Identifying counties at risk of high overdose mortality burden during the emerging fentanyl epidemic in the USA: a predictive statistical modelling study

Predicting the Population Risk of Suicide Using Routinely Collected Health Administrative Data in Quebec, Canada: Model-Based Synthetic Estimation Study

BACKGROUND

Suicide is a significant public health issue. Many risk prediction tools have been developed to estimate an individual's risk of suicide. Risk prediction models can go beyond individual risk assessment; one important application of risk prediction models is population health planning. Suicide is a result of the interaction among the risk and protective factors at the individual, health care system, and community levels. Thus, policy and decision makers can play an important role in suicide prevention. However, few prediction models for the population risk of suicide have been developed.

OBJECTIVE

This study aims to develop and validate prediction models for the population risk of suicide using health administrative data, considering individual-, health system-, and community-level predictors.

METHODS

We used a case-control study design to develop sex-specific risk prediction models for suicide, using the health administrative data in Quebec, Canada. The training data included all suicide cases (n=8899) that occurred from January 1, 2002, to December 31, 2010. The control group was a 1% random sample of living individuals in each year between January 1, 2002, and December 31, 2010 (n=645,590). Logistic regression was used to develop the prediction models based on individual-, health care system-, and community-level predictors. The developed model was converted into synthetic estimation models, which concerted the individual-level predictors into community-level predictors. The synthetic estimation models were directly applied to the validation data from January 1, 2011, to December 31, 2019. We assessed the performance of the synthetic estimation models with four indicators: the agreement between predicted and observed proportions of suicide, mean average error, root mean square error, and the proportion of correctly identified high-risk regions.

RESULTS

The sex-specific models based on individual data had good discrimination (male model: C=0.79; female model: C=0.85) and calibration (Brier score for male model 0.01; Brier score for female model 0.005). With the regression-based synthetic models applied in the validation data, the absolute differences between the synthetic risk estimates and observed suicide risk ranged from 0% to 0.001%. The root mean square errors were under 0.2. The synthetic estimation model for males correctly predicted 4 of 5 high-risk regions in 8 years, and the model for females correctly predicted 4 of 5 high-risk regions in 5 years.

CONCLUSIONS

Using linked health administrative databases, this study demonstrated the feasibility and the validity of developing prediction models for the population risk of suicide, incorporating individual-, health system-, and community-level variables. Synthetic estimation models built on routinely collected health administrative data can accurately predict the population risk of suicide. This effort can be enhanced by timely access to other critical information at the population level.

Identification of factors associated with opioid-related and hepatitis C virus-related hospitalisations at the ZIP code area level in the USA: an ecological and modelling study

BACKGROUND

Opioid overdose and related diseases remain a growing public health crisis in the USA. Identifying sociostructural and other contextual factors associated with adverse health outcomes is needed to improve prediction models to inform policy and interventions. We aimed to identify high-risk communities for targeted delivery of screening and prevention interventions for opioid use disorder and hepatitis C virus (HCV).

METHODS

In this ecological and modelling study, we fit mixed-effects negative binomial regression models to identify factors associated with, and predict, opioid-related and HCV-related hospitalisations for ZIP code tabulation areas (ZCTAs) in South Carolina, USA. All individuals aged 18 years or older living in South Carolina from Jan 1, 2016, to Dec 31, 2021, were included. Data on opioid-related and HCV-related hospitalisations, as well as data on additional individual-level variables, were collected from medical claims records, which were obtained from the South Carolina Revenue and Fiscal Affairs Office. Demographic and socioeconomic variables were obtained from the United States Census Bureau (American Community Survey, 2021) with additional structural health-care barrier data obtained from South Carolina's Center for Rural and Primary Health Care, and the American Hospital Directory.

