Rurality and opioid prescribing rates in U.S. counties from 2006 to 2018: A spatiotemporal investigation

Sun, Feinuo

doi:10.1016/j.socscimed.2022.114788

Cited by 6 publications

(7 citation statements)

References 38 publications

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“…It should be noted rst that the analysis is only based on counties that have observed any prescription opioid-, heroin-, or synthetic opioid-involved deaths during 2010-2018, with a larger number of becomingmore-rural counties, mostly in the Great Plains area, being excluded since they have observed no such deaths compared to becoming-more-urban counties. This is consistent with evidence that some rural counties in the Great Plains have the lowest prescribing rates [34] and illicit drug markets are rstly developed in urban and metropolitan areas [3,8,21]. The overall developing trends of three opioidinvolved mortality rates are similar for both groups of counties, especially that the average synthetic opioid-involved mortality has very similar values between two groups during its rapid growing period (i.e., 2014-2018).…”

Section: Conclusion and Discussionsupporting

confidence: 85%

“…Thus, heroin-involved overdoses in rural areas are growing and warrant attention. Another unexpected nding is that increasing rurality does not necessarily predict the increases in the prescription opioid-involved mortality, though prescribing rates in rural areas are found higher than that in urban areas [3,34]. This indicates that overdoses involving prescription opioids continue to be a problem in areas that becoming more urbanized.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…Third, the contrary impacts of social capital on the development of the opioid crisis, which include protecting people from drug addiction and overdoses through family and community supports [39][40][41] and facilitating the circulation of drugs [34,42,43], are shown in becoming-more-urban counties, while becoming-more-rural counties are found to suffer more from the downside of social capital. For both groups of counties, increasing social capital is related to increasing heroin-and synthetic opioid-involved mortality, which may be explained by the positive impact of social capital on the growth of illicit drug markets [44,45].…”

Section: Conclusion and Discussionmentioning

confidence: 99%

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Opioid crisis in rural and urban counties from 2010 to 2018: in light of the index of relative rurality

Sun

2023

Preprint

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Previous studies have documented the rural-urban disparities of the opioid crisis, but rarely consider the heterogeneities within rural and urban areas, nor the changing dynamics of the rural-urban boundaries. Taking advantage of a continuous measure of rurality, this study separates counties into “becoming-more-rural” and “becoming-more-urban” groups according to the change of rurality between 2000 and 2010, and compares the determinants of the development of prescription opioid-, heroin-, and synthetic opioid-related opioid mortality in the two groups of counties. The analysis uses longitudinal data of 2010–2018 from a variety of national datasets such as the confidential Multiple-Cause of Death data, the U.S. Opioid Dispensing Rate Maps, and the American Community Survey. The results show that while increasing rurality does not predict increasing prescription opioid- and synthetic opioid-involved mortality, the growth of heroin-involved mortality could be a severe issue for becoming-more-rural counties that have already observed heroin overdose deaths. The demographic groups that have higher risk of opioid overdoses may be different in different areas, for example increasing males and people engaged in manual labor occupations are associated with increasing prescription opioid-involved mortality for becoming-more-rural counties but in becoming-more-urban areas, heroin-involved mortality correlates to a higher percentage of Hispanics. Concentrations of socioeconomically disadvantaged populations like veterans and people in poverty increase opioid overdose deaths particularly for becoming-more-urban counties, while healthcare services shown more beneficial to becoming-more-rural areas. The findings suggest that the programs and regulations to reduce opioid-involved mortality should consider the changing nature of counties in the degree of rurality.

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Section: Conclusion and Discussionsupporting

confidence: 85%

Section: Conclusion and Discussionmentioning

confidence: 99%

Section: Conclusion and Discussionmentioning

confidence: 99%

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Opioid crisis in rural and urban counties from 2010 to 2018: in light of the index of relative rurality

Sun

2023

Preprint

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“… 26 , 27 Studies have shown significant spatial clustering in demographic, income, and health status characteristics. 28 , 29 …”

Section: Methodsmentioning

confidence: 99%

Drivers of Individual and Regional Variation in CMS Hierarchical Condition Categories Among Florida Beneficiaries

