Asia)

8,920 4,885 54.8% 1,826 4,379 89.6 4.6 7.8 416 685 227 383

High-Income East Asia (e) 447 482 (d) 107.8% 13 241 50.0 1.3 1.6 n/a n/a 6 8

Southeast Asia 4,966 2,765 55.7% 1,317 2,353 85.1 1.8 1.4 73 66 49 38

Pacific (e) 18 18 99.7% 3 9 50.0 1.0 1.3 n/a n/a 0 0

Total (whole world) 44,574 27,435 61.6% 7,092 20,593 75.1 3.3 4.6 1,402 1,880 899 1,265

Total 22 HBC 34,961 21,382 61.2% 5,828 17,668 82.6 3.3 5.3 1,136 1,719 712 1,138

Total 22 HBC and LLMIC (114 countries in all)

42,867

26,033

60.7%

6,923

21,488

82.5

3.4

4.8

1,402

1,880

880

1,240

(a) Difference between the total number of cases detected and a scenario in which no investment is made to increase case detection. (b) Based on estimates proposed by Dye et al. and Espinal et al. (see Section 2.3.4). (c) Based on estimates proposed by WHO and Becerra (see Section 2.3.4). (d) More cases are detected than estimated because the number of cases notified to WHO is higher than the number estimated by incidence rates. This scenario

occurs in a small number of cases, as explained in Section 2.3.4. (e) Clusters not included in the financial estimates.

59

Table 9b. Worldwide Caseload Estimates (2001 – 2005) by Cluster: TB-HIV Co-Infection

(in thousands of cases or people)

Caseload of TB-HIV

co-infections (as of 2000)

Total number of TB-HIV co-infection cases

Number of co-infected people

treated by prophylactic therapy

Scenario 1

Scenario 2

Incidence of

TB-HIV co-

infections Scenario 1

Scenario 2

Number of people going

through ProTEST

programs or equivalent

Number of HIV+ cases detected by ProTEST

programs or equivalent

Scenario 1

Scenario 2

Austral Africa (AA) 1,903 2,537 525 2,428 3,061 4,556 921 348 461


7,072 10,107 2,303 9,367 12,411 31,636 2,921 1,002 1,460


8 17 1 9 18 0 0 0 0


67 201 19 86 220 5,088 20 3 10

Urbanized South Cone (South America) (USC)

138 273 14 153 287 4,342 26 7 13


256 351 38 293 389 1,621 28 11 14

Indian sub-continent 1,703 1,940 283 1,986 2,223 25,639 179 79 90

China, Indochina, and Miscellaneous (Asia)

587 683 97 684 779 33,565 261 98 131

Southeast Asia 366 424 48 414 473 1,584 32 13 16

Total (whole world) 12,203 3,370 108,033 4,388 1,562 2,194

Total 22 HBC 8,827 11,895 2,350 11,177 14,245 95,339 3,361 1,201 1,680

Total 12 “ProTEST” countries(a)

5,944 8,463 1,910 7,854 10,373 28,253 3,396 1,191 1,698

Total 22 HBC and LLMIC (114 countries in all)

12,090

16,532

3,330

15,420

19,862

108,033

4,388

1,562

2,194

(a) First-wave ProTEST countries include Malawi, South Africa, Uganda, and Zambia. Second-wave ProTEST countries include Ethiopia, Kenya, Mozambique, and Tanzania. Third-wave ProTEST countries include Congo DR, Côte d’Ivoire, Rwanda, and Senegal.

60

Table 10. Transformation of the TB-Control Panorama Between 2000 and 2005 in Low and Lower Middle-Income Countries (caseloads in millions)

Data cover the 2001-2005 period

Scenario 1: No modification in

TB-control strategies and coverage

Scenario 2: WHO goals are reached by

2005

Achievements in TB control:

differential between Scenario 2 and

Scenario 1

Cure rates Increase in number of

patients cured (all

cases) TB as a whole All cases SS+ cases All cases SS+ cases All

cases SS+

cases

Total caseload (incidence) 42.9 18.9 42.9 18.9 n.a.(a) n.a. n.a. Total cases detected and treated Case detection rate Covered by DOTS Covered by other programs and

protocols Cases not detected

19.1 44.6%

8.7

10.4

23.8

7.7 41.0%

4.7 3.0

11.2

26.0 60.7%

21.5 4.5

16.9

11.3 59.8%

10.0 1.3

7.6

+ 6.9

+ 12.8 – 5.9

– 6.9

+ 3.5

+ 5.3 – 1.8

– 3.5

n.a.

85% 60%

30%

+5.3

+10.9 –3.5

–2.1

MDR-TB cases (incidence) (subset of total incidence, assuming the worst-case scenario of 4.6% prevalence among TB cases)

1.9 1.9 None (a) n.a. n.a.

Cases of MDR-TB detected and treated Covered by DOTS-Plus programs Covered by other programs or not

appropriately treated

0.9 0.0 0.9

1.2 0.75 0.45

+ 0.3 + 0.75 – 0.45

85% 50%

+0.4 +0.6 –0.2

TB-HIV co-infected people (caseload of co-infections as of 2000 and incidence, assuming no reduction in initial caseload)

15.6 15.6 None (a) n.a. n.a.

Number of TB-HIV co-infected people receiving prophylactic treatment

0.2 (1%)

1.6 (10%)

+ 1.4 n.a. n.a.

(a) The GPSTB assumes that up-scaled and strengthened strategies will not have a measurable impact on the total number of new cases, the proportion

of MDR-TB cases relative to total TB cases, or the size of the pool of TB-HIV co-infected people over the five -year period. However, efficient and massive TB-control did significantly reduce TB incidence, as shown in Cuba and Peru, and should do so elsewhere; but the plan has assumed, conservatively, that this improvement may not occur by the end of 2005.

61

Table 11. Transformation of the TB-Control Panorama Between 2000 and 2005 in Low and Lower Middle-Income Countries (LLMIC) (cases in thousands)

1999 1999 2001 2002 2003 2004 2005 2001 – 2005

Whole world

LLMIC countries

Sc. 1(a) Sc. 2(b) Sc. 1(a) Sc. 2(b) Sc. 1(a) Sc. 2(b) Sc. 1(a) Sc. 2(b) Sc. 1(a) Sc. 2(b) Sc. 1(a) Sc. 2(b) Sc. 2 / Sc. 1

Total incidence (1)

8,417 8,102 8,332 8,332 8,451 8,451 8,571 8,571 8,694 8,694 8,818 8,818 42,867 42,867 0

Incidence rate(c)

141 171 176 176 179 179 181 181 184 184 187 187

SS+ incidence (2)

3,724 3,564 3,665 3,665 3,717 3,717 3,770 3,770 3,823 3,823 3,878 3,878 18,853 18,853 0

Incidence rate(c)

62 75 78 78 79 79 80 80 81 81 82 82

SS+ (% of total) (2)/(1)

44.2% 44.0% 44.0% 44.0% 44.0% 44.0% 44.0% 44.0% 44.0% 44.0% 44.0% 44.0% 44.0% 44.0% 0.0

Total detection (3)

3,690 3,500 3,723 4,054 3,772 4,615 3,821 5,191 3,871 5,783 3,923 6,391 19,110 26,033 6,923

Detection rate (3)/(1)

43.8% 43.2% 44.7% 48.7% 44.6% 54.6% 44.6% 60.6% 44.5% 66.5% 44.5% 72.5% 44.6% 60.7%

SS+ detection (4)

1,486 1,379 1,505 1,732 1,525 1,987 1,547 2,249 1,568 2,518 1,590 2,795 7,735 11,281 3,546

SS+ detection rate (4)/(2)

39.9% 38.7% 41.1% 47.3% 41.0% 53.5% 41.0% 59.7% 41.0% 65.9% 41.0% 72.1% 41.0% 59.8%

SS+ (% of total) (4)/(3)

40.3% 39.4% 40.4% 42.7% 40.4% 43.1% 40.5% 43.3% 40.5% 43.5% 40.5% 43.7% 40.5% 43.3% 2.9

Total cases treated by DOTS (5)

1,679 1,564 1,695 2,406 1,720 3,253 1,744 4,197 1,769 5,242 1,795 6,391 8,723 21,488 12,764

Proportion treated by DOTS (5)/(3)

45.5% 44.7% 45.5% 59.3% 45.6% 70.5% 45.6% 80.9% 45.7% 90.6% 45.8% 100.0%

45.6% 82.5%

SS+cases treated by DOTS (6)

868 821 904 1,245 917 1,608 930 1,980 944 2,363 957 2,757 4,653 9,953 5,300

Proportion treated by (6)/(4)

58.4% 59.5% 60.1% 71.9% 60.1% 80.9% 60.2% 88.1% 60.2% 93.9% 60.2% 98.6% 60.2% 88.2%

62

SS+ DOTS /Total DOTS (6)/(5)

51.7% 52.5% 53.3% 51.7% 53.3% 49.4% 53.3% 47.2% 53.3% 45.1% 53.3% 43.1% 53.3% 46.3% -7.0

SS+ DOTS / SS+ Incidence (6)/(2)

23.3% 23.0% 24.7% 34.0% 24.7% 43.2% 24.7% 52.5% 24.7% 61.8% 24.7% 71.1% 24.7% 52.8% 28.1

MDR-TB incidence (7) 368 368 372 372 376 376 380 380 384 384 1,880 1,880 0

MDR-TB in % of all TB (7)/(1)

4.4% 4.4% 4.4% 4.4% 4.4% 4.4% 4.4% 4.4% 4.4% 4.4% 4.4% 4.4%

MDR-TB cases detected (8)

165 199 166 223 168 248 169 273 171 298 838 1,240 402

Cases covered by DOTS-Plus (9)

16 20 17 67 17 136 17 218 17 298 84 739 655

Coverage rate (9)/(8)

10.0% 10.0% 10.0% 30.0% 10.0% 55.0% 10.0% 80.0% 10.0% 100.0%

10.0% 59.6%

TB-HIV co-infected cases(d) (10)

12,733 12,733 654 654 666 666 678 678 690 690 15,420 15,420 0

Detected cases receiving prophylactic therapy (11)

10 13 11 106 12 225 13 429 15 852 61 1,625 1,564

Detection rate (11)/(10) 0.4% 10.5% 10.1

(a) Scenario 1 assumes that no additional effort to expand DOTS or raise case detection rates is done after 2000. (b) Scenario 2 includes WHO goals for 2005 (100% DOTS coverage, 70% SS+ case detection rate, and 85% cure rate). (c) Incidence rates are in number of new cases per 100,000 population. (d) For 2001, figures include the total caseload (or “stock”) as of 2000 and the 2001 incidence. For other years, they only include incidence in those years.

63

4.2. Total Financial Requirements for TB Control during the 2001 – 2005 Period in Low- and Lower Middle-Income Countries Technical Note: All costs are given in 2000 U.S. dollars. Evaluations presented hereafter are provided for each of the nine clusters where low-income and lower middle-income countries are located, and in total. Figures have been calculated for all 22 HBCs combined, as well as for all other low and lower middle-income countries, that is countries that have a GNP/capita less than $2,996 per year and per capita. This cut-off uses the World Bank’s 1999 classification 101,102 of countries as “lower-middle income,” according to the exchange rate method. Some countries among the 22 HBCs, however, have a GNP/capita greater than $2,996, as in the case of Brazil ($3,570) or South Africa ($3,020). Appendix B lists all 114 countries included in the financial estimates, while Appendix D displays cost estimates by country. Gross National Product figures, recalculated according to the “Purchasing Power Parity” (PPP) system, are thought to more accurately reflect the real purchasing capability of an economy. It could indeed seem a better system in the present case, because most of the costs of national TB programs consist of local inputs usually not tradable or not imported, such as labor, transportation, communication expenses, publication of material and, in some cases, locally produced drugs and equipment. However, no such classification for GNP/capita calculated with the “PPP” system exists, as in the case of GNP/capita calculated with the exchange rate system. The $2,996 cut-off and the use of the exchange rate-based GNPs might be revised in the future, especially as some countries may face a very disease burden (and associated cost) while being close to the lower middle-income category. This is the case of Botswana, which, with a $3,240 GNP/capita, ranks among the “upper middle-income” countries, but has the highest incidence of TB in the world (756 cases per 100,000 population, as of 2000 and according to the most recent estimates103). This is a country where at least 7% of the population is dually infected with MTB and HIV, which implies a potentially huge economic burden should prophylactic strategies against TB in co-infected persons be widely implemented. Botswana was included in the present estimates. As mentioned in Section 2.6, some results are given in terms of ranges. The GPSTB presents only one set of results, which have been derived from the following estimates. When estimates are given with ranges, mid-point figures or higher ends have been used for the GPSTB, which are also presented here. Costs associated with direct interventions run by national or local programs have been combined with activities designed at the international level to help strengthen and expand these programs. Costs associated with national or local programs are also called “intervention costs” or “direct costs.” Costs (budgets) of international working groups and of the international TB partnership (that is, the Global Partnership to Stop TB) are also called “Working Groups” and “Partnership” costs or budgets, respectively. Detailed activities and budgets of the working groups and the partnership are provided in the GPSTB (Annex 2, p. 144), and are therefore not discussed here. They are merely added to the intervention costs in order for the total cost to match those used in the GPSTB. Appendix E summarizes working group budgets.

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Bringing tuberculosis under control in all low- and lower middle-income countries during the 2001 – 2005 period will cost an estimated $8.1 to $9.9 billionXL (Table 12). Of these costs:

• $2.2 billion will cover direct costsXLI of NTPs (essentially for first-line strategies) • The cost to general health-care services of attending first-line patients will be an additional $3.2 to

$4.5 billion (including hospitalization and outpatient costs, and excluding drug and diagnostic supply costs, and sometimes salaries already accounted for in NTP direct costs)

• $0.6 to 0.8 billion will cover direct costs of TB-HIV co-infection-specific programs • $0.7 million to $1.1 billion will be necessary for MDR-TB treatment • $1 billion will be accounted for by R & D efforts • $0.4 billion will cover WHO working groups dedicated to each of the main TB-control activities and

the Partnership to Stop TB’s own activities in the 2001 – 2005 period.XLII These total cost estimates imply that increased efforts in TB control for all low and lower middle-income countries will require $2.7 to $4.4 billion beyond what would be spent if no additional efforts are put in place (Table 12). These funds cover:

• Expansion of DOTS and strengthening of NTPs ($820 million for NTPs) • Up-scaling of general health-care services (with a range of $0 to $1.3 billion, depending on whether

or not there is enough capacity in the current systems to absorb the incremental increase in the number of TB cases)

• Implementation and expansion of TB-HIV co-infection prophylaxis through integrated TB-HIV co-infection programs such as ProTEST ($570 to $700 million for national programs)

• Implementation and expansion of DOTS-Plus programs for the management of MDR-TB patients ($660 to $960 million for NTPs)

• Acceleration of research and development programs (approximately $250 million) • WHO working groups activities ($314 million) • Partnership-specific actions ($75 million).

Tables 12 and 13 summarize these costs. Table 15 presents them by regional cluster. Table 14 summarizes costs by principal TB-control activity, grouping intervention costs with working group and the Partnership to Stop TB budgets; Table 14 parallels Table 2 (page 17) of the GPSTB. Sections 4.3 through 4.7 of the present document discuss in detail each of the main aspects of TB control evaluated. Excluding R & D, international working groups, and the partnership’s specific costs, the 22 HBCs,XLIII which will generate 79% of all new TB cases, will roughly account for 77 to 80% (or $5.2 to $6.8 billion) of all intervention costs (including first-line strategies, MDR-TB, TB-HIV co-infection, and health-care services). These expenditures will increase year after year as more cases are detected, national program XL Ranges are due to factors explicated in Section 2.2.6. XLI These costs cover drugs, diagnostic consumables, training, NTP management, monitoring, and supervision, operational research at the national level, as well as all investment costs needed to up-scale DOTS programs in order to achieve WHO goals. These latter investments (for microscopes and other equipment, as well as initial training and other initial costs) account for $360 million. Costs of dedicated TB clinics, dispensaries, and sanatoriums, which can be managed by NTPs (as in China) are excluded here. They are included with the cost of health-care services. XLII WHO’s working groups cover the following areas: DOTS Expansion ($225 million over 5 years), TB-HIV co-infection ($12 million), MDR-TB ($16 million), new drugs ($30 million), new vaccines ($4 million), and new diagnostics ($27 million). Total expenditure over the 2001 – 2005 period for working groups is therefore currently estimated at $314 million. The Partnership’s budget over the 2001 – 2005 period is currently estimated at $75 million. XLIII The HBCs are (in decreasing order of annual number of new cases): India, China, Indonesia, Nigeria, Bangladesh, Pakistan, Philippines, Ethiopia, South Africa, Russian Federation, DR Congo, Vietnam, Kenya, Brazil, Tanzania, Thailand, Mozambique, Myanmar, Uganda, Afghanistan, Zimbabwe, Cambodia, Peru. (WHO Report 2001, Global Tuberculosis Control.)

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capabilities are strengthened, and effective management of MDR-TB and TB-HIV co-infection become more common. It is estimated that the total cost of TB control in low- and lower middle-income countries will increase from approximately $1.3 to $1.4 billion in 2001 to around $2.1 to $2.7 billion in 2005. Costs of health-care services make up most of the gap within the range of estimates. Table 13 summarizes the projected annual costs. These numbers assume that WHO goals for 2005 are achieved; that 100% of first-line cases are managed by DOTS by 2005; and that other major aspects of TB control are consistently strengthened. The cost associated with Bacille Calmette-Guérin (BCG) vaccination (given in childhood) is not accounted for here, as the vaccination is normally part of the Expanded Program on Immunization (EPI) promoted by WHO. Therefore, its cost is included with that of the EPI intervention. If evaluated separately, the cost would be on the order of $50 million over the next five years, assuming that all new-borns received it. (This cost covers only the cost of the vaccine.) By the mid-1990s, approximately 85% of the world’s infants (ages 0 – 1), or 100 million children, were being immunized each year.104 The financial shortfall associated with all TB control costs evaluated for the 2001 – 2005 period for all low- and middle-income countries is presently estimated to be between $2.1 and $2.8 billion. Using the GDEP’s attempt to evaluate the financial shortfall for the 22 HBCs, and extrapolating costs to all other countries included in the present evaluation, it is estimated that approximately $4.7 billion is available out of NTP budgets and health-care services in the 2001 – 2005 period to finance their activities. This $4.7 billion represents costs associated with first-line treatments (including dedicated health-care services) and is classified as “DOTS Expansion” in Table 14. Another $200 million is available through grant funds105 (Table 14). Another $50 million has been pledged by the Canadian government to finance needs of the GDF and to pay part of participating countries’ drug costs. Altogether, of the 114 countries covered by the GPSTB, $4.7 billion is therefore currently identified as available to cover costs of TB control associated with NTPs and health-care services in the 2001 – 2005 period. For the 22 HBCs surveyed by the GDEP, roughly $3.6 billion would be available, including a share of the $50 million already committed for the GDF (approximately $40 million) during the 2001 – 2005 period. Consequently, total shortfall for first-line treatments is estimated at $1.7 billion for NTP and case management activities, or about 28% of total costs. If costs of WHO’s Working Group on DOTS Expansion are included in these estimates ($225 million; see Appendix E), total requirements for DOTS expansion become $6.2 billion, and the financial shortfall, $1.6 billion. No data are currently available for estimating the financial shortfall for TB-HIV co-infection- and MDR-TB-related interventions and programs. Consequently, estimates of financial shortfalls proposed in the GPSTB will have to be revised as more becomes known regarding DOTS-Plus, ProTEST, and similar initiatives. Given that the problem of multidrug resistance tends to affect some of the largest and poorest countries (India and China in particular), it has been estimated that approximately 80% of the cost of MDR-TB treatment will not be covered by national resources: this amount is not yet budgeted by the NTPs (at least not among the 22 HBCs) except in the case of Peru. As for activities associated with the control of TB-HIV co-infection, it has been estimated that roughly 50% of the total cost of interventions will lack funding. Interventions and programs targeting this problem will, at least in an initial phase, be concentrated principally in sub-Saharan Africa, with South Africa alone (a nation with significantly higher health-care costs) making up two thirds of the total financial need. Sections 4.5. and 4.6. further discuss these assumptions. Choosing a mid-point for all ranges—as done in the GPSTB—and taking into account all TB-control activities evaluated in this work, the total financial shortfall for all TB-control activities during the 2001 – 2005 period could be on the order of $3.8 billion. This would represent approximately 40% of a total cost estimated at $ 9.1 billion for all HBCs and other low and lower middle-income countries (Table 14). Taking into account only costs borne by direct (or “vertical”) interventions and programs, total costs are estimated

66

at $7.7 billion—or $3 billion more than with no incremental effort in TB control—and financial shortfall is estimated at $2.9 billion, or 37% of costs (Tables 14 and 15). Table 12. TB-control Financial Panorama during the 2001 – 2005 Period in Low and Lower Middle-

Income Countries (in millions of 2000 U.S.$)

Section in text

TB-Control Category Total Needs Incremental Cost(a) Financial Shortfall(b)

4.3 Investments to expand DOTS(c) (costs to NTPs only)

350 350

4.3 NTP recurrent direct costs(d) (costs to NTPs only—does not include use of health-care facilities)

1,800

470

4.4 Use of health-care services (HCS) 3,200 – 4,500

0 – 1,300

Could be

on the order of 1,000 – 1,900

combined

4.5 TB-HIV co-infection prophylaxis (intervention/program costs only)

630 – 760 570 – 700 (≈ 90% of the cost)

590 – 720

4.6 MDR-TB Treatment (intervention/program costs only)

730 – 1,070 660 – 960 (≈ 90% of the cost)

550 – 820 (≈ 75% of the

cost) Sub-total: national TB-control and

case management costs (1) 6,710 – 8,480 2,050 – 3,780 2,140 – 3,440

4.7 R & D(e) 1,037 250 660

All the above

WHO working groups and Global Partnership to Stop TB

389 389 253

TOTALS (all TB-control activities) (rounded to nearest 10)

8,140 – 9,910

2,690 – 4,420

3,050 – 4,350

Sub-totals for the 22 HBCs only (R & D, working groups and TB partnership excluded) As a % of (1)

5,150 – 6,830

77 – 80%

1,640 – 3,160

80 – 84 %

1,750 – 2,820

82 %

(a) The incremental cost represents the additional financial expenditure incurred by National TB Control

Programs as a result of higher TB detection rates in the 2001 – 2005 period. It is estimated that approximately 6.9 million more TB cases will be treated in low and lower middle-income countries as a result of investments in expanded programs.

(b) The financial shortfall estimates the gap between currently pledged funds for the 2001 – 2005 period and the total costs presented in the “Total Needs” column.

(c) These costs cover equipment, initial training, and other implementation costs. (d) These costs cover first-line drugs, diagnostic supplies, training of health-care professionals, program

administration, monitoring, supervision, incentives, and operational research. (e) This figure does not include $180 million for operational research already accounted for in NTP recurrent,

direct costs ($1,800 million). Research and development costs accounted for here are supposedly concentrated in high-income countries. However, a number of other countries might have research capabilities that could participate in these efforts: China, India, South Africa, Brazil or Cuba.

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Table 13. Annualization of Projected TB-Control Costs during the 2001 – 2005 Period

(low- and lower middle-income countries, in millions of U.S.$)

2001 2002 2003 2004 2005 Totals

DOTS investments 50 100 90 70 40 350

NTP direct costs (first-line treatments) Drugs Other

280

125 155

315

140 175

355

155 200

400

170 230

450

190 260

1,800

780 1,020

Use of HCS Higher estimate Lower estimate Mid-point (GPSTB)

780 640 710

840 640 740

900 640 770

960 640 800

1,020 640 830

4,500 3,200 3,850

TB/HIV Higher estimate Lower estimate

15 10

50 40

110 90

195 160

390 330

760 630

MDR-TB Higher estimate Lower estimate

20 15

75 50

160 110

295 200

520 355

1,070 730

Sub-total: national TB-control and case management costs

1,145 995

1,380 1,145

1,615 1,285

1,920 1,470

2,420 1,815

8,480 6,710

R & D 210 210 209 204 204 1,037

Working groups and TB Partnership

60 83 83 83 80 389

Totals Higher estimate Lower estimate Used in the GPSTB

1,415 1,265

1,340

1,673 1,438

1,563

1,907 1,577

1,757

2,207 1,757

2,012

2,704 2,099

2,454

9,906 8,136

9,126

Note: Numbers in the “Totals” column used in the GPSTB are shown in bold characters.

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Table 14. Summary of Costs for 114 Countries by TB-Control Activity, as Shown in the GPSTB (in millions of U.S.$)

Costs(a)

(1) Current Resources Gap

(1)-(4)

National (2)

External (3)

Sub-total (4)=(2)+(3)

DOTS Expansion 6,225 4,300 359 4,659 1,566

Country needs—TB programs (HBC) Country needs—health-care services (HBC)

1,560 3,000

3,300 250 3,550 1,010

Country needs—TB programs (non-HBC) Country needs—health-care services (non-HBC)

590 850

1,000 0 1,000 440

Working Group on DOTS Expansion 225 0 109 109 116 Research and Development (totals) 1,098 0 390 390 708 New diagnostics Research needs Working Group on New TB Diagnostics

177 150 27

0 0 0

53 47 6

53 47 6

124 103 21

New drugs Research needs Working Group on TB Drug Development

347 317 30

0 0 0

136 130

6

136 130

6

211 187 24

New vaccines Research needs Working Group on TB Vaccine Development

424 420

4

0 0 0

96 95 1

96 95 1

328 325

3 Health policy systems research 150 0 105 105 45

Partnership Partnership secretariat Advocacy Resource Development and Financing Monitoring

75 27 20 13 15

0 0 0 0 0

10 10 0 0 0

10 10 0 0 0

65 17 20 13 15

TOTALS (all TB-control activities)

9,126

4,530

819

5,349

3,777

Totals without R & D, working groups, and the Stop TB Partnership (only direct intervention and program costs)

7,700

4,530

306

4,836

2,864

Note: See Table 2 in the GPSTB (p. 17) for correspondence of presentation. (The present table reallocates working group budgets to each of the corresponding areas of TB control.) (a) Intervention cost and working groups budgets combined.

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Figure 3: TB-Control Costs per Main Area of Intervention (GPSTB numbers)

Figure 4: Financial Shortfall Currently Identified (GPSTB numbers)

TB Control (2001–2005)—Total Costs

68%

7%

12%

12%1%

DOTSExpansion

TB-HIV co-infection

MDR-TB

Total R & D

Stop TBPartnership

6,225

1,098

1,086

642

75

TB control (2001–2005)—Total Financial Shortfall

41%

16%

22%

19%

2%

DOTSExpansion

TB-HIV co-infection

MDR TB

Total R & D

Stop TBPartnership

1,566

65

604

708

834

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Table 15. Global Results: Cost Estimates by Regional Cluster for National Programs and Interventions of TB-Control, Covering the 114 Countries Encompassed by the GPSTB (R & D, Stop TB Working Groups and Stop TB Partnership Excluded)—2001 – 2005

(in millions of 2000 U.S. $)

Recurrent NTP costs during the

five-year period

Total Cost

of TB-Control Activities

Cost of DOTS Expansion

(initial investment

cost)(a) Drugs and diagnostic

costs

Other program costs(b)

Costs associated with

the use of health-care

services

TB-HIV co-

infection prophylaxis

MDR-TB-specific

costs (DOTS-Plus programs)

Austral Africa (AA) 1,746 – 1,995 2.4 55.9 52.9 1,123 – 1,344 495.3 – 513.4 16.7 – 25.9


675 – 741 35.2 220.3 83.9 136 – 188 134.7 – 175.6 64.7 – 38.1

Middle East and northern Africa 414 – 482 61.6 75.1 77.4 166 – 246 0 34.0 – 22.3


1,515 – 1,824 59.6 143.3 42.2 1,143 – 1,362 0 – 6.0 126.8 – 211.0

Urbanized South Cone (USC) (South America)

250 – 270 7.7 32.1 69.5 134 – 149 0 – 2.8 6.2 – 8.9


192 – 212 3.3 32.2 56.3 85 – 101 0 – 3.3 15.7 – 16.1

Indian Sub-Continent 676 – 1,159 62.8 155.1 156.0 69 – 381 0 – 22.3 233.5 – 381.6

China, Indochina, and miscellaneous (Asia)

918 – 1,308 105.0 96.8 242.1 285 – 506 0 – 29.4 189.1 – 328.8

Southeast Asia 324 – 488 12.5 89.3 119.7 59 – 222 0 – 7.0 43.3 – 37.2

Total for the 114 countries covered by the GPSTB (figures in italic are those used for the GPSTB) % of total

6,710 – 8,480

(7,700) 100%

350

(350) 4.5%

900

(900) 11.7%

900

(900) 11.7%

3,200 – 4,500

(3,850) 50.0%

630 – 760

(630) 8.2%

730 – 1,070

(1,070) 13.9%

Sub-total 22 HBC 5,145 – 6,825 240 630 690 2,400 – 3,600 590 – 685 595 – 980

Incremental costs (for 114 countries)

2,050 – 3,780 (2,990)

350 250 220 0 – 1,300 570 – 700 (≈ 90% of

total)

660 – 960 (≈ 90% of total)

Financial gap (for 114 countries in the GPSTB)

2,140 – 3,440 (2,860 in GPSTB or 37% of cost)

1,000 – 1,900 altogether

(1,450 in GPSTB)

590 – 720

(594 in GPSTB)

550 – 820

(≈ 75% of total) (820 in GPSTB)

(a) Costs associated with equipment, initial training, and all other initial (nonrecurring) program actions. (b) Costs associated with recurrent training, program administration, supervision, monitoring, surveillance, incentives, and operational research.

