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

Is fentanyl in everything? Examining the unexpected occurrence of illicit opioids in British Columbia's drug supply

BACKGROUND

Illicit opioids, including fentanyl, are linked to unprecedented levels of overdose in Canada and elsewhere. The risks associated with illicit opioids can include high potency, unpredictable concentration and the unexpected presence in other drugs. Within this context, we examine drug checking data to better understand the presence of illicit opioids such as fentanyl in other drugs and possible ways to interpret these results.

METHODS

Three years (2021-2023) of data (18,474 samples) from Substance Drug Checking in British Columbia, Canada were examined to investigate the risks associated with the detection of opioids in other drugs such as cocaine and methamphetamine, as well as in other drug categories. Samples were tested by paper spray mass spectrometry (PS-MS), fentanyl test strips and Fourier-Transform infrared spectroscopy (FTIR). We examine the 8889 samples not expected to include fentanyl to confirm; if the expected drug was detected, if unexpected opioids were detected, and when the unexpected opioids are in trace concentration.

RESULTS

Unexpected opioids were rarely detected (2%) in other drugs (189 of 8889 samples) with most (61.4%) detected at trace concentration levels. Unexpected opioids are far more likely to be found in samples that did not contain the expected drug than in samples that were confirmed to contain the expected drug. The least common scenario (below 1%) were substances that included the expected drug plus unexpected opioid above trace concentration. These findings raise questions on how to interpret and communicate the detection of fentanyl and related opioids in other drugs. We present three potential interpretations: (1) mistaken and misrepresented samples where the expected drug was never detected, (2) cross contamination when opioids were at trace concentration levels, or (3) adulteration as the least frequent scenario where opioids were detected above trace concentrations in combination with the expected drug.

CONCLUSIONS

In a region where fentanyl is associated with extreme rates of overdose, it remains rare to find such opioids in other drugs. However, the risk of fentanyl in other drugs remains an ongoing threat that warrants responses by individuals and public health. We provide possible interpretations to inform such responses. Our data raises questions on how to interpret and communicate the detection of fentanyl and other opioids in other drugs.

"Sometimes I'm interested in seeing a fuller story to tell with numbers" Implementing a forecasting dashboard for harm reduction and overdose prevention: a qualitative assessment

OBJECTIVES

The escalating overdose crisis in the United States points to the urgent need for new and novel data tools. Overdose data tools are growing in popularity but still face timely delays in surveillance data availability, lack of completeness, and wide variability in quality by region. As such, we need innovative tools to identify and prioritize emerging and high-need areas. Forecasting offers one such solution. Machine learning methods leverage numerous datasets that could be used to predict future vulnerability to overdose at the regional, town, and even neighborhood levels. This study aimed to understand the multi-level factors affecting the early stages of implementation for an overdose forecasting dashboard. This dashboard was developed with and for statewide harm reduction providers to increase data-driven response and resource distribution at the neighborhood level.

METHODS

As part of PROVIDENT (Preventing OVerdose using Information and Data from the EnvironmeNT), a randomized, statewide community trial, we conducted an implementation study where we facilitated three focus groups with harm reduction organizations enrolled in the larger trial. Focus group participants held titles such as peer outreach workers, case managers, and program coordinators/managers. We employed the Exploration, Preparation, Implementation, Sustainment (EPIS) Framework to guide our analysis. This framework offers a multi-level, four-phase analysis unique to implementation within a human services environment to assess the exploration and preparation phases that influenced the early launch of the intervention.

RESULTS

Multiple themes centering on organizational culture and resources emerged, including limited staff capacity for new interventions and repeated exposure to stress and trauma, which could limit intervention uptake. Community-level themes included the burden of data collection for program funding and statewide efforts to build stronger networks for data collection and dashboarding and data-driven resource allocation.

