Project description:Importance:Treatment with methadone or buprenorphine is the current standard of care for opioid use disorder. Given the paucity of research identifying which patients will respond best to which medication, both medications should be accessible to all patients so that patients can determine which works best for them. However, given differences in the historical contexts of their initial implementation, access to each of these medications may vary along racial/ethnic lines. Objective:To examine the extent to which capacity to provide methadone and buprenorphine vary with measures of racial/ethnic segregation. Design, Setting, and Participants:This cross-sectional study included all counties and county-equivalent divisions in the US in 2016. Data on racial/ethnic population distribution were derived from the American Community Survey, and data on locations of facilities providing methadone and buprenorphine were obtained from Substance Abuse and Mental Health Services Administration databases. Data were analyzed from August 22, 2018, to September 11, 2019. Exposures:Two county-level measures of racial/ethnic segregation, including dissimilarity (representing the proportion of African American or Hispanic/Latino residents who would need to move census tracts to achieve a uniform spatial distribution of the population by race/ethnicity) and interaction (representing the probability that an African American or Hispanic/Latino resident will interact with a white resident and vice versa, assuming random mixing across census tracts). Main Outcomes and Measures:County-level capacity to provide methadone or buprenorphine, defined as the number of facilities providing a medication per 100?000 population. Results:Among 3142 US counties, there were 1698 facilities providing methadone (0.6 facilities per 100?000 population) and 18?868 facilities providing buprenorphine (5.9 facilities per 100?000 population). Each 1% decrease in probability of interaction of an African American resident with a white resident was associated with 0.6 more facilities providing methadone per 100?000 population. Similarly, each 1% decrease in probability of interaction of a Hispanic/Latino resident with a white resident was associated with 0.3 more facilities providing methadone per 100?000 population. Each 1% decrease in the probability of interaction of a white resident with an African American resident was associated with 8.17 more facilities providing buprenorphine per 100?000 population. Similarly, each 1% decrease in the probability of interaction of a white resident with a Hispanic/Latino resident was associated with 1.61 more facilities providing buprenorphine per 100?000 population. Conclusions and Relevance:These findings suggest that the racial/ethnic composition of a community was associated with which medications residents would likely be able to access when seeking treatment for opioid use disorder. Reforms to existing regulations governing the provisions of these medications are needed to ensure that both medications are equally accessible to all.

Project description:Importance:Opioid overdose deaths in the United States continue to increase, reflecting a growing need to treat those with opioid use disorder (OUD). Little is known about counties with high rates of opioid overdose mortality but low availability of OUD treatment. Objective:To identify characteristics of US counties with persistently high rates of opioid overdose mortality and low capacity to deliver OUD medications. Design, Setting, and Participants:In this cross-sectional study of data from 3142 US counties from January 1, 2015, to December 31, 2017, rates of opioid overdose mortality were compared with availability in 2017 of OUD medication providers (24 851 buprenorphine-waivered clinicians [physicians, nurse practitioners, and physician assistants], 1517 opioid treatment programs [providing methadone], and 5222 health care professionals who could prescribe extended-release naltrexone). Statistical analysis was performed from April 20, 2018, to May 8, 2019. Exposures:Demographic, workforce, lack of insurance, road density, urbanicity, opioid prescribing, and regional division county-level characteristics. Main Outcome and Measures:The outcome variable, "opioid high-risk county," was a binary indicator of a high (above national) rate of opioid overdose mortality with a low (below national) rate of provider availability to deliver OUD medication. Spatial logistic regression models were used to determine associations with being an opioid high-risk county. Results:Of 3142 counties, 751 (23.9%) had high rates of opioid overdose mortality. A total of 1457 counties (46.4%), and 946 of 1328 rural counties (71.2%), lacked a publicly available OUD medication provider in 2017. In adjusted models, compared with the West North Central division, counties in the East North Central, Mountain, and South Atlantic divisions had increased odds of being opioid high-risk counties (East North Central: odds ratio [OR], 2.21; 95% CI, 1.19-4.12; Mountain: OR, 4.15; 95% CI, 1.34-12.89; and South Atlantic: OR, 2.99; 95% CI, 1.26-7.11). A 1% increase in unemployment was associated with increased odds (OR, 1.09; 95% CI, 1.03-1.15) of a county being an opioid high-risk county. Counties with an additional 10 primary care clinicians per 100 000 population had a reduced risk of being opioid high-risk counties (OR, 0.89; 95% CI, 0.85-0.93), as did counties that were micropolitan (vs metropolitan) (OR, 0.67; 95% CI, 0.50-0.90) and those that had an additional 1% of the population younger than 25 years (OR, 0.95; 95% CI, 0.92-0.98). Conclusions and Relevance:Counties with low availability of OUD medication providers and high rates of opioid overdose mortality were less likely to be micropolitan and have lower primary care clinician density, but were more likely to be in the East North Central, South Atlantic, or Mountain division and have higher rates of unemployment. Strategies to increase medication treatment must account for these factors.

