Prevalence and pattern of HIV-related malnutrition among women in sub-Saharan Africa: a meta-analysis of demographic health surveys

Abstract

Background: The world's highest HIV infection rates are found in Sub-Saharan Africa (SSA), where adult prevalence in most countries exceeds 25%. Food shortages and malnutrition have combined with HIV/AIDS to bring some countries to the brink of crisis. The aim of this study was to describe prevalence of malnutrition among HIV-infected women and variations across socioeconomic status using data from 11 countries in SSA.

Methods: This study uses meta-analytic procedures to synthesize the results of most recent data sets available from Demographic and Health Surveys of 11 countries in SSA. Pooled prevalence estimates and 95% confidence intervals were calculated using random-and fixed-effects models. Subgroup and leave-one-country-out sensitivity analyses were also carried out.

Results: Pooling the prevalence estimates of HIV-related malnutrition yielded an overall prevalence of 10.3% (95% CI 7.4% to 14.1%) with no statistically significant heterogeneity (I2 = 0.0%, p = .903). The prevalence estimates decreased with increasing wealth index and education attainment. The pooled prevalence of HIV-related malnutrition was higher among women residing in rural areas than among women residing in urban areas; and lower among women that were professionally employed than unemployed or women in agricultural or manual work.

Conclusion: Prevalence of HIV-related malnutrition among women varies by wealth status, education attainment, occupation, and type of residence (rural/urban). The observed socioeconomic disparities can help provide more information about population subgroups in particular need and high risk groups, which may in turn lead to the development and implementation of more effective intervention programs.

Figure 1

Forest plot of meta-analysis of the prevalence estimates from 11 studies. *HIV-related, proportion of HIV-seropositive women (N) that are underweight (n). **Pooled effect estimate is the same for both random- and fixed-effects model. The vertical line represents the prevalence estimates of the pooled result. ***Inverse variance weight.

Figure 2

Plot indicating the influence of each country on the overall pooled result- "leave-one-country-out" sensitivity analysis.