Prescription opioid poisoning across urban and rural areas: identifying vulnerable groups and geographic areas

Risk factors for persistent fatal opioid-involved overdose clusters in Massachusetts 2011-2021: a spatial statistical analysis with socio-economic, accessibility, and prescription factors

BACKGROUND

Fatal opioid-involved overdose rates increased precipitously from 5.0 per 100,000 population to 33.5 in Massachusetts between 1999 and 2022.

METHODS

We used spatial rate smoothing techniques to identify persistent opioid overdose-involved fatality clusters at the ZIP Code Tabulation Area (ZCTA) level. Rate smoothing techniques were employed to identify locations of high fatal opioid overdose rates where population counts were low. In Massachusetts, this included areas with both sparse data and low population density. We used Local Indicators of Spatial Association (LISA) cluster analyses with the raw incidence rates, and the Empirical Bayes smoothed rates to identify clusters from 2011 to 2021. We also estimated Empirical Bayes LISA cluster estimates to identify clusters during the same period. We constructed measures of the socio-built environment and potentially inappropriate prescribing using principal components analysis. The resulting measures were used as covariates in Conditional Autoregressive Bayesian models that acknowledge spatial autocorrelation to predict both, if a ZCTA was part of an opioid-involved cluster for fatal overdose rates, as well as the number of times that it was part of a cluster of high incidence rates.

RESULTS

LISA clusters for smoothed data were able to identify whether a ZCTA was part of a opioid involved fatality incidence cluster earlier in the study period, when compared to LISA clusters based on raw rates. PCA helped in identifying unique socio-environmental factors, such as minoritized populations and poverty, potentially inappropriate prescribing, access to amenities, and rurality by combining socioeconomic, built environment and prescription variables that were highly correlated with each other. In all models except for those that used raw rates to estimate whether a ZCTA was part of a high fatality cluster, opioid overdose fatality clusters in Massachusetts had high percentages of Black and Hispanic residents, and households experiencing poverty. The models that were fitted on Empirical Bayes LISA identified this phenomenon earlier in the study period than the raw rate LISA. However, all the models identified minoritized populations and poverty as significant factors in predicting the persistence of a ZCTA being part of a high opioid overdose cluster during this time period.

CONCLUSION

Conducting spatially robust analyses may help inform policies to identify community-level risks for opioid-involved overdose deaths sooner than depending on raw incidence rates alone. The results can help inform policy makers and planners about locations of persistent risk.

Association between trajectories of prescription opioid use and risk of opioid use disorder and overdose among US nonmetastatic breast cancer survivors

PURPOSE

To examine the association between prescription opioid use trajectories and risk of opioid use disorder (OUD) or overdose among nonmetastatic breast cancer survivors by treatment type.

METHODS

This retrospective cohort study included female nonmetastatic breast cancer survivors with at least 1 opioid prescription fill in 2010-2019 Surveillance, Epidemiology and End Results linked Medicare data. Opioid mean daily morphine milligram equivalents (MME) calculated within 1.5 years after initiating active breast cancer therapy. Group-based trajectory models identified distinct opioid use trajectory patterns. Risk of time to first OUD/overdose event within 1 year after the trajectory period was calculated for distinct trajectory groups using Cox proportional hazards models. Analyses were stratified by treatment type.

RESULTS

Four opioid use trajectories were identified for each treatment group. For 38,030 survivors with systemic endocrine therapy, 3 trajectories were associated with increased OUD/overdose risk compared with early discontinuation: minimal dose (< 5 MME; adjusted hazard ratio [aHR] = 1.73 [95% CI  1.43-2.09]), very low dose (5-25 MME; 2.67 [2.05-3.48]), and moderate dose (51-90 MME; 6.20 [4.69-8.19]). For 9477 survivors with adjuvant chemotherapy, low-dose opioid use was associated with higher OUD/overdose risk (aHR = 7.33 [95% CI 2.52-21.31]) compared with early discontinuation. For 3513 survivors with neoadjuvant chemotherapy, the differences in OUD/OD risks across the 4 trajectories were not significant.

CONCLUSIONS

Among Medicare nonmetastatic breast cancer survivors receiving systemic endocrine therapy or adjuvant chemotherapy, compared with early discontinuation, low-dose or moderate-dose opioid use were associated with six- to sevenfold higher OUD/overdose risk. Breast cancer survivors at high-risk of OUD/overdose may benefit from targeted interventions (e.g., pain clinic referral).

Disrupted resting-state functional connectivity of frontal network in opium use disorder

Opioid use disorder (OUD) as a chronic relapsing disorder is initially driven by dysfunction of brain reward networks and associated with several psychiatric disorders. Resting-state EEG was recorded in 24 healthy participants as well as 31 patients with OUD. Healthy participants do not meet OUD criteria. After pre-processing of the raw EEG, functional connectivity in the frontal network using eLORETA and all networks using graph analysis method were calculated. Patients with OUD had higher electrical neuronal activity compared to healthy participants in higher frequency bands. The statistical analysis revealed that patients with OUD had significantly decreased phase synchronization in β1 and β2 frequency bands compared with the healthy group in the frontal network. Regarding global network topology, we found a significant decrease in the characteristic path length and an increase in global efficiency, clustering coefficient, and transitivity in patients compared with the healthy group. These changes indicated that local specialization and global integration of the brain were disrupted in OUD and it suggests a tendency toward random network configuration of functional brain networks in patients with OUD. Disturbances in EEG-based brain network indices might reflect an altered cortical functional network in OUD. These findings might provide useful biomarkers to understand cortical brain pathology in opium use disorder.

