Investigating the socio-demographic determinants of medical travel burden in the U.S.: insights from the 2017 National Household Travel Survey
A.D. Lidbe, M. Dutta, M. Cheshire, E.K.
Adanu, X. Li, P. Penmetsa
Pages: 89-110
Abstract:
Medical travel burden is a significant
challenge for vulnerable subgroups in the United States, particularly rural
residents, low-income individuals, and racial minorities, who often face
longer travel distances and limited access to healthcare services. This study
aims to investigate and identify the various socio-demographic factors
associated with medical travel burden trips using the 2017 National Household
Travel Survey (NHTS). The study specifically focuses on intricate interplay
of demographic, socio-economic, spatial, and temporal factors that influence
whether individuals perceive medical travel as a burden. To this end, a
random parameter multinomial logit model was developed to examine the
exogenous factors impacting medical travel burden. This study applies a
Random Parameters Multinomial Logit (RPMNL) model to national-level household
travel data, allowing us to capture unobserved heterogeneity that traditional
models overlook. The findings reveal significant gender differences, with
more females than males making medical trips. Regarding race and ethnicity,
the white population accounted for the majority of medical trips, followed by
Hispanics and then African Americans. Households with income less than
$50,000 predominantly undertook these medical trips. The results indicate
that individuals with poor health conditions, low-income households,
single-adult households, rural residents, and households without vehicles are
more likely to experience travel burden in accessing healthcare. To address
these disparities, future interventions aimed at improving health-related
transportation should focus on these vulnerable subgroups, thereby mitigating
the existing gaps in healthcare accessibility and health outcomes.
Keywords: medical care; travel burden; NHTS; random
parameter logit model; healthcare access
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