FINDINGS

Between Jan 1, 2016, and Dec 31, 2021, 41 691 individuals were hospitalised for opioid misuse and 26 860 were hospitalised for HCV. There were a median of 80 (IQR 24-213) opioid-related hospitalisations and 61 (21-196) HCV-related hospitalisations per ZCTA. A standard deviation increase in ZCTA-level uninsured rate (relative risk 1·24 [95% CI 1·17-1·31]), poverty rate (1·24 [1·17-1·31]), mortality (1·18 [1·12-1·25]), and social vulnerability index (1·17 [1·10-1·24]) was significantly associated with increased combined opioid-related and HCV-related hospitalisation rates. A standard deviation increase in ZCTA-level income (0·79 [0·75-0·84]) and unemployment rate (0·87 [0·82-0·93]) was significantly associated with decreased combined opioid-related and HCV-related hospitalisations. Using 2016-20 hospitalisations as training data, our models predicted ZCTA-level opioid-related hospitalisations in 2021 with a median of 80·4% (IQR 66·8-91·1) accuracy and HCV-related hospitalisations in 2021 with a median of 75·2% (61·2-87·7) accuracy. Several underserved high-risk ZCTAs were identified for delivery of targeted interventions.

INTERPRETATION

Our results suggest that individuals from economically disadvantaged and medically under-resourced communities are more likely to have an opioid-related or HCV-related hospitalisation. In conjunction with hospitalisation forecasts, our results could be used to identify and prioritise high-risk, underserved communities for delivery of field-level interventions.

FUNDING

South Carolina Center for Rural and Primary Healthcare, National Institute on Drug Abuse, and National Library of Medicine.

National and regional trends in fentanyl seizures in the United States, 2017-2023

BACKGROUND

Rates of synthetic opioid-related deaths over time and across regions have been compared within the US, but other indicator data could help inform prevention and harm reduction as well. We compared regional trends in fentanyl seizures to examine potential shifts in illicit fentanyl availability.

METHODS

Annual trends in fentanyl seizures were examined using data from High Intensity Drug Trafficking Areas for the US overall and by region from 2017 through 2023. Multiple measures included the number of seizures, the number of powder seizures, the number of pill seizures, the total weight of seizures, the number of pills seized, and the percentage of the number of pill seizures relative to the number of total seizures.

RESULTS

The percentage of seizures in pill form in the US increased from 10.3 % in 2017 to 49.0 % in 2023 (adjusted annual percentage change [AAPC]=25.2, 95 % CI: 17.6, 33.2), with 115.6 million individual pills seized in 2023. Pill weight related to total seizure weight also increased from 0.4 % to 54.5 % (AAPC=112.6, 95 % CI: 78.6, 153.2). In 2023, the plurality of seizures was in the West, in seven out of eight of our measures, with 77.8 % of seizures in the West being in pill form. Although the Midwest had lower prevalence of seizures than the West, there were notable increases in the Midwest in the number of pill seizures (AAPC=142.2, 95 % CI: 91.9, 205.8) and number of pills seized (AAPC=421.0, 95 % CI: 272.7, 628.4). Total weight of fentanyl seized increased the most in the West (AAPC=84.6, 95 % CI: 72.3, 97.8).

CONCLUSIONS

The number and size of fentanyl seizures is increasing in the US, with the majority of seizures, especially in pill form, in the West. Continued monitoring of regional shifts in the fentanyl supply can help inform targeted prevention and public health response.

Opioid Use Increases the Rate of Pseudarthrosis and Revision Surgery in Patients Undergoing Anterior Cervical Discectomy and Fusion

STUDY DESIGN

Retrospective Cohort.

OBJECTIVES

To (1) quantify the risk opioids impart on pseudarthrosis development, (2) analyze the effect of pseudarthrosis on clinical outcomes, and (3) identify if the amount of opioids prescribed are predictive of pseudarthrosis revision.

METHODS

Patients who underwent ACDF at a single institution between 2017-2019 were retrospectively identified. Postoperative dynamic cervical spine radiographs were reviewed to assess fusion status. Logistic regression models measured the effect of morphine milligram equivalents (MME) prescribed on the likelihood of pseudarthrosis development. Receiver operating characteristic (ROC) curves were generated to predict the probability of surgical revision based on MME prescribed.

