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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ABSTRACT: Background:
Local governments on the front lines of the opioid epidemic often collaborate
SUBMITTER: Swann W
PROVIDER: S-EPMC9284196 | biostudies-literature |
REPOSITORIES: biostudies-literature
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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.
| S-EPMC7177200 | biostudies-literature
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.
| S-EPMC6604101 | biostudies-literature
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.
| S-EPMC7500307 | biostudies-literature
Project description:ObjectivesThe DSM-5 diagnosis 'opioid use disorder' (OUD) was established to better describe and detect significant impairment or distress related to opioid use. There is no data on rates of OUD in chronic non-cancer pain (CNCP) in European countries. Therefore, our objective was to screen patients in specialised pain centres for signs of OUD.DesignCross-sectional questionnaire study.SettingFour outpatient pain clinics in the area of Bonn, Germany.Participantsn=204 patients participated in the study (response rate: 87.9%). All adult patients with opioid pain therapy >6 months for CNCP were included. Excluded were patients with malignant disease, patients who could not collect their prescription themselves due to age or multimorbidity and patients on opioid-maintenance therapy.Primary and secondary outcome measurePrimary outcome measure was the proportion of patients with mild to severe OUD.ResultsOne-fourth (26.5%) of participants were diagnosed with OUD. Moderate to severe disorder was found in 9.3. Young age was the only connected risk factor (OR 0.96 [95% CI 0.94 to 0.99], p: 0.003).ConclusionsOUD is a relevant diagnosis in patients on long-term opioid therapy for CNCP in the Bonn area. Careful follow-up by the attending physicians is advisable, especially in patients with moderate or severe disorder.
| S-EPMC6500335 | biostudies-literature
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.
| S-EPMC7928489 | biostudies-literature
Project description:BackgroundWithin the United States, there is a shortage of opioid treatment programs (OTPs), facilities which dispense methadone for opioid use disorder. It is unknown how pharmacy-based methadone dispensing, as available internationally, could affect methadone access. We aimed to compare drive times to the nearest OTP with drive times to the nearest chain pharmacy in urban and rural census tracts.MethodsCross-sectional geospatial analysis of 2018 OTP location data and 2017 pharmacy location data. We included census tracts with non-zero population in Indiana, Kentucky, Ohio, Virginia, and West Virginia, states with highest rates of opioid overdose deaths. Our outcome was minimum drive time in minutes from census tract mean center of population to the nearest dispensing facility.ResultsAmong 7918 census tracts, median (IQR) drive time to OTPs increased from urban to increasingly rural census tract classification [16.1 min (10.2-25.9) to 48.4 min (34.0-63.3);p < .001]. Median (IQR) drive time to OTPs was greater than drive time to chain pharmacies among all census tracts: 19.6 min (11.6-35.1) versus 4.4 min (2.9-7.7) respectively; p < .001. The median (IQR) difference in drive time was greater for increasingly rural census tracts [11.5 min (6.1-19.2) to 35.2 min (19.6-49.7); p <.001] with pharmacy-based methadone dispensing.ConclusionRural census tracts have disproportionately long drive times to OTPs. Drawing from policies to increase methadone access in countries like Canada and Australia, this geographic methadone disparity could be mitigated through implementation of pharmacy-based methadone dispensing.
| S-EPMC7529685 | biostudies-literature
Project description:The authors present 12-month and lifetime prevalence, correlates, psychiatric comorbidity, and treatment of nonmedical prescription opioid use (NMPOU) and DSM-5 NMPOU disorder (NMPOUD).Data were derived from the 2012-2013 National Epidemiologic Survey on Alcohol and Related Conditions-III (NESARC-III) (N = 36,309).Prevalences of 12-month and lifetime NMPOU were 4.1% and 11.3%, exceeding rates in the 2001-2002 NESARC (1.8%, 4.7%). Twelve-month and lifetime rates of DSM-5 NMPOUD were 0.9% and 2.1%. NESARC-III DSM-IV NMPOUD rates (0.8%, 2.9%) were greater than those observed in the 2001-2002 NESARC (0.4% and 1.4%). Rates of NMPOU were greater among men, but no sex differential was observed for NMPOUD. Prevalences of NMPOU and NMPOUD were generally greater among 18- to 64-year-old individuals, whites, and Native Americans, and individuals with lower socioeconomic status. Associations were observed between 12-month and lifetime NMPOU and NMPOUD and other drug use disorders, posttraumatic stress disorder, and borderline, schizotypal, and antisocial personality disorders; persistent depression and major depressive disorder (for NMPOU); and bipolar I disorder (for NMPOUD). Only 5.5% and 17.7% of individuals with 12-month NMPOU and NMPOUD were ever treated.NMPOU and NMPOUD have considerably increased over the past decade, are associated with a broad array of risk factors and comorbidities, and largely go untreated in the United States. More information on the determinants, characteristics, and outcomes of NMPOU and NMPOUD is needed to support evidence-based interventions and prevention.