DISCUSSION

Using an implementation framework within the larger study allowed us to identify multi-level and contextual factors affecting the early implementation of a forecasting dashboard within the PROVIDENT community trial. Additional investments to build organizational and community capacity may be required to create the optimal implementation setting and integration of forecasting tools.

Demographic, Social, Behavioral, and Clinical Characteristics Associated with Long-Term Opioid Therapy and Any Opioid Prescription in High-Risk VA Patients

BACKGROUND

Social risks (individual social and economic conditions) have been implicated as playing a major role in the opioid epidemic and may be more prevalent in the most medically vulnerable patients. However, the extent to which specific social risks and other patient factors are associated with opioid use among high-risk patients has not been comprehensively assessed.

OBJECTIVE

To identify patient-reported and electronic health record (EHR)-derived demographic, social, behavioral/psychological, and clinical characteristics associated with opioid use in Veterans Affairs (VA) patients at high risk for hospitalization or death.

DESIGN

We used generalized estimating equations to calculate the probability of long-term opioid therapy (LTOT) and the probability of filling any opioid prescription (regardless of duration) over five intervals during a 4-year period (12/2016-12/2020).

PARTICIPANTS

Prospective cohort of 4121 medically high-risk VA patients not receiving palliative or end-of-life care, and who responded to a survey mailed to a nationally representative sample of 10,000 high-risk VA patients.

MAIN MEASURES

Patient-reported demographic, social risk, behavioral/psychological, and clinical measures, and linked EHR-derived data.

KEY RESULTS

The average age was 69.8 years, 6.7% were female, and 17.5% were Non-Hispanic Black race/ethnicity. The majority had diagnosed chronic pain (76.1%). LTOT and any opioid prescription were positively associated with the following: younger age, non-Hispanic White race/ethnicity (compared to non-Hispanic Black race/ethnicity), male sex assigned at birth (LTOT only), not being currently employed, current tobacco use, no alcohol use, higher grit (any opioid prescription only), functional limitations, diagnosed chronic pain, lower comorbidity burden (LTOT only), obesity class I or class II/III (any opioid prescription only), undergoing surgery (any opioid prescription only), and diagnosed cancer (any opioid prescription only).

CONCLUSIONS

Multifactor screening could help identify individuals at elevated risk for adverse opioid-related outcomes and augment current multifaceted initiatives, as several social risks and patient characteristics were predictors of LTOT and any opioid prescription.

Spatiotemporal Cluster Detection for COVID-19 Outbreak Surveillance: Descriptive Analysis Study

BACKGROUND

During the peak of the winter 2020-2021 surge, the number of weekly reported COVID-19 outbreaks in Washington State was 231; the majority occurred in high-priority settings such as workplaces, community settings, and schools. The Washington State Department of Health used automated address matching to identify clusters at health care facilities. No other systematic, statewide outbreak detection methods were in place. This was a gap given the high volume of cases, which delayed investigations and decreased data completeness, potentially leading to undetected outbreaks. We initiated statewide cluster detection using SaTScan, implementing a space-time permutation model to identify COVID-19 clusters for investigation.

OBJECTIVE

To improve outbreak detection, the Washington State Department of Health initiated a systematic cluster detection model to identify timely and actionable COVID-19 clusters for local health jurisdiction (LHJ) investigation and resource prioritization. This report details the model's implementation and the assessment of the tool's effectiveness.

METHODS

In total, 6 LHJs participated in a pilot to test model parameters including analysis type, geographic aggregation, cluster radius, and data lag. Parameters were determined through heuristic criteria to detect clusters early when they are smaller, making interventions more feasible. This study reviews all clusters detected after statewide implementation from July 17 to December 17, 2021. The clusters were analyzed by LHJ population and disease incidence. Clusters were compared with reported outbreaks.