RESULTS

Of 298 included patients, an average of 2.01 ± 0.82 levels were included in the construct and 121 (40.9%) patients were diagnosed with a pseudarthrosis. However, only 14 (4.7%) required a pseudarthrosis revision. Patients requiring pseudarthrosis revision had worse one-year postoperative Δ PCS-12 (-1.70 vs. 7.58, P = 0.004), Δ NDI (3.33 vs. -15.26, P = 0.002), and Δ VAS Arm (2.33 vs. -2.48, P = .047). Multivariate logistic regression analyses found the three-month postoperative (OR=1.00, P = .010), one-year postoperative (OR=1.001, P = 0.025), and combined pre- and postoperative MME (OR=1.000, P = .035) increased the risk of pseudarthrosis. ROC analysis identified cutoff values to predict pseudarthrosis revision at 90.00 (area under the curve (AUC): 0.693, confidence interval (CI): 0.554-0.832), 132.86 (0.710, CI: 0.589-0.840), 224.76 (0.687, CI: 0.558-0.817) and 285.00 (0.711, CI: 0.585-0.837) MME in the preoperative, three-month postoperative, one-year postoperative, and combined pre-and postoperative period.

CONCLUSION

Increased prescription of opioid medications following ACDF procedures may increase the risk of pseudarthrosis development and revision surgery.

LEVEL OF EVIDENCE

Therapeutic Level III.

Forecasting drug-overdose mortality by age in the United States at the national and county levels

The drug-overdose crisis in the United States continues to intensify. Fatalities have increased 5-fold since 1999 reaching a record high of 108,000 deaths in 2021. The epidemic has unfolded through distinct waves of different drug types, uniquely impacting various age, gender, race, and ethnic groups in specific geographical areas. One major challenge in designing interventions and efficiently delivering treatment is forecasting age-specific overdose patterns at the local level. To address this need, we develop a forecasting method that assimilates observational data obtained from the CDC WONDER database with an age-structured model of addiction and overdose mortality. We apply our method nationwide and to three select areas: Los Angeles County, Cook County, and the five boroughs of New York City, providing forecasts of drug-overdose mortality and estimates of relevant epidemiological quantities, such as mortality and age-specific addiction rates.

Drug supply measures and drug overdose mortality in the era of fentanyl and stimulants

Background

Illicitly-manufactured fentanyl and stimulants have replaced prescription opioids as the primary contributors to fatal overdoses in the United States (US), yet the street supply of these substances is challenging to quantify. Building on the foundation of prior research on law enforcement drug reports, the present study compares publicly available forensic laboratory drug report measures to identify which measures account for the most variation in drug overdose mortality between states, within states over time, and in various demographic groups.

Methods

Drug reports from the National Forensic Laboratory Information System and drug overdose mortality rates from the Centers for Disease Control and Prevention were examined for all US states and the District of Columbia, 2013-2021 (459 state-years). State- and year- fixed effects models regressed drug overdose mortality rates (in the overall population and subpopulations by sex, age, and race/ethnicity) on various drug report measures, including rates per population and proportional shares of drug reports positive for fentanyl/fentanyl-related compounds, heroin, cocaine, methamphetamine, and xylazine.

Results

For drug overdose death rates in the overall population and nearly all subpopulations examined by sex, race/ethnicity, and age, the model including all drug report proportional measures represented the best-performing model (as identified via the lowest Akaike Information Criterion and highest within R-squared value), followed by the model including only the fentanyl/fentanyl-related compounds proportion.

Conclusions

Findings support the utility of publicly available drug report composition measures, particularly the proportion of fentanyl/fentanyl-related compounds, as predictors of drug overdose mortality in the US and in various subpopulations.

Xylazine in Drug Seizure Reports and Overdose Deaths in the US, 2018-2022

Xylazine is increasingly reported in street drugs and fatal overdoses in the United States (US), often in combination with synthetic opioids, yet state-level xylazine data are limited, hampering local public health responses. The present study analyzed 2018-2022 state-level data from the National Forensic Laboratory Information System (xylazine-positive reports of seized drugs analyzed by forensic laboratories), the Centers for Disease Control and Prevention (population estimates, synthetic opioid overdose mortality rates), and individual states' medical examiner/public health agency reports (numbers of xylazine-involved overdose deaths). An ordinary least squares regression model predicted state-level synthetic opioid overdose mortality rates by xylazine seizure report rates, adjusting for US Census Region. In 2018, relatively low rates of xylazine seizure reports were observed, with 21 states reporting zero xylazine seizures. In 2022, only three states reported zero xylazine seizures, and the highest xylazine seizure report rates (per 100,000 residents) were observed in New Jersey (30.4), Rhode Island (22.7), Maryland (18.9), Virginia (15.5), New Hampshire (13.0), and Ohio (10.9). Data on 2019-2022 xylazine-involved overdose deaths were available for 21 states/DC (60 state-years), with the highest 2022 xylazine-involved overdose death rates (per 100,000 residents) in Vermont (10.5) and Connecticut (9.8). Finally, in 2021, at the state level, each additional reported xylazine seizure per 100,000 residents was associated with a 2% higher synthetic opioid overdose mortality rate (b=0.02, robust standard error=0.01; p=0.049). Overall, study results emphasize xylazine's increasing involvement in US law enforcement drug seizure reports and overdose deaths, primarily in the East, yet also extending across the country.

