Opioid Mortality Research Articles

Treatment retention in opioid agonist therapy: comparison of methadone versus buprenorphine/naloxone by analysis of daily-witnessed dispensed medication in a Canadian Province

BackgroundThe last decade has shown a remarkable increase in the rates of illicit opioid use in Canada and internationally, which is associated with large increases in opioid related morbidity and mortality. While the differences between methadone and buprenorphine/naloxone in terms of retention have been studied outside Canada, the unique location and design of this study, gives it a specific significance.ObjectivesThis study aims to describe the relative treatment retention rates for first episode opioid replacement treatment between methadone and buprenorphine/naloxone for patients receiving daily witnessed dispensed medications in Nova Scotia.MethodsA longitudinal retrospective descriptive study analyzing secondary data from the Nova Scotia Prescription Monitoring Program on patients 18 years of age and older who started first episode opioid agonist therapy with methadone or buprenorphine/naloxone for opioid use disorder in Nova Scotia between 2014 and 2018. Treatment episode was defined as date of initial opioid agonist prescription until there is a gap of greater than 6 days without receiving opioid agonist medication at a pharmacy.ResultsOne thousand eight hundred sixty-seven of whom were analyzed as they had at least 1 day in treatment. There was significant treatment dropout within the first 2 weeks of treatment, which did not show a significant difference between OAT medication (23.4% of buprenorphine/naloxone; 22.2% methadone). Median duration of retention in treatment was 58 days for those treated with buprenorphine/naloxone and 101 days for patients treated with methadone.Multivariate cox proportional hazards model showed that buprenorphine/naloxone use as compared to methadone lead to increased hazard of treatment dropout by 62% (HR = 1.62).Hazard rate of treatment dropout for patients below 25 years of age was calculated. (HR 1.53).Median duration of retention in treatment for this subgroup of patients younger than age 25 was 37.5 days for patients treated with buprenorphine/naloxone and 69 days for patients treated with methadone.ConclusionsOur data suggests that methadone is a numerically superior medication for opioid use disorder when the metric of treatment retention is viewed in isolation, for our population in Nova Scotia. However, the results should be interpreted carefully considering the number of limitations of this study. There are social/accessibility, pharmacologic/safety, and patient preference factors which are also key in decision making when prescribing opioid agonist therapy. These must all be considered when deciding on which medication to initiate for a patient beginning a new treatment episode with OAT for opioid use disorder. This study should stimulate further research into this important area in addiction medicine.

Drug court utilization of medications for opioid use disorder in high opioid mortality communities

IntroductionA 2012 national survey found low utilization of medication for opioid use disorder (MOUD) in US drug courts. This study provides an update on MOUD policies and practices among drug courts in communities that the opioid epidemic has substantially impacted. MethodsThe study surveyed adult drug courts (N = 169, 80 % response rate) in US counties with high opioid mortality rates or numbers of opioid-related deaths about their policies and practices relating to MOUD and the overdose-reversal medication, naloxone. ResultsNearly three quarters of the programs (73 %) reported providing access to all FDA-approved MOUD medications, >90 % offer agonist medications (buprenorphine and/or methadone), 80 % provide naloxone training, and 62 % distribute naloxone overdose-reversal kits to their clients. Most programs rely principally on medical judgment for medication decisions (75 %), have received staff training on MOUD (65 %), and have arranged for clients to continue receiving agonist medications while serving jail sanctions for program violations (63 %). Nevertheless, only about one quarter to one half of clients with OUDs receive the medications in most programs, and respondents offered few explanations for this disconnect between policy and practice. In addition, 24 % of the programs continue to overrule medication decisions and 36 % of the jails in these communities do not offer agonist medication for drug court clients serving custodial sanctions. ConclusionsPrograms have achieved substantial progress in the past decade in improving drug court policies concerning MOUD in communities enduring the worst brunt of the opioid epidemic; however, programs require further guidance to help them understand and rectify service barriers and put intended MOUD policies into effective operation. The authors provide recommendations to enhance MOUD utilization in drug courts and the broader criminal justice system.

