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HIV epidemic drivers in South Africa: A model-based evaluation of factors accounting for inter-provincial differences in HIV prevalence and incidence trends

BACKGROUND: HIV prevalence differs substantially between South Africa’s provinces, but the factors accounting for this difference are poorly understood. OBJECTIVES: To estimate HIV prevalence and incidence trends by province, and to identify the epidemiological factors that account for most of the v...

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Autores principales: Johnson, Leigh F., Dorrington, Rob E., Moolla, Haroon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AOSIS 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5843035/
https://www.ncbi.nlm.nih.gov/pubmed/29568631
http://dx.doi.org/10.4102/sajhivmed.v18i1.695
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author Johnson, Leigh F.
Dorrington, Rob E.
Moolla, Haroon
author_facet Johnson, Leigh F.
Dorrington, Rob E.
Moolla, Haroon
author_sort Johnson, Leigh F.
collection PubMed
description BACKGROUND: HIV prevalence differs substantially between South Africa’s provinces, but the factors accounting for this difference are poorly understood. OBJECTIVES: To estimate HIV prevalence and incidence trends by province, and to identify the epidemiological factors that account for most of the variation between provinces. METHODS: A mathematical model of the South African HIV epidemic was applied to each of the nine provinces, allowing for provincial differences in demography, sexual behaviour, male circumcision, interventions and epidemic timing. The model was calibrated to HIV prevalence data from antenatal and household surveys using a Bayesian approach. Parameters estimated for each province were substituted into the national model to assess sensitivity to provincial variations. RESULTS: HIV incidence in 15–49-year-olds peaked between 1997 and 2003 and has since declined steadily. By mid-2013, HIV prevalence in 15–49-year-olds varied between 9.4% (95% CI: 8.5%–10.2%) in Western Cape and 26.8% (95% CI: 25.8%–27.6%) in KwaZulu-Natal. When standardising parameters across provinces, this prevalence was sensitive to provincial differences in the prevalence of male circumcision (range 12.3%–21.4%) and the level of non-marital sexual activity (range 9.5%–24.1%), but not to provincial differences in condom use (range 17.7%–21.2%), sexual mixing (range 15.9%–19.2%), marriage (range 18.2%–19.4%) or assumed HIV prevalence in 1985 (range 17.0%–19.1%). CONCLUSION: The provinces of South Africa differ in the timing and magnitude of their HIV epidemics. Most of the heterogeneity in HIV prevalence between South Africa’s provinces is attributable to differences in the prevalence of male circumcision and the frequency of non-marital sexual activity.
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spelling pubmed-58430352018-03-22 HIV epidemic drivers in South Africa: A model-based evaluation of factors accounting for inter-provincial differences in HIV prevalence and incidence trends Johnson, Leigh F. Dorrington, Rob E. Moolla, Haroon South Afr J HIV Med Original Research BACKGROUND: HIV prevalence differs substantially between South Africa’s provinces, but the factors accounting for this difference are poorly understood. OBJECTIVES: To estimate HIV prevalence and incidence trends by province, and to identify the epidemiological factors that account for most of the variation between provinces. METHODS: A mathematical model of the South African HIV epidemic was applied to each of the nine provinces, allowing for provincial differences in demography, sexual behaviour, male circumcision, interventions and epidemic timing. The model was calibrated to HIV prevalence data from antenatal and household surveys using a Bayesian approach. Parameters estimated for each province were substituted into the national model to assess sensitivity to provincial variations. RESULTS: HIV incidence in 15–49-year-olds peaked between 1997 and 2003 and has since declined steadily. By mid-2013, HIV prevalence in 15–49-year-olds varied between 9.4% (95% CI: 8.5%–10.2%) in Western Cape and 26.8% (95% CI: 25.8%–27.6%) in KwaZulu-Natal. When standardising parameters across provinces, this prevalence was sensitive to provincial differences in the prevalence of male circumcision (range 12.3%–21.4%) and the level of non-marital sexual activity (range 9.5%–24.1%), but not to provincial differences in condom use (range 17.7%–21.2%), sexual mixing (range 15.9%–19.2%), marriage (range 18.2%–19.4%) or assumed HIV prevalence in 1985 (range 17.0%–19.1%). CONCLUSION: The provinces of South Africa differ in the timing and magnitude of their HIV epidemics. Most of the heterogeneity in HIV prevalence between South Africa’s provinces is attributable to differences in the prevalence of male circumcision and the frequency of non-marital sexual activity. AOSIS 2017-07-28 /pmc/articles/PMC5843035/ /pubmed/29568631 http://dx.doi.org/10.4102/sajhivmed.v18i1.695 Text en © 2017. The Authors http://creativecommons.org/licenses/by/2.0/ Licensee: AOSIS. This work is licensed under the Creative Commons Attribution License.
spellingShingle Original Research
Johnson, Leigh F.
Dorrington, Rob E.
Moolla, Haroon
HIV epidemic drivers in South Africa: A model-based evaluation of factors accounting for inter-provincial differences in HIV prevalence and incidence trends
title HIV epidemic drivers in South Africa: A model-based evaluation of factors accounting for inter-provincial differences in HIV prevalence and incidence trends
title_full HIV epidemic drivers in South Africa: A model-based evaluation of factors accounting for inter-provincial differences in HIV prevalence and incidence trends
title_fullStr HIV epidemic drivers in South Africa: A model-based evaluation of factors accounting for inter-provincial differences in HIV prevalence and incidence trends
title_full_unstemmed HIV epidemic drivers in South Africa: A model-based evaluation of factors accounting for inter-provincial differences in HIV prevalence and incidence trends
title_short HIV epidemic drivers in South Africa: A model-based evaluation of factors accounting for inter-provincial differences in HIV prevalence and incidence trends
title_sort hiv epidemic drivers in south africa: a model-based evaluation of factors accounting for inter-provincial differences in hiv prevalence and incidence trends
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5843035/
https://www.ncbi.nlm.nih.gov/pubmed/29568631
http://dx.doi.org/10.4102/sajhivmed.v18i1.695
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