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

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.

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.

"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.

Implementation of a Pharmacy Follow-Up Program for Dispensed Opioid Medications

BACKGROUND

There have been multiple reported pharmacy initiatives to reduce opioid misuse and accidental overdose to address our nation's public health crisis. To date, there has not been a description in the literature of a community pharmacy follow-up initiative for dispensed opioids.

METHODS

A follow-up program was designed and implemented in community pharmacies as part of a previously developed opioid overdose and misuse prevention program (ONE Program). Five to twelve days after the dispensing of an opioid, pharmacy technicians called the patient to follow up on opioid safety topics. Pharmacy technicians used a questionnaire to inquire about medication disposal plans, if the patient was taking the medication more than prescribed, medication side effects, and if the patient needed a pharmacist consultation. The results from that questionnaire were documented.

RESULTS

During the first 18 months of the follow-up program, 1789 phone calls were completed. Of those contacted, 40% were still using their opioid medication, and over 10% were experiencing side effects which triggered a pharmacist consult. Patients were reminded of proper medication disposal methods, and most patients (78%) desired to dispose of unused medication at the pharmacy medication disposal box.

CONCLUSIONS

Follow-up phone calls post-opioid medication dispensing were shown to add value to a previously established opioid misuse and accidental overdose prevention program and allowed for the fulfillment of the Pharmacist Patient Care Process.

Early warnings and slow deaths: A sociology of outbreak and overdose

In this paper, we offer a sociological analysis of early warning and outbreak in the field of drug policy, focusing on opioid overdose. We trace how 'outbreak' is enacted as a rupturing event which enables rapid reflex responses of precautionary control, based largely on short-term and proximal early warning indicators. We make the case for an alternative view of early warning and outbreak. We argue that practices of detection and projection that help to materialise drug-related outbreaks are too focused on the proximal and short-term. Engaging with epidemiological and sociological work investigating epidemics of opioid overdose, we show how the short-termism and rapid reflex response of outbreak fails to appreciate the slow violent pasts of epidemics indicative of an ongoing need and care for structural and societal change. Accordingly, we gather together ideas of 'slow emergency' (Ben Anderson), 'slow death' (Lauren Berlant) and 'slow violence' (Rob Nixon), to re-assemble outbreaks in 'long view'. This locates opioid overdose in long-term attritional processes of deindustrialisation, pharmaceuticalisation, and other forms of structural violence, including the criminalisation and problematisation of people who use drugs. Outbreaks evolve in relation to their slow violent pasts. To ignore this can perpetuate harm. Attending to the social conditions that create the possibilities for outbreak invites early warning that goes 'beyond outbreak' and 'beyond epidemic' as generally configured.

Small Area Forecasting of Opioid-Related Mortality: Bayesian Spatiotemporal Dynamic Modeling Approach

BACKGROUND

Opioid-related overdose mortality has remained at crisis levels across the United States, increasing 5-fold and worsened during the COVID-19 pandemic. The ability to provide forecasts of opioid-related mortality at granular geographical and temporal scales may help guide preemptive public health responses. Current forecasting models focus on prediction on a large geographical scale, such as states or counties, lacking the spatial granularity that local public health officials desire to guide policy decisions and resource allocation.

OBJECTIVE

The overarching objective of our study was to develop Bayesian spatiotemporal dynamic models to predict opioid-related mortality counts and rates at temporally and geographically granular scales (ie, ZIP Code Tabulation Areas [ZCTAs]) for Massachusetts.

METHODS

We obtained decedent data from the Massachusetts Registry of Vital Records and Statistics for 2005 through 2019. We developed Bayesian spatiotemporal dynamic models to predict opioid-related mortality across Massachusetts' 537 ZCTAs. We evaluated the prediction performance of our models using the one-year ahead approach. We investigated the potential improvement of prediction accuracy by incorporating ZCTA-level demographic and socioeconomic determinants. We identified ZCTAs with the highest predicted opioid-related mortality in terms of rates and counts and stratified them by rural and urban areas.

RESULTS

Bayesian dynamic models with the full spatial and temporal dependency performed best. Inclusion of the ZCTA-level demographic and socioeconomic variables as predictors improved the prediction accuracy, but only in the model that did not account for the neighborhood-level spatial dependency of the ZCTAs. Predictions were better for urban areas than for rural areas, which were more sparsely populated. Using the best performing model and the Massachusetts opioid-related mortality data from 2005 through 2019, our models suggested a stabilizing pattern in opioid-related overdose mortality in 2020 and 2021 if there were no disruptive changes to the trends observed for 2005-2019.

