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Vol. 298 No. 16, October 24/31, 2007

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Improving Child Survival Through
Environmental and Nutritional Interventions

The Importance of Targeting Interventions Toward the
Poor

Emmanuela
Gakidou, PhD; Shefali Oza, SB; Cecilia
Vidal Fuertes, MA; Amy Y. Li, AB; Diana K.
Lee, BS; Angelica Sousa, MS; Margaret C.
Hogan, AB; Stephen Vander Hoorn, MSc;
Majid Ezzati, PhD

JAMA. 2007;298:1876-1887.

ABSTRACT

Context  The United Nations Millennium
Development Goals (MDGs) set targets related to important
global poverty, health, and sustainability issues. A
critical but underinvestigated question for planning and
allocating resources toward the MDGs is how interventions
related to one MDG might affect progress toward other
goals.

Objectives  To estimate the reduction in child
mortality as a result of interventions related to the
environmental and nutritional MDGs (improving child
nutrition and providing clean water, sanitation, and
fuels) and to estimate how the magnitude and distribution
of the effects of interventions vary based on the
economic status of intervention recipients.

Design, Setting, and Population  Population-level
comparative risk assessment modeling the mortality
effects of interventions on child nutrition and
environmental risk factors, stratified by economic
status. Data on economic status, child underweight, water
and sanitation, and household fuels were from the nationally
representative Demographic and Health Surveys for 42
countries in Latin America and the Caribbean, South Asia,
and sub-Saharan Africa. Data on disease-specific child
mortality were from the World Health Organization. Data
on the hazardous effects of each MDG-related risk factor
were from systematic reviews and meta-analyses of
epidemiological studies.

Main Outcome Measure  Child mortality, stratified by
comparable international quintiles of economic
status.

Results  Implementing interventions that improve
child nutrition and provide clean water and sanitation
and clean household fuels to all children younger than 5
years would result in an estimated annual reduction in
child deaths of 49 700 (14%) in Latin America and
the Caribbean, 0.80 million (24%) in South Asia, and 1.47
million (31%) in sub-Saharan Africa. These benefits are
equivalent to 30% to 48% of the current regional gaps toward
the MDG target on reducing child mortality. Fifty percent
coverage of the same environmental and nutritional
interventions, as envisioned by the MDGs, would reduce
child mortality by 26 900, 0.51 million, and 1.02
million in the 3 regions, respectively, if the
interventions are implemented among the poor first. These
reductions are 30% to 75% larger than those expected if
the same 50% coverage first reached the wealthier
households, who nonetheless are in need of similar
interventions.

Conclusions  Interventions related to nutritional and
environmental MDGs can also provide substantial gains
toward the MDG of reducing child mortality. To maximize
the reduction in childhood mortality, such integrated
management of interventions should prioritize the
poor.

INTRODUCTION

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The
Millennium Development Goals (MDGs) were established in
2000 in a declaration adopted unanimously by the member
countries of the United Nations to focus resources and
efforts toward the critical global poverty, health, and
sustainability problems (http://millenniumindicators.un.org/unsd/mdg/Default.aspx).
The MDGs set numerical targets to be achieved by 2015 and
use socioeconomic, environmental, nutritional, and health
indicators to monitor progress toward these targets.
There are concerns that the progress toward some of the
health-related MDGs has been slow in many countries,
making it very difficult to achieve them by the target
date.1-3
A crucial question for efficient allocation of resources
is whether some interventions may help achieve targets
related to multiple MDGs, or whether additional and/or
separate interventions are needed.

A second characteristic of the current MDG-related policies,
programs, and monitoring is that they do not explicitly
take into account within-country disparities in
intervention needs and coverage.4
However, there are at least 2 compelling reasons to
examine how the factors that comprise the MDGs and the related
interventions are distributed within countries: first,
better nutritional, environmental, and health outcomes
are important for improving the welfare of the poor and,
therefore, can complement the broader, explicitly stated
MDG of reducing poverty3;
and second, the absolute health benefits from specific
nutritional and environmental interventions may depend on
which segment of the population receives the
interventions.

We evaluated how programs and policies related to 2 MDGs and
their specific indicators—eradicating extreme poverty
and hunger (goal 1) and ensuring environmental
sustainability (goal 7)—affect a third MDG, reducing
child mortality (goal 4). Specifically, we estimated (1)
to what extent interventions related to children's
nutritional and environmental status are expected to
reduce child mortality and (2) how the total benefits, as
well as the distribution of benefits, from these interventions
are expected to vary based on economic status of the
intervention recipients. We evaluated these integrated
interventions in 3 regions: Latin America and the
Caribbean, South Asia, and sub-Saharan Africa. To examine
the role of economic status of intervention recipients,
we compared 2 hypothetical ways of implementing programs
and policies: improving nutritional and environmental MDG
indicators equally across all levels of economic status
vs giving priority to either poorer or wealthier
households.

