Prescription Opioid Laws and Opioid Dispensing in US Counties: Identifying Salient Law Provisions With Machine Learning

Scaling Interventions to Manage Chronic Disease: Innovative Methods at the Intersection of Health Policy Research and Implementation Science

Policy implementation is a key component of scaling effective chronic disease prevention and management interventions. Policy can support scale-up by mandating or incentivizing intervention adoption, but enacting a policy is only the first step. Fully implementing a policy designed to facilitate implementation of health interventions often requires a range of accompanying implementation structures, like health IT systems, and implementation strategies, like training. Decision makers need to know what policies can support intervention adoption and how to implement those policies, but to date research on policy implementation is limited and innovative methodological approaches are needed. In December 2021, the Johns Hopkins ALACRITY Center for Health and Longevity in Mental Illness and the Johns Hopkins Center for Mental Health and Addiction Policy convened a forum of research experts to discuss approaches for studying policy implementation. In this report, we summarize the ideas that came out of the forum. First, we describe a motivating example focused on an Affordable Care Act Medicaid health home waiver policy used by some US states to support scale-up of an evidence-based integrated care model shown in clinical trials to improve cardiovascular care for people with serious mental illness. Second, we define key policy implementation components including structures, strategies, and outcomes. Third, we provide an overview of descriptive, predictive and associational, and causal approaches that can be used to study policy implementation. We conclude with discussion of priorities for methodological innovations in policy implementation research, with three key areas identified by forum experts: effect modification methods for making causal inferences about how policies' effects on outcomes vary based on implementation structures/strategies; causal mediation approaches for studying policy implementation mechanisms; and characterizing uncertainty in systems science models. We conclude with discussion of overarching methods considerations for studying policy implementation, including measurement of policy implementation, strategies for studying the role of context in policy implementation, and the importance of considering when establishing causality is the goal of policy implementation research.

Recent trends in prescription drug misuse in the United States by age, race/ethnicity, and sex

BACKGROUND AND OBJECTIVES

To examine changes in United States past-year opioid, stimulant, and benzodiazepine prescription drug misuse (PDM) and poly-PDM by demographics.

METHODS

Data were from the 2015-2019 National Survey on Drug Use and Health (N = 282,768), examining annualized PDM change by demographics.

RESULTS

Opioid and poly-PDM significantly declined among those under 35 years, White, and multiracial residents.

DISCUSSION AND CONCLUSIONS

Age and race/ethnicity are important moderators of recent PDM trends, warranting investigation of mechanisms.

SCIENTIFIC SIGNIFICANCE

Results highlight ongoing PDM declines in younger groups but expand the literature by showing limited changes in adults 35 and older and non-opioid PDM.

Characteristics of statewide prescription drug monitoring programs and potentially inappropriate opioid prescribing to patients with non-cancer chronic pain: A machine learning application

Unnecessary/unsafe opioid prescribing has become a major public health concern in the U.S. Statewide prescription drug monitoring programs (PDMPs) with varying characteristics have been implemented to improve safe prescribing practice. Yet, no studies have comprehensively evaluated the effectiveness of PDMP characteristics in reducing opioid-related potentially inappropriate prescribing (PIP) practices. The objective of the study is to apply machine learning methods to evaluate PDMP effectiveness by examining how different PDMP characteristics are associated with opioid-related PIPs for non-cancer chronic pain (NCCP) treatment. This was a retrospective observational study that included 802,926 adult patients who were diagnosed NCCP, obtained opioid prescriptions, and were continuously enrolled in plans of a major U.S. insurer for over a year. Four outcomes of opioid-related PIP practices, including dosage ≥50 MME/day and ≥90 MME/day, days supply ≥7 days, and benzodiazepine-opioid co-prescription were examined. Machine learning models were applied, including logistic regression, least absolute shrinkage and selection operation regression, classification and regression trees, random forests, and gradient boost modeling (GBM). The SHapley Additive exPlanations (SHAP) method was applied to interpret model results. The results show that among 1,886,146 NCCP opioid-related claims, 22.8% had an opioid dosage ≥50 MME/day and 8.9% ≥90 MME/day, 70.3% had days supply ≥7 days, and 10.3% were when benzodiazepine was filled ≤7 days ago. GBM had superior model performance. We identified the most salient PDMP characteristics that predict opioid-related PIPs (e.g., broader access to patient prescription history, monitoring Schedule IV controlled substances), which could be informative to the states considering the redesign of PDMPs.

Effects of State Opioid Prescribing Laws on Use of Opioid and Other Pain Treatments Among Commercially Insured U.S. Adults

BACKGROUND

There is concern that state laws to curb opioid prescribing may adversely affect patients with chronic noncancer pain, but the laws' effects are unclear because of challenges in disentangling multiple laws implemented around the same time.

OBJECTIVE

To study the association between state opioid prescribing cap laws, pill mill laws, and mandatory prescription drug monitoring program query or enrollment laws and trends in opioid and guideline-concordant nonopioid pain treatment among commercially insured adults, including a subgroup with chronic noncancer pain conditions.

DESIGN

Thirteen treatment states that implemented a single law of interest in a 4-year period and unique groups of control states for each treatment state were identified. Augmented synthetic control analyses were used to estimate the association between each state law and outcomes.

SETTING

United States, 2008 to 2019.

PATIENTS

7 694 514 commercially insured adults aged 18 years or older, including 1 976 355 diagnosed with arthritis, low back pain, headache, fibromyalgia, and/or neuropathic pain.

MEASUREMENTS

Proportion of patients receiving any opioid prescription or guideline-concordant nonopioid pain treatment per month, and mean days' supply and morphine milligram equivalents (MME) of prescribed opioids per day, per patient, per month.

RESULTS

Laws were associated with small-in-magnitude and non-statistically significant changes in outcomes, although CIs around some estimates were wide. For adults overall and those with chronic noncancer pain, the 13 state laws were each associated with a change of less than 1 percentage point in the proportion of patients receiving any opioid prescription and a change of less than 2 percentage points in the proportion receiving any guideline-concordant nonopioid treatment, per month. The laws were associated with a change of less than 1 in days' supply of opioid prescriptions and a change of less than 4 in average monthly MME per day per patient prescribed opioids.

LIMITATIONS

Results may not be generalizable to non-commercially insured populations and were imprecise for some estimates. Use of claims data precluded assessment of the clinical appropriateness of pain treatments.

CONCLUSION

This study did not identify changes in opioid prescribing or nonopioid pain treatment attributable to state laws.

PRIMARY FUNDING SOURCE

National Institute on Drug Abuse.