CONCLUSIONS

Our Bayesian spatiotemporal models focused on opioid-related overdose mortality data facilitated prediction approaches that can inform preemptive public health decision-making and resource allocation. While sparse data from rural and less populated locales typically pose special challenges in small area predictions, our dynamic Bayesian models, which maximized information borrowing across geographic areas and time points, were used to provide more accurate predictions for small areas. Such approaches can be replicated in other jurisdictions and at varying temporal and geographical levels. We encourage the formation of a modeling consortium for fatal opioid-related overdose predictions, where different modeling techniques could be ensembled to inform public health policy.

Geographic information science and the United States opioid overdose crisis: A scoping review of methods, scales, and application areas

BACKGROUND

The Opioid Overdose Crisis (OOC) continues to generate morbidity and mortality in the United States, outpacing other prominent accident-related reasons. Multiple disciplines have applied geographic information science (GIScience) to understand geographical patterns in opioid-related health measures. However, there are limited reviews that assess how GIScience has been used.

OBJECTIVES

This scoping review investigates how GIScience has been used to conduct research on the OOC. Specific sub-objectives involve identifying bibliometric trends, the location and scale of studies, the frequency of use of various GIScience methodologies, and what direction future research can take to address existing gaps.

METHODS

The review was pre-registered with the Open Science Framework ((https://osf.io/h3mfx/) and followed the PRISMA-ScR guidelines. Scholarly research was gathered from the Web of Science Core Collection, PubMed, IEEE Xplore, ACM Digital Library. Inclusion criteria was defined as having a publication date between January 1999 and August 2021, using GIScience as a central part of the research, and investigating an opioid-related health measure.

RESULTS

231 studies met the inclusion criteria. Most studies were published from 2017 onward. While many (41.6%) of studies were conducted using nationwide data, the majority (58.4%) occurred at the sub-national level. California, New York, Ohio, and Appalachia were most frequently studied, while the Midwest, north Rocky Mountains, Alaska, and Hawaii lacked studies. The most common GIScience methodology used was descriptive mapping, and county-level data was the most common unit of analysis across methodologies.

CONCLUSIONS

Future research of GIScience on the OOC can address gaps by developing use cases for machine learning, conducting analyses at the sub-county level, and applying GIScience to questions involving illicit fentanyl. Research using GIScience is expected to continue to increase, and multidisciplinary research efforts amongst GIScientists, epidemiologists, and other medical professionals can improve the rigor of research.

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.

Predicting accidental drug overdose as the cause of fatality in near real-time using the Suspected Potential Overdose Tracker (SPOT): public health implications

BACKGROUND

Effective responses to the worsening drug overdose epidemic require accurate and timely drug overdose surveillance data. The objectives of this paper are to describe the development, functionality, and accuracy of the Suspected Potential Overdose Tracker (SPOT) for predicting accidental drug overdose as the cause and manner of death in near real-time, and public health implications of adopting the tool.

METHODS

SPOT was developed to rapidly identify overdose deaths through a simple and duplicable process using data collected by death investigators. The tool assigns each death a ranking of 1 through 3 based on the likelihood of it being an unintentional drug overdose, with 1 representing the highest likelihood that the death will be confirmed as an unintentional drug overdose and 3 representing the lowest. We measured the accuracy of the tool for predicting overdose deaths by comparing potential overdose deaths in New York City from 2018-2020 that were identified using SPOT to finalized death certificates. We also calculated the proportion of death certificate-confirmed overdoses that were missed by the SPOT tool and the proportion of type 1 errors.

RESULTS

SPOT captured up to 77% of unintentional drug overdose deaths using data collected within 72 h of fatality. The tool predicted unintentional drug overdose from 2018 to 2020 with 93-97% accuracy for cases assigned a ranking of 1, 87-91% accuracy for cases assigned a ranking of 2, and 62-73% accuracy for cases assigned a ranking of 3. Among all unintentional overdose deaths in 2018, 2019, and 2020, 21%, 28%, and 33% were missed by the SPOT tool, respectively. During this timeframe, the proportion of type 1 errors ranged from 15%-23%.

