Evidence map›Paper›PMID 31421797›Full record

ArticleSpatial and spatio-temporal epidemiology2019

Investigating spatial disparities in high-risk women and HIV infections using generalized additive models: Results from a cohort of South African women.

Handan Wand, Tarylee Reddy, Gita Ramjee

Abstract read
In one paragraph

Article in Spatial and spatio-temporal epidemiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

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5citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Handan WandKirby Institute, University of New South Wales, Kensington 2052, New South Wales, Australia. Electronic address: hwand@kirby.unsw.edu.au.
Tarylee ReddyBiostatistics Unit, South African Medical Research Council, Durban, Kwazulu-Natal, South Africa. Electronic address: tarylee.reddy@mrc.ac.za.
Gita RamjeeHIV Prevention Research Unit, South African Medical Research Council, Westville, 3630 KwaZulu-Natal, South Africa; Department of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK; Department of Global Health, School of Medicine, University of Washington, United States. Electronic address: gita.ramjee@mrc.ac.za.

Funding

South African Medical Research Council Clinical Trials Unit (MRC CTU)UM1AI069422 · NIAID · MEDICAL RESEARCH COUNCIL OF SOUTH AFRICA · PI ABDULLAH, MAHOMED FAREED ABOOBAKER, GOGA, AMEENA · 2012 to 2020
$37.4M
NIAID NIH HHS UM1 AI069422
6 · The paper itself

Abstract

objectiveWe identified the geographical clustering of HIV as well as those at highest risk of infection using a decade long data (2002-2012) from KwaZulu-Natal, South Africa.

methodsA total of 5,776 women who enrolled in several HIV prevention trials were included in the study. Geo-coded individual-level data were linked to the community-level characteristics using the South African Census. High-risk women were identified using a risk scoring algorithm. Generalized additive models were used to identify the significant geographical clustering of high-risk women and HIV.

resultsOverall, 60% of the women were classified as high risk of HIV. HIV infection rates were estimated as high as 10 to 15 per 100 person year. Areas with high rates of HIV infections were spatially clustered and overlapped particularly in the Northern part of Durban.

conclusionTargeting multifactorial and complex nature of the epidemic is urgently needed to identify the "high transmission" areas.

Indexed as

Health Services Needs and DemandHIV InfectionsWomen's HealthAdultCluster AnalysisDelivery of Health CareFemaleHumansPrevalenceRisk AssessmentRisk FactorsSouth AfricaCommunity-level characteristicsGeneralized additive modelsHIVSouth AfricaSpatial clustering

Identifiers

PMID31421797
PMCPMC6914769

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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.