What factors influence HIV testing? Modeling preference heterogeneity using latent classes and class-independent random effects.

Autor: Ostermann J; Department of Health Services Policy & Management, University of South Carolina, 915 Greene Street, Columbia, SC, USA.; South Carolina Smart State Center for Healthcare Quality, University of South Carolina, Columbia, SC, USA.; Duke Global Health Institute, Duke University, Durham, NC, USA.; Center for Health Policy & Inequalities Research, Duke University, Durham, NC, USA., Flaherty BP; Center for Health Policy & Inequalities Research, Duke University, Durham, NC, USA.; Department of Psychology, University of Washington, Seattle, WA, USA., Brown DS; Center for Health Policy & Inequalities Research, Duke University, Durham, NC, USA.; Brown School, Washington University in St. Louis, St. Louis, MO, USA., Njau B; Kilimanjaro Christian Medical Centre, Moshi, Tanzania., Hobbie AM; Duke Global Health Institute, Duke University, Durham, NC, USA.; Center for Health Policy & Inequalities Research, Duke University, Durham, NC, USA., Mtuy TB; Kilimanjaro Christian Medical Centre, Moshi, Tanzania.; Department of Global Health and Development, London School of Hygiene and Tropical Medicine, London, UK., Masnick M; Selway Labs, LLC, Englewood, CO, USA., Mühlbacher AC; Center for Health Policy & Inequalities Research, Duke University, Durham, NC, USA.; Institut Gesundheitsökonomie und Medizinmanagement, Hochschule Neubrandenburg, Neubrandenburg, Germany.; Department of Population Health Sciences, Duke University, Durham, NC, USA., Thielman NM; Duke Global Health Institute, Duke University, Durham, NC, USA.; Center for Health Policy & Inequalities Research, Duke University, Durham, NC, USA.
Jazyk: angličtina
Zdroj: Journal of choice modelling [J Choice Model] 2021 Sep; Vol. 40. Date of Electronic Publication: 2021 Jul 11.
DOI: 10.1016/j.jocm.2021.100305
Abstrakt: Efforts to eliminate the HIV epidemic will require increased HIV testing rates among high-risk populations. To inform the design of HIV testing interventions, a discrete choice experiment (DCE) with six policy-relevant attributes of HIV testing options elicited the testing preferences of 300 female barworkers and 440 male Kilimanjaro mountain porters in northern Tanzania. Surveys were administered between September 2017 and July 2018. Participants were asked to complete 12 choice tasks, each involving first- and second-best choices from 3 testing options. DCE responses were analyzed using a random effects latent class logit (RELCL) model, in which the latent classes summarize common participant preference profiles, and the random effects capture additional individual-level preference heterogeneity with respect to three attribute domains: (a) privacy and confidentiality (testing venue, pre-test counseling, partner notification); (b) invasiveness and perceived accuracy (method for obtaining the sample for the HIV test); and (c) accessibility and value (testing availability, additional services provided). The Bayesian Information Criterion indicated the best model fit for a model with 8 preference classes, with class sizes ranging from 6% to 19% of participants. Substantial preference heterogeneity was observed, both between and within latent classes, with 12 of 16 attribute levels having positive and negative coefficients across classes, and all three random effects contributing significantly to participants' choices. The findings may help identify combinations of testing options that match the distribution of HIV testing preferences among high-risk populations; the methods may be used to systematically design heterogeneity-focused interventions using stated preference methods.
Competing Interests: Declaration of competing interest The authors declare that they have no conflict of interests.
Databáze: MEDLINE
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