Trends in opioid use disorder and overdose among opioid-naive individuals receiving an opioid prescription in Massachusetts from 2011 to 2014

An Analysis of Benzodiazepine Prescribing to Primary Care Patients in a Large Healthcare System from 2019-2020

We sought to quantify benzodiazepine prescribing by primary care providers from 2019 to 2020 and identify correlates of prescribing. We hypothesized prescribing would increase post-COVID-19 lockdown. We conducted a retrospective cohort study of adult patients with primary care visits in 2019 or 2020 in a large Ohio healthcare system. Demographics, diagnosis codes, and receipt of benzodiazepine prescriptions were collected. Using multivariable logistic regression, we examined factors associated with benzodiazepine prescription receipt during the whole study period and post-lockdown. 455,537 adult patients had 1,643,473 visits. Benzodiazepines were prescribed in 3.2% (53,049/1,643,473) of visits. Effect sizes for positive associations with benzodiazepine prescription were largest for anxiety disorders. For negative associations, they were largest for Black patients and patients with cocaine use disorder. Benzodiazepine prescribing was positively associated with multiple groups having contraindications, though effect sizes were small. Contrary to our hypothesis, odds of receiving a prescription were 8.8% lower post-lockdown. Benzodiazepine prescribing rates in our system compared favorably to national rates. Year over year odds of receiving a prescription were slightly lower post-lockdown. Racial disparities were present and deserve further study. Strategies to reduce benzodiazepine prescribing to patients with anxiety may yield the largest reductions for benzodiazepine prescribing in primary care settings.

Expert Views on State Policies to Improve Engagement and Retention in Treatment for Opioid Use Disorder: A Qualitative Analysis of an Online Modified Delphi Process

OBJECTIVES

The aim of this study was to examine expert views on the effectiveness and implementability of state policies to improve engagement and retention in treatment for opioid use disorder (OUD).

METHODS

We conducted a 3-round modified Delphi process using the online ExpertLens platform. Participants included 66 experts on OUD treatment policies. Experts commented on 14 hypothetical state policies targeting treatment engagement and quality of care. Using the GRADE Evidence-to-Decision framework, we conducted reflexive thematic analysis to develop patterns of meaning from the dataset.

RESULTS

Only policies for providing continued access to evidence-based treatment for highly at-risk populations, settings, and periods were seen as effective in meaningfully reducing population-level opioid-related overdose mortality. Experts commented that, although the general public increasingly supports policies expanding medications for OUD and evidence-based care, ongoing stigma about OUD encourages public acceptance of punitive and paternalistic policies. Experts viewed all policies as at least moderately feasible given the current infrastructure and resources, with affordability reliant on long-term cost savings from reduced opioid-related harms. Equitability depended on whether experts perceived a policy as inherently equitable in its design as well as concerns about the potential for inequitable implementation due to structural oppression and interpersonal biases in criminal-legal, healthcare, and other systems.

CONCLUSIONS

Experts believe that supportive (rather than punitive) policies improve engagement and retention in OUD treatment. States could prioritize implementing supportive policies that are patient-centered and take a harm-reduction approach to enhance medications for OUD access and utilization. States could consider deimplementing punitive policies that are coercive, take an abstinence-only approach, and use punitive and restrictive measures.

Multinational appraisal of the epidemiological distribution of opioid fatalities: a systematic review and meta-analysis

Background

The global or multinational scientific evidence on the distribution of opioid fatality is unknown. Hence, the current study collects epidemiological characteristics to shed light on the ongoing global or multinational opioid crisis and to promote the development of public health prevention/management strategies.

Method

All documents on PRISMA standards were retrieved via electronic databases.

Results

Among the 47 articles relevant to our studies, which depict a total population size of 10,191 individuals, the prevalence of opioid fatal overdose was 15,022 (14.74%). Among the 47 articles, 14 of them reported the gender of the participants, with 22,125 (15.79%) male individuals and 7,235 (5.17%) female individuals, and the age distribution of the participants that was most affected by the overdose was as follows: 29,272 (31.13%) belonged to the 18-34-year-old age group and 25,316 (26.92%) belonged to the less than 18-year-old age group. Eighteen studies qualified for the meta-analysis of the multinational prevalence of fatal opioid overdose, depicting an overall pooled prevalence estimate of 19.66%, with 95% CIs (0.13-0.29), I2 = 99.76% determined using the random-effects model, and Q statistic of 7198.77 (p < 0.0001). The Egger test models of publication bias revealed an insubstantial level of bias (p = 0.015). The subgroup analysis of the study design (cohort or other) revealed that others have the highest prevalence estimate of 34.37, 95% CIs (0.1600-0.5901), I2 = 97.04%, and a sample size of less than 1,000 shows the highest prevalence of 34.66, 95% CIs (0.2039-0.5234), I2 = 97.82%, compared to that of more than 1,000 with a prevalence of 12.28, 95% CIs (0.0675-0.2131), I2 = 99.85%. The meta-regression analysis revealed that sample size (less-than or greater-than 1,000), (p = 0.0098; R2 = 3.83%) is significantly associated with the observed heterogeneity.