RESULTS

A weekly, LHJ-specific retrospective space-time permutation model identified 2874 new clusters during this period. While the weekly analysis included case data from the prior 3 weeks, 58.25% (n=1674) of all clusters identified were timely-having occurred within 1 week of the analysis and early enough for intervention to prevent further transmission. There were 2874 reported outbreaks during this same period. Of those, 363 (12.63%) matched to at least one SaTScan cluster. The most frequent settings among reported and matched outbreaks were schools and youth programs (n=825, 28.71% and n=108, 29.8%), workplaces (n=617, 21.46% and n=56, 15%), and long-term care facilities (n=541, 18.82% and n=99, 27.3%). Settings with the highest percentage of clusters that matched outbreaks were community settings (16/72, 22%) and congregate housing (44/212, 20.8%). The model identified approximately one-third (119/363, 32.8%) of matched outbreaks before cases were associated with the outbreak event in our surveillance system.

CONCLUSIONS

Our goal was to routinely and systematically identify timely and actionable COVID-19 clusters statewide. Regardless of population or incidence, the model identified reasonably sized, timely clusters statewide, meeting the objective. Among some high-priority settings subject to public health interventions throughout the pandemic, such as schools and community settings, the model identified clusters that were matched to reported outbreaks. In workplaces, another high-priority setting, results suggest the model might be able to identify outbreaks sooner than existing outbreak detection methods.

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.

Predicting U.S. county opioid poisoning mortality from multi-modal social media and psychological self-report data

Opioid poisoning mortality is a substantial public health crisis in the United States, with opioids involved in approximately 75% of the nearly 1 million drug related deaths since 1999. Research suggests that the epidemic is driven by both over-prescribing and social and psychological determinants such as economic stability, hopelessness, and isolation. Hindering this research is a lack of measurements of these social and psychological constructs at fine-grained spatial and temporal resolutions. To address this issue, we use a multi-modal data set consisting of natural language from Twitter, psychometric self-reports of depression and well-being, and traditional area-based measures of socio-demographics and health-related risk factors. Unlike previous work using social media data, we do not rely on opioid or substance related keywords to track community poisonings. Instead, we leverage a large, open vocabulary of thousands of words in order to fully characterize communities suffering from opioid poisoning, using a sample of 1.5 billion tweets from 6 million U.S. county mapped Twitter users. Results show that Twitter language predicted opioid poisoning mortality better than factors relating to socio-demographics, access to healthcare, physical pain, and psychological well-being. Additionally, risk factors revealed by the Twitter language analysis included negative emotions, discussions of long work hours, and boredom, whereas protective factors included resilience, travel/leisure, and positive emotions, dovetailing with results from the psychometric self-report data. The results show that natural language from public social media can be used as a surveillance tool for both predicting community opioid poisonings and understanding the dynamic social and psychological nature of the epidemic.

Understanding research methods, limitations, and applications of drug data collected by the National Forensic Laboratory Information System (NFLIS-Drug)

The National Forensic Laboratory Information System (NFLIS) is a drug surveillance program of the US Drug Enforcement Administration that systematically collects data on drugs that are seized by law enforcement and submitted to and analyzed by the Nation's forensic laboratories (NFLIS-Drug). NFLIS-Drug data are increasingly used in predictive modeling and drug surveillance to examine drug availability patterns. Given the complexity of the data and data collection, there are some common methodological pitfalls that we highlight with the aim of helping researchers avoid these concerns. The analysis done for this Technical Note is based on a review of the scientific literature that includes 428 unique, refereed article citations in 182 distinct journals published between January 1, 2005, and April 30, 2021. Each article was analyzed according to how NFLIS-Drug data were mentioned and whether NFLIS-Drug data were included. A sample of 37 articles was studied in-depth, and data issues were summarized. Using examples from the literature, this Technical Note highlights eight broad concerns that have important implications for the proper applications, interpretations, and limitations of NFLIS-Drug data with suggestions for improving research methods and accurate reporting of forensic drug data. NFLIS-Drug data are timely and provide key information to inform drug use trends across the United States; however, our present analysis shows that NFLIS-Drug data are misunderstood and represented in the literature. In addition to highlighting these issues, DEA has created several resources to assist NFLIS data users and researchers, which are summarized in the discussion.