An external exposome-wide association study of opioid use disorder diagnosed during pregnancy in Florida

The prevalence of opioid use disorder (OUD) during pregnancy has quadrupled in recent years and widely varies geographically in the US. However, few studies have examined which environmental factors are associated with OUD during pregnancy. We conducted an external exposome-wide association study (ExWAS) to investigate the associations between external environmental factors and OUD diagnosed during pregnancy. Data were obtained from a unique, statewide database in Florida comprising linked individual-level birth and electronic health records. A total of 255,228 pregnancies with conception dates between 2012 and 2016 were included. We examined 82 exposome measures characterizing seven aspects of the built and social environment and spatiotemporally linked them to each individual record. A two-phase procedure was utilized for the external ExWAS. In Phase 1, we randomly divided the data into a discovery set (50 %) and a replication set (50 %). Associations between exposome measures (normalized and standardized) and OUD initially diagnosed during pregnancy were examined using logistic regression. A total of 15 variables were significant in both the discovery and replication sets. In Phase 2, multivariable logistic regression was used to fit all variables selected from Phase 1. Measures of walkability (the national walkability index, OR: 1.23, 95 % CI: 1.17, 1.29), vacant land (the percent vacant land for 36 months or longer, OR: 1.06, 95 % CI: 1.00, 1.12) and food access (the percentage of low food access population that are seniors at 1/2 mile, OR: 1.47, 95 % CI: 1.38, 1.57) were each associated with diagnosis of OUD during pregnancy. This is the first external ExWAS of OUD during pregnancy, and the results suggest that low food access, high walkability, and high vacant land in under-resourced neighborhoods are associated with diagnosis of OUD during pregnancy. These findings could help develop complementary tools for universal screening for substance use and provide direction for future studies.

A counterfactual analysis of opioid-involved deaths during the COVID-19 pandemic using a spatiotemporal random forest modeling approach

The global pandemic of SARS-CoV-2 (COVID-19) has been linked to adversely impacting individuals with opioid use disorder in the United States. This study focuses on analyzing opioid-involved mortality in the context of COVID-19 in the U.S. from a geospatial perspective. We investigated spatiotemporal patterns of opioid-involved deaths during 2020 and compared the spatiotemporal pattern of these deaths with patterns for the previous three years (2017-2019) to understand changes in the context of the COVID-19 pandemic. A counterfactual analysis framework together with a space-time random forest (STRF) model were used to estimate the increase in opioid-involved deaths related to the pandemic. To gain further insight into the relationship between opioid deaths and COVID-19-related factors, we built a space-time random forest model for the City of Chicago, that experienced a steep increase in opioid-related deaths during 2020. High ranking indicators identified by the model such as the number of positive COVID-19 cases adjusted by population and the change in stay-at-home dwell time during the pandemic were used to generate a vulnerability index for opioid overdoses during the COVID-19 pandemic in Chicago.

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.

The spatial prevalence and associated factors of opioid overdose mortality in Milwaukee County, Wisconsin (2003-2018)

Mortality from opioid overdose has become the leading cause of non-natural death in Milwaukee County, Wisconsin in recent years. In order to better understand the opioid epidemic and formulate pro-active responses to the crisis at the local level, this study examines the spatial prevalence and associated factors of opioid overdoses that end in mortality in Milwaukee, WI using the spatial econometrics model. The social determinants of health framework is used to identify the potential related socioeconomic factors associated with opioid use and misuse. Using principal component analysis, 6 primary components are identified from the chosen social determinants and used as explanatory variables in the spatial econometric analysis. The age-adjusted standardized mortality rate is calculated for each census tract as the dependent variable in the analysis. Overall low socioeconomic status, labor-intensive occupations, income inequality, and the 20-34-year-old age group are identified as variables with a significant contribution to high overdose mortality rates, both directly and indirectly. A significant global spillover effect is also identified at the census tract level, indicating the severity of the opioid epidemic in Milwaukee County. This study reveals the overall contribution that socioeconomic factors have on the opioid epidemic and their associated feedback effects, providing targeted information on the opioid epidemic.

The Impact of COVID-19 on Opioid-Related Overdose Deaths in Texas

Prior to the COVID-19 pandemic, the United States was facing an epidemic of opioid overdose deaths, clouding accurate inferences about the impact of the pandemic at the population level. We sought to determine the existence of increases in the trends of opioid-related overdose (ORO) deaths in the Greater Houston metropolitan area from January 2015 through December 2021, and to describe the social vulnerability present in the geographic location of these deaths. We merged records from the county medical examiner's office with social vulnerability indexes (SVIs) for the region and present geospatial locations of the aggregated ORO deaths. Time series analyses were conducted to determine trends in the deaths, with a specific focus on the years 2019 to 2021. A total of 2660 deaths were included in the study and the mean (standard deviation, SD) age at death was 41.04 (13.60) years. Heroin and fentanyl were the most frequent opioids detected, present in 1153 (43.35%) and 1023 (38.46%) ORO deaths. We found that ORO deaths increased during the years 2019 to 2021 (p-value ≤ 0.001) when compared with 2015. Compared to the year 2019, ORO deaths increased for the years 2020 and 2021 (p-value ≤ 0.001). The geographic locations of ORO deaths were not associated with differences in the SVI. The COVID-19 pandemic had an impact on increasing ORO deaths in the metropolitan Houston area; however, identifying the determinants to guide targeted interventions in the areas of greatest need may require other factors, in addition to community-level social vulnerability parameters.

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.

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.

Identifying Predictors of Opioid Overdose Death at a Neighborhood Level With Machine Learning

Predictors of opioid overdose death in neighborhoods are important to identify, both to understand characteristics of high-risk areas and to prioritize limited prevention and intervention resources. Machine learning methods could serve as a valuable tool for identifying neighborhood-level predictors. We examined statewide data on opioid overdose death from Rhode Island (log-transformed rates for 2016-2019) and 203 covariates from the American Community Survey for 742 US Census block groups. The analysis included a least absolute shrinkage and selection operator (LASSO) algorithm followed by variable importance rankings from a random forest algorithm. We employed double cross-validation, with 10 folds in the inner loop to train the model and 4 outer folds to assess predictive performance. The ranked variables included a range of dimensions of socioeconomic status, including education, income and wealth, residential stability, race/ethnicity, social isolation, and occupational status. The R2 value of the model on testing data was 0.17. While many predictors of overdose death were in established domains (education, income, occupation), we also identified novel domains (residential stability, racial/ethnic distribution, and social isolation). Predictive modeling with machine learning can identify new neighborhood-level predictors of overdose in the continually evolving opioid epidemic and anticipate the neighborhoods at high risk of overdose mortality.

