The relationship between national income and child mortality has been understood for many years. However, what is less well known is whether the association differs for neonatal mortality compared to postneonatal and early childhood deaths. Our study extends knowledge by analysing the relationship between gross national income (GNI) and neonatal, postneonatal, and early child mortality. The study draws on mortality estimates from Demographic and Household Surveys and World Bank data for GNI. It uses multivariate multiple regression analysis to examine the relationship between GNI and neonatal, postneonatal, and early child mortality rates (NMR, PNMR, and ECMR) using cross-sectional data from 65 countries and trend data from 49 countries. No significant relationship can be found between NMR and GNI for cross-sectional data once adjusted for region. The trend data confirms that increases over time in GNI are associated with lower reductions in NMR than other component rates. Thus, economic growth alone may have a weaker effect on reducing neonatal deaths than for older age groups; achieving improvements in neonatal mortality requires investment in maternal and new born health services alongside growth.
Over the last few decades neonatal mortality (death of an infant before 28 days) has fallen at a slower rate than postneonatal or early childhood mortality (death of a child between the ages of 28 days and 12 months and between 12 and 60 months, resp.). As a result around 40% of all child deaths now occur in the first month of life [
The relationship between national income and overall child mortality (deaths in all children under the age of five) has been understood for many years. A strong negative relationship between national income level (using per capita gross domestic product or gross national income: GDP or GNI) and child mortality has been well documented in a number of studies using both cross-sectional and trend data that examines the association between change over time. One of the most frequently cited studies is Pritchett and Summers’ analysis [
Evidence on the relationship between GDP and neonatal mortality is, however, extremely sparse. A broader study using principal component analysis suggests that contextual factors (income, female literacy, sanitation, and access to clean water) explain less of the variation between country mortality rates for neonatal mortality than for postneonatal and early child mortality [
Our study extends knowledge by analysing and presenting the relationship between gross national income (GNI) and the three separate component rates of child mortality: neonatal, postneonatal, and early childhood mortality using both cross-sectional and trend data.
Data for the neonatal mortality rate, postneonatal mortality rate, and early childhood mortality rate (NMR, PNMR, and ECMR) are taken from Demographic and Household Surveys (DHS). Where available, the trend data also draws on estimates from the World Fertility Surveys (WFS), which were the predecessor to DHS. In both cases the estimates are based on births occurring up to five years prior to the survey. The GNI data is taken from the World Bank database and is adjusted to constant 2010 US dollars. As the NMR estimates are from a five-year period prior to the survey the GNI is taken from the midyear.
The data for cross-sectional analysis includes estimates from 65 countries from 2000 onwards. Thirty-five of the countries were in Sub-Saharan Africa, with eleven in Latin America and the Caribbean, nine in South or South East Asia and the remaining ten in West and Central Asia, North Africa or Europe. There were 49 countries where at least two estimates are available for a period ranging from 6 to 32 years. Over half of these countries were in Sub-Saharan Africa (25), with a further 11 in Latin America and the Caribbean, seven in South or South East Asia, and six in West and Central Asia, North Africa, or Europe. The vast majority of countries used for both cross-sectional and trend analysis were low income or lower middle income countries based on the World Bank classifications.
DHS/WFS data are among the most commonly used source of direct estimates for child mortality. However, there are issues about data reliability, particularly for neonatal deaths, which are more likely to be underreported [
The limitations of GNI as a measure of living standards are fully accepted: in particular, it is not able to capture national fractionalisation or inequalities, and may be particularly inaccurate in countries where the informal economy or nonmonetised sectors are important. However, as the focus of this paper is economic growth rather than family-level wealth or socioeconomic wellbeing, GNI is the most valid measure. It is also fully recognised that a number of other socioeconomic factors such as education and public spending also impact on child mortality and these will be strongly correlated with GNI. However, as this study is specifically focussed on addressing the gap in evidence on the relationship between GNI and neonatal mortality, it does not attempt to unravel these links.
