Spatiotemporal Trends in Opioid Overdose Deaths by Race for Counties in Ohio

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.

The impact of fentanyl on state- and county-level psychostimulant and cocaine overdose death rates by race in Ohio from 2010 to 2020: a time series and spatiotemporal analysis

BACKGROUND

Over the past decade in the USA, increases in overdose rates of cocaine and psychostimulants with opioids were highest among Black, compared to White, populations. Whether fentanyl has contributed to the rise in cocaine and psychostimulant overdoses in Ohio is unknown. We sought to measure the impact of fentanyl on cocaine and psychostimulant overdose death rates by race in Ohio.

METHODS

We conducted time series and spatiotemporal analyses using data from the Ohio Public Health Information Warehouse. Primary outcomes were state- and county-level overdose death rates from 2010 to 2020 for Black and White populations. Measures of interest were overdoses consisting of four drug involvement classes: (1) all cocaine overdoses, (2) cocaine overdoses not involving fentanyl, (3) all psychostimulant overdoses, and (4) psychostimulant overdoses not involving fentanyl. We fit a time series model of log standardized mortality ratios (SMRs) using a Bayesian generalized linear mixed model to estimate posterior median rate ratios (RR). We conducted a spatiotemporal analysis by modeling the SMR for each drug class at the county level to characterize county-level variation over time.

RESULTS

In 2020, the greatest overdose rates involved cocaine among Black (24.8 deaths/100,000 people) and psychostimulants among White (10.1 deaths/100,000 people) populations. Annual mortality rate ratios were highest for psychostimulant-involved overdoses among Black (aRR = 1.71; 95% CI (1.43, 2.02)) and White (aRR = 1.60, 95% CI (1.39, 1.80)) populations. For cocaine not involving fentanyl, annual mortality rate ratios were similar among Black (aRR = 1.04; 95% CI (0.96,1.16)) and White (aRR = 1.02; 95% CI (0.87, 1.20)) populations. Within each drug category, change over time was similar for both racial groups. The spatial models highlighted county-level variation for all drug categories.

CONCLUSIONS

Without the involvement of fentanyl, cocaine overdoses remained constant while psychostimulant overdoses increased. Tailored harm reduction approaches, such as distribution of fentanyl test strips and the removal of punitive laws that influence decisions to contact emergency services, are the first steps to reduce cocaine overdose rates involving fentanyl among urban populations in Ohio. In parallel, harm reduction policies to address the increase in psychostimulant overdoses are warranted.

"We adjusted for race": now what? A systematic review of utilization and reporting of race in American Journal of Epidemiology and Epidemiology, 2020-2021

Race is a social construct, commonly used in epidemiologic research to adjust for confounding. However, adjustment of race may mask racial disparities, thereby perpetuating structural racism. We conducted a systematic review of articles published in Epidemiology and American Journal of Epidemiology between 2020 and 2021 to (1) understand how race, ethnicity, and similar social constructs were operationalized, used, and reported; and (2) characterize good and poor practices of utilization and reporting of race data on the basis of the extent to which they reveal or mask systemic racism. Original research articles were considered for full review and data extraction if race data were used in the study analysis. We extracted how race was categorized, used-as a descriptor, confounder, or for effect measure modification (EMM)-and reported if the authors discussed racial disparities and systemic bias-related mechanisms responsible for perpetuating the disparities. Of the 561 articles, 299 had race data available and 192 (34.2%) used race data in analyses. Among the 160 US-based studies, 81 different racial categorizations were used. Race was most often used as a confounder (52%), followed by effect measure modifier (33%), and descriptive variable (12%). Fewer than 1 in 4 articles (22.9%) exhibited good practices (EMM along with discussing disparities and mechanisms), 63.5% of the articles exhibited poor practices (confounding only or not discussing mechanisms), and 13.5% were considered neither poor nor good practices. We discuss implications and provide 13 recommendations for operationalization, utilization, and reporting of race in epidemiologic and public health research.