METHODS

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Overview

We conducted a population-level comparative risk assessment
(CRA) using data that were defined and measured
consistently and comparably across countries for the
following indicators related to the aforementioned 3
MDGs: economic status, child mortality (mortality rate in
children younger than 5 years), child nutrition as
measured by anthropometry (prevalence of underweight
children younger than 5 years, an indicator for the MDG
of eradicating extreme poverty and hunger), water and
sanitation (proportion of population with sustainable
access to an improved water source and with access to
improved sanitation, indicators for the MDG of ensuring
environmental sustainability), and household fuel use
(proportion of population using solid fuels, another
indicator for the MDG of ensuring environmental
sustainability) (Table
1). The CRA estimates the number of child deaths that
would be prevented if exposures to the MDG-related risk
factors were reduced through effective interventions.6
Inputs to the CRA are (1) risk factor exposure, based on
Demographic and Health Surveys (DHS), (2) relative risks
for each disease outcome causally associated with risk
factor exposure, based on systematic reviews conducted in
the World Health Organization (WHO) CRA project,6-7
and (3) total number of child deaths from each disease
based on the WHO databases and the Child Health
Epidemiology Reference Group.

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Table 1. Variables Used for
Measuring Nutritional and Environmental Risk
Factors for Child Mortality From the Demographic
and Health Surveys Data

To consider the role of the economic status of intervention
recipients, all analyses were conducted stratified on a
comparable measure of wealth (described below) and
repeated for each of the intervention scenarios in Table
2. To take into account country-specific
epidemiological characteristics (eg, the diseases that
contribute to child mortality), we conducted all calculations
separately for each country, following the steps in Figure
1. All analyses were conducted using Stata
statistical software, version 9.2 (Stata Corp, College
Station, Texas).

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Table 2. Intervention Scenarios Used
in the Analysis

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Figure 1. Analysis Steps

To take into account country-specific
epidemiological characteristics, these steps were
conducted for each country in each region.
Regional PAF estimates of preventable deaths for
each quintile were aggregated from the estimates
for individual countries and applied to regional
disease-specific deaths. DHS indicates Demographic
and Health Surveys; PAF, population-attributable
fraction; WHO, World Health Organization.

aThe 3 risk factors are
child undernutrition (underweight), unsafe water
and sanitation, and indoor air pollution from
solid fuels (Table
1).

Data Sources

Demographic and Health Surveys. The DHS program, which was
established in the 1980s, is a nationally representative
household survey program that primarily focuses on
maternal and child health in developing countries. The
survey instruments are extensively validated through
pretesting and are standardized across countries. The DHS
constitutes one of the largest consistent and comparable
health survey programs in the world and is used in many
analyses related to maternal and child health including
those performed by WHO, the United Nations Children's
Fund, and the United Nations Population Division. (For
detailed information on DHS design, instruments, and
implementation and access to the data sets, see http://www.measuredhs.com/ and
http://www.statcompiler.com/.)
In each country, the DHS questionnaire is administered to
a nationally representative sample of women aged 15 to 49
years and includes questions on basic sociodemographic
characteristics; household asset ownership; water sources
and sanitation facilities; fuel use; complete birth
history and children's survival; contraception use;
antenatal, delivery, and postpartum care; breastfeeding
and nutrition; and children's health. Each DHS includes
measured weight and height data for children born in the
3 or 5 years preceding the interview.

The DHS surveys use a multistage cluster design. The sample
sizes for all but 2 surveys used in our analysis ranged
from more than 5000 to more than 90 000; 2 surveys
from smaller countries had sample sizes of more than
3000. The response rates for all but 3 of the DHS surveys
used in our analysis were greater than 95%; 2 other
surveys had response rates greater than 93% and 1 survey
had a response rate of 88% (see aforementioned Web
sites). We used the most recent DHS survey data (surveys
conducted before 1990 were not used) from 42 countries in
the 3 regions, Latin America and the Caribbean, South
Asia, and sub-Saharan Africa, for estimating risk factor
exposure and child mortality (these 42 surveys were among
the 144 used for estimating economic status). We did not
use surveys that included only preliminary data. The
countries with surveys covered 58% of the regional
population younger than 5 years in Latin America and the
Caribbean, 96% in South Asia, and 83% in sub-Saharan
Africa (listed in eTable
1).

CRA Project Reviews of Risk Factor Epidemiology. The CRA
project was coordinated at WHO between 1999 and 2002. For
each risk factor, an expert working group conducted a
comprehensive review of the published literature as well
as other sources (government reports, international
databases, etc) to obtain data on effects size (relative
risk [RR]). The work included a number of reanalyses of
original data, systematic reviews, and meta-analyses of
randomized intervention studies and observational
epidemiological studies. There was particular emphasis on
ensuring that the bias due to confounding in RRs was
adequately dealt with by using RRs from randomized
studies or studies with adjustment for multiple
covariates. Each risk factor chapter was anonymously
peer-reviewed by external reviewers (more than 160 peer
reviews plus multiple rereviews as appropriate), by the
CRA editorial group, and in the process of publication in
scientific journals.6,
9
Data sources and methods are described in detail in
individual risk factor chapters and summarized
elsewhere.5-6,10-11
All chapters are available online at http://www.who.int/publications/cra/en/.
The CRA framework, data, and estimates formed the
technical basis of the World Health Report
2002.7