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4.3. Costs to National TB Control Programs (First-Line Treatments and Retreatments)

Expanding DOTS is the cornerstone of global TB control and the most important aspect of National TB Control Programs (NTPs). To achieve WHO goals regarding global TB control and DOTS in particular by 2005,XLIV NTPs will have to:

• implement DOTS throughout national territories • increase case detection rates • strengthen efficient diagnostic capabilities • regularly procure reliable and affordable drugs • train health personnel • educate (or “increase awareness in”) the general public about the disease • ensure directly-observed treatment • conduct operational research • supervise and monitor increasingly large and complex programs • ensure the continuous commitment of political leadership as well as of financial sources.

All these activities should lead to an increased number of new TB cases being detected and managed with DOTS. Altogether the model used in the present study predicts that an additional 7 million cases could be managed under DOTS in low and lower middle-income countries, or 6.9 million cases among the 114 countries covered by financial estimates. The proportion of all cases detected and treated annually among those countries that are managed under DOTS will increase from a current annual rate of 46% to 100% by 2005, and will account for 21.5 million cases out of 26 million cases detected in all throughout the five-year period. It is assumed that all additional cases detected due to the strengthening of the NTPs will be compliant with DOTS protocols. A few countries, such as Vietnam (2002) and Cambodia (2004), have planned to reach WHO goals before the 2005 mark. Between 2001 and 2005, it is estimated that NTPs will spend approximately $2.2 billion to achieve and expand the detection of new cases, implement and run DOTS programs on a larger scale, and ensure TB patients’ regular access to efficient diagnostic and therapeutic tools. Tables 16 and 17 detail these costs by item, year, and regional cluster. Appendix D1 provides estimates by country. These costs do not include the use of health-care services, which have been evaluated separately, as they normally do not depend on the NTP specifically. (In fact, most NTPs actually do not account for this use in their budgets.) These costs are presented and discussed in Section 4.4, but have been regrouped with NTP direct costs in the GPSTB in order to show how much the management of first-line TB patients actually costs to national health systems as a whole (see also the “DOTS Expansion” section in Table 14). Direct budgetary costs to the NTPsXLV include investments to strengthen and upgrade TB control programs (DOTS expansion), requiring an estimated $350 million in all ($240 million will be needed for the 22 HBCs). Approximately $230 million will be invested in equipment for laboratories ($60 million), supervision and monitoring. A provision has been added to the initial calculation, in order to account for the construction and equipment of national (and, in some cases, provincial) reference laboratories. From information provided by some NTPs, such as the Philippines’, it appears that there is a need for more such

XLIV Reminder: the three objectives set by WHO for the year 2005 are: a) 100% population coverage by DOTS ; b) 70% of all new smear-positive cases detected and managed through DOTS programs; and c) 85% treatment success, at least for cases covered by DOTS programs. XLV See Section 2.1 and Appendix A for definitions and classification of costs.

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laboratories.XLVI Thus, it is possible that the provision (approximately $20 million) will prove insufficient, if national TB control plans start developing this kind of infrastructure on a wider scale. $120 million should be spent mainly on initial training for program personnel (microscopists, supervision, and monitoring personnel) and health-care workers. Recurrent budgets for the NTPs (excluding costs of dedicated TB clinics, dispensaries, and sanatoriums) will rise to $1,800 million—approximately $470 million above what it would cost to run the NTPs if detection rates were left unchanged during the five-year period. The overall incremental cost to the NTPs of achieving WHO goals for DOTS, including the $350 million required in initial investments, is therefore approximately $820 million during the five years. Altogether, drugs account for approximately 36%, or $780 million, of the total budgetary needs of the NTPs from 2001 to 2005. This is the largest single item for first-line treatments and programs. It includes all major categories of cases (infectious and noninfectious pulmonary and extra-pulmonary cases) as well as retreatment cases, but not MDR-TB regimens. Per patient drug costs for a complete course (six to eight months) range from $14.40 in India and $16 in Ethiopia, to $60.80 in Russia and $75 in the Middle East (see Appendix C2). Actual prices from price lists were lower in budgetary data available for the present study (as low as $11 in some cases), but a mark-up had to be applied to account for losses, procurement, and storage costs, as well as for drugs used against adverse effects of anti-TB drugs. The mark-up also had to take into account the fact that many countries have to build reserve stocks (sufficient to last up to one-and-a-half years) to ensure the availability of drugs. These buffer stocks have been explicitly budgeted by some NTPs. The model has taken these mark-ups into account systematically. As a consequence of these important factors affecting the actual cost of delivering a reliable and continuous supply of treatment drugs, price list numbers have been inflated by approximately 20%, unless available NTP budgets already include that provision. Laboratory supplies will require approximately $120 million. This is principally for reagents and other consumables needed for smear tests, X-rays, and cultures, where such procedures are conducted on a regular basis (such as in the countries of eastern Europe and the former Soviet Union). Salaries of microscopists have been included here, whenever national programs specifically identified them. This cost has not been identified by most NTPs, however, probably because it is part of the cost of health-care services as a whole, which do not fall under the direct responsibility of the NTPs. Microscopists may share their time between several types of tests. Apportioning their time and cost systematically might be useful to reflect the total cost of any TB intervention requiring these laboratory tests, but is a lengthy exercise that would have to be done for each country, to reflect national specifics regarding the allocation of tasks within laboratories. Reagents and other basic consumables for sputum smear tests or X-rays (when done on a regular basis, as in Russia) are budgeted by the NTPs, which procure them. Other recurrent costs, less directly related to the number of patients within the NTPs (but related in part to the population coverage and to the intensity and effectiveness of the program), are regular training costs ($120 million or 6% of the total), as well as administration, monitoring, and supervision activities, which are likely to require $480 million (22% of total). These figures include staff costs at national and provincial levels. The last number is probably the least precisely evaluated of all, due to the relative lack of data or details at national or provincial levels. Decentralization in many countries sometimes makes it impossible for heads of national programs to know the amount spent on administrative accounts for TB activities for the entire country.XLVII Provincial coordinators and their teams might indeed be included in the provincial or

XLVI From conversations with the head of the Philippines’ national TB control program, in November 2000. The expenditure was at first budgeted, but was not explicitly included in the budgetary data eventually provided. XLVII Dr. Elizabeth Ferreira of the Mexican NTP (federal level), personal communication, October 2000. Mexico is not included in the present evaluation, however, because its GNP/capita was $4,400 per year in 1999. Dr.

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local state budgets. This is the case in many Latin American countries, for example, such as Mexico and Brazil. The program in DR Congo also faces some difficulty in assessing how much is currently being spent on TB control throughout the country. Cost of staff there is easily several times higher when paid for by an NGO running the program locally. Lower government salaries actually seem less attractive despite or because of the difficulties experienced by the country.XLVIII Estimates have taken these facts into account; but it is rather difficult, based on this knowledge of national programs, to check whether the facts have been taken into account accurately. Incentives (financial or other types) are likely to amount to $110 million altogether (5% of total costs). Though not one of the largest single items, this expenditure is a very important item of the NTP panoply. It covers financial incentives to boost detection and compliance with treatment regimens; supervision/observation; and socioeconomic support such as food (in Peru, for example). In China, 100 yuans (about $12.50) are paid to local physicians for each case detected and treated. This will represent about $20 million in China for the 2001 – 2005 period. In Brazil, bonuses ($55.50) are paid to municipalities (there are 5,600 of them in the country) for each case notified. When the case is smear-positive and treatment is directly observed, the bonus paid is $83.30, once the patient is cured. These incentives should represent about $3.2 million per year. In Peru, food provided to patients amounts to $4 million a year, or up to 50% of the direct costs of the national program. Though the importance of such incentives and socioeconomic support for the programs has not yet been evaluated, it is likely that more such tools should be used in more countries, thereby increasing the total cost for this item and for the programs. Some current policies in a number of countries not classified here as “incentives,” and not specifically evaluated in the present study, are in fact quasi-incentives. For example, in Nicaragua, poor patients are hospitalized for longer periods than clinically necessary in order to provide them with nutrition and support they might not get once discharged.XLIX In this particular case, the cost of inpatient care beyond the normal length of stay could be reallocated as a socio-economic support item and accounted for as an incentive. More examples are likely to be found if further investigation continues, as incentives have only been evaluated if they have been reported in TB control plans. Other recurrent NTP activities in the next five years include operational research and surveillance (some programs for which details were scarce might have included this under “administration”), accounting for approximately $190 million (9% of total costs). This amount will probably be revised as more details become available from multiyear national TB control plans. From currently available data, it is estimated that approximately $180 million (95% of total costs) will be used for operational research. This should be considered in juxtaposition with an estimate for the establishment of Health Policy Systems and Services Research (HPSSR), as given in Chapter 5 (page 101) of the GPSTB. There, it is estimated that in all, $210 million will be necessary to establish HPSSR, with $110 million earmarked for the 22 HBCs and $100 million for all other countries with high TB incidence. Though these estimates, arrived at by means of different methodologies, tend to be similar, it is possible that they do not cover the exact same aspects of the programs. Further analysis will be required to confirm the magnitude of the operational research portion of the NTP costs.

Ferreira estimated that drug and diagnostic costs alone for first-line treatments were on the order of $11 million for the entire country as of 1999. XLVIII Dr. Henriette Wembanyama, national coordinator of the NTP, DR of the Congo, personal communication, November 2000. XLIX Dr. José Ramón Cruz, PAHO, personal communication, 2001.

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Box 3: Links with Latest Estimates from the Global DOTS Expansion Plan (GDEP) The Stop TB team based in WHO headquarters in Geneva has conducted a financial evaluation of TB-control needs, focusing exclusively on DOTS expansion plans. Results were first published in the Global DOTS Expansion Plan in 2001, and updated in 2002 (see Global TB Control Report, 2002, WHO). As with the GPSTB, the GDEP uses data from those of the 22 high-burden countries that have elaborated multiyear TB-control plans to estimate unit costs of treatment for first-line patients. It also aggregates health system costs based on published and unpublished cost estimates. Using simple techniques, it extrapolates these unit costs on the basis of GDP/capita to all other low- and lower middle-income countries. It also estimates known and possible additional financial gaps between resources needed (total costs) and resources available (government budgets, grants, loans, and other external contributions). The GDEP does not cover MDR-TB treatment, TB-HIV co-infection-related interventions, or R & D programs. Figures presented here have been compared to the first version of the GDEP, as well as to the newest estimates. Overall, the GDEP estimates are consistent with the GPSTB’s; they show a total requirement of $4.8 billion for the 22 HBC over the 2001 – 2005 period, to be compared with $4.5 billion estimated in the present document. The overall difference is 5% of total costs for these countries. However, as shown here, estimates—which include both NTP budgets and the cost of health-care services—vary significantly, except for five countries. These five countries account for 60% of total costs.

Projections of cases Variations NTP + HCS costs Variation treated (in millions) ((2) – (1))/(1) in millions of U.S. $ (4) – (3) ((4) – (3))/(3)

GDEP (1) GPSTB (2) (%) GDEP (3) GPSTB (4) (millions of U.S. $) (%) India 7.26 7.10 –2% 556 486 –70 –12% China 2.00 3.70 +85% 485 678 +193 +40% Indonesia 1.07 1.46 +36% 175 204 +29 +17% Nigeria 0.70 0.81 +16% 90 73 –17 –19% Bangladesh 0.96 0.83 –14% 120 44 –76 –63% Ethiopia 0.98 0.69 –30% 89 62 –27 –30% Pakistan 1.01 0.75 –26% 120 47 –73 –61% Philippines 0.96 0.86 –10% 109 82 –27 –25% South Africa 0.84 0.75 –11% 1,149 1,257 +108 +9% Russian Fed. 0.96 0.88 –8% 838 893 + 55 +7% Congo (DR) 0.55 0.38 –31% 63 32 –31 –49% Kenya 0.76 0.31 –59% 180 76 –104 –58% Vietnam 0.48 0.52 +8% 97 79 –18 –19% UR Tanzania 0.48 0.33 –31% 49 31 –18 –37% Brazil 0.37 0.45 +22% 258 250 –8 –3% Thailand 0.26 0.29 +12% 104 45 –59 –57% Uganda 0.25 0.22 –12% 42 15 –27 –64% Myanmar 0.19 0.19 — 12 7 –5 –42% Cambodia 0.15 0.15 — 49 30 –19 –39% Zimbabwe 0.34 0.29 –15% 111 55 –56 –50% Afghanistan 0.16 0.20 +25% 11 15 +4 +36% Peru 0.16 0.23 +44% 80 78 –2 –3% TOTAL 20.9 21.4 +2% 4,787 4,532 –255 –5% After revision of both sets of estimates, authors of both studies concluded that these differences arise mainly from different assumptions regarding the cost of health-care services. These different assumptions arose from the lack of knowledge regarding this particular subject. On the other hand, NTP budget evaluations and projections were generally very consistent, which is due to the similarity of the data bases that were used. Extrapolation methodologies vary

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Table 16. Total Financial Requirements for DOTS Implementation and Expansion, and for the Running of National Tuberculosis Control Programs (2001 – 2005), in Low and Middle-Income

Countries: General Results (in millions of U.S. $)

Total Needs % of

total Incremental

cost(a) Financial shortfall(b)

New microscopes 60 2.8 60

Other equipment (vehicles, computers, etc.) 170 7.9 170

Training (initial) 120 5.6 120

Drugs (for both DOTS and non-DOTS programs)(c) 780 36.3 220

Diagnostic supplies(c) 120 5.6 30

Administration (monitoring, supervision, reporting), education, and communication

480 22.3

Training (recurrent) 120 5.6

Incentives(d) 110 5.1

Operational research and surveillance 190 8.8

220 altogether

TOTAL

2,150

100.0

820

(The gap is not identified by item but as a

single total for each country.

Total shortfall

can currently be estimated at

U.S. $600 – 800 million

during the 2001 – 2005

period.)

Totals for the 22 HBCs 1,560 72.6 580 400 – 500

(a) The incremental cost represents the financial expenditure added to NTP costs resulting from higher detection rates in the 2001 – 2005 period. It is estimated that approximately 7 million more TB cases will be treated due to investments in expanded programs in low- and middle-income countries.

(b) The financial shortfall estimates the gap between currently pledged funds for the 2001 – 2005 period and the total costs presented in the “Total Needs” column.

(c) Drugs and diagnostic supplies cover all new and retreatment cases detected and treated with first-line drugs, whether managed under DOTS or not. Altogether, throughout the 2001 – 2005 period, 82% of these costs will cover DOTS (100% in 2005), while the other 18% will cover the remaining protocols. The $250 million needed for the GDF are part of the $780 shown here.

(d) As stated previously, a number of countries use financial and nonfinancial incentives to urge patients to seek care and comply with treatment regimens. In Peru, for example, food packages are provided to 65% of patients. Nicaragua presents indirect forms of incentives: hospitalization is prolonged in order to provide food and care for patients who are otherwise unable to sustain the regimen themselves. This example, however, is still classified here as a cost to health-care services, not as an incentive.

Table 17. Annualization of Financial Requirements for DOTS Implementation and Expansion, and for Running of NTPs (2001 – 2005) in Low and Lower Middle-Income Countries

(in millions of U. S. $)

2001 2002 2003 2004 2005 Total

Investments 61 98 87 65 38 350

76

Microscopes Other equipment Initial training

14 27 21

18 46 34

16 40 31

7 37 21

5 20 13

60 170 120

Recurrent costs 277 314 357 402 450 1,800 Drugs(1) Diagnostic supplies Recurrent training Admin./supervision Incentives Miscellaneous

123 20 18 73 17 27

139 22 20 82 19 33

155 24 23 94 22 38

173 26 28

108 25 43

190 28 32

123 28 49

780 120 120 480 110 190

Total (NTPs) 339 412 444 467 488 2,150

WHO Working Group on DOTS Expansion(2)

40 46 46 46 46 225

TOTAL 379 458 490 513 534 2,375

(1) Includes $250 million that will be provided for by the GDF. (2) See Appendix E for a summary of working group budgets.

Box 4: Procurement Strategies In addition to the main aspects of TB control in low- and middle-income countries presented and discussed in this section, a number of international initiatives are currently being implemented that will contribute to DOTS expansion programs and facilitate access to essential TB medicines in poor countries. The GDF,(1) sponsored by WHO, and the FSTI,(2) will help poor countries purchase medicines by developing long-term procurement strategies and reducing global drug prices. Such initiatives actually require relatively modest, recurrent financing, mostly for drugs. Presently, it is estimated that approximately $1.5 – $2.0 million is necessary to run the GDF. Total requirement is estimated at $50 million each year for first-line drugs. In this presentation of TB-control costs, financial requirements for drugs are included as NTP direct costs, which amount to $780 million for first-line drugs only (see Sections 4.2 and 4.3). Current commitments to finance the GDF’s needs amount to $50 million for the 2001 – 2005 period and are covered by the Canadian government. (1) World Health Organization, Global TB Drug Facility: A Global Mechanism to Ensure Universal, Uninterrupted Access to Quality TB Drugs for DOTS Implementation. WHO, Stop TB Secretariat, Geneva, 2000. (2) The Fonds de Solidarité Thérapeutique International was launched with the sponsorship of the French government in 1997, in association with UNAIDS and other agencies. It is currently serving programs in five countries (Bénin, Côte d’Ivoire, Morocco, Sénégal, and South Africa). It covers drugs and hospitalization costs for AIDS and opportunistic infections, including TB. To date, more than 10 countries have requested help from this fund. Price reductions of up to 70% had already been obtained from pharmaceutical companies by 1999. The current annual financial commitment is $2 million, with French and Korean funds participating. The FSTI is now being integrated with a new initiative to create bilateral cooperation between hospitals from the “north” with counterparts from the “south.”

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Based on an investment of $350 million, and including recurrent costs associated with the management of first-line TB patients (health-care services excluded), the average unit cost per case detected and treated under DOTS programsL would be approximately $93 for the 114 countries covered by the GPSTB (Table 18). This cost is made up of $16.3 (on average) for initial investments and $76.4 for recurrent costs of drugs, diagnostic supplies, and other program costs. If costs of health-care services are included ($148, for the mid-point scenario used in the GPSTB) the average unit cost of addressing the case management of the entire 21.5 million cases managed by DOTS programs will be $241. The absolute cost will range from $77.3 in the Indian subcontinent to $1,522 in Austral Africa, in comparison to an estimated average cost per case treated of $237, as of 2000. Costs of health-care services are discussed in Section 4.4. If the cost of the Working Group on DOTS Expansion ($225 million during five the years) is included, the average unit cost of treating the 21.5 million patients covered by DOTS programs will become $103 (without health-care services), and $251 if all aspects of the management of cases through DOTS programs are covered (see Section 4.4). If all of the 26 million cases expected to be detected by NTPs in the five-year period are taken into account, the average unit cost of treatment will range from $205.5 to $255.5, according to the scenario retained for the use of health-care services. Using the mid-point scenario for health-care services (see Section 4.4), this unit cost will be $230.5 on average. If the DOTS Expansion Working Group is included in these considerations, the unit cost of managing first-line cases will range from $214 to $264, with a mid-point at $239. The cost of managing one first-line case as of 2000 is estimated at $237, on average, for all 114 countries covered by the plan ($216.4 for the 22 HBCs). The marginal cost throughout the five-year period of detecting and treating each additional patient through the strengthened TB-control programs that will result from the investments discussed here will be $119 on average, for the NTPs of all 114 countries (and $151.5 if the Working Group on DOTS Expansion is included). It will be $106 for the 22 HBCs. Assuming that most cases added to TB control throughout the 2001 – 2005 period will actually be managed by DOTS programs with a cure rate of approximately 85%, the cost per additional case cured is expected to average around $140 (and $178 if the DOTS expansion Working Group is included). With the inclusion of health-care systems costs, this would become $213 on average (using the mid-point cost for the use of health-care services, as done in the GPSTB’s main document). The average cost per additional case cured (assuming a 30% natural cure rate and at least 5.3 million additional cases cured over the five-year period; see Table 10) – would then be $175 for all LLMICs. The wide range in regional marginal cost, as shown in Table 19, reflects, in part, the variety of situations among the regions of the world. A high detection rate (80%) with low DOTS coverage (11%) and relatively high prices in eastern Europe and the former Soviet Union translate into a high marginal cost for detecting every additional case and managing it under DOTS. On the other hand, relatively high DOTS coverage (69%) and very low case detection rates (29%) coupled with cheaper inputs generate a very low marginal cost in southeast Asia, where a modest investment in DOTS expansion will be coupled with an anticipated large increase in case detection rates. However, these results indicate that more needs to be known about NTPs in these regions. Indeed, they might reflect the level of uncertainty that has characterized the construction of estimates for these two regions, especially the former cluster (eastern Europe and the former Soviet Union), as no fully documented national plan was available for any of its countries (see Appendix C1). But they also illustrate that DOTS expansion is the only aspect of TB control for which investment

L Approximately 21.5 million cases will be detected and treated via DOTS programs, as estimated in Section 4.1. An additional 6.9 million cases are expected to be detected and treated during the five-year period for the 114 countries covered by the GPSTB estimates. But, as shown in Table 10, this means that an additional 12.8 million cases will be managed by DOTS programs. Investments in DOTS expansion refer to these additional cases.

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costs (in training, equipment, and other items) is currently documented. The amplitude of the range in marginal costs also reflects the significant differences in unit cost levels of running national programs, due to great variations in costs of drugs, labor, and infrastructure. Table 18. Unit Costs of First-Line Treatments, Low and Lower Middle-Income Countries, 2001 – 2005

(in 2000 U.S. $)

Areas of TB-Control Costs Included NTP and HCS(a) NTP and

WGDE(b) NTP, HCS, and WGDE

Unit cost per:

NTP

Low HCS cost scenario

High HCS cost scenario

Mid-point HCS cost scenario

Low HCS cost scenario

High HCS cost scenario

Mid-point HCS cost scenario

Case Treated First-line DOTS only

82.6 92.7

205.5 215.7

255.4 265.6

230.5 240.6

91.2 103.2

214.2 226.1

264.1 276.1

239.1 251.1

Additional Case Treated First-line DOTS only (c)

119.0 119.0

119.0 119.0

306.7 306.7

212.8 212.8

151.5 151.5

151.5 151.5

339.2 339.2

245.3 245.3

Cured Case First-line DOTS only

102.4 109.1

254.7 253.7

316.6 312.5

285.7 283.1

113.1 121.4

265.6 266.0

327.5 324.8

296.5 295.4.

Additional Cured Case First-line DOTS only(c)

140.0 140.0

140.0 140.0

360.9 360.9

250.4 250.4

178.2 178.2

178.2 178.2

399.1 399.1

288.6 288.6

(a) NTP = National TB Control Program HCS = Health-Care Services.

(b) WGDE = Working Group on DOTS Expansion. (c) It is assumed that all additional cases treated by the NTPs will be DOTS-compliant, therefore unit costs of

additional cases are the same, be they viewed as labeled “first-line” or “DOTS.”

79

Table 19. Total Costs to National TB Control Programs, Low and Lower Middle-Income Countries, 2001 – 2005 (in millions of 2000 U. S. $)

National TB Control Programs

(DOTS and Other First-Line Strategies) Initial Investment

Costs (c) Recurrent Costs

(d)

Number of cases detected

and treated

(in 000’s)

(a)

Additional number of

cases detected

and treated

Addi-tional

number of cases detected

and treated

by DOTS

(b)

Total NTP

Budge-tary

Needs

Equip-ment

(microscopes,

cars, compu-

ters)

Other (incl.

Training)

Drugs (First- line)

Diag-nostic costs

Contin- uous

training

Program mgmt., super-vision, moni-toring

Incentives

Other (operational research,

surveil-lance)

Cost ($)

per add. case detected

and treated

by DOTS(1)

[c + (b/a x d)] / b

Austral Africa (AA) 889 42 195 116.1 0.5 1.9 47.6 8.4 2.1 55.3 0.0 0.4 173.8


4,975 1,766 1,954 349.8 21.7 13.5 200.0 20.3 28.1 59.1 0.0 7.2 79.5


930 144 440 226.1 9.9 51.7 72.9 2.1 12.9 66.4 0.0 10.1 707.2


1,678 290 913 242.8 54.5 5.1 105.9 37.4 13.5 20.7 0.0 5.7 281.1

Industrial South Cone (South America)

454 41 268 109.9 6.4 1.3 28.1 4.0 3.9 21.7 38.1 6.3 410.6

Latin America and Caribbean 630 80 163 96.7 2.9 0.4 26.6 5.6 6.0 25.7 24.8 4.8 152.3 Indian sub-continent 8,909 1,423 5,059 370.7 49.4 13.4 135.5 19.6 9.4 95.4 12.0 36.1 78.1


4,885 1,826 2,069 440.6 82.6 22.4 82.4 14.4 11.6 92.9 35.1 99.2 127.3

Southeast Asia 2,675 1,310 1,698 197.2 2.2 10.3 81.0 8.3 32.5 42.8 0.0 20.2 91.5

Total for 114 countries covered by GPSTB

26,032

6,923

12,760

2,150

230

120

780

120

120

480

110

190

119.0

Total 22 HBCs 21,501 5,828 10,735 1,560 180 60 550 80 90 340 100 160 105.9

Total (Low and Middle income countries)

26,545

7,015

13,023

2,270

240

130

800

130

130

520

120

200

N/A

(1) This cost only includes direct NTP costs (therefore excludes costs to health-care services of TB case management).

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4.4. Costs of the Use of Health-Care Services National Tuberculosis programs depend on local primary health-care systems for the provision of both ambulatory and inpatient care, a major component of TB-control efforts, even though they do not, usually, supervise these resources. Hospitalization is still routine in many countries, such as South Africa (where 50% of patients are hospitalized for over two months on average) and the former socialist countries. China has a dedicated network of TB clinics. In Russia, all TB patients are interned in sanatoriums and hospitals for the full length of treatment. As case detection rates rise, ensuring that existing health-care delivery systems are being used efficiently will be necessary. For all 114 countries covered by the GPSTB, it is presently estimated that costs for visits to health centers and hospitals—including inpatient care costs for severe and infectious TB patients—will be between approximately $3.2 billion and $4.5 billion during the 2001 – 2005 period. For all 147 low and middle-income countries, total cost of health-care services of TB case management will be on the order of $3.5 to $5.0 billion. Table 20 displays these costs by cluster. Appendix D1 provides country by country estimates. These evaluations encompass regimens that are first-line treatments. However, no differentiation between case categories (smear-positive, smear-negative, relapse, re-treatment) has been introduced. Cases have not been separated by category due to the lack of specific data regarding the different regimens for the various types of TB, country by country. Moreover, these evaluations do not include specific additional costs for surgery. Should they be further investigated, the latter might increase current estimates slightly. The proportion of TB patients being operated on remains a very small fraction of the total. The cost of health-care services associated with MDR-TB treatment has been included with the cost of that intervention, as discussed in Section 4.6. It is assumed that this cost will principally be added to the cost already incurred by first-line treatments and retreatments. Indeed, the majority of the cost of health-care services in these cases is concentrated during the intensive phase of the DOTS protocol, which is when hospitalization takes place and when direct observation of treatment (that is, visits) is most intensively used. When MDR-TB patients are identified at an early stage of their treatment, part of the cost associated with the use of health-care services should then be allocated to second-line treatments; however, this is a complex evaluation and does not represent a very significant share of the total cost. Out of the $3.2 to $4.5 billion estimated for first-line treatments, South Africa alone could account for an estimated $1.03 to $1.24 billion during the 2001 – 2005 period. The minimum annual cost for first-line treatment in the Russian Federation is estimated at $120 million (as of 2000).106 During the five-year period, the cost for Russia could range from $660 to $860 million. Just five countries (South Africa, the Russian Federation, China, India, and Indonesia) will concentrate 61% to 67% of all health-care services’ costs over the period (Table 20). The ranges presented here reflect whether or not current capacities of health-care services can absorb the additional 6.9 million TB cases to be treated. The lower end of the range corresponds to a situation in which no additional cases are added or no additional investment is needed in the health-care system, even with a larger caseload. The upper end assumes that every new case requires a proportionate amount of investment and increase in operations. In economic terms, the latter scenario means that health-care systems are already at full capacity and that the marginal cost of adding one case is equivalent to the cost of treating one patient in the present configuration of the system. This equivalency will be arguable in some instances, but requires far more data than are currently available in order to be verified and adapted.

