Systematic Identification and Categorization of Opioid Prescribing and Dispensing Policies in 16 States and Washington, DC.

OBJECTIVES: State policies can impact opioid prescribing or dispensing. Some state opioid policies have been widely examined in empirical studies, including prescription drug monitoring programs and pain clinic licensure requirements. Other relevant policies might exist that have received limited attention. Our objective was to identify and categorize a wide range of state policies that could affect opioid prescribing/dispensing. METHODS: We used stratified random sampling to select 16 states and Washington, DC, for our sample. We collected state regulations and statutes effective during 2020 from each jurisdiction, using search terms related to opioids, pain management, and prescribing/dispensing. We then conducted qualitative template analysis of the data to identify and categorize policy categories. RESULTS: We identified three dimensions of opioid prescribing/dispensing laws: the prescribing/dispensing rule, its applicability, and its disciplinary consequences. Policy categories of prescribing/dispensing rules included clinic licensure, staff credentials, evaluating the appropriateness of opioids, limiting the initiation of opioids, preventing the diversion or misuse of opioids, and enhancing patient safety. Policy categories related to applicability of the law included the pain type, substance type, practitioner, setting, payer, and prescribing situation. The disciplinary consequences dimension included specific consequences and inspection processes. DISCUSSION: Policy categories within each dimension of opioid prescribing/dispensing laws could become a foundation for creating variables to support empirical analyses of policy effects, improving operationalization of policies in empirical studies, and helping to disentangle the effects of multiple state laws enacted at similar times to address the opioid crisis. Several of the policy categories we identified have been underexplored in previous empirical studies.

Changes in firearm mortality following the implementation of state laws regulating firearm access and use.

Although 39,000 individuals die annually from gunshots in the US, research examining the effects of laws designed to reduce these deaths has sometimes produced inconclusive or contradictory findings. We evaluated the effects on total firearm-related deaths of three classes of gun laws: child access prevention (CAP), right-to-carry (RTC), and stand your ground (SYG) laws. The analyses exploit changes in these state-level policies from 1970 to 2016, using Bayesian methods and a modeling approach that addresses several methodological limitations of prior gun policy evaluations. CAP laws showed the strongest evidence of an association with firearm-related death rate, with a probability of 0.97 that the death rate declined at 6 y after implementation. In contrast, the probability of being associated with an increase in firearm-related deaths was 0.87 for RTC laws and 0.77 for SYG laws. The joint effects of these laws indicate that the restrictive gun policy regime (having a CAP law without an RTC or SYG law) has a 0.98 probability of being associated with a reduction in firearm-related deaths relative to the permissive policy regime. This estimated effect corresponds to an 11% reduction in firearm-related deaths relative to the permissive legal regime. Our findings suggest that a small but meaningful decrease in firearm-related deaths may be associated with the implementation of more restrictive gun policies.

Economic Evaluation Design within the HEAL Prevention Cooperative.

The rapid rise in opioid misuse, disorder, and opioid-involved deaths among older adolescents and young adults is an urgent public health problem. Prevention is a vital part of the nation's response to the opioid crisis, yet preventive interventions for those at risk for opioid misuse and opioid use disorder are scarce. In 2019, the National Institutes of Health (NIH) launched the Preventing Opioid Use Disorder in Older Adolescents and Young Adults cooperative as part of its broader Helping to End Addiction Long-term (HEAL) Initiative ( https://heal.nih.gov/ ). The HEAL Prevention Cooperative (HPC) includes ten research projects funded with the goal of developing effective prevention interventions across various settings (e.g., community, health care, juvenile justice, school) for older adolescent and young adults at risk for opioid misuse and opioid use disorder (OUD). An important component of the HPC is the inclusion of an economic evaluation by nine of these research projects that will provide information on the costs, cost-effectiveness, and sustainability of these interventions. The HPC economic evaluation is integrated into each research project's overall design with start-up costs and ongoing delivery costs collected prospectively using an activity-based costing approach. The primary objectives of the economic evaluation are to estimate the intervention implementation costs to providers, estimate the cost-effectiveness of each intervention for reducing opioid misuse initiation and escalation among youth, and use simulation modeling to estimate the budget impact of broader implementation of the interventions within the various settings over multiple years. The HPC offers an extraordinary opportunity to generate economic evidence for substance use prevention programming, providing policy makers and providers with critical information on the investments needed to start-up prevention interventions, as well as the cost-effectiveness of these interventions relative to alternatives. These data will help demonstrate the valuable role that prevention can play in combating the opioid crisis.