Analysis of Opioid Poisoning in Medically Underserved Rural Areas: An Evaluation of International Statistical Classification of Diseases Codes from the State of South Dakota

Background

Rural hospitals and patient population tend to be medically underserved. The states with more rural population dispensed the most opioids per person in the last 10 years. We aimed to explore if rurality contributed to the likelihood of higher opioid adversity and how it affected substance-use rehabilitation in federally designated Medically Underserved Areas (MUAs).

Methods

We analyzed data dispensed by the South Dakota Department of Health (DOH) on opioid-led poisoning International Classification of Disease (ICD) codes that were active within the state in the last decade. After locating MUA rural and partially rural counties, we cross profiled the counties to the state datasets. Assessments were conducted using the PROC SURVEY methods in SAS version 9.3 (SAS Institute) and checked for multicollinearity with the Belsley-Kuh-Welsch technique. Finally, we used the American Hospital Association (AHA) database for analyzing substance use rehabilitation availability on per hospital basis.

Results

The chi-square statistic for comparing opioid codes against non-opioid codes distributed among three categories, rural, non-rural, and partially rural was significant at the limit of p <0.05. 81.134% of opioid-led poisoning codes were activated in a rural county. Only four hospitals had substance-use rehabilitation, three of which were in a non-rural area. More people from the teenage and early-adulthood years (10-19) were prone to opioid usage.

Conclusions

Rural counties in South Dakota were more likely to dispense opioid care and not have access to rehabilitation. We also found that as the opioid dispensing rate at hospitals within a state decreased as the state had less rural counties. Introducing public programs to train more physicians and cutting down cost of non-opioid based care may lower opioid distribution and increase rehabilitation options in rural hospitals.

Spatiotemporal Analysis of the Association Between Pain Management Clinic Laws and Opioid Prescribing and Overdose Deaths

Pain management clinic (PMC) laws were enacted by 12 states to promote appropriate opioid prescribing, but their impact is inadequately understood. We analyzed county-level opioid overdose deaths (National Vital Statistics System) and patients filling long-duration (≥30 day) or high-dose (≥90 morphine milligram equivalents per day) opioid prescriptions (IQVIA, Inc.) in the United States in 2010-2018. We fitted Besag-York-Mollié spatiotemporal models to estimate annual relative rates (RRs) of overdose and prevalence ratios (PRs) of high-risk prescribing associated with any PMC law and 3 provisions: payment restrictions, site inspections, and criminal penalties. Laws with criminal penalties were significantly associated with reduced PRs of long-duration and high-dose opioid prescriptions (adjusted PR = 0.82, 95% credible interval (CrI): 0.82, 0.82, and adjusted PR = 0.73, 95% CI: 0.73, 0.74 respectively) and reduced RRs of total and natural/semisynthetic opioid overdoses (adjusted RR = 0.86, 95% CrI: 0.80, 0.92, and adjusted RR = 0.84, and 95% CrI: 0.77, 0.92, respectively). Conversely, PMC laws were associated with increased relative rates of synthetic opioid and heroin overdose deaths, especially criminal penalties (adjusted RR = 1.83, 95% CrI: 1.59, 2.11, and adjusted RR = 2.59, 95% CrI: 2.22, 3.02, respectively). Findings suggest that laws with criminal penalties were associated with intended reductions in high-risk opioid prescribing and some opioid overdoses but raise concerns regarding unintended consequences on heroin/synthetic overdoses.

Association between county-level sociodemographic characteristics and county-level differences in opioid dispensing

Background

While overall opioid prescribing has been decreasing in the United States, the rates of prescribing at the county level have been variable. Previous studies show that social determinants of health (the social and economic conditions in which we live) may play a role in opioid prescribing; however, researchers have not examined this relationship across US counties. This cross-sectional study seeks to determine whether county-level sociodemographic characteristics (e.g., economic, housing, social environment, healthcare environment, and population characteristics) are associated with county level differences in opioid dispensing.

Methods

Data from 2,881 counties in the United States from 2017 to 2018 were used for this study. Opioid dispensing was measured using morphine milligram equivalents (MME) per capita. Spatial error models were used to measure the association between county-level sociodemographic characteristics and MME per capita while adjusting for spatial correlation between neighboring counties.

Results

In the adjusted model, counties with a higher percentage of people below the poverty line, with less than a 4-year college degree, and without health insurance were associated with higher MME dispensed per capita, as were counties with higher percentages of families headed by a single parent, persons separated or divorced, and those with disabilities. Conversely, minority race/ethnicity and rural population were associated with lower opioid dispensing.

Conclusions

County-level sociodemographics can differ in their association with opioid dispensing, hence examining which county-level factors help in improving opioid prescribing, and implementing overdose prevention strategies that tackle these factors is important.

Urban scaling of opioid analgesic sales in the United States

Opioid misuse is a public health crisis in the United States. The origin of this crisis is associated with a sharp increase in opioid analgesic prescribing. We used the urban scaling framework to analyze opioid prescribing patterns in US commuting zones (CZs), i.e., groups of counties based on commuting patterns. The urban scaling framework postulates that a set of scaling relations can be used to predict health outcomes and behaviors in cities. We used data from the Drug Enforcement Administration’s Automated Reports and Consolidated Ordering System (ARCOS) to calculate counts of oxycodone/hydrocodone pills distributed to 607 CZs in the continental US from 2006 to 2014. We estimated the scaling coefficient of opioid pill counts by regressing log(pills) on log(population) using a piecewise linear spline with a single knot at 82,363. Our results show that CZs with populations below the knot scaled superlinearly (β = 1.36), i.e., larger CZs had disproportionally larger pill counts compared to smaller CZs. On the other hand, CZs with populations above the knot scaled sublinearly (β = 0.92), i.e., larger CZs had disproportionally smaller pill counts compared to smaller CZs. This dual scaling pattern was consistent across US census regions. For CZs with population below the knot, the superlinear scaling of pills is consistent with the explanation that an increased number of successful matches between prescribers and users will lead to higher prescribing rates. The non-linear scaling behavior observed could be the result of a combination of factors, including stronger health care systems and prescribing regulation in largely populated commuting zones, as well as high availability of other opioids such as heroin in these commuting zones. Future research should explore potential mechanisms for the non-linearity of prescription opioid pills.

