The Combined Relationship of Prescription Drug Monitoring Program Enactment and Medical Cannabis Laws with Chronic Pain-Related Healthcare Visits.

BACKGROUND: U.S. state electronic prescription drug monitoring programs (PDMPs) are associated with reduced opioid dispensing among people with chronic pain and may impact use of other chronic pain treatments. In states with medical cannabis laws (MCLs), patients can use cannabis for chronic pain management, reducing their need for chronic-pain related treatment visits and moderating effects of PDMP laws. OBJECTIVE: Given high rates of chronic pain among Medicaid enrollees, we examined associations between PDMP enactment in the presence or absence of MCL on chronic pain-related outpatient and emergency department (ED) visits. DESIGN: We created annual cohorts of Medicaid enrollees with chronic pain diagnoses using national Medicaid claims data from 2002-2013 and 2016. Negative binomial hurdle models produced adjusted odds ratios (aOR) for the likelihood of any chronic pain-related outpatient or ED visit and incident rate ratios (IRR) for the rate of visits among patients with ≥ 1 visit. PARTICIPANTS: Medicaid enrollees aged 18-64 years with chronic pain (N = 4,878,462). MAIN MEASURES: A 3-level state-year variable with the following categories: 1) no PDMP, 2) PDMP enactment in the absence of MCL, or 3) PDMP enactment in the presence of MCL. Healthcare codes for chronic pain-related outpatient and ED visits each year. KEY RESULTS: The sample was primarily female (67.2%), non-Hispanic White (51.2%), and ages 40-55 years (37.2%). Compared to no-PDMP states, PDMP enactment in the absence of MCL was not associated with chronic pain-related outpatient visits but PDMP enactment in the presence of MCL was associated with lower odds of chronic pain-related outpatient visits (aOR = 0.81, 95% CI:0.71-0.92). PDMP enactment was not associated with ED visits, irrespective of MCL. CONCLUSIONS: During a period of PDMP and MCL expansion, our findings suggest treatment shifts for persons with chronic pain away from outpatient settings, potentially related to increased use of cannabis for chronic pain management.

Trends in Nonfatal Overdose Rates Due to Alcohol and Prescription and Illegal Substances in Colombia, 2010‒2021.

Objectives. To examine drug overdoses in Colombia by type of substance, sex, age, and intent using data from a health surveillance system from 2010 to 2021. Methods. We characterized data by year, type of substance, and sociodemographic variables. We calculated age-adjusted overdose rates by substance type, sex, age groups, and intent. We used Poisson regression models to examine trend differences across sex and age groups. Results. Age-adjusted rates of drug overdoses increased from 8.51 to 40.52 per 100 000 during 2010 to 2021. Men, compared with women, had higher overdose rates for every substance, except for opioids and psychotropics. Drug overdose rates involving cannabis and stimulants increased steadily until 2017 but decreased afterward. Overdose rates involving psychotropic medication increased greatly during 2018 to 2021, mainly because of intentional overdoses in young women. Conclusions. Overdoses involving illegal drugs decreased in recent years in Colombia; however, the continuous increase in intentional psychotropic overdose rates highlights the need for prevention efforts to curb this trend. Health surveillance systems are an important tool that can guide overdose prevention efforts in countries with limited data resources. (Am J Public Health. 2024;114(11):1252-1260. https://doi.org/10.2105/AJPH.2024.307786).

Improving Estimates of the Prevalence of Opioid Use Disorder in the United States: Revising Keyes et al.

OBJECTIVES: The United States faces an ongoing drug overdose crisis, but accurate information on the prevalence of opioid use disorder (OUD) remains limited. A recent analysis by Keyes et al used a multiplier approach with drug poisoning mortality data to estimate OUD prevalence. Although insightful, this approach made stringent and partly inconsistent assumptions in interpreting mortality data, particularly synthetic opioid (SO)-involved and non-opioid-involved mortality. We revise that approach and resulting estimates to resolve inconsistencies and examine several alternative assumptions. METHODS: We examine 4 adjustments to Keyes and colleagues' estimation approach: (A) revising how the equations account for SO effects on mortality, (B) incorporating fentanyl prevalence data to inform estimates of SO lethality, (C) using opioid-involved drug poisoning data to estimate a plausible range for OUD prevalence, and (D) adjusting mortality data to account for underreporting of opioid involvement. RESULTS: Revising the estimation equation and SO lethality effect (adj. A and B) while using Keyes and colleagues' original assumption that people with OUD account for all fatal drug poisonings yields slightly higher estimates, with OUD population reaching 9.3 million in 2016 before declining to 7.6 million by 2019. Using only opioid-involved drug poisoning data (adj. C and D) provides a lower range, peaking at 6.4 million in 2014-2015 and declining to 3.8 million in 2019. CONCLUSIONS: The revised estimation equation presented is feasible and addresses limitations of the earlier method and hence should be used in future estimations. Alternative assumptions around drug poisoning data can also provide a plausible range of estimates for OUD population.

