The geography of HIV/AIDS prevalence rates in Botswana
Ngianga-Bakwin Kandala,1 Eugene K Campbell,2 Serai Dan Rakgoasi,2 Banyana C Madi-Segwagwe,3 Thabo T Fako4
1University of Warwick, Warwick Medical School, Division of Health Sciences; Populations, Evidence and Technologies Group, Warwick Evidence, Coventry, UK; 2Department of Population Studies, University of Botswana, 3SADC Secretariat, Directorate of Social and Human Development and Special Programmes, 4Vice Chancellor's Office, University of Botswana, Gaborone, Botswana
Background: Botswana has the second-highest human immunodeficiency virus (HIV) infection rate in the world, with one in three adults infected. However, there is significant geographic variation at the district level and HIV prevalence is heterogeneous with the highest prevalence recorded in Selebi-Phikwe and North East. There is a lack of age-and location-adjusted prevalence maps that could be used for targeting HIV educational programs and efficient allocation of resources to higher risk groups.
Methods: We used a nationally representative household survey to investigate and explain district level inequalities in HIV rates. A Bayesian geoadditive mixed model based on Markov Chain Monte Carlo techniques was applied to map the geographic distribution of HIV prevalence in the 26 districts, accounting simultaneously for individual, household, and area factors using the 2008 Botswana HIV Impact Survey.
Results: Overall, HIV prevalence was 17.6%, which was higher among females (20.4%) than males (14.3%). HIV prevalence was higher in cities and towns (20.3%) than in urban villages and rural areas (16.6% and 16.9%, respectively). We also observed an inverse U-shape association between age and prevalence of HIV, which had a different pattern in males and females. HIV prevalence was lowest among those aged 24 years or less and HIV affected over a third of those aged 25–35 years, before reaching a peak among the 36–49-year age group, after which the rate of HIV infection decreased by more than half among those aged 50 years and over. In a multivariate analysis, there was a statistically significant higher likelihood of HIV among females compared with males, and in clerical workers compared with professionals. The district-specific net spatial effects of HIV indicated a significantly higher HIV rate of 66% (posterior odds ratio of 1.66) in the northeast districts (Selebi-Phikwe, Sowa, and Francistown) and a reduced rate of 27% (posterior odds ratio of 0.73) in Kgalagadi North and Kweneng West districts.
Conclusion: This study showed a clear geographic distribution of the HIV epidemic, with the highest prevalence in the east-central districts. This study provides age- and location-adjusted prevalence maps that could be used for the targeting of HIV educational programs and efficient allocation of resources to higher risk groups. There is need for further research to determine the social, cultural, economic, behavioral, and other distal factors that might explain the high infection rates in some of the high-risk areas in Botswana.
Keywords: Botswana, HIV prevalence, geographic location, spatial autocorrelation
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