DHS ANALYTICAL 

STUDIES 38

Association between Economic Growth, 

Coverage of Maternal and Child Health

Interventions,and Under-Five Mortality:

A Repeated Cross-Sectional Analysis of 36

Sub-Saharan African Countries

JANUARY 2014

This publication was produced for review by the United States Agency for International Development. It was prepared by 

S.V. Subramanian and Daniel J. Corsi of the Department of Social and Behavioral Sciences, Harvard School of Public Health.

DHS Analytical Studies No. 38 

Association among Economic Growth, Coverage of Maternal and 

Child Health Interventions, and Under-Five Mortality: A Repeated 

Cross-Sectional Analysis of 36 Sub-Saharan African Countries 

S.V. Subramanian 

Daniel J. Corsi 

ICF International 

Rockville, Maryland, USA 

January 2014 

Corresponding author: Professor S. V. Subramanian, Department of Social and Behavioral Sciences, 

Harvard School of Public Health, 677 Huntington Avenue, Kresge Building 7th Floor, 716, Boston, 

Massachusetts 02115-6096, Phone: 617-432-6299, Fax: 617-432-3123
Acknowledgment: The authors thank USAID for their support, and for access to the original data, and 

thank in particular Thomas Pullum, director of research for The Demographic and Health Surveys (DHS) 

Program. 

Editor: Bryant Robey 

Document Production: Yuan Cheng 

This research was carried out with support provided by the United States Agency for International 

Development (USAID) through the MEASURE DHS project (#GPO-C-00-08-00008-00). The views 

expressed are those of the authors and do not necessarily reflect the views of USAID or the United States 

Government. 

The DHS Program assists countries worldwide in the collection and use of data to monitor and evaluate 

population, health, and nutrition programs. Additional information can be obtained by contacting The 

DHS Program, ICF International, 530 Gaither Road, Suite 500, Rockville, MD 20850; Telephone: 301-

407-6500; Fax: 301-407-6501; E-mail: reports@DHSprogram.com; Internet: www. DHSprogram.com. 

Recommended citation: 

Subramanian, S.V., and Daniel J. Corsi. 2014. Association among Economic Growth, Coverage of 

Maternal and Child Health Interventions, and Under-Five Mortality: A Repeated Cross-Sectional 

Analysis of 36 Sub-Saharan African Countries. DHS Analytical Studies No. 38. Rockville, Maryland, 

USA: ICF International. 
iii 

Contents 

List of Tables ............................................................................................................................................... v 

List of Figures ............................................................................................................................................ vii 

Preface ......................................................................................................................................................... ix 

Executive Summary ................................................................................................................................... xi 

Background ................................................................................................................................................. 1 

Methods ........................................................................................................................................................ 5 

Data Sources ......................................................................................................................................... 5 

Sampling Plan ....................................................................................................................................... 5 

Study Population, Data Designs, and Sample Sizes ............................................................................. 5 

Outcomes .............................................................................................................................................. 6 

Exposures .............................................................................................................................................. 7 

Analysis ................................................................................................................................................. 8 

Results ........................................................................................................................................................ 13 

Trends in U5MR, Economic Growth, and MNCH Intervention Coverage ......................................... 13 

Change in U5MR against Change in Pcgdp and Change in MNCH Intervention Coverage ............. 19 

Multilevel Analyses Examining the Probability of Child-Level Mortality against Indicators of 

Economic Growth and MNCH Intervention Coverage ............................................................... 27 

Summary of Fixed Effects Models at the Survey Period and Child Levels ......................................... 30 

Sensitivity Analyses Using Random Effects Models ........................................................................... 31

Discussion .................................................................................................................................................. 33 

Limitations .......................................................................................................................................... 33 

Concluding Remarks ........................................................................................................................... 34 

References .................................................................................................................................................. 37 

v 

List of Tables 

Table 1. Descriptive statistics and correlation matrix of all of the economic and coverage indicators 

measured in the first survey period, from Demographic and Health Surveys conducted in 

36 sub-Saharan African countries ............................................................................................... 11 

Table 2. Year of survey, under-five mortality rates, and country-level economic and coverage 

indicators of maternal and child health interventions in 36 sub-Saharan African countries 

for first and repeated surveys ...................................................................................................... 14 

Table 3. U5MR in 36 sub-Saharan African countries and rates of change according to urban/rural, 

household wealth, and education population subgroups ............................................................ 16 

Table 4. Results of linear regression analyses of U5MR on country-level economic and health 

coverage indicators from the ecological time series data structure treating countries as 

fixed and random effects ............................................................................................................ 20 

Table 5. Results of the multivariable ecological time series regressions of U5MR on country-level 

covariates .................................................................................................................................... 20 

Table 6. Odds ratios for child mortality for country-level economic and maternal/child intervention 

coverage indicators, adjusted for child, maternal, and household-level covariates across 36 

sub-Saharan African countries from a three-level random intercepts logistic regression 

model .......................................................................................................................................... 28 

Table 7. Odds ratios for child mortality for multivariable models of country-level economic and 

maternal/child intervention coverage indicators adjusted for child, maternal, and 

household-level covariates across 36 sub-Saharan African countries from a three-level 

random intercepts logistic regression model .............................................................................. 29 

Table 8. Risks of child mortality according to child, maternal, and household-level covariates 

across 36 sub-Saharan African countries .................................................................................... 29

Table 9. Summary of the associations between pcGDP and coverage of MNCH interventions on 

U5MR at the survey period/country level and the probability of child mortality at age 0-59 

months at the child level from both the fixed effects and random effects model 

specifications .............................................................................................................................. 31 

vii 

List of Figures 

Figure 1. Hypothesized relational structure among child mortality; child, household/maternal, and 

community determinants; and country-level economic growth, social determinants, and 

health system and policy variables ............................................................................................... 2 

Figure 2. Classification diagram for the ecological time series data structure with survey periods 

nested within countries ................................................................................................................. 5 

Figure 3. Classification diagram for the multilevel data structure with children nested within 

primary sample units (PSU), within survey periods, and within countries .................................. 6 

Figure 4. Change in U5MR and against change in log of pcGDP (left panel); change in U5MR 

against change in the composite coverage index, 25 sub-Saharan African countries ................. 21 

Figure 5. Change in U5MR and against change in coverage of MNCH interventions delivered 

during the infancy and childhood period .................................................................................... 23

Figure 6. Change in U5MR and against change in coverage of MNCH interventions delivered 

during the pre-pregnancy period and improved sanitary facilities and drinking water 

source .......................................................................................................................................... 25 

ix 

Preface 

One of the most significant contributions of The DHS Program is the creation of an internationally 

comparable body of data on the demographic and health characteristics of populations in developing 

countries. 

The DHS Comparative Reports series examines these data across countries in a comparative framework. 

The DHS Analytical Studies series focuses on analysis of specific topics. The principal objectives of both 

series are to provide information for policy formulation at the international level and to examine 

individual country results in an international context. 

While Comparative Reports are primarily descriptive, Analytical Studies provide in-depth, focused 

studies on a variety of substantive topics. The studies are based on a varying number of data sets, 

depending on the topic being examined. These studies employ a range of methodologies, including 

multivariate statistical techniques. 

The DHS Program staff, in conjunction with the U.S. Agency for International Development (USAID), 

select the topics covered in Analytical Studies. 

It is anticipated that the DHS Analytical Studies will enhance the understanding of analysts and 

policymakers regarding significant issues in the fields of international population and health. 

Sunita Kishor 

Director, The DHS Program 

xi 

Executive Summary 

Infant and child mortality rates are among the most important indicators of child health, nutrition, 

implementation of key survival interventions, and the overall social and economic development of a 

population. In this report, using data from 99 Demographic and Health Surveys (DHS) conducted in 36 

sub-Saharan African countries, we investigate factors that have contributed to the declines in under-five 

mortality rates (U5MR) in sub-Saharan Africa. Specifically, we focus on the extent to which changes in 

country-level economic growth and changes in the coverage of key maternal, neonatal, and child health 

(MNCH) interventions have contributed to reductions in under-five mortality. 

For this analysis we constructed two distinct data structures: (1) an ecological time series (with countries 

repeatedly observed) and (2) a multilevel repeated cross-section (which in addition took account of the 

variability between children within a country at any time). We employed a country-level fixed effects 

regression to model changes in U5MR across survey periods as a function of changes in economic growth 

and coverage of MNCH interventions for ecological time series data. The multilevel repeated crosssectional data was used to examine the probability of a child being reported to have died at age 0-59 

months, corresponding with different levels of economic growth and coverage, while accounting for 

within-country between-child factors that could influence both child mortality and the country-level 

economic development and coverage indicators. 

Our results show that changes in country-level per capita GDP (pcGDP) are not consistently associated 

with a reduction in U5MR across different model specifications. In ecological time series models, a unit 

increase in pcGDP is associated with a reduction in U5MR of 11.6 deaths per 1000 live births (95% CI: -

29.1, 5.9), while a composite index of MNCH interventions is associated with a reduction in U5MR of 

31.9 deaths per 1000 live births (95% CI: -48.6, -15.3). The results of the multilevel repeated crosssection data structure suggest that MNCH coverage indicators are important. For example, pcGDP is 

associated for a decreased likelihood of child mortality with an odds ratio of 0.96 (95% CI: 0.92 -1.00) 

and an increase of 1 standard deviation in the composite coverage index (CCI) is also associated with a 

decrease in child mortality [odds ratio 0.92 (95% CI: 0.88 - 0.96)]. A measure of improvements in 

sanitary facilities is associated with an odds ratio of 0.57 (95% CI: 0.50-0.65) for child mortality. 

Together, these results indicate that MNCH interventions are important in reducing U5MR, while the 

effects of economic growth in sub-Saharan Africa remain weak and inconsistent. Sub-Saharan Africa 

continues to have the highest U5MR globally, and progress toward reducing mortality rates has been 

slow. Our findings indicate that improved coverage of proven life-saving interventions and access to 

clean water and sanitation will likely contribute to further reductions in U5MR in sub-Saharan Africa in 

the future. 