Conclusion

Research-based findings of fatal opioid overdose are grossly lacking in middle- and low-income nations. We established that there is a need for opioid fatality surveillance systems in developing nations.

Is age of onset and duration of stimulant therapy for ADHD associated with cocaine, methamphetamine, and prescription stimulant misuse?

BACKGROUND

To assess whether age of onset and duration of stimulant therapy for attention-deficit/hyperactivity disorder (ADHD) are associated with cocaine, methamphetamine, and prescription stimulant misuse during adolescence.

METHODS

Nationally representative samples of US 10th and 12th grade students (N = 150,395) from the Monitoring the Future study were surveyed via self-administered questionnaires from 16 annual surveys (2005-2020).

RESULTS

An estimated 8.2% of youth received stimulant therapy for ADHD during their lifetime (n = 10,937). More than one in 10 of all youth reported past-year prescription stimulant misuse (10.4%)-past-year cocaine (4.4%) and methamphetamine (2.0%) use were less prevalent. Youth who initiated early stimulant therapy for ADHD (≤9 years old) and for long duration (≥6 years) did not have significantly increased adjusted odds of cocaine or methamphetamine use relative to population controls (ie, non-ADHD and unmedicated ADHD youth). Youth who initiated late stimulant therapy for ADHD (≥10 years old) and for short duration (<1 year) had significantly higher odds of past-year cocaine or prescription stimulant misuse in adolescence than those initiating early stimulant therapy for ADHD (≤9 years old) and for long duration (≥6 years). Youth who initiated late stimulant therapy for ADHD (≥10 years) for short duration (<1 year) had significantly higher odds of past-year cocaine, methamphetamine, and prescription stimulant misuse versus population controls during adolescence. No differences in past-year cocaine, methamphetamine, and prescription stimulant misuse were found between individuals who only used non-stimulant therapy for ADHD relative to youth who initiated early stimulant therapy (≤9 years old) and for long duration (≥6 years).

CONCLUSIONS

An inverse relationship was found between years of stimulant therapy and illicit and prescription stimulant misuse. Adolescents with later initiation and/or shorter duration of stimulant treatment for ADHD should be monitored for potential illicit and prescription stimulant misuse.

The antidepressant agomelatine attenuates morphine-induced reinstatement but not self-administration or precipitated withdrawal

BACKGROUND

Exogenous melatonin appears to have anti-addictive properties and was recently shown to improve mental health and metabolic measures in patients receiving chronic opioid maintenance therapy. Agomelatine is a marketed antidepressant which acts as a melatonin agonist. We evaluated its effects using a rat model of morphine-reinforced behavior.

METHODS

After pretreatment with noncontingent morphine, male Wistar rats were trained to self-administer intravenous morphine (1.0 mg/kg-injection) under a progressive-ratio schedule. Rats were pretreated with vehicle or agomelatine during extinction, reinstatement, and reacquisition of morphine-reinforced behavior.

RESULTS

Daily treatment with 10 mg/kg-day of agomelatine decreased the number of ratios completed and prolonged latency during morphine-induced reinstatement. There were no significant effects on cue-induced reinstatement, morphine self-administration, or naloxone-precipitated withdrawal. Treatment with 32 mg/kg-day of agomelatine caused postural changes. That dose prolonged withdrawal-induced loss of body weight and caused delayed reductions in food reinforcement.

SUMMARY

In addition to postural effects, high-dose agomelatine worsened the course of spontaneous withdrawal and produced nonspecific effects on food-reinforced behavior. When administered at a selective dose, agomelatine did not modify morphine self-administration or precipitated withdrawal, but decreased morphine-induced reinstatement. Our findings show potential detrimental effects of high-dose agomelatine, with reductions in opioid-seeking behavior after a lower, more selective dose.

