The role of prescription opioid and cannabis supply policies on opioid overdose deaths.

Mandatory prescription drug monitoring programs and cannabis legalization have been hypothesized to reduce overdose deaths. We examined associations between prescription monitoring programs with access mandates ("must-query PDMPs"), legalization of medical and recreational cannabis supply, and opioid overdose deaths in United States counties in 2013-2020. Using data on overdose deaths from the National Vital Statistics System, we fit Bayesian spatiotemporal models to estimate risk differences and 95% credible intervals (CrI) in county-level opioid overdose deaths associated with enactment of these state policies. Must-query PDMPs were independently associated with on average 0.8 (95% CrI: 0.5, 1.0) additional opioid-involved overdose deaths per 100,000 person-years. Legal cannabis supply was not independently associated with opioid overdose deaths in this time period. Must-query PDMPs enacted in the presence of legal (medical or recreational) cannabis supply were associated with 0.7 (95% CrI: 0.4, 0.9) more opioid-involved deaths, relative to must-query PDMPs without any legal cannabis supply. In a time when overdoses are driven mostly by non-prescribed opioids, stricter opioid prescribing policies and more expansive cannabis legalization were not associated with reduced overdose death rates.

Utilization and disparities in medication treatment for opioid use disorder among patients with comorbid opioid use disorder and chronic pain during the COVID-19 pandemic.

BACKGROUND: The COVID-19 pandemic's impact on utilization of medications for opioid use disorder (MOUD) among patients with opioid use disorder (OUD) and chronic pain is unclear. METHODS: We analyzed New York State (NYS) Medicaid claims from pre-pandemic (August 2019-February 2020) and pandemic (March 2020-December 2020) periods for beneficiaries with and without chronic pain. We calculated monthly proportions of patients with OUD diagnoses in 6-month-lookback windows utilizing MOUD and proportions of treatment-naïve patients initiating MOUD. We used interrupted time series to assess changes in MOUD utilization and initiation rates by medication type and by race/ethnicity. RESULTS: Among 20,785 patients with OUD and chronic pain, 49.3% utilized MOUD (versus 60.3% without chronic pain). The pandemic did not affect utilization in either group but briefly disrupted initiation among patients with chronic pain (ß=-0.009; 95% CI [-0.015, -0.002]). Overall MOUD utilization was not affected by the pandemic for any race/ethnicity but opioid treatment program (OTP) utilization was briefly disrupted for non-Hispanic Black individuals (ß=-0.007 [-0.013, -0.001]). The pandemic disrupted overall MOUD initiation in non-Hispanic Black (ß=-0.007 [-0.012, -0.002]) and Hispanic individuals (ß=-0.010 [-0.019, -0.001]). CONCLUSIONS: Adults with chronic pain who were enrolled in NYS Medicaid before the COVID-19 pandemic had lower MOUD utilization than those without chronic pain. MOUD initiation was briefly disrupted, with disparities especially in racial/ethnic minority groups. Flexible MOUD policy initiatives may have maintained overall treatment utilization, but disparities in initiation and care continuity remain for patients with chronic pain, and particularly for racial/ethnic minoritized subgroups.

Risks of opioid overdose among New York State Medicaid recipients with chronic pain before and during the COVID-19 pandemic.

OBJECTIVE: The COVID-19 pandemic contributed to healthcare disruptions for patients with chronic pain. Following initial disruptions, national policies were enacted to expand access to long-term opioid therapy (LTOT) for chronic pain and opioid use disorder (OUD) treatment services, which may have modified risk of opioid overdose. We examined associations between LTOT and/or OUD with fatal and non-fatal opioid overdoses, and whether the pandemic moderated overdose risk in these groups. METHODS: We analyzed New York State Medicaid claims data (3/1/2019-12/31/20) of patients with chronic pain (N = 236,391). We used generalized estimating equations models to assess associations between LTOT and/or OUD (neither LTOT or OUD [ref], LTOT only, OUD only, and LTOT and OUD) and the pandemic (03/2020-12/2020) with opioid overdose. RESULTS: The pandemic did not significantly (ns) affect opioid overdose among patients with LTOT and/or OUD. While patients with LTOT (vs. no LTOT) had a slight increase in opioid overdose during the pandemic (pre-pandemic: aOR:1.65, 95% CI:1.05, 2.57; pandemic: aOR:2.43, CI:1.75,3.37, ns), patients with OUD had a slightly attenuated odds of overdose during the pandemic (pre-pandemic: aOR:5.65, CI:4.73, 6.75; pandemic: aOR:5.16, CI:4.33, 6.14, ns). Patients with both LTOT and OUD also experienced a slightly reduced odds of opioid overdose during the pandemic (pre-pandemic: aOR:5.82, CI:3.58, 9.44; pandemic: aOR:3.70, CI:2.11, 6.50, ns). CONCLUSIONS: Findings demonstrated no significant effect of the pandemic on opioid overdose among people with chronic pain and LTOT and/or OUD, suggesting pandemic policies expanding access to chronic pain and OUD treatment services may have mitigated the risk of opioid overdose.