Aiding the prescriber: developing a machine learning approach to personalized risk modeling for chronic opioid therapy amongst US Army soldiers.

The opioid epidemic is a major policy concern. The widespread availability of opioids, which is fueled by physician prescribing patterns, medication diversion, and the interaction with potential illicit opioid use, has been implicated as proximal cause for subsequent opioid dependence and mortality. Risk indicators related to chronic opioid therapy (COT) at the point of care may influence physicians' prescribing decisions, potentially reducing rates of dependency and abuse. In this paper, we investigate the performance of machine learning algorithms for predicting the risk of COT. Using data on over 12 million observations of active duty US Army soldiers, we apply machine learning models to predict the risk of COT in the initial months of prescription. We use the area under the curve (AUC) as an overall measure of model performance, and we focus on the positive predictive value (PPV), which reflects the models' ability to accurately target military members for intervention. Of the many models tested, AUC ranges between 0.83 and 0.87. When we focus on the top 1% of members at highest risk, we observe a PPV value of 8.4% and 20.3% for months 1 and 3, respectively. We further investigate the performance of sparse models that can be implemented in sparse data environments. We find that when the goal is to identify patients at the highest risk of chronic use, these sparse linear models achieve a performance similar to models trained on hundreds of variables. Our predictive models exhibit high accuracy and can alert prescribers to the risk of COT for the highest risk patients. Optimized sparse models identify a parsimonious set of factors to predict COT: initial supply of opioids, the supply of opioids in the month being studied, and the number of prescriptions for psychotropic medications. Future research should investigate the possible effects of these tools on prescriber behavior (e.g., the benefit of clinician nudging at the point of care in outpatient settings).

Long-term opioid use and mortality in patients with chronic non-cancer pain: Ten-year follow-up study in South Korea from 2010 through 2019.

SummaryBackgroundWe aimed to investigate the prevalence and factors associated with long-term opioid use among patients with chronic non-cancer pain (CNCP).MethodsWe extracted data from the National Health Insurance Service (NHIS) database in South Korea. As a nationwide database, the NHIS database contains information regarding all disease diagnoses and prescriptions for any drug and/or procedures. A total of 2.5% of adult patients (≥20 years of age) who were diagnosed with musculoskeletal diseases and CNCP from 2010 to 2019 were selected using a stratified random sampling technique and included in the analysis. Patients who were prescribed opioids continuously for ≥90 days were classified as long-term opioid users.FindingsA total of 19,645,161 patients with CNCP were included in the final analysis. The prevalence of long-term opioid use was 0.47% (95% confidence interval [CI]: 0.46%, 0.48%; 8421/1,808,043) in 2010, which gradually increased to 2.63% (95% CI: 2.61%, 2.66%; 49,846/1,892,913) in 2019. Among the 2010 cohort (n = 1,804,019), in multivariable logistic regression: old age, underlying disability, increased Charlson comorbidity index, use of benzodiazepine or Z-drug, rheumatoid arthritis, osteoarthritis, and low back pain were associated with an increased prevalence of long-term opioid use among patients with CNCP. In a multivariable Cox regression, the 10-year all-cause mortality in long-term opioid users was found to be 1·21-fold (hazard ratio: 1.21, 95% CI: 1.13, 1.31; P<0·001) higher than that in opioid-naive patients with CNCP.InterpretationLong-term opioid use increased in patients with CNCP in South Korea from 2010 to 2019. Certain factors were potential risk factors for long-term opioid use. Moreover, long-term opioid use was associated with increased 10-year all-cause mortality among patients with CNCP.FundingNone.

Methodological considerations for estimating policy effects in the context of co-occurring policies.