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These are, therefore, approximate evaluations that will require closer analysis and refinement. Further work is critical in order to identify, country by country, how health-care systems will be able to handle a (temporarily) increasing number of TB cases as detection rates also increase.LI DOTS is normally based on ambulatory patient management, and expansion of protocols based on DOTS might modify the way health-care systems are used. The example of Cuba, where sanatoriums were closed after the introduction of DOTS (“TADO”) in 1970,107 indicates that there is room for higher productivity in many health-care systems, and that a switch to ambulatory care would likely produce considerable cost savings within health-care infrastructures. However, a portion of TB cases will still require hospitalization for some time during the intensive phase of the treatment. Though not yet precisely known, it is estimated that in countries where inpatient care is not routine for TB patients, 1% to 2% of patients spend some time in hospitals.LII In other countries, hospitalization is widely used during the intensive phase of the treatment (that is, the first two months of short-course therapy). Moreover, hospital stays are also used to prevent poor patients from stopping the treatment due to a lack of food, which they may not be able to afford or have access to once they leave the facilities. The example of hospitals in Nicaragua108 suggests that this practice may be common in some countries, even though from a strictly clinical point of view it is not necessary. As previously mentioned, extended hospital stays may be considered an incentive and classified as such in cost estimates. While not routine, these longer-than-clinically-necessary hospital stays do contribute to the cost of TB control. However, further improvements in case management—as well as extended financial support to the NTPs—could relieve health-care facilities of these supportive, yet relatively costly, activities.

LI The number of cases estimated in the present costing analysis assumes that incidence rates will not decrease overall before the end of the 2001 – 2005 period. However, it is clear that an increased case detection rate combined with better TB-control programs will eventually bring incidence rates down. The case of Peru, where this rate is now decreasing at an estimated 4% per year, illustrates the accomplishments successful and massive TB-control programs can achieve. LII This is the case of the Philippines and India, for example, according to the coordinators of the NTPs in those countries. (Personal communications, 2000 and 2001.)

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Table 20. Costs to Health-Care Services of TB Case Management for the 114 Countries Included

in the GPSTB (by Regional Cluster), 2001 – 2005 (in millions of U.S. $)

Hypothesis:

No need for additional capacity in health-care services

Hypothesis: Health-care services are already at capacity and each incremental case

treated requires a proportional investment in those services

Ambulatory Care

(visits, supervision

of drug intakes)

Inpatient Care (hospital and sanatoriums)

Totals Ambulatory Care

(visits, supervision

of drug intakes)

Inpatient Care

(hospital and sanatoriums)

Totals

Austral Africa (AA) South Africa

395 330

729 700

1,124 1,030

555 500

789 740

1,344 1,240

Sub-Saharan Africa (AA excluded)

60 76 136 96 93 189


14 151 165 55 191 246

Eastern Europe and the former Soviet Union Russian Federation

4

2

1,139

658

1,143

661

3

2

1,359

861

1,362

863

Brazil 2 133 135 1 148 149

Latin America (USC) and Caribbean

9 76 85 12 90 102

Indian Sub-Continent India

60 42

9 7

69 49

370 320

11 8

381 328

China, Indochina, and Miscellaneous (Asia) China

199

183

86

39

285

222

388

367

118

61

506

428

Southeast Asia Indonesia

58 41

2 0

60 41

220 161

2 0

222 161

Total for 114 countries covered by GPSTB

800

2,400

3,200

1,700

2,800

4,500

Sub-total (22 HBCs) % of total

700 88%

1,700 71%

2,400 75%

1,600 94%

2,000 71%

3,600 80%

Total 5 countries % of total

597 75%

1,346 56%

1,943 61%

1,349 79%

1,670 60%

3,019 67%

Total (low and middle income countries)

800

2,700

3,500

1,800

3,200

5,000

83

4.5. Costs Associated with TB-HIV Chemoprophylaxis In regions where it is highly prevalent, HIV is fuelling rapid increases in TB epidemics. In just two years, TB incidence has increased at least 25% in South Africa and 40% in Botswana, according to recent estimates.109 The present model estimates that in all of the 144 countries between 12.1 and 16.5 million people are currently co-infected (though not diseased, with either AIDS or TB), and that an additional 3.3 million people will become co-infected in those countries during the 2001 – 2005 period (see Table 9b). This will bring the total number of co-infected people in those countries to between 15.4 to 19.9 million, roughly 99.9% of all cases worldwide for the 2001 – 2005 period. Africa alone will account for 11.8 to 15.5 million cases, or 77 – 78% of the total. India will account for roughly 11 to 12.5% of the total. These estimates will need revision, as recently released data regarding the HIV/AIDS epidemic show a rapid expansion of the disease in places like China, eastern Europe, and the former Soviet Union.110 Stopping TB requires HIV-control strategies, and vice-versa.111 TB chemoprophylaxis will have to be rapidly distributed in HIV-endemic areas to prevent TB-HIV co-morbidity. Various strategies exist to prevent the onset of TB in HIV-infected people.112 For example, the ProTEST program, promoted by both WHO and UNAIDS, is an integrated approach to HIV and TB control, using voluntary testing and counseling for HIV+ persons, who are also offered TB testing and prophylactic treatment. Those who test positive for active TB are referred to the NTP for medical treatment. Prophylaxis for those infected with TB but not diseased consists of a six-month course of isoniazid (see Section 2.3.4), which has been documented to be highly cost-effective in Uganda.113 As noted in Section 2.3.4, a scenario has been built to simulate case detection on a large scale. The starting point of the scenario (described in Tables 6 and 7) is the existence of ProTEST programs in four countries (South Africa, Malawi, Uganda, and Zambia)114 and the planned expansion of ProTEST programs to another eight sub-Saharan countriesLIII in the near future.115 A high-end scenario assumes that further expansion will include all countries where HIV and TB are significant problems (another 45 countries). For the 2001 – 2005 period, applying the detection scenario to the pool of roughly 15.4 to 19.9 million cases of TB-HIV co-infection in the 45 countries covered by the high-end estimates yields a preliminary detection rate of 10.1 to 11%, translating into approximately 1.6 to 2.2 million cases (1.4 to 1.9 million cases in Africa alone). Table 21 summarizes the number of cases that could be detected and managed, should expansion of appropriate programs be achieved. If restricted to the 12 sub-Saharan countries where ProTEST programs have already been or will soon be started, it is estimated that approximately 28.3 million people will go through the program and be tested for HIV and counseled; that roughly 3.4 million HIV infections will be detected; and that between 1.2 and 1.7 million persons with the dual infections will receive preventive therapy. The higher end of this estimate (reduced to 1.6 million due to the fact that a small portion of these patients will actually be diseased with TB and will be transferred to DOTS programs) was used for estimating costs published in the GPSTB.

LIII Congo DR, Côte d’Ivoire, Ethiopia, Kenya, Mozambique, Rwanda, Sénégal, and Tanzania.

84

Table 21. Scenario of Detection and Treatment for Cases of TB-HIV Co-Infection in the 2001 – 2005 Period in Low and Lower Middle-Income Countries(a)

(cases in thousands)

2001 2002 2003 2004 2005 2001 – 2005

A. Totals for Malawi, South Africa, Uganda, and Zambia (where ProTEST programs have already started) Number of people referring themselves to the programs

215 654 1,329 2,250 4,573 9,021

Number of HIV+ cases detected 36 106 216 366 742 1,466 Number of TB-HIV co-infection cases treated With a 50% prevalence rate of MTB among HIV+ patients With MTB prevalence rate among HIV+ patients equal to that among the general population

18

13

53

39

108

80

183

135

371

273

733

539

B. Totals for Ethiopia, Kenya, Mozambique, and Tanzania (where ProTEST programs are starting) Number of people referring themselves to the programs

0 1,416 2,166 3,682 7,509 14,774

Number of HIV+ cases detected 0 158 242 412 838 1,650 Number of TB-HIV co-infection cases treated With a 50% prevalence rate of MTB among HIV+ patients With MTB prevalence rate among HIV+ patients equal to that among the general population

0

0

79

53

121

81

206

138

419

281

825

552

C. Totals for Congo DR, Côte d'Ivoire, Rwanda, and Senegal (where ProTEST programs could be started soon) Number of people referring themselves to the programs

0 424 869 1,337 1,828 4,458

Number of HIV+ cases detected 0 26 54 84 114 280 Number of TB-HIV co-infection cases treated With a 50% prevalence rate of MTB among HIV+ patients With MTB prevalence rate among HIV+ patients equal to that among the general population

0

0

13

10

27

19

42

30

57

41

140

100

D. Totals for another 45 low and lower middle-income countries (including China, India, Russia, Brazil, Thailand, Cambodia, Vietnam, Myanmar, Haiti, the Dominican Republic, and all other countries of sub-Saharan Africa). These totals assume that ProTEST or ProTEST-equivalent programs will be available in these countries in the near future. Number of people referring themselves to the programs

0 0 7,855 23,810 48,115 79,781

Number of HIV+ cases detected 0 0 98 296 600 992 Number of TB-HIV co-infection cases treated With a 50% prevalence rate of MTB among HIV+ patients - With MTB prevalence rate among HIV+

patients equal to that among the general population

0

0

0

0

49

36

148

110

300

224

496

371

85

2001 2002 2003 2004 2005 2001 – 2005

TOTAL (for all 57 countries included in the high-end estimate) Number of people referring themselves to the programs

215 2,494 12,220 31,079 62,025 108,033

Number of HIV+ cases detected 36 292 610 1,155 2,295 4,388 Number of TB-HIV co-infection cases treated With a 50% prevalence rate of MTB among HIV+ patients - With MTB prevalence rate among HIV+


18

13

146

102

305

216

578

413

1,148

819

2,194

1,562

SUBTOTAL (for the 12 countries where ProTEST is or will soon be implemented) Number of people referring themselves to the programs

215 2,494 4,365 7,269 13,910 28,253

Number of HIV+ cases detected 36 292 514 860 1,694 3,396 Number of TB-HIV co-infection cases treated With a 50% prevalence rate of MTB among HIV+ patients - With MTB prevalence rate among HIV+


18

13

146

102

257

180

430

302

847

594

1,698

1,191

(a) The present table is based on the scenario presented in Tables 6 and 7, and calculates the number of TB-co-infection cases based on two separate scenarios concerning the prevalence of MTB (mycobacterium tuberculosis)—the bacterium responsible for TB infection). The total cost of the isionazid (INH) prophylactic strategy throughout the 2001 – 2005 period could be on the order of $630 to 760 million (see Table 22 for a summary of costs, and Table 15 for estimates by cluster). Of this total, it is estimated that at most $10 to 20 million would be needed for six-month courses of INH. The lower end of these estimates corresponds to costs likely to be accounted for by the ProTEST programs in all 12 countries where they have already been or will soon be started. The higher end encompasses an additional 45 countries. Given the fact that WHO did not have plans as of the end of 2001 to help start ProTEST or ProTEST-like programs in countries other than those mentioned, the GPSTB currently reports the lower-end of the estimates ($630 million). However, the current annex intends to simulate and show the magnitude of the human and economic effort if those plans were rapidly expanded, given that the rapidly mounting problem of dual infection by HIV and MTB could indeed trigger this expansion on a more voluntary basis than formerly expected. Program costs, covering HIV testing and counseling, TB screening and testing, administration, up-front costs (training costs, in particular), monitoring and follow-up of patients, are likely to amount to $620 to 740 million in all during the 2001 – 2005 period. These costs would be split about evenly between HIV-related and TB-related costs—or $330 – 360 million for HIV testing and counseling and $290 – 380 for TB screening and testing (see Table 22). No information was actually available regarding the up-front costs of the activities and resources needed to set up and implement such programs (initial training and equipment, for example). In order to reflect at least some of these likely costs, a provision of 50% was added to the TB screening and testing program costs available from a study on the cost-effectiveness of preventive therapy in Uganda. This provision will obviously need to be refined as more becomes known of the economic and financial aspects of ProTEST and other programs.

Table 22. Intervention Costs for Chemoprophylaxis of TB-HIV Co-Infected Patients in the

86

2001 – 2005 Period in Low and Lower Middle-Income Countries (in millions of U.S. $)

Chemo-

prophylaxis (6-month course of

INH)

HIV testing and

counseling, and related

program costs

TB screening and testing, and related

program costs

Total financial

needs

Known financial contri-butions

Financial gap

Malawi, South Africa, Uganda, and Zambia

4.4 275.7 248.2 528.3 36.0 492.3

Ethiopia, Kenya, Mozambique, and Tanzania

4.0 41.3 37.2 82.4 n.a. 82.4

Congo DR, Côte d’Ivoire, Rwanda, and Senegal

1.3 9.1 8.2 18.6 n.a. 18.6

Total for 45 other countries

10.3 33.9 86.4 130.7 n.a. 130.7

Total for 12 “ProTEST” countries

10

330

290

630

36

594

Totals for 57 countries

20

360

380

760

36

724

As noted earlier, the vast majority of TB-HIV co-infection cases are concentrated among poor countries of sub-Saharan Africa and south Asia. Total costs will therefore be concentrated in those areas as well. South Africa alone could account for two thirds of the total estimated costs for the 12 “ProTEST” countries, or $495 million. This very high share is also due to the higher cost of health-care in South Africa than in the rest of Africa. Though being an upper middle-income country, South Africa is also one of the world’s most burdened by both HIV/AIDS and TB. The enormity of the HIV/AIDS epidemic there creates a considerable economic burden (more than a quarter of South African adults are infected with HIV, according to UNAIDS116). In general, any additional strain on already modestly equipped health-care systems is likely to require an equivalent additional investment. This in turn will create an additional financial gap needing to be filled in the poorest countries affected, notably those in sub-Saharan Africa. There are very significant limitations to these estimates, and the estimates themselves will require considerable refining in the future. In particular, costs have been evaluated on the basis of a single INH-based intervention in Uganda, together with a few specific costs for tests and screening associated with TB-HIV co-infection interventions from Peru117 and very aggregated data from a ProTEST project in Zambia.118 This does not accurately reflect the variety of situations and costs that exist throughout low- and middle-income countries. Further investigation is therefore required to assess the full economic impact and financial burden of isionazid intervention. Moreover, costs associated with the implementation and expansion of such strategies still have to be fully integrated. Including the budget of the WHO’s Working Group on TB-HIV co-infection ($12 million during the 2001 – 2005 period), total financial requirements for this major area of TB control are shown in Table 23.

Table 23. Total Costs for the Control of TB-HIV Co-Infection in the 2001 – 2005 Period in Low and Lower Middle-Income Countries

(in millions of U.S. $)

87

2001 2002 2003 2004 2005 Total

needs Financial

contributions known at present

Financial gap

Intervention costs High-end Drugs Other costs of prophylactic treatments HIV testing and counseling program costs Low-end Drugs Other costs of prophylactic treatments HIV testing and counseling program costs

13 0 7 6

11 0 6 5

50 1

25 23

41 1

23 18

108

3 52 53

89 1

48 40

196

5 92 99

162

3 84 76

393 10

184 199

326

5 169 152

760 20

360 380

630 10

330 290

36

36

724

594

WG budget 1 2 3 3 3 12 2 10

Total High-End Low-End(1)

14 12

52 43

111 92

199 165

396 329

772 642

38 38

734 604

(1) Scenario used for the GPSTB. See Table 14 in this document and Table 3.1 (page 72) in the GPSTB.

4.6. Costs Associated with the Treatment of MDR-TB Treating patients with multidrug-resistant tuberculosis (MDR-TB) will be a key element of the long-term fight against both the disease and its eventual eradication. Though its exact impact on the future epidemiology of TB is only beginning to be fully understood,LIV DOTS-Plus programsLV will undeniably represent a very significant portion of TB-control costs. Cost projections have been based on current estimates of MDR-TB prevalence among new TB cases (see Section 2.3.1) ranging between 3.2% and 4.6% of all new TB cases.119 This would represent between 900,000 and 1.3 million MDR-TB cases detected worldwide in the next five years (with 80% to 90% of these cases occurring among the 22 HBCs; see Table 9a). For the GPSTB’s 114 countries, the range is 880,000 to 1,240,000 cases. Using the scenario discussed in Section 2.3.4 (see Table 5), Table 24 shows how many total cases—out of all TB cases detected that actually will be MDR—could be appropriately treated if DOTS-Plus or DOTS-Plus-equivalent programs were widely deployed by 2005. The application of the scenario described in Section 2.3.4 results in the hypothesis that in all 114 countries, roughly 40%, or approximately 494,000, of all detected cases of MDR-TB would be appropriately treated as such over the 2001 – 2005 period. These estimates have thus far been worked out on a global scale (the same proportions of annual detection are used for all countries) in the absence of DOTS-Plus or DOTS-Plus-equivalent national programs. Peru is the only exception among the 22 HBCs, with a 100% coverage rate, and approximately 450 patients being treated in 1999.120

LIV See, in particular, The Global Impact of Drug-resistant Tuberculosis (Harvard Medical School, Open Society Institute: Boston, 1999), for a comprehensive attempt to determine the extent of MDR-TB and the threat it represents for TB control worldwide. LV DOTS-Plus stands for the adaptation of DOTS programs to the specific characteristics of multidrug-resistant tuberculosis, which requires reinforced diagnostic protocols and more complex combinations of more powerful drugs. DOTS alone is deemed insufficient to treat MDR-TB patients. (For more on this subject, see The Global Impact of Drug-resistant Tuberculosis.)

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Table 24. Scenario of Detection and Treatment Rates for MDR-TB Cases in the 2001 – 2005 Period in Low and Lower Middle-Income Countries

(based on the scenarios introduced in Table 3 and hypotheses in Table 5; cases in thousands) 2001 2002 2003 2004 2005 2001-

2005 Total number of TB cases detected that are MDR Scenario 1 (a) Scenario 2 (b)

138 199

157 223

176 248

197 273

217 299

884

1,242 Proportion of detected MDR-TB cases covered by DOTS-Plus or equivalent programs

5%

15%

30%

50%

80%

40%

Number of cases treated as MDR Scenario 1(a) Scenario 2(b)

7

10

23 33

53 74

98 137

174 239

355 494

(a) Scenario 1 uses data provided by Dye et al. and Espinal et al. It estimates a worldwide proportion of MDR-TB among new TB cases of 3.2%.

(b) Scenario 2 uses data from WHO, the IUATLD, and Mercedes Becerra. It estimates a worldwide proportion of MDR-TB among new TB cases of 4.6%. Scenario 2 is used in the GPSTB’s figures.

High drug costs (ranging from $823 to $4,780 per patient, according to current data), specific diagnostic tools (culture and drug sensitivity tests in particular), additional training, and specific monitoring/surveillance resources are the main economic factors determining the overall cost of MDR-TB treatment. The use of health-care services is also an important component that has to be accounted for. Some program costs specific to DOTS-Plus, such as training, specific equipment, and administration, have not been detailed here, as there are insufficient documented data on such costs for these programs. (The only published or available material relates to programs in Peru.121) Consequently, these cost estimates will have to be revised as more information becomes available concerning DOTS-Plus programs and their up scaling and expansion. The program costs (excluding drugs) have been inflated by 10% from the available data to account for possible under-evaluation of initial program costs. All recurrent costs considered, data from Peru show that a single case of MDR-TB may cost $1,200 to $1,400, or 20 times as much as a drug-sensitive TB case in that country.122 This amount could be twice as much in countries such as Brazil and Ukraine, according to data currently available (see Section 3). The total cost of drugs for MDR-TB cases in all low and lower-middle income countries could amount to as much as $1.0 to $1.4 billion should 100% of detected cases be treated properly (see Table 25). These costs are based on currently known drug prices. However, recent trends in drug costs have shown that further reduction in costs is possible. For this reason a simulation has been conducted, suggesting that a 20% decrease in prices will gradually occur over the five-year period.LVI If an additional 30 to 40% is added to the overall cost of drugs to treat MDR-TB (in order to cover at least some of the other costs currently known to be associated with the intervention, based on both standardized and individualized regimens currently being used in Peru123), approximately $1.5 to $1.9 billion will be needed to cover the overall treatment of all MDR-TB cases in all low and middle-income countries during the 2001 – 2005 period. Using the number of cases detected (as shown in Table 24), the expected total cost, however, is more likely to range from approximately $730 million to $1,070 million, of which $460 million to $650 million will be for drugs only. Simulations using a gradual 20% reduction of drug costs show that those totals would be reduced by $70 million for the low-end scenario, and by $100 million in the high-end scenario. Table 25 summarizes costs associated with the management of MDR-TB. Tables 15 and 26 provide estimates by

LVI This price reduction has been established by the author in collaboration with Dr. Jim Kim of the Harvard Medical School. Dr. Kim also chairs the Green Light Committee for DOTS-Plus programs.

89

regional cluster. It is estimated that $400 million to $900 million will be needed for so-called “hot spots,”LVII where MDR-TB prevalence exceeds 5% of all TB cases.124 This is the case of the Chinese province of Henan, for example, (where 85.5 million inhabitants account for approximately 86,000 annual new cases of TB). A study conducted there in 1996 reported that 15.1% of all new TB cases detected were multidrug-resistant.125 Estonia had the highest proportion of documented new cases in 1998 with 14.1%. If re-treatment cases are included, the proportion in Estonia for that year rises to 18.1% of all TB cases. Generally speaking, for all TB cases tested, the highest incidence rates of MDR-TB were found throughout the former Soviet Union; in some Chinese provinces; in Mexico (Baja California, 1997: 7.3%); in one Indian state (Tamil Nadu, 1997: 7.1%); and in Iran (1998: 6.7%). Despite this threatening proportion, NTPs have not, in most cases, specifically budgeted for the management of multidrug-resistant cases; it is thus likely that a very high proportion of the funds needed to implement and run DOTS-Plus programs will not immediately be available from local sources. Moreover, most MDR-TB cases will occur in countries where resources are scarce, such as India, Pakistan, China, and Nigeria. A comprehensive understanding of the economic consequences of MDR-TB for NTP budgets has yet to be achieved. Studies to document these costs are underway, and estimates will have to be refined as the economics of MDR-TB are better defined, and as DOTS-Plus programs are set up. Specific investments in equipment and training will also have to be fully identified.

Table 25. Intervention Costs for MDR-TB Control in the 2001 – 2005 Period in Low and Lower Middle-Income Countries (in millions of 2000 U.S. $)

“Hot Spots” only(1)

All low- and middle-income countries

If all cases detected were treated as MDR-

TB Drugs only 240 – 540 460 – 650 1,050 – 1,400

Other intervention costs 60 – 150 110 – 170 230 – 350

General health care services 100 – 10 160 – 250 320 – 500

Total for 114 countries

400 – 900

730 – 1,070(2)

1,600 – 2,250

Total for 22 HBC — 590 – 980 —

Financial Shortfall

Probably up to 75% of these totals, because these costs are not currently budgeted in most low and middle-income countries, and as they will concentrate in some of the poorest countries (India, China, Pakistan, and Nigeria): 360 – 700 630 – 860(3) 1,000 – 1,400

Note: See also Table 15 for breakdown of costs by regional cluster. (1) “Hot spots” are places where more than 5% of all reported TB cases are multidrug-resistant.126 (2) If the budget of the WHO’s Working Group on DOTS-Plus for MDR-TB is included in these estimates ($16

million), the total cost for MDR-TB control would be $746 to $1,086 million during the 2001 – 2005 period. The latter figure is reported in the GPSTB (see page 87 of the GPSTB).

LVII MDR-TB is particularly prevalent in a number of “hot spots”—areas where more than 5% of all TB cases documented in recent surveys have been reported as MDR-TB. Proportions documented in only a few regions of a specific country have been extrapolated to the entire country, such as in Russia, China, and India.

90

(3) Including the working group’s budget in these estimates raises the financial shortfall to $604 to $834 million. The latter figure is reported in the GPSTB (see also Table 14).

In the GPSTB, estimates shown reflect the higher end of these costs which also cover the portion of costs regarding expansion and up-scaling of DOTS Plus programs that might have been underestimated by the present evaluations.

Table 26. Total Costs for MDR-TB Control in the 2001 – 2005 Period in Low and Middle-Income Countries

(in millions of 2000 U.S. $)

Drugs Other specific program costs

Health-care services costs

Total Costs

Scen. 1 Scen.2 Scen.1 Scen. 2 Scen. 1 Scen. 2 Scen. 1 Scen. 2

Austral Africa (AA) 6.0 9.2 4.4 6.7 6.4 10.0 16.7 25.9

Sub-Saharan Africa (AA excluded) 52.8 30.8 4.8 2.9 7.1 4.4 64.7 38.1 Middle East and northern Africa 18.9 11.6 6.1 4.3 9.0 6.4 34.0 22.3

Eastern Europe and the former Soviet Union

74.8 118.3 21.1 37.4 30.9 55.4 126.8 211.0

Urbanized South Cone (Brazil) 3.5 5.0 1.1 1.6 1.6 2.3 6.2 8.9

Latin America (USC excluded) and the Caribbean

7.9 7.5 3.7 4.3 4.0 4.4 15.7 16.1

Indian south-continent 159.0 257.8 30.2 49.9 44.3 74.0 233.5 381.6

China, Indochina, and miscellaneous Asia

113.4 190.2 30.7 55.8 45.0 82.8 189.1 328.8

Southeast Asia 23.7 19.7 7.9 7.0 11.6 10.4 43.3 37.2

Total (114 countries)

460

650

110

170

160

250

730

1,070

Subtotal (22 HBCs) 370 590 90 160 135 230 595 980

WHO Working Group on DOTS-Plus for MDR-TB

16 16

TOTAL for MDR-TB control

746

1,086

Note: Scenario 2 is the scenario used for the presentation of costs in the GPSTB.

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Table 27. Annual Estimates of MDR-TB Costs during the 2001 – 2005 Period for 114 Low and Lower Middle-Income Countries

(figures in millions of 2000 U.S. $)

2001 2002 2003 2004 2005 2001 – 2005

Drugs Scenario 1 Scenario 2

9 13

31 44

69 98

127 180

224 314

460 650

Other program costs Scenario 1 Scenario 2

2 3

8 12

17 26

30 47

53 82

110 170

Health-care services Scenario 1 Scenario 2

3 5

11 17

24 38

44 69

77

121

160 250

TOTAL Scenario 1 Scenario 2

15 22

49 73

109 162

202 296

355 517

730

1,070 Note: Scenario 2 is the scenario used for the presentation of costs in the GPSTB.

4.7. Financial Requirements for Research and Development Efforts—Chapter 5 of the Global Plan to Stop TB Research to develop new diagnostic, prophylactic, therapeutic, and policy tools for TB control is a fundamental aspect of future TB-control strategies and of our long-term ability to fight and eventually eradicate this deadly disease. Although such costs are generally not incurred by the NTPs of poor countries, including the costs of R & D here is critical. Estimating how much should and will be spent for TB R & D during the 2001 – 2005 period is not easy, as many research programs take longer than five years to reach their goals. To develop a new TB antibiotic, for example, up to $300 million might be needed over a period spanning eight to 20 years.127 In the case of developing a new TB vaccine, $1 billion could be necessary over 15 years. Based on these assumptions and restrictions, during the 2001 – 2005 period an estimated $1 billion will have to be spent on all forms of research related to TB control. This estimate does not include operational R & D totaling approximately $180 million, which will mainly be administered by the NTPs, and which has been included in estimates presented in Section 4.3. (See also Tables 16 and 19). Table 28 presents an estimate of R & D costs for the period 2001 to 2005.