1 

Background 

Infant and child mortality rates are among the most important indicators of child health, nutrition, 

implementation of key survival interventions, and the overall social and economic development of a 

population.1

 Researchers have proposed several conceptual and analytical frameworks for studying child 

mortality and survival in low-income countries.2

 Perhaps the most widely cited of these is the model 

developed by Mosley and Chen3

, which integrated the social science and medical disciplines and 

introduced the idea that background socioeconomic and cultural variables operate through proximate 

determinants that directly influence childhood disease. In this report, we further extend this model by 

expanding and conceptualizing the socioeconomic background variables as a hierarchy of ‘distal’ 

determinants, which include country-level factors relating to economic growth, rates of fertility and 

education, and health systems and policy factors, such as coverage of MNCH interventions. 

The overall directionality of country-level factors on child health is downward, from distal to proximate 

(Figure 1). Country-level factors may: (1) exert direct, cross-level effects on child mortality, (2) exert 

indirect, cross-level effects mediated through more proximal variables, such as community or household 

socioeconomic factors, and (3) modify the associations between independent and dependent variables 

operating within or across levels (e.g., country gross domestic product (GDP) modifying the effects of 

household-level disadvantage on child mortality). Of course, this conceptualization is complex and many 

interactions may produce bidirectional effects such that family-level and child-level factors may influence 

the community and country-level variables. Using this framework, we examine the effects of both 

economic growth and coverage of MNCH interventions on reducing child mortality in sub-Saharan 

Africa. We use both an ecological time series (country-level) approach and a multilevel (children nested 

within countries) approach. 

Since September 2008, the governments of 147 countries have agreed to accelerate efforts to achieve a 

series of development goals,7

 which are referred to as the Millennium Development Goals (MDGs).8 Of 

the eight goals established, the fourth (MDG-4) is “to reduce by two thirds, between 1990 and 2015, the 

under-five mortality rate”.8

 Considerable resources and efforts have gone into assessing the progress 

toward achieving MDG-4, including determining what interventions are needed to accomplish this goal.9, 

10 With less than 1,000 days remaining until the 2015 deadline for accomplishing the goals of MDG-4, 

there has been renewed attention to the success (or not) of meeting these targets.11 The UN Inter-agency 

Group on Child Mortality Estimation (UN-IGME) was established to track progress toward MDG-4 and 

assess whether countries will achieve it.12

According to the most recent UN-IGME estimates in 2010, sub-Saharan Africa as a whole has the highest 

rate of child mortality (121 deaths per 1,000 live births), with one in every eight children dying before 

their fifth birthday. This is nearly double the average in developing/low-income regions (62.7 deaths per 

1,000 live births), and nearly 18 times the average for developed/high-income regions (6.8 per 1,000). 

Sub-Saharan Africa and South Asia together accounted for 82 percent of global under-five deaths in 2010, 

although these regions account for less than half of the global population under age 5.12

The U5MR for sub-Saharan African declined from 174 per 1,000 in 1990 to 121 per 1,000 in 2010.12 This 

corresponds to a 30 percent reduction in U5MR since 1990, well short of the target of a two-thirds 

reduction.9

 The annual rate of reduction in U5MR has been 1.8 percent over the period 1990-2010, 

although the region experienced a more rapid rate of reduction from 2000-2010, at an annual rate of 2.4 

percent, double the 1.2 percent rate between 1990 and 2000. Compared with other developing regions, 

however, sub-Saharan Africa has experienced slower rates of decline in U5MR and continues to have 

higher fertility rates. 
Figure 1. Hypothesized relational structure among child mortality; child, household/maternal, and community determinants; and 

country-level economic growth, social determinants, and health system and policy variables Note: Adapted from Bronfrenbrenner4, Mosley & Chen3, Boyle5, and Bhutta.6

2 
3 

It has been argued that poor economic performance and poverty are key drivers of poor health and 

nutrition in developing countries.13 It has also been argued, however, that economic growth and prosperity 

do not automatically imply improved health and that other factors, such as greater equality in the 

distribution of wealth, seem to be important in improving health.14 Economic growth continues to be 

promoted as a policy instrument in low-income countries, under the premise that such growth will 

improve average incomes, lift people out of poverty, and improve health by encouraging greater 

consumption of health-promoting goods and services.15

An alternative policy approach to improving health has been to encourage the strengthening of health 

systems in low-income countries,16 and in particular improving the coverage of key maternal, newborn, 

and child health interventions.17, 18 Coverage of interventions such as measles and diphtheria, pertussis, 

and tetanus (DPT) vaccinations, and skilled birth attendance are widely accepted indicators of progress 

toward improving health systems and achieving the MDGs.19, 20 Further, the coverage and equity 

distribution of a set of core MNCH interventions are being tracked across a range of low- and middleincome countries as we approach the 2015 MDG deadline.21 To date, however, there has been limited 

evidence on how the coverage of these interventions is related to declines in U5MR, as these measures are 

considered long-term indicators of health systems performance and are insensitive to shorter-term 

changes in coverage.22

In this report, we investigate factors that have contributed to the recent declines in U5MR in sub-Saharan 

Africa since 1990, using data from the Demographic and Health Surveys (DHS). Specifically, we focus 

on the contributions of economic growth and MNCH interventions in reducing child mortality in subSaharan Africa. We examine those MNCH interventions that can be estimated from the DHS data and 

that have reliable evidence for reducing child mortality in low-income countries: contraceptive 

prevalence; antenatal care; skilled birth attendance; immunization; treatment of childhood illnesses; and 

improved water and sanitation facilities.6, 17, 23-38

5 

Methods 

Data Sources 

We used data from DHS surveys39 conducted since 1990. We selected surveys for each country that 

included birth histories (‘BR’ files), MNCH coverage indicators, assessments of household wealth, and 

the standard DHS wealth index included with the survey datasets.40, 41 The sample included all DHS or 

related surveys with a ‘BR’ file from which child mortality rates could be calculated. Related surveys 

include the AIDS Indicator Surveys (AIS) and the Malaria Indicator Surveys (MIS). 

In total, 99 surveys are included in this report, covering 36 countries and 93 percent of the population of 

sub-Saharan Africa.42 Thirty-eight surveys were conducted before 2000, 20 between 2000 and 2004, 24 

between 2005 and 2008, and 19 since 2009. Twenty-five of the 36 countries conducted at least two 

surveys during this period, and 20 conducted three or more. 

Sampling Plan 

The DHS household surveys use nationally representative sampling plans and have special emphasis on 

fertility, child mortality, and indicators of maternal and child health.39 The target population in DHS 

surveys considered in this report is women age 15-49 and their children. A complete birth and death 

history was collected for each eligible woman survey respondent and included the date of birth, and if 

applicable the date of death, of all children ever born. Due to the coverage, comparability, and data 

quality of DHS,43 it is possible to conduct pooled and cross-country comparative analyses.39

Study Population, Data Designs, and Sample Sizes 

In this report the study population was structured as two distinct data designs. First, we examined the 

study population as an ecological time series design, where the lowest level of analysis was the survey 

period, which was nested within countries as a hierarchical structure. This can be shown graphically, with 

rectangles representing units of analysis and arrows representing classification hierarchies. Figure 2

presents a graphical classification diagram for the ecological time series data structure. 

Second, we used a multilevel repeated cross-sectional design, where children at the lowest unit of analysis 

were nested within primary sampling units (PSU), within survey periods, and within countries (Figure 3). 

A key substantive advantage of the second approach is the ability to account for within-country betweenchild factors that can influence both child mortality and the country-level economic development and 

coverage indicators. Further, the ecological time series data structure assumes that the probability of 

dying (or U5MR) is the same for all children within a country period. This assumption is relaxed in the 

multilevel data structure, although in doing so we are modeling the probability of a child dying before the 

fifth birthday, and not U5MR. 

Figure 2. Classification diagram for the ecological time series data structure with survey periods 

nested within countries 
6 

Figure 3. Classification diagram for the multilevel data structure with children nested within 

primary sample units (PSU), within survey periods, and within countries 

Ecological time series data structure 

For the ecological time series design, the lowest unit of analysis was the survey period, nested within 

country. In total, 99 surveys were included in this analysis, covering 36 countries, with between one and 

five survey periods per country. In total, 25 countries had at least two survey periods, and the average 

number of survey periods across all countries was 2.8. The ecological time series design accounts for the 

nested structure of survey periods within countries (Figure 2). On the subset of countries (n = 25) where 

more than one survey was available, we used a change-on-change approach to examine changes in U5MR 

occurring between the first and last survey periods, corresponding to changes in the explanatory variables 

over the same period. In both approaches the U5MR corresponds to a five-year reference period 

immediately preceding the survey. 

Multilevel repeated cross-sectional data structure 

For the multilevel design, the lowest unit of analysis was at the child level. In this approach, a nested 

hierarchy was defined which included children at level one, nested in primary sample units (PSUs) at 

level two, nested in countries at level three (Figure 3). Children across all surveys were pooled, and we 

defined a reference period from which to examine the probability of child death as the five-year period 

immediately preceding the survey. In total, there were 748,363 birth histories of children born within the 

reference period. After making exclusions for missing data, the final analytical sample size was 722,353. 

Maternal height was included as a potential confounder. In models where maternal height was included, 

the sample size was 509,432, because anthropometric measurements for women were not available in all 

99 surveys. 

Outcomes 

This report uses two distinct outcomes, corresponding to the two data designs employed. In the ecologic 

time series design, the outcome is the calculated U5MR in each of the 99 surveys. In the multilevel 

design, the outcome is the probability of child death occurring within five years prior to the survey 

(reference period). 
7 

Ecological time series data structure 

Levels of child mortality are typically expressed as probabilities of dying between exact ages (x and x+n), 

which are derived from life tables and denoted by nqx.

44 The U5MR, also denoted 5q0, is formally defined 

as the probability of a child death occurring between birth and a child’s fifth birthday.12 Further, the 

U5MR is a composite measure of mortality occurring during the first five years, which can be further 

defined as the probability of dying within 1 month (neonatal mortality), 0-11 months (infant mortality, 

including neonatal deaths, or 1q0), and 12-59 months (child mortality, conditional on having reached the 

first birthday, or 4q1).45 The U5MR is expressed as deaths per 1,000 live births.12, 44

Under-five mortality rates were calculated using the DHS synthetic cohort life table methodology.46 This 

approach uses age segments 0, 1–2, 3–5, 6–11, 12–23, 24–35, 36–47, 48–59 months (completed ages) for 

the calculation of the individual probabilities of dying, without adjustment for heaping at age of death of 

12 months.46 Imputation procedures were used for children with missing ages at death. The calculation of 

the U5MR was based on the number of deaths to live-born children in the five-year reference period 

preceding the survey. Death probabilities were calculated for each of the age segments defined above and 

then combined into the mortality rate as the product of the component survival probabilities, and 

expressed as a rate per 1,000 live births. 

