Spatial analysis of HIV infection and associated individual characteristics in Burundi: indications for effective prevention

TLDR

Adequate resource allocation is critical in the battle against HIV/AIDS in Africa, requiring alignment with patients’ geographic, social, and behavioral characteristics. The study examined spatial heterogeneity of HIV prevalence in Burundi and evaluated how social and behavioral factors associate with infection while accounting for spatial variation. Using 2010 Demographic and Health Survey data, the authors applied a geostatistical workflow that interpolated HIV prevalence with kernel density estimation, identified high‑ and low‑prevalence clusters via Kulldorff spatial scan statistics, and performed multivariate spatial logistic regression. The analysis revealed a 1.4 % overall prevalence with pronounced spatial heterogeneity (0–10 %), a high‑prevalence cluster in the capital (3.9 % RR 3.7) and a low‑prevalence cluster in the south (0 %), and identified significant associations between HIV infection and female sex, older age, higher education, marital status, wealth, sexual activity, and prior STI, underscoring the need for targeted interventions and supporting UNAIDS’ country‑specific strategy.

Abstract

Adequate resource allocation is critical in the battle against HIV/AIDS, especially in Africa. The determination of the location and nature of HIV services to implement must comply with the geographic, social and behavioral characteristics of patients. We therefore investigated the spatial heterogeneity of HIV prevalence in Burundi and then assessed the association of social and behavioral characteristics with HIV infection accounting for the spatial heterogeneity.We used data from the 2010 Demographic and Health Survey. We analyzed these data with a geostatistical approach (which takes into account spatial autocorrelation) by i) interpolating HIV data using the kernel density estimation, ii) identifying the spatial clusters with high and low HIV prevalence using the Kulldorff spatial scan statistics, and then iii) performing a multivariate spatial logistic regression.Overall HIV prevalence was 1.4 %. The interpolated data showed the great spatial heterogeneity of HIV prevalence (from 0 to 10 %), independently of administrative boundaries. A cluster with high HIV prevalence was found in the capital city and adjacent areas (3.9 %; relative risk 3.7, p < 0.001) whereas a cluster with low prevalence straddled two southern provinces (0 %; p = 0.02). By multivariate spatial analysis, HIV infection was significantly associated with the female sex (posterior odds ratio [POR] 1.36, 95 % credible interval [CrI] 1.13-1.64), an older age (POR 1.97, 95 % CrI 1.26-3.08), the level of education (POR 1.50, 95 % CrI 1.22-1.84), the marital status (POR 1.86, 95 % CrI 1.23-2.80), a higher wealth index (POR 2.11, 95 % CrI 1.77-2.51), the sexual activity (POR 1.76, 95 % CrI 1.04-2.96), and a history of sexually transmitted infection (POR 2.03, 95 % CrI 1.56-2.64).Our study, which shows where and towards which populations HIV resources should be allocated, could help national health policy makers develop an effective HIV intervention in Burundi. Our findings support the strategy of the Joint United Nations Programme on HIV/AIDS (UNAIDS) for country-specific, in-depth analyses of HIV epidemics to tailor national prevention responses.