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.

Objectives

Opioid use disorder remains undertreated in the United States. One of the primary mechanisms for expanding access to treatment has been the use of buprenorphine. In this study, we compare prescribing trends of buprenorphine paid through Medicaid versus other payer sources.

Methods

Combined data from California's prescription drug monitoring program and California's Department of Health Care Services was used to obtain statewide quarterly prescription rates for buprenorphine, indicated for the treatment of opioid use disorder, from 2012 to 2018.

Results

From 2012 to 2018, the rate of individuals treated with buprenorphine in Medicaid increased by 657% (1.39-10.5 Medicaid beneficiaries per 10,000) with increases beginning in 2014 and continuing through 2018. Rate of individual prescribing among non-Medicaid sources increased by 93.7% (6.54-12.7 non-Medicaid individuals per 10,000) with most increases occurring before 2014.

Conclusions

California Medicaid has made considerable gains in buprenorphine access, with access growing steadily even after expansions through the Affordable Care Act plateaued. In contrast, recent gains in buprenorphine access for individuals without Medicaid are uninspiring, indicating that initiatives to improve buprenorphine access to patients without Medicaid are urgently needed.

Context: Health plan disenrollment has been associated with higher mortality in patients with opioid use disorder. Insurance loss and health plan disenrollment might be downstream social consequences of opioid misuse and overdose that may heighten patient mortality risks during a period of heightened need for professional assistance. Objective: To test hypotheses that: 1) overdose events in patients prescribed long-term opioids are associated with subsequent health plan disenrollment; and 2) buprenorphine initiation after overdose would attenuate this association. Study Design: Retrospective cohort study. Setting and Dataset: Data from the Optum Labs Data Warehouse which includes de-identified medical and pharmacy claims and enrollment records for commercial and Medicare Advantage enrollees. The database contains longitudinal health information on patients, representing a mixture of ages, ethnicities, and geographical regions across the United States. Population studied: Adults (aged ≥18 years) prescribed stable, opioid therapy for a 6-month baseline period (≥90% of days covered, <10% monthly fluctuation from 6-month average, average daily dose ≥10 morphine milligram equivalents). Outcome Measures: Health plan disenrollment during up to one year of follow-up. Negative binomial regression estimated adjusted incidence rate ratios of disenrollment by incident overdose and buprenorphine initiation after overdose. Results: We identified 556,676 patients who were followed up after 1,119,100 stable periods of opioid therapy. During follow-up, 17.5% of person-periods ended in health plan disenrollment. Overdose events during follow-up were associated with health plan disenrollment with a dose-response relationship [adjusted incidence rate ratio (aIRR) for 1 overdose event = 1.29 (95% CI: 1.24-1.35); aIRR for ≥2 overdose events = 1.51 (1.43-1.59)]. Among patients with overdose events, subsequent buprenorphine initiation was associated with substantially reduced risk of health plan disenrollment [aIRR 0.36 (0.17-0.74)]. Conclusions: Overdose events in patients prescribed long-term opioids may portend other social consequences, such as health insurance loss, which may exacerbate patient risk at a time of heightened need and vulnerability. Buprenorphine may mitigate the risk of health plan disenrollment in opioid-prescribed patients who overdose.

BACKGROUND: Patients on long-term opioid therapy are particularly vulnerable to disruptions in medication access, especially during traumatic and chaotic events such as wildfires and other natural disasters. OBJECTIVES: To determine whether past highly destructive California wildfires were associated with disrupted access to prescription opioids for patients receiving long-term, and therefore physically dependent on, opioid medications. METHODS: Using California prescription drug monitoring program data, this retrospective study selected patients with long-term prescription opioid use episodes residing in ZIP code tabulation areas impacted by either the Camp Fire or Tubbs Fire. Autoregressive integrated moving average time series models were fit to pre-fire data to forecast post-fire expected values and then compared with observed post-fire data, specifically for weekly proportions of long-term episodes with early fills, late fills, changes in patients prescriber and pharmacy, and fills within a different ZIP code tabulation area than the patients residence. RESULTS: After the Camp Fire, there were significant spikes in the proportions of early fills (peak at 56% of total, week 1 after fire), late fills (peak at 29%, week 6), and immediate significant increases in prescriber (peak at 37%, week 3) and pharmacy changes (peak at 71%, week 1) in high-impact ZIP code tabulation areas. Low-impact ZIP code tabulation areas experienced no similar disruptions. Disruptions due to the Tubbs Fire were far less severe. CONCLUSION: Access to prescription opioids was greatly disrupted for patients living in areas most impacted by the Camp Fire. Future research should explore effectiveness of current state and federal controlled substance prescribing policies to determine what improvements are needed to minimize disruptions in medication access due to wildfires and other natural disasters.

