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Review
. 2020 May;24(5):1294-1303.
doi: 10.1007/s10461-019-02705-3.

Preferences for Pre-exposure Prophylaxis Service Delivery Among Female Sex Workers in Malawi: A Discrete Choice Experiment

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Review

Preferences for Pre-exposure Prophylaxis Service Delivery Among Female Sex Workers in Malawi: A Discrete Choice Experiment

Kathryn E Lancaster et al. AIDS Behav. 2020 May.

Abstract

Female sex workers (FSW) in Malawi have among the highest HIV prevalence estimates worldwide. Daily oral pre-exposure prophylaxis (PrEP) is an effective HIV prevention method, yet preferences for PrEP delivery among FSW are lacking. Eight focus group discussions, a literature review, and cognitive interviews were conducted to identify modifiable PrEP delivery attributes and inform discrete-choice experiment (DCE) development for FSW in Lilongwe. Enrolled FSW received an interviewer-assisted DCE. Data were analyzed using mixed logit regression. Dispensing location was most preferred, followed by the provision of additional services. Women preferred receiving PrEP at family planning clinics or non-governmental organization run drop-in centers. Cervical cancer screening was the most preferred additional service, while pregnancy testing and partner risk reduction counseling were less valued. This study was the first study to examine PrEP delivery preferences in Malawi using DCE-a powerful elicitation tool to apply to other key populations at risk for HIV.

Keywords: Africa; Pre-exposure prophylaxis; Sex workers; Women.

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Conflict of interest statement

Conflict of interest

Authors declare that they have no conflict of interest.

Figures

Figure I.
Figure I.. Sample choice task.
Choice task shown to participants during presentation of the discrete choice experiment, in English (panel a) and Chichewa (panel b).
Figure II.
Figure II.. Preferences of PrEP delivery among female sex workers in Lilongwe, Malawi (n=150).
Panel a) Relative utilities and 95% confidence intervals between most preferred and least preferred within attributes; Panel b) Mean level utilities of attributes and standard deviations.

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