Out-of-pocket spending and health care utilization associated with initiation of different medications for opioid use disorder: Findings from a national commercially insured cohort

INTRODUCTION

Buprenorphine and naltrexone are effective medications for opioid use disorder (MOUD). Naltrexone requires complete detoxification from opioids before initiation while buprenorphine does not, which leads to a differential clinical induction challenge. Few studies have evaluated economic costs associated with MOUD initiation.

METHODS

We conducted a retrospective cohort analysis using the 2014-2019 Merative MarketScan database. We included individuals diagnosed with opioid use, abuse, or dependence from 2014 to 2019 who initiated one of three MOUD types: 1) buprenorphine, 2) extended-release naltrexone, or 3) oral naltrexone. We calculated total and monthly out-of-pocket spending, for overall and MOUD-specific claims, for the three months prior through three months after MOUD initiation. We also calculated utilization of detoxification, inpatient, and outpatient services monthly over this period.

RESULTS

Our cohort included 27,133 individuals; 19,536, 1886, and 5711 initiated buprenorphine, extended-release naltrexone, and oral naltrexone, respectively. Individuals who initiated naltrexone had the highest out-of-pocket spending over the study period. MOUD-specific spending did not contribute substantially to total out-of-pocket spending. Difference in overall spending by MOUD type was driven by a subset of individuals who initiated naltrexone and had very high out-of-pocket spending in the month prior to MOUD initiation. In this month, mean monthly out-of-pocket spending for high-spenders (above 90th percentile within MOUD type category) was $5734 (95 % confidence interval [CI]: $5181-$6286) and $4622 (95 % CI: $4161-$5082) for those who initiated oral and extended-release naltrexone, respectively, compared with $1852 (95 % CI: $1754-$1950) for those who initiated buprenorphine. In the month prior to MOUD initiation, those who initiated naltrexone also had higher detoxification, inpatient, and outpatient episode/visit frequency. In the month prior to initiation, 28.8 % (95 % CI: 27.7 %-30.0 %) and 25.5 % (95 % CI: 23.6 %-27.5 %) of individuals who initiated oral and extended-release naltrexone had detoxification episodes, compared with 9.7 % (95 % CI: 9.3 %-10.1 %) of those who initiated buprenorphine.

CONCLUSION

Findings suggest that individuals who initiated naltrexone utilized more intensive health services, including detoxification, in the period prior to MOUD initiation, resulting in significantly higher out-of-pocket spending. Out-of-pocket spending is a patient-centered outcome reflecting potential patient burden. Our results should be considered as part of the shared decision-making process between patients and providers when choosing treatment for OUD.

Prison Buprenorphine Implementation and Postrelease Opioid Use Disorder Outcomes

Importance

Agonist medications for opioid use disorder (MOUD), buprenorphine and methadone, in carceral settings might reduce the risk of postrelease opioid overdose but are uncommonly offered. In April 2019, the Massachusetts Department of Correction (MADOC), the state prison system, provided buprenorphine for incarcerated individuals in addition to previously offered injectable naltrexone.

Objective

To evaluate postrelease outcomes after buprenorphine implementation.

Design, Setting, and Participants

This cohort study with interrupted time-series analysis used linked data across multiple statewide data sets in the Massachusetts Public Health Data Warehouse stratified by sex due to differences in carceral systems. Eligible participants were individuals sentenced and released from a MADOC facility to the community. The study period for the male sample was January 2014 to November 2020; for the female sample, January 2015 to October 2019. Data were analyzed between February 2022 and January 2024.

Exposure

April 2019 implementation of buprenorphine during incarceration.

Main Outcomes and Measures

Receipt of MOUD within 4 weeks after release, opioid overdose, and all-cause mortality within 8 weeks after release, each measured as a percentage of monthly releases who experienced the outcome. Segmented linear regression analyzed changes in outcome rates after implementation.

