Explaining spatial accessibility to high-quality nursing home care in the US using machine learning.

Reddy, BP; O'Neill, SORCID logo; O'Neill, C and (2022) Explaining spatial accessibility to high-quality nursing home care in the US using machine learning. Spatial and Spatio-temporal Epidemiology, 41. 100503-. ISSN 1877-5845 DOI: 10.1016/j.sste.2022.100503
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In this study we measure and map the system-wide spatial accessibility to good quality nursing home care for all counties in the contiguous United States, and use an 'imputed post-lasso' machine learning technique to systematically examine this accessibility measure's associations with a broad range of county-level socio-demographic variables. Both steps were carried out using publicly available datasets. Analyses found clear evidence of spatial patterning in accessibility, particularly by population density, state and the populations of specific racial minorities. This has implications for outcomes that extend beyond the care homes and we highlight a number of policy measures that may help to address these shortcomings. The 'out-of-sample' predictive performance of the machine learning approach highlights the method's usefulness in identifying systematic differences in accessibility to services.


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This is an author accepted manuscript version of an article accepted for publication, and following peer review. Please be aware that minor differences may exist between this version and the final version if you wish to cite from it.
Available under Creative Commons: Attribution-NonCommercial-No Derivative Works 4.0

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