Cross-national comparisons of time trends in overweight inequality by socioeconomic status among women using repeated cross-sectional surveys from 37 developing countries, 1989-2007

Abstract

Chronic diseases are now among the leading causes of morbidity and mortality in lower income countries. Although traditionally related to higher individual socioeconomic status (SES) in these contexts, the associations between SES and chronic disease may be actively changing. Furthermore, country-level contextual factors, such as economic development and income inequality, may influence the distribution of chronic disease by SES as well as how this distribution has changed over time. Using overweight status as a health indicator, the authors studied repeated cross-sectional data from women aged 18-49 years in 37 developing countries to assess within-country trends in overweight inequalities by SES between 1989 and 2007 (n=405,550). Meta-regression was used to examine the associations between gross domestic product and disproportionate increases in overweight prevalence by SES, with additional testing for modification by country-level income inequality. In 27 of 37 countries, higher SES (vs. lower) was associated with higher gains in overweight prevalence; in the remaining 10 countries, lower SES (vs. higher) was associated with higher gains in overweight prevalence. Gross domestic product was positively related to faster increase in overweight prevalence among the lower wealth groups. Among countries with a higher gross domestic product, lower income inequality was associated with faster overweight growth among the poor.

Figure 1.

Annualized change in slope index of inequality for overweight between the first and last survey years, with 95% confidence intervals, for women in 37 developing countries in 1989–2007, ordered according to first-year gross domestic product (GDP). GDP is per capita adjusted by the purchasing power parity method and is adjusted for inflation using the 2005 international dollar value as a base. Positive values for annualized change in slope index of inequality indicate faster overweight prevalence increase among lower wealth groups (vs. higher); negative values indicate faster overweight prevalence increase among higher wealth groups (vs. lower). Rep, Republic.

Figure 2.

Predicted change in slope index of inequality in overweight by country-level gross domestic product (GDP) and country level of income inequality among women in 37 developing countries, 1989–2007. Positive values for annualized change in slope index of inequality indicate faster overweight prevalence increase among lower wealth groups (vs. higher); negative values indicate faster overweight prevalence increase among higher wealth groups (vs. lower). Estimate for highest GDP tertile and high-income-inequality countries is significantly different from the estimate for highest GDP tertile and low-income-inequality countries. Income inequality was represented by the Gini coefficient and was dichotomized; low income inequality range: 29.8–42.1 and high income inequality range: 42.2–74.3. GDP is per capita adjusted by the purchasing power parity method and adjusted for inflation using the 2005 international dollar value as a base. Baseline GDP was used to create GDP tertiles. Sample countries in the 6 categories: highest GDP tertile/high income inequality: Bolivia, Colombia, Guatemala, Namibia, Peru; highest GDP tertile/low income inequality: Armenia, Arab Republic of Egypt, Indonesia, Jordan, Morocco, Turkey, Kazahkstan; middle GDP tertile/high income inequality: Cameroon, Guinea, Haiti, Kenya, Nicaragua, Zambia, Zimbabwe; middle GDP tertile/low income inequality: Benin, Cambodia, China, Cote d'Ivoire, Ghana; lowest GDP tertile/high income inequality: Burkina Faso, Madagascar, Malawi, Mozambique, Nepal, Rwanda, Uganda; lowest GDP tertile/low income inequality: Bangladesh, Chad, Ethiopia, Mali, Tanzania, Niger.