We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
Machine Learning to Identify Persons at High-Risk of Human Immunodeficiency Virus Acquisition in Rural Kenya and Uganda.
- Authors
Balzer, Laura B; Havlir, Diane V; Kamya, Moses R; Chamie, Gabriel; Charlebois, Edwin D; Clark, Tamara D; Koss, Catherine A; Kwarisiima, Dalsone; Ayieko, James; Sang, Norton; Kabami, Jane; Atukunda, Mucunguzi; Jain, Vivek; Camlin, Carol S; Cohen, Craig R; Bukusi, Elizabeth A; Laan, Mark Van Der; Petersen, Maya L
- Abstract
Background In generalized epidemic settings, strategies are needed to prioritize individuals at higher risk of human immunodeficiency virus (HIV) acquisition for prevention services. We used population-level HIV testing data from rural Kenya and Uganda to construct HIV risk scores and assessed their ability to identify seroconversions. Methods During 2013–2017, >75% of residents in 16 communities in the SEARCH study were tested annually for HIV. In this population, we evaluated 3 strategies for using demographic factors to predict the 1-year risk of HIV seroconversion: membership in ≥1 known "risk group" (eg, having a spouse living with HIV), a "model-based" risk score constructed with logistic regression, and a "machine learning" risk score constructed with the Super Learner algorithm. We hypothesized machine learning would identify high-risk individuals more efficiently (fewer persons targeted for a fixed sensitivity) and with higher sensitivity (for a fixed number targeted) than either other approach. Results A total of 75 558 persons contributed 166 723 person-years of follow-up; 519 seroconverted. Machine learning improved efficiency. To achieve a fixed sensitivity of 50%, the risk-group strategy targeted 42% of the population, the model-based strategy targeted 27%, and machine learning targeted 18%. Machine learning also improved sensitivity. With an upper limit of 45% targeted, the risk-group strategy correctly classified 58% of seroconversions, the model-based strategy 68%, and machine learning 78%. Conclusions Machine learning improved classification of individuals at risk of HIV acquisition compared with a model-based approach or reliance on known risk groups and could inform targeting of prevention strategies in generalized epidemic settings. Clinical Trials Registration NCT01864603.
- Subjects
KENYA; UGANDA; HIV prevention; HIV infection risk factors; ALGORITHMS; PSYCHOLOGY of HIV-positive persons; MACHINE learning; RURAL conditions; STRUCTURAL models; LOGISTIC regression analysis; MEMBERSHIP; SEROCONVERSION; DESCRIPTIVE statistics
- Publication
Clinical Infectious Diseases, 2020, Vol 71, Issue 9, p2326
- ISSN
1058-4838
- Publication type
Article
- DOI
10.1093/cid/ciz1096