The salience of social context, opioid antagonist use, and prior opioid exposure as determinants of fatal and non-fatal opioid overdoses

This study examines the salience of social context for opioid overdoses in Boston from 2014 to 2019. Longitudinal negative binomial models with random effects indicated that higher levels of concentrated disadvantage, residential instability, and illicit drug activity increased annual block group counts of opioid overdoses. Logistic hierarchical and cross-classified random effects models indicated that the use of Narcan and greater exposure to drugs through previous opioid overdose and contextual lillicit drug crime activity reduced the odds of fatal opioid overdose relative to non-fatal opioid overdose. The findings suggest that the accurate tracking of both fatal and non-fatal overdoses, and a consideration of the broader social context, can facilitate effective public health resource allocation to reduce opioid overdoses.

Exploring county-level spatio-temporal patterns in opioid overdose related emergency department visits

Opioid overdoses within the United States continue to rise and have been negatively impacting the social and economic status of the country. In order to effectively allocate resources and identify policy solutions to reduce the number of overdoses, it is important to understand the geographical differences in opioid overdose rates and their causes. In this study, we utilized data on emergency department opioid overdose (EDOOD) visits to explore the county-level spatio-temporal distribution of opioid overdose rates within the state of Virginia and their association with aggregate socio-ecological factors. The analyses were performed using a combination of techniques including Moran's I and multilevel modeling. Using data from 2016-2021, we found that Virginia counties had notable differences in their EDOOD visit rates with significant neighborhood-level associations: many counties in the southwestern region were consistently identified as the hotspots (areas with a higher concentration of EDOOD visits) whereas many counties in the northern region were consistently identified as the coldspots (areas with a lower concentration of EDOOD visits). In most Virginia counties, EDOOD visit rates declined from 2017 to 2018. In more recent years (since 2019), the visit rates showed an increasing trend. The multilevel modeling revealed that the change in clinical care factors (i.e., access to care and quality of care) and socio-economic factors (i.e., levels of education, employment, income, family and social support, and community safety) were significantly associated with the change in the EDOOD visit rates. The findings from this study have the potential to assist policymakers in proper resource planning thereby improving health outcomes.

Hospitalizations for Alcohol and Opioid Use Disorders in Older Adults: Trends, Comorbidities, and Differences by Gender, Race, and Ethnicity

Background

The prevalence of substance use disorders (SUDs) among adults ages 65 and older has been increasing at a notably high rate in recent years, yet little information exists on hospitalizations for SUDs among this age group. In this study we examined trends in hospitalizations for alcohol use disorders (AUDs) and opioid use disorders (OUDs) among adults 65 and older in the United States, including differences by gender and race/ethnicity.

Methods

We used Medicare claims data for years 2007-2014 from beneficiaries ages 65 and older. We abstracted hospitalization records with an ICD-9 diagnostic code for an AUD or OUD. Hospitalization rates were calculated using population estimates from the United States Census. We examined trends in quarterly hospitalization rates for hospitalizations with AUD/OUD as primary diagnoses, and separately for those with these disorders as secondary diagnoses. We also examined comorbidities for those with a primary diagnosis of AUD/OUD. Analyses were conducted for all hospitalizations with AUD/OUD diagnoses, and separately by gender and race/ethnicity.

Results

Between the last quarter of 2007 and the third quarter of 2014, AUD hospitalization rates increased from 485 to 579 per million (19%), and OUD hospitalization rates from 46 to 101 per million (120%) and varied by gender (for AUD) and race/ethnicity (for both AUD and OUD). Hospitalization rates were particularly high for Black older adults, as was the increase in hospitalization rates. The increase in hospitalization rates was substantially higher for hospitalizations with AUD (84%) and OUD (269%) as secondary diagnoses.

Conclusions

Hospitalizations for AUDs and OUDs among older adults increased at an alarming rate during the observation period, and disparities existed in hospitalization rates for these conditions. Interventions focusing on the needs of older adults with AUD and/or OUD are needed, particularly to address the needs of a growing racially/ethnically diverse older adult population.

