presentation: rapid reductions in premature mortality in urban india
DESCRIPTION
Presented at the Asian Development Bank (ADB) by Dr. Prabhat Jha on 23 March 2015 during the joint SASS-Health Sector Group event.TRANSCRIPT
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Prabhat Jha
Centre for Global Health Research (CGHR) St. Michaels Hospital and Dalla Lana School of Public Health, University of
Toronto
[email protected] Twitter: Countthedead
ADB Consultation, March 23, 2015
Disclaimer: The views expressed in this paper/presentation are the views of the author and do not necessarily reflect the views or policies of the Asian Development Bank (ADB), or its Board of Governors, or the governments they represent. ADB does not guarantee the accuracy of the data included in this paper and accepts no responsibility for any consequence of their use. Terminology used may not necessarily be consistent with ADB official terms.
Avoidable mortality in urban and rural India: Estimates from
the Million Death Study
mailto:[email protected]
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Conclusions: Better COD systems Most deaths in India occur out of hospital and in rural areas. Appropriate systems are needed to measure the causes of deaths out of hospital. A random sample of deaths (home/hospital) is key to interpreting national results. Move beyond hospital-based comparison studies. Adopt population-based focus. Make Verbal autopsy (VA) better, simpler, faster and cheaper. Link deaths to health services and biological confirmation.
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World,
1970-
2010
Low-
income,
1970-2010
High-
income
countries,
1970-2010
50-69
0-49
5-49
0-4 Source: Norheim, Jha,
Addis et al, Lancet 2014
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* World 2010: 6%
* World 1950: 25%
Source: Norheim, Jha, Addis et al, Lancet 2014
1970-2010 trends in risk of death, 25
countries, age 0-4 years
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1970-2010 trends in risk of death, 25
countries, age 5-49 years
Source: Norheim, Jha, Addis et al, Lancet 2014
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1970-2010 trends in risk of death, 25
countries, age 50-69 years
Source: Norheim, Jha, Addis et al, Lancet 2014
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Source: Norheim, Jha, Addis et al, Lancet 2014
HIV Vodka War
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Most deaths in low and middle-income countries occur at home, and without medical attention, so causes of death (COD) are unknown Only 3% of worlds children who died in 2010 had
medically-certified COD
40% of dead African children never made it to a facility
Weak civil and vital registration systems (CRVS) 81 of all 194 countries in the world have high quality
death and COD data (Only 4/46 countries in Africa report deaths to the United Nations)
India: 10 million deaths, 5 M crude registered, causes not
Most causes of death are unknown
Source : Jha, BMC Med, 2014
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Best: Ensure that 100% of all deaths are medically certified Only possible when most deaths occur in hospitals
Such coverage took ~100 years in high-income countries
Practical: Representative samples with continuous assessments of COD using verbal autopsies every 1-3 years Hospital COD can be strengthened in parallel, but can
yield misleading results (eg malaria deaths)
The Solution
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for sanitary purposes it is indispensable
to know the relative mortality in small
and, as far as possible, well-defined
tracts to ascertain the death rates in each
of these communities; to see how far this
arises from preventable causes; and to
apply the remedies
Sanitary Commissioner of the
Government of India, 1869
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MILLION DEATH STUDY IN INDIA 1. Visit 1 M homes (true snapshot of India) with a recent death &
ask standard questions (esp. for child deaths) and get a local language narrative
2. Use non-medical surveyors (add electronic entry + GPS) 3. Web-based double coding by 350 doctors (guidelines, +
adjudication and other strict quality control) 4. Study all diseases, work with census dept, keep costs
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MILLION DEATH STUDY: current status (M=millions)
0.13 M deaths coded and reported for 2001-3 (including 25,000 child deaths) with RHIME method
0.2 M crude (household opinion) causes of death 1997-2003 reported
0.15M deaths for 2004-6 coded by Feb 2015
0.30 M deaths for 2010-13 to be coded by Fall 2015
400 physicians coding in web-based system (double coding with about 20% reconciliation and 10% adjudicated)