Spatiotemporal Analysis Exploring the Effect of Law Enforcement Drug Market Disruptions on Overdose, Indianapolis, Indiana, 2020-2021

Objectives. To test the hypothesis that law enforcement efforts to disrupt local drug markets by seizing opioids or stimulants are associated with increased spatiotemporal clustering of overdose events in the surrounding geographic area. Methods. We performed a retrospective (January 1, 2020 to December 31, 2021), population-based cohort study using administrative data from Marion County, Indiana. We compared frequency and characteristics of drug (i.e., opioids and stimulants) seizures with changes in fatal overdose, emergency medical services nonfatal overdose calls for service, and naloxone administration in the geographic area and time following the seizures. Results. Within 7, 14, and 21 days, opioid-related law enforcement drug seizures were significantly associated with increased spatiotemporal clustering of overdoses within radii of 100, 250, and 500 meters. For example, the observed number of fatal overdoses was two-fold higher than expected under the null distribution within 7 days and 500 meters following opioid-related seizures. To a lesser extent, stimulant-related drug seizures were associated with increased spatiotemporal clustering overdose. Conclusions. Supply-side enforcement interventions and drug policies should be further explored to determine whether they exacerbate an ongoing overdose epidemic and negatively affect the nation's life expectancy. (Am J Public Health. 2023;113(7):750-758. https://doi.org/10.2105/AJPH.2023.307291).

Is England facing an opioid epidemic?

The opioid crisis in the United States (US) is one of the most high-profile public health scandals of the 21st century with millions of people unknowingly becoming dependent on opioids. The United Kingdom (UK) had the world's highest rate of opioid consumption in 2019, and opiate-related drug poisoning deaths have increased by 388% since 1993 in England and Wales. This article explores the epidemiological definitions of public health emergencies and epidemics in the context of opioid use, misuse, and mortality in England, to establish whether England is facing an opioid crisis.

Antecedents of fatal overdose in an adult cohort identified through administrative record linkage in Indiana, 2015-2022

BACKGROUND

The United States continues to experience unprecedented rates of overdose mortality and need to identify effective policies or practices that can be implemented. This study aims to measure the prevalence, frequency, timing, and rate of touchpoints that occurred prior to a fatal overdose where communities might intervene.

METHODS

In collaboration with Indiana state government, we conducted record-linkage of statewide administrative datasets to vital records (January 1, 2015, through August 26, 2022) to identify touchpoints (jail booking, prison release, prescription medication dispensation, emergency department visits, and emergency medical services). We examined touchpoints within 12-months prior to a fatal overdose among an adult cohort and explored variation over time and by demographic characteristics.

RESULTS

Over the 92-month study period there were 13,882 overdose deaths (89.3% accidental poisonings, X40-X44) in our adult cohort that were record-linked to multiple administrative datasets and revealed nearly two-thirds (64.7%; n=8980) experienced an emergency department visit, the most prevalent touchpoint followed by prescription medication dispensation, emergency medical services responses, jail booking, and prison release. However, with approximately 1 out of every 100 returning citizens dying from drug overdose within 12-months of release, prison release had the highest touchpoint rate followed by emergency medical services responses, jail booking, emergency department visits, and prescription medication dispensation.

CONCLUSION

Record-linking administrative data from routine practice to vital records from overdose mortality is a viable means of identifying where resources should be situated to reduce fatal overdose, with potential to evaluate the effectiveness of overdose prevention efforts.