Invited Commentary: Toward a Better Understanding of Disparities in Overdose Mortality

The United States has been in the grip of an epidemic of drug overdose mortality for the past few decades, and deaths from drug overdose increased dramatically during the coronavirus disease 2019 pandemic. Townsend et al. (Am J Epidemiol. 2022;191(4):599-612) add to the growing literature highlighting the steep rise of drug overdose mortality among racial and ethnic minorities in the country. Using data from National Center for Health Statistics death certificates and employing principles of small-area estimation and a Bayesian hierarchical model to stabilize the estimates of smaller racial/ethnic groups and states, the authors found that combinations of opioid/stimulant drug overdose deaths saw a steep increase among racial and ethnic minorities, particularly Black Americans, between 2007 and 2019. The results from their analysis highlight the need to change the narrative around opioid deaths, to invest in targeted policies that address the growing burden of drug overdose faced by racial/ethnic minorities, and the importance of using innovative methods to address limitations to data disaggregation. The paper also demonstrates the importance of a holistic view of the challenges to the health of the American public.

Analysis of Urine Drug Test Results From Substance Use Disorder Treatment Practices and Overdose Mortality Rates, 2013-2020

IMPORTANCE

Drug overdose deaths in the US are currently the highest ever recorded; data collected from public health surveillance sources can help to identify emerging drug use patterns associated with overdose mortality rates, but the time lag in results often limits utility. Urine drug testing (UDT) is one potentially underused source that could augment surveillance efforts through timely data collection.

OBJECTIVE

To evaluate the correlation between real-time UDT results from a proprietary national database and overdose mortality data from the National Vital Statistics System.

DESIGN, SETTING, AND PARTICIPANTS

This retrospective cross-sectional study included 500 000 urine specimens submitted for UDT by substance use disorder (SUD) treatment health care practices and collected between January 1, 2013, and December 31, 2020. Real-time UDT data were obtained from the Millennium Health proprietary national database, and overdose mortality data were obtained from the National Vital Statistics System of the Centers for Disease Control and Prevention (CDC WONDER). Specimens were analyzed for specific drugs in 5 categories (cocaine, heroin, methamphetamine, synthetic opioids, and other opioids) using liquid chromatography-tandem mass spectrometry. Participants were adults aged 18 years and older who provided urine specimens at SUD treatment practices.

EXPOSURES

Urine drug testing.

MAIN OUTCOMES AND MEASURES

The primary outcome was the correlation between UDT positivity rates and overdose mortality rates at national, state, and county levels. Univariate and multivariate regression models were also used to evaluate the association between state- and county-level overdose mortality and standardized UDT positivity rates.

RESULTS

Among 500 000 unique patient specimens collected from SUD treatment practices between 2013 and 2020, 288 534 specimens (57.7%) were from men, and the median age of the study population was 34 years (IQR, 17-51 years). On a national level, synthetic opioids and methamphetamine were highly correlated with overdose mortality (Spearman ρ = 0.96 for both). When synthetic opioids were coinvolved, methamphetamine (ρ = 0.98), heroin (ρ = 0.78), cocaine (ρ = 0.94), and other opioids (ρ = 0.83) were also highly correlated with overdose mortality. In the absence of synthetic opioids, all drug categories were highly correlated (ρ = 0.75 for other opioids, 0.81 for heroin, and 0.88 for methamphetamine), with the exception of cocaine (ρ = -0.37). Synthetic opioids (ρ = 0.77) and methamphetamine (ρ = 0.80) had the strongest state-level correlations over time, whereas other opioids had the lowest correlation for both total positivity (ρ = 0.31) and positivity in the absence of synthetic opioids (ρ = 0.23). In Ohio, county-level correlation was strongest for synthetic opioids (ρ = 0.71), followed by heroin (ρ = 0.69) and methamphetamine (ρ = 0.67). At the state level, the multivariate incidence rate ratio (IRR) for synthetic opioids was 1.16 (95% CI, 1.14-1.19; P < .001), and at the county level, the IRR was 1.13 (95% CI, 1.09-1.17; P < .001), suggesting that for every 1-SD increase in the UDT positivity rate, there were 16.2% and 12.8% increases, respectively, in monthly overdose deaths. Both methamphetamine (11.7% increase per 1-SD increase in UDT positivity rate; IRR, 1.12; 95% CI, 1.09-1.14; P < .001) and cocaine (5.1% increase per 1-SD increase in UDT positivity rate; IRR, 1.05; 95% CI, 1.03-1.07; P < .001) also had significant positive associations with mortality rates, but the effect sizes were smaller than that of synthetic opioids (IRR, 1.16).

