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Influence of PBF Indicators on Health CoverageKathy Kantengwa M.D, MPA; PBF advisor, MSH

Montreux, November 2010

Rwanda IHSS Project

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Authors

Ndizeye, Cedric , USAID/IHSSP, Rwanda

De Naeyer, Ludwig , USAID/IHSSP, Rwanda

Kantengwa, Kathy , USAID/IHSSP, Rwanda

Collins, David , USAID/IHSSP, Rwanda

Karengera, Steven , MOH, Rwanda

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Outline

Background

Challenges/opportunities

Data analysis objective

What indicators defined

Results

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Opportunities

Global agenda: MDG goals: addressing the needs of the poor and for specific

health problems

National Agenda:

Vision 2020/PRSP-EDPRS goals (Poverty reduction papers) Universal Health Coverage: reduce financial barriers to quality

essential health services (minimum package) at all levels Expand the offer of preventive health services: Exploit positive

externalities Health providers motivated

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Data analysis objective

Has PBF contributed to improvement in quantity and quality indicators related to MDG 4 – 5 goals?

Methodology:Analysis of time-series and before-after evaluation of 3 different datasets (impact evaluation data, DHS data, Routine PBF indicators data)

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What indicators are defined? Health center output indicators: usually less than 15

14 maternal and child health PBF output indicators

­ A set of indicators consists of the number of visits to the facility

­ A set of indicators of the clinical content Many quality indicators (checklist of 120-150)

Each indicator associated with a specific price, but quality

indicators are modifiers

Eligibility for premium on HIV indicators subject to maintaining

our improving primary care services (PEFAR to improve Health

systems)

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Focus on getting health impact through PBF

By setting indicators which will increase the productivity and quality of care: Identifying the few most critical interventions which, if delivered at

the right time and place to the right people, will have the greatest impact

Pre-determine system requirements for achieving adequate scale Identify bottlenecks across the system building blocks target PBF efforts through indicators against those bottlenecks Validate reported data including quality

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PBF on MCH health coverage: Services data (PBF database, Rwanda)

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Increase in Volume of Services (after 39 months)

PBF Indicator January 2006 average/month/

health center( 258 health centers on

average)

March 2009average/month/

health center(297 health centers on

average)

Percentage increase (linear/log R2)

Institutional Deliveries

21 39.7 89%(log 0.77)

New Curative Consultations

985 1835 86.3%(log 0.28)

ANC new cases 100.8 76.2 -24%(log 0.05)

Family Planning new users

15.5 58.6 278%(linear 0.79)

Family Planning users at the end of the month

175.2 1005.6 473.9%(linear 0.98)

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PBF on MCH health coverage: Impact evaluation WB

(P. Basinga, P Getner & al, 2010: Paying Primary Health care centers for performance in Rwanda)

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Estimated impact of PBF on maternal and child health care services

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Trend of institutional delivery for treatment and control facilities

36.3

49.7

34.9

55.6

30.0

40.0

50.0

60.0

Baseline (2006) Follow up (2008)

Prop

ortio

n of

of i

nstit

ution

al d

eliv

erie

s

Control facilities Treatment (PBF facilities)

7.3 % increasedue to PBF

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Trend of prenatal care quality between treatment and control facility (2006-2008)

-0.10

0

-0.13

0.15

-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15

0.20

Baseline (2006) Follow up (2008)

Stan

dard

ized

Pre

nata

l eff

ort s

core

Control facilities Treatment (PBF facilities)

15 % Standard deviation increase due to PBF

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Rwanda health sector performance status: (Rwanda DHS data)

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Progress based on DHS

Source: Rwanda DHS 2005 and 2008

Indicators DHS-2000 DHS- 2005 DHS-2008Contraceptive prevalence: All methods 17% 36%Contraceptive prevalence: Modern methods 4% 10% 27%Antenatal Care 94% 96%Delivery in Health Centers 26% 39% 52%Infant Mortality rate 10786/1000 live births 62/1000 live births

Under-Five Mortality rate 196152/1000 live births 103/1000 live birthsMaternal Mortality rate 1071 Anemia Prevalence : Children 56% 48%Anemia Prevalence : Women 33% 27%Malaria prevalence: Children - 2.10%Malaria prevalence: Women - 1.10%Vaccination : All 75% 80.40%Vaccination : Measles 86% 90%Fertility 6.1 children 5.5 Children

18

85

107

86

62

28

0

20

40

60

80

100

120

1990 2000 2005 2008 2012

INFANT MORTALITY (PER 1000)

Infant mortality rate (deaths per 1,000)

1990 2000 2005 2008 2012

120

100

80

60

40

20

0

28% decrease over 2 years

62

28

8685

107

19

151

196

152

103

50

0

50

100

150

200

250

1990 2000 2005 2008 2012

UNDER FIVE CHILDREN MORTALITY (PER 1000)

Under 5 children mortality rate (deaths per 1,000)

1990 2000 2005 2008 2012

250

200

150

100

50

0

33% decline over 2 years

103

50

152

151

196

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Comparison of Maternal Mortality Ratio and Facility-Based Deliveries

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13

410

27

70

0

10

20

30

40

50

60

70

80

1990 2000 2005 2008 2015

Modern contraception prevalence (% 15-49 year-old women)

1990 2000 2005 2008 2015

80

70

60

50

40

30

20

10

0

70

2763% increase over two years

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4 10

22

2631

39

52

95

0

10

20

30

40

50

60

70

80

90

100

1990 2000 2005 2008 2015

Births attended by skilled health personnel (% of births)

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95

52

31

1990 2000 2005 2008 2015

100

90

80

70

60

50

40

30

20

10

0

39

25% increase over two years

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Conclusion

PBF positively affects health coverage of preventive services PBF is a systems strengthening reform that can help

accelerate the achievement of MDGs 4 &5 Target for choice of indicators: few most critical interventions

which, if delivered at the right time and place to the right people, will have the greatest impact

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Lessons learnt

PBF can lead to: A significant increase in service production A significant increase in quality of services.

PBF service data are reliable for systems analysis or programs impact analysis

With PBF, Health facilities reports are complete, timely and accurate : improved HMIS

Clearly defined and agreed upon measurable goals must be linked to routine and transparent reporting with an effective system for validating data.

For services that depend more on patient behavior (4 ANC visits), need community interventions

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Limitations

Denominator definition for routine data analysis

(Change of the health pyramid?)

Routine data underestimated, private for profits

sector health facilities are excluded

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Data restrictions

The data shown in this presentation should not be quoted without permission of the authors.

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