Project description:ObjectiveTo identify county characteristics associated with high versus low well-being among high-poverty counties.DesignObservational cross-sectional study at the county level to investigate the associations of 29 county characteristics with the odds of a high-poverty county reporting population well-being in the top quintile versus the bottom quintile of well-being in the USA. County characteristics representing key determinants of health were drawn from the Robert Wood Johnson Foundation County Health Rankings and Roadmaps population health model.SettingCounties in the USA that are in the highest quartile of poverty rate.Main outcome measureGallup-Sharecare Well-being Index, a comprehensive population-level measure of physical, mental and social health. Counties were classified as having a well-being index score in the top or bottom 20% of all counties in the USA.ResultsAmong 770 high-poverty counties, 72 were categorised as having high well-being and 311 as having low well-being. The high-well-being counties had a mean well-being score of 71.8 with a SD of 2.3, while the low-well-being counties had a mean well-being score of 60.2 with a SD of 2.8. Among the six domains of well-being, basic access, which includes access to housing and healthcare, and life evaluation, which includes life satisfaction and optimism, differed the most between high-being and low-well-being counties. Among 29 county characteristics tested, six were independently and significantly associated with high well-being (p<0.05). These were lower rates of preventable hospital stays, higher supply of primary care physicians, lower prevalence of smoking, lower physical inactivity, higher percentage of some college education and higher percentage of heavy drinkers.ConclusionsAmong 770 high-poverty counties, approximately 9% outperformed expectations, reporting a collective well-being score in the top 20% of all counties in the USA. High-poverty counties reporting high well-being differed from high-poverty counties reporting low well-being in several characteristics.

Project description:BackgroundAdults in rural counties in the United States (US) experience higher rates broadly of cardiovascular disease (CVD) compared with adults in urban counties. Mortality rates specifically due to heart failure (HF) have increased since 2011, but estimates of heterogeneity at the county-level in HF-related mortality have not been produced. The objectives of this study were 1) to quantify nationwide trends by rural-urban designation and 2) examine county-level factors associated with rural-urban differences in HF-related mortality rates.Methods and findingsWe queried CDC WONDER to identify HF deaths between 2011-2018 defined as CVD (I00-78) as the underlying cause of death and HF (I50) as a contributing cause of death. First, we calculated national age-adjusted mortality rates (AAMR) and examined trends stratified by rural-urban status (defined using 2013 NCHS Urban-Rural Classification Scheme), age (35-64 and 65-84 years), and race-sex subgroups per year. Second, we combined all deaths from 2011-2018 and estimated incidence rate ratios (IRR) in HF-related mortality for rural versus urban counties using multivariable negative binomial regression models with adjustment for demographic and socioeconomic characteristics, risk factor prevalence, and physician density. Between 2011-2018, 162,314 and 580,305 HF-related deaths occurred in rural and urban counties, respectively. AAMRs were consistently higher for residents in rural compared with urban counties (73.2 [95% CI: 72.2-74.2] vs. 57.2 [56.8-57.6] in 2018, respectively). The highest AAMR was observed in rural Black men (131.1 [123.3-138.9] in 2018) with greatest increases in HF-related mortality in those 35-64 years (+6.1%/year). The rural-urban IRR persisted among both younger (1.10 [1.04-1.16]) and older adults (1.04 [1.02-1.07]) after adjustment for county-level factors. Main limitations included lack of individual-level data and county dropout due to low event rates (<20).ConclusionsDifferences in county-level factors may account for a significant amount of the observed variation in HF-related mortality between rural and urban counties. Efforts to reduce the rural-urban disparity in HF-related mortality rates will likely require diverse public health and clinical interventions targeting the underlying causes of this disparity.

Dataset Information

Association Between Interorganizational Collaboration in Opioid Response and Treatment Capacity for Opioid Use Disorder in Counties of Five States: A Cross-Sectional Study

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