Modeling the evolution of the US opioid crisis for national policy development

The opioid crisis is a major public health challenge in the United States, killing about 70,000 people in 2020 alone. Long delays and feedbacks between policy actions and their effects on drug-use behavior create dynamic complexity, complicating policy decision-making. In 2017, the National Academies of Sciences, Engineering, and Medicine called for a quantitative systems model to help understand and address this complexity and guide policy decisions. Here, we present SOURCE (Simulation of Opioid Use, Response, Consequences, and Effects), a dynamic simulation model developed in response to that charge. SOURCE tracks the US population aged ≥12 y through the stages of prescription and illicit opioid (e.g., heroin, illicit fentanyl) misuse and use disorder, addiction treatment, remission, and overdose death. Using data spanning from 1999 to 2020, we highlight how risks of drug use initiation and overdose have evolved in response to essential endogenous feedback mechanisms, including: 1) social influence on drug use initiation and escalation among people who use opioids; 2) risk perception and response based on overdose mortality, influencing potential new initiates; and 3) capacity limits on treatment engagement; as well as other drivers, such as 4) supply-side changes in prescription opioid and heroin availability; and 5) the competing influences of illicit fentanyl and overdose death prevention efforts. Our estimates yield a more nuanced understanding of the historical trajectory of the crisis, providing a basis for projecting future scenarios and informing policy planning.

The effect of state laws designed to prevent nonmedical prescription opioid use on overdose deaths and treatment

Nonmedical use of prescription opioids has reached epidemic levels in the United States and globally. In response, federal, state, and local governments are taking actions to address substantial increases in prescription opioid addiction and its associated harms. This study examines the effect of two state laws specifically designed to curtail access to prescription opioids to nonmedical users: pain management clinic and doctor shopping laws. We use administrative data on overdose deaths and admissions to specialty substance use disorder treatment coupled with a differences-in-differences design. Our findings suggest that both pain management clinic and doctor shopping laws have the potential to reduce prescription opioid overdose deaths. Moreover, doctor shopping laws appear to reduce prescription opioid treatment admissions. As many states have adopted these laws in recent years, the full effects of the laws may not yet be realized. Future research using more postlaw passage data should reevaluate the effectiveness of these laws.

Preventing Overdose Using Information and Data from the Environment (PROVIDENT): protocol for a randomized, population-based, community intervention trial

BACKGROUND AND AIMS

In light of the accelerating drug overdose epidemic in North America, new strategies are needed to identify communities most at risk to prioritize geographically the existing public health resources (e.g. street outreach, naloxone distribution efforts). We aimed to develop PROVIDENT (Preventing Overdose using Information and Data from the Environment), a machine learning-based forecasting tool to predict future overdose deaths at the census block group (i.e. neighbourhood) level.

DESIGN

Randomized, population-based, community intervention trial.

SETTING

Rhode Island, USA.

PARTICIPANTS

All people who reside in Rhode Island during the study period may contribute data to either the model or the trial outcomes.

INTERVENTION

Each of the state's 39 municipalities will be randomized to the intervention (PROVIDENT) or comparator condition. An interactive, web-based tool will be developed to visualize the PROVIDENT model predictions. Municipalities assigned to the treatment arm will receive neighbourhood risk predictions from the PROVIDENT model, and state agencies and community-based organizations will direct resources to neighbourhoods identified as high risk. Municipalities assigned to the control arm will continue to receive surveillance information and overdose prevention resources, but they will not receive neighbourhood risk predictions.

MEASUREMENTS

The primary outcome is the municipal-level rate of fatal and non-fatal drug overdoses. Fatal overdoses will be defined as unintentional drug-related death; non-fatal overdoses will be defined as an emergency department visit for a suspected overdose reported through the state's syndromic surveillance system. Intervention efficacy will be assessed using Poisson or negative binomial regression to estimate incidence rate ratios comparing fatal and non-fatal overdose rates in treatment vs. control municipalities.

COMMENTS

The findings will inform the utility of predictive modelling as a tool to improve public health decision-making and inform resource allocation to communities that should be prioritized for prevention, treatment, recovery and overdose rescue services.

Understanding Regional Patterns of Overdose Deaths Related to Opioids and Psychostimulants

BACKGROUND

As overdose rates increase for multiple substances, policymakers need to identify geographic patterns of substance-specific deaths. In this study, we describe county-level opioid and psychostimulant overdose patterns and how they correlate with county-level social vulnerability measures.