Multilevel repeated cross-sectional data structure 

In the multilevel design, the outcome was defined at the child level as a child death occurring within the 

reference period. This was expressed as a binary outcome: 1 for a death occurring in the child’s first five 

years; 0 for survival through 59 months of age. This outcome is interpreted as the probability of a child 

death occurring within the first five years of life. 

Exposures 

Per capita gross domestic product (pcGDP) was used as the primary measure of a country’s economic 

growth and development. Per capita GDP is the value of all goods and services produced within a country 

in a given period of time, divided by the total population of that country in the same period. These data 

were obtained from the World Bank47 and corresponded to the five-year reference period preceding the 

survey. Since pcGDP was measured multiple times for 25 countries with repeated DHS, the inclusion of 

pcGDP in regression models allows for the estimation of the effect of within-country change in pcGDP, 

interpreted as economic growth. Analysis of pcGDP was included in regression models as the logarithm 

of pcGDP. 

The second key exposure of interest was coverage of maternal and child health interventions. We selected 

a subset of maternal, newborn, and child health interventions that have sufficient evidence in the literature 

of an effect on reducing child mortality from the major causes of under-five deaths.17, 23-38, 48 These 

interventions, classified by the timing of coverage include: 

Pre-pregnancy: Contraceptive prevalence, demand for family planning satisfied (pre-pregnancy), 1+ ANC 

visits with a skilled provider/4+ visits with any provider, neonatal tetanus protection. 

Pregnancy/birth: Skilled attendant at birth, early initiation of breastfeeding (within 24 hours), BCG, 

DTP3, measles, and full vacation coverage, Vitamin A supplementation, oral rehydration therapy (ORT), 

care-seeking for pneumonia (CPNM), children sleeping under insecticide-treated mosquito nets. 

Infancy/childhood: Improved water and sanitary facilities at last antenatal care visit by a skilled provider, 

skilled birth attendant, measles and DPT3 vaccination coverage, care seeking for pneumonia, tetanus 
8 

vaccination of mother, access to improved sanitary facilities, early initiation of breastfeeding, insecticidetreated bed net use by children, contraceptive prevalence (modern and traditional methods), and access to 

an improved drinking water source. 

Finally, we used a composite variable (the composite coverage index, or CCI) based on a weighed 

average of coverage of a set of eight interventions, giving equal weight to family planning, maternal and 

newborn care, immunization, and case management of sick children, as a way to effectively summarize 

and compare coverage of MNCH interventions across countries and over time.17

Child, maternal, and household-level covariates used with multilevel data structure 

We used a variety of theoretically important maternal and child characteristics as covariates in the 

multilevel regression models.49 Age, sex, multiple/single birth, birth order, and preceding birth interval 

were included as child characteristics, along with age of the mother at birth, maternal education, 

household wealth quintile, area of residence, and maternal height, as maternal characteristics. Child age 

was categorized as 0-1 years, 2-3 years, and 4 years. Sex and twin status were binary variables that 

indicate whether the child was a boy or a girl, and whether a single or multiple birth. Birth order was 

categorized into five groups to indicate first, second, third, fourth, or fifth/higher born. Preceding birth 

interval was recorded in months and categorized as <24 months, 24-47 months, and 48 or more months 

between births. Mother’s age at birth was calculated and categorized as under age 17, 17-19, 20-24, 25-

29, and 30-49. Maternal education was categorized in four groups: no education, any primary, incomplete 

secondary, and complete secondary/higher. 

Household wealth was defined according to an index developed from indicators of household asset 

ownership and housing characteristics (e.g. whether the household had a flush toilet, refrigerator, car, 

moped/motorcycle, television, washing machine, or telephone). Country-specific and weighted linear 

combinations of these items were constructed with weights for each item obtained from a principal 

component analysis.41 The index was then standardized with a mean 0 and standard deviation 1 and, using 

the quintiles of this distribution, the survey population in each country was divided into fifths from 

poorest to richest. Similar measures have been developed in India and other settings and shown to be a 

consistent proxy for household income and expenditure.40 

Whether the household was located in an urban or rural area was included according to country-specific 

definitions. Where available, maternal height in centimeters was included in regression models and 

categorized as: <145 cm, 145-149 cm, 150-154 cm, 155-159 cm, and 160+ cm. The relationship between 

child mortality and these covariates was examined (see Table 8). 

Analysis 

Our analysis uses two approaches that correspond to the two data structures described previously. We 

first describe the ecological time series analyses conducted at the level of surveys within countries, 

followed by the multilevel analyses conducted at the level of children within surveys and countries. 

Ecological time series data structure 

In the ecological time series models, where the survey is the unit of analysis, we fit regression models of 

the form (illustrated with the example of pcGDP as a single covariate), 

ij ijj ijij β0 BSBCy +++= β1 +epcGDP 0 , (1) 
9 

where ij y represents the U5MR for survey time i in country j; β0 represents the intercept or the average 

U5MR holding pcGDP constant ( β1 pcGDPij ), and after accounting for country differences 

( BCj ); BCj represents the country-specific dummy variables estimating differences in U5MR between 

countries; BSij represents the effects associated with dummies for survey years; β1 pcGDPij represents 

the change in U5MR for a unit change in pcGDP; and ij e0 represents the residuals at the survey-year level 

i in country j. β1 is the effect of a one-unit increase in pcGDP on U5MR. 

We first ran a series of ecological regressions that included survey period and one exposure of interest per 

model. We then fit a multivariable model that included pcGDP and the composite coverage indicator 

(CCI). The CCI is used as a summary for all of the other coverage variables to increase model parsimony 

and reduce potential multicollinearity, given the small size of the dataset. 

While these models were also fitted using the ‘random effects’ approach, we focus on the fixed effects 

approach because of the additional assumptions required by the random effects approach, even though 

random effects models are more efficient. Since we want to err on the conservative side with regard to the 

strength of the association between the independent variables and the dependent variable, we use the fixed 

effects approach as the basis for interpretation. It should be noted that, while we control for all countrylevel confounders that do not vary over time, the model also ends up controlling for any country-level 

“mediators” that could potentially underestimate the strength of association. 

This modeling approach used in our analyses is able to account for imbalance in the number of survey 

periods available for each country. In our dataset, several countries have only one survey, whereas others 

have three or more. In a traditional ‘change on change’ regression model, information is lost because 

countries without at least two surveys cannot be included in the model. Further, the fixed effects model 

can readily handle situations where multiple periods are available per country, whereas the ‘change on 

change’ model essentially removes the intervening periods and only examines the first and last. 

Multilevel repeated cross-sectional data structure 

We also conducted multilevel analyses on the dataset where children are the lowest unit of analysis. In 

these analyses the basic model is a two-level multilevel logistic regression model with a binary response 

(y, child death, or not) for child i in PSU j. 50 Countries are treated as fixed effects using country indicator 

variables in the fixed part of the model ( BCk ). The outcome of child mortality, Pr(yijk = 1), is assumed to 

be binomially distributed yijk Binomial π ijk ),1(~ with probability π ij related to the set of independent 

variables Χ and a random effect for each level by a logit link function: 

)( )( Logit π ij β 0 k BSBC jk +++= β1 pcGDPk ij ++ uBX 0 j (2) 

The right-hand side of the equation consists of the fixed part linear predictor 

( k ij jk BX ijk β 0 BSBC +++ β1 pcGDP + ) and random intercepts for PSU ( u0 jk ). The intercept, β 0

represents the log odds of child mortality for the reference group, BSij is a vector of coefficients for 

dummy variables for survey years, β1 pcGDPjk represents the log odds of child mortality for a one-unit 

increase in pcGDP, and the BX represents a vector of coefficients for the log odds of child mortality for 

a one-unit increase for each independent variable. Coefficients are exponentiated and presented as odds 
10 

ratios for interpretation. The random intercepts are assumed to be independently and identically 

distributed and have variances estimated for PSU ( 2 σ u ).51

We fitted a sequence of models where the child- and maternal-level covariates were first entered in to the 

model, followed by one country-level exposure at a time. A final multivariable multilevel model was 

then fitted that included all child and maternal covariates plus important economic and coverage 

indicators from the previous models. 

In both the ecological time series analyses and the multilevel analyses, the country-level economic and 

coverage indicators are defined in standard deviation units. This is to allow comparability of the 

coefficients across different models. Table 1 presents the mean, standard deviation, minimum, maximum, 

and a correlation matrix for all of the economic and coverage indicators at the first survey period. 
Table 1. Descriptive statistics and correlation matrix of all of the economic and coverage indicators measured in the first survey period, 

from Demographic and Health Surveys conducted in 36 sub-Saharan African countries Variable Mean S.D. Min Max 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 1. Log GDP 6.1 0.9 4.7 8.4 1.000 2. CCI 51.9 16.3 20.5 77.1 0.525 1.0003. ANC1 78.9 19.0 26.7 98.9 0.281 0.912 1.0004. ANC4 48.5 20.9 8.6 79.3 0.362 0.879 0.707 1.0005. SBA 42.7 20.9 4.0 85.5 0.508 0.852 0.702 0.632 1.0006. MSLV 63.8 18.5 22.8 94.3 0.167 0.933 0.842 0.607 0.638 1.0007. DPT3 60.4 21.8 20.0 95.7 0.107 0.886 0.820 0.572 0.526 0.959 1.0008. CPNM 45.7 16.2 13.5 75.3 0.385 0.843 0.767 0.814 0.766 0.694 0.643 1.000 9. Tetan 35.8 17.5 0.0 72.6 -0.158 0.184 0.343 0.200 0.254 0.347 0.321 0.235 1.000 10. Sanit 25.8 16.6 4.8 67.6 0.514 0.798 0.475 0.365 0.625 0.576 0.503 0.529 0.042 1.00011. Bednet 23.8 21.2 0.6 56.2 -0.144 0.296 0.337 -0.308 0.439 0.370 0.461 0.650 -0.357 0.061 1.00012. Contra 20.6 14.2 4.1 56.3 0.615 0.849 0.581 0.630 0.610 0.618 0.559 0.521 -0.135 0.542 -0.279 1.00013. Water 58.6 16.7 28.9 92.1 0.571 0.790 0.605 0.497 0.516 0.508 0.474 0.598 -0.101 0.375 0.641 0.606 1.00014. BCG 80.9 16.7 38.8 98.9 0.149 0.913 0.844 0.638 0.648 0.920 0.887 0.703 0.328 0.314 0.160 0.609 0.557 1.00015. Full vacc 48.4 23.0 0.0 86.5 -0.068 0.832 0.698 0.498 0.452 0.941 0.981 0.558 0.326 0.313 0.238 0.519 0.386 0.855 1.00016. VITA 57.6 17.8 25.9 80.7 0.245 0.315 0.120 -0.052 0.138 0.405 0.402 -0.032 -0.446 0.706 -0.240 0.565 0.080 0.286 0.486 1.00017. ORT 35.2 13.0 11.9 62.8 0.155 0.643 0.550 0.652 0.647 0.605 0.565 0.655 0.348 0.444 0.563 0.465 0.450 0.495 0.488 -0.503 1.00018. FPS 39.4 16.8 18.7 79.0 0.630 0.797 0.513 0.573 0.554 0.527 0.453 0.453 -0.186 0.497 -0.448 0.951 0.568 0.530 0.413 0.605 0.336 1.000CCI, composite coverage index; ANC1, 1 ANC visit with skilled provider; ANC4, 4+ ANC visits with any provider; SBA, skilled birth assistance; MSLV, measles vaccination; DPT3, 3 doses of DPT vaccine; CPNM, care seeking for pneumonia; Tetan, tetanus vaccination; Sanit, improved sanitation facilities; Bednet, use of insecticide treated bednets; Contra, 