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Table 28. Financial Panorama of Research and Development for TB control (2001 – 2005) (in millions of U.S. $)

5-year cost Current

Resources—external (1) Financing gap

R & D Costs Drugs(2) 317 130 187 Vaccines(3) 420 95 325 Diagnostics(4) 150 47 103 Health policy systems and services research (HPSSR)(5)

150 105 45

Subtotals (R & D) 1,037 377 660

Working group coordination and operating expenses

New drugs 30(6) 6 24 New vaccines 4 1 3 New diagnostics 27 6 21 Subtotals WHO Working Groups

61 13 48

Total R & D efforts

1,098

390

708

(1) Assumes estimated expenditure in 2000 is repeated each year for the period 2001 – 2005. (2) Costs are for development of at least one new anti-TB drug (of a different class than those currently known)

by 2010. (3) Costs are for developing a single new vaccine that is significantly more effective than BCG in the prevention

of infectious tuberculosis. (4) Costs are for developing three products of proven performance (and the guidelines for their appropriate use). (5) Another $180 million could be added to these costs to account for operational R & D efforts covered by the

NTPs during the 2001 – 2005 period. Of this additional cost, $160 million would be for the 22 HBCs. However, this amount is already included in the $2.2 billion covering direct NTP expenditures (see Section 4.3).

(6) This amount also includes advocacy activities, establishing partnerships, mapping activities, developing networks in TB high-burden countries, and regulatory harmonization.

93

5. Future Developments of This Work The results obtained so far allow for a wide-scale and detailed attempt to quantify the economic impact of TB in low- and middle-income countries. The scope is nevertheless constrained mainly to direct financial costs to health-care systems as a whole. More research and evaluations would have to be conducted, particularly regarding the indirect impact of the disease on societies and economies. One example of this impact is the lower tax revenues that nations collect due to the reduced economic output that results from the suffering and the early deaths that is experienced by the sick and their relatives, and which prevents them from engaging in productive activities. Much work remains to be done in order to refine estimates contained in this document, and to provide increasingly precise data as to costs in particular circumstances and contexts. The vast variety of TB-control measures and strategies worldwide is, indeed, a very important aspect of TB control, which calls for further understanding of these same contexts and circumstances. More specifically, additional work is required to better understand the economics of MDR-TB and TB-HIV co-infection and interaction. To date, very little is known of the specific present and future impact on TB control of MDR-TB and TB-HIV co-infection, both of which ailments play a growing role in the impact of the disease. The GPSTB currently provides only rough estimates for a limited array of inputs for interventions specifically targeting these phenomena. Initial and recurrent program costs, as well as specific investments in the health-care infrastructure, still need to be fully accounted for. More detailed studies need to be made available to the research and health-care community. Another important goal of future work is to refine the annual estimates in a truly budgetary perspective. Even though estimates have been elaborated from annualized scenarios, the annualization process remains relatively general, due to the lack of disaggregated data for some important national programs. Such data are needed to provide estimates that will better monitor the global surveillance of TB control and target financial flows even more accurately. Technically, this is not very difficult; but it requires the collaboration of all parties engaged. In addition, a number of assumptions made in the present evaluation could and should be further refined. This is notably the case for epidemiological projections. Current estimates of the number of future TB cases assume a flat incidence rate throughout the five-year period examined. As the understanding of epidemiological trends improves—particularly in light of the increasing effectiveness of interventions, and the role of MDR-TB and HIV—these numbers will have to be reconsidered. Cost estimates will also be modified by changing costs of inputs (such as drugs), as well as by modifications in the design of interventions. These will occur with the implementation of new diagnostic, therapeutic, and policy tools, and with increased effectiveness and productivity in the use of resources (a reduction in the use of hospital-based interventions, for example). The continuous economic surveillance that the GPSTB suggests could also reflect these changes. Another focus for improvement of the economic monitoring of the disease is making every effort, at the national level of each country, to improve financial planning and management of resource requirements. As a number of national TB-control plans have proved, further understanding of the actual organization, reach, and technical advancement of the programs, as well as of their economics, is required. The World Health Organization has set up a working group that will propose budgetary as well as financial planning guidelines to improve the ability of nations to monitor TB, as well as to monitor other major infectious diseases and conditions such as HIV/AIDS. This ability will be an important tool in the effort to better monitor TB-

94

control programs and—beyond this very important and basic need—to better understand the economics of TB as a whole. In all economic or financial evaluation processes, the availability and the accuracy of data is a fundamental pillar of an accurate evaluation. Efforts to further document interventions and programs will play a major role in our quest for more reliable and detailed estimates. Better estimates and better comprehension of the objects they refer to will favor a larger flow of resources to these programs and interventions, as well as favoring their better management and their greater efficiency.

95

Appendix A: Socioeconomic Panorama of Tuberculosis

Tuberculosis, like any other disease, (particularly infectious diseases), reaches deep into the socioeconomic fabric of societies. Its manifestations and associated costs are broad and varied. In this appendix, we review the socioeconomic aspects of human societies affected by TB, and the types of financial and economic costs associated with the disease. A1. Defining the Socioeconomic Panorama of TB Social and economic aspects involved in the disease and its control:

• The disease impacts society at large—primarily adults128—by depriving sick individuals and their families of the health needed to support their relatives in general—and, more specifically, to contribute to the production of resources and generation of income.

• Affected families live in difficult situations, as the sick must be cared for, while the risk to the health of relatives and neighbors is high.

• In a large number of cases (currently approximately two million annually), TB kills its victims—in terms of mortality it comes second to AIDS (among infectious diseases).

• TB implies direct costs to the general health-care system for diagnosis, treatment, and follow-up.

• In many countries where the disease’s burden is significant, fighting TB also requires a coordinated response, which requires mass training, management, monitoring, reporting, surveillance, and operational research.

• TB also requires research to discover techniques and tools (diagnostic tests, drugs, vaccines, and policy tools) to address its devastating effects.

A2. Quantification of the overall socioeconomic impact of TB There have been few attempts to evaluate the global economic impact of TB in countries where it is among the biggest health problems on a nationwide level.129,130,131 The studies that exist have combined direct costs with patient costs and indirect costs. This is the case with studies of India132 and Peru.133 In a March 2001 report on Peru134—one of the most detailed and comprehensive attempts to date to evaluate the economic impact of TB on society at large—the total economic impact was estimated at $95 million annually,LVIII or 0.2% of Peru’s current GNP. This cost is made up of $19.5 million incurred by the health-care system (direct costs, of which $4 million refer to expenditure paid for by the NTP); $24 million paid for by families (because of hospitalization and ambulatory care); and $50 million due to mortality (indirect costs to society). Health-care system costs represent 14% of public expenditure on health care, and 4% of the total national health-care expenditure. Put another way, 75% of the total $95 million (or $71 million) was directly due to the disease, while $24 million (25%) was due to the fight against the disease. Costs could also be classified as $22 million (23%) incurred by the consumption of products and commodities, while $73 million (77%) was the result of production losses.

LVIII According to mortality scenarios for Peru, the economic impact of TB ranges from $66 to $108 million.

96

TB was responsible for about 2,600 deaths throughout Peru in 1998, or 2.6% of all deaths. Thanks to a massive and efficient national program—with 100% DOTS coverage—TB fell from the sixth to the thirteenth cause of death nationwide from 1990 to 1997. In India, the economic burden of TB was evaluated at $3 billion according to a review of the national TB control program conducted in 1999.135 Five hundred million dollars are incurred at the level of direct health-care costs, while indirect costs account for another $2.5 billion. Overall, this represents 0.7% of the Indian GNP and 13% of the total health-care expenditure.LIX A3. Types of economic and financial costs associated with TB control In this section we detail major types of costs associated with the disease in general and TB control in particular. These types of costs vary widely. Taking a health-care system or social perspective, we can classify these costs into four broad categories: “direct,” “patient,” “indirect,” and “miscellaneous.” Activities covering the diagnosis, treatment, and monitoring of the TB patient make up the main bulk of direct costs. These activities are directly incurred to the health-care system by the existence of the disease, and by the will to fight against it. Direct costs also cover other important activities associated with TB control, such as research activities to develop new diagnostic and therapeutic tools; prevention activities (including prophylaxis and public information campaigns); and training and management of National TB Control Programs. Some aspects of the disease translate into indirect costs to both the patient and society at large. They mainly include financial or economic loss incurred by the disease. This is the case for family income lost due to morbidity and mortality. Family problems generated by the presence of the disease translate indirectly into economic terms, and are sometimes hard to assess. Such “indirect costs” can be estimated, for example, on the basis of lost income due to caring for the diseased person. Indirect costs, though, do not fall into the financial perspective of this study and have, therefore, been excluded. The category of patient costs covers resources paid for by patients themselves. These resources include transportation and meals, as well as other expenses that would otherwise not have been incurred. Generally, indirect costs and patient costs are not included in national TB control programs, and do not seem to be accounted for by health-care systems in the nonindustrial world. In countries where comprehensive health-care insurance is available, particularly in the countries of western Europe, days of lost work due to sickness can be compensated by the insurance plan on the basis of daily rates, as in the case of France’s social security system. From a societal point of view, however, part of that cost would have been incurred with or without the presence of the disease (such as is the case with basic consumptions, such as food), whether it is paid for by the program or by the patient him/herself. Yet, even in the absence of any insurance scheme or general subsidization system, in a few low and middle-income countries some type of financial compensations are put in place to cover basic necessities that the sick cannot afford while they are unable to work. In the case of TB, this financial or nonfinancial support is primarily aimed at helping the patient sustain the relatively long treatment, especially poor patients who cannot afford sufficient drugs and food. In Peru, for instance, food packages are provided to TB patients at a countrywide annual cost of $4 million, or almost half the cost of the entire program’s direct costs.136 In Nicaragua, poor patients can be kept in inpatient services for up to three months, to ensure that they have

LIX Author’s calculation from the Indian cost data and GNP and health expenditure numbers from the World Bank’s World Development Indicators 2000. (The World Bank, World Development Indicators 2000, Washington, D.C.)

97

enough food and support to maintain their regimen.LX These are direct costs to the NTPs, which must be accounted for in the evaluation of TB-control costs. These incentives may also play an important role in the effectiveness of the program. As they support patients who might otherwise abandon treatment, the programs may favorably impact treatment success rates. Further investigation of this impact still has to be conducted. But if such policies can increase the cure rate, then they may very well have a significant economic impact, from both the patient’s and society’s point of view. Classification of costs and inputs associated with tuberculosis and its control

Cost Element Included

in the budgetary

cost analysis (Global Plan to

Stop TB)?

Types of Costs involved

Provider or direct cost

(cost to the NTP, or to the health-

care provider)?

Patient cost Indirect cost Miscellaneous (financial costs

that are not directly borne by

NTPs, health-care providers,

or patients) Diagnosis Yes Mostly

a provider cost — — —

Treatment (all protocols)

Yes Mostly a provider cost

Cost can be borne in part by patients (drugs for example—especially when patients receive care from private providers)

— —

Program management Yes Direct cost to the program or intervention

— — —

Training Yes Direct cost to the program or intervention

— — —

Supervision Yes Direct cost to the program or intervention

— — —

Monitoring/Reporting Yes Direct cost to the program or the intervention

— — —

Surveillance Yes Direct cost to the program or the intervention

— — —

Operational research Yes Program cost — — —

LX Dr. Jose Ramon Cruz, PAHO. (Personal communication, 2001.)

98

Fundamental research and development

Yes Can be a direct cost to the program

— — Is most likely an external cost

Global Drug Fund, IAVI,LXI FSTI,LXII and other similar international initiatives

Yes — — — Direct cost to global TB control, but not to an NTP

Food Yes (when

specifically mentioned by national programs)

Can be a provider cost (in case of hospitalization)

Can be a cost borne by the patients and/or by the family. In this case, economic analysis focuses on the marginal cost of food to whomever bears it.

— —

Lodging/rent No Is rarely taken care of by the program

Mostly a cost to the patient who has to stay away from home but is not hospitalized

— —

Transportation No Is rarely taken care of by the program

Mostly a cost to the patient who has to travel to seek care

— —

Lost income No — — Family income lost due to the illness endured by the patient

—

LXI International Aids Vaccine Initiative. LXII Fonds de Solidarité Thérapeutique International.

99

Appendix B: Composition of Regional Clusters (Low and Middle-Income Countries)

Clusters

Number of countries

All low and lower middle -income countries (except 22 high-burden

countries

(1)

22 HBCs

(2)

Other upper

middle-income countries (not

included in financial

evaluation)

(3) Included

in the epidemio-logical projections (1)+(2)+(3)

Included in the cost estimates (1)+(2)

Austral Africa (AA)

7 5 Botswana(a), Lesotho, Namibia, Swaziland

South Africa Mauritius, Seychelles


41 40 Angola, Benin, Burkina Faso, Burundi, Cameroon, Cape Verde, Central African Republic, Chad, Comoros, Congo, Côte d'Ivoire, Djibouti, Eritrea, Gambia, Ghana, Guinea, Guinea-Bissau, Guinea (Equatorial), Liberia, Madagascar, Malawi, Mali, Mauritania, Mozambique(b), Niger, Rwanda, Sao Tome and Principe, Senegal, Sierra Leone, Somalia, Sudan, Togo, Zambia

Congo DR, Ethiopia, Kenya, Nigeria, Uganda, Tanzania (United Rep. of), Zimbabwe

Gabon


19 12 Algeria, Egypt, Gaza and the West Bank, Iran, Iraq, Jordan, Libya, Morocco, Syria, Tunisia, Turkey, Yemen

Bahrain, Cyprus, Jordan, Qatar, Lebanon, Saudi Arabia, United Arab Emirates


27 19 Albania, Armenia, Azerbaijan, Belarus, Bosnia-Herzegovina, Bulgaria, Georgia, Kazakhstan, Kyrgyzstan, Latvia, Lithuania, Macedonia, Moldavia, Romania, Tajikistan, Turkmenistan, Ukraine, Uzbekistan

Russian Federation

Croatia, Czech Republic, Estonia, Hungary, Poland, Slovakia, Slovenia, Yugoslavia

Urbanized South Cone (South America)

4 1 Brazil Argentina, Chile, Uruguay

100

Latin America (little industrialized) and the Caribbean

29 18 Belize, Bolivia, Colombia, Costa Rica, Cuba, El Salvador, Equator, Guatemala, Guyana, Haiti, Honduras, Jamaica, Nicaragua, Paraguay, Dominican Republic, St. Vincent and Grenadines, Surinam

Peru(c) Antigua and Barbuda, Barbados, Dominica, Grenada, Mexico, Panama, Puerto Rico, St. Kitts and St. Nevis, St. Lucy, Trinidad and Tobago, Venezuela

Indian sub-continent 7 7 Bhutan, Maldives, Nepal, Sri Lanka Bangladesh, India, Pakistan

—


8 8 Korea (Dem. Pop. Rep.), Laos, Mongolia

Afghanistan, Cambodia, China, Myanmar, Vietnam

—

Southeast Asia 5 4 Papua-New Guinea Indonesia, Philippines, Thailand

Malaysia

Pacific 22 0 — — — Total no. of countries

147 114 92 22 33

(a) Botswana had a GNP/capita of $3,240 per year in 1999. However, it was included in the present

evaluation because as of 2000 it has the highest TB incidence rate in the world , with approximately 7% of its population dually infected by MTB and HIV.

(b) Mozambique joined the list of 22 HBCs in 2001. However, cost estimates for the GPSTB are based on the 2000 list, which included Peru, but not Mozambique.

(c) Peru dropped from the 22 HBC list in 2001 and was replaced by Mozambique. See table note (b).

101

Appendix C: Data Used in the Model, by Type, Source, and Country

Appendix C1: Data Sources

Country Data available Reference

South Africa Cost of drugs, laboratory supplies, training, MDR-TB related consumables, average number of visits and bed-days; bed-day and visit costs; program administration and health-care services cost

—Sinanovic 137 —Wilkinson (1997)138 —Floyd (1997)139 —WHO (Stop TB) survey on cost estimates for TB

control (2000) —Floyd (WHO) 2000

Botswana Cost of bed-day and visits to health center —Floyd (WHO) 2000 Congo (Democratic Republic of)

Number of patients and visits, cost of drugs, microscopes, equipment to expand DOTS, training, administration, monitoring, supervision, surveillance, communication

—Plan directeur du Programme National de Lutte contre la Tuberculose (2000)140

—Programme National de Lutte contre la Tuberculose, rapport n°1 UICTMR (2000)

Ethiopia Number of patients; number and cost of microscopes; cost of equipment to expand DOTS, training and other initial costs; cost of drugs, reagents, training, administration, supervision, monitoring, surveillance.

—TB and Leprosy control program’s plan of action 2001141

—WHO (Stop TB) survey on cost estimates for TB control (2000)

Kenya Cost of training, drugs, reagents, visits to health center, bed-days, program administration, monitoring, surveillance, operational research

—WHO (Stop TB) survey on budget projections for NTP, 2000 – 2004 (2000)


—Floyd (WHO) 2000 Malawi Cost of bed-days and visits to health center —Sawert (1996)142

—Harries (2000)143 —Floyd (WHO) 2000

Nigeria All initial costs (training, equipment, and other costs) to expand DOTS; recurrent program costs; cost of drugs and laboratory supplies

—Operational plan for TB control in Nigeria 2001 – 2005 (2000)144


Uganda Number and cost of visits and bed-days; costs associated with TB-HIV co-infection prophylaxis


—Floyd (WHO) 2000 —Bell (1999)145

Tanzania Cost of training, program administration, supervision, monitoring, surveillance, operational research

—Ministry of Health, a 3-year development plan for the national tuberculosis/leprosy program (July 1997 – June 2000) (1997)


Zimbabwe Number of visits to health-care centers —WHO (Stop TB) survey on cost estimates for TB control (2000)

Egypt Number and cost of visits and bed-days; cost of drugs

—Cost-effectiveness study of DOTS (WHO, 1999)

Syria Number and cost of visits; cost of drugs —Cost-effectiveness study of DOTS (WHO, 1999) Morocco Cost of drugs —Ministère de la Santé (1996)146

—Ottmani SE – WHO (2000)

102

Russia Cost of investments in equipment, training and other initial actions to expand DOTS; cost of drugs, training, and health-care services


—Various communications (including World Bank and WHO)

Ukraine Cost of drugs, laboratory supplies —Smith (2000, unpublished) Poland Bed-day cost —Snyder (1998)147 Brazil Cost of initial training, and other initial actions to

expand DOTS; cost of first- and second-line drugs, recurrent training, administration, supervision, incentives, operational research

—Ministerio da Saude (Brazil): Strategic plan for the project of tuberculosis control implementation in Brazil for 2001 to 2005 (2000)



—Numerous communications, exchanges, and discussions with Dr. Ruffino Netto, national program coordination

Nicaragua Training —PAHO: elements of national program budget (2000)

Peru Number of patients; cost of first- and second-line drugs, laboratory supplies; cost of recurrent training, program administration, supervision, monitoring, surveillance, communication and operational research; cost of MDR-TB related direct inputs, TB-HIV co-infection associated tests costs; number and cost of visits to health-care centers and bed-days (as well as total cost of health-care services)

—Impacto Económico de la Tuberculosis en el Perú 1999 (2001)148


—Plan Estratégico para el Ministerio de Salud (Perú), Fortalecimiento del Control de la Tuberculosis en el Perú durante en Quinquenio 2001 – 2005, Programa Nacional de Control de la Tuberculosis (2000)

Bangladesh Cost of drugs, recurrent training —WHO (Stop TB) survey on cost estimates for TB control (2000)

India Number of patients; cost of equipment, initial training and other initial actions to expand DOTS; number and cost of visits and bed-days; cost of drugs, reagents, incentives, program administration, supervision, surveillance, operational research

—TB program review (2000)149 —TB control project (1997)150 —WHO (Stop TB) survey on cost estimates for TB

control (2000)

Pakistan Data available are not adaptable to 2001 – 2005 —TB control five-year plan (1995 – 2000) Afghanistan Number of patients; cost of equipment, initial

training and other initial actions to expand DOTS; cost of drugs, laboratory supplies, program administration, supervision, training; number of visits to health-care centers

—2001 plan of action and proposed budget for 2001 for Afghanistan NTP

—Plan of action, 1998 – 1999 —WHO (Stop TB) survey on budget projections for

NTP, 2000 – 2004 (2000) —WHO (Stop TB) survey on cost estimates for TB

control (2000) Cambodia Number of patients; cost of equipment, initial

training and other initial actions to expand DOTS; cost of drugs, laboratory supplies, recurrent training, program administration, supervision, monitoring, incentives, surveillance, operational research; number and cost of visits to health-care centers and bed-days

—NTP in Cambodia (TB-control strategy, 2000 – 2004) (2000)151

—Development plan of the NTP, 1993 – 1997152 —WHO (Stop TB) survey on budget projections for

NTP, 2000 – 2004 (2000)

103

China (People’s Republic of)

Number of patients; cost of equipment, initial training and other initial actions to expand DOTS; cost of drugs, laboratory supplies, recurrent training, program administration, monitoring, health education, incentives, operational research, technical assistance, loan interests; number of visits to health-care centers

—Ministry of Health, sustainability study on tuberculosis control in China (1999)



Myanmar Number of patients; cost of equipment, initial training and other initial actions to expand DOTS; cost of drugs, laboratory supplies, recurrent training, program administration (in part), supervision, monitoring, surveillance, operational research, technical support, health education; number of visits to health-care centers

—Review of national TB program 2000 – 2004 (2000)



Vietnam Number of patients; cost of equipment, initial training and other initial actions to expand DOTS; cost of drugs, laboratory supplies, recurrent training, program administration, supervision, health education, operational research, surveillance, technical assistance

—Development plan of the NTP, 2000 – 2004 (2000)153



Indonesia Cost of initial training to expand DOTS —WHO, Indonesia-WHO joint evaluation of the National Tuberculosis Program


Philippines Cost of equipment and initial training to expand DOTS; cost of drugs, laboratory supplies; elements of program costs

—Universal DOTS in 2004 (2000)154 —WHO (Stop TB) survey on budget projections for

NTP, 2000 – 2004 (2000) —WHO (Stop TB) survey on cost estimates for TB

control (2000) Thailand Number of patients; number of visits to health-care

centers; elements of cost for diagnosis and NTP —5-year development plan, 1997 – 2001 (1997)155 —WHO (Stop TB) survey on cost estimates for TB

control (2000)

Note: Numerous exchanges (through e-mails, telephone, fax, and direct conversations—in particular at the Cairo meeting of all 22 HBC national TB control coordinators in November 2000) have taken place in order to refine the interpretation of the data and double-check a number of figures and assumptions.

104

Appendix C2: Cost Database for First-Line Treatments and Programs (Including DOTS and DOTS Expansion)(a) (costs in U.S. $)

Cost of 1

microscope (including parts and

procurement costs)

Density of laboratory network (micro-

scopes per 100,000 inhab.)

Cost of misc. equip-ment

Cost of initial

training

Misc. initial costs

Total cost of drugs (first-

line, including retreatment; includes a

provision for losses,

storage, and procurement

costs)

Unit cost of drugs

(first-line, including

re-treatment)

Reagents and other laboratory supplies

(totals)

Unit cost of

laboratory supplies

Cost of regular training

Cost of program adminis-tration,

supervision, and

monitoring

Cost of incentives

Cost of surveil-lance

and operational R

& D

Austral Africa South Africa 2,000 2.3 $1 per

patient and per year in Cape Town

52.0 MRM is routine ($12.30 in urban

areas in 1997; $5.60 in rural areas in

1995; $50 – 50 in urban/rural areas)

19.5 ≈ $60 per patient

Botswana 2,000 2.3 38.0 3.6 Lesotho 2,000 2.3 52.0 3.6 Namibia 2,000 2.3 38.0 3.6 Swaziland 2,000 2.3 52.0 3.6

Sub-Saharan Africa Angola 2,000 1.5 38.0 3.6 Bénin 2,000 1.5 40.8 3.6 Burkina Faso 2,000 1.5 40.8 3.6 Burundi 2,000 1.5 30.0 3.6 Cameroon 2,000 1.5 30.0 3.6 Cape Verde 2,000 1.5 40.8 3.6 Comoros 2,000 1.5 40.8 3.6 Congo 2,000 1.5 30.0 3.6 Congo(DR of) 2,000 2.0 30.0 3.6 96,166 510,800 21,200 Côte d'Ivoire 2,000 1.5 40.8 3.6 Djibouti 1,680 1.5 16.0 3.6 Eritrea 1,680 1.5 16.0 0.8 Ethiopia 1,680 1.5 504,200 234,625 326,575 4,225,480/

316,541 cases

16.0 199,797/316,541 cases

0.8 158,860 793,977 2,000

105

Gambia 2,000 1.5 40.8 3.6 Ghana 2,000 1.5 40.8 3.6 Guinea 2,000 1.5 30.0 3.6 Guinea-Bissau 2,000 1.5 40.8 3.6 Guinea (Equ.) 2,000 1.5 30.0 3.6 Kenya 1,680 1.5 706,416 4,221,580 38.0 722,623 6.6 858,160 364,766 222,900 Liberia 2,000 1.5 40.8 3.6 Madagascar 2,000 1.5 40.8 3.6 Malawi 1,680 1.5 38.0 3.6 Mali 2,000 1.5 40.8 3.6 Mauritania 2,000 1.5 40.8 3.6 Mozambique 1,680 1.5 38.0 3.6 Niger 2,000 1.5 40.8 3.6 Nigeria 2,000 2.3 1,426,000 1,918,000 40.8 1,089,843 3.6 15,600 1,085,400 Uganda 2,000 1.5 32.0 6.6 Central African Rep. 2,000 1.5 40.8 3.6 Rwanda 2,000 1.5 30.0 3.6 Sao Tome and Principe

2,000 1.5 40.8 3.6

Senegal 2,000 1.5 40.8 3.6 Sierra Leone 2,000 1.5 40.8 3.6 Somalia 1,680 1.5 16.0 0.8 Sudan 1,680 1.5 16.0 0.8 Tanzania (Un. Rep.) 1,680 1.5 38.0 3.6 164,898 602,739 324,065 Chad 2,000 1.5 40.8 3.6 Togo 2,000 1.5 40.8 3.6 Zambia 1,680 1.5 38.0 3.6 Zimbabwe 2,000 1.5 38.0 15.6


Algeria 2,000 1 73.0 3.6 Egypt 2,000 1 73.0 3.6 Gaza and the West Bank

2,000 1 75.0 3.6

Iran 2,000 1 75.0 3.6 Iraq 2,000 1 75.0 3.6 Jordan 2,000 1 75.0 3.6 Libya 2,000 1 73.0 3.6 Morocco 2,000 1 73.0 3.6 Syria 2,000 1 75.0 3.6 Tunisia 2,000 1 73.0 3.6

106

Turkey 2,000 1 75.0 3.6 Yemen 2,000 1 75.0 3.6


Albania 1495.0 1 58.5 39.0 Armenia 1495.0 1 58.5 39.0 Azerbaijan 1495.0 1 58.5 39.0 Belarus 1495.0 1 58.5 39.0 Bosnia-Herzegovina 1495.0 1 58.5 39.0 Bulgaria 1495.0 1 58.5 39.0 Georgia 1495.0 1 58.5 39.0 Kazakhstan 1495.0 1 58.5 39.0 Kyrgyzstan 1495.0 1 58.5 39.0 Latvia 1495.0 1 58.5 39.0 Lithuania 1495.0 1 58.5 39.0 Macedonia 1495.0 1 58.5 39.0 Moldavia 1495.0 1 58.5 39.0 Uzbekistan 1495.0 1 58.5 39.0 Romania 1495.0 1 58.5 39.0 Russian Federation 1495.0 1 19,800,000 1,200,000 1,000,000 16,360,000 60.8 39.0 Tajikistan 1495.0 1 58.5 39.0 Turkmenistan 1495.0 1 58.5 39.0 Ukraine 1495.0 1 53.2/case 58.5 1,529,826 39.0


Brazil 3,000 1 155,555 800,000 3,514,766 39.4 14.4 600,000 3,888,888 3,222,222 1,111,111

Latin America (excluding South Cone) and the Caribbean

Belize 3,000 1 40.0 14.4 Bolivia 3,000 1 40.0 14.4 Colombia 3,000 1 188,084 40.0 14.4 Costa Rica 3,000 1 40.0 14.4 Cuba 3,000 1 40.0 14.4 El Salvador 3,000 1 40.0 14.4 Equator 3,000 1 40.0 14.4 Guatemala 3,000 1 40.0 14.4

107

Guyana 3,000 1 40.0 14.4 Haiti 3,000 1 40.0 14.4 Honduras 3,000 1 40.0 14.4 Jamaica 3,000 1 40.0 14.4 Nicaragua 3,000 1 15,000 40.0 14.4 15,000 Paraguay 3,000 1 40.0 14.4 Peru 3,000 1 40.0 $0.40 per smear

and $1.60 per culture

14.4 181,000 1,089,000 4,000,000 162,000

Dominican Republic 3,000 1 40.0 14.4 St. Vincent and Grenadines

3,000 1 40.0 14.4

Surinam 3,000 1 40.0 14.4

Indian sub-continent

Bangladesh 910 1 5,000,000 14.4 3.6 100,000 Bhutan 910 1 14.4 3.6 India 910 1 $600 per

laboratory and $7,792,000 for vehicles and other equipment for 30% of population

$6,335,000 for 30% of population

$7,185,000 for 30% of population

$12/case 14.4 $0.10/slide 3.6 ≈ $6,000,000 for 30% of population

$2.47 per patient with

DOTS for the person

who supervises treatment

Maldives 910 1 14.4 3.6 Nepal 910 1 14.4 3.6 Pakistan 910 1 14.4 3.6 Sri Lanka 910 1 14.4 3.6

China, Indochina, and Other (Asia)

Afghanistan 2,400 1 91,846 98,777 77,048 27/case (NTP)

27.0 3.6 147,569

Cambodia 1,600 1 940,000 1,018,000 720,000 40.3 66,000 3.6 64,800 100,000 1,172,641 (annual

food program:

NTP, 1998)

345,400

108

China (Pop. Rep.) 910 1 $20,638,000 for all of the infra-structure

28.1 92 yuans per case 5.2 100 yuans per cured

case

Korea (Dém. Pop. Rep.)