Objective: To describe trends in prevalent and incident stimulant prescribing between 2008 and 2017 in California. Method: Statewide trends were estimated by age and sex category for prevalent (any) and incident (no prescriptions in the preceding 2 years) stimulant prescribing while adjusting for area-level covariates. Results: Prevalent prescribing rates increased by 126%, while incident prescribing increased 23%. Patients aged 25 to 44 years experienced over 200% increases in prevalent prescribing and 34% to 55% increases in incident prescribing. Among patients older than 25, women had consistently higher prescribing rates than men. ZIP code tabulation areas with the largest minority populations had the lowest baseline prescribing rates but experienced the greatest annual prescription rate increases. Conclusion: Adult stimulant prescribing increased substantially for early working aged adults. Prescription rates were greater for women than men.

Background

Obesity and overweight have increased dramatically in the United States over the last decades. The complexity of interrelated causal factors that result in obesity needs to be addressed within the cultural dynamic of sub-populations. In this study, we sought to estimate the effects of a multifaceted, community-based intervention on body mass index (BMI) among Mexican-heritage children.

Methods

Niños Sanos, Familia Sana (Healthy Children, Healthy Family) was a quasi-experimental intervention study designed to reduce the rate of BMI growth among Mexican-heritage children in California's Central Valley. Two rural communities were matched based on demographic and environmental characteristics and were assigned as the intervention or comparison community. The three-year intervention included parent workshops on nutrition and physical activity; school-based nutrition lessons and enhanced physical education program for children; and a monthly voucher for fruits and vegetables. Eligible children were between 3 and 8 years old at baseline. Intent-to-treat analyses were estimated using linear mixed-effect models with random intercepts. We ran a series of models for each gender where predictors were fixed except interactions between age groups and obesity status at baseline with intervention to determine the magnitude of impact on BMI.

Results

At baseline, mean (SD) BMI z-score (zBMI) was 0.97 (0.98) in the intervention group (n = 387) and 0.98 (1.02) in the comparison group (n = 313) (NS). The intervention was significantly associated with log-transformed BMI (β = 0.04 (0.02), P = 0.03) and zBMI (β = 0.25 (0.12), P = 0.04) among boys and log-transformed BMI among obese girls (β = - 0.04 (0.02), P = 0.04). The intervention was significantly and inversely associated with BMI in obese boys and girls across all age groups and normal weight boys in the oldest group (over 6 years) relative to their counterparts in the comparison community.

Conclusions

A community-based, multifaceted intervention was effective at slowing the rate of BMI growth among Mexican-heritage children. Our findings suggest that practitioners should consider strategies that address gender disparities and work with a variety of stakeholders to target childhood obesity.

Trial registration

clinicaltrials.gov Identifier: NCT01900613 . Registered 16th July 2013.

BACKGROUND: Both increases and decreases in patients prescribed daily opioid dose have been linked to increased overdose risk, but associations between 30-day dose trajectories and subsequent overdose risk have not been systematically examined. OBJECTIVE: To examine the associations between 30-day prescribed opioid dose trajectories and fatal opioid overdose risk during the subsequent 15 days. DESIGN: Statewide cohort study using linked prescription drug monitoring program and death certificate data. We constructed a multivariable Cox proportional hazards model that accounted for time-varying prescription-, prescriber-, and pharmacy-level factors. PARTICIPANTS: All patients prescribed an opioid analgesic in California from March to December, 2013 (5,326,392 patients). MAIN MEASURES: Dependent variable: fatal drug overdose involving opioids. Primary independent variable: a 16-level variable denoting all possible opioid dose trajectories using the following categories for current and 30-day previously prescribed daily dose: 0-29, 30-59, 60-89, or ≥90 milligram morphine equivalents (MME). KEY RESULTS: Relative to patients prescribed a stable daily dose of 0-29 MME, large (≥2 categories) dose increases and having a previous or current dose ≥60 MME per day were associated with significantly greater 15-day overdose risk. Patients whose dose decreased from ≥90 to 0-29 MME per day had significantly greater overdose risk compared to both patients prescribed a stable daily dose of ≥90 MME (aHR 3.56, 95%CI 2.24-5.67) and to patients prescribed a stable daily dose of 0-29 MME (aHR 7.87, 95%CI 5.49-11.28). Patients prescribed benzodiazepines also had significantly greater overdose risk; being prescribed Z-drugs, carisoprodol, or psychostimulants was not associated with overdose risk. CONCLUSIONS: Large (≥2 categories) 30-day dose increases and decreases were both associated with increased risk of fatal opioid overdose, particularly for patients taking ≥90 MME whose opioids were abruptly stopped. Results align with 2022 CDC guidelines that urge caution when reducing opioid doses for patients taking long-term opioid for chronic pain.

