The impact of California wildfires on patient access to prescription opioids.

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 patient's 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.

A Risk Prediction Model for Long-term Prescription Opioid Use.

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.

Attitudes and Social Norms Are Related to Attendance at Childhood Obesity Prevention Classes in a Rural Mexican-Heritage Community.

OBJECTIVE: To examine factors related to attendance of Mexican-heritage parents at community-based nutrition classes to prevent childhood obesity. METHODS: Starting in 2011, interviewers collected baseline data from Niños Sanos Familia Sana (Healthy Children, Healthy Families) participants in rural California. Educators maintained attendance logs from 2012 to 2014. Informed by the Theory of Planned Behavior, interviewers administered an exit survey in 2015 to collect data on attitudes, subjective norms, health motivations, and perceived control related to attendance. Multivariable ordinal logistic regression analysis examined the correlates of attendance (n = 194, intervention group only). RESULTS: Controlling for mother's age, marital status, acculturation, and employment, attitudes and subjective norms were significantly related to attendance (odds ratio = 1.27; 95% confidence interval [CI], 1.18-1.37; P < .001). CONCLUSIONS AND IMPLICATIONS: In these Mexican-heritage participants, attitudes and subjective norms were significant correlates of attendance. The Theory of Planned Behavior may shed light on attendance of high-risk groups but further testing of instruments is needed.