Results

A total of 15 225 individuals were included. In the male sample there were 14 582 releases among 12 688 individuals (mean [SD] age, 35.0 [10.8] years; 133 Asian and Pacific Islander [0.9%], 4079 Black [28.0%], 4208 Hispanic [28.9%], 6117 White [41.9%]), a rate of 175.7 releases per month; the female sample included 3269 releases among 2537 individuals (mean [SD] age, 34.9 [9.8] years; 328 Black [10.0%], 225 Hispanic [6.9%], 2545 White [77.9%]), a rate of 56.4 releases per month. Among male participants at 20 months postimplementation, the monthly rate of postrelease buprenorphine receipt was higher than would have been expected under baseline trends (21.2% vs 10.6% of monthly releases; 18.6 additional releases per month). Naltrexone receipt was lower than expected (1.0% vs 6.0%; 8.8 fewer releases per month). Monthly rates of methadone receipt (1.4%) and opioid overdose (1.8%) were not significantly different than expected. All-cause mortality was lower than expected (1.9% vs 2.8%; 1.5 fewer deaths per month). Among female participants at 7 months postimplementation, buprenorphine receipt was higher than expected (31.6% vs 9.5%; 12.4 additional releases per month). Naltrexone receipt was lower than expected (3.4% vs 7.2%) but not statistically significantly different. Monthly rates of methadone receipt (1.1%), opioid overdose (4.8%), and all-cause mortality (1.6%) were not significantly different than expected.

Conclusions and Relevance

In this cohort study of state prison releases, postrelease buprenorphine receipt increased and naltrexone receipt decreased after buprenorphine became available during incarceration.

Telemedicine Buprenorphine Initiation and Retention in Opioid Use Disorder Treatment for Medicaid Enrollees

Importance

Early COVID-19 mitigation strategies placed an additional burden on individuals seeking care for opioid use disorder (OUD). Telemedicine provided a way to initiate and maintain transmucosal buprenorphine treatment of OUD.

Objective

To examine associations between transmucosal buprenorphine OUD treatment modality (telemedicine vs traditional) during the COVID-19 public health emergency and the health outcomes of treatment retention and opioid-related nonfatal overdose.

Design, Setting, and Participants

This retrospective cohort study was conducted using Medicaid claims and enrollment data from November 1, 2019, to December 31, 2020, for individuals aged 18 to 64 years from Kentucky and Ohio. Data were collected and analyzed in June 2022, with data updated during revision in August 2023.

Exposures

The primary exposure of interest was the modality of the transmucosal buprenorphine OUD treatment initiation. Relevant patient demographic and comorbidity characteristics were included in regression models.

Main Outcomes and Measures

There were 2 main outcomes of interest: retention in treatment after initiation and opioid-related nonfatal overdose after initiation. For outcomes measured after initiation, a 90-day follow-up period was used. The main analysis used a new-user study design; transmucosal buprenorphine OUD treatment initiation was defined as initiation after more than a 60-day gap in buprenorphine treatment. In addition, uptake of telemedicine for buprenorphine was examined, overall and within patients initiating treatment, across quarters in 2020.

Results

This study included 41 266 individuals in Kentucky (21 269 women [51.5%]; mean [SD] age, 37.9 [9.0] years) and 50 648 individuals in Ohio (26 425 women [52.2%]; mean [SD] age, 37.1 [9.3] years) who received buprenorphine in 2020, with 18 250 and 24 741 people initiating buprenorphine in Kentucky and Ohio, respectively. Telemedicine buprenorphine initiations increased sharply at the beginning of 2020. Compared with nontelemedicine initiation, telemedicine initiation was associated with better odds of 90-day retention with buprenorphine in both states (Kentucky: adjusted odds ratio, 1.13 [95% CI, 1.01-1.27]; Ohio: adjusted odds ratio, 1.19 [95% CI, 1.06-1.32]) in a regression analysis adjusting for patient demographic and comorbidity characteristics. Telemedicine initiation was not associated with opioid-related nonfatal overdose (Kentucky: adjusted odds ratio, 0.89 [95% CI, 0.56-1.40]; Ohio: adjusted odds ratio, 1.08 [95% CI, 0.83-1.41]).

Conclusions and Relevance

In this cohort study of Medicaid enrollees receiving buprenorphine for OUD, telemedicine buprenorphine initiation was associated with retention in treatment early during the COVID-19 pandemic. These findings add to the literature demonstrating positive outcomes associated with the use of telemedicine for treatment of OUD.

Validating opioid use disorder diagnoses in administrative data: a commentary on existing evidence and future directions

BACKGROUND

A valid opioid use disorder (OUD) identification algorithm for use in administrative medical record data would enhance investigators' ability to study consequences of OUD, OUD treatment seeking and treatment outcomes.