WHO and Child Health Epidemiology Reference Group
Mortality Data. WHO reports child mortality by
disease for each of its 193 member states. The 2004
disease-specific mortality rates were based on the work
of the Child Health Epidemiology Reference Group (CHERG),
established in 2001 to develop estimates of the
proportion of deaths in children younger than 5 years
(including separate estimates for causes of death in the
first 28 days of life). For countries without a vital
registration system, CHERG used a 2-step approach: in the
first step, all available historical data were used to
divide total deaths (from demographic methods) into 3
broad groups: communicable, noncommunicable, and
injuries. This step also used information on the country's
economic development and regional characteristics. In the
next step, CHERG used single-cause and multicause
proportionate mortality models, together with all
available population- or community-based epidemiological
data, to divide deaths into specific diseases. Estimates
for human immunodeficiency virus/AIDS, tuberculosis, and
vaccine-preventable diseases also used data and epidemiological
and natural history models from UNAIDS and WHO technical
groups. This effort has led to better estimates of the
causes of death in children than ever before—arguably
substantially better estimates than for causes of death
in adults. Further details of the methods are described
elsewhere.12-15

Measuring Economic Status

There are several ways of measuring economic status in
household surveys, including monetary measures (eg,
self-reported income and expenditure) and nonmonetary
indices derived from information on ownership of
household assets (eg, television, radio, car,
motorcycle). Asset-based approaches use the information
content in the set of assets owned by a given household
to estimate a "wealth index" for that household and are
therefore based on a long-term conceptualization of
wealth, one that is arguably more relevant as a
determinant of health. Asset-based approaches are also
more robust to common reporting biases that make monetary
income and expenditure at the lower end of the spectrum
especially problematic.16-17
Asset-based indices have been widely used in equity
analyses in low- and middle-income countries, including
analyses of the DHS, the World Health Surveys, and several
country health surveys.3,
8,
18-29

The asset-based index used in this analysis (step 1 in Figure
1) was developed by Ferguson et al30
and has been applied in several other studies, especially
ones that required cross-population comparability.18-19,21,
23-24,26
The method of Ferguson et al30
is conceptually similar to other commonly used asset-based
approaches and is ideal for comparative cross-country
analysis because (1) it incorporates both asset ownership
and household sociodemographic characteristics that are
related to household wealth, such as the education, age,
and sex of the household head and urban-rural residence
and (2) the wealth indices from all DHS surveys are
placed on the same international scale.

The effects of asset ownership and household characteristics
on household wealth were simultaneously estimated using a
random-effects probit model, with the hierarchical error
term at the household level. The output of the model is a
set of covariate coefficients and asset cut points. The
covariate coefficients represent the underlying
relationship between each sociodemographic predictor and
the "latent wealth variable." The asset cut points represent
the point or threshold on the (latent) wealth scale above
which a household is more likely to own a particular
asset. This "asset ladder" was then applied to every
household in each survey to produce adjusted estimates of
household wealth.

To obtain measures of wealth that are comparable across
countries, we first set a single international scale for
wealth, then rescaled all country-specific results to
this scale. The international scale refers to all
developing countries for which DHS data were available at
the time of analysis; this includes 144 DHS surveys in 66
countries between 1985 and 2005. To set the international
wealth scale, we calculated international asset cut points
as the weighted means of the compiled country-specific
ones (weights account for the sample sizes for surveys
from each country and the population of each country).
Rescaling to the common international scale used a simple
linear transformation. For each country, the
country-specific cut points were regressed against the
international cut points. The coefficients were applied
to the estimates of wealth for each household, resulting
in estimates on the international scale while preserving
within-country rank order.

To estimate international wealth quintiles, rescaled
estimates of wealth from individual surveys were appended
and quintile cutoffs were set using appropriate sample
weights. These weights represent the probability of
choosing the household from the international population
(note that "international" refers to all country-years
represented by the 144 surveys used in the analysis). To
convert sample weights for each household from individual
surveys into an "international" weight, we multiplied the
former by the ratio of the country population to the total
population of all countries with a survey. The results of
this analysis provide estimates of average economic
status at the country level that are highly correlated
with gross domestic product per capita (correlation
coefficient = 0.83) (data available on
request), supporting that the index can be used for
cross-country comparative analysis. Copies of statistical
programs to replicate the analysis are available from the
authors.

Because the quintiles of wealth index are international, in
countries with very high poverty, most people fall in the
poorest quintile(s) and few in the wealthiest. For
example, 60% or more of the populations of the Central
African Republic, Chad, Ethiopia, Madagascar, and Niger
lived in the poorest quintile vs 5% or less in the
wealthiest quintile. In contrast, some countries in Latin
America and the Caribbean had no or few households in the
poorest quintile, with 10% and 7% of the regional population
in the poorest 2 quintiles vs 34% and 37% in the
wealthiest 2 quintiles (eTable
2).