METHODS

A cross-sectional observational study, we used nationwide 2016-2018 restricted access Centers for Disease Prevention and Control county-level mortality files for 1,024 counties. We estimated quartiles of opioid and psychostimulant overdose mortality and provided estimates of their association with county-level Social Vulnerability Index (SVI) percentile.

RESULTS

There was high opioid and psychostimulant overdose mortality in the Middle Atlantic, South Atlantic, East North Central, and Mountain regions. The Central US had the lowest opioid and psychostimulant overdose mortality rates. Counties with higher SVI scores (i.e. higher social vulnerability) were significantly more likely to experience high opioid and high psychostimulant overdose (high-high) mortality. A 10-percentile increase in SVI score was associated with a 3.1 percentage point increase in the likelihood of being a high-high county (p < 0.001) in unadjusted models and a 1.5 percentage point increase (p < 0.05) in models adjusting for region.

CONCLUSION

Our results illustrated the heterogenous geographic distribution of the growing concurrent opioid and psychostimulant overdose crisis. The substantial regional variation we identified highlights the need for local data to guide policymaking and treatment planning. The association of opioid-psychostimulant overdose mortality with social vulnerability demonstrates the critical need in impacted counties for tailored treatment that addresses the complex medical and social needs of people who use both opioids and psychostimulants.

Medicaid expansion and opioid deaths

The United States is in the midst of an opioid epidemic, and drug overdose deaths are becoming a leading cause of death. Meanwhile, in 2010, the United States passed comprehensive health care reform providing access to care for millions of individuals who previously lacked care. Part of the new access came from expanding Medicaid, the insurance program for low-income individuals. Expanding Medicaid was optional for states. Those individuals living in expansion states gained prescription drug coverage and hence more access to opioid pain-relievers that are known to be addictive. However, they also gained access to medication-assisted treatment for addiction. This paper uses a difference-in-differences approach and state-level data from 2010 to 2017 to compare opioid death rates in expansion and non-expansion states to determine if Medicaid expansion was a potential cause of rising opioid deaths. We find no evidence that Medicaid expansion is related to opioid deaths.

Opioid overdose risk during and after drug treatment for heroin dependence: An incidence density case-control study nested in the VEdeTTE cohort

INTRODUCTION AND AIMS

To corroborate protective effects of a range of drug treatment modalities against overdose mortality risk.

DESIGN AND METHODS

Nested case-control study, with incidence density sampling, selecting controls retrospectively at each case event. Cases and controls came from a sub-cohort of opioid-dependent patients (n = 4444) from two Italian regions (Lazio and Piedmont). From 1998 to 2005, there were 91 overdose deaths (cases) matched to 352 controls. The primary outcome was overdose mortality and the primary exposure was drug treatment: opioid agonist treatment (OAT), opioid detoxification, residential community, psychosocial and other pharmacological treatment. Conditional logistic regression models generated intervention effects comparing mortality risk in and out of treatment, adjusting for confounding variables.

RESULTS

Overall, drug treatment reduced overdose mortality risk by 80% [adjusted odds ratio (AOR) 0.18, 95% confidence interval (CI) 0.10-0.33, P < 0.001] compared to being out of treatment. There was a particularly strong protective effect of OAT on overdose mortality (AOR 0.08, 95% CI 0.03-0.23, P < 0.001) compared to being out of treatment. There was evidence of a substantially elevated risk of overdose in the first month of leaving treatment (AOR 23.50, 95% CI 7.84-70.19, P < 0.001) compared to being in treatment.

DISCUSSION AND CONCLUSIONS

The nested case-control design strengthened earlier findings that OAT in Italy has strong protective effects on overdose mortality risk, much stronger than has been previously seen in other Western European settings.

Inequities in naloxone administration among fatal overdose decedents by race and ethnicity in Pennsylvania, 2019-21

AIMS

The aim of this study was to characterize the circumstances of drug overdose deaths and determine whether naloxone administration differed by overdose decedent race and ethnicity.

DESIGN AND SETTING

Analysis of data on unintentional and undetermined intent drug overdose deaths in Pennsylvania (2019-21) was collected from death certificates and the State Unintentional Drug Overdose Reporting System. Multivariable logistic regression models were adjusted for overdose death circumstances and the odds of naloxone administration were estimated by race/ethnicity and year.

CASES

The analytical sample included 3386 fatal overdose decedents in 2019, 3864 in 2020 and 3816 in 2021.