Abrupt Discontinuation From Long-Term Opioid Therapy in Massachusetts, 2015-2018

INTRODUCTION

In response to the opioid overdose crisis, providers were urged to taper and discontinue patients from long-term opioid therapy; however, abrupt discontinuation may lead to poor health outcomes. This study aims to determine abrupt and tapered discontinuation rates and identify the patient and provider characteristics associated with abrupt discontinuation.

METHODS

Data were from the Massachusetts Prescription Monitoring Program, 2015-2018. Patients discontinued from long-term opioid therapy were included in the analysis. Differences between abrupt and tapered discontinuations were identified with bivariate correlations, and variables independently associated with abrupt discontinuation were identified using multivariable Poisson regression analyses. Data were analyzed during 2019-2021.

RESULTS

In total, 277,485 patients experienced 359,320 discontinuations, of which 33.7% (n=120,964) were abrupt. Of all discontinuations, 55.7% were among female patients, and 57.9% were among patients aged >55 years. The ratio of abrupt to tapered discontinuations increased from 1:2.11 in 2015 to 1:1.75 in 2018. In bivariate analysis, prescribers with more patients receiving monthly opioid prescriptions were less likely to abruptly discontinue patients (29.0, IQR=13.9, 55.3 vs 18.8, IQR=5.84, 43.9, p<0.001), as were prescribers who wrote more monthly opioid prescriptions (36.0, IQR=16.8, 70.8 vs 25.4, IQR=7.40, 58.3, p<0.001). Multivariable results indicated that abrupt discontinuation was independently associated with male sex (RR=1.31, 95% CI=1.29, 1.1.32), younger age (RR=0.872, 95% CI=0.869, 0.874), greater distance between patient and prescriber (RR=1.0075, 95% CI=1.0072, 1.0078), and longer long-term opioid therapy duration (RR=1.021, 95% CI=1.021, 1.0122 for every month increase).

CONCLUSIONS

Among all long-term opioid therapy discontinuations, abrupt discontinuation is increasing. Evidence-based approaches to managing and tapering long-term opioid therapy are urgently needed.

Risk factors of chronic opioid use after surgical procedures in noncancer patients: A nationwide case-control study

BACKGROUND

Surgery is an indication for opioid prescription in noncancer patients, and chronic use of opioids is associated with overdose and abuse.

OBJECTIVES

We aimed to evaluate the prevalence and risk factors associated with chronic opioid use (COU) following surgery among noncancer patients.

DESIGN

A nationwide case-control study.

SETTING

Retrospective analysis of the annual national patient sample data from 2012 to 2018 in South Korea.

PATIENTS

Adults without cancer who had undergone surgery and received noninjectable opioids during hospital stay.

MAIN OUTCOME MEASURES

COU during 3 months following surgery.

RESULTS

A total of 15 543 participants were included, and the prevalence overall and in opioid-naïve users was 8.1 and 5.7%, respectively. Prior exposure patterns of opioids [intermittent user, adjusted odds ratio (aOR) 2.35; 95% CI, 2.00 to 2.77, and continuous user, aOR 8.58; 95% CI, 6.54 to 11.24] and concomitant use of benzodiazepine (in continuous user, aOR 18.60; 95% CI 11.70 to 29.55) were strongly associated with COU compared with naïve users. Morphine milligram equivalent, type of opioid strength at discharge and prescription of nonopioid analgesics at discharge were also associated with COU. Compared with minor surgery, knee (aOR 1.49; 95% CI 1.17 to 1.89), spine (aOR 1.65; 95% CI 1.33 to 2.06) and shoulder (aOR 2.54; 95% CI 1.97 to 3.27) procedures showed a significantly positive association with COU. Sensitivity analysis in opioid-naïve patients showed similar results.

CONCLUSION

About 8.1% of noncancer patients who had undergone surgery and were prescribed noninjectable opioids became chronic opioid users in Korea. Identified risk factors could be used to derive strategies for safe opioid use in noncancer patients in the future.