CONCLUSIONS

SPOT may be used by health departments, epidemiologists, public health programs, and others to monitor overdose fatalities before death certificate data becomes available. Improved monitoring of overdose fatalities allows for rapid data-driven decision making, identification of gaps in public health and public safety overdose response, and evaluation and response to overdose prevention interventions, programs, and policies.

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.

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

BACKGROUND

The emergence of fentanyl around 2013 represented a new, deadly stage of the opioid epidemic in the USA. We aimed to develop a statistical regression approach to identify counties at the highest risk of high overdose mortality in the subsequent years by predicting annual county-level overdose death rates across the contiguous USA and to validate our approach against observed overdose mortality data collected between 2013 and 2018.

METHODS

We fit mixed-effects negative binomial regression models to predict overdose death rates in the subsequent year for 2013-18 for all contiguous state counties in the USA (ie, excluding Alaska and Hawaii). We used publicly available county-level data related to health-care access, drug markets, socio-demographics, and the geographical spread of opioid overdose as model predictors. The crude number of county-level overdose deaths was extracted from restricted US Centers for Disease Control and Prevention mortality records. To predict county-level overdose rates for the year 201X: (1) a model was trained on county-level predictor data for the years 2010-201(X-2) paired with county-level overdose deaths for the year 2011-201(X-1); (2) county-level predictor data for the year 201(X-1) was fed into the model to predict the 201X county-level crude number of overdose deaths; and (3) the latter were converted to a population-adjusted rate. For comparison, we generated a benchmark set of predictions by applying the observed slope of change in overdose death rates in the previous year to 201(X-1) rates. To assess the predictive performance of the model, we compared predicted values (of both the model and benchmark) to observed values by (1) calculating the mean average error, root mean squared error, and Spearman's correlation coefficient and (2) assessing the proportion of counties in the top decile (10%) of overdose death rates that were correctly predicted as such. Finally, in a post-hoc analysis, we sought to identify variables with greatest predictive utility.

FINDINGS

Between 2013 and 2018, among the 3106 US counties included, our modelling approach outperformed the benchmark strategy across all metrics. The observed average county-level overdose death rate rose from 11·8 per 100 000 people in 2013 to 15·4 in 2017 before falling to 14·6 in 2018. Our negative binomal modelling approach similarly identified an increasing trend, predicting an average 11·8 deaths per 100 000 in 2013, up to 15·1 in 2017, and increasing further to 16·4 in 2018. The benchmark model over-predicted average death rates each year, ranging from 13·0 per 100 000 in 2013 to 18·3 in 2018. Our modelling approach successfully ranked counties by overdose death rate identifying between 42% and 57% of counties in the top decile of overdose mortality (compared with 29% and 43% using the benchmark) each year and identified 194 of the 808 counties with emergent overdose outbreaks (ie, newly entered the top decile) across the study period, versus 31 using the benchmark. In the post-hoc analysis, we identified geospatial proximity of overdose in nearby counties, opioid prescription rate, presence of an urgent care facility, and several economic indicators as the variables with the greatest predictive utility.

INTERPRETATION

Our model shows that a regression approach can effectively predict county-level overdose death rates and serve as a risk assessment tool to identify future high mortality counties throughout an emerging drug use epidemic.

FUNDING

National Institute on Drug Abuse.

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.

Types of child maltreatment and child welfare involvement among opioid-using mothers involved in substance use treatment

Although there is a significant link between maternal substance use and child maltreatment risk, extant literature has not investigated this link specifically among the growing number of parents abusing opioids. Underreporting of opioid use within child welfare presents further challenges in elucidating relations between maternal opioid use and child maltreatment. The purpose of the current study is to examine the link between maternal opioid use in women in substance use treatment and self-reported rates of child maltreatment and child welfare involvement of their children. We examined maternal substance use, severity of substance use, severity and type of child maltreatment of their children, and child welfare involvement across mothers who misuse opioids and misuse other substances using self-report surveys with 89 mothers. Results suggest similarities and differences among mothers who use opioids and other substances. Mothers who use opioids endorsed more significant and prolonged involvement with child welfare than mothers who use other substances. Participants did not endorse significant differences between rates of child maltreatment, and treatment engagement across groups. Given increased awareness of significant risks associated with opioid abuse, including greater risk for child maltreatment, a better understanding of its intersection with child welfare is necessary.