Expert views on state-level naloxone access laws: a qualitative analysis of an online modified-Delphi process.

BACKGROUND: Expanding availability to naloxone is a core harm reduction strategy in efforts to address the opioid epidemic. In the US, state-level legislation is a prominent mechanism to expand naloxone availability through various venues, such as community pharmacies. This qualitative study aimed to identify and summarize the views of experts on state-level naloxone access laws. METHODS: We conducted a three-round modified-Delphi process using the online ExpertLens platform. Participants included 46 key stakeholders representing various groups (advocates, healthcare providers, human/social service practitioners, policymakers, and researchers) with expertise naloxone access laws. Participants commented on the effectiveness and implementability of 15 state-level naloxone access laws (NALs). We thematically analyzed participant comments to summarize views on NALs overall and specific types of NAL. RESULTS: Participants commented that the effectiveness of NALs in reducing opioid-related mortality depends on their ability to make sustained, significant impacts on population-level naloxone availability. Participants generally believed that increased naloxone availability does not have appreciable negative impacts on the prevalence of opioid misuse, opioid use disorder (OUD), and non-fatal opioid overdoses. Implementation barriers include stigma among the general public, affordability of naloxone, and reliance on an inequitable healthcare system. CONCLUSIONS: Experts believe NALs that significantly increase naloxone access are associated with less overdose mortality without risking substantial unintended public health outcomes. To maximize impacts, high-value NALs should explicitly counter existing healthcare system inequities, address stigmatization of opioid use and naloxone, maintain reasonable prices for purchasing naloxone, and target settings beyond community pharmacies to distribute naloxone.

Individual and Community Factors Associated with Naloxone Co-prescribing Among Long-term Opioid Patients: a Retrospective Analysis.

BACKGROUND: Naloxone co-prescribing to individuals at increased opioid overdose risk is a key component of opioid overdose prevention efforts. OBJECTIVE: Examine naloxone co-prescribing in the general population and assess how co-prescribing varies by individual and community characteristics. DESIGN: Retrospective cross-sectional study. We conducted a multivariable logistic regression of 2017-2018 de-identified pharmacy claims representing 90% of all prescriptions filled at retail pharmacies in 50 states and the District of Columbia. PATIENTS: Individuals with opioid analgesic treatment episodes > 90 days MAIN MEASURES: Outcome was co-prescribed naloxone. Predictor variables included insurance type, primary prescriber specialty, receipt of concomitant benzodiazepines, high-dose opioid episode, county urbanicity, fatal overdose rates, poverty rates, and primary care health professional shortage areas. KEY RESULTS: Naloxone co-prescribing occurred in 2.3% of long-term opioid therapy episodes. Medicaid (aOR 1.87, 95%CI 1.84 to 1.90) and Medicare (aOR 1.48, 95%CI 1.46 to 1.51) episodes had higher odds of naloxone co-prescribing than commercial insurance episodes, while cash pay (aOR 0.77, 95%CI 0.74 to 0.80) and other insurance episodes (aOR 0.81, 95%CI 0.79 to 0.83) had lower odds. Odds of naloxone co-prescribing were higher among high-dose opioid episodes (aOR 3.19, 95%CI 3.15 to 3.23), when concomitant benzodiazepines were prescribed (aOR 1.12, 95%CI 1.10 to 1.14), and in counties with higher fatal overdose rates. CONCLUSION: Co-prescription of naloxone represents a tangible clinical action that can be taken to help prevent opioid overdose deaths. However, despite recommendations to co-prescribe naloxone to patients at increased risk for opioid overdose, we found that co-prescribing rates remain low overall. States, insurers, and health systems should consider implementing strategies to facilitate increased co-prescribing of naloxone to at-risk individuals.

