Spatial disparities of HIV prevalence in South Africa. Do sociodemographic, behavioral, and biological factors explain this spatial variability

Bibliographic Data

ID22080672
AuthorsChigozie Louisa Jane Ugwu (0000-0001-5013-2253, University of KwaZulu-Natal), Jabulani Ncayiyana (0000-0002-2151-1251, University of KwaZulu-Natal, corresponding author), Jabulani R Ncayiyana
Year2022
Volume10
Pages994277-994277
Publication date2022-11-11
Open AccessYes
TypeARTICLE
VenueFrontiers in Public Health (JOURNAL)
Journal identifiersISSN: 2296-2565 • E-ISSN: 2296-2565
PublisherFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2022.994277
PMID36438270
OpenAlexW4309182281
LanguageEN
Citations received4
References cited38

Background: In 2021, an estimated 38 million people were living with human immunodeficiency virus (HIV) globally, with over two-thirds living in African regions. In South Africa, ~20% of South African adults are living with HIV. Accurate estimation of the risk factors and spatial patterns of HIV risk using individual-level data from a nationally representative sample is invaluable for designing geographically targeted intervention and control programs. Methods: Data were obtained from the 2016 South Africa Demographic and Health Survey (SDHS16). The study involved all men and women aged 15 years and older, who responded to questions and tested for HIV in the SDHS. Generalized additive models (GAMs) were fitted to our data with a nonparametric bivariate smooth term of spatial location parameters (X and Y coordinates). The GAMs were used to assess the spatial disparities and the potential contribution of sociodemographic, biological, and behavioral factors to the spatial patterns of HIV prevalence in South Africa. Results: A significantly highest risk of HIV was observed in east coast, central and north-eastern regions. South African men and women who are widowed and divorced had higher odds of HIV as compared to their counterparts. Additionally, men and women who are unemployed had higher odds of HIV as compared to the employed. Surprisingly, the odds of HIV infection among men residing in rural areas were 1.60 times higher (AOR 1.60, 95% CI 1.12, 2.29) as compared to those in urban areas. But men who were circumcised had lower odds of HIV (AOR 0.73, 95% CI 0.52, 0.98), while those who had STI in the last 12 months prior to the survey had higher odds of HIV (AOR 1.76, 95% CI 1.44, 3.68). Conclusion: Spatial heterogeneity in HIV risk persisted even after covariate adjustment but differed by sex, suggesting that there are plausible unobserved influencing factors contributing to HIV uneven variation. This study's findings could guide geographically targeted public health policy and effective HIV intervention in South Africa

Environmental health · Geography · Logistic regression · Odds · Odds ratio · Rural area · Adolescent Sexual and Reproductive Health · Demography · HIV/AIDS Impact and Responses · HIV/AIDS Research and Interventions · Medicine · Gerontology · Immunology

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