A Demographic and Regional Comparison of Opioid-Related Hospital Visits within Community Type in the United States

BACKGROUND

The opioid epidemic is a complex national crisis in the United States with a 400% increase in related deaths over the past two decades with no signs of slowing. The purpose of this study was to assess the incidence of opioid use, based on the geographic and population characteristics.

METHODS

The opioid-related hospital inpatient stays and emergency department visits obtained from the 2010 to 2018 Healthcare Cost and Utilization Project and demographic confounders, including age, race, education, and income gathered from US Census data were analyzed through generalized linear mixed models and reported by community size and region.

RESULTS

Opioid use varies among population center sizes and the region analyzed. In general, opioid visits in the southwest region were greatest across the majority of population center sizes. Rural usage was greatest in the northeast, southeast, and southwest. Unemployment and diverse ethnicities were commonly associated with opioid use in the metro areas studied but these associations were not seen in rural areas.

CONCLUSION

Opioid use remains significant among diverse populations across the United States. Understanding the unique dynamics associated with opioid usage in populations within the regions studied is important in guiding future interventions to fight this crisis.

Assessing opioid overdose risk: a review of clinical prediction models utilizing patient-level data

Drug, and specifically opioid-related, overdoses remain a major public health problem in the United States. Multiple studies have examined individual risk factors associated with overdose risk, but research developing clinical risk prediction tools for overdose has only emerged in the last few years. We conducted a comprehensive review of the literature on patient-level factors associated with opioid-related overdose risk, with an emphasis on clinical risk prediction models for opioid-related overdose in the United States. Studies that developed and/or validated clinical prediction models were closely reviewed and evaluated to determine the state of the field. We identified 12 studies that reported risk prediction models for opioid-related overdose risk. Published models were developed from a variety of data sources, including Veterans Health Administration data, Medicare data, commercial insurance data, and statewide linked datasets. Studies reported model performance using measures of discrimination, usually at good-to-excellent levels, though they did not always assess calibration. C-statistics were better for models that included clinical predictors (c-statistics: 0.75-0.95) compared to models without them (c-statistics: 0.69-0.82). External validation of models was rare, and we found no studies evaluating implementation of models or risk prediction tools into clinical practice. A common feature of these models was a high rate of false positives, largely because opioid-related overdose is rare in the general population. Thus, efforts to implement prediction models into practice should take into account that published models overestimate overdose risk for many low-risk patients. Future prediction models assessing overdose risk should employ external validation and address model calibration. In order to translate findings from prediction models into clinical public health benefit, future studies should focus on developing clinical prediction tools based on prediction models, implementing these tools into clinical practice, and evaluating the impact of these models on treatment decisions, patient outcomes, and, ultimately, opioid overdose rates.

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.

Longitudinal Dose Trajectory Among Patients Tapering Long-Term Opioids

OBJECTIVE

To evaluate the dose trajectory of new opioid tapers and estimate the percentage of patients with sustained tapers at long-term follow-up.

DESIGN

Retrospective cohort study.

SETTING

Data from the OptumLabs Data Warehouse® which includes de-identified medical and pharmacy claims and enrollment records for commercial and Medicare Advantage enrollees, representing a diverse mixture of ages, ethnicities, and geographical regions across the United States.

SUBJECTS

Patients prescribed stable, higher-dose opioids for ≥12 months from 2008 to 2018.

METHODS

Tapering was defined as ≥15% relative reduction in average MME/day during any of six overlapping 60-day periods in the initial 7 months of follow-up after the period of stable baseline dosing. Average monthly dose was ascertained during consecutive 60-day periods up to 16 months of follow-up. Linear regression estimated the geometric mean relative dose by tapering status and follow-up duration. Poisson regression estimated the percentage of tapered patients with sustained dose reductions at follow-up and patient-level predictors of failing to sustain tapers.

RESULTS

The sample included 113,618 patients with 203,920 periods of stable baseline dosing (mean follow-up = 13.7 months). Tapering was initiated during 37,170 follow-up periods (18.2%). After taper initiation, patients had a substantial initial mean dose reduction (geometric mean relative dose .73 [95% CI: .72-.74]) that was sustained through 16 months of follow-up; at which point, 69.8% (95% CI: 69.1%-70.4%) of patients who initiated tapers had a relative dose reduction ≥15%, and 14.2% (95% CI: 13.7%-14.7%) had discontinued opioids. Failure to sustain tapers was significantly less likely among patients with overdose events during follow-up (adjusted incidence rate ratio [aIRR]: .56 [95% CI: .48-.67]) and during more recent years (aIRR: .93 per year after 2008 [95% CI: .92-.94]).

CONCLUSIONS

In an insured and Medicare Advantage population, over two-thirds of patients who initiated opioid dose tapering sustained long-term dose reductions, and the likelihood of sustaining tapers increased substantially from 2008 to 2018.