| S-EPMC5555044 | biostudies-other
Project description:BACKGROUND:In light of the accelerating and rapidly evolving overdose crisis in the United States (US), new strategies are needed to address the epidemic and to efficiently engage and retain individuals in care for opioid use disorder (OUD). Moreover, there is an increasing need for novel approaches to using health data to identify gaps in the cascade of care for persons with OUD. METHODS AND FINDINGS:Between June 2018 and May 2019, we engaged a diverse stakeholder group (including directors of statewide health and social service agencies) to develop a statewide, patient-centered cascade of care for OUD for Rhode Island, a small state in New England, a region highly impacted by the opioid crisis. Through an iterative process, we modified the cascade of care defined by Williams et al. for use in Rhode Island using key national survey data and statewide health claims datasets to create a cross-sectional summary of 5 stages in the cascade. Approximately 47,000 Rhode Islanders (5.2%) were estimated to be at risk for OUD (stage 0) in 2016. At the same time, 26,000 Rhode Islanders had a medical claim related to an OUD diagnosis, accounting for 55% of the population at risk (stage 1); 27% of the stage 0 population, 12,700 people, showed evidence of initiation of medication for OUD (MOUD, stage 2), and 18%, or 8,300 people, had evidence of retention on MOUD (stage 3). Imputation from a national survey estimated that 4,200 Rhode Islanders were in recovery from OUD as of 2016, representing 9% of the total population at risk. Limitations included use of self-report data to arrive at estimates of the number of individuals at risk for OUD and using a national estimate to identify the number of individuals in recovery due to a lack of available state data sources. CONCLUSIONS:Our findings indicate that cross-sectional summaries of the cascade of care for OUD can be used as a health policy tool to identify gaps in care, inform data-driven policy decisions, set benchmarks for quality, and improve health outcomes for persons with OUD. There exists a significant opportunity to increase engagement prior to the initiation of OUD treatment (i.e., identification of OUD symptoms via routine screening or acute presentation) and improve retention and remission from OUD symptoms through improved community-supported processes of recovery. To do this more precisely, states should work to systematically collect data to populate their own cascade of care as a health policy tool to enhance system-level interventions and maximize engagement in care.
| S-EPMC6863520 | biostudies-literature
Project description:ObjectiveDescribe methods to compile a unified database from disparate state agency datasets linking person-level data on controlled substance prescribing, overdose, and treatment for opioid use disorder in Connecticut.MethodsA multidisciplinary team of university, state and federal agency experts planned steps to build the data analytic system: stakeholder engagement, articulation of metrics, funding to establish the system, determination of needed data, accessing data and merging, and matching patient-level data.ResultsStakeholder meetings occurred over a 6-month period driving selection of metrics and funding was obtained through a grant from the Food and Drug Administration. Through multi-stakeholder collaborations and memoranda of understanding, we identified relevant data sources, merged them and matched individuals across the merged dataset. The dataset contains information on sociodemographics, treatments and outcomes. Step-by-step processes are presented for dissemination.ConclusionsCreation of a unified database linking multiple sources in a timely and ongoing fashion may assist other states to monitor the public health impact of controlled substances, identify and implement interventions, and assess their effectiveness.
| S-EPMC7780404 | biostudies-literature
Project description:BACKGROUND:Previous studies have associated elevated mortality risk in central Appalachia with coal-mining activities, but few have explored how different non-coal factors influence the association within each county. Consequently, there is a knowledge gap in identifying effective ways to address health disparities in coal-mining counties. To specifically address this knowledge gap, this study estimated the effect of living in a coal-mining county on non-malignant respiratory diseases (NMRD) mortality, and defined this as "coal-county effect." We also investigated what factors may accentuate or attenuate the coal-county effect. METHODS:An ecological epidemiology protocol was designed to observe the characteristics of three populations and to identify the effects of coal-mining on community health. Records for seven coal-mining counties (n?=?19,692) were obtained with approvals from the Virginia Department of Health Office of Vital Statistics for the years 2005 to 2012. Also requested were records from three adjacent coal counties (n?=?10,425) to provide a geographic comparison. For a baseline comparison, records were requested for eleven tobacco-producing counties (n?=?27,800). We analyzed the association of 57,917 individual mortality records in Virginia with coal-mining county residency, county-level socioeconomic status, health access, behavioral risk factors, and coal production. The development of a two-level hierarchical model allowed the coal-county effect to vary by county-level characteristics. Wald tests detected sets of significant factors explaining the variation of impacts across counties. Furthermore, to illustrate how the model estimations help explain health disparities, two coal-mining county case studies were presented. RESULTS:The main result revealed that coal-mining county residency increased the probability of dying from NMRD. The coal-county effect was accentuated by surface coal mining, high smoking rates, decreasing health insurance coverage, and a shortage of doctors. In Virginia coal-mining regions, the average coal-county effect increased by 147% (p-value<?0.01) when one doctor per 1000 left, and the effect increased by 68% (p-value<?0.01) with a 1% reduction of health insurance rates, holding other factors fixed. CONCLUSIONS:This study showed a high mortality risk of NMRD associated with residents living in Virginia coal-mining counties. Our results also revealed the critical role of health access in reducing health disparities related to coal exposure.
| S-EPMC6839055 | biostudies-literature