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Low ill defined deaths with RHIME (VA)
Low ill-defined rates (
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Similar MDS results of RGI staff and independent
re-sample teams ages 5-69 years Disease MDS RE-
SAMPLED OR: Re-sampled vs. original
Malaria 3.3 2.2 NS Tuberculosis 9.0 7.7 NS HIV/STI 0.7 0.4 NS Other infectious diseases1 11.1 12.9 P
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Rank order OR: hospital vs. home Disease Home Hospital
Malaria 3.7% 3.0% NS Tuberculosis 10.8% 5.9% p
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Different MDS results of rural and urban
ages 5-69 years
Rank order OR: rural vs. urban Disease Rural Urban
Malaria 3.6 2.1 P
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MILLION DEATH STUDY: selected results (M=Millions, K=thousands)
4-12M girls aborted before birth since 1980 (1/2 of these since 2000)
1M smoking deaths (more than expected) and 0.1M alcohol deaths
200K malaria deaths: WHO predicted only 15K
100K HIV deaths: UNAIDS predicted 400K
60K pedestrian traffic deaths: Police estimate=9K
50K snakebite: WHO worldwide estimate=50K
33K cervical cancer: only 7K at Kashmir/Assam rate
Each common disease is rare somewhere in India, & hence is largely avoidable
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Maternal deaths concentrated in
rural areas of poorer states
27
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www.cghr.org/child
Cause Mortality rate per 1000
live births
Pneumonia
Girls in Central India 21
Boys in South India 4
Diarrhoea
Girls in Central India 18
Boys in West India 4
~ 5 fold difference
~ 4 fold difference
Huge gender variation in specific
causes at ages 1-59 months
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www.cghr.org/child
Measles mortality by state and district
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Under-5 mortality progress 2001-2012
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81 districts are home to 37% of the national deaths
in children < 5 years
68 of these 81 districts are in poorer states
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Girl disadvantage in 1-59 month mortality
Nationally: for every 100 boys who died at 1-59 months, 131 girls died.
Female mortality at these ages exceeds male mortality by more than 25% in 303 districts
Excess female mortality is seen in nearly all states including Kerala and Tamil Nadu
Nationally: about 74 000 excess deaths in girls at these ages
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0
10
20
30
40
50
60
70
80
Dea
thra
tepe
r1
00
00
0
Age range
0 4 5 14 15 29 30 44 45 59 60 69
591
349
388
319
500
538
Age-specific India malaria-attributed death
rates estimated from the MDS and those
estimated indirectly for WHO
WHO indirect estimates of Indian malaria mortality rates
MDS-attributed Indian malaria mortality rates
Source: Dhingra, et al; Lancet Oct 2010
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Geographic distributions of malaria-attributed
mortality and slide P. falciparum rate
0 0.75%
0.75 1.5%
1.5 2.5%
2.5 5%
over 5%
Study-attributed malaria mortality
as percent of all mortality
at ages 1 month to 70 years
a
Slide P. falciparum rate 1995-2005
derived from the National Vector-borne
Disease Control Programme
b
0 0.58
0.58 0.81
0.81 1.14
1.14 1.53
over 1.53
High-malaria states
ORCG
JH
NE
ORCG
JH
NE
Source: Dhingra, et al; Lancet Oct 2010
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Malaria was a minority cause of
rural, unattended fever deaths in
2005 (1.3M
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Death rates from malaria in other
African settings: INDEPTH sites and
national surveys
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INDIA:
1 million tobacco
deaths per year during
the 2010s
Jha et al, NEJM 2008
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Men who smoke bidis 6 years
Women who smoke bidis 8 years
Men who smoke cigarettes 10 years
INDIA: Years of life lost among 30 year old smokers* (MDS results)
* At current risks of death versus non-smokers, adjusted for age, alcohol use and education
(note that currently, few females smoke cigarettes)
Source: Jha et al, NEJM, Feb 2009
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CGHR.ORG
Category Smokers
(%)
Risk Ratio *
Residence
Rural 56.4 1.6 (1.6-1.8)
Urban 51.3 1.9 (1.6-2.1)
Education
None 58.2 1.6 (1.5-1.7)
Primary 56.8 1.7 (1.5-1.8)
Secondary 47.8 1.7 (1.6-1.9)
Alcohol
No 44.0 1.6 (1.5-1.7)
Yes 75.7 1.6 (1.5-1.8)
Total 55.4 1.7 (1.6-1.8)
Smoker vs Nonsmoker
Risk Ratio
Smoking kills all categories of men results for men aged 30-69
*adjusted for age, alcohol use and education