Understanding how best to estimate state-level policy effects is important, and several unanswered questions remain, particularly about the ability of statistical models to disentangle the effects of concurrently enacted policies. In practice, many policy evaluation studies do not attempt to control for effects of co-occurring policies, and this issue has not received extensive attention in the methodological literature to date. In this study, we utilized Monte Carlo simulations to assess the impact of co-occurring policies on the performance of commonly-used statistical models in state policy evaluations. Simulation conditions varied effect sizes of the co-occurring policies and length of time between policy enactment dates, among other factors. Outcome data (annual state-specific opioid mortality rate per 100,000) were obtained from 1999 to 2016 National Vital Statistics System (NVSS) Multiple Cause of Death mortality files, thus yielding longitudinal annual state-level data over 18years from 50 states. When co-occurring policies are ignored (i.e., omitted from the analytic model), our results demonstrated that high relative bias (> 82%) arises, particularly when policies are enacted in rapid succession. Moreover, as expected, controlling for all co-occurring policies will effectively mitigate the threat of confounding bias; however, effect estimates may be relatively imprecise (i.e., larger variance) when policies are enacted in near succession. Our findings highlight several key methodological issues regarding co-occurring policies in the context of opioid-policy research yet also generalize more broadly to evaluation of other state-level policies, such as policies related to firearms or COVID-19, showcasing the need to think critically about co-occurring policies that are likely to influence the outcome when specifying analytic models.

The Effects of Naloxone Access Laws on Opioid Abuse, Mortality, and Crime

The United States is experiencing an epidemic of opioid abuse. In response, states have implemented policies including increased access to naloxone, a drug that can save lives when administered during an overdose. There is a concern that widespread naloxone access may unintentionally lead to increased or riskier opioid use by reducing the risk of death from overdose, however. In this paper, we use the staggered timing of state-level naloxone access laws as a natural experiment to measure the effects of broadening access to this lifesaving drug. We find that broadened access led to more opioid-related emergency room visits and more opioid-related theft, with no net measurable reduction in opioid-related mortality. We conclude that naloxone has a clear and important role in harm reduction, yet its ability to combat the opioid epidemic’s death toll may be limited without complementary efforts.

United States marijuana legalization and opioid mortality epidemic during 2010–2020 and pandemic implications

BackgroundThe hypothesis that marijuana availability reduces opioid mortality merits more complete testing, especially in a country with the world's highest opioid death rate and 2nd highest cannabis-use-disorder prevalence. MethodsThe United States opioid mortality rate was compared in states and District of Columbia that had implemented marijuana legalization with states that had not, by applying joinpoint methodology to Centers for Disease Control and Prevention data. Variables included race/ethnicity and fentanyl-type opioids (fentanyls). ResultsAfter the same rates during 2010–2012, the opioid mortality rate increased more rapidly in marijuana-legalizing than non-legalizing jurisdictions (2010–2020 annual pairwise comparison p = 0.003 for all opioids and p = 0.0004 for fentanyls). During the past decade, all four major race/ethnicities in the U.S. had evidence for a statistically-significant greater increase in opioid mortality rates in legalizing than non-legalizing jurisdictions. Among legalizing jurisdictions, the greatest mortality rate increase for all opioids was in non-Hispanic blacks (27%/year, p = 0.0001) and for fentanyls in Hispanics (45%/year, p = 0.0000008). The greatest annual opioid mortality increase occurred in 2020, the first year of the COVID-19 pandemic, with non-Hispanic blacks having the greatest increase in legalizing vs. non-legalizing opioid-death-rate difference, from 32% higher in legalizing jurisdictions in 2019 to more than double in 2020. ConclusionsInstead of supporting the marijuana protection hypothesis, ecologic associations at the national level suggest that marijuana legalization has contributed to the U.S.’s opioid epidemic in all major races/ethnicities, and especially in blacks. If so, the increased use of marijuana during the 2020–2022 pandemic may thereby worsen the country's opioid crisis.

Identifying bright spot communities: Socioecological, workforce, and healthcare delivery factors influencing opioid mortality.