contraceptive prevalence; Water, improved water; BCG, BCG vaccination coverage; Full vacc, full vaccination coverage; VITA, vitamin A supplementation; ORT, oral rehydration 

therapy; FPS, family planning needs satisfied 

11 

13 

Results 

Trends in U5MR, Economic Growth, and MNCH Intervention Coverage 

Between 1990 and 2012, the U5MR declined in a majority (22 of 25) of sub-Saharan African countries 

where repeated DHS surveys were available, although the rate of change varied across countries (Table 

2). The U5MR ranged from 59.4 deaths per 1,000 live births in South Africa to 317.3 per 1,000 in Niger 

in the initial round of surveys (median year: 1996), corresponding to a five-fold difference across 

countries. In the most recent round of DHS surveys (median year: 2009), the U5MR ranged from 64.6 

deaths per 1,000 live births in Gabon to 197.2 per 1,000 in Niger, indicating a three-fold difference across 

countries. 

Table 2 also shows that for the countries included in this analysis, GDP per capita increased on average 

by US$165 during the study period, although ten countries saw declines in pcGDP between repeated 

surveys. The coverage of maternal, neonatal, and child health interventions generally increased between 

1990 and 2012. For example, the coverage of at least one antenatal care (ANC) visit with a skilled 

provider increased from 60 percent in Burkina Faso in 1993 to nearly 95 percent in 2010. There were 

modest declines (from 2 percent to 7 percent) observed in measles vaccination coverage in a number of 

countries during the study period. 

The rate of decline in U5MR was faster in urban than rural areas and in the richest groups compared with 

the poorest groups for two-thirds (16 of 25) of countries. In 40 percent of the ountries (10 of 25), the rate 

of mortality decline was faster in the highest education groups compared with those with no education 

(Table 3). 
Table 2. Year of survey, under-five mortality rates, and country-level economic and coverage indicators of maternal and child health 

interventions in 36 sub-Saharan African countries for first and repeated surveys 

First survey Repeated survey 

Country Year N U5MR pcGDP ANC SBA MSLV Water Year N U5MR pcGDP ANC SBA MSLV Water Angola 2011 8,354 90.5 2,441 75.7 . . 53.4 . . . . . Burundi 2010 7,883 96.1 144 98.9 60.3 94.3 74.2 . . . . . Benin 1996 5,274 166.4 383 80.5 37.1 64.3 62.6 2006 16,403 124.8 348 88.0 77.7 61.1 71.5 

Burkina Faso 1993 5,923 187.0 313 60.0 41.4 59.6 47.2 2010 15,356 128.4 407 94.9 67.1 87.3 78.2 

Central African Republic 

1994 4,857 157.4 445 66.4 27.3 52.4 59.6 . . . 

Cote d'Ivoire 1994 6,990 149.4 833 83.2 26.5 53.1 76.6 2005 3,703 124.7 646 87.3 54.9 78.6 Cameroon 1991 3,400 125.2 992 78.2 58.1 56.0 50.3 2011 12,018 121.8 965 84.7 63.6 70.6 74.4 

Congo, Dem. Rep. 

2007 9,,181 147.7 112 85.3 52.8 62.9 45.3 . . . 

Congo, Rep. 2005 4,932 116.5 1,030 86.9 31.2 66.2 71.6 . . . Comoros 1996 2,062 103.9 584 83.9 29.7 63.4 90.0 . . . Ethiopia 2000 11,120 166.1 133 26.7 5.6 26.6 28.9 2011 11,969 88.0 194 42.6 10.0 55.7 49.0 Gabon 2000 4,463 88.6 4,589 95.1 85.5 54.8 85.3 2012 6,173 64.6 7,994 95.0 89.3 74.3 92.2 

Ghana 1993 3,849 119.4 375 85.7 25.5 64.4 58.9 2008 3,047 80.0 376 95.4 58.7 90.2 83.4 Guinea 1999 6,050 176.6 450 73.5 34.8 52.1 62.0 2005 6,573 163.0 342 82.1 32.4 50.2 68.9 Kenya 1993 6,271 96.0 382 94.8 45.1 83.8 45.5 2008 6,173 73.6 440 91.7 44.3 85.0 58.5 Liberia 2007 5,890 109.5 175 79.3 46.3 63.0 69.4 2009 4,296 114.4 147 71.9 Lesotho 2004 3,784 112.8 436 90.4 55.4 84.9 78.8 2009 4,072 116.7 645 91.8 72.6 80.3 78.0 

Madagascar 1992 6,473 162.6 243 78.8 56.7 54.0 30.7 2008 12,740 71.7 317 86.3 43.9 69.6 45.2 

Mali 1995 10,506 237.2 304 47.5 13.9 50.8 36.7 2006 14,526 190.3 249 37.2 26.8 68.4 56.3 Mozambique 1997 7,320 200.8 137 72.0 25.3 57.5 39.4 2011 11,356 96.9 329 90.6 54.3 81.5 47.2 

Malawi 1992 4,587 233.3 145 90.6 54.9 85.9 45.9 2010 20,437 112.1 213 97.5 71.3 93.0 81.8 

Namibia 1992 4,000 83.8 1,551 87.7 68.2 75.6 67.6 2006 5,281 69.3 1,836 94.6 81.4 83.8 89.3 Niger 1992 7,029 317.3 315 31.0 14.9 27.8 36.2 2006 9,353 197.2 171 46.1 17.6 47.0 46.8 

Nigeria 1990 8,091 192.5 339 58.9 32.0 45.6 47.2 2010 6,111 142.8 804 57.5 60.6 

(Continued...) 

14 
Table 2. – Continued

First survey Repeated survey 

Country Year N U5MR pcGDP ANC SBA MSLV Water Year N U5MR pcGDP ANC SBA MSLV Water Rwanda 1992 5,663 150.7 320 94.3 25.9 89.8 62.3 2010 9,237 75.7 274 98.0 69.0 95.0 68.7 Senegal 1992 5,765 131.7 736 74.2 47.2 57.2 60.9 2010 12,542 71.6 772 93.3 65.0 82.1 72.7 Sierra Leone 2008 5,785 139.6 291 86.9 42.4 59.7 54.5 . . . Sao Tome and Principe 

2008 1,972 62.5 665 97.9 81.7 84.0 92.1 . . . 

Swaziland 2006 2,866 119.8 1,255 97.1 74.3 91.5 62.9 . . . Chad 1996 5,785 194.0 306 32.6 4.0 22.8 42.8 2004 5,785 190.5 192 42.6 14.5 22.8 46.7 

Togo 1998 7,188 146.1 302 82.8 29.7 42.6 52.3 . . . Tanzania 1991 8,310 140.8 219 92.0 53.1 81.2 54.9 2010 8,148 81.1 375 87.7 48.9 84.5 53.4 Uganda 1995 7,344 147.3 245 91.5 30.5 59.6 48.7 2011 8,057 90.0 336 94.9 59.2 75.8 74.8 South Africa 1998 5,167 59.4 3,480 95.0 84.4 82.2 85.5 . . . Zambia 1992 6,411 190.9 313 92.2 50.4 77.0 49.4 2001 6,499 168.0 360 93.4 43.4 84.4 54.5 

Zimbabwe 1994 4,187 77.1 815 93.1 41.2 86.3 79.4 2010 5,630 84.1 453 89.8 66.2 79.1 79.9 

U5MR, under-five mortality rate pcGDP, per capita gross domestic product ANC, 1+ antenatal care visit with skilled provider SBA, skilled birth attendant; MSLV, measles vaccination coverage; Water, coverage of improved drinking water sources 

15 
Table 3. U5MR in 36 sub-Saharan African countries and rates of change according to urban/rural, household wealth, and education

population subgroups 

 U5MR Annualized % Change 

Country Year Overall Urban Rural 

Poorest 

20% 

Richest 

20% 

No 

education

Secondary+ Overall Urban Rural Poorest 20% Richest 20% No education Second- ary+ 

Angola 2011 90.5 74.4 98.6 85.8 77.1 90.1 56.9 . . . . . . . Burundi 2010 96.1 66.5 98.7 116.4 66.7 110.7 42.8 . . . . . . . Benin 1996 166.4 142.5 177.6 188.6 114.4 175.1 75.0 . . . . . . . 

2006 124.8 105.9 134.3 138.1 75.3 131.1 66.8 -2.5 -2.6 -2.4 -2.7 -3.4 -2.5 -1.1 

Burkina Faso 1993 187.0 132.1 196.4 179.7 137.1 192.5 88.2 . . . . . . . 

2010 128.4 81.7 137.5 149.0 78.3 136.8 55.8 -1.8 -2.2 -1.8 -1.0 -2.5 -1.7 -2.2 

CAR 1994 157.4 133.8 173.2 167.5 108.5 174.2 86.9 . . . . . . . Cote d'Ivoire 1994 149.4 126.5 161.2 186.9 105.2 158.5 107.4 . . . . . . . 