910 1 30.9 3.6

Laos 1,600 1 40.3 3.6 Mongolia 910 1 30.9 3.6 Myanmar 1,440 1 943,000 121,800 25,000 $14.14 per

case, according

to NTP

17.0 $4 per SS+ case (NTP)

3.6 69,825 93,030 45,000

Vietnam 1,800 1 1,868,886 860,636 305,500 26.1 $1.90 per case for smear test + $1.40

for culture

3.3 103,150 377,840 263,021

Southeast Asia Indonesia 1,560 1 30.0 4.5 Papua-New Guinea 1,560 1 30.0 4.5 Philippines 1,560 1 55,556 388,889 900

pesos/case (first-line),

1,400 pesos/case

(second-line)

30.0 13 pesos/case 4.5

Thailand 1,560 1 30.0 8.9

(a) Red signifies high-burden country; blue signifies data available and replicated in neighboring countries. Entry left blank where no data are available.

109

Appendix C3: Inputs Used in the Evaluation of Costs Associated with the Use of Health-Care Services, MDR-TB Treatment, and TB-HIV Co-Infection Prophylactic Intervention

(all costs in U.S. $)(a) Proportion

of patients hospitalized or placed in

sanatoria

Average length of

stay (days)

Cost of 1 bed-day

Average number of visits

(per patient)

Average cost of 1

visit

Cost of drugs for

MDR (includes a

provision for losses,

storage, and procurement

costs)

Other costs associated with MDR-

TB treatment

Cost of PPD test

Cost of isionazid (6-

month course)

Program costs

associated with TB-HIV prophylactic

treatment

Other program costs of ProTEST programs

Austral Africa South Africa 50.0% 60 30.69 84 7.28 1,200 517.62 0.14 2.17 295.58 264.00 Botswana 0.0% 0 24.97 84 8.32 1,200 375.37 0.10 2.17 214.35 191.45 Lesotho 50.0% 60 7.59 84 1.80 1,200 128.07 0.03 2.17 73.13 65.32 Namibia 50.0% 60 19.81 84 4.70 1,200 334.11 0.09 2.17 190.79 170.40 Swaziland 50.0% 60 15.50 84 3.68 1,200 261.36 0.07 2.17 149.25 133.30 Sub-Saharan Africa Angola 22.0% 60 0.22 14 0.11 1,200 39.33 0.01 2.17 22.46 20.06 Bénin 22.0% 60 0.31 14 0.16 1,200 55.14 0.01 2.17 31.48 28.12 Burkina Faso 21.5% 60 0.32 14 0.16 1,200 55.88 0.01 2.17 31.91 28.50 Burundi 20.9% 60 2.36 14 1.13 1,200 34.41 0.01 2.17 19.65 17.55 Cameroon 21.3% 60 0.51 14 0.25 1,200 89.86 0.02 2.17 51.31 45.83 Cape Verde 21.3% 60 1.23 14 0.62 1,200 217.62 0.06 2.17 124.27 110.99 Comoros 22.9% 60 5.80 14 2.79 1,200 84.63 0.02 2.17 48.33 43.16 Congo 21.4% 60 0.32 40 0.16 1,200 55.82 0.01 2.17 31.87 28.47 Congo (Dem. Rep. of) 21.6% 60 0.26 40 0.13 1,200 45.49 0.01 2.17 25.98 23.20 Côte d'Ivoire 21.0% 60 0.54 14 0.27 1,200 96.21 0.03 2.17 54.94 49.07 Djibouti 26.3% 60 8.54 50 4.10 1,200 124.46 0.03 2.17 71.07 63.48 Eritrea 0.0% 0 4.32 50 2.08 1,200 62.98 0.02 2.17 35.96 32.12 Ethiopia 0.0% 0 2.56 50 1.23 1,200 37.28 0.01 2.17 21.29 19.01 Gambia 17.7% 60 0.53 14 0.26 1,200 92.85 0.02 2.17 53.02 47.35 Ghana 15.9% 60 0.63 14 0.32 1,200 111.58 0.03 2.17 63.71 56.91

110

Guinea 15.4% 60 0.62 14 0.31 1,200 109.59 0.03 2.17 62.58 55.89 Guinea-Bissau 14.1% 60 0.21 14 0.10 1,200 37.03 0.01 2.17 21.14 18.88 Guinea (Equatorial) 16.2% 60 0.70 14 0.35 1,200 124.46 0.03 2.17 71.07 63.48 Kenya 41.5% 60 4.16 7 2.00 1,200 60.67 0.02 2.17 34.65 30.95 Liberia 18.8% 60 0.11 14 0.05 1,200 18.67 0.00 2.17 10.66 9.52 Madagascar 21.0% 60 3.27 7 1.57 1,200 47.67 0.01 2.17 27.22 24.31 Malawi 20.6% 60 3.12 7 1.00 1,200 36.16 0.01 2.17 20.65 18.44 Mali 27.4% 60 0.24 14 0.12 1,200 43.12 0.01 2.17 24.63 21.99 Mauritania 23.6% 60 0.54 14 0.27 1,200 94.71 0.03 2.17 54.08 48.31 Mozambique 20.8% 60 3.40 7 1.63 1,200 49.60 0.01 2.17 28.32 25.30 Niger 12.5% 60 0.26 14 0.13 1,200 45.24 0.01 2.17 25.83 23.07 Nigeria 21.6% 60 0.26 14 0.13 1,200 46.30 0.01 2.17 26.44 23.61 Uganda 42.6% 42 0.40 14 0.20 1,200 70.69 0.02 2.17 40.37 36.06 Central African Rep. 11.0% 60 0.40 14 0.20 1,200 70.38 0.02 2.17 40.19 35.90 Rwanda 13.8% 60 1.07 14 0.51 1,200 15.56 0.00 2.17 8.88 7.93 Sao Tome and Principe 21.4% 60 0.47 14 0.24 1,200 83.08 0.02 2.17 47.44 42.37 Senegal 19.0% 60 0.47 14 0.24 1,200 83.45 0.02 2.17 47.65 42.56 Sierra Leone 25.4% 60 0.15 14 0.07 1,200 25.76 0.01 2.17 14.71 13.14 Somalia 0.0% 0 0.07 7 0.04 1,200 12.45 0.00 2.17 7.11 6.35 Sudan 23.2% 60 0.46 7 0.23 1,200 80.77 0.02 2.17 46.12 41.20 Tanzania (Un. Rep. of) 21.3% 60 2.04 7 0.98 1,200 29.75 0.01 2.17 16.99 15.17 Chad 15.7% 60 0.29 14 0.14 1,200 50.78 0.01 2.17 29.00 25.90 Togo 23.3% 60 0.47 14 0.24 1,200 83.76 0.02 2.17 47.83 42.72 Zambia 22.4% 60 2.93 7 1.41 1,200 42.69 0.01 2.17 24.38 21.77 Zimbabwe 50.0% 5 10.54 9 5.07 1,200 153.71 0.04 2.17 87.77 78.39 Middle East and northern Africa

Algeria 22.1% 60 17.17 76 1.00 1,200 295.78 0.08 8.21 168.90 150.85 Egypt 20.4% 60 11.93 76 0.70 1,200 205.54 0.05 8.21 117.37 104.83 Gaza and the West Bank 60.8% 60 10.84 76 0.63 1,200 186.69 0.05 8.21 106.60 95.22 Iran 25.5% 60 18.65 76 0.83 1,200 321.29 0.09 8.21 183.47 163.87 Iraq 23.1% 60 9.03 76 0.40 1,200 155.57 0.04 8.21 88.84 79.35 Jordan 21.9% 60 12.80 76 0.57 1,200 220.42 0.06 8.21 125.86 112.42 Libya 19.8% 60 18.07 76 1.06 1,200 311.15 0.08 8.21 177.67 158.69 Morocco 23.4% 60 11.53 76 0.67 1,200 198.51 0.05 8.21 113.36 101.25 Syria 22.0% 60 9.98 76 0.44 1,200 171.82 0.05 8.21 98.11 87.63 Tunisia 24.8% 60 19.79 76 1.16 1,200 340.89 0.09 8.21 194.66 173.86

111

Turkey 24.1% 60 22.13 76 1.29 1,200 381.22 0.10 8.21 217.69 194.43 Yemen 23.1% 60 2.49 76 0.11 1,200 42.81 0.01 8.21 24.45 21.84 Eastern Europe and the former-Soviet Union

Albania 47.8% 60 14.85 2 0.73 1,500 179.97 0.05 1.73 13.82 91.79 Armenia 100.0% 260 1.39 1 0.56 1,500 137.53 0.04 1.73 10.56 70.14 Azerbaijan 100.0% 260 1.47 1 0.58 1,500 144.50 0.04 1.73 11.09 73.70 Belarus 100.0% 260 4.11 1 1.64 1,500 405.61 0.11 1.73 31.14 206.87 Bosnia-Herzegovina 45.1% 60 25.68 2 1.26 1,500 311.15 0.08 1.73 23.89 158.69 Bulgaria 42.1% 60 25.24 2 1.24 1,500 305.79 0.08 1.73 23.48 155.96 Georgia 100.0% 260 2.28 1 0.91 1,500 224.40 0.06 1.73 17.23 114.45 Kazakhstan 100.0% 260 2.78 1 1.11 1,500 274.31 0.07 1.73 21.06 139.90 Kyrgyzstan 100.0% 260 1.40 1 0.56 1,500 138.34 0.04 1.73 10.62 70.55 Latvia 100.0% 260 3.75 1 1.49 1,500 369.52 0.10 1.73 28.37 188.46 Lithuania 100.0% 260 3.84 1 1.53 1,500 379.16 0.10 1.73 29.11 193.38 Macedonia 46.2% 60 22.28 2 1.09 1,500 270.01 0.07 1.73 20.73 137.71 Moldavia 100.0% 260 1.49 1 0.59 1,500 146.74 0.04 1.73 11.27 74.84 Uzbekistan 100.0% 260 1.32 1 0.53 1,500 130.18 0.03 1.73 10.00 66.40 Romania 41.9% 60 29.00 2 1.42 1,500 351.41 0.09 1.73 26.98 179.23 Russian Federation 100.0% 260 4.00 1 1.59 1,500 394.47 0.10 1.73 30.29 201.19 Tajikistan 100.0% 260 0.62 1 0.25 1,500 61.05 0.02 1.73 4.69 31.14 Turkmenistan 100.0% 260 1.96 1 0.78 1,500 192.85 0.05 1.73 14.81 98.36 Ukraine 100.0% 260 1.98 1 0.79 1,500 195.52 0.05 1.73 15.01 99.72 Urbanized South Cone (South America)

Brazil 10.0% 60 50.40 1 2.03 2,230 393.10 0.10 2.17 30.18 200.49 Latin America (excluding South Cone) and the Caribbean

Belize 45.8% 20 35.84 15 1.45 1125.366 279.53 0.07 2.17 21.46 142.57 Bolivia 20.0% 45 17.50 15 0.71 1,125.366 136.47 0.04 2.17 10.48 69.60 Colombia 10.9% 20 45.55 15 1.84 1,125.366 355.27 0.09 2.17 27.28 181.20 Costa Rica 15.0% 45 46.03 15 1.86 1,125.366 359.06 0.10 2.17 27.57 183.13 Cuba 8.1% 20 15.96 15 0.64 1,125.366 124.46 0.03 2.17 9.56 63.48 El Salvador 15.0% 45 32.30 15 1.30 1,125.366 251.90 0.07 2.17 19.34 128.48 Equator 10.0% 45 20.78 15 0.84 1,125.366 162.11 0.04 2.17 12.45 82.68

112

Guatemala 15.0% 45 28.06 15 1.13 1,125.366 218.86 0.06 2.17 16.80 111.62 Guyana 34.0% 20 25.87 15 1.04 1,125.366 201.75 0.05 2.17 15.49 102.90 Haiti 10.0% 45 11.23 15 0.45 1,125.366 87.56 0.02 2.17 6.72 44.66 Honduras 45.1% 20 17.98 15 0.73 1,125.366 140.26 0.04 2.17 10.77 71.54 Jamaica 4.6% 20 26.14 15 1.06 1,125.366 203.86 0.05 2.17 15.65 103.98 Nicaragua 6.1% 20 17.18 15 0.69 1,125.366 134.04 0.04 2.17 10.29 68.36 Paraguay 47.9% 20 33.45 15 1.35 1,125.366 260.93 0.07 2.17 20.03 133.08 Peru 17.5% 20 35.00 15 1.41 1,125.366 273.00 0.07 2.17 20.96 139.24 Dominican Republic 41.0% 20 37.12 15 1.50 1,125.366 289.55 0.08 2.17 22.23 147.68 St. Vincent and Grenadines

31.3% 20 37.23 15 1.50 1,125.366 290.42 0.08 2.17 22.30 148.12

Surinam 50.4% 20 27.92 15 1.13 1,125.366 217.80 0.06 2.17 16.72 111.09 Indian Sub-Continent Bangladesh 1.5% 60 2.29 32 0.69 1,200 91.79 0.02 2.17 7.05 46.81 Bhutan 1.0% 60 2.33 32 0.70 1,200 93.10 0.02 2.17 7.15 47.48 India 0.5% 60 3.34 42 1.00 1,200 133.73 0.04 2.17 10.27 68.21 Maldives 1.0% 60 5.51 42 1.65 1,200 220.60 0.06 2.17 16.94 112.51 Nepal 1.0% 60 1.90 42 0.57 1,200 75.86 0.02 2.17 5.82 38.69 Pakistan 1.0% 60 2.73 32 0.82 1,200 109.34 0.03 2.17 8.39 55.76 Sri Lanka 1.0% 60 4.75 42 1.42 1,200 190.17 0.05 2.17 14.60 96.99 China, Indochina, and Others (Asia)

Afghanistan 0.62 117 0.16 1,200 24.89 0.01 2.17 1.91 12.70 Cambodia 67.5% 60 2.00 36 0.50 1,200 80.03 0.02 2.17 6.14 40.82 China (Pop. Rep.) 5.0% 60 5.12 72 1.28 1,200 204.80 0.05 2.17 15.72 104.45 Korea (Dém. Pop. Rep.) 0.0% 0 1.56 1 0.39 1,200 62.23 0.02 2.17 4.78 31.74 Laos 100.0% 45 2.68 36 0.67 1,200 107.41 0.03 2.17 8.25 54.78 Mongolia 0.0% 0 2.33 72 0.58 1,200 93.10 0.02 2.17 7.15 47.48 Myanmar 0.0% 0 1.56 5 0.39 1,200 62.23 0.02 2.17 4.78 31.74 Vietnam 45.1% 60 2.73 15 1.50 1,200 109.21 0.03 2.17 8.39 55.70 Southeast Asia Indonesia 0.0% 0 3.79 108 0.95 1,200 151.78 0.04 2.17 11.65 77.41 Papua-New Guinea 2.0% 60 3.52 36 0.88 1,200 140.82 0.04 2.17 10.81 71.82 Philippines 2.0% 15 5.93 36 1.48 1,200 237.40 0.06 2.17 18.23 121.08 Thailand 0.0% 0 8.71 11 2.18 1,200 348.42 0.09 2.17 26.75 177.70

113

Note: Data in bold characters were used to compute data for other countries in the same cluster, and when missing, in other clusters. (a) Red signifies high-burden country; blue signifies data available and replicated in neighboring countries.

114

Appendix C4: Economic, Demographic, and Epidemiological Data(a) GNP/capita

in 1999 U.S. $

(exchange rate

method)

Population (1999, in millions)

1999 TB incidence

(per 100,000 inhab.)

1999 estimated detection rate (all cases)

1999 estimated detection rate for

SS+ cases

2001 – 2005

new TB cases

(incidence)

2001 – 2005 new SS+ cases (incidence

)

2001 – 2005 TB

cases detected

2001 – 2005 total estimated detection

rate

2001 – 2005 SS+

cases detected

2001 – 2005 TB

cases detected covered

by DOTS

2001 – 2005

additional cases

covered by DOTS

Austral Africa South Africa 3,160 42,600 495 65.3% 97.0 1,096,803 445,975 747,262 68.1% 432,595 659,588 158,726 Botswana 3,240 1,588 702 73.1% 90.8 57,758 22,774 42,222 73.1% 20,679 42,222 0 Lesotho 550 2,105 542 50.0% 50.0 60,810 24,500 37,779 62.1% 15,221 23,880 23,880 Namibia 1,890 1,701 490 95.8% 94.4 44,556 18,132 42,683 95.8% 17,116 42,683 0 Swaziland 1,360 1,018 564 50.0% 50.0 30,478 10,290 18,933 62.1% 6,392 11,960 11,960 Total (Austral Africa) 2,969 49,012 505 62.1% 93.6 1,290,405 521,670 888,879 68.9% 492,004 780,333 194,566 Sub-Saharan Africa Angola 220 12,352 271 47.6% 60.8 186,894 82,141 99,550 53.3% 54,559 93,761 18,519 Bénin 380 6,114 266 17.3% 34.9 89,968 39,645 34,791 38.7% 22,323 34,791 19,259 Burkina Faso 240 11,600 319 5.7% 18.3 202,469 86,875 75,198 37.1% 43,255 75,198 63,758 Burundi 120 5,700 382 25.4% 43.5 117,971 49,383 48,940 41.5% 29,437 39,528 26,476 Cameroon 580 15,500 335 15.5% 53.9 282,608 120,403 71,490 25.3% 76,690 53,266 42,225 Cape Verde 1,330 429 173 29.1% 34.7 4,009 1,707 2,026 50.5% 959 1,345 1,345 Comoros 350 544 63 50.0% 50.0 1,869 855 1,161 62.2% 532 736 736 Congo 670 2,859 318 55.0% 65.3 50,478 21,646 29,187 57.8% 14,756 29,187 1,448 Congo (D.R.) 110 50,500 301 39.2% 60.7 851,857 368,316 382,757 44.9% 244,512 382,757 48,439 Côte d'Ivoire 710 14,729 375 27.7% 58.6 294,305 123,493 101,976 34.6% 80,903 101,976 20,343 Djibouti 790 648 651 91.6% 79.4 22,851 12,038 20,935 91.6% 9,558 20,935 0 Eritrea 200 3,991 272 80.0% 6.0 59,485 22,794 70,792 119.0% 10,253 47,985 44,462 Ethiopia 100 59,700 373 31.6% 28.0 1,211,077 509,489 692,347 57.2% 272,826 692,347 309,419 Gambia 340 1,251 260 60.0% 60.0 17,750 6,267 11,731 66.1% 4,141 7,282 7,282 Ghana 390 18,949 281 18.8% 40.8 291,068 92,603 106,542 36.6% 54,241 99,738 55,618 Guinea 510 7,247 255 27.1% 58.5 99,893 30,741 34,057 34.1% 20,132 34,057 6,984 Guinea-Bissau 160 1,185 267 20.0% 20.0 17,008 4,786 8,565 50.4% 2,410 5,880 5,880 Guinea (Equatorial) 1,170 443 241 40.1% 84.5 5,784 1,871 3,370 58.3% 1,581 3,370 1,053 Kenya 360 29,410 417 46.6% 68.1 655,417 272,219 312,819 47.7% 188,519 312,819 7,556 Liberia 200 3,044 271 24.3% 43.8 44,674 16,811 17,960 40.2% 10,041 17,960 7,115 Madagascar 250 15,051 236 41.2% 64.3 198,069 83,376 88,493 44.7% 56,513 88,493 6,894 Malawi 190 10,788 443 51.7% 56.8 260,737 107,278 155,770 59.7% 69,554 155,770 20,950 Mali 240 10,911 261 15.7% 16.7 158,509 86,883 76,492 48.3% 42,788 75,870 51,642 Mauritania 380 2,598 241 20.0% 20.0 34,358 16,184 17,335 50.5% 8,166 11,932 11,932

115

Mozambique 230 17,264 407 25.6% 60.8 380,466 157,968 118,677 31.2% 104,879 118,677 21,279 Niger 190 10,493 252 13.6% 33.4 149,032 37,360 53,703 36.0% 20,844 46,201 35,121 Nigeria 310 113,800 301 7.3% 11.7 1,888,582 816,988 809,758 42.9% 386,073 809,758 671,498 Uganda 320 22,800 343 48.4% 66.7 428,233 182,383 215,975 50.4% 125,316 215,975 8,608 Central African Rep. 290 3,540 415 34.0% 83.0 78,310 17,204 43,709 55.8% 14,279 28,760 28,760 Rwanda 250 8,200 381 24.3% 63.7 170,423 47,007 47,974 28.1% 31,746 47,974 6,535 Sao Tome and Principe 270 145 126 20.0% 20.0 987 423 498 50.4% 213 342 342 Senegal 510 9,285 258 30.7% 61.7 131,058 49,911 46,814 35.7% 33,318 46,814 6,626 Sierra Leone 130 4,949 274 26.2% 35.7 73,001 37,040 34,322 47.0% 20,942 34,322 15,212 Somalia 150 9,388 365 12.1% 17.4 185,508 98,912 81,735 44.1% 48,839 81,735 59,319 Sudan 330 28,993 195 41.3% 42.1 307,326 142,764 179,026 58.3% 84,333 147,203 84,150 Tanzania (Un. Rep. of) 240 32,923 340 47.0% 58.0 606,370 258,469 329,442 54.3% 168,767 329,442 44,235 Chad 200 7,486 270 23.4% 20.0 112,346 35,208 60,554 53.9% 17,791 41,467 41,467 Togo 320 4,567 313 8.8% 14.5 77,284 36,043 32,900 42.6% 17,387 32,900 26,075 Zambia 320 9,881 495 50.0% 50.0 261,622 117,180 162,558 62.1% 72,810 102,815 102,815 Zimbabwe 520 11,904 562 77.5% 59.1 349,261 140,549 293,534 84.0% 92,323 293,534 23,008 Total (Sub-Saharan Africa)

283 581,161 326 30.7% 41.9 10,358,919

4,333,211 4,975,463 48.0% 2,558,510 4,764,901 1,954,386

Middle East and Northern Africa

Algeria 1,550 29,950 45 112.1% 123.7 72,784 32,133 81,563 112.1% 39,748 49,488 49,488 Egypt 1,400 66,900 39 49.2% 46.8 138,494 56,583 87,650 63.3% 34,435 64,633 45,542 Gaza and the West Bank

1,610 3,100 28 6.0% 2.3 4,918 5,982 2,334 47.5% 2,615 1,694 1,694

Iran 1,760 66,200 54 33.4% 28.3 189,939 97,006 111,456 58.7% 51,996 97,920 57,408 Iraq 1,500 22,797 156 89.2% 56.4 192,555 88,852 187,610 97.4% 57,458 118,290 113,139 Jordan 1,500 4,700 11 58.0% 37.9 2,708 1,185 2,100 77.6% 681 2,100 529 Libya 2,500 5,419 24 121.0% 50.0 7,152 2,836 9,525 133.2% 1,763 5,905 5,905 Morocco 1,200 28,238 119 88.9% 87.6 178,274 83,539 158,549 88.9% 73,180 158,549 0 Syria 970 15,653 85 40.8% 27.7 72,489 31,909 48,263 66.6% 17,049 38,523 26,442 Tunisia 2,100 9,457 37 64.3% 70.1 18,292 9,065 12,390 67.7% 6,354 7,577 7,577 Turkey 2,900 65,900 38 92.7% 31.5 130,178 62,736 150,943 116.0% 34,369 95,295 95,295 Yemen 350 17,048 108 67.7% 58.0 103,210 47,638 77,399 75.0% 31,123 56,029 37,454 Total (Middle East and northern Africa)

1,737 335,362 62 70.8% 54.2 1,110,993 519,462 929,782 83.7% 350,773 696,001 440,472


Albania 870 3,375 29 84.2% 49.9 5,008 2,393 4,823 96.3% 1,485 2,993 2,993 Armenia 490 3,809 58 72.7% 65.5 11,186 3,967 8,431 75.4% 2,706 8,069 827 Azerbaijan 550 7,980 62 96.9% 36.4 25,611 10,770 30,009 117.2% 6,105 19,609 17,822

116

Belarus 2,630 10,208 80 89.2% 167.1 39,815 14,653 35,520 89.2% 24,485 21,241 21,241 Bosnia-Herzegovina 3,000 3,881 87 84.7% 44.3 17,454 7,871 17,491 100.2% 4,707 15,398 5,315 Bulgaria 1,380 8,216 46 93.9% 82.4 18,285 7,705 17,176 93.9% 6,349 10,257 10,257 Georgia 620 5,452 72 133.7% 33.7 19,579 8,722 30,435 155.4% 4,838 30,435 4,261 Kazakhstan 1,230 15,438 130 118.5% 78.8 101,558 37,527 120,307 118.5% 29,571 74,295 69,363 Kyrgyzstan 300 4,744 130 105.4% 41.4 32,219 10,708 39,523 122.7% 6,284 33,012 13,896 Latvia 2,470 2,430 105 77.6% 74.0 12,327 4,383 9,566 77.6% 3,243 9,566 0 Lithuania 2,620 3,699 99 82.4% 59.2 18,270 6,633 16,233 88.9% 4,356 9,891 9,891 Macedonia 1,690 2,021 50 62.1% 38.8 5,124 2,368 4,145 80.9% 1,363 2,621 2,621 Moldavia 370 4,281 130 47.7% 32.5 27,610 7,115 19,369 70.2% 3,911 12,434 12,434 Uzbekistan 720 24,600 97 64.8% 40.8 126,513 47,386 104,308 82.4% 27,712 66,425 65,769 Romania 1,520 22,458 130 90.0% 88.6 144,262 60,411 129,811 90.0% 53,524 129,811 0 Russian Federation 2,270 146,512 123 74.1% 27.0 885,107 396,506 883,785 99.9% 209,082 568,527 549,875 Tajikistan 290 6,218 105 40.1% 18.3 34,579 13,048 24,695 71.4% 6,475 16,394 16,394 Turkmenistan 660 4,779 90 103.2% 55.0 22,864 8,454 25,669 112.3% 5,418 15,829 15,829 Ukraine 750 49,908 73 88.7% 72.6 177,004 69,267 157,083 88.7% 50,288 93,745 93,745 Total (Eastern Europe and the former Soviet Union)

1,607 330,009 105 80.5% 45.4 1,724,374 719,888 1,678,379 97.3% 451,903 1,140,553 912,534

Urbanized South Cone

Brazil 4,420 168,066 70 66.8% 79.0 617,962 274,993 453,754 73.4% 235,526 288,205 267,984 Total (USC) 4,420 168,066 70 66.8% 79.0 617,962 274,993 453,754 73.4% 235,526 288,205 267,984 Latin America and the Caribbean