Child Mortality and Risk Factor Exposure by Economic
Status

We used total and disease-specific mortality among children
younger than 5 years for each country from the WHO
databases. We divided disease-specific deaths for each
country into wealth quintiles using weights that were
proportional to the total number of under-5 deaths in the
quintile, ni, as given by equation 1
(step 3 in Figure
1):

The number of under-5 deaths in each wealth quintile,
ni, is a product of 2 factors: (1) the
population younger than 5 years in the quintile and (2)
the annual mortality rate in the quintile,
ri. To calculate annual mortality rate by
wealth quintile, we used the DHS data. We applied
standard demographic techniques31
to information on complete birth histories from the DHS
data, described in detail in the DHS supporting
documentation,32
to estimate the probability of death between birth and
the fifth birthday, 5q0, for the
5-year period prior to the survey (step 2 in Figure
1). The probability 5q0 was converted to
annual mortality rates using the relationship shown in
equation 2, a common demographic relationship.31
The denominator of equation 2 represents the total number
of person-years lived between birth and age 5 years,
which is the sum of the years lived by those who survived
to age 5 years (first term) plus the years lived by those
who died during the interval (second term). The number 5
in the first term in the denominator is included because
those who survive to age 5 years contribute 5
person-years. The value of 0.8 is the estimated average
number of years lived by those who died before age 5
years, because child mortality declines rapidly with age,
with most deaths occurring during the first month and a
large proportion during the first year of life.

The levels of risk factors for child mortality (Table
1) by wealth quintile for each country were
calculated directly from the DHS data (step 4 in Figure
1; national data provided in eTable
2).

Mortality Prevented Through Risk Factor
Interventions

To estimate the child mortality effects of interventions for
nutritional and environmental MDGs at the population
level, we used the disease-specific mortality and risk
factor exposure by wealth quintile in each country, as
calculated above, together with data on the hazardous
effects of each risk (measured by RR). The RRs for each
risk factor came from systematic reviews and
meta-analyses of the epidemiological literature in the CRA
project (Table
3).

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Table 3. Relative Risks of
Disease-Specific Child Mortality Associated With
Nutritional and Environmental Health Risks

The proportional reduction in mortality that would be
achieved by decreasing exposure to a risk factor from its
current level to some alternative postintervention
scenario can be estimated using the
population-attributable fraction (PAF) relationship in
equation 3 (also known as the potential impact fraction
relationship).6,
34-35

where n is the number of exposure categories
(n = 4 for undernutrition and unsafe water and
sanitation and n = 3 for fuel use) (Table
3);

Pi is the proportion of population currently in
the ith exposure category;

P'i is the proportion of population in
the ith exposure category in alternative exposure
scenarios, as defined in the intervention scenarios in Table
2; and

RRi is the RR of disease-specific mortality for
the ith exposure category.

For each disease caused by exposure to these risk factors,
we first computed PAFs for individual risks using
equation 3 (step 5 in Figure
1). Disease-specific deaths may be caused by multiple
risk factors acting simultaneously and, hence, can be
prevented by intervening on each of the risks. For
example, some deaths from childhood pneumonia may be
prevented by removing exposure to indoor air pollution
from solid fuels or by improving child nutrition.9
As a result of multicausality, the PAFs for interventions
related to multiple risk factors overlap and cannot be
combined by simple addition.9
Under specific assumptions, described below, the combined
(joint) PAF that avoids double-counting the overlap of
interventions on multiple risk factors is given by equation
4.9,
35-36
In equation 4, the proportion of the disease that can be
prevented by reducing exposure to the ith risk factor
equals PAFi, and
(1 – PAFi) is the proportion
that is not preventable by intervention on the ith
factor. The second term in the right-hand side of
equation 4 (ie, the product of all
[1 – PAFi] terms) is the fraction
of disease that is not preventable through interventions
on any of the n risk factors. One minus this term
is the fraction attributable to the combined effects of
interventions on the n risk factors.

where PAFi is the PAF of the ith
individual risk factors.

For diseases affected by multiple risk factors (diarrhea and
lower respiratory tract infections), we estimated the
combined PAFs using equation 4 (step 6 in Figure
1). This provided the proportional reduction in
deaths from each disease as a result of the combined
effects of intervention on all 3 risks and properly
accounted for multicausality. The proportional reduction
was used in equation 5 to calculate the number of deaths
from each disease that would be prevented by
interventions on all 3 risk factors (step 7 in Figure
1).

where AMj is deaths from disease j
prevented as a result of interventions on all 3 risk
factors;

PAFj is the PAF for disease j from
interventions on all 3 risk factors combined; and

Mj is total deaths from disease j.

The preventable deaths were then summed across diseases to
obtain the total (all-cause) mortality that would be
prevented through interventions on all 3 risks (step 8 in
Figure
1). Preventable deaths from different diseases can be
added because in cause-of-death statistics, following the
International Classification of Diseases system,
each death is categorically assigned to a single underlying
cause (disease) and, unlike risk factors, there is no
overlap between disease-specific deaths.

There are 2 key assumptions when using equation 4. First,
exposures to these risks are uncorrelated. Environmental
and nutritional risk factors for child mortality may be
correlated because undernutrition, unsafe water and
sanitation, and use of solid fuels are more common among
the poor. Positive correlation among risk factors in
relation to economic status is accounted for by conducting
all analyses stratified on wealth quintile. We examined
whether there was within-quintile correlation of risk
factors using data from 6 large DHS and found that there
was little remaining correlation beyond what was
accounted for by stratification on the wealth quintiles
(data available on request). Because positive correlation
between risks increases the estimated PAFs, remaining
correlation between risks would strengthen the findings
of our analysis.

