Abstract
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.
Highlights
Ambitious targets have been set by the Joint United Nations Programme on HIV/AIDS (UNAIDS), the President’s Emergency Plan for AIDS Relief (PEPFAR), and by Ministries of Health across the globe to eliminate the HIV epidemic
To identify patterns of preferences, we modeled discrete choice experiment (DCE) responses using a random effects latent class logit (RELCL) model
Compared to a national sample of adults ages 18–49 residing in mainland urban Tanzania who participated in the 2016-17 Tanzania HIV IMPACT Survey (THIS) (Tanzania Commission for AIDS, 2018), female barworkers were less likely to be married, had more education, and both groups were somewhat more likely to have ever tested for HIV
Summary
Ambitious targets have been set by the Joint United Nations Programme on HIV/AIDS (UNAIDS), the President’s Emergency Plan for AIDS Relief (PEPFAR), and by Ministries of Health across the globe to eliminate the HIV epidemic. For the year 2030, these targets include what is known as 95-95-95 - diagnosing 95% of all persons living with HIV (PLWH), initiating treatment for 95% of those diagnosed, and achieving viral suppression for 95% of those treated (UNAIDS, 2014). Progress towards diagnosing 95% of PLWH by 2030 is contingent on accelerating the uptake of HIV testing, both among higher-risk populations and across the population at large. The number of un diagnosed HIV infections is considered a major hindrance to achieving the UNAIDS targets and ending the epidemic (The Lancet, 2017). DCE results can be used to develop targeted, preference-informed interventions; optimal in terventions may vary across and within population subgroups. DCEs have not been used to systematically characterize the distribution of HIV testing preferences among populations at high risk of HIV infection
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