Belize 2,730 247 46 70.0% 70.0 586 268 410 70.0% 188 248 248 Bolivia 1,010 8,135 238 53.6% 74.5 104,214 49,934 66,238 63.6% 37,201 66,238 10,390 Colombia 2,250 38,600 51 43.7% 75.4 104,635 46,239 62,385 59.6% 34,864 62,385 16,672 Costa Rica 2,740 3,588 17 106.6% 189.9 3,207 1,456 3,420 106.6% 2,765 2,070 2,070 Cuba 1,000 11,150 15 77.3% 84.4 8,544 4,472 6,601 77.3% 3,774 6,601 0 El Salvador 1,900 6,189 67 42.0% 53.7 22,168 10,897 11,492 51.8% 6,929 11,212 2,525 Equator 1,310 12,409 172 43.8% 71.7 113,293 48,724 67,625 59.7% 34,935 48,689 37,763 Guatemala 1,660 11,086 85 30.0% 51.3 51,253 24,520 21,197 41.4% 15,368 21,197 5,830 Guyana 760 856 101 36.9% 29.0 4,607 1,567 2,844 61.7% 844 1,874 1,874 Haiti 460 7,803 361 34.3% 53.0 150,069 63,516 66,990 44.6% 40,211 47,429 37,330 Honduras 760 6,325 92 86.6% 88.3 31,778 14,636 27,505 86.6% 12,924 16,904 16,464 Jamaica 2,330 2,598 8 63.3% 92.1 1,042 472 702 67.3% 435 695 51 Nicaragua 430 4,919 88 61.6% 86.4 23,614 10,650 15,755 66.7% 9,201 15,755 1,205 Paraguay 1,580 5,359 68 52.3% 49.9 19,669 9,419 12,690 64.5% 5,850 8,026 8,026 Peru 2,390 26,600 228 70.1% 94.1 322,089 144,016 225,908 70.1% 135,520 225,908 0 Dominican Republic 1,910 8,404 135 52.6% 53.2 59,709 26,276 37,492 62.8% 16,650 24,536 22,191

117

St Vincent and Grenadines 2,700 114 36 11.9% 18.6 214 83 92 43.0% 41 92 67 Surinam 1,660 413 77 30.0% 30.0 1,682 848 912 54.2% 460 607 607 Total (Latin America) 1,741 154,795 124 53.9% 75.0 1,022,374 457,994 630,259 61.6% 358,160 560,466 163,314 Indian sub-continent Bangladesh 370 125,700 241 25.7% 27.5 1,608,533 723,261 827,920 51.5% 385,159 800,551 428,569 Bhutan 510 782 140 46.0% 21.2 5,936 2,694 4,492 75.7% 1,370 4,492 1,761 India 450 986,600 185 66.5% 42.0 9,615,945 4,303,135 7,100,000 73.8% 2,536,468 4,744,255 4,115,842 Maldives 1,160 278 66 98.1% 82.2 991 595 972 98.1% 489 972 0 Nepal 220 24,300 209 56.0% 51.5 275,838 126,826 185,586 67.3% 79,588 165,323 56,420 Pakistan 470 146,500 177 34.2% 5.2 1,418,662 634,106 750,000 52.9% 283,251 563,188 454,844 Sri Lanka 820 18,985 59 64.4% 74.7 58,641 26,974 39,759 67.8% 20,149 39,759 1,970 Total (Indian sub-continent)

446 1,303,145

188 57.8% 36.7 12,984,545

5,817,590 8,908,730 68.6% 3,306,474 6,318,541 5,059,407

China, Indochina, and Others (Asia)

Afghanistan 200 25,869 325 4.6% 5.4 455,213 204,491 199,779 43.9% 91,349 199,779 178,770 Cambodia 260 11,757 560 31.4% 53.6 349,768 156,639 144,593 41.3% 99,525 144,593 34,759 China (Pop. Rep.) 780 1,254,100 103 35.4% 36.0 6,616,011 2,966,190 3,698,215 55.9% 1,675,745 3,378,966 1,616,798 Korea (Dem. Pop. Rep.) 400 23,414 176 2.8% 2.2 211,574 95,702 92,368 43.7% 41,218 67,153 67,153 Laos 280 5,097 171 24.5% 37.7 47,597 21,534 21,013 44.1% 12,352 20,964 9,391 Mongolia 350 2,623 195 58.1% 56.2 27,087 13,021 17,998 66.4% 8,407 17,556 2,846 Myanmar 300 48,100 169 19.6% 27.7 424,570 190,979 191,969 45.2% 101,725 191,969 108,542 Vietnam 370 79,500 189 59.6% 80.0 788,113 355,179 519,375 65.9% 284,143 518,362 50,814 Total (China, Indochina, and Others (Asia))

718 1,450,460

119 34.4% 37.9 8,919,934 4,003,735 4,885,310 54.8% 2,314,464 4,539,343 2,069,073

Southeast Asia Indonesia 580 211,800 282 11.7% 12.2 3,132,911 1,410,859 1,461,734 46.7% 665,301 1,461,734 1,095,133 Papua-New Guinea 800 4,705 236 103.9% 40.9 59,720 28,319 72,597 121.6% 16,586 47,986 42,222 Philippines 1,020 74,700 297 65.9% 70.0 1,188,640 563,530 855,466 72.0% 428,663 599,908 428,928 Thailand 1,960 61,691 141 34.1% 21.3 449,634 197,838 285,641 63.5% 100,293 285,641 132,168 Total (Southeast Asia) 917 352,896 260 28.1% 28.0 4,830,905 2,200,546 2,675,438 55.4% 1,210,843 2,395,269 1,698,451 Pacific Fiji 2,210 801 35 59.7% 22.6 1,518 1,781 1,344 916 874 874 Kiribati 910 81 87 380.3% 152.4 383 174 1,458 266 886 886 Marshall (Islands) 1,560 59 87 91.9% 46.2 279 126 297 76 186 186 Micronesia 1,810 116 87 30.5% 52.8 549 64 225 41 144 144 Nauru 11 35 70.0% 70.0 21 22 15 15 9 9 Niue 2 35 142.9% 70.0 4 2 5 2 3 3

118

Solomon (Islands) 750 429 87 80.9% 84.3 2,031 924 1,642 779 998 998 Samoa 1,060 168 35 36.0% 10.3 318 355 230 165 155 155 Tokelau 2 35 70.0% 70.0 4 2 3 2 2 2 Tonga 1,720 99 35 86.6% 41.0 188 211 195 124 123 123 Tuvalu 1,170 10 35 514.3% 70.0 19 22 97 15 59 59 Vanuatu 189 87 112.2% 52.1 895 412 1,101 259 682 682 Total 1,438 1,967 58 94.5% 46.7 6,208 4,095 6,613 106.5% 2,659 4,123 4,123 WORLD TOTAL 928

4,726,873 171 44.6% 41.1 42,866,61

9 18,853,18

4 26,032,60

7 60.7% 11,281,31

6 21,487,73

5

12,764,310

Total for the 22 HBCs

874 3,731,132

178 44.6% 39.0 34,960,756

15,516,111

21,382,034

61.2% 9,142,682 17,667,805

10,734,51

5 (a) Red signifies high-burden country; blue signifies data available and replicated in neighboring countries.

119

Appendix C5. Epidemiological Data Associated with MDR-TB and TB-HIV Co-Infection(a)

(cases in units)

Number of co-infected persons dealt with by

ProTEST

Propor-tion of MDR-

TB (scen. 1)

Propor-tion of

MDR-TB (scen. 2)

New cases of

MDR-TB (scen. 1)

New cases of

MDR-TB (scen. 2)

MDR-TB cases

detected (scen. 1)

MDR-TB cases

detected (scen. 2)

MDR-TB cases

covered by DOTS-Plus programs (scen.1)

MDR-TB cases

covered by DOTS-Plus programs (scen.2)

% of popula-

tion infected

with MTB

% of popula-

tion infected

with HIV

New cases of TB-HIV co-infection

Stock of co-infected persons as

of 2000

Number of persons

attended by ProTEST 50% of

HIV+ are MTB+

MTB+ is % in popul.

Austral Africa South Africa 2.5% 1.5% 27,420 16,593 18,682 11,305 10,479 6,342 38 9.9% 433,549 1,610,176 4,389,082 438,908 333,201 Botswana 1.2% 0.5% 693 272 507 199 281 110 38 18.3% 31,628 111,759 40,782 7,341 5,607 Lesotho 1.2% 0.9% 730 553 453 343 266 202 29 11.4% 27,837 70,468 55,192 6,623 3,828 Namibia 1.0% 3.0% 446 1,319 427 1,264 238 705 37 9.4% 18,544 60,986 44,731 4,473 3,353 Swaziland 1.9% 0.9% 579 274 360 170 211 100 37 12.8% 13,838 49,225 26,613 3,327 2,482 Total (Austral Africa)

2.3% 1.5% 29,868 19,010 20,428 13,281 11,475 7,458 37.5% 10.2% 525,397 1,902,615 4,556,399 460,672 348,472

Sub-Saharan Africa Angola 1.0% 2.3% 1,869 4,328 995 2,305 576 1,334 36 1.3% 20,077 58,396 335,472 5,032 3,573 Bénin 0.3% 0.3% 270 270 104 104 67 67 35 1.1% 8,331 25,460 164,603 2,469 1,752 Burkina Faso 1.0% 2.6% 2,025 5,270 752 1,957 518 1,348 32 3.0% 42,134 112,914 310,474 10,867 6,854 Burundi 1.0% 3.1% 1,180 3,669 489 1,522 302 940 36 6.3% 41,798 131,903 151,222 8,317 5,975 Cameroon 1.0% 2.4% 2,826 6,832 715 1,728 442 1,068 31 3.5% 63,409 170,915 412,429 16,497 10,228 Cape Verde 1.0% 1.0% 40 40 20 20 13 13 37 0.0% 84 38 n/a 0 0 Comoros 1.0% 1.0% 19 19 12 12 7 7 19 0.1% 18 76 n/a 0 0 Congo 1.0% 1.4% 505 704 292 407 166 232 36 3.0% 10,617 31,481 77,196 2,702 1,928 Congo (D.R.) 1.0% 1.5% 8,519 12,746 3,828 5,727 2,228 3,334 36 2.2% 149,144 403,847 2,751,107 68,778 49,078 Côte d'Ivoire 5.3% 5.3% 15,598 15,635 5,405 5,417 3,176 3,183 36 5.2% 86,507 274,901 772,368 42,480 30,247 Djibouti 1.0% 1.0% 229 229 209 209 117 117 52 5.7% 4,416 19,590 17,192 1,031 1,071 Eritrea 1.0% 1.6% 595 943 708 1,122 432 684 36 1.3% 6,142 18,528 106,819 1,602 1,161 Ethiopia 1.0% 2.3% 12,111 27,671 6,923 15,819 4,342 9,920 36 5.0% 366,469 1,093,422 6,354,064 349,473 249,508 Gambia 1.0% 1.5% 178 265 117 175 67 101 35 1.0% 1,405 4,662 33,385 334 234 Ghana 1.0% 2.6% 2,911 7,664 1,065 2,805 675 1,777 38 1.8% 39,939 132,965 505,684 10,114 7,740 Guinea 1.5% 0.6% 1,498 556 511 190 301 112 35 0.8% 6,389 19,627 192,264 1,923 1,345 Guinea-Bissau 1.0% 2.3% 170 394 86 198 56 129 35 1.2% 1,657 4,955 31,254 469 326 Guinea (Eq.al) 1.0% 1.0% 58 58 34 34 20 20 37 0.2% 127 374 11,753 59 43 Kenya 1.0% 0.0% 6,554 0 3,128 0 1,756 0 36 7.1% 229,427 763,372 3,093,555 216,549 154,804 Liberia 1.0% 1.0% 447 447 180 180 111 111 37 1.3% 4,605 14,700 80,758 1,211 895 Madagascar 1.0% 2.3% 1,981 4,551 885 2,033 508 1,167 37 0.1% 1,321 4,199 n/a 0 0 Malawi 1.0% 2.1% 2,607 5,460 1,558 3,262 904 1,893 35 7.4% 96,644 282,918 1,151,579 92,126 63,758

120

Mali 1.0% 2.4% 1,585 3,866 765 1,865 508 1,238 35 0.9% 12,871 36,255 295,472 2,955 2,086 Mauritania 1.0% 1.0% 344 344 173 173 113 113 36 0.3% 784 2,601 69,535 348 253 Mozambique 3.5% 3.5% 13,316 13,324 4,154 4,156 2,435 2,436 35 7.0% 143,546 429,016 1,832,069 119,085 83,479 Niger 1.0% 2.8% 1,490 4,171 537 1,503 353 987 32 0.6% 8,267 20,871 286,650 1,433 908 Nigeria 1.0% 1.7% 18,886 31,606 8,098 13,552 5,558 9,301 36 2.4% 313,386 1,000,054 3,063,754 76,594 55,355 Uganda 0.8% 0.5% 3,426 2,290 1,728 1,155 979 654 34 3.6% 99,816 287,261 2,440,966 97,639 66,871 Central African Republic

2.8% 1.1% 2,193 844 1,224 471 755 290 40 6.8% 27,236 98,042 92,816 6,497 5,225

Rwanda 1.0% 1.9% 1,704 3,304 480 930 279 540 35 4.9% 45,980 143,110 437,660 24,071 16,853 Sao Tome and Principe 1.0% 1.0% 10 10 5 5 3 3 29 0.1% 0 29 n/a 0 0 Senegal 1.0% 2.2% 1,311 2,840 468 1,015 272 590 36 0.9% 9,333 29,045 497,026 4,970 3,573 Sierra Leone 2.6% 0.9% 1,898 624 892 293 558 184 34 1.4% 7,875 23,316 130,912 1,964 1,322 Somalia 1.0% 1.9% 1,855 3,483 817 1,535 547 1,027 54 2.1% 9,598 109,175 n/a 0 0 Sudan 1.0% 10.1% 3,073 30,916 1,790 18,010 1,081 10,871 38 0.5% 8,852 58,167 771,455 3,857 2,930 Tanzania (United Republic of)

1.0% 1.2% 6,064 7,513 3,294 4,082 1,909 2,365 23 3.9% 149,274 306,259 3,493,815 139,753 64,556

Chad 1.0% 2.9% 1,123 3,221 606 1,736 392 1,124 40 1.2% 11,579 37,374 202,722 3,041 2,406 Togo 1.0% 2.3% 773 1,795 329 764 223 519 37 2.8% 15,024 48,902 121,163 3,635 2,681 Zambia 1.0% 1.8% 2,616 4,820 1,626 2,995 955 1,759 36 8.8% 100,315 320,992 1,039,355 103,935 75,413 Zimbabwe 2.4% 1.9% 8,382 6,717 7,045 5,645 3,994 3,200 36 12.6% 159,047 543,634 307,529 38,441 27,561 Total (Sub-Saharan Africa)

1.2% 2.1% 132,236 219,437 62,047 105,113 37,697 64,759 35.3% 3.4% 2,303,444 7,063,347 31,636,076 1,460,251 1,001,992

n/a Middle East and northern Africa

n/a

Algeria 2.0% 0.7% 1,456 491 1,631 551 910 307 25 0.0% 387 2,788 n/a 0 0 Egypt 2.0% 5.6% 2,770 7,772 1,753 4,919 1,035 2,905 14 0.0% 195 1,167 n/a 0 0 Gaza and the West Bank

2.0% 2.0% 98 98 47 47 32 32 12 0.0% 3 0 n/a 0 0

Iran 6.7% 5.8% 12,726 10,991 7,468 6,449 4,653 4,019 19 0.0% 114 192 n/a 0 0 Iraq 2.0% 2.0% 3,851 3,851 3,752 3,752 2,146 2,146 34 0.0% 62 104 n/a 0 0 Jordan 2.0% 2.8% 54 76 42 59 25 35 5 0.0% 15 39 n/a 0 0 Libya 2.0% 2.0% 143 143 190 190 109 109 11 0.0% 35 158 n/a 0 0 Morocco 2.2% 2.2% 3,922 3,845 3,488 3,420 1,941 1,903 41 0.0% 206 2,101 n/a 0 0 Syria 2.0% 6.7% 1,450 4,826 965 3,213 597 1,985 28 0.0% 36 229 n/a 0 0 Tunisia 2.0% 2.5% 366 460 248 312 140 176 16 0.0% 57 360 n/a 0 0 Turkey 2.0% 4.8% 2,604 6,297 3,019 7,302 1,769 4,279 15 0.0% 79 380 n/a 0 0 Yemen 2.0% 12.4% 2,064 12,830 1,548 9,621 894 5,555 24 0.0% 46 225 n/a 0 0 Total (Middle -East and northern Africa)

2.6% 4.3% 31,504 51,680 24,151 39,834 14,251 23,452 20.9% 0.0% 1,235 7,742 0 0 0

121


Albania 1.0% 1.0% 50 50 48 48 28 28 14 0.0% 5 0 n/a 0 0 Armenia 5.0% 7.5% 559 836 422 630 235 351 15 0.0% 9 75 n/a 0 0 Azerbaijan 5.0% 5.0% 1,281 1,269 1,500 1,487 871 863 15 0.0% 16 76 n/a 0 0 Belarus 5.0% 2.0% 1,991 792 1,776 707 973 387 16 0.1% 641 2,229 251,391 377 121 Bosnia-Herzegovina 1.0% 1.0% 175 175 175 175 101 101 17 0.0% 35 137 n/a 0 0 Bulgaria 1.0% 1.0% 183 183 172 172 94 94 15 0.0% 11 45 n/a 0 0 Georgia 5.0% 6.2% 979 1,214 1,522 1,888 869 1,078 16 0.0% 11 80 n/a 0 0 Kazakhstan 5.0% 3.9% 5,078 3,983 6,015 4,718 3,317 2,601 18 0.0% 217 632 n/a 0 0 Kyrgyzstan 5.0% 11.2% 1,611 3,624 1,976 4,445 1,139 2,563 17 0.0% 16 17 n/a 0 0 Latvia 12.0% 9.0% 1,479 1,109 1,148 861 627 470 16 0.1% 71 206 n/a 0 0 Lithuania 10.0% 4.1% 1,827 752 1,623 668 910 375 16 0.0% 17 80 n/a 0 0 Macedonia 1.0% 4.4% 51 226 41 183 24 107 15 0.0% 2 15 n/a 0 0 Moldavia 5.0% 5.0% 1,380 1,380 968 968 579 579 16 0.1% 273 719 n/a 0 0 Uzbekistan 5.0% 5.4% 6,326 6,841 5,215 5,640 3,072 3,322 17 0.0% 68 17 n/a 0 0 Romania 3.6% 2.8% 5,193 3,968 4,673 3,571 2,564 1,959 18 0.0% 125 1,256 n/a 0 0 Russian Federation 13.0% 6.0% 115,064 53,389 114,892 53,309 67,531 31,334 18 0.1% 7,763 23,306 3,619,022 3,619 1,303 Tajikistan 5.0% 5.7% 1,729 1,967 1,235 1,405 770 876 17 0.0% 18 17 n/a 0 0 Turkmenistan 5.0% 7.8% 1,143 1,778 1,283 1,996 730 1,136 16 0.0% 6 16 n/a 0 0 Ukraine 3.9% 8.9% 6,815 15,666 6,048 13,903 3,304 7,595 16 0.5% 9,801 38,093 1,217,994 6,090 1,949 Total (Eastern Europe and the former Soviet Union)

9.0% 5.8% 152,914 99,203 150,734 96,774 87,737 55,820 17.1% 0.1% 19,105 67,017 5,088,407 10,086 3,372

Urbanized South Cone

Brazil 1.3% 0.9% 8,034 5,604 5,899 4,115 3,355 2,341 25 0.3% 14,229 138,489 4,341,835 13,026 6,608 Total (CSU) 1.3% 0.9% 8,034 5,604 5,899 4,115 3,355 2,341 25.4% 0.3% 14,229 138,489 4,341,835 13,026 6,608 Latin America Belize 1.0% 1.0% 6 6 4 4 2 2 19 1.0% 49 455 6,362 64 24 Bolivia 2.1% 1.2% 2,188 1,256 1,391 798 810 465 47 0.1% 357 2,025 n/a 0 0 Colombia 1.0% 0.5% 1,046 521 624 310 372 185 24 0.2% 1,710 17,423 n/a 0 0 Costa Rica 1.0% 1.0% 32 32 34 34 19 19 11 0.3% 177 1,318 n/a 0 0 Cuba 0.9% 0.0% 77 0 59 0 33 0 11 0.0% 39 213 n/a 0 0 El Salvador 1.0% 4.0% 222 890 115 461 67 270 25 0.3% 550 5,117 n/a 0 0 Equator 1.0% 4.0% 1,133 4,524 676 2,700 404 1,612 38 0.2% 1,139 7,355 n/a 0 0 Guatemala 1.0% 1.7% 513 887 212 367 127 221 28 0.7% 2,892 20,604 294,980 2,065 1,142 Guyana 1.0% 1.0% 46 46 28 28 18 18 22 1.8% 419 3,387 22,378 336 149 Haiti 1.0% 4.1% 1,501 6,161 670 2,750 397 1,629 54 2.7% 19,792 115,886 204,589 5,115 5,556 Honduras 1.0% 2.5% 318 797 275 689 154 386 26 1.0% 2,025 16,944 168,298 1,683 887

122

Jamaica 1.0% 2.0% 10 21 7 14 4 8 10 0.4% 112 999 n/a 0 0 Nicaragua 1.2% 1.2% 283 293 189 196 108 112 34 0.1% 210 1,681 n/a 0 0 Paraguay 1.0% 3.2% 197 620 127 400 75 235 25 0.1% 93 771 n/a 0 0 Peru 4.3% 3.1% 13,850 9,883 9,714 6,932 5,409 3,860 44 0.2% 3,531 21,583 695,376 1,391 1,232 Dominican Republic 8.6% 6.6% 5,135 3,941 3,224 2,475 1,872 1,437 30 1.5% 4,705 39,112 218,401 3,276 1,946 St. Vincent and Grenadines

1.0% 1.0% 2 2 1 1 1 1 10 0.0% 0 0 n/a 0 0

Surinam 1.0% 1.0% 17 17 9 9 6 6 24 0.7% 79 718 10,797 70 33 Total (Latin America)

2.8% 2.9% 26,576 29,896 17,360 18,170 9,877 10,465 31.0% 0.4% 37,881 255,591 1,621,180 13,999 10,969

Indian sub-continent Bangladesh 2.0% 1.4% 32,171 22,701 16,558 11,684 10,478 7,394 46 0.0% 1,193 6,084 n/a 0 0 Bhutan 2.0% 2.0% 119 119 90 90 56 56 37 0.0% 7 37 n/a 0 0 India 7.1% 3.4% 682,732 325,540 504,100 240,365 295,817 141,051 44 0.4% 273,965 1,649,518 25,639,494 89,738 78,987 Maldives 1.4% 1.4% 14 14 14 14 8 8 27 0.0% 3 28 n/a 0 0 Nepal 1.4% 1.0% 3,862 2,652 2,598 1,784 1,520 1,044 45 0.1% 3,088 15,513 n/a 0 0 Pakistan 2.0% 9.6% 28,373 136,105 15,000 71,954 9,619 46,144 40 0.1% 4,657 30,058 n/a 0 0 Sri Lanka 1.0% 1.0% 586 608 398 412 224 232 22 0.0% 269 1,693 n/a 0 0 Total (Indian sub-continent)

6.0% 3.7% 747,857 487,739 538,758 326,303 317,720 195,927 43.4% 0.3% 283,183 1,702,932 25,639,494 89,738 78,987


Afghanistan 1.0% 7.3% 4,552 33,273 1,998 14,603 1,387 10,141 34 0.0% 108 34 n/a 0 0 Cambodia 1.0% 4.2% 3,498 14,779 1,446 6,109 858 3,625 64 1.9% 36,508 143,081 307,351 6,147 7,877 China (Pop. Rep.) 9.8% 5.1% 651,265 335,562 364,044 187,572 221,763 114,262 36 0.0% 22,048 180,287 32,015,522 112,054 80,246 Korea (Dém. Pop. Rep.)

1.0% 1.0% 2,116 2,116 924 924 643 643 37 0.0% 91 37 n/a 0 0

Laos 1.0% 8.1% 476 3,868 210 1,707 132 1,071 39 0.0% 108 561 n/a 0 0 Mongolia 1.0% 6.1% 271 1,656 180 1,100 104 634 45 0.0% 4 46 n/a 0 0 Myanmar 1.0% 1.5% 4,246 6,324 1,920 2,859 1,231 1,833 41 1.1% 30,539 218,551 1,242,621 12,426 10,137 Vietnam 2.3% 2.3% 18,127 18,471 11,946 12,173 6,809 6,938 44 0.1% 7,275 44,410 0 0 Total (China, Indochina, and Others (Asia))

7.8% 4.6% 684,550 416,049 382,667 227,048 232,926 139,147 36.7% 0.1% 96,680 587,008 33,565,494 130,628 98,260

Southeast Asia Indonesia 1.0% 0.7% 31,329 21,231 14,617 9,906 9,785 6,631 49 0.0% 5,575 25,735 n/a 0 0 Papua-New Guinea 1.0% 7.7% 597 4,577 726 5,564 421 3,228 44 0.1% 488 2,430 n/a 0 0 Philippines 1.0% 3.2% 11,886 38,076 8,555 27,403 4,881 15,635 47 0.0% 2,894 13,498 n/a 0 0 Thailand 5.0% 2.1% 22,482 9,491 14,282 6,029 8,921 3,766 43 1.2% 39,461 324,178 1,584,292 15,843 13,484

123

Total (Southeast Asia)

1.4% 1.8% 66,294 73,375 38,180 48,902 24,008 29,260 47.4% 0.2% 48,418 365,840 1,584,292 15,843 13,484

WORLD TOTAL 4.8% 3.4% 1,879,89

3 1,402,05

5 1,240,29

0 879,607 739,086 528,667 36.1% 0.7% 3,329,585 12,090,579 108,033,177 2,194,241 1,562,144

7.7% 5.8% 4.8% 3.4% 60% 60% 12.8% 50.0% 35.6% Total for the 22 HBCs

5.3% 3.3% 1,718,969

1,135,565

1,137,696

712,299 679,089 430,071 38.7% 0.6% 2,349,859 8,826,838 95,339,386 1,680,379 1,200,809

8.6% 5.9% 5.3% 3.3% 60% 60% 11.0% Proportion of total number of persons attended

50.0% 35.7%

22 HBCs (total) 88.3% 76.6% 76.9% (a) Red signifies high-burden country; blue signifies data available and replicated in neighboring countries.