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.

Multiscale dimensions of county-level disparities in opioid use disorder rates among older Medicare beneficiaries

Background

Opioid use disorder (OUD) among older adults (age ≥ 65) is a growing yet underexplored public health concern and previous research has mainly assumed that the spatial process underlying geographic patterns of population health outcomes is constant across space. This study is among the first to apply a local modeling perspective to examine the geographic disparity in county-level OUD rates among older Medicare beneficiaries and the spatial non-stationarity in the relationships between determinants and OUD rates.

Methods

Data are from a variety of national sources including the Centers for Medicare & Medicaid Services beneficiary-level data from 2020 aggregated to the county-level and county-equivalents, and the 2016-2020 American Community Survey (ACS) 5-year estimates for 3,108 contiguous US counties. We use multiscale geographically weighted regression to investigate three dimensions of spatial process, namely "level of influence" (the percentage of older Medicare beneficiaries affected by a certain determinant), "scalability" (the spatial process of a determinant as global, regional, or local), and "specificity" (the determinant that has the strongest association with the OUD rate).

Results

The results indicate great spatial heterogeneity in the distribution of OUD rates. Beneficiaries' characteristics, including the average age, racial/ethnic composition, and the average hierarchical condition categories (HCC) score, play important roles in shaping OUD rates as they are identified as primary influencers (impacting more than 50% of the population) and the most dominant determinants in US counties. Moreover, the percentage of non-Hispanic white beneficiaries, average number of mental health conditions, and the average HCC score demonstrate spatial non-stationarity in their associations with the OUD rates, suggesting that these variables are more important in some counties than others.

Conclusions

Our findings highlight the importance of a local perspective in addressing the geographic disparity in OUD rates among older adults. Interventions that aim to reduce OUD rates in US counties may adopt a place-based approach, which could consider the local needs and differential scales of spatial process.

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.

What Are the Correlates of Global Variations in the Prevalence of Opioid Use Disorders? An Analysis of Data From the Global Burden of Disease Study, 2019

Introduction

The recent opioid crisis in North America has brought the problem of opioid use disorders (OUD) into clinical and public health focus, with experts warning that other countries or regions may be at future risk of experiencing such crises. The existing literature suggests that a wide range of social, cultural and economic factors may be associated with the onset, course and outcome of OUD in individuals. The current study uses data on the estimated prevalence of OUDs across 115 countries, obtained from the Global Burden of Disease Study, 2019, to examine the bivariate and multivariate associations between national prevalence of OUD and these factors.

Methods

Data on the estimated prevalence of OUDs was obtained via a database query from the Global Burden of Disease (GBD) Collaborative Network database for the year 2019. Recent (2018-2019) data on 10 relevant variables identified in the literature (gross national income, economic inequality, urbanization, social capital, religious affiliation and practice, unemployment, divorce, cultural individualism, and prevalence of depression) were obtained from the GBD, World Bank and Our World in Data databases. After transformation to a normal distribution, bivariate and univariate analyses were conducted to identify the significance and strength of the associations between these variables and the prevalence of OUD.

Results

Of the 10 variables studied, all variables except the divorce rate and religious affiliation were significantly correlated with the prevalence of OUD on bivariate analyses, though the strength of these associations was in the poor to fair range. On multivariate analysis, a significant association was observed only for the prevalence of depression, with trends towards a positive association for cultural individualism and unemployment, and a protective trend observed for religious practice.

Discussion

Though subject to certain limitations inherent in cross-sectional analyses, these results suggest that certain variables may be associated with a higher prevalence of OUD at the national level. Replication and refinement of these analyses may prove useful in identifying countries or regions at risk of a future opioid epidemic or crisis, which could facilitate the institution of preventive measures or early intervention strategies.