{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T16:28:33Z","timestamp":1753892913522,"version":"3.41.2"},"reference-count":41,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,9,26]],"date-time":"2022-09-26T00:00:00Z","timestamp":1664150400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Opioid use disorder (OUD) among older adults (age \u2265 65) is a growing yet underexplored public health concern and previous research has mainly assumed that the spatial process underlying geographic patterns of population health outcomes is constant across space. This study is among the first to apply a local modeling perspective to examine the geographic disparity in county-level OUD rates among older Medicare beneficiaries and the spatial non-stationarity in the relationships between determinants and OUD rates.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Data are from a variety of national sources including the Centers for Medicare &amp;amp; Medicaid Services beneficiary-level data from 2020 aggregated to the county-level and county-equivalents, and the 2016\u20132020 American Community Survey (ACS) 5-year estimates for 3,108 contiguous US counties. We use multiscale geographically weighted regression to investigate three dimensions of spatial process, namely \u201clevel of influence\u201d (the percentage of older Medicare beneficiaries affected by a certain determinant), \u201cscalability\u201d (the spatial process of a determinant as global, regional, or local), and \u201cspecificity\u201d (the determinant that has the strongest association with the OUD rate).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The results indicate great spatial heterogeneity in the distribution of OUD rates. Beneficiaries' characteristics, including the average age, racial\/ethnic composition, and the average hierarchical condition categories (HCC) score, play important roles in shaping OUD rates as they are identified as primary influencers (impacting more than 50% of the population) and the most dominant determinants in US counties. Moreover, the percentage of non-Hispanic white beneficiaries, average number of mental health conditions, and the average HCC score demonstrate spatial non-stationarity in their associations with the OUD rates, suggesting that these variables are more important in some counties than others.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Our findings highlight the importance of a local perspective in addressing the geographic disparity in OUD rates among older adults. Interventions that aim to reduce OUD rates in US counties may adopt a place-based approach, which could consider the local needs and differential scales of spatial process.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fpubh.2022.993507","type":"journal-article","created":{"date-parts":[[2022,9,26]],"date-time":"2022-09-26T06:05:35Z","timestamp":1664172335000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Multiscale dimensions of county-level disparities in opioid use disorder rates among older Medicare beneficiaries"],"prefix":"10.3389","volume":"10","author":[{"given":"Tse-Chuan","family":"Yang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Carla","family":"Shoff","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seung-won Emily","family":"Choi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feinuo","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2022,9,26]]},"reference":[{"key":"B1","unstructured":"Provisional Life Expectancy Estimates for January through June, 2020 NVSS vital statistics rapid release; report no. 010. 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