A case-control study on predicting population risk of suicide using health administrative data: a research protocol

INTRODUCTION

Suicide has a complex aetiology and is a result of the interaction among the risk and protective factors at the individual, healthcare system and population levels. Therefore, policy and decision makers and mental health service planners can play an important role in suicide prevention. Although a number of suicide risk predictive tools have been developed, these tools were designed to be used by clinicians for assessing individual risk of suicide. There have been no risk predictive models to be used by policy and decision makers for predicting population risk of suicide at the national, provincial and regional levels. This paper aimed to describe the rationale and methodology for developing risk predictive models for population risk of suicide.

METHODS AND ANALYSIS

A case-control study design will be used to develop sex-specific risk predictive models for population risk of suicide, using statistical regression and machine learning techniques. Routinely collected health administrative data in Quebec, Canada, and community-level social deprivation and marginalisation data will be used. The developed models will be transformed into the models that can be readily used by policy and decision makers. Two rounds of qualitative interviews with end-users and other stakeholders were proposed to understand their views about the developed models and potential systematic, social and ethical issues for implementation; the first round of qualitative interviews has been completed. We included 9440 suicide cases (7234 males and 2206 females) and 661 780 controls for model development. Three hundred and forty-seven variables at individual, healthcare system and community levels have been identified and will be included in least absolute shrinkage and selection operator regression for feature selection.

ETHICS AND DISSEMINATION

This study is approved by the Health Research Ethnics Committee of Dalhousie University, Canada. This study takes an integrated knowledge translation approach, involving knowledge users from the beginning of the process.

Supply-side predictors of fatal drug overdose in the Washington/Baltimore HIDTA region: 2016-2020

BACKGROUND

Rising rates of fentanyl- and polydrug-involved drug overdose deaths have prompted inquiry into the role of drug supply in fatal overdose outcomes in the United States. To date, however, there have been few empirical investigations of drug enforcement strategies on fatal overdose rates, despite knowledge that both drug use and supply are often geographically distributed. To address this limitation, we examined measures of drug enforcement as predictors of next-year fatal overdose rates in the Washington/Baltimore High Intensity Drug Trafficking Area (W/B HIDTA).

METHODS

We conducted mixed-effects models to examine the role of drug seizures and disruption in drug trafficking organizations (DTOs) and money laundering organizations (MLOs) on fatal overdose rates over a 5-year period (2016-2020) across 45 local jurisdictions in the W/B HIDTA region. Outcomes included any, opioid-involved, and fentanyl-involved fatal overdose.

RESULTS

Adjusting for covariates, both the total number of drug seizures and amount of cocaine seized (in dosage units per capita) positively predicted next-year opioid- and fentanyl-involved fatal overdose rates. Disruption to DTO and MLO operations did not significantly predict next-year fatal overdose rates for any outcome.

CONCLUSION

Supply-side enforcement activities alone may have limited impact on reducing fatal overdose rates, but may serve as important markers to identify communities at high risk of fatal overdose and facilitate targeted intervention. Our findings underscore the importance of comprehensive law enforcement approaches that extend beyond drug enforcement to integrate prevention, linkage to treatment, and harm reduction strategies as needed to address the overdose epidemic.

Marijuana Use and its Effect on Clinical Outcomes and Revision Rates in Patients Undergoing Anterior Cervical Discectomy and Fusion

STUDY DESIGN

A retrospective cohort study.

OBJECTIVE

To determine if (1) preoperative marijuana use increased complications, readmission, or reoperation rates following anterior cervical discectomy and fusion (ACDF), (2) identify if preoperative marijuana use resulted in worse patient-reported outcome measures (PROMs), and (3) investigate if preoperative marijuana use affects the quantity of opioid prescriptions in the perioperative period.

SUMMARY OF BACKGROUND DATA

A growing number of states have legalized recreational and/or medical marijuana, thus increasing the number of patients who report preoperative marijuana use. The effects of marijuana on clinical outcomes and PROMs in the postoperative period are unknown.

METHODS

All patients 18 years of age and older who underwent primary one- to four-level ACDF with preoperative marijuana use at our academic institution were retrospectively identified. A 3:1 propensity match was conducted to compare patients who used marijuana versus those who did not. Patient demographics, surgical characteristics, clinical outcomes, and PROMs were compared between groups. Multivariate regression models measured the effect of marijuana use on the likelihood of requiring a reoperation and whether marijuana use predicted inferior PROM improvements at the one-year postoperative period.

