Prabhat Jha

Centre for Global Health Research (CGHR) St. Michaels Hospital and Dalla Lana School of Public Health, University of

Toronto

Prabhat.jha@utoronto.ca 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:Prabhat.jha@utoronto.ca

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.

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

* 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

1970-2010 trends in risk of death, 25

countries, age 5-49 years

Source: Norheim, Jha, Addis et al, Lancet 2014

1970-2010 trends in risk of death, 25

countries, age 50-69 years

Source: Norheim, Jha, Addis et al, Lancet 2014

Source: Norheim, Jha, Addis et al, Lancet 2014

HIV Vodka War

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

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

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

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

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)

Low ill defined deaths with RHIME (VA)

Low ill-defined rates (

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

Rank order OR: hospital vs. home Disease Home Hospital

Malaria 3.7% 3.0% NS Tuberculosis 10.8% 5.9% p

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

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

Maternal deaths concentrated in

rural areas of poorer states

27

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

www.cghr.org/child

Measles mortality by state and district

Under-5 mortality progress 2001-2012

81 districts are home to 37% of the national deaths

in children < 5 years

68 of these 81 districts are in poorer states

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

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

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

Malaria was a minority cause of

rural, unattended fever deaths in

2005 (1.3M

Death rates from malaria in other

African settings: INDEPTH sites and

national surveys

INDIA:

1 million tobacco

deaths per year during

the 2010s

Jha et al, NEJM 2008

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

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

Cumulative risk of death, Bangladeshi men age 25-69, smokers vs. nonsmokers

*adjusted for age, alcohol use and

education Source: Alam et al, 2013

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

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

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

Gaps contributing to Death:

importance of right controls for

neonatal deaths

Source : CGHR unpublished

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

Ram et al 2014 in press

Risks of death at 15-69 years in 2013

and malaria patterns in 1948

Source : Ram et al, Lancet GH under review

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

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

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

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

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.