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Cumulative risk of death, Bangladeshi men age 25-69, smokers vs. nonsmokers
*adjusted for age, alcohol use and
education Source: Alam et al, 2013
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0 1 2 3
UK/US/Japan
India-cig
Hong Kong males
South Africa-Coloureds
Agincourt-Black
South Africa-White
South Africa-Black
RELATIVE RISKS DARK BAR=NOT CAUSED BY SMOKING
Current mortality risks for smokers vs
never; Males
Source: Jha and Peto, 2013; Alawn, 2013, Sitas, 2013, CGHR unpublished
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Ambient air pollution
Nationally representative estimates of
AAP= half of WHOs estimates, and
mostly from COPD/ARI
Source: Yurie Maher, in press
Geospatial approaches for random samples
Geospatial linkages of exposures
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Gaps contributing to Death (GCD)
child deaths in MDS (pilot)
Exposure (row) and cause of death (column) based on dual physician coding
Pneumonia (n=3432)
Diarrhoeal diseases (n=2716)
Malaria other infectious diseases (2736)
Relative risks (crude) versus 757 control deaths Became thin 10.5 16.4 10.3
Lack of blood or appear pale 6.6 9.8 7.5
Repeat illness 3.7 3.7 2.8
Small size/underweight babies 1.7 1.7 1.1
Not breastfed 1.7 1.1 1.1
Premature 1.0 1.0 1.1
Immunized (any) 0.8 0.8 0.8
Measles injection 0.5 0.5 0.2
Source : CGHR unpublished
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Gaps contributing to Death:
importance of right controls for
neonatal deaths
Source : CGHR unpublished
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DESH Random intervention: Local information to leaders on (A) general health; (B) tobacco Target: MPs, MLAs, doctors, health workers and technocrats in 600 districts A No A
B 150 150
No B 150 150 Outcome: Quit rates
Outcome: Service use &
healthcare spending Control
Intervention
Randomize politicians to enforce laws
Source: CGHR unpublished,
- Risks of death
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Ram et al 2014 in press
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Risks of death at 15-69 years in 2013
and malaria patterns in 1948
Source : Ram et al, Lancet GH under review
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Why are COD studies with random
sampling not scaled up?
Obstacle Solutions
COD data not perceived as useful
1. Utility of MDS 2. Link COD to health care access
Random sampling difficult
Use modern geospatial methods, novel sample frames
Too costly 1. Design for lower marginal costs (esp. field work) 2. Link to Census
Training field staff/doctors
E-training and e-certification
Physician coding Standardize e-coding + machine
Concerns on quality of COD
Random re-sampling, biological confirmation
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59%
6%
13%
23%
Field Work
Physician coding
Equipment/IT
Overhead
Overall monthly costs for 2 million people per district: $26,500 Cost per death: $12.50 Cost per household: 7 cents
Marginal costs of MDS per district
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Focus on scaling up CRVS priorities in 10 countries (birth/death registration in hospitals, random sample of home deaths, census mortality)
Phase 1: Bangladesh, Ethiopia, Ghana, Nigeria, Senegal Phase 2: + 5 other countries
Partner with UN PD/SD, RGI, UNECA, World Bank, SEARO
Use innovative Pay for Performance approach for incentives
Combine operational research, cyber infrastructure and demonstration projects
Eventual goal: 25 countries by 25 and 75 countries by 2030 (25X25 and 75X30)
SAVE: Statistical Alliance
for Vital Events
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Establish demonstration projects Helps to improve technical capability and political support
Comprises e-training and certification, software and mobile computers with demo projects
Incentives for 3 key outputs: hospital statistics (birth/death registered with COD), representative COD surveys in community, census use of data
MOUs, payment schedules negotiated
Key inputs are local human resources
PforP should build local ownership, lower costs of transactions and be more sustainable
SAVE: Pay for Performance
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Conclusions: Better COD systems Most deaths in India occur out of hospital and in rural areas. Appropriate systems are needed to measure the causes of deaths out of hospital. A random sample of deaths (home/hospital) is key to interpreting national results. Move beyond hospital-based comparison studies. Adopt population-based focus. Make Verbal autopsy (VA) better, simpler, faster and cheaper. Link deaths to health services and biological confirmation.