MAIN BODY

Existing studies indicate ICD-9 and ICD-10 codes for opioid abuse and dependence do not accurately measure OUD. However, critical appraisal of existing literature suggests alternative validation methods would improve the validity of OUD identification algorithms in administrative data. Chart abstraction may not be sufficient to validate OUD, and primary data collection via structured diagnostic interviews might be an ideal gold standard.

CONCLUSION AND COMMENTARY

Generating valid OUD identification algorithms is critical for OUD research and quality measurement in real world health care settings.

Estimated Reductions in Opioid Overdose Deaths With Sustainment of Public Health Interventions in 4 US States

Importance

In 2021, more than 80 000 US residents died from an opioid overdose. Public health intervention initiatives, such as the Helping to End Addiction Long-term (HEALing) Communities Study (HCS), are being launched with the goal of reducing opioid-related overdose deaths (OODs).

Objective

To estimate the change in the projected number of OODs under different scenarios of the duration of sustainment of interventions, compared with the status quo.

Design, Setting, and Participants

This decision analytical model simulated the opioid epidemic in the 4 states participating in the HCS (ie, Kentucky, Massachusetts, New York, and Ohio) from 2020 to 2026. Participants were a simulated population transitioning from opioid misuse to opioid use disorder (OUD), overdose, treatment, and relapse. The model was calibrated using 2015 to 2020 data from the National Survey on Drug Use and Health, the US Centers for Disease Control and Prevention, and other sources for each state. The model accounts for reduced initiation of medications for OUD (MOUDs) and increased OODs during the COVID-19 pandemic.

Exposure

Increasing MOUD initiation by 2- or 5-fold, improving MOUD retention to the rates achieved in clinical trial settings, increasing naloxone distribution efforts, and furthering safe opioid prescribing. An initial 2-year duration of interventions was simulated, with potential sustainment for up to 3 additional years.

Main Outcomes and Measures

Projected reduction in number of OODs under different combinations and durations of sustainment of interventions.

Results

Compared with the status quo, the estimated annual reduction in OODs at the end of the second year of interventions was 13% to 17% in Kentucky, 17% to 27% in Massachusetts, 15% to 22% in New York, and 15% to 22% in Ohio. Sustaining all interventions for an additional 3 years was estimated to reduce the annual number of OODs at the end of the fifth year by 18% to 27% in Kentucky, 28% to 46% in Massachusetts, 22% to 34% in New York, and 25% to 41% in Ohio. The longer the interventions were sustained, the better the outcomes; however, these positive gains would be washed out if interventions were not sustained.

Conclusions and Relevance

In this decision analytical model study of the opioid epidemic in 4 US states, sustained implementation of interventions, including increased delivery of MOUDs and naloxone supply, was found to be needed to reduce OODs and prevent deaths from increasing again.

Prior Incarceration Is Associated with Poor Mental Health at Midlife: Findings from a National Longitudinal Cohort Study

BACKGROUND

People with mental illnesses and people living in poverty have higher rates of incarceration than others, but relatively little is known about the long-term impact that incarceration has on an individual's mental health later in life.

OBJECTIVE

To evaluate prior incarceration's association with mental health at midlife.

DESIGN

Retrospective cohort study PARTICIPANTS: Participants from the National Longitudinal Survey of Youth 1979 (NLSY79)-a nationally representative age cohort of individuals 15 to 22 years of age in 1979-who remained in follow-up through age 50.

MAIN MEASURES

Midlife mental health outcomes were measured as part of a health module administered once participants reached 50 years of age (2008-2019): any mental health history, any depression history, past-year depression, severity of depression symptoms in the past 7 days (Center for Epidemiologic Studies Depression [CES-D] scale), and mental health-related quality of life in the past 4 weeks (SF-12 Mental Component Score [MCS]). The main exposure was any incarceration prior to age 50.

KEY RESULTS

Among 7889 participants included in our sample, 577 (5.4%) experienced at least one incarceration prior to age 50. Prior incarceration was associated with a greater likelihood of having any mental health history (predicted probability 27.0% vs. 16.6%; adjusted odds ratio [aOR] 1.9 [95%CI: 1.4, 2.5]), any history of depression (22.0% vs. 13.3%; aOR 1.8 [95%CI: 1.3, 2.5]), past-year depression (16.9% vs. 8.6%; aOR 2.2 [95%CI: 1.5, 3.0]), and high CES-D score (21.1% vs. 15.4%; aOR 1.5 [95%CI: 1.1, 2.0]) and with a lower (worse) SF-12 MCS (-2.1 points [95%CI: -3.3, -0.9]; standardized mean difference -0.24 [95%CI: -0.37, -0.10]) at age 50, when adjusting for early-life demographic, socioeconomic, and behavioral factors.