There were 50,000 U.S. opioid overdose deaths in 2019. Millions suffer from opioid addiction. Identifying protective factors for low community opioid mortality may have important implications for addressing the opioid epidemic. This study was funded through the Virginia (VA) Department of Medical Assistance Services (DMAS) through a SUPPORT Act Grant. To identify "Bright Spot" communities in Virginia with protective factors associated with reduced opioid mortality and morbidity. Ecologic study. Virginia All Payer Claims Database (APCD), Virginia Department of Health (VDH) statewide medical examiner registry, and American Community Survey (ACS). 2016-2019; 2019 data cited here. APCD includes VA residents with medical claims through commercial, Medicaid, and Medicare coverage. VDH data includes fatal drug overdoses. ACS surveys all VA residents. Primary outcome: fatal opioid overdoses. Secondary outcomes: emergency room visits for overdoses and opioid-related diagnoses, outpatient diagnoses for opioid-related disorder, prescription rate for opioids, and prescription rate for buprenorphine. Opioid mortality was associated with higher rates of community poverty (r=.38, p<.0001) and disability (r=.52, r<.0001). Opioid mortality was associated with inequality, with higher Gini index associated with higher opioid mortality (r=.23, p<.0001). A higher percentage of black residents was associated with increased fatal opioid overdoses (r=.37, p<.0001) and ED visits for overdoses (r=.30, p<.0001). A higher percentage of white residents correlated with increased outpatient visits for opioid use disorder (r=.24, p<.0001) and higher rates of buprenorphine (r=.34, p<.0001) and opioid prescriptions (r=.31, p <.0001). These findings suggest significant racial disparities in opioid outcomes. Communities with a higher percentage of black residents are more likely to have higher opioid mortality and a lower rate of outpatient treatment. This association may be affected by the time period used in the analysis (2015-2019), as nationally there has been an increasing rate of synthetic opioid deaths in Black communities. These measures have been incorporated into a multivariate analysis to identify Bright Spot communities, which will be discussed during the presentation.

Evaluation of adherence monitoring in buprenorphine treatment: A pilot study using timed drug assays to determine accuracy of testing.

Buprenorphine is effective at reducing relapse to opioid misuse, morbidity and mortality in opioid-dependent patients. Urine drug screening (UDS) to assess adherence is used routinely in opioid agonist treatment (OAT). The primary aim of this study was to determine factors which may be associated with a negative qualitative urine drug screen for buprenorphine in OAT patients. This prospective pilot study was conducted at a tertiary addiction medicine centre. Twenty participants on stable treatment underwent supervised administration of sublingual buprenorphine. Matched urine and blood samples were collected prior to and 2, 4 and 6 hours after buprenorphine administration. Qualitative urine drug screen results were obtained using gas chromatography-mass spectrometry (GC-MS), while quantitative blood and urine results were obtained using ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). Qualitative urine assay yielded a negative result for buprenorphine in 57% of tested samples. The median concentration of urinary buprenorphine was 167 mcg/L (range: 2-1730 mcg/L). Thirty percent of all blood samples did not detect buprenorphine (range 0-18 mcg/L). Positive qualitative urine drug screen results were associated with higher urine (343 mcg/L compared with 75 mcg/L; P < .05) and blood (4mcg/L compared with 2mcg/L; P < .05) buprenorphine concentrations. Median urine concentrations of buprenorphine were highest at 2hours and were higher in participants receiving CYP3A4 inhibitors. Interpretation of qualitative urine drug screens to assess adherence in OAT is complex. Poor adherence with treatment cannot be assumed in patients returning a negative qualitative GC-MS urine drug screen.

Urban scaling of opioid overdose deaths in the USA: a cross-sectional study in three periods between 2005 and 2017

ObjectivesTo describe the association between population size, population growth and opioid overdose deaths—overall and by type of opioid—in US commuting zones (CZs) in three periods between 2005 and 2017.Settings741 CZs...

Is green space associated with opioid-related mortality? An ecological study at the U.S. county level