Trends in Cannabis Involvement and Risk of Alcohol Involvement in Motor Vehicle Crash Fatalities in the United States, 2000‒2018.

Objectives. To assess cannabis and alcohol involvement among motor vehicle crash (MVC) fatalities in the United States. Methods. In this repeated cross-sectional analysis, we used data from the Fatality Analysis Reporting System from 2000 to 2018. Fatalities were cannabis-involved if an involved driver tested positive for a cannabinoid and alcohol-involved based on the highest blood alcohol concentration (BAC) of an involved driver. Multinomial mixed-effects logistic regression models assessed cannabis as a risk factor for alcohol by BAC level. Results. While trends in fatalities involving alcohol have remained stable, the percentage of fatalities involving cannabis and cannabis and alcohol increased from 9.0% in 2000 to 21.5% in 2018, and 4.8% in 2000 to 10.3% in 2018, respectively. In adjusted analyses, fatalities involving cannabis had 1.56 (95% confidence interval [CI] = 1.48, 1.65), 1.62 (95% CI = 1.52, 1.72), and 1.46 (95% CI = 1.42, 1.50) times the odds of involving BACs of 0.01% to 0.049%, 0.05% to 0.079%, and 0.08% or higher, respectively. Conclusions. The percentage of fatalities involving cannabis and coinvolving cannabis and alcohol doubled from 2000 to 2018, and cannabis was associated with alcohol coinvolvement. Further research is warranted to understand cannabis- and alcohol-involved MVC fatalities. (Am J Public Health. 2021;111(11):1976-1985. https://doi.org/10.2105/AJPH.2021.306466).

Moving beyond the classic difference-in-differences model: a simulation study comparing statistical methods for estimating effectiveness of state-level policies.

BACKGROUND: Reliable evaluations of state-level policies are essential for identifying effective policies and informing policymakers' decisions. State-level policy evaluations commonly use a difference-in-differences (DID) study design; yet within this framework, statistical model specification varies notably across studies. More guidance is needed about which set of statistical models perform best when estimating how state-level policies affect outcomes. METHODS: Motivated by applied state-level opioid policy evaluations, we implemented an extensive simulation study to compare the statistical performance of multiple variations of the two-way fixed effect models traditionally used for DID under a range of simulation conditions. We also explored the performance of autoregressive (AR) and GEE models. We simulated policy effects on annual state-level opioid mortality rates and assessed statistical performance using various metrics, including directional bias, magnitude bias, and root mean squared error. We also reported Type I error rates and the rate of correctly rejecting the null hypothesis (e.g., power), given the prevalence of frequentist null hypothesis significance testing in the applied literature. RESULTS: Most linear models resulted in minimal bias. However, non-linear models and population-weighted versions of classic linear two-way fixed effect and linear GEE models yielded considerable bias (60 to 160%). Further, root mean square error was minimized by linear AR models when we examined crude mortality rates and by negative binomial models when we examined raw death counts. In the context of frequentist hypothesis testing, many models yielded high Type I error rates and very low rates of correctly rejecting the null hypothesis (< 10%), raising concerns of spurious conclusions about policy effectiveness in the opioid literature. When considering performance across models, the linear AR models were optimal in terms of directional bias, root mean squared error, Type I error, and correct rejection rates. CONCLUSIONS: The findings highlight notable limitations of commonly used statistical models for DID designs, which are widely used in opioid policy studies and in state policy evaluations more broadly. In contrast, the optimal model we identified--the AR model--is rarely used in state policy evaluation. We urge applied researchers to move beyond the classic DID paradigm and adopt use of AR models.

An Observational Study of Retail Pharmacy Naloxone Prescriptions: Differences Across Provider Specialties and Patient Populations.