RESULTS

Of the 240 patients included, 60 (25.0%) used marijuana preoperatively. Multivariate logistic regression analysis identified marijuana use (odds ratio=5.62, P <0.001) as a predictor of a cervical spine reoperation after ACDF. Patients who used marijuana preoperatively had worse one-year postoperative Physical Component Scores of the Short-Form 12 (PCS-12) ( P =0.001), Neck Disability Index ( P =0.003), Visual Analogue Scale (VAS) Arm ( P =0.044) and VAS Neck ( P =0.012). Multivariate linear regression found preoperative marijuana use did not independently predict improvement in PCS-12 (β=-4.62, P =0.096), Neck Disability Index (β=9.51, P =0.062), Mental Component Scores of the Short-Form 12 (MCS-12) (β=-1.16, P =0.694), VAS Arm (β=0.06, P =0.944), or VAS Neck (β=-0.44, P =0.617).

CONCLUSION

Preoperative marijuana use increased the risk of a cervical spine reoperation after ACDF, but it did not significantly change the amount of postoperative opioids used or the magnitude of improvement in PROMs.

LEVEL OF EVIDENCE

Levwl III.

Opioid Overdoses Increase at Home During the COVID-19 Stay-At-Home Order Period in Cook County, Illinois

Introduction

Stay-at-home orders during the COVID-19 pandemic decreased population mobility to reduce SARS-CoV-2 infection rates. We empirically tested the hypothesis that this public health measure was associated with a higher likelihood of opioid- and stimulant-involved deaths occurring in homes located in Cook County, Illinois.

Methods

The stay-at-home period was from March 21, 2020 to May 30, 2020. We analyzed overdose data from the Cook County Medical Examiner's Office using a death location description from case investigations categorized as home, medical, motel, scene, and other. Two groups of decedents were defined as either having an opioid or stimulant listed in the primary cause of death field. We modeled a weekly time series to detect changes in deaths (number) and trends during segmented time periods. Chi-square or Fisher's exact and adjusted logistic regression was used for testing the differences between the stay-at-home and a 13-week preceding period.

Results

There were 4,169 and 2,012 opioid- and stimulant-involved deaths, respectively, from 2018 to 2020. Both groups were demographically similar: 75% male, 52% White, and aged 45 years (mean). In the 13 weeks before stay-at-home orders, 51% of opioid-involved deaths occurred in homes, which increased to 59% (p<0.0001) during the 10 weeks of the order and decreased back to 51% in the 18 weeks after the order expired. For stimulant-involved deaths, 51% were residential immediately before the orders, with a nonsignificant increase to 52% during the stay-at-home period. Before the pandemic, there were 20 deaths/week, increasing to 37 deaths/week (p<0.0001) during stay-at-home enactment. Deaths involving fentanyl among the opioid-involved group increased from 76% to 89%, whereas those involving heroin decreased from 55% to 37%. The adjusted OR for opioid-involved fatal overdoses occurring at home during this period compared with that occurring the 13 weeks before was 1.37 (95% CI=1.05, 1.79).

Conclusions

The likelihood of a death occurring at home, especially for people using opioids, increased during the stay-at-home order period. Findings have implications for mitigating overdose risks during social isolation.

Conceptualizing the Socio-Built Environment: An Expanded Theoretical Framework to Promote a Better Understanding of Risk for Nonmedical Opioid Overdose Outcomes in Urban and Non-Urban Settings

Nonmedical opioid (NMO) use has been linked to significant increases in rates of NMO morbidity and mortality in non-urban areas. While there has been a great deal of empirical evidence suggesting that physical features of built environments represent strong predictors of drug use and mental health outcomes in urban settings, there is a dearth of research assessing the physical, built environment features of non-urban settings in order to predict risk for NMO overdose outcomes. Likewise, there is strong extant literature suggesting that social characteristics of environments also predict NMO overdoses and other NMO use outcomes, but limited research that considers the combined effects of both physical and social characteristics of environments on NMO outcomes. As a result, important gaps in the scientific literature currently limit our understanding of how both physical and social features of environments shape risk for NMO overdose in rural and suburban settings and therefore limit our ability to intervene effectively. In order to foster a more holistic understanding of environmental features predicting the emerging epidemic of NMO overdose, this article presents a novel, expanded theoretical framework that conceptualizes "socio-built environments" as comprised of (a) environmental characteristics that are applicable to both non-urban and urban settings and (b) not only traditional features of environments as conceptualized by the extant built environment framework, but also social features of environments. This novel framework can help improve our ability to identify settings at highest risk for high rates of NMO overdose, in order to improve resource allocation, targeting, and implementation for interventions such as opioid treatment services, mental health services, and care and harm reduction services for people who use drugs.