Jacobs¹,

Morris²,

Haleem³

et al. 2023

RMHP

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Objective To explore hierarchical condition categories (HCC) risk score variation among Florida Fee for Service (FFS) Medicare beneficiaries between 2016 and 2018. Data Sources This study analyzed HCC risk score variation using Medicare claims data for Florida beneficiaries enrolled in Parts A & B between 2016 and 2018. Study Design The CMS methodology analyzed HCC risk score variation using annual mean county- and beneficiary-level risk score changes. The association between variation and beneficiary characteristics, diagnoses, and geographic location was characterized using mixed-effects negative binomial regression models. Data Collection Not applicable. Principal Findings Counties in the Northeast [marginal effect (ME)=−0.003], Central (ME=−0.021), and Southwest (ME=−0.009) Florida have relatively lower mean risk scores. A higher number of lifetime (ME=0.246) and treatable (ME=0.288) conditions were associated with higher county-level risk scores, while more preventable conditions (ME=−0.249) were associated with lower risk scores. Counties with older beneficiaries (ME=0.015) and more Blacks (ME=0.070) have higher risk scores, while having female beneficiaries reduced risk scores (ME=−0.005). Individual risk scores did not vary by age (ME=0.000), but Blacks (ME=0.001) had higher rates of variation relative to Whites, while other races had comparatively lower variation (ME=−0.003). In addition, individuals diagnosed with more lifetime (ME=0.129), treatable (ME=0.235), and preventable (ME=0.001) conditions had higher risk score variation. Most condition-specific indicators showed small associations with risk score changes; however, metastatic cancer/acute leukemia, respirator dependence/tracheostomy, and pressure ulcers of the skin were significantly associated with both types of HCC risk score variation. Conclusion Results showed demographics, HCC condition classifications (ie, lifetime, preventable, and treatable), and some specific conditions were associated with higher variation in mean county-level and individual risk scores. Results suggest consistent coding and reductions in the prevalence of certain treatable or preventable conditions could reduce the county and individual HCC risk score year-to-year change.

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“…One such measure is the Index of Relative Rurality (IRR) [49], which is calculated using population size, population density, distance to nearest metropolitan area, and percent urban population. The IRR has been used in several recent studies on health and aging, including health services utilization [50], geographic inequities in healthcare providers [51], availability of hospice care [52], opioid prescribing patterns [53], obesity [54], and COVID-19-related outcomes [55,56].…”

Section: Defining Rurality: Methodological Considerationsmentioning

confidence: 99%

Aging in Rural Communities

Cohen

Greaney

2022

Curr Epidemiol Rep

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Purpose of Review Population aging is occurring worldwide, particularly in developed countries such as the United States (US). However, in the US, the population is aging more rapidly in rural areas than in urban areas. Healthy aging in rural areas presents unique challenges. Understanding and addressing those challenges is essential to ensure healthy aging and promote health equity across the lifespan and all geographies. This review aims to present findings and evaluate recent literature (2019)(2020)(2021)(2022) on rural aging and highlight future directions and opportunities to improve population health in rural communities. Recent FindingsThe review first addresses several methodological considerations in measuring rurality, including the choice of measure used, the composition of each measure, and the limitations and drawbacks of each measure. Next, the review considers important concepts and context when describing what it means to be rural, including social, cultural, economic, and environmental conditions. The review assesses several key epidemiologic studies addressing rural-urban differences in population health among older adults. Health and social services in rural areas are then discussed in the context of healthy aging in rural areas. Racial and ethnic minorities, indigenous peoples, and informal caregivers are considered as special populations in the discussion of rural older adults and healthy aging. Lastly, the review provides evidence to support critical longitudinal, place-based research to promote healthy aging across the rural-urban divide is highlighted. Summary Policies, programs, and interventions to reduce rural-urban differences in population health and to promote health equity and healthy aging necessitate a context-specific approach. Considering the cultural context and root causes of rural-urban differences in population health and healthy aging is essential to support the real-world effectiveness of such programs, policies, and interventions.

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Rurality and opioid prescribing rates in U.S. counties from 2006 to 2018: A spatiotemporal investigation

Cited by 6 publications

References 38 publications

Opioid crisis in rural and urban counties from 2010 to 2018: in light of the index of relative rurality

Opioid crisis in rural and urban counties from 2010 to 2018: in light of the index of relative rurality

Drivers of Individual and Regional Variation in CMS Hierarchical Condition Categories Among Florida Beneficiaries

Aging in Rural Communities

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