APCDs can Provide Important Insights for Surveilling the Opioid Epidemic, With Caveats

State-level all-payer claims databases (APCDs) are a possible new public health surveillance tool, but their reliability is unclear. We compared Colorado's APCD with other state-level databases for use in monitoring the opioid epidemic (Colorado Hospital Association and Colorado's Prescription Drug Monitoring Program database for 2010-2017), using descriptive analyses comparing quarterly counts/rates of opioid-involved inpatient and emergency department visits and counts/rates of 30-day opioid fills between databases. Utilization is lower in the Colorado APCD than the other databases for all outcomes but trends are parallel and consistent between databases. State APCDs hold promise for researchers, but they may be better suited to individual-level analyses or comparisons of providers than for surveillance of public health trends related to addiction.

Opioid analgesic prescribing for opioid-naïve individuals prior to identification of opioid use disorder in British Columbia, Canada

BACKGROUND AND AIMS

Prescription opioid analgesics have contributed to the development of opioid use disorder (OUD) in many individuals. We aimed to characterize non-cancer opioid prescribing for opioid-naive individuals prior to OUD identification.

DESIGN

Population-based retrospective cohort study using six linked health administrative databases.

SETTING

British Columbia (BC), Canada.

PARTICIPANTS

People with OUD between 1 January 2001 and 30 September 2018 who initiated opioid analgesic therapy for non-cancer pain prior to OUD identification.

MEASUREMENTS

Dose (morphine milligram equivalent per day), days prescribed and clinical guideline non-concordance for initial opioid prescriptions (dose ≥ 90 morphine milligram equivalent per day; ≥ 7 days prescribed; concomitant sedative prescription). We estimated the probability of non-concordant initial prescriptions by source (inpatient post-discharge, non-inpatient acute, non-acute) using logistic regression, adjusting for individual characteristics and comorbidities.

FINDINGS

Among 66 372 individuals identified with OUD from 2001 to 2018, 21 331 (32.1%) received opioid analgesics prior to OUD identification. This proportion increased from 3.0% in 2001 to 41.0% in 2011, before decreasing to 34.2% in 2017. Roughly half of opioid prescriptions were attributed to non-acute care visits, peaking at 56.8% in 2007, while the proportion from inpatient visits increased from 19.7% in 2001 to 28.5% in 2017. The predicted probability of receiving non-guideline concordant prescriptions declined over time-periods across all three measures for inpatient and non-inpatient acute care, while remaining stable for non-acute care. In particular, the predicted probability of receiving ≥ 7-day prescriptions following inpatient visits decreased from 53.3% [95% confidence interval (CI) = 50.9, 55.8%] in 2001-06 to 37.2% (95% CI = 33.9, 40.5%) in 2013-18.

CONCLUSIONS

Among the 66 372 individuals in British Columbia, Canada diagnosed with opioid use disorder between 2001 and 2018, more than 32% were earlier prescribed non-cancer opioid analgesics. The proportion who had received an opioid analgesic prescription prior to OUD identification peaked at more than 40% in 2011, before stabilizing between 2011 and 2016 and declining thereafter. Guideline concordance improved over time for high-dose and concomitant sedative prescribing.

Nonmedical Opioid Use After Short-term Therapeutic Exposure in Children: A Systematic Review

CONTEXT

Opioid-related harms continue to rise for children and youth. Analgesic prescribing decisions are challenging because the risk for future nonmedical opioid use or disorder is unclear.

OBJECTIVE

To synthesize research examining the association between short-term therapeutic opioid exposure and future nonmedical opioid use or opioid use disorder and associated risk factors.

DATA SOURCES

We searched 11 electronic databases.

STUDY SELECTION

Two reviewers screened studies. Studies were included if: they were published in English or French, participants had short-term (≤14 days) or an unknown duration of therapeutic exposure to opioids before 18 years, and reported opioid use disorder or misuse.

DATA EXTRACTION

Data were extracted, and methodologic quality was assessed by 2 reviewers. Data were summarized narratively.

RESULTS

We included 21 observational studies (49 944 602 participants). One study demonstrated that short-term therapeutic exposure may be associated with opioid abuse; 4 showed an association between medical and nonmedical opioid use without specifying duration of exposure. Other studies reported on prevalence or incidence of nonmedical use after medical exposure to opioids. Risk factors were contradictory and remain unclear.

LIMITATIONS

Most studies did not specify duration of exposure and were of low methodologic quality, and participants might not have been opioid naïve.

CONCLUSIONS

Some studies suggest an association between lifetime therapeutic opioid use and nonmedical opioid use. Given the lack of clear evidence regarding short-term therapeutic exposure, health care providers should carefully evaluate pain management options and educate patients and caregivers about safe, judicious, and appropriate use of opioids and potential signs of misuse.