A state-level history of opioid overdose deaths in the United States: 1999-2021.

We examined a natural history of opioid overdose deaths from 1999-2021 in the United States to describe state-level spatio-temporal heterogeneity in the waves of the epidemic. We obtained overdose death counts by state from 1999-2021, categorized as involving prescription opioids, heroin, synthetic opioids, or unspecified drugs. We developed a Bayesian multivariate multiple change point model to flexibly estimate the timing and magnitude of state-specific changes in death rates involving each drug type. We found substantial variability around the timing and severity of each wave across states. The first wave of prescription-involved deaths started between 1999 and 2005, the second wave of heroin-involved deaths started between 2010 and 2014, and the third wave of synthetic opioid-involved deaths started between 2014 and 2021. The severity of the second and third waves was greater in states in the eastern half of the country. Our study highlights state-level variation in the timing and severity of the waves of the opioid epidemic by presenting a 23-year natural history of opioid overdose mortality in the United States. While reinforcing the general notion of three waves, we find that states did not uniformly experience the impacts of each wave.

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.

Pain Management Treatments and Opioid Use Disorder Risk in Medicaid Patients.

INTRODUCTION: People with chronic pain are at increased risk of opioid misuse. Less is known about the unique risk conferred by each pain management treatment, as treatments are typically implemented together, confounding their independent effects. This study estimated the extent to which pain management treatments were associated with risk of opioid use disorder (OUD) for those with chronic pain, controlling for baseline demographic and clinical confounding variables and holding other pain management treatments at their observed levels. METHODS: Data were analyzed in 2024 from 2 chronic pain subgroups within a cohort of non-pregnant Medicaid patients aged 35-64 years, 2016-2019, from 25 states: those with (1) chronic pain and physical disability (CPPD) (N=6,133) or (2) chronic pain without disability (CP) (N=67,438). Nine pain management treatments were considered: prescription opioid (1) dose and (2) duration; (3) number of opioid prescribers; opioid co-prescription with (4) benzo- diazepines, (5) muscle relaxants, and (6) gabapentinoids; (7) nonopioid pain prescription, (8) physical therapy, and (9) other pain treatment modality. The outcome was OUD risk. RESULTS: Having opioids co-prescribed with gabapentin or benzodiazepine was statistically significantly associated with a 37-45% increased OUD risk for the CP subgroup. Opioid dose and duration also were significantly associated with increased OUD risk in this subgroup. Physical therapy was significantly associated with an 18% decreased risk of OUD in the CP subgroup. DISCUSSION: Coprescription of opioids with either gabapentin or benzodiazepines may substantially increase OUD risk. More positively, physical therapy may be a relatively accessible and safe pain management strategy.

A simple heuristic for allocating opioid settlement funding to reduce overdose mortality in the United States.

As resolution for opioid-related claims and litigation against pharmaceutical manufacturers and other stakeholders, state and local governments are newly eligible for millions of dollars of settlement funding to address the overdose crisis in the United States. To inform effective use of opioid settlement funds, we propose a simple framework that highlights the principal determinants of overdose mortality: the number of people at risk of overdose each year, the average annual number of overdoses per person at risk, and the average probability of death per overdose event. We assert that the annual number of overdose deaths is a function of these three determinants, all of which can be modified through public health intervention. Our proposed heuristic depicts how each of these drivers of drug-related mortality - and the corresponding interventions designed to address each term - operate both in isolation and in conjunction. We intend for this framework to be used by policymakers as a tool for identifying and evaluating public health interventions and funding priorities that will most effectively address the structural forces shaping the overdose crisis and reduce overdose deaths.

Estimation of the prevalence of opioid misuse in New York State counties, 2007-2018: a bayesian spatiotemporal abundance model approach.

An important challenge to addressing the opioid overdose crisis is the lack of information on the size of the population of people who misuse opioids (PWMO) in local areas. This estimate is needed for better resource allocation, estimation of treatment and overdose outcome rates using appropriate denominators (ie, the population at risk), and proper evaluation of intervention effects. In this study, we used a bayesian hierarchical spatiotemporal integrated abundance model that integrates multiple types of county-level surveillance outcome data, state-level information on opioid misuse, and covariates to estimate the latent (hidden) numbers of PWMO and latent prevalence of opioid misuse across New York State counties (2007-2018). The model assumes that each opioid-related outcome reflects a partial count of the number of PWMO, and it leverages these multiple sources of data to circumvent limitations of parameter estimation associated with other types of abundance models. Model estimates showed a reduction in the prevalence of PWMO during the study period, with important spatial and temporal variability. The model also provided county-level estimates of rates of treatment and opioid overdose using the numbers of PWMO as denominators. This modeling approach can identify the sizes of hidden populations to guide public health efforts in confronting the opioid overdose crisis across local areas. This article is part of a Special Collection on Mental Health.