Background
Many states have implemented opioid days’ supply restriction policies, leading to reductions in opioid prescribing. Although research within certain provider types exist, no study has ...evaluated a restriction policy by various provider types.
Objective
To evaluate changes in opioid utilization following a days’ supply restriction policy stratified by provider type: surgery, emergency medicine, primary care, specialty care, and dentistry.
Design
Interrupted time series (ITS)
Participants
Opioid prescription claims of patients in a private health plan serving a large Florida employer from 1/1/2015 to 3/31/2019. Provider types were determined using the Healthcare Provider Taxonomy Code associated with the national provider identifier (NPI).
Interventions
Florida’s opioid restriction policy implemented on July 1, 2018.
Main Measures
Changes in mean morphine milligram equivalent (MMEs), mean days’ supply, and mean number of units dispensed per opioid prescription before and after policy implementation.
Key Results
There were 10,583 opioid initial prescriptions dispensed. Treating providers were classified as surgery (16.4%; n = 1732), emergency care (14.3%; n = 1516), primary care (21.2%; n = 2241), specialty care (11.4%; n = 1207), and dentistry providers (23.7%; n = 2511). Significant reductions in mean days’ supply were observed across most provider types ranging from 14% reduction for dentistry providers to 41% reduction for specialty care providers. Significant changes were observed for emergency care and specialty care providers with a 30% (p = 0.001)and 29% (p < 0.001) reduction in mean MME, respectively, and a 27% (p = 0.040) reduction in mean number of units dispensed in emergency care providers, after implementation. Pre-implementation trends in opioid prescribing varied by provider type impacting the effects of the opioid days’ supply restriction policy.
Conclusions
Pre-policy opioid prescribing varied by provider type with a differential impact on mean MMEs, mean days’ supply, and mean number of units dispensed per prescription following implementation.
Objective To examine the utility of the Healthy Start Screen (HSS), which is an assessment of health, environment, and behavioral risk factors offered to all pregnant women in the state of Florida, ...in identifying women at risk for developing postpartum depression (PPD). Methods The sample for this Institutional Review Board (IRB)-approved, retrospective study consisted of patients who presented to a women's clinic for a new prenatal visit. Those patients who completed both the HSS at their prenatal visit and the Edinburgh Postnatal Depression Scale (EPDS) at their postpartum visit were included. We focused on items 1-10 of the HSS, where patients could respond with either "yes" or "no", and identified a positive EPDS as any score greater than or equal to 12. Results Women who identified as feeling down, depressed or hopeless, feeling alone when facing problems, to having ever received mental health services, or to having any trouble paying bills were more likely to have an EPDS score greater than or equal to 12. Conclusion The HSS, currently mandated by the state of Florida to be offered to all pregnant women, is a useful tool for identifying women at increased risk of developing PPD.
Buprenorphine is an effective evidence-based medication for opioid use disorder (OUD). Yet premature discontinuation undermines treatment effectiveness, increasing the risk of mortality and overdose. ...We developed and evaluated a machine learning (ML) framework for predicting buprenorphine care discontinuity within 12 months following treatment initiation.
This retrospective study used United States (US) 2018–2021 MarketScan commercial claims data of insured individuals aged 18–64 who initiated buprenorphine between July 2018 and December 2020 with no buprenorphine prescriptions in the previous six months. We measured buprenorphine prescription discontinuation gaps of ≥30 days within 12 months of initiating treatment. We developed predictive models employing logistic regression, decision tree classifier, random forest, extreme gradient boosting, Adaboost, and random forest-extreme gradient boosting ensemble. We applied recursive feature elimination with cross-validation to reduce dimensionality and identify the most predictive features while maintaining model robustness. For model validation, we used several statistics to evaluate performance, such as C-statistics and precision-recall curves. We focused on two distinct treatment stages: at the time of treatment initiation and one and three months after treatment initiation. We employed SHapley Additive exPlanations (SHAP) analysis that helped us explain the contributions of different features in predicting buprenorphine discontinuation. We stratified patients into risk subgroups based on their predicted likelihood of treatment discontinuation, dividing them into decile subgroups. Additionally, we used a calibration plot to analyze the reliability of the models.