Applying the NIA Health Disparities Research Framework to Identify Needs and Opportunities in Chronic Musculoskeletal Pain Research

Disparities in the experience of chronic musculoskeletal pain in the United States stem from a confluence of a broad array of factors. Organized within the National Institute on Aging Health Disparity Research Framework, a literature review was completed to evaluate what is known and what is needed to move chronic musculoskeletal pain research forward specific to disproportionately affected populations. Peer-reviewed studies published in English, on human adults, from 2000 to 2019, and conducted in the United States were extracted from PubMed and Web of Science. Articles were reviewed for key words that focused on underrepresented ethnic/race groups with chronic musculoskeletal pain applying health factor terms identified in the NIAHealth Disparity Research Framework four levels of analysis: 1) environmental, 2) sociocultural, 3) behavioral, and 4) biological. A total of 52 articles met inclusion criteria. There were limited publications specific to underrepresented ethnic/race groups with chronic musculoskeletal pain across all levels with particular research gaps under sociocultural and biological categories. Current limitations in evidence may be supplemented by a foundation of findings specific to the broader topic of "chronic pain" which provides guidance for future investigations. Study designs including a focus on protective factors and multiple levels of analyses would be particularly meritorious. PERSPECTIVE: Chronic musculoskeletal pain unequally burdens underrepresented ethnic/race groups. In order to move research forward and to systematically investigate the complex array of factors contributing toward health disparities, an organized approach is necessary. Applying the NIA Health Disparities Research Framework, an overview of the current state of evidence specific to chronic musculoskeletal pain and underrepresented ethnic/race groups is provided with future directions identified.

Assessing the Relationships Between COVID-19 Stay-at-Home Orders and Opioid Overdoses in the State of Pennsylvania

COVID-19 is compounding opioid use disorder throughout the United States. While recent commentaries provide useful policy recommendations, few studies examine the intersection of COVID-19 policy responses and patterns of opioid overdose. We examine opioid overdoses prior to and following the Pennsylvania stay-at-home order implemented on April 1, 2020. Using data from the Pennsylvania Overdose Information Network, we measure change in monthly incidents of opioid-related overdose pre- versus post-April 1, and the significance of change by gender, age, race, drug class, and naloxone doses administered. Findings demonstrate statistically significant increases in overdose incidents among both men and women, White and Black groups, and several age groups, most notably the 30–39 and 40–49 ranges, following April 1. Significant increases were observed for overdoses involving heroin, fentanyl, fentanyl analogs or other synthetic opioids, pharmaceutical opioids, and carfentanil. The study emphasizes the need for opioid use to be addressed alongside efforts to mitigate and manage COVID-19 infection.

Incorporating space and time into random forest models for analyzing geospatial patterns of drug-related crime incidents in a major U.S. metropolitan area

The opioid crisis has hit American cities hard, and research on spatial and temporal patterns of drug-related activities including detecting and predicting clusters of crime incidents involving particular types of drugs is useful for distinguishing hot zones where drugs are present that in turn can further provide a basis for assessing and providing related treatment services. In this study, we investigated spatiotemporal patterns of more than 52,000 reported incidents of drug-related crime at block group granularity in Chicago, IL between 2016 and 2019. We applied a space-time analysis framework and machine learning approaches to build a model using training data that identified whether certain locations and built environment and sociodemographic factors were correlated with drug-related crime incident patterns, and establish the top contributing factors that underlaid the trends. Space and time, together with multiple driving factors, were incorporated into a random forest model to analyze these changing patterns. We accommodated both spatial and temporal autocorrelation in the model learning process to assist with capturing the changes over time and tested the capabilities of the space-time random forest model by predicting drug-related activity hot zones. We focused particularly on crime incidents that involved heroin and synthetic drugs as these have been key drug types that have highly impacted cities during the opioid crisis in the U.S.

Assessing County-Level Vulnerability for Opioid Overdose and Rapid Spread of Human Immunodeficiency Virus and Hepatitis C Infection in South Dakota

BACKGROUND

Key indicators of vulnerability for the syndemic of opioid overdose, human immunodeficiency virus (HIV), and hepatitis C virus (HCV) due to injection drug use (IDU) in rural reservation and frontier counties are unknown. We examined county-level vulnerability for this syndemic in South Dakota.

METHODS

Informed by prior methodology from the Centers for Disease Control and Prevention, we used acute and chronic HCV infections among persons aged ≤40 years as a proxy measure of IDU. Twenty-nine county-level indicators potentially associated with HCV infection rates were identified. Using these indicators, we examined relationships through bivariate and multivariate analysis and calculated a composite index score to identify the most vulnerable counties (top 20%) to this syndemic.

RESULTS

Of the most vulnerable counties, 69% are reservation counties and 62% are rural. The county-level HCV infection rate is 4 times higher in minority counties than nonminority counties, and almost all significant indicators of opioid-related vulnerability in our analysis are structural and potentially modifiable through public health interventions and policies.

CONCLUSIONS

Our assessment gives context to the magnitude of this syndemic in rural reservation and frontier counties and should inform the strategic allocation of prevention and intervention services.

Measuring Relationships Between Proactive Reporting State-level Prescription Drug Monitoring Programs and County-level Fatal Prescription Opioid Overdoses

BACKGROUND

Prescription drug monitoring programs (PDMPs) that collect and distribute information on dispensed controlled substances have been adopted by nearly all US states. We know little about program characteristics that modify PDMP impact on prescription opioid (PO) overdose deaths.

METHODS

We measured associations between adoption of any PDMP and changes in fatal PO overdoses in 2002-2016 across 3109 counties in 49 states and D.C. We then measured changes related to the adoption of "proactive PDMPs," which report outlying prescribing/dispensing patterns and provide broader access to PDMP data by law enforcement. Comparisons were made within 3 time intervals that broadly represent the evolution of PDMPs (2002-2004, 2005-2009, and 2010-2016). We modeled overdoses using Bayesian space-time models.

RESULTS

Adoption of electronic PDMP access was associated with 9% lower rates of fatal PO overdoses after three years (rate ratio [RR] = 0.91, 95% credible interval [CI]: 0.88-0.93) with well-supported effects for methadone (RR = 0.86,95% CI: 0.82-0.90) and other synthetic opioids (RR = 0.82, 95% CI: 0.77-0.86). Compared with states with no/weak PDMPs, proactive PDMPs were associated with fewer deaths attributed to natural/semi-synthetic opioids (2002-2004: RR = 0.72 [0.66-0.78]; 2005-2009: RR = 0.93 [0.90-0.97]; 2010-2016: 0.89 [0.86-0.92]) and methadone (2002-2004: RR = 0.77 [0.69-0.85]; 2010-2016: RR = 0.90 [0.86-0.94]). Unintended effects were observed for synthetic opioids other than methadone (2005-2009: RR = 1.29 [1.21-1.38]; 2010-2016: RR = 1.22 [1.16-1.29]).