Assessing opioid overdose risk: a review of clinical prediction models utilizing patient-level data

Drug, and specifically opioid-related, overdoses remain a major public health problem in the United States. Multiple studies have examined individual risk factors associated with overdose risk, but research developing clinical risk prediction tools for overdose has only emerged in the last few years. We conducted a comprehensive review of the literature on patient-level factors associated with opioid-related overdose risk, with an emphasis on clinical risk prediction models for opioid-related overdose in the United States. Studies that developed and/or validated clinical prediction models were closely reviewed and evaluated to determine the state of the field. We identified 12 studies that reported risk prediction models for opioid-related overdose risk. Published models were developed from a variety of data sources, including Veterans Health Administration data, Medicare data, commercial insurance data, and statewide linked datasets. Studies reported model performance using measures of discrimination, usually at good-to-excellent levels, though they did not always assess calibration. C-statistics were better for models that included clinical predictors (c-statistics: 0.75-0.95) compared to models without them (c-statistics: 0.69-0.82). External validation of models was rare, and we found no studies evaluating implementation of models or risk prediction tools into clinical practice. A common feature of these models was a high rate of false positives, largely because opioid-related overdose is rare in the general population. Thus, efforts to implement prediction models into practice should take into account that published models overestimate overdose risk for many low-risk patients. Future prediction models assessing overdose risk should employ external validation and address model calibration. In order to translate findings from prediction models into clinical public health benefit, future studies should focus on developing clinical prediction tools based on prediction models, implementing these tools into clinical practice, and evaluating the impact of these models on treatment decisions, patient outcomes, and, ultimately, opioid overdose rates.

Drug type and risk behaviors associated with non-fatal overdose among people who use drugs: a systematic review and meta-analysis

The present study aimed to determine the association between drug type, risk behaviors and non-fatal overdose among people who use drugs (PWUD). We searched for studies in English published before February 1, 2021, on PubMed, Scopus, Cochrane, and Web of Science to identify primary studies on the factors associated with non-fatal overdose among PWUD. After reviewing for study duplicates, the full-text of selected articles were assessed for eligibility using Population, Intervention, Comparator, Outcomes (PICO) criteria. After a detailed assessment of over 13,845 articles, a total of 49 studies met the eligibility criteria. We found that non-injection opioid use, heroin injection, cocaine use, concurrent use of buprenorphine and benzodiazepines, benzodiazepine use, incarceration, injecting drugs, and duration of injecting were associated with greater odds of non-fatal overdose among PWUD. The findings of the current meta-analysis support the requirement to improve suitable harm reduction strategies for drug users, such as peer-based overdose management, and further focusing on the need to balance the current emphasis on enforcement-based responses to illegal drug use with health-related interventions.

Influence of Opioid Prescription Policy on Overdoses and Related Adverse Effects in a Primary Care Population

BACKGROUND

In response to the opioid epidemic, many states have enacted policies limiting opioid prescriptions. There is a paucity of evidence of the impact of opioid prescribing interventions in primary care populations, including whether unintended consequences arise from limiting the availability of prescribed opioids.

OBJECTIVE

Our aim was to compare changes in opioid overdose and related adverse effects rate among primary care patients following the implementation of state-level prescribing policies.

DESIGN

A cohort of primary care patients within an interrupted time series model.

PARTICIPANTS

Electronic medical record data for 62,776 adult (18+ years) primary care patients from a major medical center in Vermont from January 1, 2016, to June 30, 2018.

INTERVENTIONS

State-level opioid prescription policy changes limiting dose and duration.

MAIN MEASURES

Changes in (1) opioid overdose rate and (2) opioid-related adverse effects rate per 100,000 person-months following the July 1, 2017, prescription policy change.

KEY RESULTS

Among primary care patients, there was no change in opioid overdose rate following implementation of the prescribing policy (incidence rate ratio; IRR: 0.64, 95% confidence interval; CI: 0.22-1.88). There was a 78% decrease in the opioid-related adverse effects rate following the prescribing policy (IRR: 0.22, 95%CI: 0.09-0.51). This association was moderated by opioid prescription history, with decreases observed among opioid-naïve patients (IRR: 0.18, 95%CI: 0.06-0.59) and among patients receiving chronic opioid prescriptions (IRR: 0.17, 95%CI: 0.03-0.99), but not among those with intermittent opioid prescriptions (IRR: 0.51, 95%CI: 0.09-2.82).