Multivariate multiple regression was used to capture the association between the three component mortality rates and GNI per capita using cross-sectional data from 65 countries. Multivariate multiple regression is preferable to using separate ordinary least squares (OLS) analysis for each outcome variable as posttest estimation allows for the difference in coefficients across equations to be tested for significance. The models can either be expressed as three separate equations
A double log model was used as the data are nonlinear and this provided the best fit. An advantage of the double log model is that the slope coefficient measures the average percentage reduction in mortality that is associated with a percentage increase in national income, thus making interpretation easier. Dummy variables for region were also added.
The examination of the relationship between GNI and mortality using cross-sectional data has the disadvantage that it cannot allow for country-specific factors that may affect the association. In order to address this, the study also examines the relationship between the change in mortality and the change in GNI for 49 countries, where more than one survey is available. The use of these data has the advantage that external variables that differ between countries are factored out. Again, a double log model is used to create three separate models, with log of average annual rate of change in per capita GNI per annum as the independent variable and the log of the average annual rate of change in the three component mortality rates as the dependent variable. Postestimation testing was then carried out to produce a Wald
Descriptive statistics for the 65 countries with cross-sectional data available can be found in Table
Descriptive statistics for cross-sectional data from 65 countries.
Mean NMR per 1000 live births (with standard deviation) and range | Mean PNMR per 1000 live births (with deviation) |
Mean ECMR per 1000 live births (with deviation) | Mean GNI per capita (with deviation) in $ |
---|---|---|---|
27.8 (11.7) | 27.7 (17.7) | 33.7 (31.4) | 1084 (1066) |
5–62 | 4–64 | 1–126 | 120–4210 |
The results from the regression analysis of the 65 countries, where cross-sectional data is available can be seen in Table
Results for multivariate multiple regression using natural log of gross national income (GNI) per capita as independent variable (with dummy variables for region) and natural log of neonatal, postneonatal, and early childhood mortality rates (NMR, PNMR, and ECMR) as dependent variable.
Coefficient |
|
Confidence interval | ||
---|---|---|---|---|
Log of NMR ( |
||||
Log of GNI | −0.09 | 0.10 | −0.19 | 0.02 |
West/central Asia, North Africa, and Europe (reference) | ||||
Sub-Saharan Africa | 0.57 | 0.00 | 0.26 | 0.87 |
South/South East Asia | 0.33 | 0.07 | −0.03 | 0.69 |
Latin America/Caribbean | 0.00 | 1.00 | −0.33 | 0.33 |
Constant | 3.44 | 0.00 | 2.67 | 4.21 |
Log of PNMR ( |
||||
Log of GNI | −0.21 | 0.01 | −0.35 | −0.07 |
West/central Asia, North Africa, and Europe (reference) | ||||
Sub-Saharan Africa | 0.99 | 0.00 | 0.58 | 1.40 |
South/South East Asia | 0.12 | 0.61 | −0.37 | 0.61 |
Latin America/Caribbean | 0.10 | 0.66 | −0.35 | 0.55 |
Constant | 3.84 | 0.00 | 2.79 | 4.89 |
Log of ECMR ( |
||||
Log of GNI | −0.25 | 0.01 | −0.43 | −0.07 |
West/central Asia, North Africa, and Europe (reference) | ||||
Sub-Saharan Africa | 1.96 | 0.00 | 1.43 | 2.48 |
South/South East Asia | 0.69 | 0.03 | 0.06 | 1.33 |
Latin America/Caribbean | 0.33 | 0.26 | −0.25 | 0.91 |
Constant | 3.41 | 0.00 | 2.05 | 4.77 |
Data sources: mortality rates were taken from Demographic and Household surveys (DHS) and GNI adjusted to constant 2010 US dollars were taken from the World Bank Database.
When the regression results for the trend data were examined (see Table
Results of multivariate multiple regression using natural log of change in GNI over time as the independent variable (and dummy variables for region) and the natural log of the change in NMR, PNMR, and ECMR as dependent variables.