2005 124.7 105.4 137.2 151.4 105.5 128.7 97.0 -1.5 -1.5 -1.4 -1.7 0.0 -1.7 -0.9 

Cameroon 1991 125.2 107.0 136.7 186.8 80.0 180.3 73.6 . . . . . . . 

2011 121.8 89.6 144.8 178.3 69.1 166.0 73.6 -0.1 -0.8 0.3 -0.2 -0.7 -0.4 0.0 

Congo, Dem. Rep. 

2007 147.7 114.9 168.3 174.6 83.0 199.1 107.2 . . . . . . . 

Congo, Rep. 2005 116.5 91.9 137.1 123.5 80.7 166.0 92.1 . . . . . . . Comoros 1996 103.9 67.1 115.4 122.5 82.6 117.7 53.0 . . . . . . . Ethiopia 2000 166.1 134.9 169.7 137.6 140.7 174.7 93.0 . . . . . . . 

2011 88.0 68.7 90.9 112.1 73.7 95.5 34.4 -4.3 -4.5 -4.2 -1.7 -4.3 -4.1 -5.7 

Gabon 2000 88.6 83.6 102.6 93.5 40.3 112.1 81.4 . . . . . . . 

2012 64.6 62.0 78.0 68.1 50.1 104.1 60.4 -2.3 -2.2 -2.0 -2.3 2.0 -0.6 -2.2 

Ghana 1993 119.4 96.7 127.9 147.2 78.0 149.1 39.0 . . . . . . . 

2008 80.0 72.1 84.7 97.2 62.3 89.2 65.6 -2.2 -1.7 -2.3 -2.3 -1.3 -2.7 4.5 

Guinea 1999 176.6 135.3 190.1 206.4 127.8 184.5 83.5 . . . . . . . 

2005 163.0 124.3 174.6 190.5 106.4 167.1 78.4 -1.3 -1.4 -1.4 -1.3 -2.8 -1.6 -1.0 

Kenya 1993 96.0 85.2 97.6 126.7 70.5 98.7 56.3 . . . . . . . 

2008 73.6 64.6 75.5 88.7 68.4 83.1 60.1 -1.6 -1.6 -1.5 -2.0 -0.2 -1.1 0.5 

(Continued...) 

16 
Table 3. – Continued

 U5MR Annualized % Change 

Country Year Overall Urban Rural 

Poorest 

20% 

Richest 

20% 

No 

education

Secondary+ Overall Urban Rural Poorest 20% Richest 20% No education Second- ary+ 

Liberia 2007 109.5 110.1 109.3 112.5 103.3 105.1 105.7 . . . . . . . 

2009 114.4 102.2 121.7 121.5 109.1 121.5 103.6 2.2 -3.6 5.7 4.0 2.8 7.8 -1.0 

Lesotho 2004 112.8 118.3 111.3 109.5 103.6 201.7 100.2 . . . . . . . 

2009 116.7 94.7 123.6 118.0 84.9 77.3 97.4 0.7 -4.0 2.2 1.6 -3.6 -12.3 -0.6 

Madagascar 1992 162.6 124.5 168.4 146.3 217.5 199.5 100.8 . . . . . . . 

2008 71.7 56.9 73.5 88.2 47.9 82.8 55.2 -3.5 -3.4 -3.5 -2.5 -4.9 -3.7 -2.8 

Mali 1995 237.2 180.3 256.3 275.3 164.2 250.2 81.3 . . . . . . . 

2006 190.3 133.3 210.8 211.3 107.7 198.0 81.9 -1.8 -2.4 -1.6 -2.1 -3.1 -1.9 0.1 

Mozambique 1997 200.8 174.2 208.4 236.7 156.4 210.1 108.8 . . . . . . . 

2011 96.9 90.6 99.3 110.2 87.5 95.3 73.5 -3.7 -3.4 -3.7 -3.8 -3.1 -3.9 -2.3 

Malawi 1992 233.3 216.6 235.6 242.0 189.9 233.6 143.4 . . . . . . . 

2010 112.1 117.3 111.2 109.7 95.3 112.1 88.8 -2.9 -2.5 -2.9 -3.0 -2.8 -2.9 -2.1 

Namibia 1992 83.8 73.9 88.7 90.5 73.1 91.5 73.3 . . . . . . . 

2006 69.3 60.6 75.7 90.8 36.2 60.5 60.1 -1.2 -1.3 -1.0 0.0 -3.6 -2.4 -1.3 

Niger 1992 317.3 203.1 337.8 310.6 237.7 326.7 104.3 . . . . . . . 

2006 197.2 126.3 209.5 182.5 146.2 202.5 76.9 -2.7 -2.7 -2.7 -2.9 -2.7 -2.7 -1.9 

Nigeria 1990 192.5 132.8 208.6 225.2 125.8 216.4 115.5 . . . . . . . 

2010 142.8 107.8 152.9 166.1 76.3 172.5 90.0 -1.3 -0.9 -1.3 -1.3 -2.0 -1.0 -1.1 

Rwanda 1992 150.7 150.9 150.7 128.3 141.6 160.6 104.5 . . . . . . . 

2010 75.7 66.1 76.9 85.3 54.9 93.6 58.3 -2.8 -3.1 -2.7 -1.9 -3.4 -2.3 -2.5 

Senegal 1992 131.7 88.5 153.3 172.6 64.6 143.3 49.0 . . . . . . . 

2010 71.6 51.2 84.2 96.2 43.2 81.7 26.3 -2.5 -2.3 -2.5 -2.5 -1.8 -2.4 -2.6 

Sierra Leone 2008 139.6 153.9 134.4 171.1 140.6 139.6 126.8 . . . . . . . Sao Tome and Principe 

2008 62.5 76.1 49.2 91.2 27.4 146.3 47.0 . . . . . . . 

Swaziland 2006 119.8 137.8 114.4 129.0 116.1 175.5 108.9 . . . . . . . 

(Continued...) 

17 
Table 3. – Continued

 U5MR Annualized % Change 

Country Year Overall Urban Rural 

Poorest 

20% 

Richest 

20% 

No 

education

Secondary+ Overall Urban Rural Poorest 20% Richest 20% No education Second- ary+ 

Chad 1996 194.0 197.3 193.0 159.4 183.1 195.5 171.2 . . . . . . . 

2004 190.5 167.3 195.8 161.4 177.1 186.0 154.6 -0.2 -1.9 0.2 0.2 -0.4 -0.6 -1.2 

Togo 1998 146.1 104.7 158.9 172.3 94.3 159.8 83.8 . . . . . . . Tanzania 1991 140.8 156.4 136.7 135.8 111.9 142.1 90.8 . . . . . . . 

2010 81.1 100.6 76.2 88.8 87.3 77.1 76.2 -2.2 -1.9 -2.3 -1.8 -1.2 -2.4 -0.8 

Uganda 1995 147.3 129.2 149.7 170.2 105.9 161.1 100.8 . . . . . . . 

2011 90.0 63.7 94.1 105.6 56.5 101.3 67.6 -2.4 -3.2 -2.3 -2.4 -2.9 -2.3 -2.1 

South Africa 1998 59.4 47.3 71.7 92.3 24.9 98.4 45.3 . . . . . . . Zambia 1992 190.9 173.0 206.5 221.6 133.6 204.0 159.4 . . . . . . . 

2001 168.0 151.5 175.8 173.9 98.3 189.2 127.5 -1.3 -1.4 -1.7 -2.4 -2.9 -0.8 -2.2 

Zimbabwe 1994 77.1 66.1 81.0 82.4 66.6 79.5 65.0 . . . . . . . 

2010 84.1 86.7 83.0 85.8 58.1 72.6 80.1 0.6 2.0 0.2 0.3 -0.8 -0.5 1.5 

Annualized % change is change in U5MR divided by number of years between first and last repeated survey, expressed as a percentage 

18 
19 

Change in U5MR against Change in Pcgdp and Change in MNCH Intervention Coverage 

Figures 4-6 plot the association between changes in U5MR and changes in pcGDP or coverage of MNCH 

interventions over the study period, along with the fitted linear regression line for a subset of 25 subSaharan African countries with at least two DHS surveys conducted during the study period. The 

regression equation including the intercept and slope from these models is also displayed in the figures. 

Many of these plots demonstrate a negative association, indicating that increases in pcGDP or coverage of 

MNCH interventions are associated with decreases in U5MR. 

Table 4 presents the results of the regression from the ecological time series data of U5MR on country 

pcGDP and coverage of MNCH interventions. These models were fitted with one economic/coverage 

indicator at a time and included an indicator for survey period. We fitted models using fixed effects 

specifications, as described in the methods section, and coefficients represent the change in U5MR per 

one standard deviation increase in each of the economic/coverage indicators. Due to the more 

conservative approach and relatively small sample size (99 survey period units), there is less precision in 

the effect sizes from the fixed effects model compared with random effects models because between-unit 

effects are modeled as variables in the fixed part. Although less efficient (compared with a random effects 

approach), this approach is less biased if the causal model is one where between-unit effects are entirely 

confounders. 

In the fixed effects analyses, per capita GDP has a negative but not statistically significant effect (β = -

11.6, 95% CI: -29.1 to 5.9). The fixed effects specification indicates that the largest effects are observed 

for the composite coverage index (CCI) (β = -31.9, 95% CI: -48.6 to -15.3), 1+ ANC visit with a skilled 

provider (β = -21.9, 95% CI: -40.6 to -3.2), 4+ ANC visits with any provider (β = -17.9, 95% CI: -32.1 to 

-3.8), and skilled attendance at birth (β = -16.5, 95% CI: -28.2 to -4.8). 