CONCLUSIONS

Limiting prescription opioids did not change the opioid overdose rate among primary care patients, but it reduced the rate of opioid-related adverse effects in the year following the state-level policy change, particularly among patients with chronic opioid prescription history and opioid-naïve patients. Limiting the quantity and duration of opioid prescriptions may have beneficial effects among primary care patients.

Demographic Pattern and Mortality Risk Factors for Prescription Opioid Overdose Hospitalizations: Results From Nationwide Inpatient Sample Analysis

Objectives To explore the demographic patterns of hospitalizations related to prescription opioid overdose (POD) and evaluate the mortality risk of association in POD inpatients. Methodology We conducted a cross-sectional study using the Nationwide Inpatient Sample of 184,711 POD inpatients. A binomial logistic regression model was used to evaluate the odds ratio (OR) of association for mortality risk due to comorbidities (substance use disorders (SUD) and medical complications) in POD inpatients. Results POD inpatients were majorly females (54.1%), older adults aged 51-75 years (48.5%), whites (81.5%), and from lower household income quartet (32.8%). The most prevalent comorbid SUD among POD inpatients was alcohol (15.7%), followed by cannabis (5.7%), cocaine (4.2%), and amphetamine (1.8%). Comorbid alcohol use disorders had a minimally increased association with mortality but were not statistically significant (OR = 1.036; P = 0.438). POD in patients with cardiac arrest had the highest risk of mortality (OR = 103.423; P < 0.001), followed by shock (OR = 15.367; P < 0.001), coma (OR = 13.427; P < 0.001), and respiratory failure (OR = 12.051; P < 0.001). Conclusions Our study indicates that the hospitalizations related to POD were more prevalent among females, elders between 51 and 75 years of age, whites, and those in the lower household income quartet. The prevalence of prescription opioid use and the hospitalization related to POD remains a significant public health issue. POD inpatients with medical complications were at a higher risk of mortality than with comorbid SUD.

Development of a Medicare Claims-Based Model to Predict Persistent High-Dose Opioid Use After Total Knee Replacement

OBJECTIVE

To develop a claims-based model to predict persistent high-dose opioid use amongst patients undergoing total knee replacement (TKR).

METHODS

Using Medicare claims (2010-2014), we identified patients ≥65 years who underwent TKR with no history of high-dose opioid use (>25 mean morphine equivalents (MME)/day) in the year prior. We used group-based trajectory modeling to identify distinct opioid use patterns. The primary outcome was persistent high-dose opioid use in the year after TKR. We split the data into training (2010-2013) and test (2014) sets and used logistic regression with least absolute shrinkage and selection operator (LASSO) regularization utilizing a total of 83 pre-operative patient characteristics as candidate predictors. A reduced model with ten pre-specified variables which included demographics, opioid use and medication history was also considered.

RESULTS

The final study cohort included 142,089 patients who underwent TKR. The group-based trajectory model identified 4 distinct trajectories of opioid use (Group 1- short-term, low-dose, Group 2- moderate-duration, low-dose, Group 3- moderate-duration, high-dose, and Group 4-persistent high-dose). The model predicting persistent high-dose opioid use achieved high discrimination (area under the receiver operating characteristic curve (AUC) of 0.85; 95% CI, 0.84-0.86)) in the test set. The reduced model with ten predictors performed equally well (AUC=0.84; 95% CI, 0.84-0.85).

CONCLUSIONS

In this cohort of older patients, 10.6% became persistent high dose (mean=22.4 MME/day) opioid users after TKR. Our model with 10 readily available clinical factors may help identify patients at high risk of future adverse outcomes from persistent opioid use after TKR.

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.

Opioid Prescription and Persistent Opioid Use After Ectopic Pregnancy

OBJECTIVE

To evaluate outpatient opioid dispensing and the incidence of persistent opioid use after ectopic pregnancy.

METHODS

This cohort study used U.S. employer-based claims data to identify opioid-naïve individuals experiencing ectopic pregnancy from November 1, 2008, to September 30, 2015. Treatment was categorized as surgical, medical (using methotrexate), or unknown. New opioid prescriptions were defined as prescriptions filled from 1 week before to 1 week after an ectopic pregnancy treatment. For those who filled a new opioid prescription, we calculated the incidence and risk factors for persistent opioid use, defined as having filled at least one opioid prescription both from 8 to 90 days after treatment and from 91 to 365 days after treatment.

