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Patient preferences for HIV service delivery models; a Discrete Choice Experiment in Kisumu, Kenya

Bibliographic Data

ID19595048
AuthorsRaphael Onyango Mando (0000-0002-6148-0218, Kenya Medical Research Institute, corresponding author), Michelle Moghadassi (University of California, San Francisco), Eric Juma (Kenya Medical Research Institute), Cirilus Ogollah (Kenya Medical Research Institute), Laura J Packel (0000-0002-8186-5246, University of California, Berkeley), Jayne Lewis Kulzer (University of California, San Francisco), Julie Kadima (Kenya Medical Research Institute), Francesca Odhiambo (0000-0002-3503-2294, Kenya Medical Research Institute), Ingrid Eshun-Wilson (0000-0002-4049-9868, Washington University in St. Louis), Hae‐young Kim (0000-0002-5124-1555, New York University), Hae-Young Kim (0000-0003-2043-2575), Craig R Cohen (0000-0001-8654-6634, University of California, San Francisco), Elizabeth A Bukusi (0000-0002-2031-2808, University of California, San Francisco), Elvin Geng (0000-0002-0825-1424, Washington University in St. Louis)
EditorsJeannine Uwimana-Nicol
Year2022
Volume2
Issue10
Pagese0000614
Publication date2022-10-27
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenuePLOS Global Public Health (JOURNAL)
Journal identifiersISSN: 2767-3375 • E-ISSN: 2767-3375
PublisherPublic Library of Science (PLoS) (PUBLISHER)
DOI10.1371/journal.pgph.0000614
PMID36962597
OpenAlexW4307412453
LanguageEN
Citations received3
References cited19

Novel “differentiated service delivery” models for HIV treatment that reduce clinic visit frequency, minimize waiting time, and deliver treatment in the community promise retention improvement for HIV treatment in Sub-Saharan Africa. Quantitative assessments of differentiated service delivery (DSD) feature most preferred by patient populations do not widely exist but could inform selection and prioritization of different DSD models. We used a discrete choice experiment (DCE) to elicit patient preferences of HIV treatment services and how they differ across DSD models. We surveyed 18+year-olds, enrolled in HIV care for ≥6 months between February-March, 2019 at four facilities in Kisumu County, Kenya. DCE offered patients a series of comparisons between three treatment models, each varying across seven attributes: ART refill location, quantity of dispensed ART at each refill, medication pick-up hours, type of adherence support, clinical visit frequency, staff attitude, and professional cadre of person providing ART refills. We used hierarchical Bayesian model to estimate attribute importance and relative desirability of care characteristics, latent class analysis (LCA) for groups of preferences and mixed logit model for willingness to trade analysis. Of 242 patients, 128 (53.8%) were females and 150 (62.8%) lived in rural areas. Patients placed greatest importance on ART refill location [19.5% (95% CI 18.4, 10.6) and adherence support [19.5% (95% CI 18.17, 20.3)], followed by staff attitude [16.1% (95% CI 15.1, 17.2)]. In the mixed logit, patients preferred nice attitude of staff (coefficient = 1.60), refill ART health center (Coeff = 1.58) and individual adherence support (Coeff = 1.54), 3 or 6 months for ART refill (Coeff = 0.95 and 0.80, respectively) and pharmacists (instead of lay health workers) providing ART refill (Coeff = 0.64). No differences were observed by gender or urbanicity. LCA revealed two distinct groups (59.5% vs. 40.5%). Participants preferred 3 to 6-month refill interval or clinic visit spacing, which DSD offers stable patients. While DSD has encouraged community ART group options, our results suggest strong preferences for ART refills from health-centers or pharmacists over lay-caregivers or community members. These preferences held across gender&urban/rural subpopulations

Business · Family medicine · Latent class model · Logistic regression · Logit · Mixed logit · Ordered logit · Service delivery framework · Service provider · Statistics · Global Maternal and Child Health · HIV/AIDS Research and Interventions · Medicine · Pregnancy and Medication Impact · Internal Medicine · Marketing

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Unique citing works3
Citations per year3
Citation span2025 - 2025 (1)
Citation velocityrecent
Highly citedNo
Citation typesNeutral: 3
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