Opioid consumption, both legal and illicit, has risen precipitously in the U.S. over the past few decades, as has the number of deaths due to the use and misuse of opioids. Exposure to green spaces may help to alleviate the problematic levels of opioid use. Such exposure has been tied to health benefits relevant to opioid use. To explore the potential influence of green space on opioid-related health outcomes, we analyzed the association between tree canopy cover and mortality attributable to opioid use and abuse using 2008–2018 death rate data on a county level (n = 3087) across the contiguous U. S. We fitted spatial general additive model while controlling for socioeconomic factors, healthcare access measures, opioid prescription rates, and particulate air pollution. Contrary to expectations, canopy cover was positively associated with opioid mortality. A sensitivity analysis with forest land cover showed similar results while a sensitivity analysis with total greenness (NDVI) was nonsignificant. Stratified models by urbanicity level suggested suburban and rural counties drove the positive associations observed in the nationwide models. The findings for forest and canopy cover are unexpected, given the myriad health benefits of green spaces, yet might be explained by heavily forested areas (i.e., Appalachia) being home to injury-prone natural resource extraction employment sectors. The steady decline of these industries has created poor socioeconomic conditions that exacerbate the already elevated risk of opioid use and misuse. Alternatively, the magnitude of the protective effects of greenspace on pain reduction are insufficient to counter opioid demand. Further research is warranted, especially in studies with individual-level data. Entities with responsibility or interest in reducing the incidence of deaths from opioids are cautioned that green spaces might not be a viable option for reducing opioid mortality.

Medicaid expansion and opioid overdose mortality among socioeconomically disadvantaged populations in the US: A difference in differences analysis

BackgroundOpioid-related overdoses are a major cause of mortality in the US. Medicaid Expansion is posited to reduce opioid overdose-related mortality (OORM), and may have a particularly strong effect among people of lower socioeconomic status. This study assessed the association between state Medicaid Expansion and county-level OORM rates among individuals with low educational attainment. MethodsThis quasi-experimental study used lagged multilevel difference-in-difference models to test the relationship of state Medicaid Expansion to county-level OORM rates among people with a high-school diploma or less. Longitudinal (2008–2018) OORM data on 2978 counties nested in 48 states and the District of Columbia (DC) were drawn from the National Center for Health Statistics. The state-level exposure was a time-varying binary-coded variable capturing pre- and post-Medicaid Expansion under the Affordable Care Act (an “on switch”-type variable).The main outcome was annual county-level OORM rates among low-education adults adjusted for potential underreporting of OORM. FindingsThe adjusted county-level OORM rates per 100,000 among the study population rose on average from 10.26 (SD = 13.56) in 2008–14.51 (SD = 18.20) in 2018. In the 1-year lagged multivariable model that controlled for policy and sociodemographic covariates, the association between state Medicaid Expansion and county-level OORM rates was statistically insignificant. ConclusionsWe found no evidence that expanding Medicaid eligibility reduced OORM rates among adults with lower educational attainment. Future work should seek to corroborate our findings and also identify – and repair – breakdowns in mechanisms that should link Medicaid Expansion to reduced overdoses.

Disparities by Sex in COVID-19 Risk and Related Harms Among People with Opioid Use Disorder.

Objectives: (1) Report sex-specific prevalence of coronavirus disease 2019 (COVID-19) test positivity among an opioid use disorder (OUD) cohort (2) Assess sex-specific rates of opioid overdose and mortality. Methods: A retrospective cohort study was performed on all adult patients with OUD who received a COVID-19 test in calendar year 2020 at a large academic medical center in Richmond, Virginia. Our study outcomes were positive COVID-19 test, opioid overdose, and all-cause in-hospital mortality. Sex-stratified multivariable logistic regression assessed sociodemographic factors associated with COVID-19 test positivity. Results: A total of 2,600 patients (males = 1,294, females = 1,306) with OUD received a COVID-19 test. Approximately 5% across both sexes tested positive for COVID-19 (p = 0.420), whereas 7% presented with an opioid overdose (males 10%; females 4%; p < 0.0001). However, mortality rates were similar across sex. Among males, individuals in the other racial group had increased odds of COVID-19 test positivity (adjusted odds ratio or AOR: 5.03, 95% confidence interval [CI]: 1.70-14.88), whereas black females had higher odds of COVID-19 test positivity (AOR: 1.92, 95% CI: 1.01-3.62) compared to their white counterparts. Conclusions: Opioid overdose, more often than COVID-19, impacted the health of patients with OUD presenting to a public safety net health system. Despite a female advantage documented in the general population for COVID-19 susceptibility, COVID-19 test positivity rates were similar across sex in an OUD cohort; yet, racial disparities emerged with notable sex-related variation. Sex and gender are important variables that modify health outcomes, including OUD and COVID-19, and should be further investigated using an intersectionality framework.