CONCLUSIONS

State adoption of PDMPs was associated with fewer PO deaths overall while proactive PDMPs alone were associated with fewer deaths related to natural/semisynthetic opioids and methadone, the specific targets of these programs. See video abstract at, http://links.lww.com/EDE/B619.

[Poisoning inquiries from Berlin and Brandenburg from 1999-2018: an urban-rural comparison]

Hintergrund und Ziel

Der Berliner Giftnotruf ist seit 1963 die zentrale Anlaufstelle beim Thema „Vergiftungen“ für die Berliner und Brandenburger Bevölkerung. Ferner nimmt die Einrichtung eine wichtige Funktion im Bereich der Vergiftungsprävention wahr. Ziel dieser Arbeit ist es, die Entwicklung des Beratungsaufkommens und der Inhalte von 1999 bis 2018 zu beschreiben. Unterschiede bei städtischer und ländlicher Herkunft der Anrufenden sowie bei privatem oder beruflichem Hintergrund der Anfragen werden betrachtet. Die Ergebnisse sollen der Verbesserung der Präventionsarbeit dienen.

Methoden

Die Falldaten des Giftnotrufs (1999–2018) wurden aufbereitet und einer explorativen Datenanalyse unterzogen. Über Verfahren der deskriptiven Statistik wurden die Daten ausgewertet und analysiert. Zusammenhänge zwischen der „Herkunft des Anrufs“ (Stadt oder Land), dem „Hintergrund“ (privat oder beruflich) und der jeweiligen „Noxenkategorie“ wurden mittels Pearsons Chi-Quadrat-Test analysiert.

Ergebnisse

Das jährliche Beratungsvolumen stieg tendenziell an. Insbesondere Anfragen zu Expositionen von Erwachsenen und Senioren nahmen zu. Häufigste Themen waren Vergiftungen mit Medikamenten und Publikumsmitteln. Anfragen zu illegalen Drogen nahmen am stärksten zu (durchschnittliche jährliche Wachstumsrate 6,3 %). Anfragenden Privatpersonen kann in den meisten Fällen direkt geholfen werden (86,8 %), sodass nur selten eine medizinische Behandlung empfohlen wird. Privatpersonen rufen häufiger aus der Stadt an, auf dem Land überwiegen Anrufe von medizinischem Personal. Im ländlichen Raum wurden Anfragen zu Schädlingsbekämpfungsmitteln, Pilzen, Tieren und Pflanzen häufiger gestellt. Anrufe zu Lebensmitteln, Fremdkörpern, Genussmitteln oder illegalen Drogen gingen hingegen vermehrt aus dem städtischen Raum ein.

A large-scale retrospective study of opioid poisoning in New York State with implications for targeted interventions

Opioid overdose related deaths have increased dramatically in recent years. Combating the opioid epidemic requires better understanding of the epidemiology of opioid poisoning (OP). To discover trends and patterns of opioid poisoning and the demographic and regional disparities, we analyzed large scale patient visits data in New York State (NYS). Demographic, spatial, temporal and correlation analyses were performed for all OP patients extracted from the claims data in the New York Statewide Planning and Research Cooperative System (SPARCS) from 2010 to 2016, along with Decennial US Census and American Community Survey zip code level data. 58,481 patients with at least one OP diagnosis and a valid NYS zip code address were included. Main outcome and measures include OP patient counts and rates per 100,000 population, patient level factors (gender, age, race and ethnicity, residential zip code), and zip code level social demographic factors. The results showed that the OP rate increased by 364.6%, and by 741.5% for the age group > 65 years. There were wide disparities among groups by race and ethnicity on rates and age distributions of OP. Heroin and non-heroin based OP rates demonstrated distinct temporal trends as well as major geospatial variation. The findings highlighted strong demographic disparity of OP patients, evolving patterns and substantial geospatial variation.

A spatiotemporal analysis of opioid poisoning mortality in Ohio from 2010 to 2016

Opioid-related deaths have severely increased since 2000 in the United States. This crisis has been declared a public health emergency, and among the most affected states is Ohio. We used statewide vital statistic data from the Ohio Department of Health (ODH) and demographics data from the U.S. Census Bureau to analyze opioid-related mortality from 2010 to 2016. We focused on the characterization of the demographics from the population of opioid-related fatalities, spatiotemporal pattern analysis using Moran's statistics at the census-tract level, and comorbidity analysis using frequent itemset mining and association rule mining. We found higher rates of opioid-related deaths in white males aged 25-54 compared to the rest of Ohioans. Deaths tended to increasingly cluster around Cleveland, Columbus and Cincinnati and away from rural regions as time progressed. We also found relatively high co-occurrence of cardiovascular disease, anxiety or drug abuse history, with opioid-related mortality. Our results demonstrate that state-wide spatiotemporal and comorbidity analysis of the opioid epidemic could provide novel insights into how the demographic characteristics, spatiotemporal factors, and/or health conditions may be associated with opioid-related deaths in the state of Ohio.

Unclassified drug overdose deaths in the opioid crisis: emerging patterns of inequity

OBJECTIVE

Examine whether individual, geographic, and economic phenotypes predict missing data on specific drug involvement in overdose deaths, manifesting inequities in overdose mortality data, which is a key data source used in measuring the opioid epidemic.

MATERIALS AND METHODS

We combined national data sources (mortality, demographic, economic, and geographic) from 2014-2016 in a multi-method analysis of missing drug classification in the overdose mortality records (as defined by the use of ICD-10 T50.9 on death certificates). We examined individual disparities in decedent-level multivariate logistic regression models, geographic disparities in spatial analysis (heat maps), and economic disparities in a combination of temporal trend analyses (descriptive statistics) and both decedent- and county-level multivariate logistic regression models.

