Social Vulnerabilities and Spatial Access to Primary Healthcare through Car and Public Transportation System in the Albuquerque, NM, Metropolitan Area: Assessing Disparities through GIS and Multilevel Modeling

Abstract

Primary healthcare (PHC) is a keystone component of population health. However, inequities in public transportation access hinder equitable usage of PHC services by minoritized populations. Using the multimodal enhanced 2-step floating catchment area method and data in 2018 and 2019 for spatial access to PHC providers ( n = 1166) and social vulnerability markers through census block ( n = 543) and tract data ( n = 226), a generalized linear mixed-effect model (GLMEM) was constructed to test the effects of sociodemographic and community area correlates on both car and bus transit spatial access to PHC in the Albuquerque, New Mexico (NM) metropolitan area. Results for bus spatial access to PHC showed lower access for Hispanics ( B = − 0.097 ± 0.029 [− 0.154, − 0.040]) and non-Hispanic Whites ( B = − 0.106 ± 0.032 [− 0.169, − 0.043]) and a positive association between single-family households and bus spatial access ( B = 1.573 ± 0.349 [0.866, 2.261]). Greater disability vulnerability ( B = − 0.569 ± 0.173 [− 0.919, − 0.259]) and language vulnerability ( B = − 0.569 ± 0.173 [− 0.919, − 0.259]) were associated with decreased bus spatial access. For car spatial access to PHC, greater SES vulnerability ( B = − 0.338 ± 0.021 [− 1.568, -0.143]), disability ( B = − 0.721 ± .092 [− 0.862, − 0.50 9]), and language vulnerability ( B = − 0.686 ± 0.172 [− 1.044, − 0.362]) were associated with less car spatial access. Results indicate a disproportionate burden of low PHC access among disadvantaged population groups who rely heavily on public transportation. These results necessitate targeted interventions to reduce these disparities in access to PHC.

The accessibility analysis of multiple public facilities in the central urban areas based on multi‐transportation modes: A study in Chengdu

“…In recent years, scholars at home and abroad have applied accessibility in the planning and construction of public facilities such as medical care (Baier et al., 2020; Chen et al., 2021), education (Pizzol et al., 2021; Wang et al., 2021), commerce (Xue, 2022; Zhou et al., 2021), transportation (Bernard, 2022; Xue et al., 2020) and leisure (Liao et al., 2022; Tomasiello & Giannotti, 2022). The methods of accessibility analysis included the shortest distance, buffer analysis, network analysis, gravity model, and the opportunity‐based cumulative methods (Singh & Sarkar, 2022), as well as the two‐step floating catchment area (2SFCA) (Fransen et al., 2015), and improved models (Gong et al., 2021; Lardier et al., 2023). The shortest distance method only considers the shortest straight‐line distance between the start and end points, ideally omitting the traffic factors that may impact the result (Fan et al., 2017).…”