CONCLUSIONS

Prior incarceration was associated with worse mental health at age 50 across five measured outcomes. Incarceration is a key social-structural driver of poor mental health.

Small Area Forecasting of Opioid-Related Mortality: Bayesian Spatiotemporal Dynamic Modeling Approach

BACKGROUND

Opioid-related overdose mortality has remained at crisis levels across the United States, increasing 5-fold and worsened during the COVID-19 pandemic. The ability to provide forecasts of opioid-related mortality at granular geographical and temporal scales may help guide preemptive public health responses. Current forecasting models focus on prediction on a large geographical scale, such as states or counties, lacking the spatial granularity that local public health officials desire to guide policy decisions and resource allocation.

OBJECTIVE

The overarching objective of our study was to develop Bayesian spatiotemporal dynamic models to predict opioid-related mortality counts and rates at temporally and geographically granular scales (ie, ZIP Code Tabulation Areas [ZCTAs]) for Massachusetts.

METHODS

We obtained decedent data from the Massachusetts Registry of Vital Records and Statistics for 2005 through 2019. We developed Bayesian spatiotemporal dynamic models to predict opioid-related mortality across Massachusetts' 537 ZCTAs. We evaluated the prediction performance of our models using the one-year ahead approach. We investigated the potential improvement of prediction accuracy by incorporating ZCTA-level demographic and socioeconomic determinants. We identified ZCTAs with the highest predicted opioid-related mortality in terms of rates and counts and stratified them by rural and urban areas.

RESULTS

Bayesian dynamic models with the full spatial and temporal dependency performed best. Inclusion of the ZCTA-level demographic and socioeconomic variables as predictors improved the prediction accuracy, but only in the model that did not account for the neighborhood-level spatial dependency of the ZCTAs. Predictions were better for urban areas than for rural areas, which were more sparsely populated. Using the best performing model and the Massachusetts opioid-related mortality data from 2005 through 2019, our models suggested a stabilizing pattern in opioid-related overdose mortality in 2020 and 2021 if there were no disruptive changes to the trends observed for 2005-2019.

CONCLUSIONS

Our Bayesian spatiotemporal models focused on opioid-related overdose mortality data facilitated prediction approaches that can inform preemptive public health decision-making and resource allocation. While sparse data from rural and less populated locales typically pose special challenges in small area predictions, our dynamic Bayesian models, which maximized information borrowing across geographic areas and time points, were used to provide more accurate predictions for small areas. Such approaches can be replicated in other jurisdictions and at varying temporal and geographical levels. We encourage the formation of a modeling consortium for fatal opioid-related overdose predictions, where different modeling techniques could be ensembled to inform public health policy.

Variation in Initiation, Engagement, and Retention on Medications for Opioid Use Disorder Based on Health Insurance Plan Design

BACKGROUND

The association between cost-sharing and receipt of medication for opioid use disorder (MOUD) is unknown.

METHODS

We constructed a cohort of 10,513 commercially insured individuals with a new diagnosis of opioid use disorder and information on insurance cost-sharing in a large national deidentified claims database. We examined 4 cost-sharing measures: (1) pharmacy deductible; (2) medical service deductible; (3) pharmacy medication copay; and (4) medical office copay. We measured MOUD (naltrexone, buprenorphine, or methadone) initiation (within 14 d of diagnosis), engagement (second receipt within 34 d of first), and 6-month retention (continuous receipt without 14-d gap). We used multivariable logistic regression to assess the association between cost-sharing and MOUD initiation, engagement, and retention. We calculated total out-of-pocket costs in the 30 days following MOUD initiation for each type of MOUD.

RESULTS

Of 10,513 individuals with incident opioid use disorder, 1202 (11%) initiated MOUD, 742 (7%) engaged, and 253 (2%) were retained in MOUD at 6 months. A high ($1000+) medical deductible was associated with a lower odds of initiation compared with no deductible (odds ratio: 0.85, 95% confidence interval: 0.74-0.98). We found no significant associations between other cost-sharing measures for initiation, engagement, or retention. Median initial 30-day out-of-pocket costs ranged from $100 for methadone to $710 for extended-release naltrexone.