Evaluating chronic pain as a risk factor for COVID-19 complications among New York State Medicaid beneficiaries: a retrospective claims analysis.

OBJECTIVE: To assess whether chronic pain increases the risk of COVID-19 complications and whether opioid use disorder (OUD) differentiates this risk among New York State Medicaid beneficiaries. DESIGN, SETTING, AND SUBJECTS: This was a retrospective cohort study of New York State Medicaid claims data. We evaluated Medicaid claims from March 2019 through December 2020 to determine whether chronic pain increased the risk of COVID-19 emergency department (ED) visits, hospitalizations, and complications and whether this relationship differed by OUD status. We included beneficiaries 18-64 years of age with 10 months of prior enrollment. Patients with chronic pain were propensity score-matched to those without chronic pain on demographics, utilization, and comorbidities to control for confounders and were stratified by OUD. Complementary log-log regressions estimated hazard ratios (HRs) of COVID-19 ED visits and hospitalizations; logistic regressions estimated odds ratios (ORs) of hospital complications and readmissions within 0-30, 31-60, and 61-90 days. RESULTS: Among 773 880 adults, chronic pain was associated with greater hazards of COVID-related ED visits (HR = 1.22 [95% CI: 1.16-1.29]) and hospitalizations (HR = 1.19 [95% CI: 1.12-1.27]). Patients with chronic pain and OUD had even greater hazards of hospitalization (HR = 1.25 [95% CI: 1.07-1.47]) and increased odds of hepatic- and cardiac-related events (OR = 1.74 [95% CI: 1.10-2.74]). CONCLUSIONS: Chronic pain increased the risk of COVID-19 ED visits and hospitalizations. Presence of OUD further increased the risk of COVID-19 hospitalizations and the odds of hepatic- and cardiac-related events. Results highlight intersecting risks among a vulnerable population and can inform tailored COVID-19 management.

Naloxone expansion is not associated with increases in adolescent heroin use and injection drug use: Evidence from 44 US states.

BACKGROUND: Naloxone distribution is central to ongoing efforts to address the opioid overdose crisis. Some critics contend that naloxone expansion may inadvertently promote high-risk substance use behaviors among adolescents, but this question has not been directly investigated. METHODS: We examined relationships between naloxone access laws and pharmacy naloxone distribution with lifetime heroin and injection drug use (IDU), 2007-2019. Models generating adjusted odds ratios (aOR) and 95% confidence intervals (CI) included year and state fixed effects, controlled for demographics and sources of variation in opioid environments (e.g., fentanyl penetration), as well as additional policies expected to impact substance use (e.g., prescription drug monitoring). Exploratory and sensitivity analyses further examined naloxone law provisions (e.g., third-party prescribing) and applied e-value testing to assess vulnerability to unmeasured confounding. RESULTS: Adoption of any naloxone law was not associated with changes in adolescent lifetime heroin or IDU. For pharmacy dispensing, we observed a small decrease in heroin use (aOR: 0.95 [CI: 0.92, 0.99]) and a small increase in IDU (aOR: 1.07 [CI: 1.02, 1.11]). Exploratory analyses of law provisions suggested that third-party prescribing (aOR: 0.80, [CI: 0.66, 0.96]) and non-patient-specific dispensing models (aOR: 0.78, [CI: 0.61, 0.99]) were associated with decreased heroin use but not decreased IDU. Small e-values associated with the pharmacy dispensing and provision estimates indicate that unmeasured confounding may explain observed findings. CONCLUSION: Naloxone access laws and pharmacy naloxone distribution were more consistently associated with decreases rather than increases in lifetime heroin and IDU among adolescents. Our findings therefore do not support concerns that naloxone access promotes high-risk adolescent substance use behaviors. As of 2019, all US states have adopted legislation to improve naloxone access and facilitate use. However, further removal of adolescent naloxone access barriers is an important priority given that the opioid epidemic continues to affect people of all ages.