The second assumption is that the hazardous effects of one
risk are not mediated through other risks. Child growth
(eg, weight for age) may be affected by diet (eg,
protein-energy consumption) as well as previous
infections.37-39
Therefore, some of the hazardous effects of indoor air
pollution from solid fuels and unsafe water and
sanitation (which result in lower respiratory tract
infections and diarrhea, respectively) may be mediated
through low weight for age. To account for potential
mediated effects in a sensitivity analysis, we repeated
all the analysis assuming that 50% of the excess risk
(RR – 1) from indoor air pollution from solid
fuels and unsafe water and sanitation are mediated
through underweight, based on reviews of epidemiological
studies reported elsewhere.9
The results of this sensitivity analysis for Table
4 are shown in eTable
3.

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Table 4. Estimated Reduction in
Child Deaths in Latin America and the Caribbean,
South Asia, and Sub-Saharan Africa After
Implementing Specific Intervention
Scenariosa

RESULTS

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Child
Mortality and Risk Factor Exposure by Economic
Status

An estimated 0.36 million, 3.38 million, and 4.79 million
children younger than 5 years died in Latin America and
the Caribbean, South Asia, and sub-Saharan Africa in
2004, respectively (Table
4), with corresponding annual mortality rates of 6.5,
19.9, and 39.1 per 1000 children. Child mortality
declined with increasing wealth in all regions (Figure
2). In South Asia, child mortality declined across
all quintiles, with the greatest decline between
quintiles 2 and 4. In Latin America and the Caribbean and
in sub-Saharan Africa, the differences in child mortality
were smaller in the first 3 quintiles, but there was a
sharp decline in the higher wealth levels. This may
reflect more demarcated inequalities in socioeconomic
status and in access to preventive and clinical services
in these 2 regions compared with those in South Asia.
Beyond the economic gradient within each region, Figure
2 illustrates that child mortality among the poor was
substantially higher in sub-Saharan Africa (46.9 and 42.7
deaths per 1000 children in quintiles 1 and 2,
respectively) than in Latin America and the Caribbean
(9.6 and 10.2 deaths per 1000 children), with South Asia
having levels between these 2 regions. Our analysis also
revealed significant variation in mortality across
countries in each region. Countries with the highest
mortality rate among the poor were Niger (75.6 per 1000
children), Mali (58.6), and Nigeria (58.1); those with
the lowest mortality among the poor were the Dominican
Republic (6.0 per 1000 children), Paraguay (8.3), and
Peru (10.3) (data available on request).

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Figure 2. Annual Child Mortality
Rate by Quintile of Wealth in Latin America and
the Caribbean, South Asia, and Sub-Saharan Africa

The chart shows current mortality rate as well
as rates expected after implementing the
intervention scenarios shown in Table
2. The percentages of households in each
wealth quintile in each region are as follows: in
Latin America and the Caribbean: Q1, 10%; Q2, 7%;
Q3, 13%; Q4, 34%; Q5, 37%; in South Asia: Q1, 16%;
Q2, 16%; Q3, 24%; Q4, 21%; Q5, 23%; and in
sub-Saharan Africa: Q1, 29%; Q2, 17%; Q3, 22%; Q4,
19%; Q5, 14%. Regional mortality rate estimates
for each quintile are weighted averages of rates
for individual countries in the same quintile,
with weights for each country proportional to the
number of children younger than 5 years in the
quintile in that country. The scales on the panels
are different to increase resolution. Child
mortality is reported as annual rate (ie, with the
numerator as the number of deaths and the
denominator as the number of children in 1 year)
to be consistent with the literature on
disease-specific mortality; eg, a recent analysis
of the global burden of disease.13
Infant and child mortality can also be reported as
per 1000 live births (5q0),
with the 2 units related as described in equation
2. For comparison, the annual mortality rate was
1.3 per 1000 children in high-income countries in
2001.13

Child mortality varied less for households in the wealthiest
quintiles, ranging from 21.5 per 1000 children in
sub-Saharan Africa to 4.4 per 1000 children in Latin
America and the Caribbean in quintile 5. In other words,
children in wealthier households had more similar (and
better) survival across nations and regions compared with
children in poor households, whose survival varied
enormously. For example, in the poorest quintile, the
child mortality rate varied between 6.0 per 1000 children
(Dominican Republic) and 31.1 (Haiti) in Latin America
and the Caribbean, between 20.4 (Nepal) and 27.2
(Pakistan) in South Asia, and between 15.5 (Namibia) and
75.6 (Niger) in sub-Saharan Africa. The larger variation
among the poor may be due to differential exposure to
nutritional and environmental causes of child mortality
and/or differential access to health care for treatment of
childhood diseases (eg, oral rehydration therapy for
diarrhea and antibiotics for pneumonia and other
infections).