124

Appendix D: Results (Output of the Financial Model)

Appendix D1. Results for NTP Budgets (First-line Treatments—DOTS Expansion) and Health-Care Services(a) (in thousands of U.S. $)

Budgetary Costs to National TB Control Programs Costs to Health Care Services

Implementation costs Recurrent costs Total Costs Inpatient care Ambulatory care

Total NTP

costs (includes DOTS and DOTS expansion)

Micro-scopes

(includes parts and

logistical costs)

Other equip-ment

Initial training

and Misc. initial costs

Drugs (first-line

treat-ments and

retreat-ments)

Diagnos-tic costs

Regular training

Program admini-stration, super-vision,

and moni-toring

Incen-tives

Opera-tional

research and

surveil-lance

With proportion-

ate increment of

capacity

With no change in capacity

Propor-tionate

increment of capacity

Using current

capacity (as of 2000)

Visits (propor-tionate

increment of

capacity)

Visits (using current

capacity)

Austral Africa South Africa 102,917 0 0 1,368 41,048 8,087 1,949 50,091 0 375 1,235,421 1,045,687 742,520 702,192 492,901 343,496 Botswana 3,891 0 0 0 1,695 82 53 2,052 0 10 31,828 32,856 0 0 31,828 32,856 Lesotho 3,392 265 54 268 2,075 75 24 627 0 5 15,453 0 9,288 0 6,165 0 Namibia 3,705 0 0 0 1,713 85 50 1,847 0 10 45,548 44,776 27,376 26,460 18,173 18,316 Swaziland 2,159 128 26 265 1,040 31 24 641 0 5 15,805 0 9,499 0 6,306 0

Total (Austral Africa)

116,066 (5.4%)

394 80 1,901 47,571 8,360 2,099 55,258 0 404 1,344,056 1,123,320 788,683 728,652 555,373 394,668

Sub-Saharan Africa

Angola 6,457 321 201 309 3,995 215 215 1,160 0 41 488 394 315 264 173 131 Bénin 2,240 0 0 0 1,499 75 149 488 0 29 240 102 155 65 85 37 Burkina Faso 4,832 9 6 13 3,241 158 286 1,063 0 55 516 76 330 48 186 28 Burundi 2,828 218 137 184 1,551 101 87 534 0 17 2,432 1,189 1,565 917 866 272 Cameroon 6,068 404 253 888 2,266 150 615 1,374 0 118 785 355 501 293 284 62 Cape Verde 340 17 11 93 87 4 41 78 0 8 54 21 34 19 19 2 Comoros 334 22 14 46 50 3 145 21 0 33 150 130 100 121 51 9 Congo 1,483 0 0 0 925 61 71 412 0 14 327 300 128 114 199 185 Congo (D.R.) 28,180 5,238 267 1,454 17,233 812 203 2,853 0 121 3,504 2,928 1,379 1,123 2,125 1,805 Côte d'Ivoire 9,557 540 338 1,270 4,395 210 626 2,057 0 120 1,187 936 754 585 433 351

125

Djibouti 980 0 0 0 354 54 60 511 0 1 7,678 7,545 3,048 2,920 4,630 4,625 Eritrea 2,640 55 41 55 1,198 28 186 1,076 0 2 7,923 586 0 0 7,923 586 Ethiopia 19,339 68 565 768 11,604 848 1,040 4,435 0 11 45,862 25,263 0 0 45,862 25,263 Gambia 1,016 51 32 116 506 20 51 231 0 10 119 26 70 23 48 2 Ghana 9,275 232 145 632 4,592 166 934 2,395 0 180 1,218 562 693 339 525 223 Guinea 2,305 0 0 0 1,079 51 351 755 0 68 375 291 210 160 165 131 Guinea-Bissau 623 48 30 44 369 12 19 97 0 4 30 3 16 2 14 0 Guinea (Equat.) 227 1 0 2 107 5 24 82 0 5 43 29 25 16 19 13 Kenya 37,116 0 0 966 22,298 4,927 5,616 2,038 0 1,272 39,959 37,890 35,008 33,021 4,950 4,869 Liberia 1,146 74 47 34 774 33 25 154 0 5 38 22 23 13 15 9 Madagascar 8,011 37 23 43 3,814 183 2,258 1,142 0 512 5,043 4,404 3,943 3,411 1,100 993 Malawi 8,513 0 0 0 6,252 315 172 1,742 0 33 7,709 6,397 6,476 5,337 1,233 1,059 Mali 4,900 80 50 84 3,297 206 208 935 0 40 477 146 331 101 146 45 Mauritania 1,680 106 66 245 747 40 109 346 0 21 214 166 142 155 72 11 Mozambique 9,970 30 22 43 4,763 242 2,695 1,566 0 611 6,962 5,525 5,427 4,266 1,535 1,259 Niger 4,179 320 201 354 2,315 66 210 673 0 40 219 63 112 39 107 24 Nigeria 69,978 6,791 1,597 2,623 46,480 6,320 102 6,063 0 0 4,626 757 2,971 479 1,655 278 Uganda 12,510 0 0 0 7,301 828 712 3,532 0 137 2,342 2,161 1,668 1,520 674 641 Central African Republic

3,258 144 90 248 1,884 47 110 713 0 21 260 85 124 73 136 12

Rwanda 2,041 0 0 0 1,520 65 56 388 0 11 841 705 457 376 384 329 Sao Tome and Principe

59 6 4 12 21 1 5 9 0 1 5 4 3 3 2 0

Senegal 3,368 0 0 0 2,018 88 342 855 0 66 445 369 273 222 172 147 Sierra Leone 1,962 0 0 0 1,479 85 56 330 0 11 121 66 82 44 39 22 Somalia 2,439 154 115 30 1,383 45 87 624 0 1 23 6 0 0 23 6 Sudan 9,270 397 296 508 3,030 86 1,736 3,198 0 19 1,554 978 1,230 833 324 145 Tanzania (United Republic of)

20,208 0 0 0 13,222 689 1,079 3,367 0 1,851 11,831 9,903 9,275 7,692 2,556 2,210

Chad 4,588 305 191 379 2,610 93 168 810 0 32 312 80 177 72 136 8 Togo 2,297 0 0 0 1,418 75 169 602 0 33 357 72 235 47 122 25 Zambia 11,497 338 252 420 6,524 357 1,327 1,977 0 301 8,712 3,497 6,902 3,254 1,810 243 Zimbabwe 32,105 427 267 1,603 11,781 2,513 5,758 8,450 0 1,305 23,422 21,827 8,349 7,571 15,074 14,255

Total (Sub-Saharan Africa)

349,821 (16.3%)

16,434 5,261 13,463 199,982 20,279 28,105 59,137 0 7,159 188,401 135,856 92,532 75,543 95,869 60,313

Middle East and Northern Africa

126

Algeria 6,467 0 0 0 6,290 177 0 0 0 0 26,731 19,527 20,019 19,349 6,712 178 Egypt 35,018 1,327 1,246 9,996 6,759 176 2,243 11,519 0 1,752 18,848 11,646 13,835 10,464 5,013 1,182 Gaza and the West Bank

1,591 84 79 576 185 14 94 485 0 74 1,117 118 996 118 121 0

Iran 40,753 557 524 6,566 8,830 279 3,469 17,817 0 2,710 41,941 21,713 34,376 18,901 7,564 2,812 Iraq 23,550 557 524 3,179 14,864 425 578 2,971 0 452 31,480 22,506 25,315 22,193 6,165 313 Jordan 1,340 0 0 0 166 5 169 868 0 132 478 343 381 270 98 72 Libya 4,620 147 138 1,679 735 19 275 1,412 0 215 3,033 1,942 2,209 1,923 825 20 Morocco 18,916 0 0 0 12,227 365 914 4,696 0 714 36,479 35,942 27,722 27,036 8,757 8,906 Syria 8,433 179 168 1,125 3,824 104 439 2,253 0 343 8,612 4,474 6,861 4,020 1,752 453 Tunisia 8,331 257 241 3,210 955 30 526 2,701 0 411 5,109 3,692 3,934 3,662 1,175 30 Turkey 69,148 1,790 1,682 25,017 11,959 357 4,098 21,045 0 3,201 68,134 41,214 52,124 40,870 16,010 344 Yemen 7,911 222 209 349 6,132 175 119 611 0 93 3,575 2,602 2,875 2,426 700 176

Total (Middle East and northern Africa)

226,077 (10.5%)

5,121 4,810 51,696 72,926 2,126 12,924 66,379 0 10,096

245,538 165,719 190,646 151,232 54,892 14,487


Albania 1,260 69 538 33 298 123 83 117 0 0 2,224 1,918 2,216 1,911 8 7 Armenia 995 15 121 6 521 159 71 101 0 0 3,304 3,201 3,299 3,196 5 6 Azerbaijan 4,223 143 1,119 56 1,855 671 157 223 0 0 12,356 10,130 12,337 10,099 19 31 Belarus 6,315 207 1,626 227 2,196 695 563 800 0 0 41,052 42,451 40,989 42,316 63 135 Bosnia-Herzegovina

2,456 58 451 48 1,081 419 164 233 0 0 13,161 11,048 13,114 11,007 47 42

Bulgaria 3,887 167 1,309 138 1,062 385 341 486 0 0 11,872 12,333 11,827 12,283 46 50 Georgia 3,016 1 9 1 1,881 721 166 236 0 0 19,460 17,072 19,430 17,045 30 27 Kazakhstan 14,080 301 2,361 223 7,437 2,364 575 819 0 0 94,032 94,925 93,888 94,629 144 296 Kyrgyzstan 3,358 0 0 0 2,443 699 89 127 0 0 15,579 13,180 15,555 13,150 24 30 Latvia 1,068 0 0 0 591 181 122 174 0 0 10,072 10,493 10,057 10,477 15 17 Lithuania 2,520 75 589 77 1,004 313 191 271 0 0 17,538 16,613 17,511 16,560 27 53 Macedonia 931 41 322 30 256 102 74 105 0 0 2,773 2,141 2,764 2,133 10 8 Moldavia 2,473 87 682 34 1,197 265 85 121 0 0 8,098 5,641 8,086 5,623 12 18 Uzbekistan 14,129 495 3,880 174 6,448 2,078 435 619 0 0 38,692 29,691 38,633 29,597 59 94 Romania 17,801 438 3,435 415 8,025 2,891 1,072 1,526 0 0 102,468 105,169 102,070 104,744 398 425 Russian Federation 130,563 2,826 22,176 3,009 56,761 21,055 7,853 11,172 0 5,711 863,351 660,857 861,832 658,471 1,519 2,386 Tajikistan 3,285 126 991 21 1,527 496 52 73 0 0 4,296 2,348 4,289 2,341 7 7

127

Turkmenistan 3,304 97 761 51 1,587 505 125 178 0 0 14,105 12,626 14,084 12,586 22 40 Ukraine 27,157 1,013 7,952 535 9,711 3,269 1,326 3,352 0 0 87,515 91,318 87,381 91,027 134 291

Total (Eastern Europe and the former Soviet Union)

242,820 (11.3%)

6,159 48,322 5,076 105,882 37,391 13,544 20,735 0 5,711 1,361,949 1,143,155 1,359,362 1,139,192 2,587 3,963


Brazil 108,647 6,369 0 1,307 28,105 3,965 3,926 21,724 36,906 6,345 149,060 134,322 148,065 132,509 996 1,813

Total (CSU) 108,647 (5.1%)

6,369 0 1,307 28,105 3,965 3,926 21,724 36,906 6,345 149,060 134,322 148,065 132,509 996 1,813

Latin America Belize 111 10 0 2 17 4 10 58 0 9 155 318 145 315 10 3 Bolivia 4,831 143 5 9 2,799 623 181 930 0 141 12,012 9,757 11,255 9,118 757 639 Colombia 14,068 1,101 39 180 2,636 541 2,237 5,586 0 1,747 8,527 6,150 6,672 4,768 1,855 1,383 Costa Rica 1,802 146 5 40 145 30 192 1,079 0 164 1,250 1,136 1,147 1,122 103 14 Cuba 1,893 0 0 0 279 68 207 1,162 0 177 254 259 185 186 69 72 El Salvador 2,364 25 1 5 486 111 232 1,306 0 199 2,946 2,308 2,703 2,121 242 187 Equator 6,220 450 16 56 2,858 571 328 1,685 0 256 7,743 5,095 6,825 4,874 918 221 Guatemala 3,798 0 0 0 896 199 361 2,033 0 309 4,721 3,280 4,332 3,000 388 280 Guyana 373 35 1 5 120 19 26 145 0 22 588 318 540 314 48 4 Haiti 4,352 184 7 12 2,831 557 102 572 0 87 4,143 2,838 3,652 2,723 491 116 Honduras 2,692 256 9 27 1,162 249 132 743 0 113 5,134 4,643 4,811 4,595 323 47 Jamaica 626 0 0 0 30 6 79 444 0 67 30 28 18 17 12 11 Nicaragua 1,540 0 0 0 666 140 98 552 0 84 535 477 358 315 177 162 Paraguay 2,503 218 8 44 536 119 228 1,171 0 178 4,665 3,448 4,388 3,418 277 30 Peru 43,739 0 0 0 9,546 1,983 1,184 6,083 24,017 925 35,026 34,273 29,863 29,037 5,163 5,235 Dominican Republic

4,838 175 6 39 1,584 324 362 2,038 0 310 13,220 10,246 12,311 10,092 909 154

St. Vincent and Grenadines

41 0 0 0 4 1 5 28 0 4 25 7 23 6 2 1

Surinam 168 17 1 3 39 9 13 75 0 11 294 196 277 194 17 2

Total (Latin America)

95,958 (4.5%)

2,760 99 422 26,632 5,554 5,977 25,692 24,017 4,805 101,266 84,776 89,506 76,215 11,761 8,562

128

Indian Sub-Continent

Bangladesh 28,731 155 455 118 12,594 1,827 654 9,379 0 3,548 21,381 9,859 1,845 896 19,536 8,962 Bhutan 167 0 0 0 68 10 4 62 0 23 115 69 7 4 108 66 India 298,979 10,487 30,719 11,605 108,004 15,597 7,483 73,289 14,072

27,723 329,208 48,465 7,682 6,763 321,526 41,701

Maldives 93 0 0 0 15 3 3 52 0 20 76 76 3 3 73 73 Nepal 5,920 249 730 156 2,823 419 105 1,043 0 394 4,995 3,023 228 181 4,767 2,842 Pakistan 33,830 1,666 4,880 1,507 11,409 1,646 908 8,572 0 3,243 22,484 4,500 1,327 816 21,157 3,684 Sri Lanka 5,093 12 34 18 605 90 205 2,996 0 1,133 2,683 2,612 122 114 2,560 2,497

Total (Indian sub-continent)

372,814 (17.3%)

12,570 36,818 13,405 135,518 19,592 9,363 95,393 14,072 36,084 380,942 68,603 11,214 8,778 369,728 59,825


Afghanistan 13,006 725 103 377 8,896 441 290 824 0 1,350 3,920 412 0 0 3,920 412 Cambodia 16,092 435 1,053 1,392 3,803 450 424 559 6,004 1,973 15,406 11,466 12,639 9,335 2,767 2,131 China (Pop. Rep.) 362,074 5,580 68,645 18,089 46,903 11,757 8,735 87,565 25,360

89,439 428,636 222,090 61,277 38,650 367,359 183,440

Korea (Dém. Pop. Rep.)

9,762 289 1,428 246 3,016 205 657 865 0 3,055 39 5 0 0 39 5

Laos 3,361 32 90 500 972 47 247 325 0 1,148 3,286 1,746 2,739 1,447 547 299 Mongolia 1,873 1 5 1 1,056 42 110 145 0 512 813 675 0 0 813 675 Myanmar 6,658 303 1,056 201 3,441 424 457 520 0 257 362 161 0 0 362 161 Vietnam 27,727 744 2,093 1,595 14,301 1,064 675 2,111 3,642 1,502 53,964 48,332 41,364 36,720 12,600 11,612

Total (China, Indochina, and Others (Asia))

440,552 (20.5%)

8,110 74,474 22,402 82,388 14,429 11,595 92,914 35,006 99,235

506,426 284,888 118,019 86,152 388,407 198,736

Southeast Asia Indonesia 103,624 449 31 9,301 46,324 4,061 14,488 19,086 0 9,885 161,414 41,416 0 0 161,414 41,416 Papua-New Guinea

3,281 91 6 32 2,113 212 299 393 0 135 2,810 578 331 273 2,479 305

Philippines 50,506 996 62 532 23,507 2,502 7,993 10,529 0 4,385 50,897 13,350 1,643 1,454 49,254 11,896 Thailand 39,823 536 37 464 9,052 1,529 9,687 12,762 0 5,756 7,241 4,016 0 0 7,241 4,016

Total (Southeast Asia)

197,233 (9.2%)

2,072 136 10,328 80,996 8,305 32,466 42,770 0 20,161

222,362 59,360 1,974 1,727 220,388 57,633

129

WORLD TOTAL

2,150,000 100.0%

60,000 170,000 120,000 780,000 120,000 120,000 480,000 110,000 190,000

4,500,000 3,200,000 2,800,000 2,400,000 1,700,000 800,000

Total for the 22 HBCs

1,586,353 73.8%

43,796 134,005 58,278 553,612 93,325 81,217 345,005 110,000 167,114

3,509,317 2,379,935 1,968,707 1,668,250 1,540,611 711,684

(a) Red signifies high-burden country; blue signifies data available and replicated in neighboring countries.

130

Appendix D2. Results for TB-HIV Co-Infection Prophylaxis and MDR-TB Treatment(a) (in thousands of U.S. $)

Cost of Managing TB-HIV co-infected

patients MDR-TB Treatment

Other costs associated with MDR-TB treatment Total Cost Drug costs Scenario 1

(higher end) Scenario 2 (lower end)

Total Cost 6-month isionazid preventive regimen

cost

Program costs

associated with

isionazid therapy

Other program

costs associated

with ProTEST

Scenario 1 (higher end)

Scenario 2 (lower end)

Scenario 1 (higher

end)

Scenario 2 (lower end)

DOTS-Plus program

costs

Visits and hospital

care

DOTS-Plus program

costs

Visits and hospital

care

Austral Africa South Africa 494,879 2,801 258,955 233,123 24,334 14,827 8,408 5,069 6,415 9,511 3,954 5,804 Botswana 6,106 138 3,141 2,827 533 210 225 88 124 184 50 73 Lesotho 1,929 92 967 870 317 240 216 163 41 60 31 46 Namibia 3,313 76 1,703 1,534 424 1,262 191 563 94 139 283 416 Swaziland 1,944 61 991 892 334 159 171 81 66 98 32 47 Total (Austral Africa) 508,172 3,168 265,757 239,247 25,942 16,698 9,210 5,963 6,740 9,992 4,350 6,385 Sub-Saharan Africa Angola 503 74 226 203 532 1,231 465 1,074 27 40 64 93 Bénin 229 32 103 93 67 67 56 55 5 7 5 7 Burkina Faso 1,273 146 593 534 522 1,356 433 1,123 36 53 95 139 Burundi 785 166 326 294 279 866 248 768 13 19 40 58 Cameroon 3,431 220 1,690 1,521 481 1,163 362 872 48 71 118 173 Cape Verde 0 0 0 0 19 19 10 10 3 5 3 5 Comoros 0 0 0 0 7 7 6 6 1 1 1 1 Congo 320 40 147 133 161 225 134 186 11 16 16 23 Congo (D.R.) 7,403 627 3,566 3,210 2,103 3,142 1,803 2,688 121 179 184 270 Côte d'Ivoire 9,268 416 4,658 4,194 3,481 3,491 2,574 2,571 365 541 373 547 Djibouti 302 23 146 132 137 137 94 93 17 26 18 26 Eritrea 242 23 115 104 434 688 353 557 33 49 53 78 Ethiopia 29,885 1,670 14,848 13,367 4,055 9,248 3,568 8,122 196 291 456 670 Gambia 73 6 35 32 73 109 54 81 7 11 11 17 Ghana 2,610 166 1,286 1,158 783 2,062 556 1,458 91 135 245 360

131

Guinea 481 25 240 216 343 127 245 90 40 59 15 22 Guinea-Bissau 31 6 13 12 52 121 46 106 3 4 6 9 Guinea (Equatorial) 32 0 17 15 24 24 17 17 3 5 3 5 Kenya 29,604 1,147 14,976 13,482 1,722 0 1,409 0 126 187 0 0 Liberia 67 18 26 23 97 97 91 91 3 4 3 4 Madagascar 5 5 0 0 481 1,104 410 938 29 43 67 99 Malawi 7,725 511 3,797 3,418 828 1,730 731 1,525 39 58 83 122 Mali 322 46 145 131 489 1,190 422 1,025 27 40 67 98 Mauritania 75 3 38 34 126 126 94 93 13 19 13 20 Mozambique 13,448 655 6,732 6,060 2,331 2,330 1,973 1,967 144 214 147 216 Niger 168 27 74 67 341 953 293 816 20 29 56 82 Nigeria 8,933 1,252 4,042 3,639 5,435 9,083 4,647 7,749 317 470 541 794 Uganda 15,470 520 7,867 7,083 991 662 787 524 82 122 56 82 Central African Rep. 1,111 121 521 469 777 299 618 237 64 95 25 37 Rwanda 1,028 217 427 384 238 460 225 435 5 8 10 15 Sao Tome and Principe 2,917 2,917 0 0 3 3 3 3 0 0 0 0 Senegal 942 44 473 426 288 623 220 476 27 40 60 88 Sierra Leone 139 30 58 52 502 165 459 150 17 26 6 9 Somalia 122 122 0 0 476 890 455 851 8 12 16 23 Sudan 742 67 355 320 1,142 11,483 881 8,831 105 156 1,075 1,578 Tanzania (U. Rep. Of) 9,507 503 4,738 4,266 1,711 2,115 1,543 1,905 68 100 85 125 Chad 381 47 176 158 385 1,101 324 926 24 36 71 104 Togo 721 61 347 312 244 566 186 431 23 34 54 80 Zambia 10,181 571 5,057 4,553 895 1,646 774 1,421 49 72 91 134 Zimbabwe 13,473 675 6,735 6,063 5,019 4,030 3,213 2,564 728 1,079 594 872 Total (Sub-Saharan Africa)

173,950 13,199 84,595 76,156 38,071 64,740 30,782 52,834 2,936 4,354 4,824 7,082


Algeria 12 12 0 0 1,518 515 729 245 318 471 109 160 Egypt 5 5 0 0 1,472 4,144 840 2,349 255 377 727 1,068 Gaza and the West Bank 0 0 0 0 46 46 27 27 7 11 8 11 Iran 1 1 0 0 8,308 7,211 3,819 3,286 1,808 2,681 1,591 2,335 Iraq 1 1 0 0 2,714 2,720 1,730 1,723 397 588 404 593 Jordan 0 0 0 0 37 52 20 28 7 10 9 14 Libya 1 1 0 0 188 189 88 88 40 60 41 60

132

Morocco 9 9 0 0 2,683 2,639 1,554 1,518 455 674 454 667 Syria 1 1 0 0 796 2,656 489 1,621 124 184 420 616 Tunisia 2 2 0 0 252 319 112 141 56 84 72 106 Turkey 2 2 0 0 3,433 8,350 1,433 3,454 805 1,194 1,984 2,912 Yemen 1 1 0 0 835 5,182 722 4,470 46 68 288 423 Total (Middle East and northern Africa)

34 34 0 0 22,282 34,022 11,564 18,949 4,317 6,401 6,108 8,966


Albania 0 0 0 0 52 52 37 37 6 9 6 9 Armenia 0 0 0 0 408 610 314 467 38 57 58 85 Azerbaijan 0 0 0 0 1,546 1,533 1,174 1,159 150 222 151 222 Belarus 303 2 39 262 2,446 977 1,294 513 464 688 188 276 Bosnia-Herzegovina 0 0 0 0 229 229 136 135 37 55 38 56 Bulgaria 0 0 0 0 209 209 125 124 34 50 34 50 Georgia 0 0 0 0 1,741 2,163 1,167 1,442 231 343 292 429 Kazakhstan 1 1 0 0 7,079 5,565 4,418 3,452 1,072 1,589 856 1,257 Kyrgyzstan 0 0 0 0 1,998 4,495 1,533 3,435 188 278 430 631 Latvia 0 0 0 0 1,510 1,136 834 623 272 404 208 305 Lithuania 0 0 0 0 2,230 921 1,217 499 408 605 171 251 Macedonia 0 0 0 0 52 232 33 144 8 12 35 52 Moldavia 1 1 0 0 1,039 1,039 786 783 102 151 104 152 Uzbekistan 0 0 0 0 5,339 5,773 4,152 4,474 478 709 527 773 Romania 1 1 0 0 6,043 4,633 3,411 2,597 1,060 1,572 825 1,211 Russian Federation 4,233 24 547 3,662 170,216 79,279 91,218 42,166 31,820 47,178 15,038 22,075 Tajikistan 0 0 0 0 1,194 1,356 1,053 1,194 57 84 66 97 Turkmenistan 0 0 0 0 1,395 2,172 980 1,519 167 248 265 389 Ukraine 1,441 37 182 1,222 6,277 14,446 4,392 10,058 759 1,126 1,778 2,610 Total (Eastern Europe and the former Soviet Union)

5,980 66 769 5,146 211,003 126,821 118,272 74,820 37,352 55,379 21,071 30,931


Brazil 10,221 156 1,308 8,757 8,903 6,232 5,019 3,488 1,565 2,320 1,112 1,632

133

Total (Urbanized Southern Cone)

10,221 156 1,308 8,757 8,903 6,232 5,019 3,488 1,565 2,320 1,112 1,632

Latin America Belize 21 1 3 18 4 4 2 2 1 1 1 1 Bolivia 2 2 0 0 942 542 615 351 132 195 77 113 Colombia 20 20 0 0 678 339 284 141 159 235 80 118 Costa Rica 2 2 0 0 34 34 14 14 8 12 8 12 Cuba 0 0 0 0 36 0 25 0 5 7 0 0 El Salvador 6 6 0 0 102 409 51 205 20 30 83 122 Equator 9 9 0 0 503 2,013 308 1,225 78 116 319 469 Guatemala 404 24 49 331 180 314 97 168 33 50 59 87 Guyana 84 4 10 69 24 24 14 13 4 6 4 6 Haiti 663 135 69 460 405 1,665 302 1,236 42 62 174 255 Honduras 298 19 36 242 179 450 116 289 26 38 65 96 Jamaica 1 1 0 0 5 11 3 6 1 1 2 3 Nicaragua 2 2 0 0 124 129 82 84 17 26 18 27 Paraguay 1 1 0 0 114 362 57 178 23 34 75 109 Peru 585 25 73 487 9,775 7,021 4,064 2,889 3,126 2,585 2,272 1,860 Dominican Republic 1,163 44 145 973 3,023 2,331 1,419 1,085 646 958 505 741 St. Vincent and Grenadines

0 0 0 0 1 1 0 0 0 0 0 0

Surinam 15 1 2 12 8 8 4 4 2 2 2 2 Total (Latin America) 3,275 295 387 2,593 16,139 15,658 7,457 7,892 4,323 4,359 3,745 4,021 Indian Sub-Continent Bangladesh 7 7 0 0 11,522 8,133 8,625 6,063 1,167 1,730 839 1,231 Bhutan 0 0 0 0 61 61 46 45 6 9 6 9 India 22,139 1,919 2,627 17,592 357,046 170,517 239,736 113,882 47,252 70,058 22,948 33,687 Maldives 0 0 0 0 11 11 6 6 2 3 2 3 Nepal 18 18 0 0 1,572 1,080 1,231 842 138 204 96 141 Pakistan 35 35 0 0 11,118 53,380 7,941 37,950 1,280 1,897 6,252 9,178 Sri Lanka 2 2 0 0 305 317 180 186 50 75 53 78 Total (Indian sub-continent)

22,201 1,982 2,627 17,592 381,635 233,499 257,764 158,974 49,895 73,976 30,197 44,328

134


Afghanistan 0 0 0 0 1,268 9,247 1,162 8,464 43 63 317 466 Cambodia 754 174 75 505 901 3,807 697 2,934 82 122 354 519 China (Pop. Rep.) 27,270 205 3,517 23,548 316,828 163,769 181,110 92,966 54,666 81,051 28,689 42,114 Korea (D. Pop. Rep.) 0 0 0 0 661 661 538 536 49 73 50 74 Laos 1 1 0 0 151 1,227 108 877 17 25 142 208 Mongolia 0 0 0 0 112 686 84 510 11 17 71 105 Myanmar 1,163 251 119 793 1,247 1,856 1,015 1,507 93 138 141 207 Vietnam 52 52 0 0 7,674 7,827 5,483 5,566 883 1,308 916 1,345 Total (China, Indochina, and Others (Asia))

29,240 683 3,711 24,846 328,841 189,079 190,198 113,359 55,845 82,798 30,681 45,038

Southeast Asia Indonesia 31 31 0 0 12,661 8,598 8,140 5,496 1,821 2,700 1,257 1,845 Papua-New Guinea 3 3 0 0 516 3,962 341 2,600 71 105 552 810 Philippines 16 16 0 0 7,346 23,622 3,931 12,546 1,376 2,039 4,488 6,588 Thailand 6,879 369 846 5,664 16,659 7,071 7,323 3,080 3,761 5,576 1,617 2,374 Total (Southeast Asia) 6,928 418 846 5,664 37,183 43,252 19,735 23,722 7,028 10,420 7,914 11,617 WORLD TOTAL 760,000 20,000 360,000 380,000 1,070,000 730,000 650,000 460,000 170,000 250,000 110,000 160,000 Total 22 HBCs 682,538 12,458 324,840 345,241 978,535 593,465 590,844 367,616 156,986 230,705 92,110 133,738 22 HBCs/World total 90% 62% 90% 91% 91% 81% 91% 80% 92% 92% 84% 84%

(a) Red signifies high-burden country; blue signifies data available and replicated in neighboring countries.

135

Appendix E: Budgets of WHO’s TB Working Groups and TB Partnership (in 2000 U.S. $)

2001 2002 2003 2004 2005 Total (2001 – 2005)

Working Group on DOTS Expansion

40,430,000 46,200,000 46,200,000 46,200,000 46,200,000 225,230,000

Working Group on TB-HIV co-infection

900,000 1,885,000 3,235,000 3,165,000 3,165,000 12 350,000

Working Group on DOTS-Plus for MDR-TB

4,280,000 4,580,000 3,980,000 3,550,000 90,000 16,480,000

Working Group on New TB Diagnostics

7,080,000 6,510,000 6,440,000 7,025,000 27,055,000

Working Group on TB Drug Development

7,800,000 7,800,000 7,300,000 6,800,000 29,700,000

Working Group on TB Vaccine Development

1,150,000 1,300,000 1,000,000 1,050,000 4,500,000

Subtotals for working groups 45,610,000 68,695,000 69,025,000 67,655,000 64,330,000 315,315,000

Costs associated with the “Stop TB Partnership”

74,500,000

TOTALS

389,815,000

136

Endnotes

1 World Health Organization, Global Tuberculosis Control, Report 2001. WHO, Communicable Diseases, WHO/CDS/TB/2001.287, Geneva, 2001. 2 World Health Organization, Global DOTS Expansion Plan. Progress in TB Control in High-Burden Countries: One Year after Amsterdam. WHO/CDS/STB/2001.11, World Health Organization, Geneva, 2001. 3 World Health Organization. Global Tuberculosis Control, Report 2001. WHO, Communicable Diseases, WHO/CDS/TB/2001.287, Geneva, 2001. 4 Ministerio de Salud (Peru). Impacto Económico de la Tuberculosis en el Perú 1999. Ministerio de Salud, Proyecto Vigía, Lima, 2001; and Suarez et al., “Feasibility and cost-effectiveness of standardised second-line drug treatment for chronic tuberculosis patients: a national cohort study in Peru.” Lancet, 2002, June 8; 359(9322):1980 – 9.