BACKGROUND: Despite exponential growth in pharmacy-dispensed naloxone, little information is available regarding variation in naloxone prescribing pattern across specialty groups, regions, and patient populations. OBJECTIVE: Explore variation in pharmacy-dispensed naloxone by prescriber specialty and patient characteristics. DESIGN: Cross-sectional analysis of the 2016 national retail pharmacy naloxone prescription claims from the IQVIA Real Longitudinal Prescriptions database. PARTICIPANTS: Naloxone prescribers and individuals filling naloxone prescriptions. MAIN MEASURES: Descriptive statistics assess differences across prescriber specialty groups in number of naloxone prescribers, patient and prescription characteristics, and geographic variation in naloxone dispensation and naloxone market share across prescriber specialty groups or formulation. KEY RESULTS: In 2016, 100,958 naloxone prescriptions written by 14,026 prescribers were filled by 88,735 patients. Primary care physicians accounted for the largest share of naloxone prescribers (45.9%); pain and anesthesia physicians and non-physicians prescribed to significantly greater numbers of patients (means of 10 and 8, respectively). While responsible for a relatively small share of naloxone dispensed (6.1%), psychiatrists and addiction specialists disproportionately served younger individuals, accounting for 49.5% of all prescriptions for individuals aged 35 and younger. Naloxone fill rates differed greatly across geographic regions, with the highest per capita rates in New England and the most concentrated prescribing in the West South Central and South Atlantic regions, where naloxone prescribers had the highest average numbers of patients (9.7 and 7.9, respectively). The South Atlantic and West South Central also had naloxone markets dominated by the Evzio® auto-injector, responsible for 50.3% and 43.8% of all naloxone dispensed in the regions; in contrast, New England's naloxone market was predominantly comprised of generic formulations (48.8%) and Narcan® nasal spray (45.4%). CONCLUSIONS: Our findings reflect a need to better understand barriers to uptake of naloxone prescribing behavior among physicians and other prescribers to ensure individuals have adequate opportunity to receive naloxone from their treating clinicians.

Impact on Nonfirearm Deaths of Firearm Laws Affecting Firearm Deaths: A Systematic Review and Meta-Analysis.

Background. There is debate whether policies that reduce firearm suicides or homicides are offset by increases in non-firearm-related deaths.Objectives. To assess the extent to which changes in firearm homicides and suicides following implementation of various gun laws affect nonfirearm homicides and suicides.Search Methods. We performed a literature search on 13 databases for studies published between 1995 and October 31, 2018 (PROSPERO CRD42019120105).Selection Criteria. We included studies if they (1) estimated an effect of 1 of 18 included classes of gun policy on firearm homicides or suicides, (2) included a control group or comparison group and evaluated time series data to establish that policies preceded their purported effects, and (3) provided estimated effects of the policy and inferential statistics for either total or nonfirearm homicides or suicides.Data Collection and Analysis. We extracted data from each study, including study timeframe, population, and statistical methods, as well as point estimates and inferential statistics for the effects of firearm policies on firearm deaths as well as either nonfirearm or overall deaths. We assessed quality at the estimate (study-policy-outcome) level by using prespecified criteria to evaluate the validity of inference and causal identification. For each estimate, we derived the mortality multiplier (i.e., the ratio of the policy's effect on total homicides or suicides; expressed as a change in the number of deaths) as a proportion of its effect on firearm homicides or suicides. Finally, we performed a meta-analysis to estimate overall mortality multipliers for suicide and homicide that account for both within- and between-study heterogeneity.Main Results. We identified 16 eligible studies (study timeframes spanning 1977-2015). All examined state-level policies in the United States, with most estimating effects of multiple policies, yielding 60 separate estimates of the mortality multiplier. From these, we estimated that a firearm law's effect on homicide, expressed as a change in the number of total homicide deaths, is 0.99 (95% confidence interval = 0.76, 1.22) times its effect on the number of firearm homicides. Thus, on average, changes in the number of firearm homicides caused by gun policies are neither offset nor compounded by second-order effects on nonfirearm homicides. There is insufficient evidence in the existing literature on suicide to indicate the extent to which the effects of gun policy changes on firearm suicides are offset or compounded by their effects on nonfirearm suicides.Authors' Conclusions. State gun policies that reduce firearm homicides are likely to reduce overall homicides in the state by approximately the same number. It is currently unknown whether the same holds for state gun policies that significantly reduce firearm suicides. The small number of studies meeting our inclusion criteria, issues of methodological quality within those studies, and the possibility of reporting bias are potential limitations of this review.Public Health Implications. Policies that reduce firearm homicides likely have large benefits for public health as there is little evidence to support a strong substitution effect between firearm and nonfirearm homicides at the population level. Further research is needed to determine whether policies that produce population-level reductions in firearm suicides will translate to overall declines in suicide rates.