We also fitted a series of multivariable regressions to the ecological time series data, which included an 

indicator for survey period, pcGDP, and CCI (Table 5). In models with both pcGDP and CCI, the CCI is 

the stronger predictor of reduction in U5MR. For example, in the fixed effects model the coefficient for 

CCI is -31.8 (95% CI: -48.4 to -15.1) compared with a coefficient of -9.2 (95% CI: -27.9 to 9.4) for 

pcGDP, which is negative but not statistically significant. Further, the coefficient for CCI is essentially 

unchanged even after the inclusion of pcGDP to the model; the coefficient for pcGDP remained 

statistically non-significant in both models (p>0.05). 
20 

Table 4. Results of linear regression analyses of U5MR on country-level economic and health 

coverage indicators from the ecological time series data structure treating countries as fixed and 

random effects

 Fixed effects 

Economic and coverage indicators β SE 95% CI p-value 

GDP (log) -11.6 8.9 -29.1 , 5.9 0.195 

Composite coverage index -31.9 8.5 -48.6 , -15.3 0.000 

Antenatal care (1+ visits with skilled provider) -21.9 9.5 -40.6 , -3.2 0.021 

Antenatal care (4+ visits with any provider) -0.4 6.4 -13.0 , 12.2 0.948 

Skilled birth attendant -7.0 6.6 -19.8 , 5.9 0.289 

Early breastfeeding -6.6 4.7 -15.8 , 2.5 0.154 

BCG vaccination coverage -5.8 4.6 -14.8 , 3.1 0.200 

DPT 3 vaccination coverage -3.9 4.5 -12.8 , 4.9 0.386 

Measles vaccination coverage -10.4 5.2 -20.6 , -0.2 0.047 

Full vaccination coverage -4.8 4.4 -13.5 , 3.8 0.274 

Vitamin A supplementation -1.0 4.5 -9.8 , 7.9 0.832 

ORT for diarrhea -5.7 5.4 -16.2 , 4.9 0.290 

Care seeking for pneumonia -14.3 5.3 -24.7 , -4.0 0.006 

Insecticide-treated bednets for children -14.3 5.0 -24.1 , -4.5 0.004 

Contraceptive prevalence -17.9 7.2 -32.1 , -3.8 0.013 

Family planning need satisfied -16.5 6.0 -28.2 , -4.8 0.006 

Tetanus vaccination coverage -5.6 4.0 -13.3 , 2.2 0.159 

Improved sanitary facilities -36.5 15.2 -66.2 , -6.8 0.016 

Safe water -14.7 9.0 -32.3 , 2.8 0.100 

Table 5. Results of the multivariable ecological time series regressions of U5MR on country-level 

covariates 

Economic and coverage indicators β* SE 95% CI 

Period -12.2 3.7 -19.5 , -5.0 

GDP -9.2 9.5 -27.9 , 9.4 

CCI -31.8 8.5 -48.4 , -15.1 

*Country as fixed effects 

Models include an indicator for period, pcGDP, and CCI 

 
21 

Figure 4. Change in U5MR and against change in log of pcGDP (left panel); change in U5MR 

against change in the composite coverage index, 25 sub-Saharan African countries 

-0.5 0.0 0.5

-120 -100 -80 -60 -40 -20 0

Change in log GDP

Change in log GDP

Change in U5MR (per 1000 live births)

BEN

BFA

CIV

CMR

ETH

GAB

GHA

GIN

KEN

LBR LSO

MLI

MOZ

NAM

NER

NGA

RWA

SEN

TCD

TZA UGA

ZMB

ZWE

y= -39.79 + -20.923x
22 

0 5 10 15 20

-120 -100 -80 -60 -40 -20 0

Change in composite coverage index

Change in composite coverage index

Change in U5MR (per 1000 live births)

BEN

BFA

ETH

GAB

GIN

MLI

MOZ

NER

TCD

UGA

ZWE

y = -9.586 + -4.229x
23 

Figure 5. Change in U5MR and against change in coverage of MNCH interventions delivered 

during the infancy and childhood period 

-10 0 10 20 30

-120 -80 -40 0

Change in coverage of 1+ ANC (skilled)

Change in coverage of 1+ ANC (skilled)

Change in U5MR (per 1000 live births)

BEN

BFA

CIV

CMR

ETH

GAB

GHA

GIN

KEN

LSO LBR

MLI

MOZ

NAM

NER

NGA

RWA

SEN

TCD

TZA UGA

ZMB

ZWE

y = -34.192 + -1.037x

-10 0 10 20

-120 -80 -40 0

Change in coverage of BCG vaccination

Change in coverage of BCG vaccination

Change in U5MR (per 1000 live births)

BEN

BFA

CMR

ETH

GAB

GHA

GIN

KEN

LSO

MLI

MOZ

NAM

NER

NGA

RWA

SEN

TCD

TZA UGA

ZMB

ZWE

y = -35.601 + -1.859x

-20 -15 -10

-120 -80 -40 0

Change in coverage of vitamin A suppl

Change in coverage of vitamin A suppl

Change in U5MR (per 1000 live births)

ETH

LSO

y = -111.963 + -4.96x

-30 -10 0 10 20 30

-120 -80 -40 0

Change in coverage of 4+ ANC (any)

Change in coverage of 4+ ANC (any)

Change in U5MR (per 1000 live births)

BEN

BFA

CIV

CMR

ETH

GAB

GHA

GIN

KEN

LSO

MLI

MOZ

NAM

NER

RWA

SEN

TCD

TZA UGA

ZMB

ZWE

y = -40.858 +-0.378x

-10 0 10 20 30 40 50

-120 -80 -40 0

Change in coverage of DPT 3 vaccination

Change in coverage of DPT 3 vaccination Change in U5MR (per 1000 live births)

BEN

BFA

CMR

ETH

GAB

GHA

GIN

KEN

LSO

MLI

MOZ

NAM

NER

RWA

SEN

TCD

TZAUGA

ZWE

y = -30.63 +-1.036x

-10 0 5 10 20

-120 -80 -40 0

Change in coverage of oral rehydration therapy

Change in coverage of oral rehydration therapy Change in U5MR (per 1000 live births)

BEN

BFA

ETH

GAB

GIN

MLI

MOZ

NER

TCD

UGA

ZWE

y = -33.154 +-1.535x

y
24 

-60 -40 -20 0 20 40

-120 -80 -40 0

Change in coverage of skilled birth attendant

Change in coverage of skilled birth attendant Change in U5MR (per 1000 live births)

BEN

BFA

CIV

CMR

ETH

GAB

GHA

GIN

KEN

LSO

MLI

MOZ

NAM

NER

RWA

SEN

TCD

TZA UGA

ZMB

ZWE

y = -40.829 +-0.153x

0 10 20 30

-120 -80 -40 0

Change in coverage of Measles vaccination

Change in coverage of Measles vaccination Change in U5MR (per 1000 live births)

BEN

BFA

CMR

ETH

GAB

GHA

GIN

KEN

LSO

MLI

MOZ

NAM

NER

RWA

SEN

TCD

TZA UGA

ZMB

WE

y = -24.448 +-1.732x

-10 0 10 20 30

-120 -80 -40 0

Change in coverage of care seeking for pneumoni

Change in coverage of care seeking for pneumonia Change in U5MR (per 1000 live births)

BEN

BFA

CMR

ETH

GAB

GHA

GIN

KEN

LSO

MLI

MOZ

NAM

NER

RWA

SEN

TCD

TZA UGA

ZMB

ZWE

y = -30.921 +-1.866x

-60 -40 -20 0 20 40

-120 -80 -40 0

Change in coverage of early breastfeeding

Change in coverage of early breastfeeding Change in U5MR (per 1000 live births)

BEN

BFA

CMR

ETH

GAB

GHA

GIN

KEN

LSO

MLI

MOZ

NAM

NER

RWA

SEN

TCD

UGA TZA

ZMB

ZWE

y = -40.619 + -0.221x

-20 0 20 40

-120 -80 -40 0

Change in coverage of Full vaccination

Change in coverage of Full vaccination

Change in U5MR (per 1000 live births)

BEN

BFA

CMR

ETH

GAB

GHA

GIN

KEN

LSO

MLI

MOZ

NAM

NER

NGA

RWA

SEN

TCD

TZA UGA

ZWE

y = -38.962 + -0.759x

pneumonia 
25 

Figure 6. Change in U5MR and against change in coverage of MNCH interventions delivered 

during the pre-pregnancy period and improved sanitary facilities and drinking water source 

-10 0 10 20 30

-120 -100 -80 -60 -40 -20 0

Change in Contraceptive prevalence

Change in Contraceptive prevalence

Change in U5MR (per 1000 live births)

BEN

BFA

CMR

ETH

GAB

GHA

GIN

KEN

LSO

MLI

MOZ

NAM

NER

RWA

SEN

TCD

TZA UGA

ZMB

ZWE

y = -38.871 + -0.516x

-5 0 5 10 15 20 25

-120 -100 -80 -60 -40 -20 0

Change in coverage of Improved sanitary facilities

Change in coverage of Improved sanitary facilities

Change in U5MR (per 1000 live births)

BEN

BFA

CIV

CMR

ETH

GAB

GHA

GIN

KEN

LSOLBR

MLI

MOZ

NAM

NER

NGA

RWA

SEN

TCD

TZAUGA

ZMB

ZWE

y = -30.259 + -2.393x
26 

-10 0 10 20 30 40

-120 -100 -80 -60 -40 -20 0

Change in Family Planning needs satisfied

Change in Family Planning needs satisfied

Change in U5MR (per 1000 live births)

BEN

BFA

CMR

ETH

GAB

GHA

GIN

KEN

LSO

MLI

MOZ

NAM

NER

RWA

SEN

TCD

TZA UGA

ZMB

ZWE

y= -29.075 + -1.18x

0 5 10 15 20 25 30

-120 -100 -80 -60 -40 -20 0

Change in coverage of Improved drinking water source

Change in coverage of Improved drinking w ater source

Change in U5MR (per 1000 live births)

BEN

BFA

CIV

CMR

ETH

GAB

GHA

GIN

KEN

LSO LBR

MLI

MOZ

NAM

NER

NGA

RWA

SEN

TCD

TZA UGA

ZMB

ZWE

y= -31.83 + -0.816x
27 

-40 -30 -20 -10 0 10 20

-120 -100 -80 -60 -40 -20 0

Change in coverage of Tetanus vaccination

Change in coverage of Tetanus vaccination

Change in U5MR (per 1000 live births)

BEN

BFA

CMR

ETH

GAB

GHA

GIN

KEN

LSO

MLI

MOZ

NAM

NER

RWA

SEN

TCD

TZA UGA

ZMB

ZWE

y = -44.129 + 0.006x

Multilevel Analyses Examining the Probability of Child-Level Mortality against Indicators of 

Economic Growth and MNCH Intervention Coverage

Our second major set of analyses examined factors associated with the probability of a child dying at age 

0-59 months within a period of five years preceding each survey. We first examined the association 

between child and maternal covariates and child mortality at an individual level. These analyses used 

three-level logistic multilevel models with children (level one) nested in PSU (level two), nested within 

countries (level three). The full equation was given previously in equation 2. The primary motivation was 

to examine the probability of mortality at the child level so as to further control for child, maternal, and 

household-level factors that may be associated with child mortality and pcGDP and CCI/MNCH, which is 

not possible to do using ecological analysis. 

