Heterogeneity of provider preferences for HIV Care Coordination Program features: latent class analysis of a discrete choice experiment

潜在类模型 人类免疫缺陷病毒(HIV) 班级(哲学) 离散选择 计算机科学 心理学 计量经济学 医学 病毒学 经济 人工智能 机器学习
作者
Chunki Fong,Madellena Conte,Rebecca Zimba,Jennifer Carmona,Gina Gambone,Abigail Baim‐Lance,McKaylee Robertson,Mary K. Irvine,Denis Nash
出处
期刊:HIV research & clinical practice [Taylor & Francis]
卷期号:25 (1) 被引量:1
标识
DOI:10.1080/25787489.2023.2300923
摘要

The PROMISE study assessed revisions designed to facilitate implementation of an HIV care coordination program (CCP) addressing gaps in care and treatment engagement among people living with HIV in New York City (NYC). Through latent class analysis (LCA) of a discrete choice experiment (DCE), we explored heterogeneity of provider preferences regarding CCP features. From January-March 2020, 152 NYC CCP providers completed a DCE with 3–4 levels on each of 4 program attributes: 1) Help with Adherence to Antiretroviral Therapy (ART), 2) Help with Primary Care Appointments, 3) Help with Issues Other than Primary Care, and 4) Where Program Visits Happen. We used LCA to assess patterns of preference, and choice simulation to estimate providers’ endorsement of hypothetical CCPs. LCA identified three subgroups. The two larger subgroups (n = 133) endorsed more intensive individual program features, including directly observed therapy, home visits, and appointment reminders with accompaniment of clients to their appointments. The smallest subgroup (n = 19) endorsed medication reminders only, appointment reminders without accompaniment, and meeting at the program location rather than clients’ homes. Choice simulation analysis affirmed the highest degree of endorsement (62%) for hypothetical programs combining the intensive features described above. Results indicated providers’ preference for intensive program features and also reinforced the need for flexible service delivery options. Provider perspectives on service delivery approaches can inform program adjustments for successful long-term implementation, which in turn can improve patient outcomes.
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