Evidence map›Paper›PMID 40238564›Full record

ArticleInternational journal of environmental research and public health2025

Spatial Analysis of HIV Determinants Among Females Aged 15-34 in KwaZulu Natal, South Africa: A Bayesian Spatial Logistic Regression Model.

Exaverio Chireshe, Retius Chifurira, Knowledge Chinhamu, Jesca Mercy Batidzirai, Ayesha B M Kharsany

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Exaverio ChiresheSchool of Mathematics, Statistics and Computer Science, College of Agriculture, Engineering and Science, University of KwaZulu-Natal, Durban 4001, South Africa.
Retius ChifuriraSchool of Mathematics, Statistics and Computer Science, College of Agriculture, Engineering and Science, University of KwaZulu-Natal, Durban 4001, South Africa.
Knowledge ChinhamuSchool of Mathematics, Statistics and Computer Science, College of Agriculture, Engineering and Science, University of KwaZulu-Natal, Durban 4001, South Africa.ORCID 0000-0003-4296-7677
Jesca Mercy BatidziraiSchool of Mathematics, Statistics and Computer Science, College of Agriculture, Engineering and Science, University of KwaZulu-Natal, Durban 4001, South Africa.ORCID 0000-0003-3116-1301
Ayesha B M KharsanyCentre for the AIDS Programme of Research in South Africa (CAPRISA), Doris-Duke Medical Research Institute, Nelson R Mandela School of Medicine, University of KwaZulu-Natal, Durban 4001, South Africa.ORCID 0000-0001-7457-0194

Funding

Research Training on Harnessing Data Science for Global Health Priorities in AfricaU2RTW012140 · FIC · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI BARNIGHAUSEN, TILL, FAWZI, WAFAIE W · 2021 to 2025
$1.7M
FIC NIH HHS U2R TW012140
6 · The paper itself

Abstract

HIV remains a major public health challenge in sub-Saharan Africa, with South Africa bearing the highest burden. This study confirms that KwaZulu-Natal (KZN) is a hotspot, with a high HIV prevalence of 47.4% (95% CI: 45.7-49.1) among females aged 15-34. We investigated the spatial distribution and key socio-demographic, behavioural, and economic factors associated with HIV prevalence in this group using a Bayesian spatial logistic regression model. Secondary data from 3324 females in the HIV Incidence Provincial Surveillance System (HIPSS) (2014-2015) in uMgungundlovu District, KZN, were analysed. Bayesian spatial models fitted using the Integrated Nested Laplace Approximation (INLA) identified key predictors and spatial clusters of HIV prevalence. The results showed that age, education, marital status, income, alcohol use, condom use, and number of sexual partners significantly influenced HIV prevalence. Older age groups (20-34 years), alcohol use, multiple partners, and STI/TB diagnosis increased HIV risk, while tertiary education and condom use were protective. Two HIV hotspots were identified, with one near Greater Edendale being statistically significant. The findings highlight the need for targeted, context-specific interventions to reduce HIV transmission among young females in KZN.

Indexed as

HIV InfectionsAdolescentAdultBayes TheoremFemaleHumansLogistic ModelsPrevalenceRisk FactorsSouth AfricaSpatial AnalysisYoung AdultBayesian logistic regressionHIV prevalenceKulldorf’s spatial scan statisticsodds ratiosspatial clustering

Identifiers

PMID40238564
PMCPMC11942239

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.