CONCLUSIONS

Among insurance plan cost-sharing measures, only medical services deductible showed an association with decreased MOUD initiation. Policy and benefit design should consider ways to reduce cost barriers to initiation and retention in MOUD.

Association of Incarceration With Mortality by Race From a National Longitudinal Cohort Study

IMPORTANCE

The association between incarceration and long-term mortality risk is unknown and may contribute to racial disparities in overall life expectancy.

OBJECTIVE

To determine whether incarceration in the US is associated with an increase in mortality risk and whether this association is different for Black compared with non-Black populations.

DESIGN, SETTING, AND PARTICIPANTS

This generational retrospective cohort study used data from the National Longitudinal Survey of Youth 1979, a nationally representative cohort of noninstitutionalized youths aged 15 to 22 years, from January 1 to December 31, 1979, with follow-up through December 31, 2018. A total of 7974 non-Hispanic Black and non-Hispanic non-Black participants were included. Statistical analysis was performed from October 26, 2019, to August 31, 2021.

EXPOSURES

Time-varying exposure of having experienced incarceration during follow-up.

MAIN OUTCOMES AND MEASURES

The main outcome was time to death. Cox proportional hazards regression models were used to estimate hazard ratios (HRs) and 95% CIs, adjusted for baseline sociodemographic, economic, and behavioral risk factors. Models were evaluated for the full cohort and stratified by race.

RESULTS

Of the 7974 individuals included in our sample, 4023 (50.5%) were male, and 2992 (37.5%) identified as Black (median age, 18 [IQR, 17-20] years). During a median follow-up of 35 years (IQR, 33-37 years), 478 participants were incarcerated and 818 died. Unadjusted exposure to at least 1 incarceration between 22 and 50 years of age was 11.5% (95% CI, 10.4%-12.7%) for Black participants compared with 2.5% (95% CI, 2.1%-2.9%) for non-Black participants. In the multivariable Cox proportional hazards model with the full cohort, time-varying exposure to incarceration was associated with an increased mortality rate (adjusted HR [aHR], 1.35; 95% CI, 0.97-1.88), a result that was not statistically significant. In the models stratified by race, incarceration was significantly associated with increased mortality among Black participants (aHR, 1.65; 95% CI, 1.18-2.31) but not among non-Black participants (aHR, 1.17; 95% CI, 0.68-2.03).

CONCLUSIONS AND RELEVANCE

In this cohort study with 4 decades of follow-up, incarceration was associated with a higher mortality rate among Black participants but not among non-Black participants. These findings suggest that incarceration, which was prevalent and unevenly distributed, may have contributed to the lower life expectancy of the non-Hispanic Black population in the US.

Operationalizing and selecting outcome measures for the HEALing Communities Study

BACKGROUND

The Helping to End Addiction Long-termSM (HEALing) Communities Study (HCS) is a multisite, parallel-group, cluster randomized wait-list controlled trial evaluating the impact of the Communities That HEAL intervention to reduce opioid overdose deaths and associated adverse outcomes. This paper presents the approach used to define and align administrative data across the four research sites to measure key study outcomes.

METHODS

Priority was given to using administrative data and established data collection infrastructure to ensure reliable, timely, and sustainable measures and to harmonize study outcomes across the HCS sites.

RESULTS

The research teams established multiple data use agreements and developed technical specifications for more than 80 study measures. The primary outcome, number of opioid overdose deaths, will be measured from death certificate data. Three secondary outcome measures will support hypothesis testing for specific evidence-based practices known to decrease opioid overdose deaths: (1) number of naloxone units distributed in HCS communities; (2) number of unique HCS residents receiving Food and Drug Administration-approved buprenorphine products for treatment of opioid use disorder; and (3) number of HCS residents with new incidents of high-risk opioid prescribing.

CONCLUSIONS

The HCS has already made an impact on existing data capacity in the four states. In addition to providing data needed to measure study outcomes, the HCS will provide methodology and tools to facilitate data-driven responses to the opioid epidemic, and establish a central repository for community-level longitudinal data to help researchers and public health practitioners study and understand different aspects of the Communities That HEAL framework.