RESULTS

Our analyses consistently found higher rates of unclassified overdoses in decedents of female gender, White race, non-Hispanic ethnicity, with college education, aged 30-59 and those from poorer counties. Despite the fact that unclassified drug overdose death rates have reduced over time, gaps persist between the richest and poorest counties. There are also striking geographic differences both across and within states.

DISCUSSION

Given the essential role of mortality data in measuring the scale of the opioid epidemic, it is important to understand the individual and community inequities underlying the missing data on specific drug involvements. Knowledge of these inequities could enhance our understanding of the opioid crisis and inform data-driven interventions and policies with more equitable resource allocations.

CONCLUSION

Multiple individual, geographic, and economic disparities underlie unclassified overdose deaths, with important implications for public health informatics and addressing the opioid crisis.

Changes in opioid prescribing after implementation of mandatory registration and proactive reports within California's prescription drug monitoring program

BACKGROUND

In 2016, California updated its prescription drug monitoring program (PDMP), adding two key features: automated proactive reports to prescribers and mandatory registration for prescribers and pharmacists. The effects of these changes on prescribing patterns have not yet been examined. We aimed to evaluate the joint effect of these two PDMP features on county-level prescribing practices in California.

METHODS

Using county-level quarterly data from 2012 to 2017, we estimated the absolute change associated with the implementation of these two PDMP features in seven prescribing indicators in California versus a control group comprising counties in Florida and Washington: opioid prescription rate per 1000 residents; patients' mean daily opioid dosage in milligrams of morphine equivalents[MME]; prescribers' mean daily MME prescribed; prescribers' mean number of opioid prescriptions per day; percentage of patients getting >90 MME/day; percentage of days with overlapping prescriptions for opioids and benzodiazepines; multiple opioid provider episodes per 100,000 residents.

RESULTS

Proactive reports and mandatory registration were associated with a 7.7 MME decrease in patients' mean daily opioid dose (95 %CI: -11.4, -2.9); a 1.8 decrease in the percentage of patients prescribed high-dose opioids (95 %CI: -2.3, -0.9); and a 6.3 MME decrease in prescribers' mean daily dose prescribed (95 %CI: -10.0, -1.3).

CONCLUSIONS

California's implementation of these two PDMP features was associated with decreases in the total quantity of opioid MMEs prescribed, and indicators of patients prescribed high-dose opioids compared to states that had PDMP's without these features. Rates of opioid prescribing and other high-risk prescribing patterns remained unchanged.

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

BACKGROUND

The rapid growth of opioid abuse and the related mortality across the United States has spurred the development of predictive models for the allocation of public health resources. These models should characterize heterogeneous growth across states using a drug epidemic framework that enables assessments of epidemic onset, rates of growth, and limited capacities for epidemic growth.

METHODS

We used opioid overdose mortality data for 146 North and South Carolina counties from 2001 through 2014 to compare the retrodictive and predictive performance of a logistic growth model that parameterizes onsets, growth, and carrying capacity within a traditional Bayesian Poisson space-time model.

RESULTS

In fitting the models to past data, the performance of the logistic growth model was superior to the standard Bayesian Poisson space-time model (deviance information criterion: 8,088 vs. 8,256), with reduced spatial and independent errors. Predictively, the logistic model more accurately estimated fatality rates 1, 2, and 3 years in the future (root mean squared error medians were lower for 95.7% of counties from 2012 to 2014). Capacity limits were higher in counties with greater population size, percent population age 45-64, and percent white population. Epidemic onset was associated with greater same-year and past-year incidence of overdose hospitalizations.

CONCLUSION

Growth in annual rates of opioid fatalities was capacity limited, heterogeneous across counties, and spatially correlated, requiring spatial epidemic models for the accurate and reliable prediction of future outcomes related to opioid abuse. Indicators of risk are identifiable and can be used to predict future mortality outcomes.

The Opioid/Overdose Crisis as a Dialectics of Pain, Despair, and One-Sided Struggle

The opioid/overdose crisis in the United States and Canada has claimed hundreds of thousands of lives and has become a major field for research and interventions. It has embroiled pharmaceutical companies in lawsuits and possible bankruptcy filings. Effective interventions and policies toward this and future drug-related outbreaks may be improved by understanding the sociostructural roots of this outbreak. Much of the literature on roots of the opioid/overdose outbreak focuses on (1) the actions of pharmaceutical companies in inappropriately promoting the use of prescription opioids; (2) "deaths of despair" based on the deindustrialization of much of rural and urban Canada and the United States, and on the related marginalization and demoralization of those facing lifetimes of joblessness or precarious employment in poorly paid, often dangerous work; and (3) increase in occupationally-induced pain and injuries in the population. All three of these roots of the crisis-pharmaceutical misconduct and unethical marketing practices, despair based on deindustrialization and increased occupational pain-can be traced back, in part, to what has been called the "one-sided class war" that became prominent in the 1970s, became institutionalized as neo-liberalism in and since the 1980s, and may now be beginning to be challenged. We describe this one-sided class war, and how processes it sparked enabled pharmaceutical corporations in their misconduct, nurtured individualistic ideologies that fed into despair and drug use, weakened institutions that created social support in communities, and reduced barriers against injuries and other occupational pain at workplaces by reducing unionization, weakening surviving unions, and weakening the enforcement of rules about workplace safety and health. We then briefly discuss the implications of this analysis for programs and policies to mitigate or reverse the opioid/overdose outbreak.

Rural risk environments, opioid-related overdose, and infectious diseases: A multidimensional, spatial perspective

BACKGROUND

Much remains unknown in rural risk environments, despite a growing crisis in these areas. We adapt a risk environment framework to characterize rural southern Illinois and describe the relations of risk environments, opioid-related overdose, HIV, Hepatitis C, and sexually transmitted infection rates between 2015 and 2017.