A modified Delphi process to identify experts' perceptions of the most beneficial and harmful laws to reduce opioid-related harm.

BACKGROUND: States have enacted multiple types of laws, with a variety of constituent provisions, in response to the opioid epidemic, often simultaneously. This temporal proximity and variation in state-to-state operationalization has resulted in significant challenges for empirical research on their effects. Thus, expert consensus can be helpful to classify laws and their provisions by their degree of helpfulness and impact. METHODS: We conducted a four-stage modified policy Delphi process to identify the top 10 most helpful and 5 most harmful provisions from eight opioid-related laws. This iterative consultation with six types of opioid experts included a preliminary focus group (n=12), two consecutive surveys (n=56 and n=40, respectively), and a final focus group feedback session (n=5). RESULTS: On a scale of very harmful (0) to very helpful (4), overdose Good Samaritan laws received the highest average helpfulness rating (3.62, 95% CI: 3.48-3.75), followed by naloxone access laws (3.37, 95% CI: 3.22-3.51), and pain management clinic laws (3.08, 95% CI: 2.89-3.26). Drug-induced homicide (DIH) laws were rated the most harmful (0.88, 95% CI: 0.66-1.11). Impact ratings aligned similarly, although Medicaid laws received the second highest overall impact rating (3.71, 95% CI: 3.45, 3.97). The two most helpful provisions were naloxone standing orders (3.94, 95% CI: 3.86-4.02) and Medicaid coverage of medications for opioid use disorder (MOUD) (3.89, 95% CI: 3.82). Mandatory minimum DIH laws were the most harmful provision (0.73, 95% CI 0.53-0.93); followed by requiring prior authorization for Medicaid coverage of MOUD (1.00 95% CI: 0.72-1.27). CONCLUSION: Overall, experts rated laws and provisions that facilitated harm reduction efforts and access to MOUD as most helpful. Laws and provisions rated as most harmful criminalized substance use and placed restrictions on access to MOUD. These ratings provide a foundation for evaluating the overall overdose policy environment for each state.

Spatiotemporal Analysis of the Association Between Pain Management Clinic Laws and Opioid Prescribing and Overdose Deaths.

Pain management clinic (PMC) laws were enacted by 12 states to promote appropriate opioid prescribing, but their impact is inadequately understood. We analyzed county-level opioid overdose deaths (National Vital Statistics System) and patients filling long-duration (≥30 day) or high-dose (≥90 morphine milligram equivalents per day) opioid prescriptions (IQVIA, Inc.) in the United States in 2010-2018. We fitted Besag-York-Mollié spatiotemporal models to estimate annual relative rates (RRs) of overdose and prevalence ratios (PRs) of high-risk prescribing associated with any PMC law and 3 provisions: payment restrictions, site inspections, and criminal penalties. Laws with criminal penalties were significantly associated with reduced PRs of long-duration and high-dose opioid prescriptions (adjusted PR = 0.82, 95% credible interval (CrI): 0.82, 0.82, and adjusted PR = 0.73, 95% CI: 0.73, 0.74 respectively) and reduced RRs of total and natural/semisynthetic opioid overdoses (adjusted RR = 0.86, 95% CrI: 0.80, 0.92, and adjusted RR = 0.84, and 95% CrI: 0.77, 0.92, respectively). Conversely, PMC laws were associated with increased relative rates of synthetic opioid and heroin overdose deaths, especially criminal penalties (adjusted RR = 1.83, 95% CrI: 1.59, 2.11, and adjusted RR = 2.59, 95% CrI: 2.22, 3.02, respectively). Findings suggest that laws with criminal penalties were associated with intended reductions in high-risk opioid prescribing and some opioid overdoses but raise concerns regarding unintended consequences on heroin/synthetic overdoses.