Background

Although participatory action research (PAR) studies have proliferated in recent years, the development of technological resources to manage these types of projects has not kept pace. Few studies show how Web-based applications can be used to efficiently manage the data collection process.

Objective

This study described the development, use, and impact of a Web-based application to facilitate data management in Niños Sanos, Familia Sana (Healthy Children, Healthy Family), an interventional multifaceted PAR field study.

Methods

We described the transformation of the data management process and evaluated the impact of the application in terms of time efficiency of data collection and engagement of community-based data collectors. We defined time efficiency as the total number of days it took to collect 3 main surveys, per year of data collection. The engagement of data collectors was assessed based on qualitative reports.

Results

The amount of time it took to perform a round of data collection was reduced after implementation of the field team application (between 382 and 383 days and 198 and 233 days). Secondary data were also collected in a tighter time frame around collection of the primary outcome, and communication among data collectors, the field staff, and the research team was streamlined. In focus groups, community-based data collectors reported feeling more empowered and engaged in the data collection process after implementation of the application.

Conclusions

A Web-based management application was successful in improving data collection time efficiency and engagement among data collectors.

Background

Tools are needed to aid clinicians in estimating their patients' risk of transitioning to long-term opioid use and to inform prescribing decisions.

Objective

The objective of this study was to develop and validate a model that predicts previously opioid-naive patients' risk of transitioning to long-term use.

Research design

This was a statewide population-based prognostic study.

Subjects

Opioid-naive (no prescriptions in previous 2 y) patients aged 12 years old and above who received a pill-form opioid analgesic in 2016-2018 and whose prescriptions were registered in the California Prescription Drug Monitoring Program (PDMP).

Measures

A multiple logistic regression approach was used to construct a prediction model with long-term (ie, >90 d) opioid use as the outcome. Models were developed using 2016-2017 data and validated using 2018 data. Discrimination (c-statistic), calibration (calibration slope, intercept, and visual inspection of calibration plots), and clinical utility (decision curve analysis) were evaluated to assess performance.

Results

Development and validation cohorts included 7,175,885 and 2,788,837 opioid-naive patients with outcome rates of 5.0% and 4.7%, respectively. The model showed high discrimination (c-statistic: 0.904 for development, 0.913 for validation), was well-calibrated after intercept adjustment (intercept, -0.006; 95% confidence interval, -0.016 to 0.004; slope, 1.049; 95% confidence interval, 1.045-1.053), and had a net benefit over a wide range of probability thresholds.

Conclusions

A model for the transition from opioid-naive status to long-term use had high discrimination and was well-calibrated. Given its high predictive performance, this model shows promise for future integration into PDMPs to aid clinicians in formulating opioid prescribing decisions at the point of care.

Background

Limiting the incidence of opioid-naïve patients who transition to long-term opioid use (i.e., continual use for > 90 days) is a key strategy for reducing opioid-related harms.

Objective

To identify variables constructed from data routinely collected by prescription drug monitoring programs that are associated with opioid-naïve patients' likelihood of transitioning to long-term use after an initial opioid prescription.

Design

Statewide cohort study using prescription drug monitoring program data PARTICIPANTS: All opioid-naïve patients in California (no opioid prescriptions within the prior 2 years) age ≥ 12 years prescribed an initial oral opioid analgesic from 2010 to 2017.

Methods and main measures

Multiple logistic regression models using variables constructed from prescription drug monitoring program data through the day of each patient's initial opioid prescription, and, alternatively, data available up to 30 and 60 days after the initial prescription were constructed to identify probability of transition to long-term use. Model fit was determined by the area under the receiver operating characteristic curve (C-statistic).

Key results

Among 30,569,125 episodes of patients receiving new opioid prescriptions, 1,809,750 (5.9%) resulted in long-term use. Variables with the highest adjusted odds ratios included concurrent benzodiazepine use, ≥ 2 unique prescribers, and receipt of non-pill, non-liquid formulations. C-statistics for the day 0, day 30, and day 60 models were 0.81, 0.88, and 0.94, respectively. Models assessing opioid dose using the number of pills prescribed had greater discriminative capacity than those using milligram morphine equivalents.

Conclusions

Data routinely collected by prescription drug monitoring programs can be used to identify patients who are likely to develop long-term use. Guidelines for new opioid prescriptions based on pill counts may be simpler and more clinically useful than guidelines based on days' supply or milligram morphine equivalents.