Spatial Analysis of HIV Infection and Associated Risk Factors in Botswana

Abstract

Botswana has the third highest human immunodeficiency virus (HIV) prevalence globally, and the severity of the epidemic within the country varies considerably between the districts. This study aimed to identify clusters of HIV and associated factors among adults in Botswana. Data from the Botswana Acquired Immunodeficiency Syndrome (AIDS) Impact Survey IV (BIAS IV), a nationally representative household-based survey, were used for this study. Multivariable logistic regression and Kulldorf’s scan statistics were used to identify the risk factors and HIV clusters. Socio-demographic characteristics were compared within and outside the clusters. HIV prevalence among the study participants was 25.1% (95% CI 23.3–26.4). HIV infection was significantly higher among the female gender, those older than 24 years and those reporting the use of condoms, while tertiary education had a protective effect. Two significant HIV clusters were identified, one located between Selibe-Phikwe and Francistown and another in the Central Mahalapye district. Clusters had higher levels of unemployment, less people with tertiary education and more people residing in rural areas compared to regions outside the clusters. Our study identified high-risk populations and regions with a high burden of HIV infection in Botswana. This calls for focused innovative and cost-effective HIV interventions on these vulnerable populations and regions to curb the HIV epidemic in Botswana.

Effectiveness of human immunodeficiency virus prevention strategies by mapping the geographic dispersion pattern of human immunodeficiency virus prevalence in Nanning, China

“…Subsequently, we tracked the spatiotemporal variation in the reported HIV infection cases in Nanning by identifying centroids and their shifts. The results also showed an evident trend: the centroids of the HIV-reported case area displayed a southeast–northwest direction, specifically in Phase II of the GACP, suggesting that Nanning City might experience a diffusion of HIV outward from cities into rural areas, consistent with other countries and globally [ 23 , 30 ]. There are several possible reasons for this shift in spatiotemporal centroids.…”

“…Spatial cluster analysis of HIV-1 case reporting was implemented using SaTScan [ 22 ]. Statistically significant windows were evaluated using Monte Carlo simulations; the primary and secondary clusters were determined based on window likelihood values [ 23 ]. In this study, the relative risk (RR) was calculated using the observed number of individuals within the windows [ 24 ].…”

Psychiatric disorders in adolescents living with HIV in Botswana

“…These disparities in their psychosocial needs and the association between lack of satisfaction with services and internalizing disorders supports the need to rethink the generally practiced ‘broad-brush approach’ to health care and prevention in ALWHIV in many African settings [ 10 , 32 ]. Also, more research should focus on the benefits and proper implementation of individualized management plans that consider adolescents' peculiar factors, such as gender, MOI and other psychosocial needs since this is not being currently done.…”

Epidemiological and spatial analyses of HIV/AIDS prevalence in Shandong, China from 2016 and 2022: A population-based study

“…A wide of economic and socio-cultural factors are responsible for increasing the vulnerability of African women to HIV infection [22]. Some spatial analyses of the HIV also found that HIV clusters tend to be in economically developed areas [11,13].…”