Child health inequities in developing countries: differences across urban and rural areas.

OBJECTIVES

To document and compare the magnitude of inequities in child malnutrition across urban and rural areas, and to investigate the extent to which within-urban disparities in child malnutrition are accounted for by the characteristics of communities, households and individuals.

METHODS

The most recent data sets available from the Demographic and Health Surveys (DHS) of 15 countries in sub-Saharan Africa (SSA) are used. The selection criteria were set to ensure that the number of countries, their geographical spread across Western/Central and Eastern/Southern Africa, and their socioeconomic diversities, constitute a good yardstick for the region and allow us to draw some generalizations. A household wealth index is constructed in each country and area (urban, rural), and the odds ratio between its uppermost and lowermost category, derived from multilevel logistic models, is used as a measure of socioeconomic inequalities. Control variables include mother's and father's education, community socioeconomic status (SES) designed to represent the broad socio-economic ecology of the neighborhoods in which families live, and relevant mother- and child-level covariates.

RESULTS

Across countries in SSA, though socioeconomic inequalities in stunting do exist in both urban and rural areas, they are significantly larger in urban areas. Intra-urban differences in child malnutrition are larger than overall urban-rural differentials in child malnutrition, and there seem to be no visible relationships between within-urban inequities in child health on the one hand, and urban population growth, urban malnutrition, or overall rural-urban differentials in malnutrition, on the other. Finally, maternal and father's education, community SES and other measurable covariates at the mother and child levels only explain a slight part of the within-urban differences in child malnutrition.

CONCLUSION

The urban advantage in health masks enormous disparities between the poor and the non-poor in urban areas of SSA. Specific policies geared at preferentially improving the health and nutrition of the urban poor should be implemented, so that while targeting the best attainable average level of health, reducing gaps between population groups is also on target. To successfully monitor the gaps between urban poor and non-poor, existing data collection programs such as the DHS and other nationally representative surveys should be re-designed to capture the changing patterns of the spatial distribution of population.