MEASUREMENTS

Evidence of naloxone administration (yes/no) was defined using scene evidence and toxicology reports from coroner and medical examiner records, while race/ethnicity (Hispanic, non-Hispanic Black, non-Hispanic White) was based on the death certificate.

FINDINGS

In the analytic sample, overdose death rates were the highest among Black people and increased over time (rate per 10 000 population, 2019: 4.3; 2020: 6.1; 2021: 6.5); rates were lowest among White people and remained constant over time (rate per 10 000 population, 2019: 2.6; 2020: 2.7; 2021: 2.6). Throughout all years, Black decedents had approximately 40-50% lower odds of naloxone administration compared with White decedents as referent [2019: odds ratio (OR) = 0.7, 95% confidence interval (CI) = 0.5-0.9; 2020: OR = 0.5, 95% CI = 0.4-0.7; 2021: OR = 0.6, 95% CI = 0.5-0.8], while Hispanic decedents had similar odds of naloxone administration to that of White decedents.

CONCLUSION

After controlling for overdose circumstances in drug overdose deaths in Pennsylvania, USA, from 2019 to 2021, Black people had lower odds of naloxone administration compared with White people, while there were no differences between Hispanic and White people.

Characteristics of Overdose Deaths Related to Illicitly Manufactured Fentanyl - Arizona, July 2019 - June 2020

Using the Arizona State Unintentional Drug Overdose Reporting System (AZ-SUDORS), the study aims to identify the social and drug characteristics of illicitly manufactured fentanyl (IMF)-related overdose deaths. The data include drug overdose deaths from July 1, 2019 to June 30, 2020. Decedents were categorized into four groups by types of opioids detected: (1) IMF-positive; (2) heroin-positive (negative for IMF); (3) pharmaceutical opioid-positive (negative for heroin or IMF); (4) nonopioid. Bivariate statistics were used to compare differences between IMF and other groups. Among 2,029 decedents, 77.8% tested positive for opioids. The IMF group included 57.9%, the heroin group included 9.5%, the pharmaceutical opioid group 10.5%, and the nonopioid group 22.2%. The IMF group was younger (mean age 35.0), more likely to be from a large urban area (78.2%), and with a greater proportion of ethnic/racial minorities (48.6%), compared to the other three groups. The IMF group was less likely to test positive for methamphetamine (24.9%), compared to heroin (63.7%) or pharmaceutical opioid groups (34.0%), but more likely to test positive for cannabis (31.3%), compared to the other three groups. Our data show disproportionate IMF impacts on younger persons and ethnic minorities. Interventions need to be tailored to account for distinct psychosocial profiles associated with IMF use.

Buprenorphine, oxycodone, hydrocodone, and methadone mortality in the United States (2010‒2017)

Objective

Opioid overdose survivors present to emergency departments (EDs) and many EDs have developed programs to initiate buprenorphine. The impact of the increasing use of buprenorphine in ED and by other providers is unknown while opioid mortality continues to rise. Public mortality data do not distinguish buprenorphine from other prescription opioids. Our objective was to determine when changes in overdose mortality trends occurred comparing deaths involving buprenorphine to oxycodone, hydrocodone, and methadone.

Methods

This observational study utilized the drug-involved mortality database including US death certificates (2010‒2017) in which buprenorphine, oxycodone, hydrocodone, or methadone were contributing causes of death (determined through textual analysis). Population- and drug utilization-adjusted mortality rates were examined using disjointed linear regression. Buprenorphine-involved deaths were stratified by polysubstance involvement.

Results

The population-adjusted mortality rates for buprenorphine-involved deaths were lowest compared to other opioids; however, the change in rate for buprenorphine increased faster than oxycodone, hydrocodone, and methadone at 8.9% each quarter-year (95% confidence interval [CI]: 8.0, 9.8) from 2010 to mid-2016 when it stabilized. After adjusting for changes in dispensing over the study period, buprenorphine-involved mortality rates were increasing at 5.3% (95% CI: 4.6, 6.1) each quarter-year. In 2017, 94% buprenorphine-involved deaths had at least one other drug contributing to the cause of death.

Conclusions

Given the low mortality, high proportions of polysubstance mortality, and the mixed agonist/antagonist mechanism of action, use of buprenorphine alone likely presents a lower risk for overdose than comparators. Mortality rose faster than dispensing, signaling need to ensure people understand buprenorphine risks, particularly polysubstance use, balanced against importance for treating opioid use disorders.