Based on DHS risk factor data (eTable
2), an estimated 56%, 87%, and 83% of the 339 million
children in the 3 regions were exposed to preventable
risks associated with undernutrition, unsafe water and
sanitation, and indoor air pollution from solid fuels,
respectively, and, hence, in need of interventions. The
poor in every region had higher exposure to all 3 risks,
but there was heterogeneity in regional and socioeconomic
patterns of risk factor exposure (Figure
3, Figure
4, and Figure
5). Unlike child mortality, which was highest in
sub-Saharan Africa, underweight was highest in South Asia
in every wealth quintile. The poor in Latin America and
the Caribbean had slightly worse access to safe water and
sanitation than those in South Asia and sub-Saharan
Africa, possibly reflecting a decade or longer of
targeted water and sanitation interventions in the latter
2 regions. In all regions, the poor relied on solid fuels,
while wealthier households (quintiles 4 and 5) in
sub-Saharan Africa used solid fuels substantially more
than households of the same affluence in the other 2
regions. This reflects nearly universal reliance on
biomass fuels in both rural and urban parts of Africa.33

View
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Figure 3. Levels of Child Weight for
Age by Quintile of Wealth in Latin America and the
Caribbean, South Asia, and Sub-Saharan Africa

Exposures are measured using the variables
described in Table
1. Regional estimates for each quintile are
weighted averages of those for individual
countries in the same quintile, with weights for
each country proportional to the number of
children in the quintile in that country. See eTable
2 for exposure data from individual countries.

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Figure 4. Levels of Unsafe Water and
Sanitation Risk Exposure by Quintile of Wealth in
Latin America and the Caribbean, South Asia, and
Sub-Saharan Africa

Exposures are measured using the variables
described in Table
1. Regional estimates for each quintile are
weighted averages of those for individual
countries in the same quintile, with weights for
each country proportional to the number of
children in the quintile in that country. See eTable
2 for exposure data from individual countries.

View
larger version (48K):
[in
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[in a new window]
[as
a PowerPoint slide]

Figure 5. Levels of Fuel Use by
Quintile of Wealth in Latin America and the
Caribbean, South Asia, and Sub-Saharan Africa

Exposures are measured using the variables
described in Table
1. Regional estimates for each quintile are
weighted averages of those for individual
countries in the same quintile, with weights for
each country proportional to the number of
children in the quintile in that country. See eTable
2 for exposure data from individual countries.

Total Health Benefits of
Interventions

The MDG-4 target is to reduce the mortality rate of children
younger than 5 years by two-thirds between 1990 and 2015.
Between 1990 and 2004, child mortality declined by 39%,
28%, and 9% in Latin America and the Caribbean, South
Asia, and sub-Saharan Africa, respectively (see Figure 1
in Lopez et al13
for 1990 levels). To achieve the MDG target, the
reduction required as a proportion of 2004 mortality
rates would be approximately 45% in Latin America and the
Caribbean, 53% in South Asia, and 63% in sub-Saharan
Africa. If interventions for the 3 nutritional and
environmental MDGs reached all children who need them
("equal-100" scenario), an estimated 49 700 annual
child deaths in Latin America and the Caribbean (14%),
0.80 million in South Asia (24%), and 1.47 million in
sub-Saharan Africa (31%) would be prevented (Table
4). These effects are equivalent to 30% to 48% of the
regional gaps between mortality levels in 2004 and the
MDG target (Table
4 and Figure
2). Excluding deaths during the neonatal period,
which can be prevented primarily through better
antenatal, maternal, and neonatal health services,40
the corresponding proportional reductions in mortality
would be 22% in Latin America and the Caribbean, 39% in
South Asia, and 39% in sub-Saharan Africa (data available
on request). The benefits of these environmental and
nutritional interventions are smallest in Latin America
and the Caribbean, in both absolute and relative terms,
because the majority of the population of this region is
in the wealthier quintiles and has already achieved low
levels of child mortality and improved environmental and
nutritional status. The 14% reduction in child mortality
in this region would nonetheless contribute substantially
to achieving the MDG target. The benefits would be larger
in poorer countries in the region such as Haiti (28%) and
Nicaragua (24%) (data available on request).

If environmental and nutritional risks are reduced by 50%,
as envisioned by the MDGs, and if the reductions are
distributed proportionally to current exposures across
all wealth quintiles ("equal-50" scenario), there would
be an estimated 24 900 (7%), 0.40 million (12%), and
0.74 million (15%) fewer child deaths in Latin America
and the Caribbean, South Asia, and sub-Saharan Africa,
respectively. If the same interventions were delivered
preferentially to the poor ("pro-poor" scenario), the
estimated total number of preventable deaths would be
26 900 (7%) in Latin America and the Caribbean, 0.51
million (15%) in South Asia, and 1.02 million (21%) in
sub-Saharan Africa. These reductions are 8% to 39% higher
than those expected in the "equal-50" scenario. These
"pro-poor" programs would reduce the remaining gap toward
the child mortality MDG target by 16% to 34%. In contrast,
the aggregate benefits would be 19 000 (5%), 0.39
million (12%), and 0.58 million (12%) averted deaths if
the 50% interventions first reached the wealthier
households ("pro-wealthy" scenario).