Table 6 presents results of the multilevel logistic regression analyses of individual-level child mortality on 

the economic and MNCH indicators, with adjustment for all child, maternal, and household-level 

covariates. The first set of models includes one economic and MNCH indicator, along with all child, 

maternal, and household-level covariates, and countries are treated as a fixed effect. These models show 

that pcGDP is associated with a reduced probability of child death, with an odds ratio of 0.92 (95% CI: 

0.88-0.95). The CCI is also associated with an even greater reduction in the probability of child mortality, 

with an odds ratio of 0.84 (95% CI: 0.81-0.87). Further, ANC with a skilled provider, care seeking for 

pneumonia, use of insecticide-treated bednets for children, contraceptive prevalence, family planning 

need satisfied, tetanus vaccination of mothers, and access to safe water and improved sanitation facilities 

are associated with reduced odds of child mortality, independent of the child and maternal factors. 
28 

Table 6. Odds ratios for child mortality for country-level economic and maternal/child intervention 

coverage indicators, adjusted for child, maternal, and household-level covariates across 36 subSaharan African countries from a three-level random intercepts logistic regression model 

 Fixed effects 

Economic and coverage indicators OR 95% CI 

 GDP (log) 0.92 (0.88 - 0.95) 

 Composite coverage index 0.84 (0.81 - 0.87) 

 Antenatal care (1+ visits with skilled provider) 0.89 (0.86 - 0.92) 

 Antenatal care (4+ visits with any provider) 1.06 (1.04 - 1.09) 

 Skilled birth attendant 1.01 (0.99 - 1.03) 

 Early breastfeeding 1.01 (1.00 - 1.03) 

 BCG vaccination coverage 1.01 (1.00 - 1.03) 

 DPT 3 vaccination coverage 1.01 (1.00 - 1.03) 

 Measles vaccination coverage 1.00 (0.99 - 1.02) 

 Full vaccination coverage 0.99 (0.98 - 1.01) 

 Vitamin A supplementation 0.99 (0.97 - 1.02) 

 ORT for diarrhea 0.99 (0.97 - 1.01) 

 Care seeking for pneumonia 0.91 (0.90 - 0.93) 

 Insecticide-treated bednets for children 0.89 (0.85 - 0.93) 

 Contraceptive prevalence 0.89 (0.86 - 0.91) 

 Family planning need satisfied 0.92 (0.90 - 0.94) 

 Tetanus vaccination coverage 0.95 (0.93 - 0.96) 

 Improved sanitary facilities 0.72 (0.68 - 0.77) 

 Safe water 0.91 (0.88 - 0.95) 

In these models a three-level structure is specified where children are nested in PSU, with countries as fixed effects. 

All models include an indicator of survey period, sex of child, multiple birth, birth order, birth interval, child’s age, 

mother’s age at birth, mother’s education, household wealth quintile, and area of residence in addition to one countrylevel economic/coverage indicator per model. In fixed effects models, countries are modeled as a set of indicator 

variables entered into the fixed part of a two-level random intercepts logistic regression model. The second level is for 

PSU. 

The second set of models, presented in Table 7, includes all child and maternal covariates in addition to 

three country-level variables shown to be important in predicting child mortality in the previous models: 

pcGDP, the CCI, and access to improved sanitary facilities. PcGDP, the CCI, and improved sanitary 

facilities remain strong predictors of reduced probability of mortality among children age 0-59 months. 

We further examined child, maternal, and household-level factors without the inclusion of the countrylevel economic and coverage indicators. Table 8 presents the unadjusted and adjusted odds ratios for these 

associations. Boys, multiple births, higher order births, older children, children with shorter preceding 

birth intervals, children of mothers with lower levels of education, children in relatively poorer 

households, and children in rural areas have increased odds of death within the first five years of life. 

Women’s education demonstrates a strong protective effect against child death (OR: 0.61, 95% CI: 0.57-

0.66) for women with a secondary or higher level of education. 
29 

Table 7. Odds ratios for child mortality for multivariable models of country-level economic and 

maternal/child intervention coverage indicators adjusted for child, maternal, and household-level 

covariates across 36 sub-Saharan African countries from a three-level random intercepts logistic 

regression model 

 Fixed effects 

Economic and coverage indicators OR 95% CI 

 GDP 0.96 (0.92 - 1.00) 

 CCI 0.92 (0.88 - 0.96) 

 Improved sanitary facilities 0.57 (0.50 - 0.65) 

In these models a three-level structure is specified where children are nested in PSU, nested in countries. PSU and 

countries are treated as random effects in the random effects models. All models include an indicator of survey 

period, sex of child, multiple birth, birth order, birth interval, child’s age, mother’s age at birth, mother’s education, 

household wealth quintile, and area of residence, in addition to four country-level economic/coverage indicators per 

model. In fixed effects models, countries are modeled as a set of indicator variables entered into the fixed part of a 

three-level random intercepts logistic regression model. 

Table 8. Risks of child mortality according to child, maternal, and household-level covariates 

across 36 sub-Saharan African countries 

Maternal/child 

covariates 

Children 

(n) % 

Deaths 

(n) OR 95% CI aOR1 95% CI aOR2 95% CI 

Total 748,363 100.0 75,302 0.97 (0.97 -0.97) 0.97 (0.97 - 0.97) 0.97 (0.97 -0.97) 

 

Sex of the child 

 Girl 370,220 49.5 35,166 0.88 (0.87 -0.89) 0.88 (0.86 - 0.89) 0.88 (0.86 -0.90) 

 Boy (reference) 378,143 50.5 40,136 1.00 1.00 1.00 

Multiple birth 

 Singleton 

(reference) 

722,481 96.5 68,219 1.00 1.00 1.00 

 Multiple birth 25,882 3.5 7,083 3.69 (3.58 -3.80) 4.03 (3.91 - 4.16) 4.21 (4.06 -4.37) 

Birth order 

 1st (reference) 162,013 21.6 17,287 1.00 1.00 1.00 

 2nd 136,874 18.3 12,514 0.82 (0.80 -0.84) 1.32 (1.28 - 1.36) 1.32 (1.27 -1.37) 

 3rd 111,471 14.9 10,133 0.80 (0.78 -0.82) 1.31 (1.27 - 1.35) 1.28 (1.23 -1.33) 

 4th 91,560 12.2 8,464 0.80 (0.78 -0.82) 1.30 (1.25 - 1.35) 1.26 (1.21 -1.32) 

 5th or higher 246,445 32.9 26,904 0.92 (0.90 -0.94) 1.39 (1.34 - 1.44) 1.32 (1.27 -1.38) 

Birth interval 

 <24 mo (reference) 280,326 37.5 35,510 1.00 1.00 1.00 

 24-47 mo 345,177 46.2 30,975 0.65 (0.64 -0.66) 0.56 (0.55 - 0.58) 0.56 (0.55 -0.58) 

 48+ mo 121,311 16.2 8,368 0.53 (0.52 -0.54) 0.47 (0.45 - 0.48) 0.46 (0.44 -0.47) 

Child’s age 

 0-1 y (reference) 293,348 39.2 20,909 1.00 1.00 1.00 

 2-3 y 288,883 38.6 32,671 1.68 (1.65 -1.71) 1.64 (1.61 - 1.67) 1.71 (1.67 -1.75) 

 4 y 166,132 22.2 21,722 1.97 (1.93 -2.01) 1.88 (1.84 - 1.92) 1.95 (1.90 -2.00) 

(Continued...)
30 

Table 8. – Continued

Maternal/child 

covariates 

Children 

(n) % 

Deaths 

(n) OR 95% CI aOR1 95% CI aOR2 95% CI 

Mother's age at birth 

 <17 y 61,250 8.2 8,310 1.56 (1.51 -1.61) 1.36 (1.31 - 1.42) 1.32 (1.26 -1.38) 

 17-19 y 112,626 15.1 12,165 1.22 (1.19 -1.25) 1.15 (1.11 - 1.18) 1.11 (1.07 -1.16) 

 20-24 y 207,676 27.8 19,781 1.07 (1.04 -1.09) 1.05 (1.02 - 1.08) 1.04 (1.01 -1.07) 

 25-29 y (reference) 165,196 22.1 14,889 1.00 1.00 1.00 

 30-49 y 200,854 26.9 20,125 1.12 (1.10 -1.15) 1.09 (1.07 - 1.12) 1.08 (1.05 -1.12) 

Maternal education 

 No education 

(reference) 

347,674 46.5 40,692 1.00 1.00 1.00 

 Any primary 280,612 37.5 26,790 0.88 (0.86 -0.89) 0.92 (0.90 - 0.94) 0.93 (0.90 -0.95) 

 Incomplete 

secondary 

92,008 12.3 6,292 0.65 (0.63 -0.67) 0.73 (0.71 - 0.76) 0.74 (0.71 -0.77) 

 Complete 

secondary or higher 

28,051 3.7 1,527 0.49 (0.47 -0.52) 0.60 (0.56 - 0.64) 0.61 (0.57 -0.66) 

Household wealth 

quintile 

 

 Poorest 174,536 23.3 19,019 1.00 1.00 1.00 

 2nd 156,252 20.9 17,121 0.99 (0.97 -1.01) 1.01 (0.98 - 1.03) 0.99 (0.97 -1.02) 

 3rd 147,946 19.8 15,417 0.93 (0.91 -0.96) 0.97 (0.95 - 0.99) 0.97 (0.94 -1.00) 

 4th 139,045 18.6 13,681 0.87 (0.85 -0.89) 0.94 (0.92 - 0.97) 0.94 (0.91 -0.97) 

 Richest 130,513 17.4 10,055 0.65 (0.63 -0.67) 0.78 (0.75 - 0.81) 0.77 (0.74 -0.80) 

Area of residence 

 Urban 201,107 26.9 16,909 0.76 (0.75 -0.78) 0.94 (0.91 - 0.96) 0.94 (0.91 -0.97) 

 Rural (reference) 547,256 73.1 58,393 1.00 1.00 1.00 

Maternal height 

160+ cm 214,085 41.0 21,491 1.00 1.00 

155-159 cm 154,440 29.6 16,184 1.09 (1.06 -1.11) 1.07 (1.05 -1.10) 