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

BACKGROUND: Hundreds of laws aimed at reducing inappropriate prescription opioid dispensing have been implemented in the United States, yet heterogeneity in provisions and their simultaneous implementation have complicated evaluation of impacts. We apply a hypothesis-generating, multistage, machine-learning approach to identify salient law provisions and combinations associated with dispensing rates to test in future research. METHODS: Using 162 prescription opioid law provisions capturing prescription drug monitoring program (PDMP) access, reporting and administration features, pain management clinic provisions, and prescription opioid limits, we used regularization approaches and random forest models to identify laws most predictive of county-level and high-dose dispensing. We stratified analyses by overdose epidemic phases-the prescription opioid phase (2006-2009), heroin phase (2010-2012), and fentanyl phase (2013-2016)-to further explore pattern shifts over time. RESULTS: PDMP patient data access provisions most consistently predicted high-dispensing and high-dose dispensing counties. Pain management clinic-related provisions did not generally predict dispensing measures in the prescription opioid phase but became more discriminant of high dispensing and high-dose dispensing counties over time, especially in the fentanyl period. Predictive performance across models was poor, suggesting prescription opioid laws alone do not strongly predict dispensing. CONCLUSIONS: Our systematic analysis of 162 law provisions identified patient data access and several pain management clinic provisions as predictive of county prescription opioid dispensing patterns. Future research employing other types of study designs is needed to test these provisions' causal relationships with inappropriate dispensing and to examine potential interactions between PDMP access and pain management clinic provisions. See video abstract at, http://links.lww.com/EDE/B861.

Predicting the Future Course of Opioid Overdose Mortality: An Example From Two US States.

BACKGROUND: The rapid growth of opioid abuse and the related mortality across the United States has spurred the development of predictive models for the allocation of public health resources. These models should characterize heterogeneous growth across states using a drug epidemic framework that enables assessments of epidemic onset, rates of growth, and limited capacities for epidemic growth. METHODS: We used opioid overdose mortality data for 146 North and South Carolina counties from 2001 through 2014 to compare the retrodictive and predictive performance of a logistic growth model that parameterizes onsets, growth, and carrying capacity within a traditional Bayesian Poisson space-time model. RESULTS: In fitting the models to past data, the performance of the logistic growth model was superior to the standard Bayesian Poisson space-time model (deviance information criterion: 8,088 vs. 8,256), with reduced spatial and independent errors. Predictively, the logistic model more accurately estimated fatality rates 1, 2, and 3 years in the future (root mean squared error medians were lower for 95.7% of counties from 2012 to 2014). Capacity limits were higher in counties with greater population size, percent population age 45-64, and percent white population. Epidemic onset was associated with greater same-year and past-year incidence of overdose hospitalizations. CONCLUSION: Growth in annual rates of opioid fatalities was capacity limited, heterogeneous across counties, and spatially correlated, requiring spatial epidemic models for the accurate and reliable prediction of future outcomes related to opioid abuse. Indicators of risk are identifiable and can be used to predict future mortality outcomes.

Association of Recreational Cannabis Laws in Colorado and Washington State With Changes in Traffic Fatalities, 2005-2017.