RESULTS

Of the 15,338 individuals in our study, 7,047 (45.9%, 95% CI 45.2-46.7%) filled an opioid prescription at the time of treatment, of whom 4.1% (95% CI 3.6-4.6%) developed persistent opioid use. The risk of persistent opioid use was lower among those who had surgical compared with medical treatment (3.7% and 6.8%, respectively; relative risk [RR] 0.54, 95% CI 0.38-0.77). Variables most strongly associated with persistent opioid use were a history of benzodiazepine use (RR 1.99; 95% CI 1.43-2.78; adjusted relative risk [aRR] 1.57, 95% CI 1.11-2.22), antidepressant use (RR 1.91, 95% CI 1.45-2.53; aRR 1.53, 95% CI 1.08-2.18), and a pre-existing pain disorder (RR 1.58, 95% CI 1.26-1.99; aRR 1.47, 95% CI 1.16-1.85) in the year before treatment.

CONCLUSION

New opioid use is common after an ectopic pregnancy; approximately 4% of those with new opioid use develop persistent opioid use, with the rate higher in those treated medically. New pain-management guidelines need to be developed to prevent persistent opioid use after ectopic pregnancy.

Association of Medicare Part D Benzodiazepine Coverage Expansion With Changes in Fall-Related Injuries and Overdoses Among Medicare Advantage Beneficiaries

IMPORTANCE

Benzodiazepines, which are associated with safety-related harms for older adults, were not covered when the US Medicare Part D prescription drug benefit began. Coverage was extended to benzodiazepines in 2013.

OBJECTIVE

To examine whether the expansion of benzodiazepine coverage among Medicare Advantage (MA) beneficiaries was associated with increases in fall-related injuries or overdoses among older adults.

DESIGN, SETTING, AND PARTICIPANTS

This ecological study used interrupted time-series with comparison-series analyses of MA claims data from 4 635 312 age-eligible MA beneficiaries and 940 629 commercially insured individuals (comparison group) stratified by age (65-69, 70-74, 75-79, and ≥80 years) to separately compare trends in fall-related injury and overdose before (January 1, 2010, to December 31, 2012) and after (January 1, 2013, to December 31, 2015) coverage expansion for benzodiazepines. Data analysis was performed from September 1, 2018, to August 31, 2019.

EXPOSURES

Expansion of benzodiazepine coverage in Medicare Part D in 2013.

MAIN OUTCOMES AND MEASURES

Monthly rate of fall-related injury and overdose.

RESULTS

In 2012 (the year before the policy change), women constituted 57.5% of the MA group and 47.4% of the comparison group. A total of 25.8% of individuals in the MA group were aged 65 to 69 years, and 29.3% were 80 years or older (mean [SD], 75.1 [6.4] years); 56.7% of individuals in the comparison group were aged 65 to 69 years, and 15.1% were 80 years or older (mean [SD] age, 70.9 [6.5] years). In the MA group, 4 635 312 individuals contributed 156 754 749 person-months from 2010 through 2015; in the comparison group, 940 629 individuals contributed 25 104 534 person-months. After coverage of benzodiazepines began, the rate (ie, slope) of fall-related injury among MA beneficiaries increased from before to after coverage among all age groups. Compared with the comparison group, the increase in rate was statistically significant for those 80 years or older (rate changes for the MA vs comparison groups: 0.12 [95% CI, 0.07 to 0.17] vs -0.01 [95% CI, -0.11 to 0.10]; P = .04 for interaction). The overdose trend changed from decreasing to increasing among MA beneficiaries after coverage for all age groups, with a statistically significant increase compared with the comparison group among those aged 65 to 69 years (rate changes for the MA vs comparison groups: 0.23 [95% CI, 0.17 to 0.30] vs 0.02 [95% CI, -0.06 to 0.11]; P < .001 for interaction) and among those 80 years or older (rate changes for the MA vs comparison groups: 0.07 [95% CI, 0.00 to 0.14] vs -0.20 [95% CI, -0.35 to -0.05]; P = .002 for interaction). Results among MA beneficiaries were consistent when stratified by sex and when limited to those prescribed opioids.

CONCLUSIONS AND RELEVANCE

Medicare's expansion of benzodiazepine coverage may have been associated with increases in the rates of overdose among adults ages 65 to 69 years and in the rates of overdose and fall-related injury among those 80 years or older.