Trends in socioeconomic inequalities in HIV prevalence among young people in Eastern and Southern Africa

Hargreaves, JR; Davey, C; Fearon, E; Hensen, B; Krishnaratne, S PLoS One, 2015;

Earlier in the epidemic, HIV prevalence was higher among better educated young men and women in East and Southern Africa. Has this begun to change? To find out, Dr James Hargreaves and other LSHTM researchers compared data from national surveys conducted in 2003-2005 and again in 2008-2012 to assess HIV prevalence among differently educated groups. They hypothesised that those with higher levels of education would have better access to HIV prevention programmes and better ability to take preventive action. Therefore, they argued, HIV prevalence would reduce faster over time among this group.

However, as this paper explains, only the evidence from Tanzania supports this hypothesis. There, prevalence fell faster amongst young men with higher education. In other surveyed countries and amongst young women, there was little evidence that the pattern of HIV prevalence by education had changed over the time period.

The variation in findings across settings and by gender affirm the need to continue to monitor and analyse equities in access to HIV prevention services on a local basis.


Fourteen nationally representative population-based surveys were collated from seven countries:

  • Ethiopia (2005; 2011)
  • Kenya (2004; 2008)
  • Lesotho (2004; 2009)
  • Malawi (2004; 2010)
  • Rwanda (2005; 2010)
  • Tanzania (2003; 2011/12)
  • Zimbabwe (2005; 2010)

The analysis was restricted to young people aged 15-24 years as patterns of HIV prevalence among this group should be closely related to patterns of HIV incidence. Educational attainment was measured in terms of highest level of education attended using three groups: none, primary and some secondary or more. All analyses were stratified by sex and accounted for the sampling strategy deployed in the surveys by using probability weighting.

The analysis had five steps:

  1. Describe the populations of participants.
  2. Assess the association between education and prevalent HIV infection within each survey by calculating HIV prevalence and associated 95% confidence intervals for the different educational groups.
  3. Pool the data from the two surveys in each country and assess whether the association between education and HIV changed over time in each country by fitting adjusted, country-specific logistic regression models for the association between education and HIV and fitting an interaction term between education and survey (first survey/second survey).
  4. Assess whether the association between education and HIV changed over time across the whole region by recoding the primary sampling units so that they were unique for each primary sampling unit in each country.
  5. Repeat this final analysis but including the full set of individuals aged 15 - 49 years and additionally fitting another three way interaction term (education/survey/age group) to assess whether the overall pattern among 15 - 24 year olds was systematically different from that observed in 25 - 49 year olds.


  • HIV prevalence was higher among young women than young men in all educational groups in all countries.
  • In Ethiopia and Malawi, HIV prevalence was higher in more educated women in both surveys.
  • In Lesotho, Kenya and Zimbabwe, HIV prevalence was lower in higher educated women in both surveys.
  • Only among young men in Tanzania there was some evidence that the association between education and HIV changed over time.
  • In only one country, Ethiopia, the country with the lowest HIV prevalence, was there strong evidence of interaction between survey year and the relationship between education and HIV prevalence among young women.
  • In line with the priori hypothesis, HIV prevalence increased among Tanzanian young men with no education, was stable among those with primary education and fell among those with secondary education.

These findings confirm that higher education became less of a risk factor for HIV over time. Earlier in the epidemic, HIV risk rose with level of wealth, but this trend has reversed, with HIV risk is increasingly associated with poverty and relative disadvantage. Further explanations for the social epidemiology of HIV in Africa will need to account for time-trends and inter-country differences.Geographic variation in the association between educational attainment and HIV prevalence among young women

Countries in green showed a higher HIV prevalence among more educated young women in both earlier and later surveys (Ethiopia, Malawi). Countries in blue showed a lower HIV prevalence among more educated young women in both earlier and later surveys, in countries with a low prevalence of no education (Lesotho, Kenya, Zimbabwe). Countries in pink showed no association between education and HIV in at least one of the surveys (Rwanda, Tanzania).


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