Understanding the Southern African ‘Anomaly’ Poverty, Endemic Disease, and HIV

Abstract

Background: Adult HIV prevalence in the nine countries of southern Africa averages more than 16 times the prevalence in other low- and middle-income countries. Previous studies argue that the intensity of the HIV epidemic in southern Africa results from regional characteristics, such as apartheid labor regulations and regional mineral wealth, which contributed to circular migration patterns and highly skewed income distribution. The present study also emphasizes the importance of cofactor diseases, which are suspected of raising HIV prevalence by increasing HIV viral load in infected persons or by making uninfected persons more vulnerable to HIV infection through lower immunity or genital lesions and/or inflammation. Method: the study uses multiple regression analysis on country-level data with HIV prevalence as the dependent variable. Regressors are ten socio-economic variables used in most previous cross-national analyses of HIV, two measures of cross-border migration, and measures of six cofactor infections. Results: The 10 socio-economic variables “explain” statistically only 25% of the difference in HIV prevalence between southern Africa and other low- and middle-income countries, but adding the four cofactor infection variables to the model allows us to “explain” 80% of the southern Africa difference in HIV prevalence. Conclusion: The relative affluence of southern Africa and historical migration patterns have tended to mask the vulnerability of the majority of the population who are poor and who have very high prevalence of infectious and parasitic diseases. Those diseases replicate a cycle of poverty that can lead not just to social vulnerability to HIV through risky behaviors but also to biological vulnerability through coinfections. An important implication of this research is that integrating treatment of endemic diseases with other HIVprevention policies may be necessary to slow the spread of HIV. Treatment of cofactor infections is a lowcost, policy-sensitive, high-impact variable.