Yifan Cui, Sikhulile Moyo, Molly Pretorius Holme, Kathleen E Hurwitz, Wonderful Choga, Kara Bennett, Unoda Chakalisa, James Emmanuel San, Kutlo Manyake, Coulson Kgathi, Ame Diphoko, Simani Gaseitsiwe, Tendani Gaolathe, M Essex, Eric Tchetgen Tchetgen, Joseph M Makhema, Shahin Lockman
{"title":"Predictors of HIV seroconversion in Botswana: machine learning analysis in a representative, population-based HIV incidence cohort.","authors":"Yifan Cui, Sikhulile Moyo, Molly Pretorius Holme, Kathleen E Hurwitz, Wonderful Choga, Kara Bennett, Unoda Chakalisa, James Emmanuel San, Kutlo Manyake, Coulson Kgathi, Ame Diphoko, Simani Gaseitsiwe, Tendani Gaolathe, M Essex, Eric Tchetgen Tchetgen, Joseph M Makhema, Shahin Lockman","doi":"10.1097/QAD.0000000000004055","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>To identify predictors of HIV acquisition in Botswana.</p><p><strong>Design: </strong>We applied machine learning approaches to identify HIV risk predictors using existing data from a large, well-characterized HIV incidence cohort.</p><p><strong>Methods: </strong>We applied machine learning (randomForestSRC) to analyze data from a large population-based HIV incidence cohort enrolled in a cluster-randomized HIV prevention trial in 30 communities across Botswana. We sought to identify the most important risk factors for HIV acquisition, starting with 110 potential predictors.</p><p><strong>Results: </strong>During a median 29-month follow-up of 8,551 HIV-negative adults, 147 (1.7%) acquired HIV. Our machine learning analysis found that for females, the most important variables for predicting HIV acquisition were the use of injectable hormonal contraception, frequency of sex in the prior 3 months with the most recent partner and residing in a community with HIV prevalence of 29% or higher. For the small proportion (0.3%) of females who had all three risk factors, their estimated probability of acquiring HIV during 29 months of follow-up was 34% (approximate annual incidence of 14%). For males, non-long-term relationships with the most recent partner and community HIV prevalence of 34% or higher were the most important HIV risk predictors. The 6% of males who had both risk factors had a 5.1% probability of acquiring HIV during the follow-up period (approximate annual incidence of 2.1%).</p><p><strong>Conclusions: </strong>Machine learning approaches allowed us to analyze a large number of variables to efficiently identify key factors strongly predictive of HIV risk. These factors could help target HIV prevention interventions in Botswana.</p><p><strong>Clinical trials registration: </strong>NCT01965470.</p>","PeriodicalId":7502,"journal":{"name":"AIDS","volume":null,"pages":null},"PeriodicalIF":3.4000,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"AIDS","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1097/QAD.0000000000004055","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"IMMUNOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Objective: To identify predictors of HIV acquisition in Botswana.
Design: We applied machine learning approaches to identify HIV risk predictors using existing data from a large, well-characterized HIV incidence cohort.
Methods: We applied machine learning (randomForestSRC) to analyze data from a large population-based HIV incidence cohort enrolled in a cluster-randomized HIV prevention trial in 30 communities across Botswana. We sought to identify the most important risk factors for HIV acquisition, starting with 110 potential predictors.
Results: During a median 29-month follow-up of 8,551 HIV-negative adults, 147 (1.7%) acquired HIV. Our machine learning analysis found that for females, the most important variables for predicting HIV acquisition were the use of injectable hormonal contraception, frequency of sex in the prior 3 months with the most recent partner and residing in a community with HIV prevalence of 29% or higher. For the small proportion (0.3%) of females who had all three risk factors, their estimated probability of acquiring HIV during 29 months of follow-up was 34% (approximate annual incidence of 14%). For males, non-long-term relationships with the most recent partner and community HIV prevalence of 34% or higher were the most important HIV risk predictors. The 6% of males who had both risk factors had a 5.1% probability of acquiring HIV during the follow-up period (approximate annual incidence of 2.1%).
Conclusions: Machine learning approaches allowed us to analyze a large number of variables to efficiently identify key factors strongly predictive of HIV risk. These factors could help target HIV prevention interventions in Botswana.
期刊介绍:
Publishing the very latest ground breaking research on HIV and AIDS. Read by all the top clinicians and researchers, AIDS has the highest impact of all AIDS-related journals. With 18 issues per year, AIDS guarantees the authoritative presentation of significant advances. The Editors, themselves noted international experts who know the demands of your work, are committed to making AIDS the most distinguished and innovative journal in the field. Submitted articles undergo a preliminary review by the editor. Some articles may be returned to authors without further consideration. Those being considered for publication will undergo further assessment and peer-review by the editors and those invited to do so from a reviewer pool.