Who receives naloxone from emergency medical services? Characteristics of calls and recent trends.

Background: With the rapid rise in opioid overdose-related deaths, state policy makers have expanded policies to increase the use of naloxone by emergency medical services (EMS). However, little is known about changes in EMS naloxone administration in the context of continued worsening of the opioid crisis and efforts to increase use of naloxone. This study examines trends in patient demographics and EMS response characteristics over time and by county urbanicity. Methods: We used data from the 2013-2016 National EMS Information System to examine trends in patient demographics and EMS response characteristics for 911-initiated incidents that resulted in EMS naloxone administration. We also assessed temporal, regional, and urban-rural variation in per capita rates of EMS naloxone administrations compared with per capita rates of opioid-related overdose deaths. Results: From 2013 to 2016, naloxone administrations increasingly involved young adults and occurred in public settings. Particularly in urban counties, there were modest but significant increases in the percentage of individuals who refused subsequent treatment, were treated and released, and received multiple administrations of naloxone before and after arrival of EMS personnel. Over the 4-year period, EMS naloxone administrations per capita increased at a faster rate than opioid-related overdose deaths across urban, suburban, and rural counties. Although national rates of naloxone administration were consistently higher in suburban counties, these trends varied across U.S. Census Regions, with the highest rates of suburban administration occurring in the South. Conclusions: Naloxone administration rates increased more quickly than opioid deaths across all levels of county urbanicity, but increases in the percentage of individuals requiring multiple doses and refusing subsequent care require further attention.

Early evidence of the impact of cannabis legalization on cannabis use, cannabis use disorder, and the use of other substances: Findings from state policy evaluations.

Background: The past decade has seen unprecedented shifts in the cannabis policy environment, and the public health impacts of these changes will hinge on how they affect patterns of cannabis use and the use and harms associated with other substances.Objectives: To review existing research on how state cannabis policy impacts substance use, emphasizing studies using methods for causal inference and highlighting gaps in our understanding of policy impacts on evolving cannabis markets.Methods: Narrative review of quasi-experimental studies for how medical cannabis laws (MCLs) and recreational cannabis laws (RCLs) affect cannabis use and use disorders, as well as the use of or harms from alcohol, opioids, and tobacco.Results: Research suggests MCLs increase adult but not adolescent cannabis use, and provisions of the laws associated with less regulated supply may increase adult cannabis use disorders. These laws may reduce some opioid-related harms, while their impacts on alcohol and tobacco use remain uncertain. Research on RCLs is just emerging, but findings suggest little impact on the prevalence of adolescent cannabis use, potential increases in college student use, and unknown effects on other substance use.Conclusions: Research on how MCLs influence cannabis use has advanced our understanding of the importance of heterogeneity in policies, populations, and market dynamics, but studies of how MCLs relate to other substance use often ignore these factors. Understanding effects of cannabis laws requires greater attention to differences in short- versus long-term effects of the laws, nuances of policies and patterns of consumption, and careful consideration of appropriate control groups.

Medical Marijuana and Marijuana Legalization.

State-level marijuana liberalization policies have been evolving for the past five decades, and yet the overall scientific evidence of the impact of these policies is widely believed to be inconclusive. In this review we summarize some of the key limitations of the studies evaluating the effects of decriminalization and medical marijuana laws on marijuana use, highlighting their inconsistencies in terms of the heterogeneity of policies, the timing of the evaluations, and the measures of use being considered. We suggest that the heterogeneity in the responsiveness of different populations to particular laws is important for interpreting the mixed findings from the literature, and we highlight the limitations of the existing literature in providing clear insights into the probable effects of marijuana legalization.

A New Era for Firearm Violence Prevention Research.

This Viewpoint discusses the expansion of firearm injury research that involves diverse disciplinary perspectives that could potentially lead to lifesaving policy innovation.