A total of 30,373 patients initiated buprenorphine and 14.98% (4551) discontinued treatment. C-statistic varied between 0.56 and 0.76 for the first-stage models including patient-level demographic and clinical variables. Inclusion of proportion of days covered (PDC) measured after one month and three months following treatment initiation significantly increased the models’ discriminative power (C-statistics: 0.60 to 0.82). Random forest (C-statistics: 0.76, 0.79 and 0.82 with baseline predictors, one-month PDC and three-months PDC, respectively) outperformed other ML models in discriminative performance in all stages (C-statistics: 0.56 to 0.77). Most influential risk factors of discontinuation included early stage medication adherence, age, and initial days of supply.
ML algorithms demonstrated a good discriminative power in identifying patients at higher risk of buprenorphine care discontinuity. The proposed framework may help healthcare providers optimize treatment strategies and deliver targeted interventions to improve buprenorphine care continuity.
•Machine learning framework predicts the risk of discontinuing buprenorphine treatment for opioid use disorder.•Prediction models are applied at two distinct treatment stages: at the time of treatment initiation and one- and three-months after initiation.•Random Forest model outperformed logistic regression and other tree-based models.•Identified and explained the influential risk factors associated with buprenorphine discontinuation.•Stratified patients into risk subgroups based on their predicted likelihood of treatment discontinuation to improveclinical utility.
Variation in the use of inferior vena cava filters (IVCFs) across hospitals has been observed, suggesting differences in quality of care. Hospitalization metrics associated with venous ...thromboembolism (VTE) patients have not been compared based on IVCF utilization rates using a national sample. We conducted a descriptive retrospective study using the Nationwide Readmissions Database (NRD) to delineate the variability of hospitalization metrics across the hospital quartiles of IVCF utilization for VTE patients. The NRD included all-payer administrative inpatient records drawn from 22 states. Adult (≥ 18 years) patients with VTE hospitalizations with or without IVCF were identified from January 1, 2013 through December 31, 2014 and hospitals were divided into quartiles based on the IVCF utilization rate as a proportion of VTE patients. Primary outcome measures were observed rates of in-hospital mortality, 30-day all-cause readmissions and VTE-related readmissions, cost, and length of stay. Patient case-mix characteristics and hospital-level factors by hospital quartiles of IVCF utilization rates, were compared. Overall, 12.29% of VTE patients had IVCF placement, with IVCF utilization ranging from 0% to 46.84%. The highest quartile had fewer pulmonary embolism patients relative to deep vein thrombosis patients, and older patient ages were present in higher quartiles. The highest quartile of hospitals placing IVCFs were more often private, for-profit, and non-teaching. Patient and hospital characteristics and hospitalization metrics varied by IVCF utilization rates, but hospitalization outcomes for non-IVCF patients varied most between quartiles. Future work investigating the implications of IVCF utilization rates as a measure of quality of care for VTE patients is needed.
The Cannabis Clinical Outcomes Research Conference (CCORC) 2021 was held virtually on April 8 and 9, 2021. The conference was hosted by the Consortium for Medical Marijuana Clinical Outcomes ...Research, a research organization instituted by the state legislature of Florida in the United States. The inaugural annual CCORC 2021 was organized as a scientific meeting to foster and disseminate research on medical marijuana (MM) clinical outcomes, while promoting engagement among MM researchers, patients, clinicians, policymakers, and industry partners. Key conference themes included: (a) the disconnect between policy, practice, and evidence and steps towards reconciliation, (b) approaches to overcome common barriers to MM research, and (c) the use of focused translational approaches utilizing both mechanistic and clinical research methodology to tackle the complexities of MM outcomes. CCORC 2022 is planned for spring 2022 in Orlando, Florida, United States.
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