Coefficient |
|
Confidence interval | ||
---|---|---|---|---|
Log of difference in NMR ( |
||||
Log of GNI | −0.29 | 0.02 | −0.53 | −0.06 |
West/central Asia, North Africa, and Europe (reference) | ||||
Sub-Saharan Africa | 0.26 | 0.25 | −0.19 | 0.71 |
South/South East Asia | 0.29 | 0.15 | −0.11 | 0.68 |
Latin America/Caribbean | 0.01 | 0.98 | −0.40 | 0.41 |
Constant | −0.39 | 0.04 | −0.77 | −0.01 |
Log of difference in PNMR ( |
||||
Log of GNI | −0.48 | 0.00 | −0.76 | −0.20 |
West/central Asia, North Africa, and Europe (reference) | ||||
Sub-Saharan Africa | 0.01 | 0.98 | −0.53 | 0.55 |
South/South East Asia | 0.16 | 0.50 | −0.32 | 0.63 |
Latin America/Caribbean | −0.21 | 0.39 | −0.70 | 0.27 |
Constant | −0.41 | 0.07 | −0.87 | 0.04 |
Log of difference in ECMR ( |
||||
Log of GNI | −0.53 | 0.03 | −1.00 | −0.06 |
West/central Asia, North Africa, and Europe (reference) | ||||
Sub-Saharan Africa | 0.26 | 0.25 | −0.19 | 0.71 |
South/South East Asia | 0.29 | 0.15 | −0.11 | 0.68 |
Latin America/Caribbean | 0.01 | 0.98 | −0.40 | 0.41 |
Constant | −0.39 | 0.04 | −0.77 | −0.01 |
Data sources: mortality rates were taken from Demographic and Household surveys (DHS) and GNI adjusted to constant 2010 US dollars were taken from the World Bank Database.
If the model includes only those 31 countries with estimates at least 15 years apart (where the differences are likely to be less affected by random fluctuations or sampling error) the coefficients are larger but the same pattern remains, with a 10% increase in GNI associated with decreases in 3.3%, 5.8%, and 6.1% in NMR, PNMR, and ECMR (all significant at the 1% level). Removing the five countries with a baseline NMR less than 20 per 1000 made almost no difference to the model. In nine of the countries (all in Sub-Saharan Africa) there was actually a fall in GNI during the time period. If these countries were removed from the model, the coefficients for all three groups increased, although again the patterns remained the same: a 10% increase in GNI was associated with decreases in 3.7%, 6.2%, and 6.9% in NMR, PNMR, and ECMR (NMR and PNMR significant at the 1% level and ECMR significant at the 5% level).
This study suggests changes in GNI are associated with less impact on neonatal than postneonatal and early childhood mortality. Indeed, with the model controlled for region there appeared to be no significant association between NMR and GNI for the cross-sectional data. This may partially explain why progress in reducing deaths in the neonatal period has been poorer than for older ages. While improved child health services have undoubtedly contributed to the reduction of child mortality in some countries [
These findings may well reflect the differing underlying causes of death for newborns and older children. Infectious diseases are the main cause of death in older children, while the main causes of neonatal mortality are intrinsically linked to the health of the mother and the care she receives during pregnancy and childbirth [
If neonatal mortality is less affected by changes in GNI, it has implications for the extent to which increased wealth will reduce child mortality in different countries. For countries with lower overall child mortality, and therefore a higher proportion of neonatal deaths, the impact may be less than for countries with high overall mortality. This offers a novel perspective to the debate on the degree to which synergies between economic growth and the heath sector can contribute to the achievement of the Millennium Development Goals MDGs [
Further work is needed to establish the drivers for reduction in neonatal mortality at the national level. Research clearly shows that increased maternal education [
The potentially limited impact of economic growth on the reduction of neonatal mortality point to the importance of targeted, cost-effective health care interventions to be introduced if neonatal mortality is to be effectively lowered in developing countries.
The study was funded by the ESRC under Grant no. PTA-026-27-2613/ES/H038485/1. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the paper.
The authors have declared that no competing interests exist.
The authors would like to that Professor John Micklewright for his extensive and helpful comments on earlier drafts and Dr. David Holmes for his advice on statistical methods.