Evidence map›Paper›PMID 29568631›Full record

ArticleSouthern African journal of HIV medicine2017

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

Leigh F Johnson, Rob E Dorrington, Haroon Moolla

Abstract read
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Article in Southern African journal of HIV medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

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0cells of the map it votes in
19citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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3 · Its place in the literature

Who cites it

19 citing papers in PubMed.

  1. Review
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  4. Evaluation of the impact of PEPFAR transition on retention in care in South Africa's Western Cape Province.South African medical journal = Suid-Afrikaanse tydskrif vir geneeskunde · 2023
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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

3 authors.

Leigh F JohnsonCentre for Infectious Disease Epidemiology and Research, University of Cape Town, South Africa.ORCID 0000-0001-7769-8417
Rob E DorringtonCentre for Actuarial Research, University of Cape Town, South Africa.ORCID 0000-0002-6865-9739
Haroon MoollaCentre for Infectious Disease Epidemiology and Research, University of Cape Town, South Africa.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHIV prevalence differs substantially between South Africa's provinces, but the factors accounting for this difference are poorly understood.

objectivesTo estimate HIV prevalence and incidence trends by province, and to identify the epidemiological factors that account for most of the variation between provinces.

methodsA 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.

resultsHIV 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%).

conclusionThe 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.

Identifiers

PMID29568631
PMCPMC5843035

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.