Global, regional, and national trends in opioid analgesic consumption from 2015 to 2019: a longitudinal study

BACKGROUND

Previous studies have reported an extremely unbalanced global access to opioid analgesics. We aimed to determine contemporary trends and patterns of opioid analgesic consumption at the global, regional, and national levels.

METHODS

We analysed the global pharmaceutical sales data of 66 countries or regions from the IQVIA-Multinational Integrated Data Analysis System database on opioid analgesics between 2015 and 2019. Opioid analgesic consumption was measured in milligram morphine equivalent per 1000 inhabitants per day (MME per 1000/day). The global, regional, and national trend changes were estimated using linear regressions. Factors associated with consumption patterns and trend changes were explored in multivariable linear regression analyses.

FINDINGS

Overall opioid analgesic sales in the 66 countries or regions increased from 27·52 MME per 1000/day (16·63-45·54) in 2015 to 29·51 MME per 1000/day (17·85-48·79) in 2019 (difference per year 3·96%, 95% CI 0·26 to 7·80). Sales reduced yearly in North America (-12·84%; 95% CI -15·34 to -10·27) and Oceania (-2·96%; -4·20 to -1·70); increased in South America (28·69%; 7·18 to 54·53), eastern Europe (7·68%; 3·99 to 11·49), Asia (5·74%; 0·61 to 11·14), and western and central Europe (1·64%; 0·52 to 2·78); and did not differ in Africa or central America and the Caribbean. The global opioid consumption patterns were associated with country-level Human Development Index (p=0·040), cancer death rate excluding leukaemia (p=0·0072), and geographical location (p<0·0001). In 2019, opioid analgesic consumption ranged from 0·01 MME per 1000/day to 5·40 MME per 1000/day in the 17 countries and regions in the lowest consumption quartile, despite high income levels and cancer death rates in some of them.

INTERPRETATION

Global opioid analgesic consumption increased from 2015 to 2019. The trend changes were distinctive across regions, which could reflect the different actions in response to known issues of opioid use and misuse. Disparities in opioid analgesic consumption remained, indicating potential inadequate access to essential pain relief in countries with low consumption.

FUNDING

None.

Using Economic Evaluation to Inform Responses to the Opioid Epidemic in the United States: Challenges and Suggestions for Future Research

Background: Several aspects of the opioid epidemic and of public health care organization in the United States (US) make the conduct of economic evaluation and the design of policies to respond to this crisis particularly challenging. Objectives: This commentary offers suggestions for how economic evaluation may address and overcome four key features of the opioid epidemic: 1) its magnitude and geographical distribution, 2) its intersection with multiple epidemics, 3) its rapidly changing dynamics, 4) its multi-sectoral causes and consequences. Results: We first offer pragmatic suggestions to address the difficulties in delivering a coordinated response given the fragmented nature of health care in the US. In view of the broad suite of responses required to address opioid use disorder and its associated comorbidities, we highlight the need for economic evaluations which consider interventions throughout the continuum of care (i.e. primary, secondary and tertiary levels of prevention). We examine how the use of predictive modelling alongside economic evaluation might be adopted to address the rapidly evolving situation affecting distinct populations and geographic areas and encourage investments in epidemic preparedness. Finally, we propose methods to capture the interdependence of various sectors of government affected by the opioid crisis in economic evaluations to ensure optimal levels of investment towards a comprehensive response. Conclusions: The opioid epidemic in the US represents an unprecedented public health challenge, but sound epidemiological modelling and economic analysis can help to guide use of limited resources committed to addressing it in ways that can have greatest impact in limiting its adverse consequences.