Evidence map›Paper›PMID 35100612›Full record

ArticleClinical infectious diseases : an official publication of the Infectious Diseases Society of America2022

Predicting the Risk of Human Immunodeficiency Virus Type 1 (HIV-1) Acquisition in Rural South Africa Using Geospatial Data.

D Allen Roberts, Diego Cuadros, Alain Vandormael, Dickman Gareta, Ruanne V Barnabas, Kobus Herbst, Frank Tanser, Adam Akullian

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Clinical infectious diseases : an official publication of the Infectious Diseases Society of America, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.1field-weighted citation impact, top 22% of its field
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

5 citing papers in PubMed, 13 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. HIV infection.Nature reviews. Disease primers · 2023
    Review
  5. 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

8 authors at 6 institutions in 4 countries.

D Allen RobertsDepartment of Epidemiology, University of Washington, Seattle, USA.ORCID 0000-0003-3660-6330
Diego CuadrosDepartment of Geography, University of Cincinnati, Cincinnati, Ohio, USA.
Alain VandormaelHeidelberg Institute of Global Health, Heidelberg University, Heidelberg, Germany.
Dickman GaretaAfrica Health Research Institute, KwaZulu-Natal, South Africa.
Ruanne V BarnabasDepartment of Epidemiology, University of Washington, Seattle, USA.
Kobus HerbstAfrica Health Research Institute, KwaZulu-Natal, South Africa.
Frank TanserAfrica Health Research Institute, KwaZulu-Natal, South Africa.
Adam AkullianDepartment of Global Health, University of Washington, Seattle, Washington, USA.
Africa Health Research Institute · ZAUniversity of Washington · USCentre for the AIDS Programme of Research in South Africa · ZAGates Foundation · USHeidelberg University · DEUniversity of Cincinnati · US

Funding

University of Washington/Fred Hutch Center for AIDS ResearchP30AI027757 · NIAID · UNIVERSITY OF WASHINGTON · PI EDWIN ROBERTO ORELLANA · 1988 to 2026
$104.9M
Estimating the cost-effectiveness of HIV pre-exposure prophylaxis delivery through maternal and child health clinics in KenyaF30MH122300 · NIMH · UNIVERSITY OF WASHINGTON · PI ROBERTS, DAVID ALLEN · 2019 to 2021
$145k
NIAID NIH HHS P30 AI027757NIMH NIH HHS F30 MH122300Wellcome TrustWellcome Trust 201433/Z/16/Z
6 · The paper itself

Abstract

backgroundAccurate human immunodeficiency virus (HIV) risk assessment can guide optimal HIV prevention. We evaluated the performance of risk prediction models incorporating geospatial measures.

methodsWe developed and validated HIV risk prediction models in a population-based cohort in South Africa. Individual-level covariates included demographic and sexual behavior measures, and geospatial covariates included community HIV prevalence and viral load estimates. We trained models on 2012-2015 data using LASSO Cox models and validated predictions in 2016-2019 data. We compared full models to simpler models restricted to only individual-level covariates or only age and geospatial covariates. We compared the spatial distribution of predicted risk to that of high incidence areas (≥ 3/100 person-years).

resultsOur analysis included 19 556 individuals contributing 44 871 person-years and 1308 seroconversions. Incidence among the highest predicted risk quintile using the full model was 6.6/100 person-years (women) and 2.8/100 person-years (men). Models using only age group and geospatial covariates had similar performance (women: AUROC = 0.65, men: AUROC = 0.71) to the full models (women: AUROC = 0.68, men: AUROC = 0.72). Geospatial models more accurately identified high incidence regions than individual-level models; 20% of the study area with the highest predicted risk accounted for 60% of the high incidence areas when using geospatial models but only 13% using models with only individual-level covariates.

conclusionsGeospatial models with no individual measures other than age group predicted HIV risk nearly as well as models that included detailed behavioral data. Geospatial models may help guide HIV prevention efforts to individuals and geographic areas at highest risk.

Indexed as

Acquired Immunodeficiency SyndromeHIV-1HIV InfectionsFemaleHumansIncidenceMaleRisk FactorsRural PopulationSouth AfricaHIV-1risk predictionSouth Africa

Identifiers

PMID35100612
PMCPMC9525068
OpenAlexW4210503295

What OpenQuestion holds

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LicenceCC BY
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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.