Community dashboards to support data-informed decision-making in the HEALing communities study

BACKGROUND

With opioid misuse, opioid use disorder (OUD), and opioid overdose deaths persisting at epidemic levels in the U.S., the largest implementation study in addiction research-the HEALing Communities Study (HCS)-is evaluating the impact of the Communities That Heal (CTH) intervention on reducing opioid overdose deaths in 67 disproportionately affected communities from four states (i.e., "sites"). Community-tailored dashboards are central to the CTH intervention's mandate to implement a community-engaged and data-driven process. These dashboards support a participating community's decision-making for selection and monitoring of evidence-based practices to reduce opioid overdose deaths.

METHODS/DESIGN

A community-tailored dashboard is a web-based set of interactive data visualizations of community-specific metrics. Metrics include opioid overdose deaths and other OUD-related measures, as well as drivers of change of these outcomes in a community. Each community-tailored dashboard is a product of a co-creation process between HCS researchers and stakeholders from each community. The four research sites used a varied set of technical approaches and solutions to support the scientific design and CTH intervention implementation. Ongoing evaluation of the dashboards involves quantitative and qualitative data on key aspects posited to shape dashboard use combined with website analytics.

DISCUSSION

The HCS presents an opportunity to advance how community-tailored dashboards can foster community-driven solutions to address the opioid epidemic. Lessons learned can be applied to inform interventions for public health concerns and issues that have disproportionate impact across communities and populations (e.g., racial/ethnic and sexual/gender minorities and other marginalized individuals).

TRIAL REGISTRATION

ClinicalTrials.gov (NCT04111939).

Association of tramadol with risk of myocardial infarction among patients with osteoarthritis

OBJECTIVE

Tramadol has been widely used among patients with osteoarthritis (OA); however, there is paucity of information on its cardiovascular risk. We aimed to examine the association of tramadol with risk of myocardial infarction (MI) among patients with OA.

DESIGN

Among OA patients aged 50-90 years without history of MI, cancer, or opioid use disorder in The Health Improvement Network database in the United Kingdom (2000-2016), three sequential propensity-score matched cohort studies were assembled, i.e., (1) patients who initiated tramadol or naproxen (negative comparator); (2) patients who initiated tramadol or diclofenac (positive comparator); and (3) patients who initiated tramadol or codeine (a commonly used weak opioid). The outcome was incident MI over six-months.

RESULTS

Among tramadol and naproxen initiators (n = 33,024 in each cohort), 77 (4.8/1000 person-years) and 46 (2.8/1000 person-years) incident MI occurred, respectively. The rate difference (RD) and hazard ratios (HR) for incident MI with tramadol initiation were 1.9 (95% confidence interval [CI] 0.6 to 2.3)/1000 person-years and 1.68 (95% CI 1.16 to 2.41) relative to naproxen initiation, respectively. Among tramadol and diclofenac initiators (n = 18,662 in each cohort), 58 (6.4/1000 person-years) and 47 (5.1/1000 person-years) incident MIs occurred, respectively. The corresponding RD and HR for incident MI were 1.2 (95%CI -2.1 to 14.1)/1000 person-years and 1.24 (95%CI 0.84 to 1.82), respectively. Among tramadol and codeine initiators (n = 42,722 in each cohort), 127 (6.1/1000 person-years) and 103 (5.0/1000 person-years) incident MI occurred, respectively, and the corresponding RD and HR were 1.1 (95%CI:-0.3 to 2.5)/1000 person-years and 1.23 (95%CI:0.95 to 1.60), respectively.

CONCLUSIONS

In this population-based cohort of patients with OA, the six-month risk of MI among initiators of tramadol was higher than that of naproxen, but comparable to, if not lower than, those of diclofenac or codeine.

Sociodemographic factors and social determinants associated with toxicology confirmed polysubstance opioid-related deaths

BACKGROUND AND AIMS

While prescribed and illicit opioid use are primary drivers of the national surges in overdose deaths, opioid overdose deaths in which stimulants are also present are increasing in the U.S. We determined the social determinants and sociodemographic factors associated with opioid-only versus polysubstance opioid overdose deaths in Massachusetts. Particular attention was focused on the role of stimulants in opioid overdose deaths.