ROMPER: The RAND/USC OPTIC Method for Policy Expert Ratings.

We developed an expert panel approach for identifying expert views on the effectiveness and implementability of population-level policy interventions. ROMPER-the RAND/USC OPTIC Method for Policy Expert Ratings-involves an online, three-round, modified-Delphi process:•Experts rate and comment on policies according to domains of the Grading of Recommendations Assessment, Development and Evaluation (GRADE) Evidence-to-Decision framework.•To identify consensus on policy effectiveness and implementability, expert ratings are analyzed using the Inter-Percentile Range Adjusted for Symmetry (IPRAS) technique from the RAND/UCLA Appropriateness Method and visualized using a forest plot. To explain consensus, expert comments are analyzed using reflexive thematic analysis and reported following the Standards for Reporting Qualitative Research.•To provide actionable information for decisionmakers, each policy is summarized in a "Policy Profile" adapted from GRADEPro Evidence-to-Decision tables.We validated ROMPER in two studies that successfully recruited the targeted sample size, retained experts through all three rounds, and examined consensus on which policies are (not) effective and implementable. ROMPER protocols, materials, data, and code are openly available on the Open Science Framework with Creative Commons licensing for replication and reuse. ROMPER provides a validated, replicable, open access approach for eliciting expert views on both policy effectiveness and implementability-and for summarizing (lack of) consensus specifically for policymakers.

Household Firearm Ownership and Firearm Mortality.

Importance: Causal associations between household firearm ownership rates (HFRs) and firearm mortality rates are not well understood. Objective: To assess the population-level temporal sequencing of firearm death rates and HFRs. Design, Setting, and Participants: This cohort study used autoregressive cross-lagged models to analyze HFRs, firearm suicide rates, and firearm homicide rates in the US from 1990 to 2018. The suicide analyses included 16 demographic subgroups of adults, defined by study year, state, sex, race and ethnicity, marital status, and urbanicity. The homicide analyses consisted of adult subgroups living in urban or rural areas. Data analysis was conducted from March to December 2023. Exposures: Firearm mortality rates and HFRs. Main Outcomes and Measures: Firearm homicide and suicide rates with HFRs as the exposure, and HFR with mortality as the exposure. Results: A total of 10 416 observations of 16 demographic subgroups by state and 2-year periods were included in the suicide analyses, while 1302 observations from 2 demographic subgroups by state and 2-year period were included in the homicide analysis. At baseline, the mean (SD) rate per 100 000 population across strata was 7.46 (7.21) for firearm suicides and 3.32 (2.13) for firearm homicides. The mean (SD) baseline HFR was 36.9% (20.2%) for firearm suicides and 36.9% (14.8%) for firearm homicides. Higher HFR preceded increases in suicide rates: demographic strata with equal firearm suicide rates but which differ by 18.6 percentage points on HFR (1 SD) would be expected to have firearm suicide rates that diverged by 0.19 (95% CI, 0.15-0.23) deaths per 100 000 population per period. With these differences accumulated over 8 years, firearm suicide rates in subgroups with the highest decile HFR would be expected to have 1.93 (95% CI, 1.64-2.36) more suicides per 100 000 population than strata with lowest decile HFR, a difference of 25.7% of the overall firearm suicide rate in 2018 and 2019. Firearm suicide rates had a smaller magnitude of association with subsequent changes in HFR: strata with equal HFRs but which differ by 1 SD in firearm suicide rates had minimal subsequent change in HFRs (-0.02 [95% CI, -0.04 to 0.01] percentage points). A 1-SD difference in HFRs was associated with little difference in next-period overall firearm homicides rates (0.03 [95% CI, -0.02 to 0.08] per 100 000 population), but a 1-SD difference in homicide rates was associated with a decrease in HFR (-0.09 [95% CI, -0.16 to -0.04] percentage points). Conclusions and Relevance: This cohort study found an association between high HFRs and subsequent increases in rates of firearm suicide. In contrast, higher firearm homicide rates preceded decreases in HFRs. By demonstrating the temporal sequencing of firearm ownership and mortality, this study may help to rule out some theories of why gun ownership and firearm mortality are associated at the population level.