Impact of overdose on health plan disenrollment among patients prescribed long-term opioids: Retrospective cohort study.

CONTEXT: Health plan disenrollment may disrupt chronic or preventive care for patients prescribed long-term opioid therapy (LTOT). PURPOSE: To assess whether overdose events in patients prescribed LTOT are associated with subsequent health plan disenrollment. 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 and geographical regions across the United States. PATIENTS: Adults prescribed stable opioid therapy (≥10 morphine milligram equivalents/day) for a 6-month baseline period prior to an index opioid prescription from January 1, 2018 to December 31, 2018. MAIN MEASURES: Health plan disenrollment during follow-up. RESULTS: The cohort comprised 404,151 patients who were followed up after 800,250 baseline periods of stable opioid dosing. During a mean follow-up of 9.1 months, unadjusted disenrollment rates among primary commercial beneficiaries and Medicare Advantage enrollees were 37.2 and 13.9 per 100 person-years, respectively. Incident overdoses were associated with subsequent health plan disenrollment with a statistically significantly stronger association among primary commercial insurance beneficiaries [adjusted incidence rate ratio (aIRR) 1.48 (95% CI: 1.33-1.64)] as compared to Medicare Advantage enrollees [aIRR 1.15 (95% CI: 1.07-1.23)]. CONCLUSIONS: Among patients prescribed long-term opioids, overdose events were strongly associated with subsequent health plan disenrollment, especially among primary commercial insurance beneficiaries. These findings raise concerns about the social consequences of overdose, including potential health insurance loss, which may limit patient access to care at a time of heightened vulnerability.

Comparison of record linkage software for deduplicating patient identities in California's Prescription Drug Monitoring Program.

BACKGROUND: To help prevent overdose deaths involving prescription drugs, accurate linkage of prescription drug monitoring program (PDMP) records for individual patients is essential. OBJECTIVES: To compare the accuracy of the linkage program used by California's PDMP against various record linkage programs with respect to accuracy in deduplicating patient identities in the PDMP, with implications for identifying high-risk opioid use and outlier behaviors. RESEARCH DESIGN: We evaluated California's program, Link Plus, LinkSolv, and The Link King on 557 861 PDMP identity records with addresses in two 3-digit zip code areas for patients who filled a controlled substance prescription in 2013. Manual review was performed on a stratified sample of 720 paired records identified as matches by at least one program. MEASURES: We estimated sensitivity and positive predictive value, and computed PDMP patient alerts for the patient entities identified by each program. RESULTS: Sensitivity was 95% for LinkSolv and The Link King, 84% for Link Plus, and 73% for California's program; positive predictive value was ≥93% for all programs. The number of patient entities prompting a PDMP alert was similar among the programs for all alerts except multiple provider episodes (obtaining prescriptions from ≥6 prescribers or ≥6 pharmacies in the last 6 months), which were 10.9%, 26.6%, and 16.9% greater using The Link King, Link Plus, and LinkSolv, respectively, compared to California's program. CONCLUSIONS: PDMPs should assess the accuracy of record linkage algorithms and the impacts of these algorithms on patient safety alerts and develop national best practices for PDMP record linkage.

Impact of 30-day prescribed opioid dose trajectory on fatal overdose risk: A population-based, statewide cohort study.

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.

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.

Using Prescription Drug Monitoring Program Data to Assess Likelihood of Incident Long-Term Opioid Use: a Statewide Cohort Study.

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.

Trends in Buprenorphine to Treat Opioid Use Disorder in California, 2012 to 2018: Medicaid Outpaces the Rest of the State.

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.

Association between Opioid Overdose and Health Plan Disenrollment with Mitigating Impact of Buprenorphine Initiation.

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.

Age- and Sex-Specific Increases in Stimulant Prescribing Rates-California, 2008-2017.

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.

A three-year multifaceted intervention to prevent obesity in children of Mexican-heritage.

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 (ß = 0.04 (0.02), P = 0.03) and zBMI (ß = 0.25 (0.12), P = 0.04) among boys and log-transformed BMI among obese girls (ß = - 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.

A Web-Based Application to Improve Data Collection in an Interventional Study Targeting Childhood Obesity: Pre-Post Analysis.

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.

Prevalence and Perception of Childhood Obesity in California's Farmworker Communities.

In California's central valley, childhood obesity rates are above the national average. The majority of families living in the rural, agricultural communities of this region are immigrant of Mexican heritage, and face numerous social and environmental challenges. Demographic and anthropometric data were collected from a population of Mexican-heritage children 3-8 years (N = 609) and families (N = 466) living in two central valley communities. Overall, 45 % of children and 82 % of mothers were classified as overweight or obese. Multivariable analyses indicated that mother's BMI and acculturation level were positively associated with child BMI z-score. Most children classified as overweight or obese (92 % and 53 %, respectively) were perceived as having 'normal' weight by their mothers. Childhood obesity remains a major public health issue in Mexican-heritage, central valley communities. Our model indicates that mother's BMI is predictor of child obesity, and parents tend to underestimate their child's weight status. These findings highlight a need for family-targeted and culturally-tailored approaches to address relevant perceptions of obesity and risk factors in these communities.