METHODS

We analyzed all opioid-related overdose deaths from 2014 to 2015 in an individually-linked population database in Massachusetts. We used linked postmortem toxicology data to identify drugs present at the time of death. We constructed a multinomial logistic regression model to identify factors associated with three mutually exclusive overdose death groups based on toxicological results: opioid-related deaths with (1) opioids only present, (2) opioids and other substances not including stimulants, and (3) opioids and stimulants with or without other substances.

RESULTS

Between 2014 and 2015, there were 2,244 opioid-related overdose deaths in Massachusetts that had accompanying toxicology results. Toxicology reports indicated that 17% had opioids only, 36% had opioids plus stimulants, and 46% had opioids plus another non-stimulant substance. Persons older than 24 years, non-rural residents, those with comorbid mental illness, non-Hispanic black residents, and persons with recent homelessness were more likely than their counterparts to die with opioids and stimulants than opioids alone.

CONCLUSIONS

Polysubstance opioid overdose is increasingly common in the US. Addressing modifiable social determinants of health, including barriers to mental health services and homelessness, is important to reduce polysubstance use and overdose deaths.

Opioid overdose deaths and potentially inappropriate opioid prescribing practices (PIP): A spatial epidemiological study

INTRODUCTION

Opioid overdose deaths quintupled in Massachusetts between 2000 and 2016. Potentially inappropriate opioid prescribing practices (PIP) are associated with increases in overdoses. The purpose of this study was to conduct spatial epidemiological analyses of novel comprehensively linked data to identify overdose and PIP hotspots.

METHODS

Sixteen administrative datasets, including prescription monitoring, medical claims, vital statistics, and medical examiner data, covering >98% of Massachusetts residents between 2011-2015, were linked in 2017 to better investigate the opioid epidemic. PIP was defined by six measures: ≥100 morphine milligram equivalents (MMEs), co-prescription of benzodiazepines and opioids, cash purchases of opioid prescriptions, opioid prescriptions without a recorded pain diagnosis, and opioid prescriptions through multiple prescribers or pharmacies. Using spatial autocorrelation and cluster analyses, overdose and PIP hotspots were identified among 538 ZIP codes.

RESULTS

More than half of the adult population (n = 3,143,817, ages 18 and older) were prescribed opioids. Nearly all ZIP codes showed increasing rates of overdose over time. Overdose clusters were identified in Worcester, Northampton, Lee/Tyringham, Wareham/Bourne, Lynn, and Revere/Chelsea (Getis-Ord Gi*; p < 0.05). Large PIP clusters for ≥100 MMEs and prescription without pain diagnosis were identified in Western Massachusetts; and smaller clusters for multiple prescribers in Nantucket, Berkshire, and Hampden Counties (p < 0.05). Co-prescriptions and cash payment clusters were localized and nearly identical (p < 0.05). Overlap in PIP and overdose clusters was identified in Cape Cod and Berkshire County. However, we also found contradictory patterns in overdose and PIP hotspots.

CONCLUSIONS

Overdose and PIP hotspots were identified, as well as regions where the two overlapped, and where they diverged. Results indicate that PIP clustering alone does not explain overdose clustering patterns. Our findings can inform public health policy decisions at the local level, which include a focus on PIP and misuse of heroin and fentanyl that aim to curb opioid overdoses.

Association of Tramadol With All-Cause Mortality Among Patients With Osteoarthritis

IMPORTANCE

An American Academy of Orthopaedic Surgeons guideline recommends tramadol for patients with knee osteoarthritis, and an American College of Rheumatology guideline conditionally recommends tramadol as first-line therapy for patients with knee osteoarthritis, along with nonsteroidal anti-inflammatory drugs.

OBJECTIVE

To examine the association of tramadol prescription with all-cause mortality among patients with osteoarthritis.

DESIGN, SETTING, AND PARTICIPANTS

Sequential, propensity score-matched cohort study at a general practice in the United Kingdom. Individuals aged at least 50 years with a diagnosis of osteoarthritis in the Health Improvement Network database from January 2000 to December 2015, with follow-up to December 2016.

EXPOSURES

Initial prescription of tramadol (n = 44 451), naproxen (n = 12 397), diclofenac (n = 6512), celecoxib (n = 5674), etoricoxib (n = 2946), or codeine (n = 16 922).

MAIN OUTCOMES AND MEASURES

All-cause mortality within 1 year after initial tramadol prescription, compared with 5 other pain relief medications.