The relationship between Social Determinants of Health (SDoH) and death from cardiovascular disease or opioid use in counties across the United States (2009\u20132018)

BackgroundDeath from cardiovascular disease (CVD) has been a longstanding public health challenge in the US, whereas death from opioid use is a recent, growing public health crisis. While population-level approaches to reducing CVD risk are known to be effective in preventing CVD deaths, more targeted approaches in high-risk communities are known to work better for reducing risk of opioid overdose. For communities to plan effectively in addressing both public health challenges, they need information on significant community-level (vs individual-level) predictors of death from CVD or opioid use. This study addresses this need by examining the relationship between 1) county-level social determinants of health (SDoH) and CVD deaths and 2) county-level SDoH and opioid-use deaths in the US, over a ten-year period (2009–2018).MethodsA single national county-level ten-year ‘SDoH Database’ is analyzed, to address study objectives. Fixed-effects panel-data regression analysis, including county, year, and state-by-year fixed effects, is used to examine the relationship between 1) SDoH and CVD death-rate and 2) SDoH and opioid-use death-rate. Eighteen independent (SDoH) variables are included, spanning three contexts: socio-economic (e.g., race/ethnicity, income); healthcare (e.g., system-characteristics); and physical-infrastructure (e.g., housing).ResultsAfter adjusting for county, year, and state-by-year fixed effects, the significant county-level positive SDoH predictors for CVD death rate were, median age and percentage of civilian population in armed forces. The only significant negative predictor was percentage of population reporting White race. On the other hand, the four significant negative predictors of opioid use death rate were median age, median household income, percent of population reporting Hispanic ethnicity and percentage of civilian population consisting of veterans. Notably, a dollar increase in median household income, was estimated to decrease sample mean opioid death rate by 0.0015% based on coefficient value, and by 20.05% based on effect size.ConclusionsThe study provides several practice and policy implications for addressing SDoH barriers at the county level, including population-based approaches to reduce CVD mortality risk among people in military service, and policy-based interventions to increase household income (e.g., by raising county minimum wage), to reduce mortality risk from opioid overdoses.

The opioid epidemic among the Latino population in California

BackgroundThe opioid epidemic in the United States has manifested differently across geographic regions and populations, with recent increases among racial/ethnic minorities and in the Western region of the U.S. This study provides an overview of the opioid overdose epidemic among Latinos in California and highlights high-risk areas in the state. MethodsUsing publicly available data from California, we examined trends in opioid-related deaths (e.g., overdose) and opioid-related emergency department (ED) visits among Latinos at the county-level, as well as changes in opioid outcomes overtime. ResultsOpioid-related death rates among Latinos (mostly Mexican-origin) in California remained relatively stable from 2006–2016, but started to increase in 2017 peaking at an age-adjusted opioid mortality rate of 5.4 deaths per 100,000 Latino residents in 2019. Prescription opioid-related deaths, compared to heroin and fentanyl, have remained the highest over time. However, fentanyl-related deaths began to increase dramatically in 2015. Lassen, Lake, and San Francisco counties had the highest 2019 opioid-related death rates among Latinos. Opioid-related ED visits among Latinos have steadily increased since 2006 with a sharp increase in rates in 2019. San Francisco, Amador, and Imperial counties had the highest 2019 rates of ED visits. ConclusionsLatinos are facing detrimental consequences associated with recent increasing trends in opioid overdoses. The identified high-risk counties may have vulnerable sub-populations of Latinos, such as those in northern rural regions, that have gone underrepresented in conventional surveillance health databases. Time sensitive policies and interventions are needed to curtail health consequences especially among “hidden” Latino populations.