Health Benefits of Interventions by Economic
Status

Because the poor have higher environmental and nutritional
risk exposures and intervention needs, all intervention
scenarios except the "pro-wealthy" scenario would also
reduce the disparity in child mortality between poor and
wealthier families. For example, the difference in
mortality rate between quintiles 1 and 5 would be reduced
from 5.2 to 3.8 per 1000 children in Latin America and
the Caribbean, from 8.6 to 5.4 per 1000 children in South
Asia, and from 25.4 to 12.8 per 1000 children in sub-Saharan
Africa in the "equal-100" scenario (100% reduction; Figure
2). The equity benefits would be even larger if the
interventions prioritized the poor ("pro-poor" scenario),
reducing the difference in mortality rate between
quintiles 1 and 5 to 3.5 per 1000 children in Latin
America and the Caribbean, 2.4 per 1000 children in South
Asia, and 10.1 per 1000 children in sub-Saharan Africa
(Figure
2).

COMMENT

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The results
of our analysis demonstrate that complete coverage of
effective interventions that address child undernutrition
and provide clean water and sanitation and clean household
fuels would reduce child mortality by an estimated 14% to
31% in Latin America and the Caribbean, South Asia, and
sub-Saharan Africa. These benefits would close 30% to 48%
of the current regional gaps toward the MDG target for
reducing child mortality. These interventions would also
be expected to reduce the absolute disparity in child
mortality between poor and better-off households. The
poor have a larger proportional reduction in child mortality
because they currently have higher exposure; they also
have larger absolute benefits (ie, greater reductions in
mortality rate) because of both larger proportional
benefits and larger initial mortality levels (ie, a
greater percentage reduction of a larger total number of
deaths). Fifty percent coverage of the same environmental
and nutritional interventions, as envisioned by the MDGs,
would have 30% to 75% larger benefits for reducing child
mortality if the interventions were targeted to the poor
first than it would if the interventions benefited
wealthier households, who nonetheless are also in need of
MDG-related interventions. In other words, pro-poor
nutritional and environmental interventions not only
reduce within-population child mortality disparities but
also are significantly more effective in achieving the
aggregate MDG target of reducing child mortality. This effect
is strongest in sub-Saharan Africa, where child mortality
is farthest from the MDG target. Pro-poor interventions
have larger aggregate benefits, especially in sub-Saharan
Africa, because they deliver environmental and
nutritional interventions to children who are most
susceptible to dying of such exposures, possibly because
of limited access to medical services.

Our results are based on population-level analyses, in
relation to economic status. Economic status is
multifaceted and its measurement is complex. Asset-based
methods provide a robust approach to comparable
measurements. For this reason, the DHS wealth index is
commonly used.3,
8,
20,
22,
25,
27-29
However, the DHS were not explicitly designed to measure
economic status and it is possible that the full set of
asset items does not fully differentiate households
throughout the distribution of income in a country. As a
result, the true gradient in child mortality and risk
factors might be even steeper than observed in our
results, which would strengthen our findings. More detailed
measurement of economic status and the identification of
the absolute poor are necessary for better reporting
toward the MDGs. Risk assessment analytics used in our
work apply relative risks from meta-analyses of
epidemiological studies to population-level exposure and
disease-specific mortality data. Although this is
necessary to develop evidence for population-level effects
of risk factors, programs, and policies based on
epidemiological evidence, the results, by definition, are
estimates (vs direct measurement). Related to this, while
interventions for the risk factors considered in our
analysis are available,33,
41
the implementation scenarios are, by definition,
hypothetical. Therefore, our analyses and results are
intended to draw attention to the important issues of
integrated intervention programs and the poverty status
of intervention recipients in future efforts.

Beyond these issues that enter all population-level analyses
of poverty, risk factors, and mortality, there are a
number of limitations and sources of uncertainty specific
to the data used in our analysis. The DHS constitutes the
largest international survey program, with implementation
procedures, sample sizes, and response rates that are
comparable with or better than those in many high-income
countries. To obtain data directly from as many countries
as possible, we have used the most recent survey for each
country, for years after 1990. The fact that surveys were
implemented in different years might affect comparability
across countries. To test how sensitive our results are to
the year of the survey, we repeated all of our analyses
excluding surveys that were older than 10 years. Although
the number of preventable deaths changed (by 2%-3% in
sub-Saharan Africa, 6%-9% in South Asia, and 18%-27% in
Latin America and the Caribbean for different scenarios;
data available on request), there were no changes in the
ordering of scenarios in terms of their benefits or in
any of the conclusions made based on the results.

The most important source of uncertainty in our estimates is
likely to be the use of disease-specific child death
estimates in countries without complete vital statistics.
Considering the full range of uncertainties for all-cause
and disease-specific deaths, Mathers et al42
estimated that regional uncertainties in deaths from
diarrhea, lower respirator tract infection, malaria, and
measles were about 11% in Latin America and the Caribbean,
10% to 17% in South Asia, and 20% to 23% in sub-Saharan
Africa (some of this uncertainty is in the total number
of deaths and some is in the distribution of deaths
across diseases; in the second component, deaths from
these diseases cannot be simultaneously underestimated or
overestimated). The recent work of the Child Health
Epidemiology Reference Group9
has led to better data and more consistent models for
these estimates. Therefore, while uncertainty in actual
death numbers remains an attribute of all quantitative
analyses in global health, the overall conclusions of our
analyses on the benefits of pro-poor integrated interventions
are unlikely to be simply an artifact of disease-specific
mortality methods.