150-154 cm 105,154 20.2 11,271 1.14 (1.11 -1.17) 1.12 (1.09 -1.15) 

145-149 cm 37,668 7.2 4,306 1.27 (1.22 -1.31) 1.23 (1.19 -1.28) 

<145 cm 

(reference) 

10,248 2.0 1,262 1.40 (1.31 -1.49) 1.34 (1.25 -1.43) 

aOR1 adjusted for sex of child, multiple birth, birth order, birth interval, child age, mother’s age at birth, mother’s 

education, household wealth quintile, and area of residence 

aOR2 includes maternal height 

Summary of Fixed Effects Models at the Survey Period and Child Levels 

Table 9 presents a summary of the associations between pcGDP and coverage of MNCH interventions on 

U5MR at the survey period/country level and the probability of child mortality before 60 months at the 

child level. In general, the effects are larger at the survey-period level compared with the child level; 

however, caution is urged when interpreting the child-level coefficients, which are presented in log odds 

and are not directly comparable to the beta coefficients from the linear models. Despite this limitation, the 

result fits our conceptual and analytical framework that these effects should be smaller because countrylevel factors are conceptualized as distal factors in the model and likely mediated through more proximal 

factors at the community, household, mother, or child levels. 
31 

Table 9. Summary of the associations between pcGDP and coverage of MNCH interventions on 

U5MR at the survey period/country level and the probability of child mortality at age 0-59 months 

at the child level from both the fixed effects and random effects model specifications 

Fixed effects 

Survey period Child 

Economic and coverage indicators β SE β SE 

GDP (log) -11.6 8.9 -0.04 0.007 

Composite coverage index -31.9 8.5 -0.08 0.008 

Antenatal care (1+ visits with skilled provider) -21.9 9.5 -0.05 0.007 

Antenatal care (4+ visits with any provider) -0.4 6.4 0.02 0.005 

Skilled birth attendant -7.0 6.6 0.00 0.005 

Early breastfeeding -6.6 4.7 0.01 0.004 

BCG vaccination coverage -5.8 4.6 0.01 0.003 

DPT 3 vaccination coverage -3.9 4.5 0.00 0.003 

Measles vaccination coverage -10.4 5.2 0.00 0.004 

Full vaccination coverage -4.8 4.4 0.00 0.004 

Vitamin A supplementation -1.0 4.5 0.00 0.006 

ORT for diarrhea -5.7 5.4 -0.01 0.004 

Care seeking for pneumonia -14.3 5.3 -0.04 0.004 

Insecticide-treated bednets for children -14.3 5.0 -0.07 0.008 

Contraceptive prevalence -17.9 7.2 -0.05 0.006 

Family planning need satisfied -16.5 6.0 -0.04 0.005 

Tetanus vaccination coverage -5.6 4.0 -0.02 0.003 

Improved sanitary facilities -36.5 15.2 -0.12 0.012 

Safe water -14.7 9.0 -0.05 0.007 

Sensitivity Analyses Using Random Effects Models 

In sensitivity analyses, we explored the use of random effects models at the country level. In general, the 

standard errors were considerably smaller in the random effects models at the country/survey period level 

due to improvements in efficiency in these models in treating countries as a distribution and thus reducing 

the effective number of parameters in the model. However, we prefer the fixed effects approach because it 

is less biased if the causal model is one that country-level factors are entirely confounders and if the 

model is used for the interpretation of ‘time trend’ effects. The choice of random or fixed specification of 

country has little substantive effect on the association between pcGDP and MNCH and the probability of 

child death before 60 months. 

33 

Discussion 

In this report we explored the contribution of economic growth and health coverage indicators to the 

declines in U5MR across 36 sub-Saharan African countries from 1990-2012. The study has two salient 

findings: 

First, improvements in MNCH coverage and interventions are strongly associated with reductions in child 

mortality; this association is universally consistent across the two types of data structures analyzed and 

regardless of the statistical specification. 

Second, the contributions of increases in economic growth are not consistently associated with the 

reductions in child mortality In only three of eight model specifications are increases in economic growth 

associated with reductions in child mortality. Even when there is a statistically significant association, the 

strength of the association is substantially weaker compared with the health coverage predictors. 

Although, as documented in this report, U5MR has been falling in sub-Saharan Africa in recent decades, 

the region continues to have the highest rates of child mortality in the world.12 It has been suggested that 

continuing high fertility in sub-Saharan Africa compared with other regions is likely related to the 

Africa’s slower declines in U5MR. The total fertility rate in sub-Saharan Africa remains twice as high as 

in the rest of the world, at 4.9 in 2011 compared with an average 2.4 elsewhere.52 Although we did not 

examine fertility rates directly, our findings based on DHS data demonstrate that increases in 

contraceptive prevalence are associated with decreases in U5MR, in both the ecological and multilevel 

analyses. This finding suggests that continuing declines in fertility would likely be associated with further 

declines in U5MR, although there has been debate about the causal association between fertility reduction 

and child mortality reduction. 

The analysis did not find the role of economic growth to be important in predicting declines in U5MR at 

the country level; the association is not consistent across different model specifications. The effect of 

coverage of selected maternal and child health interventions is generally greater than the effect of pcGDP, 

both at the ecologic and individual levels, underscoring that coverage of health interventions may have a 

particularly important role in sub-Saharan Africa, where U5MR remains high. Further, the effect of GDP 

is not consistent across different model specifications, whereas health care coverage is consistently 

associated with reductions in U5MR and child mortality. It has been suggested that in other situations and 

regions, such as China, where U5MR is already lower than in sub-Saharan Africa, economic growth may 

lead to further declines in U5MR.53

Limitations 

This study has several limitations. First, given that DHS surveys are typically conducted only at intervals 

of three to six years39, we were only able to study large changes in U5MR, and in some countries repeated 

surveys were not available, prohibiting a full time-series cross-sectional analysis. Second, due to sample 

size restrictions in the ecological analyses, we could only model about five to seven key indicators in the 

final multivariable adjusted model, and several other potentially relevant determinants of U5MR were not 

examined. 

Third, we analyzed U5MR over the five-year period preceding each survey. This method provided greater 

precision in the estimates of U5MR but may have masked information on recent trends in U5MR that 

could potentially have been revealed with a shorter reference period. Fourth, the coverage of maternal, 

newborn, and child health interventions was also calculated from each of the surveys, and where possible 

we used a similar five-year period before each survey. In spite of these efforts, it is likely that some 
34 

difficulty remains in establishing the timing of exposures and outcomes, as both were measured 

contemporaneously in the same survey. Further, although we chose a logistic regression analysis for the 

child-level models, a hazard model would have been another alternative. Regardless of model choice, 

there would be no additional information gained from the independent variables, given that the indicators 

were calculated using a five-year window. 

Fifth, the analyses did not include indicators of the incidence (or prevalence) of childhood diseases. Given 

the method in which the prevalence of diseases are captured in DHS (i.e. any diarrhea within two weeks 

preceding the survey) we were not confident that these would be comparable across countries, especially 

since surveys may have been conducted at different times and in different seasons. Finally, a more 

general limitation is that this study was based on estimates of U5MR. Any estimate of U5MR from 

survey data is subjected to sampling errors and will always be inferior to complete vital registration data.9

Countries where U5MR remains high and/or rates of mortality decline are slow typically lack 

comprehensive vital registration systems.54 Strengthening such systems is likely to improve future 

assessments of factors associated with declines in U5MR in sub-Saharan African. 

Concluding Remarks 

The results presented in this report indicate a secular decline in U5MR in a majority of countries in subSaharan Africa over the past two decades. A large part of this decline can be explained by coverage of 

selected maternal, newborn, and child health interventions and other social improvements, such as 

improved access to clean drinking water and sanitation facilities. There remains a fair amount of 

unexplained variation in the outcomes, and it is likely that other factors related to health systems as well 

as economic, social, or political factors play a role in influencing U5MR in sub-Saharan Africa. 

It has been suggested that effective implementation of available, cost-effective MNCH interventions can 

prevent much of the current burden of under-five mortality in low-income settings.55 However, many 

countries in sub-Saharan Africa are not on track to reach MDG-412, which is likely related in part to the 

low levels of coverage of key interventions in the 1990s in many countries.38, 56 In the 2000s, global 

health initiatives and resources for health increased, and along with such increases came improvements in 

coverage of life-saving child health interventions in several countries.38, 56 We would therefore expect that 

progress toward MDG-4 in such settings, while lagging behind other areas, might likely continue into 

2015 and beyond.9, 12 

It appears that health system improvements, including scaling up of key MNCH interventions, are a key 

explanation for reductions in U5MR in sub-Saharan Africa. For example, in Tanzania between 1999 and 

2004–05 the coverage of interventions relevant to child survival improved substantially.57 In particular, 

Vitamin A supplementation increased from 14 percent in 1999 to 85 percent in 2005, and other 

improvements also were seen: children sleeping under insecticide-treated nets increased from 10 percent 

to 29 percent, oral rehydration therapy for children increased from 57 percent to 70 percent, and exclusive 

breastfeeding for those younger than age 2 months increased from 58 percent to 70 percent.57 

Over this same period Tanzania's national wealth (in GDP per person) increased by 93 international 

dollars, from $819 to $912 per person (or US$256 to US$303). Improvements in the proportion of 

households living below the poverty line, in educational attainment, and in literacy rates improved only 

marginally during this time. Therefore, it is unlikely that growth in national wealth would account for 

much of the reduction in mortality, especially since poverty rates in Tanzania and other sub-Saharan 

African countries did not improve dramatically over the study period. 

Based on our multilevel analyses, it appears that the coverage of health interventions has played a 

relatively more important role in reducing child mortality compared with the role of economic growth. 
35 

Although recent gains have been made in reducing under-five mortality in sub-Saharan Africa, the U5MR 

in this region continues to be the highest anywhere. While sub-Saharan Africa as a whole has reduced 

U5MR by 30 percent, this is less than half of the MDG-4 target. As the global health community 

considers both the strong likelihood that the MDG-4 targets are not going to be accomplished by 2015,58, 

59 and looks ahead to the post-MDG era,60 it is important to sustain efforts to reduce child mortality. For 

sub-Saharan Africa, a continued focus on fertility declines, improved health coverage, greater equity in 

the coverage of proven life-saving interventions, and widespread access to clean water and sanitation, 

might be the key to reducing 

mortality. 

37 

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