Optimizing Differentiated HIV Treatment Models in Urban Zimbabwe

Assessing Patient Preferences Using a Discrete Choice Experiment

Bibliographic Data

ID15334299
AuthorsMichael Strauss (0000-0001-7849-8812, Centre for the AIDS Programme of Research in South Africa, corresponding author), Gavin George (0000-0001-7258-8470, University of KwaZulu-Natal), Joanne E Mantell (0000-0002-0748-5974, New York Psychoanalytic Society and Institute), Munyaradzi Mapingure (0000-0002-7777-9492), Tsitsi B Masvawure (0000-0001-7949-4864, College of the Holy Cross), Matthew R Lamb (0000-0001-6457-3762, ICAP Global Health), Jennifer M Zech (0000-0001-8652-0784, ICAP Global Health), Godfrey Musuka (0000-0001-9077-4429), Innocent Chingombe (0000-0001-6512-2832), Martin Msukwa (0000-0001-8934-2610), Rodrigo Boccanera (Health Resources and Services Administration), Clorata Gwanzura (Ministry of Health and Child Welfare), Tsitsi Apollo (0000-0001-9881-0914, Ministry of Health and Child Welfare), Miriam Rabkin (0000-0002-2342-4362, ICAP Global Health)
Year2020
Volume25
Issue2
Pages397-413
Publication date2020-08-18
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueAIDS and Behavior (JOURNAL)
Journal identifiersISSN: 1090-7165 • E-ISSN: 1573-3254
PublisherSpringer Science+Business Media (PUBLISHER • DE)
DOI10.1007/s10461-020-02994-z
PMID32812124
OpenAlexW3062096926
LanguageEN
Citations received4
References cited28

Differentiated service delivery holds great promise for streamlining the delivery of health services for HIV. This study used a discrete choice experiment to assess preferences for differentiated HIV treatment delivery model characteristics among 500 virally suppressed adults on antiretroviral therapy in Harare, Zimbabwe. Treatment model characteristics included location, consultation type, healthcare worker cadre, operation times, visit frequency and duration, and cost. A mixed effects logit model was used for parameter estimates to identify potential preference heterogeneity among participants, and interaction effects were estimated for sex and age as potential sources of divergence in preferences. Results indicated that participants preferred health facility-based services, less frequent visits, individual consultations, shorter waiting times, lower cost and, delivered by respectful and understanding healthcare workers. Some preference heterogeneity was found, particularly for location of service delivery and group vs. individual models; however, this was not fully explained by sex and age characteristics of participants. In urban areas, facility-based models, such as the Fast Track model requiring less frequent clinic visits, are likely to better align with patient preferences than some of the other community-based or group models that have been implemented. As Zimbabwe scales up differentiated treatment models for stable patients, a clear understanding of patient preferences can help in designing services that will ensure optimal utilization and improve the efficiency of service delivery

Business · Discrete choice · Econometrics · Economic growth · Economics · Environmental health · Family medicine · Health care · Health psychology · Human immunodeficiency virus (HIV · Logistic regression · Microeconomics · Mixed logit · Preference · Public health · Service (business · Service delivery framework · Demography · Economic and Environmental Valuation · Global Maternal and Child Health · HIV/AIDS Research and Interventions · Medicine · Nursing · Psychology · Internal Medicine · Marketing