5 World Health Organization, Anti-Tuberculosis Drug Resistance in the World, Report n°2, Prevalence and Trends. The WHO/IUATLD global project on anti-tuberculosis drug resistance surveillance, WHO/CDS/TB/2000.278, Geneva, 2000. 6 Espinal, M. et al., “Global Trends in Resistance to Antituberculosis Drugs.” New England Journal of Medicine, 2001; 344(17): 1294-1303. 7 World Health Organization, Global DOTS Expansion Plan. Progress in TB Control in High-Burden Countries: One Year after Amsterdam. WHO/CDS/STB/2001.11, World Health Organization, Geneva, 2001. 8 Ministerio de Salud (Peru), Impacto Económico de la Tuberculosis en el Perú 1999. Ministerio de Salud, Proyecto Vigía, Lima, 2001. 9 Partners In Health and World Health Organization, Global Plan to Stop TB (Chapter 5). New York: Open Society Institute, 2001. 10 World Health Organization, Global DOTS Expansion Plan. Progress in TB Control in High-Burden Countries: One Year after Amsterdam. WHO/CDS/STB/2001.11, World Health Organization, Geneva, 2001. 11 Ministerio de Salud (Peru). Impacto Económico de la Tuberculosis en el Perú 1999. Ministerio de Salud, Proyecto Vigía, Lima, 2001. 12 World Health Organization, Global TB Drug Facility: a Global Mechanism to Ensure Universal, Uninterrupted Access to Quality TB Drugs for DOTS Implementation. WHO (Stop TB Secretariat), Geneva, 2000. 13 World Health Organization, Involving private practitioners in tuberculosis control—issues, interventions, and emerging policy framework. WHO/CDS/TB/2001.285, WHO, 2001. 14 Ibid.

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15 World Health Organization, Global Tuberculosis Control, Report 2002. WHO, Communicable Diseases, WHO/CDS/TB/2001.287, Geneva, 2001. 16 World Health Organization, Involving private practitioners in tuberculosis control—issues, interventions, and emerging policy framework. WHO/CDS/TB/2001.285, WHO, 2001.

17 World Health Organization, Involving private practitioners in tuberculosis control—issues, interventions, and emerging policy framework. WHO/CDS/TB/2001.285, WHO, 2001. 18 World Health Organization, Global Tuberculosis Control, Report 2002: Surveillance, Planning, Financing. WHO, Communicable Diseases, WHO/CDS/TB/2002.295, Geneva, 2002 19 González-Ochoa E. et al., “Impacto Económico-Social del Programa Nacional de Control de la Tuberculosis (PNCT) en la Población Cubana.” Caderno de Saúde Pública, 2000; 16(3):687 – 699. 20 Ministerio de Salud (Peru), Tuberculosis en el Perú, Informe 1999. Ministerio de Salud, Dirección general de salud de las personas, dirección del programa nacional de control de enfermedades transmisibles, programa nacional de control de tuberculosis, Lima, Peru, 2000. 21 UNAIDS, Report on the Global HIV/AIDS Epidemic. Joint United Nations programme on HIV/AIDS, Geneva, June 2000. 22 World Health Organization, Global Tuberculosis Control, Report 2001. WHO, Communicable Diseases, WHO/CDS/TB/2001.287, Geneva; 2001 and World Health Organization, Global Tuberculosis Control, Report 2000. WHO, Communicable Diseases, WHO/CDS/TB/2000.275, Geneva, 2000. 23 Dye C. et al., World-Wide Incidence of Multidrug-Resistant Tuberculosis (submitted April 2000); and data prepared by Mercedes Becerra (Harvard Medical School, 2000), completed with World Health Organization, Anti-Tuberculosis Drug Resistance in the World, Report no2 Prevalence and Trends, WHO/IUATLD, 2000. 24 Espinal et al., “Global Trends in Resistance to Antituberculosis Drugs.” New England Journal of Medicine, Vol. 344, n°17, April 26, 2001. 25 World Health Organization, Anti-Tuberculosis Drug Resistance in the World, Report n°2, Prevalence and Trends. The WHO/IUATLD global project on anti-tuberculosis drug resistance surveillance, WHO/CDS/TB/2000.278, Geneva, 2000. 26 Becerra, M.C. et al., “Redefining MDR-TB Transmission ‘Hot-Spots.’” International Journal of Tuberculosis and Lung Diseases, 4(5):387 – 394, 2000. 27 World Health Organization, Progress report on ProTEST projects 2000 – 2001. 2002. Unpublished report. 28 World Health Organization, Global Tuberculosis Control, Report 2001. WHO, Communicable Diseases, WHO/CDS/TB/2001.287, Geneva, 2001 29 Dye C. et al., “Global Burden of Tuberculosis (Estimated Incidence, Prevalence and Mortality by Country).” Journal of the American Medical Association, Vol. 282, n°7, August 18, 1999. 30 World Health Organization, Global Tuberculosis Control, Report 2001. WHO, Communicable Diseases, WHO/CDS/TB/2001.287, Geneva, 2001.

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31 World Health Organization, Global Tuberculosis Control, Report 2001. WHO, Communicable Diseases, WHO/CDS/TB/2001.287, Geneva, 2001. 32 World Health Organization, Global Tuberculosis Control, Report 2002: Surveillance, Planning, Financing. WHO, Communicable Diseases, WHO/CDS/TB/2002.295, Geneva, 2002.

33 World Health Organization, Global Tuberculosis Control, Report 2001. WHO, Communicable Diseases, WHO/CDS/TB/2001.287, Geneva, 2001. 34 Harvard Medical School, The Global Impact of Drug-Resistant Tuberculosis. Program in Infectious Diseases and Social Change, Department of Social Medicine, Harvard Medical School (with the Open Society Institute), 1999.

35 World Health Organization, Global Tuberculosis Control, Report 2002: Surveillance, Planning, Financing. WHO, Communicable Diseases, WHO/CDS/TB/2002.295, Geneva, 2002. 36 World Health Organization, Global Tuberculosis Control, Report 2001. WHO, Communicable Diseases, WHO/CDS/TB/2001.287, Geneva, 2001; and World Health Organization, Global Tuberculosis Control, Report 2002: Surveillance, Planning, Financing. WHO, Communicable Diseases, WHO/CDS/TB/2002.295, Geneva, 2002. 37 World Health Organization, Global Tuberculosis Control, Report 2001. WHO, Communicable Diseases, WHO/CDS/TB/2001.287, Geneva, 2001. 38 Ministerio de Salud (Peru), Impacto Económico de la Tuberculosis en el Perú 1999. Ministerio de Salud, Proyecto Vigía, Lima, 2001.

39 Global Alliance for TB Drug Development, The Economics of TB Drug Development. 2001.

40 World Health Organization, Global Tuberculosis Control, Report 2001. WHO, Communicable Diseases, WHO/CDS/TB/2001.287, Geneva, 2001. 41 Partners In Health and World Health Organization, Global Plan to Stop TB. Open Society Institute, New York, 2002. 42 Partners In Health and World Health Organization, Global Plan to Stop TB. Open Society Institute, New York, 2002. 43 Philippe Onyebujoh (WHO ProTEST programs). Personal communication. November 2001. 44 Progress report on ProTEST Projects 2000 – 2001. Provided by Dr. Philippe Onyebujoh, ProTEST project coordinator at WHO. 45 Data communicated by Christopher Dye, epidemiologist with the Department of Communicable Diseases (WHO, Geneva).

46 Progress report on ProTEST projects 2000 – 2001. Provided by Dr. Philippe Onyebujoh (ProTEST project coordinator at WHO). 47 Khomenko A.G. et al., “Cost-Benefit of Detection and Treatment Patients with Tuberculosis in Ivanovo Oblast, Russian Federation.” Problemy Tuberkulosa, 1998; (3):9 – 13.

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48 Wilkinson, D., Floyd, K., Gilks, C.F., “Costs and Cost-Effectiveness of Alternative Tuberculosis Management Strategies in South Africa—Implications for Policy.” South African Medical Journal, Vol. 87, No. 4, April 1997. 49 World Health Organization, Treatment of Tuberculosis: Guidelines for National Programmes. WHO/TB/97.220, World Health Organization, Geneva, 1997. 50 World Health Organization, Treatment of Tuberculosis: Guidelines for National Programmes. WHO/TB/97.220, World Health Organization, Geneva, 1997. 51 Bell, J.C., Rose, D.N., Sacks, H.S., “Tuberculosis Therapy for HIV-Infected People in sub-Saharan Africa is Cost-Effective.” AIDS, 13:1549 – 1556, 1999. 52 Communication from Dr. Ian Smith (WHO Stop TB Initiative). 2001. 53 Dr. Salah-Eddine Ottmani, WHO (Geneva). Personal communication. 2001. 54 Partners In Health and World Health Organization, Global Plan to Stop TB (Chapter 3). New York: Open Society Institute, 2001. 55 Dr. Paul Nunn, WHO (Geneva). Personal communication. February 2002. 56 Progress Report on ProTEST Projects 2000 – 2001. Provided by Dr. Philippe Onyebujoh (ProTEST project coordinator at WHO).

57 Data communicated by Dr. Ruffino Neto of the Federal NTP in 2001. 58 Suarez et al., “Feasibility and Cost-Effectiveness of Standardised Second-Line Drug Treatment for Chronic Tuberculosis Patients: A National Cohort Study in Peru.” Lancet, 2002, June 8; 359(9322):1980 – 9. 59 Bell, J.C., Rose, D.N., Sacks H.S., “Tuberculosis Therapy for HIV-Infected People in sub-Saharan Africa is Cost-Effective.” AIDS, 13:1549 – 1556, 1999. 60 Wilkinson, D., Floyd, K., Gilks, C.F., “Costs and Cost-Effectiveness of Alternative Tuberculosis Management Strategies in South Africa—Implications for Policy. South African Medical Journal, Vol. 87, No. 4, April 1997. 61 Khomenko A.G. et al., “Cost-Benefit of Detection and Treatment of Patients with Tuberculosis in Ivanovo Oblast, Russian Federation.” Problemy Tuberkulosa, 1998; (3):9 – 13. 62 González-Ochoa E. et al., “Impacto Económico-Social del Programa Nacional de Control de la Tuberculosis (PNCT) en la Población Cubana.” Caderno de Saúde Pública, 16(3):687 – 699, jul. – set. 2000. 63 World Health Organization, Global Tuberculosis Control, Report 2001. WHO, Communicable Diseases, WHO/CDS/TB/2001.287, Geneva, 2001; and World Health Organization, Global Tuberculosis Control, Report 2000. WHO, Communicable Diseases, WHO/CDS/TB/2000.275, Geneva, 2000. 64 National Tuberculosis Control Programme (Egypt), Royal Tropical Institute (Netherlands), and World Health Organization, Cost-Effectiveness of Directly-Observed Treatment, Short Course (DOTS) for Tuberculosis in Egypt. World Health Organization, Eastern Mediterranean Regional Office, Alexandria, Egypt, August 1999.

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65 National Tuberculosis Control Programme (Syria) and World Health Organization, Study into the Cost, Effectiveness and Cost-Effectiveness of Directly-Observed Treatment with Short-Course Chemotherapy (DOTS) for Tuberculosis in Syria. World Health Organization, Eastern Mediterranean Regional Office, Alexandria, Egypt, August 1999. 66 World Health Organization, Global DOTS Expansion Plan. Progress in TB Control in High-Burden Countries: One Year after Amsterdam. WHO/CDS/STB/2001.11, World Health Organization, Geneva, 2001. 67 Ministry of Health, A Three-Year Development Plan for the National Tuberculosis/Leprosy Programme (July 1997 – June 2000). Ministry of Health (The United Republic of Tanzania), 1997. 68 World Health Organization, Joint Tuberculosis Programme Review, India. World Health Organization, SEA-TB-224, Regional Office for South-East Asia, New Delhi, May 2000. 69 Ministry of Health, Development Plan of the National Tuberculosis Control Programme for the Period 2000 – 2004. Ministry of Health of the S.R. of Vietnam, Hanoï, 2000. 70 World Health Organization, Global Tuberculosis Control, Report 2002. WHO, Communicable Diseases, WHO/CDS/TB/2002.295, Geneva, 2002. 71 Progress report on ProTEST projects 2000 – 2001. Provided by Dr. Philippe Onyebujoh (ProTEST project coordinator at WHO).

72 World Health Organization, Global Tuberculosis Control, Report 2000. WHO, Communicable Diseases, WHO/CDS/TB/2000.275, Geneva, 2000. 73 Dye C. et al., “Global Burden of Tuberculosis (Estimated Incidence, Prevalence and Mortality by Country), Journal of the American Medical Association, Vol. 282, n°7, August 18, 1999. 74 Federal Ministry of Health, Operational Plan for Tuberculosis Control in Nigeria (2001 – 2005). Federal Ministry of Health (Department of Primary Health Care and Disease Control, National Tuberculosis and Leprosy Control Programme), 2000. 75 Ministry of Health, A Three-Year Development Plan for the National Tuberculosis/Leprosy Programme (July 1997 – June 2000). Ministry of Health (The United Republic of Tanzania), 1997. 76 World Bank, Tuberculosis Control Project (India), January 1997. 77 World Bank, Tuberculosis Control Project (India), January 1997. 78 UNAIDS, Report on the Global HIV/AIDS Epidemic. Joint United Nations Programme on HIV/AIDS, Geneva, June 2001. 79 World Bank, World Development Indicators 2000. Washington, D.C. 80 Data provided by WHO, Geneva (through Katherine Floyd of Stop TB). 81 Based on data provided by national TB control programs of these regions, and by Katherine Floyd at WHO.

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82 Ministerio de Salud (Peru), Impacto Económico de la Tuberculosis en el Perú 1999. Ministerio de Salud, Proyecto Vigía, Lima, 2001; and Socios En Salud, data for 75 patients during the 1996 – 2000 period (personal communication and data presented at the conference on MDR-TB in Lima, Peru, January 2001). 83 Bell, J.C., Rose, D.N., Sacks H.S., “Tuberculosis Therapy for HIV-Infected People in sub-Saharan Africa is Cost-Effective. AIDS, 13:1549 – 1556, 1999. 84 Ministerio de Salud (Peru), Impacto Económico de la Tuberculosis en el Perú 1999. Ministerio de Salud, Proyecto Vigía, Lima, 2001. 85 Dr. Pokaya Ginwalla. Presentation of the ProTEST program in Zambia. Annual meeting of the International Union Against Tuberculosis and Lung Diseases, Paris, November 2001. 86 National Tuberculosis Control Programme (Syria) and World Health Organization, Study into the Cost, Effectiveness and Cost-Effectiveness of Directly-Observed Treatment with Short-Course Chemotherapy (DOTS) for Tuberculosis in Syria. World Health Organization, Eastern Mediterranean Regional Office (Alexandria, Egypt), August 1999. 87 National Tuberculosis Control Programme (Egypt), Royal Tropical Institute (Netherlands) and World Health Organization, Cost-Effectiveness of Directly-Observed Treatment, Short Course (DOTS) for Tuberculosis in Egypt. World Health Organization, Eastern Mediterranean Regional Office (Alexandria, Egypt), August 1999. 88 Snyder, D., Cost Savings from New Strategies for Tuberculosis Control in Poland. February 1998. Unpublished. 89 World Health Organization, Global DOTS Expansion Plan. Progress in TB Control in High-Burden Countries: One Year after Amsterdam. WHO/CDS/STB/2001.11, World Health Organization, Geneva, 2001. 90 World Health Organization, Global DOTS Expansion Plan. Progress in TB Control in High-Burden Countries: One Year after Amsterdam. WHO/CDS/STB/2001.11, World Health Organization, Geneva, 2001. 91 World Health Organization, Global Tuberculosis Control, Report 2002. WHO, Communicable Diseases, WHO/CDS/TB/2002.295, Geneva, 2002. 92 Progress report on ProTEST Projects 2000 – 2001. Provided by Dr. Philippe Onyebujoh (ProTEST project coordinator at WHO). 93 World Health Organization, Global Tuberculosis Control, Report 2001. WHO, Communicable Diseases, WHO/CDS/TB/2001.287, Geneva, 2001. 94 World Health Organization, Global Tuberculosis Control, Report 2002: Surveillance, Planning, Financing. WHO, Communicable Diseases, WHO/CDS/TB/2002.295, Geneva, 2002. 95 World Health Organization, Global Tuberculosis Control, Report 2001. WHO, Communicable Diseases, WHO/CDS/TB/2001.287, Geneva, 2001. 96 World Health Organization, Global Tuberculosis Control, Report 2002: Surveillance, Planning, Financing. WHO, Communicable Diseases, WHO/CDS/TB/2002.295, Geneva, 2002.

97 World Health Organization, Global Tuberculosis Control, Report 2000. WHO, Communicable Diseases, WHO/CDS/TB/2000.275, Geneva, 2000.

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98 Murray, C.J.L., Styblo, K., and Rouillon, A., “Tuberculosis in Developing Countries: Burden, Intervention and Cost.” Journal of the International Union Against Tuberculosis and Lung Disease, Vol. 65, No. 1, March 1990. 99 Progress Report on ProTEST Projects 2000-2001. Provided by Dr. Philippe Onyebujoh (ProTEST project coordinator at WHO). 100 ONUSIDA/OMS, “Le Point sur l’Epidémie de SIDA.” Décembre 2001, ONUSIDA 01/75F. 101 The World Bank, World Development Indicators 2001. Washington D.C. 102 The World Bank, World Development Indicators 2001. Washington D.C. 103 World Health Organization, Global Tuberculosis Control, Report 2002. WHO, Communicable Diseases, WHO/CDS/TB/2002.295, Geneva, 2002. 104 World Health Organization, Issues Relating to the Use of BCG in Immunisation Programmes (A Discussion Document). WHO/V&B/99.23, World Health Organization, Geneva, 1999. 105 World Health Organization, Issues Relating to the Use of BCG in Immunisation Programmes (A Discussion Document). WHO/V&B/99.23, World Health Organization, Geneva, 1999. 106 Katherine Floyd, WHO. Personal communication. 2001. 107 González-Ochoa E. et al., “Impacto Económico-Social del Programa Nacional de Control de la Tuberculosis (PNCT) en la Población Cubana.” Caderno de Saúde Pública, 16(3):687 – 699, jul. – set., 2000. 108 Personal communication by Dr. José Ramón Cruz of the Pan-American Health Organization. 2001. 109 World Health Organization, Global Tuberculosis Control, Report 2001. WHO, Communicable Diseases, WHO/CDS/TB/2001.287, Geneva, 2001. 110 ONUSIDA/OMS, Le point sur l’épidémie de sida. Décembre 2001, ONUSIDA 01/75F. 111 World Health Organization, Managing the Dual Epidemics of Tuberculosis and HIV/AIDS (A Review in Challenge and Response in Five Countries: Brazil, India, Indonesia, Kenya, Thailand). Global Tuberculosis Programme, WHO/TB/98.243, World Health Organization, March 1998. 112 Bell, J., Rose, D., and Sacks, H., “Tuberculosis Preventive Therapy for HIV-Infected People in sub-Saharan Africa is Cost-Effective.” AIDS, 13:1549 – 1556, 1999. 113 Bell, J., Rose, D., and Sacks, H., “Tuberculosis Preventive Therapy for HIV-Infected People in sub-Saharan Africa is Cost-Effective.” AIDS, 13:1549 – 1556, 1999. 114 World Health Organization, Global Plan to Stop TB. Open Society Institute: New York, 2001. 115 Philippe Onyebujoh (WHO ProTEST programs coordinator). Personal communication. November 2001. 116 UNAIDS, Report on the Global HIV/AIDS Epidemic. Joint United Nations Programme on HIV/AIDS, Geneva, June 2001.

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117 Ministerio de Salud (Peru), Impacto Económico de la Tuberculosis en el Perú 1999. Ministerio de Salud, Proyecto Vigía, Lima, 2001. 118 Dr. Pokaya Ginwalla. Presentation of the ProTEST program in Zambia. Annual meeting of the International Union Against Tuberculosis and Lung Diseases, Paris, November 2001.

119 Dye, C. et al., Worldwide Incidence of Multidrug-Resistant Tuberculosis. Submitted April 2000; and data prepared by Mercedes Becerra (Harvard Medical School, 2000), completed with World Health Organization, Anti-Tuberculosis Drug Resistance in the World, Report n°2 Prevalence and Trends, WHO/IUATLD, 2000. 120 Ministerio de Salud (Peru), Impacto Económico de la Tuberculosis en el Perú 1999. Ministerio de Salud, Proyecto Vigía, Lima, 2001.

121 Suarez et al., Feasibility and Cost-Effectiveness of Standardised Second-Line Drug Treatment for Chronic Tuberculosis Patients: A National Cohort Study in Peru. Lancet, 2002, June 8; 359(9322):1980 – 9; Ministerio de Salud (Peru), Impacto Económico de la Tuberculosis en el Perú 1999. Ministerio de Salud, Proyecto Vigía, Lima, 2001; and data from Socios En Salud, which has been treating MDR-TB patients in the northern slums of Lima since 1996. 122 Ministerio de Salud (Peru), Impacto Económico de la Tuberculosis en el Perú 1999. Ministerio de Salud, Proyecto Vigía, Lima, 2001. 123 Ministerio de Salud (Peru), Impacto Económico de la Tuberculosis en el Perú 1999. Ministerio de Salud, Proyecto Vigía, Lima, 2001; and data from Socios En Salud, Lima, Peru, January 2001. 124 Becerra, M.C. et al., “Redefining MDR-TB Transmission ‘Hot-Spots.’” International Journal of Tuberculosis and Lung Diseases, 2000; 4(5):387 – 394. 125 World Health Organization, Anti-Tuberculosis Drug Resistance in the World: The WHO/IUATLD Global Project on Anti-Tuberculosis Drug Resistance Surveillance 1994 – 1997. WHO/TB/97.229. Geneva: World Health Organization, 1997. 126 World Health Organization, Anti-Tuberculosis Drug Resistance in the World: The WHO/IUATLD Global Project on Anti-Tuberculosis Drug Resistance Surveillance 1994 – 1997. WHO/TB/97.229. Geneva: World Health Organization, 1997. 127 World Health Organization, The Global Tuberculosis Research Initiative: Research to Make a Difference. Global Program against Tuberculosis, WHO/TB/98.248, February 1998. 128 Baris E, Tuberculosis in Times of Health Sector Reform. Editorial, International Journal of Tuberculosis and Lung Diseases, vol.4 (7), July 2000, pp. 593 – 594. 129 World Health Organization. The Economic Impacts of Tuberculosis. Ministerial conference, Amsterdam, 22 – 24 March 2000; and the Stop TB Initiative 2000 series, WHO/CDS/STB/2000.5, World Health Organization, Geneva, 2000. 130 Sawert, H. et al., “Costs and Benefits of Improving Tuberculosis Control: The Case of Thailand.” Social Science and Medicine, 1997;44(12): 1805 – 1816. 131 Murray, C.J.L., Styblo, K., and Rouillon, A., “Tuberculosis in Developing Countries: Burden, Intervention and Cost.” Journal of the International Union Against Tuberculosis and Lung Disease, 1990;65( 1):6 – 24.

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132 World Health Organization, Joint Tuberculosis Programme Review, India. World Health Organization, SEA-TB-224, Regional office for South-East Asia, New Delhi, May 2000. 133 Ministerio de Salud (Peru), Impacto Económico de la Tuberculosis en el Perú 1999. Ministerio de Salud, Proyecto Vigía, Lima, 2001. 134 Ministerio de Salud (Peru), Impacto Económico de la Tuberculosis en el Perú 1999. Ministerio de Salud, Proyecto Vigía, Lima, 2001. 135 World Health Organization, Joint Tuberculosis Programme Review, India. World Health Organization, SEA-TB-224, Regional office for South-East Asia, New Delhi, May 2000. 136 Ministerio de Salud (Peru), Impacto Económico de la Tuberculosis en el Perú 1999. Ministerio de Salud, Proyecto Vigía, Lima, 2001. 137 Sinanovic E. et al., Cost and Cost-Effectiveness of Community-Based Care for Tuberculosis in Cape Town, South Africa. Unpublished. 138 Wilkinson, D., Floyd, K., and Gilks, C.F. “Costs and Cost-Effectiveness of Alternative Tuberculosis Management Strategies in South Africa—Implications for Policy.” South African Medical Journal, 1997; 87( 4): 451 – 455. 139 Floyd, K., Wilkinson, D., and Gilks, C. “Comparison of Cost-Effectiveness of Directly Observed Treatment (DOT) and Conventionally Delivered Treatment for Tuberculosis: Experience from Rural South Africa.” British Medical Journal 1997;315:1407 – 1411. 140 Ministère de la Santé (RD du Congo), Plan Directeur Quinquennal 2001 – 2005 de Lutte contre la Tuberculose en République Démocratique du Congo. Ministère de la Santé, Bureau National de la Tuberculose, Kinshasa, Novembre 2000. 141 Ministry of Health (Ethiopia), Plan of Action 2001. Tuberculosis and Leprosy Control Team, Disease Prevention and Control Department, Ministry of Health, Addis Abbaba, 2000. 142 Sawert, H., Cost Analysis and Cost Containment in Tuberculosis Control Programmes: The Case of Malawi. WHO Taskforce on Economics, World Health Organization, Global Tuberculosis Programme, Geneva, May 1996. 143 Anthony Harries (National TB Control Program). Personal communication to Dr. Maarten Bosman (Stop TB Initiative, and coordinator of Chapter 3 of the Global Plan to Stop TB). 144 Federal Ministry of Health, Operational Plan for Tuberculosis Control in Nigeria (2001 – 2005). Federal Ministry of Health, Department of Primary Health Care and Disease Control, National Tuberculosis and Leprosy Control Programme (NTBLCP), 2000. 145 Bell, J.C., Rose, D.N., and Sacks, H.S., “Tuberculosis Therapy for HIV-Infected People in sub-Saharan Africa is Cost-Effective.” AIDS 1999;13:1549 – 1556. 146 Kaddar, M., Aspects Economiques de la Lutte contre la Tuberculose au Maroc (Rapport Annexe). Revue du Programme National de Lutte Antituberculeuse au Maroc, Groupe Mixte Ministère de la Santé-OMS, Mai – Juin 1996.

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147 Snyder, D., Cost Savings from New Strategies for Tuberculosis Control in Poland. February 1998. Unpublished. 148 Ministerio de Salud (Peru). Impacto Económico de la Tuberculosis en el Perú 1999. Ministerio de Salud, Proyecto Vigía, Lima, 2001. 149 World Health Organization, Joint Tuberculosis Programme Review, India. World Health Organization, SEA-TB-224, Regional office for South-East Asia, New Delhi, May 2000. 150 World Bank, Tuberculosis Control Project (India). January 1997. 151 Ministry of Health (Cambodia), NTP in Cambodia: A TB Control Strategy for 2000 – 2004. In Outline of Health Development Plan 2001 – 2005, Ministry of Health (Cambodia), 2000. 152 Ministry of Health (Cambodia), Development Plan of the National Tuberculosis Programme of the Ministry of Health of Cambodia (01.01.1993 – 31.12.1997).

153 Ministry of Health (Vietnam), Development Plan of the National Tuberculosis Control Programme for the Period 2000 – 2004. Ministry of Health of the S.R of Vietnam, Hanoï, 2000. 154 Department of Health (Philippines), Universal DOTS in 2004, Department of Health (TB Control), 2000. 155 Ministry of Public Health (Thailand), Tuberculosis Control in Thailand (1997 – 2002), Five-Year Development Plan. Tuberculosis Division, Department of Communicable Disease Control, Ministry of Public Health, 1997.

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