Importance: An important consequence of cannabis legalization is the potential increase in the number of cannabis-impaired drivers on roads, which may result in higher rates of traffic-related injuries and fatalities. To date, limited information about the effects of recreational cannabis laws (RCLs) on traffic fatalities is available. Objective: To estimate the extent to which the implementation of RCLs is associated with traffic fatalities in Colorado and Washington State. Design, Setting, and Participants: This ecological study used a synthetic control approach to examine the association between RCLs and changes in traffic fatalities in Colorado and Washington State in the post-RCL period (2014-2017). Traffic fatalities data were obtained from the Fatality Analysis Reporting System from January 1, 2005, to December 31, 2017. Data from Colorado and Washington State were compared with synthetic controls. Data were analyzed from January 1, 2005, to December 31, 2017. Main Outcome(s) and Measures: The primary outcome was the rate of traffic fatalities. Sensitivity analyses were performed (1) excluding neighboring states, (2) excluding states without medical cannabis laws (MCLs), and (3) using the enactment date of RCLs to define pre-RCL and post-RCL periods instead of the effective date. Results: Implementation of RCLs was associated with increases in traffic fatalities in Colorado but not in Washington State. The difference between Colorado and its synthetic control in the post-RCL period was 1.46 deaths per 1 billion vehicle miles traveled (VMT) per year (an estimated equivalent of 75 excess fatalities per year; probability = 0.047). The difference between Washington State and its synthetic control was 0.08 deaths per 1 billion VMT per year (probability = 0.674). Results were robust in most sensitivity analyses. The difference between Colorado and synthetic Colorado was 1.84 fatalities per 1 billion VMT per year (94 excess deaths per year; probability = 0.055) after excluding neighboring states and 2.16 fatalities per 1 billion VMT per year (111 excess deaths per year; probability = 0.063) after excluding states without MCLs. The effect was smaller when using the enactment date (24 excess deaths per year; probability = 0.116). Conclusions and Relevance: This study found evidence of an increase in traffic fatalities after the implementation of RCLs in Colorado but not in Washington State. Differences in how RCLs were implemented (eg, density of recreational cannabis stores), out-of-state cannabis tourism, and local factors may explain the different results. These findings highlight the importance of RCLs as a factor that may increase traffic fatalities and call for the identification of policies and enforcement strategies that can help prevent unintended consequences of cannabis legalization.

Trends in school-age pedestrian and pedalcyclist crashes in the USA: 26 states, 2000-2014.

BACKGROUND: Despite substantial progress, motor vehicle crashes remain a leading killer of US children. Previously, we documented significant positive impacts of Safe Routes to School interventions on school-age pedestrian and pedalcyclist crashes. OBJECTIVE: To expand our analysis of US trends in motor vehicle crashes involving school-age pedestrians and pedalcyclists, exploring heterogeneity by age and geography. METHODS: We obtained recent police-reported crash data from 26 states, calculating population rates of pedestrian and pedalcyclist crashes, crash fatality rates and pedestrian commuter-adjusted crash rates ('pedestrian danger index') for school-age children as compared with other age groups. We estimated national and statewide trends by age, injury status, day and travel hour using hierarchical linear modeling. RESULTS: School-age children accounted for nearly one in three pedestrians and one in two pedalcyclists struck in motor vehicle crashes from 2000 to 2014. Yet, the rates of these crashes declined 40% and 53%, respectively, over that time, on average, even as adult rates rose. Average crash rates varied geographically from 24.4 to 100.8 pedestrians and 15.6 to 56.7 pedalcyclists struck per 100 000 youth. Crash rates and fatality rates were inversely correlated. CONCLUSIONS: Despite recent increases in adult pedestrian crashes, school-age and younger pedestrians experienced ongoing declines in motor vehicle crashes through 2014 across the USA. There was no evidence of displacement in crash severity; declines were observed in all outcomes. The growing body of state crash data resources can present analytic challenges but also provides unique insights into national and local pedestrian crash trends for all crash outcomes.

Development and Validation of a Google Street View Pedestrian Safety Audit Tool.

BACKGROUND: Assessing aspects of intersections that may affect the risk of pedestrian injury is critical to developing child pedestrian injury prevention strategies, but visiting intersections to inspect them is costly and time-consuming. Several research teams have validated the use of Google Street View to conduct virtual neighborhood audits that remove the need for field teams to conduct in-person audits. METHODS: We developed a 38-item virtual audit instrument to assess intersections for pedestrian injury risk and tested it on intersections within 700 m of 26 schools in New York City using the Computer-assisted Neighborhood Visual Assessment System (CANVAS) with Google Street View imagery. RESULTS: Six trained auditors tested this instrument for inter-rater reliability on 111 randomly selected intersections and for test-retest reliability on 264 other intersections. Inter-rater kappa scores ranged from -0.01 to 0.92, with nearly half falling above 0.41, the conventional threshold for moderate agreement. Test-retest kappa scores were slightly higher than but highly correlated with inter-rater scores (Spearman rho = 0.83). Items that were highly reliable included the presence of a pedestrian signal (K = 0.92), presence of an overhead structure such as an elevated train or a highway (K = 0.81), and intersection complexity (K = 0.76). CONCLUSIONS: Built environment features of intersections relevant to pedestrian safety can be reliably measured using a virtual audit protocol implemented via CANVAS and Google Street View.