The disease-specific quintile weights that we used were
based on all-cause mortality (equation 1) and reflect the
empirical finding that most under-5 deaths in developing
countries are due to neonatal conditions and a small
number of infectious diseases. These diseases (eg,
diarrhea, lower respiratory tract infections, and
malaria) are the same as those affected by the
environmental and nutritional risk factors considered in
this article. To examine the sensitivity of our results
to this assumption, we repeated the analysis using a
model in which the proportional share of diseases in each
quintile was related to the quintiles' total mortality
level (empirically, the proportional share of infectious
diseases like diarrhea and lower respiratory tract
infections is larger when total mortality is higher43).
Our conclusions on the effects of pro-poor programs were
strengthened in this alternative analysis, presented in
eTable
4. This is because when disease composition depends
on total mortality, an even larger share of infectious
disease deaths occurs among the poor, which in turn leads
to higher absolute benefits of risk factor
interventions.

The Millennium Declaration of the United Nations has
provided the motivation, framework, and resources to
initiate policies and programs to achieve specific
development goals in a sustainable manner. Although the
interdependence of multiple MDGs is broadly and
qualitatively recognized,3
there has been little systematic effort to integrate the
design, implementation, and evaluation of programs across
goals and across the sectors that are responsible for
them. Halfway through their intended timeline, however,
the slow rate of progress toward the MDGs has led to
increasing concerns about whether they will be achieved
by 2015 and has created an imperative for identifying
effective programs. The results of our analysis
demonstrate a real opportunity for leveraging the
linkages between multiple MDGs to substantially increase
the possibility of achieving them. In addition to
highlighting the need for funding, implementing, and
monitoring such integrated intervention programs, our
analysis demonstrates that the health benefits would be
largest if the MDG-related nutritional and environmental
interventions systematically and preferentially reach the
poor. It has also been argued that the absence of
pro-poor programs are driven by political and
institutional factors rather than by considerations about
their feasibility or costs.3
Above and beyond their aggregate effectiveness, equity
considerations in our analysis provide an ethical
imperative for pro-poor programs4,
8
and should motivate progressive intervention programs
that index recipient communities and households based on
their level of poverty as well as on exposure to nutritional
and environmental risks (eg, poor rural communities, urban
slums). In particular, to increase the accountability of
both national governments and international donors,
monitoring progress toward the MDGs should also be based
on reporting multiple environmental, health, and
nutritional indicators together with the economic status
of the recipient households and communities.

AUTHOR INFORMATION

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Corresponding Author: Majid Ezzati, PhD, Harvard
School of Public Health, 665 Huntington Ave, Boston, MA
02115 (majid_ezzati{at}harvard.edu

).

Author Contributions: Dr Ezzati had full access to all of
the data in the study and takes responsibility for the
integrity of the data and the accuracy of the data
analysis.

Study concept and design: Gakidou, Ezzati.

Acquisition of data: Gakidou, Ezzati.

Analysis and interpretation of data: Gakidou, Oza, Vidal
Fuertes, Li, Lee, Sousa, Hogan, Vander Hoorn,
Ezzati.

Drafting of the manuscript: Gakidou, Ezzati.

Critical revision of the manuscript for important
intellectual content: Gakidou, Oza, Vidal Fuertes,
Li, Lee, Sousa, Hogan, Vander Hoorn, Ezzati.

Statistical analysis: Gakidou, Oza, Vidal Fuertes, Lee,
Sousa, Hogan, Vander Hoorn, Ezzati.

Obtained funding: Gakidou, Ezzati.

Study supervision: Gakidou, Ezzati.

Financial Disclosures: None reported.

Funding/Support: This work was sponsored by National
Science Foundation grant 0527536, by the Grand Challenges
in Global Health, and by the Center for International
Development at Harvard University.

Role of the Sponsor: The sponsors had no role in the
design and conduct of the study; collection, management,
analysis, and interpretation of the data; preparation,
review, or approval of the manuscript; or decision to
submit the manuscript.

Additional Contributions: We thank Kenneth Hill, PhD,
Harvard University, and Kevin Thomas, PhD, Pennsylvania
State University, for discussion on demographic methods;
Colin Mathers, PhD, World Health Organization, and Robert
Black, MD, Johns Hopkins University, for discussion on
disease-specific child mortality data and methods;
Kenneth Hill and Barry Bloom, PhD, Harvard University,
for comments on the manuscript; and Stanislava Nikolova,
AB, Harvard University, for editorial help. None of those
acknowledged received compensation for their
contributions.

Author
Affiliations: Initiative for Global Health, Harvard University,
Cambridge, Massachusetts (Drs Gakidou and Ezzati and Mss Oza, Vidal
Fuertes, Li, Lee, and Sousa); Institute for Health Metrics and
Evaluation, University of Washington, Seattle (Ms Hogan); School of
Population Health, University of Queensland, Brisbane, Australia (Ms
Hogan); Clinical Trials Research Unit, University of Auckland,
Auckland, New Zealand (Mr Vander Hoorn); and Harvard School of
Public Health, Boston, Massachusetts (Dr Ezzati). Dr Gakidou is now
with the Institute for Health Metrics and Evaluation, University of
Washington.

REFERENCES

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