The Supply of Authorized Providers for Medication-Assisted Treatment in Ohio

Background: Ohio experiences among the highest drug overdose rates nationally. The Drug Addiction Treatment Act (DATA) of 2000 permits qualified clinicians to use medication-assisted treatment (MAT) to treat opioid dependency. This study characterizes clinicians authorized to perform MAT and examines whether these clinicians are geographically collocated in areas with higher opioid burdens. Methods: Data of DATA providers in 2019 came from the Substance Abuse and Mental Health Administration. Opioid overdose mortality rates were extracted from the County Health Rankings and Roadmaps to represent disease burdens in local areas. The DATA provider density is represented by the number of DATA-waivered providers per 100 000 population for each county. We used Pearson correlational tests to examine the correlations between the local DATA provider density and the opioid mortality rate.Results: Most of the DATA providers were physicians (57%), followed by nurse practitioners (27%) and physician assistants (4%). The average waivered provider density was 13.90 per 100 000 population. The local density of DATA providers and local opioid overdose death rates are moderately correlated (P &lt; 0.001).Conclusion: Physicians still represent most waivered providers in Ohio. While DATA providers were located in some areas with high needs for opioid treatments, our data suggest that other areas with high opioid burdens likely have an inadequate workforce supply to reduce opioid burdens. Without an adequate DATA workforce, policies that focus on MAT care access to address the opioid epidemic may be in vain.

Drug Overdose Mortality Among People Experiencing Homelessness, 2003 to 2018

Despite high rates of drug overdose death among people experiencing homelessness, patterns in drug overdose mortality, including the types of drugs implicated in overdose deaths, remain understudied in this population. To describe the patterns in drug overdose mortality among a large cohort of people experiencing homelessness in Boston vs the general adult population of Massachusetts and to evaluate the types of drugs implicated in overdose deaths over a continuous 16-year period of observation. This cohort study analyzed adults aged 18 years or older who received care at Boston Health Care for the Homeless Program (BHCHP) between January 1, 2003, and December 31, 2017. Individuals were followed up from the date of their initial BHCHP encounter during the study period until the date of death or December 31, 2018. Data were analyzed from December 1, 2020, to June 6, 2021. Drug overdose deaths and the types of drugs involved in each overdose death were ascertained by linking the BHCHP cohort to the Massachusetts Department of Public Health death records. In this cohort of 60 092 adults experiencing homelessness (mean [SD] age at entry, 40.4 [13.1] years; 38 084 men [63.4%]), 7130 individuals died by the end of the study period. A total of 1727 individuals (24.2%) died of a drug overdose. Of the drug overdose decedents, 456 were female (26.4%), 194 were Black (11.2%), 202 were Latinx (11.7%), and 1185 were White (68.6%) individuals, and the mean (SD) age at death was 43.7 (10.8) years. The age- and sex-standardized drug overdose mortality rate in the BHCHP cohort was 278.9 (95% CI, 266.1-292.3) deaths per 100 000 person-years, which was 12 times higher than the Massachusetts adult population. Opioids were involved in 91.0% of all drug overdose deaths. Between 2013 and 2018, the synthetic opioid mortality rate increased from 21.6 to 327.0 deaths per 100 000 person-years. Between 2004 and 2018, the opioid-only overdose mortality rate decreased from 117.2 to 102.4 deaths per 100 000 person-years, whereas the opioid-involved polysubstance mortality rate increased from 44.0 to 237.8 deaths per 100 000 person-years. Among opioid-involved polysubstance overdose deaths, cocaine-plus-opioid was the most common substance combination implicated throughout the study period, with Black individuals having the highest proportion of cocaine-plus-opioid involvement in death (0.72 vs 0.62 in Latinx and 0.53 in White individuals; P < .001). In this cohort study of people experiencing homelessness, drug overdose accounted for 1 in 4 deaths, with synthetic opioid and polysubstance involvement becoming predominant contributors to mortality in recent years. These findings emphasize the importance of increasing access to evidence-based opioid overdose prevention strategies and opioid use disorder treatment among people experiencing homelessness, while highlighting the need to address both intentional and unintentional polysubstance use in this population.

