Spatial accessibility of pre-exposure prophylaxis (PrEP): different measure choices and the implications for detecting shortage areas and examining its association with social determinants of health

Hui Luan*, Guangquan Li, Dustin Duncan, Patrick S. Sullivan, Yusuf Ransome

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    10 Citations (Scopus)

    Abstract

    Purpose: We examine how various pre-exposure prophylaxis (PrEP) accessibility measures impact the detection of PrEP shortage areas and the relation of shortage areas to social determinants of health (SDOH). Methods: Using ZIP Code Tabulation Areas (ZCTAs) in New York City as a case study, we compared 25 measures of spatial PrEP accessibility across four categories, including density, proximity, two-step floating catchment area (2SFCA), and Gaussian 2SFCA (G2SFCA). Bayesian spatial regression models were used to examine how PrEP accessibility is associated with SDOH. Results: Using density to measure PrEP accessibility for small areas such as ZCTAs poses challenges to statistical modeling because the measured accessibility values are highly skewed with excess zeros, leading to the necessity of using complex models such as the two-part mixture model. When G2SFCA measures are used, which account for distance decay effects and the competition from the PrEP demand side, findings on PrEP shortage area detection and the association between PrEP accessibility and SDOH were more consistent and less sensitive to spatial scales (i.e., varying from 10- to 30-minute driving). Conclusions: This research adds to the nascent research on PrEP accessibility measurement and sheds light on selecting an appropriate measure to assess spatial disparities in PrEP accessibility and its associations with SDOH.

    Original languageEnglish
    Pages (from-to)72-79.e3
    JournalAnnals of Epidemiology
    Volume86
    Early online date13 Jul 2023
    DOIs
    Publication statusPublished - 1 Oct 2023

    UN SDGs

    This output contributes to the following UN Sustainable Development Goals (SDGs)

    1. SDG 3 - Good Health and Well-being
      SDG 3 Good Health and Well-being
    2. SDG 5 - Gender Equality
      SDG 5 Gender Equality

    Keywords

    • spatial accessibility
    • two-part mixture model
    • HIV prevention
    • Pre-Exposure Prophylaxis
    • Bayesian spatial analysis
    • Gaussian 2SFCA
    • Spatial accessibility
    • Two-part mixture model
    • Pre-exposure prophylaxis

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