Bayesian hierarchical spatial models: Implementing the Besag York Mollié model in stan.

This report presents a new implementation of the Besag-York-Mollié (BYM) model in Stan, a probabilistic programming platform which does full Bayesian inference using Hamiltonian Monte Carlo (HMC). We review the spatial auto-correlation models used for areal data and disease risk mapping, and describe the corresponding Stan implementations. We also present a case study using Stan to fit a BYM model for motor vehicle crashes injuring school-age pedestrians in New York City from 2005 to 2014 localized to census tracts. Stan efficiently fit our multivariable BYM model having a large number of observations (n=2095 census tracts) with small outcome counts < 10 in the majority of tracts. Our findings reinforced that neighborhood income and social fragmentation are significant correlates of school-age pedestrian injuries. We also observed that nationally-available census tract estimates of commuting methods may serve as a useful indicator of underlying pedestrian densities.

Pediatric emergency department visits for pedestrian and bicyclist injuries in the US.

BACKGROUND: Despite reductions in youth pedestrian and bicyclist deaths over the past two decades, these injuries remain a substantial cause of morbidity and mortality for children and adolescents. There is a need for additional information on non-fatal pediatric pedestrian injuries and the role of traumatic brain injury (TBI), a leading cause of acquired disability. METHODS: Using a multi-year national sample of emergency department (ED) records, we estimated annual motorized-vehicle related pediatric pedestrian and bicyclist (i.e. pedalcyclist) injury rates by age and region. We modeled in-hospital fatality risk controlling for age, gender, injury severity, TBI, and trauma center status. RESULTS: ED visits for pediatric pedestrian injuries declined 19.3% (95% CI 16.8, 21.8) from 2006 to 2012, with the largest decreases in 5-to-9 year olds and 10-to-14 year olds. Case fatality rates also declined 14.0%. There was no significant change in bicyclist injury rates. TBI was implicated in 6.7% (95% CI 6.3, 7.1) of all pedestrian and bicyclist injuries and 55.5% (95% CI 27.9, 83.1) of fatalities. Pedestrian ED visits were more likely to be fatal than bicyclist injuries (aOR = 2.4, 95% CI 2.3, 2.6), with significant additive interaction between pedestrian status and TBI. CONCLUSIONS: TBI in young pedestrian ED patients was associated with a higher risk of mortality compared to cyclists. There is a role for concurrent clinical focus on TBI recovery alongside ongoing efforts to mitigate and prevent motor vehicle crashes with pedestrians and bicyclists. Differences between youth pedestrian and cycling injury trends merit further exploration and localized analyses, with respect to behavior patterns and interventions. ED data captures a substantially larger number of pediatric pedestrian injuries compared to crash reports and can play a role in those analyses.

Impact of Mandatory Carbon Monoxide Alarms: An Investigation of the Effects on Detection and Poisoning Rates in New York City.

OBJECTIVES: We sought to evaluate the impact of New York City's (NYC's) 2004 carbon monoxide (CO) alarm legislation on CO incident detection and poisoning rates. METHODS: We compared CO poisoning deaths, hospitalizations, exposures reported to Poison Control, and fire department investigations, before and after the law for 2000 to 2010. Use of CO alarms was assessed in the 2009 NYC Community Health Survey. RESULTS: Investigations that found indoor CO levels greater than 9 parts per million increased nearly 7-fold after the law (P < .001). There were nonsignificant decreases in unintentional, nonfire-related CO poisoning hospitalization rates (P = .114) and death rates (P = .216). After we controlled for ambient temperature, the law's effect on hospitalizations remained nonsignificantly protective (incidence rate ratio = 0.747; 95% confidence interval = 0.520, 1.074). By 2009, 83% of NYC residents reported having CO alarms; only 54% also recently tested or replaced their batteries. CONCLUSIONS: Mandating CO alarms significantly increased the detection of potentially hazardous CO levels in NYC homes. Small numbers and detection bias might have limited the discovery of significant decreases in poisoning outcomes. Investigation of individual poisoning circumstances since the law might elucidate remaining gaps in awareness and proper use of CO alarms.