County-level factors underlying opioid mortality in the United States

Background Mortality from overdoses involving opioids in the United States (U.S.) has reached epidemic proportions. More research is needed to examine the underlying factors contributing to opioid-related mortality regionally. This study’s objective was to identify and examine the county-level factors most closely associated with opioid-related overdose deaths across all counties in the U.S. Methods: Using a national cross-sectional ecological study design, we analyzed the relationships between 17 county-level characteristics in four categories (i.e. socio-economic, availability of medical care, health-related concerns, and demographics) with opioid mortality. Data were extracted from the Robert Wood Johnson County Health Rankings aggregate database and Centers for Disease Control and Prevention (CDC)’s Wide-ranging Online Data for Epidemiological Research (WONDER) system. Results: There were 1058 counties (33.67% of 3142 nationally) with reported opioid-related fatalities. Median opioid-related mortality was 15.61 per 100,000 persons. Multivariate regression results indicate that counties with the highest opioid-related mortality had increased rates of tobacco use, HIV, Non-Hispanic Caucasians, and females and were rural areas, but lower rates of food insecurity and uninsured adults. The rates of tobacco use and HIV had the strongest association with mortality. Availability of either mental health or primary care providers were not significantly associated with mortality. Severe housing problems, high school graduation rate, obesity, violent crime, and median household income also did not contribute to county-level differences in overdose mortality. Conclusions: Future health policies should fund further investigations and ultimately address the most influential and significant underlying county-level factors associated with opioid-related mortality.

Primary care physicians' opioid-related prevention behaviors and intentions: A descriptive analysis.

Primary care physicians (PCPs) are positioned to mitigate opioid morbidity and mortality, but their engagement in primary, secondary, and tertiary opioid-related prevention behaviors is unclear. The objective of this study was to evaluate Tennessee PCPs' engagement in and intention to engage in multiple opioid-related prevention behaviors. A survey instrument was developed, pretested, and pilot tested with practicing PCPs. Thereafter, a census of eligible Tennessee PCPs was conducted using a modified, four-wave tailored design method approach. Three patient scenarios were employed to assess physician intention to engage in 10 primary, secondary, and tertiary prevention behaviors. Respondents were asked to report, given 10 similar scenarios, the number of times (0-10) they would engage in prevention behaviors. Descriptive statistics were calculated using SPSS version 25. A total of 296 usable responses were received. Physician intention to engage in prevention behaviors varied across the 10 behaviors studied. Physicians reported frequently communicating risks associated with prescription opioids to patients (8.9 ± 2.8 out of 10 patients), infrequently utilizing brief questionnaires to assess for risk of opioid misuse (1.7 ± 3.3 out of 10 patients), and screening for current opioid misuse (3.1 ± 4.3 out of 10 patients). Physicians reported seldomly co-prescribing naloxone for overdose reversal and frequently discharging from practice patients presenting with an opioid use disorder. This study noted strengths and opportunities to increase engagement in prevention behaviors. Understanding PCPs' engagement in opioid-related prevention behaviors is important to effectively target and implement morbidity and mortality reducing interventions.

Network-based Analysis of Prescription Opioids Dispensing Using Exponential Random Graph Models (ERGMs).

The United States has been experiencing an unprecedented level of opioid overdose-related mortality due in part to excessive use of prescription opioids. Peer-driven network interventions may be beneficial. A key assumption of social network interventions is that of some members of the network act as key players and can influence the behavior of others in the network. We used opioid prescription records to create a social network of patients who use prescription opioid in the state of Rhode Island. The study population was restricted to patients on stable opioid regimens who used one source of payment and received the same opioid medication from ≥ 3 prescribers and pharmacies. An exponential random graph model (ERGM) was employed to examine the relationship between patient attributes and the likelihood of tie formation and modularity was used to assess for homophily (the tendency of individuals to associate with similar people). We used multivariable logistic regression to assess predictors of high betweenness centrality, a measure of influence within the network. 372 patients were included in the analysis; average age was 51 years; 53% were female; 57% were prescribed oxycodone, 34% were prescribed hydrocodone and 9% were prescribed buprenorphine/naloxone. After controlling for the main effects in the ERGM model, homophily was associated with age group, method of payment, number and type of opioid prescriptions filled, mean daily dose, and number of providers seen. Type of opioid and number of prescribers were identified as significant predictors of high betweenness centrality. We conclude that patients who use multiple prescribers or have a diagnosis of opioid use disorder may help promote positive health behaviors or disrupt harmful behaviors in an opioid prescription network.