CONCLUSIONS AND RELEVANCE

In this study, UDT results were highly correlated with mortality rates at national, state, and county levels. These findings suggest that real-time UDT surveillance can help to quickly identify changes in drug use patterns that might inform targeted harm reduction strategies designed to prevent overdose deaths.

Demographic and Geospatial Analysis of Buprenorphine and Methadone Prescription Rates

Background

The medical community continues to seek to understand both the causes and consequences of opioid use disorder (OUD). The recent 2019 public release of the Automation of Reports and Consolidated Orders System (ARCOS) database from the years 2006 to 2012 provides a unique opportunity to analyze a critical period of the opioid epidemic with unprecedented data granularity.

Objectives

This study aims to use the ARCOS dataset to (1) determine significant contributory variables to opioid overdose death rates, (2) determine significant contributory variables to the relative prescription of buprenorphine and methadone, and (3) evaluate the existence of statistically significant geospatial clusters in buprenorphine and methadone prescription rates.

Methods

This study utilizes multiple databases, including the Centers for Disease Control and Prevention (CDC) Wide-ranging Online Data for Epidemiologic Research (WONDER), the Drug Enforcement Administration (DEA) prescription drug data, and the United States (US) Census demographics, to examine the relationship between the different treatments of OUD. Linear regressions are used to determine significant contributory factors in overdose rate and the buprenorphine-to-methadone ratio. Geospatial analysis is used to identify geographic clusters in opioid overdoses and treatment patterns.

Results

Methadone prescriptions, racial demographics, and poverty were found to significantly correspond to opioid overdose death rates (p < 0.05). Buprenorphine prescriptions were not found to be significant (p = 0.20). Opioid overdoses, metro character, racial categorization, and education were found to significantly correspond to the ratio of buprenorphine to methadone prescribed (p < 0.05). Cluster analysis demonstrated different geospatial distributions in the prescriptions of buprenorphine and methadone (p < 0.05).

Conclusion

Historically, methadone prescriptions have been higher in areas with high overdose rates. Buprenorphine and methadone prescribing patterns have historically demonstrated different geographic trends.

Drug overdose mortality by race/ethnicity across US-born and immigrant populations

BACKGROUND

The present study examined racial/ethnic differences in US drug overdose mortality among US-born and foreign-born men and women.

METHODS

In this cross-sectional analysis of 2010-2019 data from the National Center for Health Statistics, Bayesian hierarchical models predicted drug overdose mortality based on the interaction of race/ethnicity, nativity, and sex, adjusting for age, for 518,553 drug overdose deaths among individuals ages 15-74 identified as Non-Hispanic (NH) White, NH Black, Hispanic, or NH Asian/Pacific Islander (PI). Rate ratios with 95% Highest Posterior Density Intervals (HPDIs) were examined by race/ethnicity and nativity.

RESULTS

In the US-born population, 2017-2019 estimated overdose mortality rates were higher for NH Black than NH White men (ratio 1.48 [95% HPDI 1.28-1.72]), similar between NH Black and NH White women (ratio 1.03 [95% HPDI 0.89-1.20]), similar between Hispanic and NH White men (ratio 0.96 [95% HPDI 0.82-1.10]), and lower for NH Asian/PI than NH White men and women. In the foreign-born population, both for men and women, estimated overdose mortality rates were lower in every racial/ethnic group relative to the NH White group. For men and women of all racial/ethnic groups examined, estimated overdose mortality rates were higher in US-born than foreign-born subpopulations, yet the extent of this nativity differential was least pronounced in the NH White group.

