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BackgroundLimiting 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.ObjectiveTo 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.DesignStatewide 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 measuresMultiple 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 resultsAmong 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.ConclusionsData 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.

ObjectivesOpioid 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.MethodsCombined 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.ResultsFrom 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.ConclusionsCalifornia 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.

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.

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.

BackgroundObesity 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.MethodsNiños 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.ResultsAt 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.ConclusionsA 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 registrationclinicaltrials.gov Identifier: NCT01900613 . Registered 16th July 2013.

BackgroundAlthough 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.ObjectiveThis 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.MethodsWe 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.ResultsThe 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.ConclusionsA Web-based management application was successful in improving data collection time efficiency and engagement among data collectors.

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 [greater than or equal to] 18 years) prescribed stable, opioid therapy for a 6-month baseline period ([greater than or equal to] 90% of days covered,, Presenters Joshua Fenton, MD, MPH, Daniel Tancredi, PhD, Alicia Agnoli, MD, MHS, MPH, Stephen Henry, MD, MSc, Iraklis Erik Tseregounis, PhD, Elizabeth Magnan, MD, [...]

Context: Tapering of long-term opioid therapy (LTOT) increased after publication of the 2016 CDC opioid guidelines, followed by anecdotal reports of exacerbated pain among tapered patients. However, two systematic reviews of limited evidence from multidisciplinary pain control programs found similar or better pain ratings after tapering versus baseline. Objective: To evaluate the association between opioid dose tapering and subsequent emergency (ED) visits, outpatient primary care visits and hospitalizations for pain among patients prescribed LTOT. Study Design: Retrospective cohort study. Dataset: 2015-2019 de-identified administrative data from the Optum Labs Data Warehouse, including medical and pharmacy claims and eligibility information for commercial and Medicare Advantage enrollees, representing a mixture of ages and geographical regions. Population: Adults [greater than or equal to] 18 years old who were prescribed stable doses of LTOT [greater than or equal to] 50 morphine milligram equivalents per day during a 12-month baseline period Outcome Measures: Monthly counts of ED visits, primary care visits, and hospitalizations for pain up to 12 months after cohort entry. Pain visits were defined by diagnostic codes for musculoskeletal or other specific chronic pain in the primary position on ED and hospitalization claims, or in any position for primary care visit claims. Analysis: Monthly counts were modeled using negative binomial regression as a function of tapering ([greater than or equal to] 15% relative dose reduction during 6 overlapping 60-day periods after cohort entry), total baseline pain and non-pain ED visits, primary care visits and hospitalizations, and adjusted for patient level-covariates (sociodemographics, comorbidities). Results: Among 51,361 patients prescribed stable LTOT, 14,331 patients (27.9%) tapered after cohort entry. Tapering was associated with more subsequent ED visits (adjusted incidence rate ratio [aIRR] 1.18, 95% CI: 1.11-1.27) and fewer subsequent primary care visits (aIRR 0.95, CI: 0.92-0.99) for pain. Hospitalizations for pain control were unchanged (aIRR 1.04, CI: 0.95- 1.15). Conclusions: Among patients prescribed LTOT, opioid tapering was associated with subsequently more ED visits yet fewer primary care visits for pain, suggesting a shift in pain care from outpatient to the higher acuity emergency setting post-taper. The findings suggest tapering may have led to increased pain, disruption of primary care relationships, or some combination of these effects., Presenters Elizabeth Magnan, MD, PhD, Daniel Tancredi, PhD, Guibo Xing, Alicia Agnoli, MD, MHS, MPH, Iraklis Erik Tseregounis, PhD, Anthony Jerant, MD, Joshua Fenton, MD, [...]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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., Competing Interests: Authors report none, (2023 Annals of Family Medicine, Inc.)