RESULTS

After propensity score matching, 88 902 patients were included (mean [SD] age, 70.1 [9.5] years; 61.2% were women). During the 1-year follow-up, 278 deaths (23.5/1000 person-years) occurred in the tramadol cohort and 164 (13.8/1000 person-years) occurred in the naproxen cohort (rate difference, 9.7 deaths/1000 person-years [95% CI, 6.3-13.2]; hazard ratio [HR], 1.71 [95% CI, 1.41-2.07]), and mortality was higher for tramadol compared with diclofenac (36.2/1000 vs 19.2/1000 person-years; HR, 1.88 [95% CI, 1.51-2.35]). Tramadol was also associated with a higher all-cause mortality rate compared with celecoxib (31.2/1000 vs 18.4/1000 person-years; HR, 1.70 [95% CI, 1.33-2.17]) and etoricoxib (25.7/1000 vs 12.8/1000 person-years; HR, 2.04 [95% CI, 1.37-3.03]). No statistically significant difference in all-cause mortality was observed between tramadol and codeine (32.2/1000 vs 34.6/1000 person-years; HR, 0.94 [95% CI, 0.83-1.05]).

CONCLUSIONS AND RELEVANCE

Among patients aged 50 years and older with osteoarthritis, initial prescription of tramadol was associated with a significantly higher rate of mortality over 1 year of follow-up compared with commonly prescribed nonsteroidal anti-inflammatory drugs, but not compared with codeine. However, these findings may be susceptible to confounding by indication, and further research is needed to determine if this association is causal.

Effect of Age on Opioid Prescribing, Overdose, and Mortality in Massachusetts, 2011 to 2015

OBJECTIVES

To examine the effect of age on the likelihood of PIP of opioids and the effect of PIP on adverse outcomes.

DESIGN

Retrospective cohort study.

SETTING

Data from multiple state agencies in Massachusetts from 2011 to 2015.

PARTICIPANTS

Adult Massachusetts residents (N=3,078,163) who received at least one prescription opioid during the study period; approximately half (1,589,365) aged 50 and older.

MEASUREMENTS

We measured exposure to 5 types of PIP: high-dose opioids, coprescription with benzodiazepines, multiple opioid prescribers, multiple opioid pharmacies, and continuous opioid therapy without a pain diagnosis. We examined 3 adverse outcomes: nonfatal opioid overdose, fatal opioid overdose, and all-cause mortality.

RESULTS

The rate of any PIP increased with age, from 2% of individuals age 18 to 29 to 14% of those aged 50 and older. Older adults also had higher rates of exposure to 2 or more different types of PIP (40-49, 2.5%; 50-69, 5%; ≥70, 4%). Of covariates assessed, older age was the greatest predictor of PIP. In analyses stratified according to age, any PIP and specific types of PIP were associated with nonfatal overdose, fatal overdose, and all-cause mortality in younger and older adults.

CONCLUSION

Older adults are more likely to be exposed to PIP, which increases their risk of adverse events. Strategies to reduce exposure to PIP and to improve outcomes in those already exposed will be instrumental to addressing the opioid crisis in older adults. J Am Geriatr Soc 67:128-132, 2019.

Estimated Prevalence of Opioid Use Disorder in Massachusetts, 2011-2015: A Capture-Recapture Analysis

OBJECTIVES

To estimate the annual prevalence of opioid use disorder (OUD) in Massachusetts from 2011 to 2015.

METHODS

We performed a multisample stratified capture-recapture analysis to estimate OUD prevalence in Massachusetts. Individuals identified from 6 administrative databases for 2011 to 2012 and 7 databases for 2013 to 2015 were linked at the individual level and included in the analysis. Individuals were stratified by age group, sex, and county of residence.

RESULTS

The OUD prevalence in Massachusetts among people aged 11 years or older was 2.72% in 2011 and 2.87% in 2012. Between 2013 and 2015, the prevalence increased from 3.87% to 4.60%. The greatest increase in prevalence was observed among those in the youngest age group (11-25 years), a 76% increase from 2011 to 2012 and a 42% increase from 2013 to 2015.

CONCLUSIONS

In Massachusetts, the OUD prevalence was 4.6% among people 11 years or older in 2015. The number of individuals with OUD is likely increasing, particularly among young people.