CONCLUSIONS

In the US-born population, NH Black men experienced the highest recent rates of overdose mortality; in the foreign-born population, the highest rates of overdose mortality were observed among NH White men and women.

Unexplained mortality during the US COVID-19 pandemic: retrospective analysis of death certificate data and critical assessment of excess death calculations

OBJECTIVES

Cause-of-death discrepancies are common in respiratory illness-related mortality. A standard epidemiological metric, excess all-cause death, is unaffected by these discrepancies but provides no actionable policy information when increased all-cause mortality is unexplained by reported specific causes. To assess the contribution of unexplained mortality to the excess death metric, we parsed excess deaths in the COVID-19 pandemic into changes in explained versus unexplained (unreported or unspecified) causes.

DESIGN

Retrospective repeated cross-sectional analysis, US death certificate data for six influenza seasons beginning October 2014, comparing population-adjusted historical benchmarks from the previous two, three and five seasons with 2019-2020.

SETTING

48 of 50 states with complete data.

PARTICIPANTS

16.3 million deaths in 312 weeks, reported in categories-all causes, top eight natural causes and respiratory causes including COVID-19.

OUTCOME MEASURES

Change in population-adjusted counts of deaths from seasonal benchmarks to 2019-2020, from all causes (ie, total excess deaths) and from explained versus unexplained causes, reported for the season overall and for time periods defined a priori: pandemic awareness (19 January through 28 March); initial pandemic peak (29 March through 30 May) and pandemic post-peak (31 May through 26 September).

RESULTS

Depending on seasonal benchmark, 287 957-306 267 excess deaths occurred through September 2020: 179 903 (58.7%-62.5%) attributed to COVID-19; 44 022-49 311 (15.2%-16.1%) to other reported causes; 64 032-77 054 (22.2%-25.2%) unexplained (unspecified or unreported cause). Unexplained deaths constituted 65.2%-72.5% of excess deaths from 19 January to 28 March and 14.1%-16.1% from 29 March through 30 May.

CONCLUSIONS

Unexplained mortality contributed substantially to US pandemic period excess deaths. Onset of unexplained mortality in February 2020 coincided with previously reported increases in psychotropic use, suggesting possible psychiatric or injurious causes. Because underlying causes of unexplained deaths may vary by group or region, results suggest excess death calculations provide limited actionable information, supporting previous calls for improved cause-of-death data to support evidence-based policy.

Methodological approaches for the prediction of opioid use-related epidemics in the United States: a narrative review and cross-disciplinary call to action

The opioid crisis in the United States has been defined by waves of drug- and locality-specific Opioid use-Related Epidemics (OREs) of overdose and bloodborne infections, among a range of health harms. The ability to identify localities at risk of such OREs, and better yet, to predict which ones will experience them, holds the potential to mitigate further morbidity and mortality. This narrative review was conducted to identify and describe quantitative approaches aimed at the "risk assessment," "detection" or "prediction" of OREs in the United States. We implemented a PubMed search composed of the: (1) objective (eg, prediction), (2) epidemiologic outcome (eg, outbreak), (3) underlying cause (ie, opioid use), (4) health outcome (eg, overdose, HIV), (5) location (ie, US). In total, 46 studies were included, and the following information extracted: discipline, objective, health outcome, drug/substance type, geographic region/unit of analysis, and data sources. Studies identified relied on clinical, epidemiological, behavioral and drug markets surveillance and applied a range of methods including statistical regression, geospatial analyses, dynamic modeling, phylogenetic analyses and machine learning. Studies for the prediction of overdose mortality at national/state/county and zip code level are rapidly emerging. Geospatial methods are increasingly used to identify hotspots of opioid use and overdose. In the context of infectious disease OREs, routine genetic sequencing of patient samples to identify growing transmission clusters via phylogenetic methods could increase early detection capacity. A coordinated implementation of multiple, complementary approaches would increase our ability to successfully anticipate outbreak risk and respond preemptively. We present a multi-disciplinary framework for the prediction of OREs in the US and reflect on challenges research teams will face in implementing such strategies along with good practices.