METHODS

Over two dozen risk environment variables are summarized across zip-code (n = 128) or county levels (n = 16) based on availability and theoretical relevance. We calculate data attribute associations and characterize spatial and temporal dimensions of longitudinal health outcomes and the rural risk environment. We then use a "regional typology analysis" to generate data-driven risk regions and compare health outcomes.

RESULTS

Pervasive risk hotspots were identified in more populated locales with higher rates of overdose and HCV incidence, whereas emerging risk areas were isolated to more rural locales that had experienced an increase in analgesic opiate overdoses and generally lacked harm-reduction resources. At-risk areas were characterized with underlying socioeconomic vulnerability but in differing ways, reflecting a nuanced and shifting structural risk landscape.

CONCLUSIONS

Rural risk environment vulnerabilities and associated opioid-related health outcomes are multifaceted and spatially heterogeneous. More research is needed to better understand how refining geographies to more precisely define risk can support intervention efforts and further enrich investigations of the opioid epidemic.

Recreational Cannabis Use and Risk of Prescription Opioid Overdose: Insights from Pediatric Inpatients

Objectives

Our first goal is to evaluate the prevalence of hospital admissions for prescription opioid overdose (POD) in pediatric inpatients, and next goal is to measure the independent association between cannabis use disorders (CUD) and POD.

Methods

We used the nationwide inpatient sample (NIS) and included 27,444,239 pediatric inpatients (age ≤ 18 years), and 10,562 (0.04%) were managed primarily for POD. The odds ratio (OR) of the association of variables in POD inpatients was measured using the binomial logistic regression model that was adjusted for demographic confounders and psychiatric comorbidities.

Results

Adolescents have higher odds (OR 10.75, 95% CI 10.16-11.36) of POD-related hospitalization compared to children ≤ 12 years. Whites formed the significant proportion (67%), and those from low-income families (<50th percentile) had higher likelihood for POD-related hospitalization. The most prevalent psychiatric comorbidities were mood disorders (44.3%) and anxiety disorders (14.6%). Prevalent comorbid substance use disorders (SUDs) included cannabis (14.2%), tobacco (13.1%), and opioid (9.4%). A higher odds of association with POD-related hospitalizations were seen in pediatric inpatients with comorbid opioid (OR 8.79, 95% CI 8.08-9.56), tobacco (OR 1.58, 95% CI 1.47-1.70), and cannabis (OR 1.68, 95% CI 1.57-1.81) use disorders.

Conclusion

The prescription opioid is a bridge to opioid abuse/dependence, thereby increasing the risk of other SUDs like tobacco (by 58%) and cannabis (by 68%). Regulating the easy availability of prescription opioids and also improving the existing prescription trends are an essential way to reduce this problem. Finally, awareness and counseling are recommended strategies for harm reduction/rehabilitation among the pediatric population.

Associations between neighborhood-level factors and opioid-related mortality: A multi-level analysis using death certificate data

AIM

To identify associations between opioid-related mortality and neighborhood-level risk factors.

DESIGN

Cross-sectional study.

SETTING

Massachusetts, USA.

PARTICIPANTS

Using 2011-14 Massachusetts death certificate data, we identified opioid-related (n = 3089) and non-opioid-related premature deaths (n = 8729).

MEASUREMENTS

The independent variables consisted of four sets of neighborhood-level factors: (1) psychosocial, (2) economic, (3) built environment and (4) health-related. At the individual level we included the following compositional factors: age at death, sex, race/ethnicity, marital status, education, veteran status and nativity. The primary outcome of interest was opioid-related mortality.

FINDINGS

Multi-level models identified number of social associations per 10 000 [odds ratio (OR) = 0.84, P = 0.002, 95% confidence interval (CI) = 0.75-0.94] and number of hospital beds per 10 000 (OR = 0.78, P < 0.001, 95% CI = 0.68-0.88) to be inversely associated with opioid-related mortality, whereas the percentage living in poverty (OR = 1.01, P = 0.008, 95% CI = 1.00-1.01), food insecurity rate (OR = 1.21, P = 0.002, 95% CI = 1.07-1.37), number of federally qualified health centers (OR = 1.02, P = 0.028, 95% CI = 1.02-1.08) and per-capita morphine milligram equivalents of hydromorphone (OR = 1.05, P = 0.003, 95% CI = 1.01-1.08) were positively associated with opioid-related mortality.

CONCLUSIONS

Opioid-related deaths between 2011 and 2014 in the state of Massachusetts appear to be positively associated with the percentage living in poverty, food insecurity rate, number of federally qualified health centers and per-capita morphine milligram equivalents of hydromorphone, but inversely associated with number of social associations per 10 000 and number of hospital beds per 10 000.

Optimizing the Distribution of Pharmacy-Dispensed Naloxone Using Spatial Mapping Techniques in Rural Areas

With the worsening opioid epidemic, recent changes allow pharmacies to sell naloxone through standing orders to reverse opioid overdoses. This study assesses direct dispensed naloxone availability through pharmacy chains in California in 2016, and utilizes spatial analysis techniques to suggest optimal stocking of naloxone. Rural counties were less likely to have a pharmacy selling naloxone even though overdose death rates were higher in rural counties (odds ratio [OR] = 0.225; 95% confidence interval [CI] = [0.059, 0.854]). Pharmacies closest to the center of the population in these rural counties were identified as ideal stocking locations. Rural counties with high overdose death rates have the lowest rates of naloxone, but spatial analysis can determine pre-existing chain pharmacies that can stock naloxone.

Geospatial Analyses of Pain Intensity and Opioid Unit Doses Prescribed on the Day of Discharge Following Orthopedic Surgery

OBJECTIVE

Inappropriate opioid prescribing after surgery contributes to opioid use disorder and risk of opioid overdose. In this cross-sectional analysis of orthopedic surgical patients, we examined the role of patient location on postoperative pain intensity and opioids prescribed on hospital discharge.

METHODS

We used geospatial analyses to characterize spatial patterns of mean pain intensity on the day of discharge (PiDoD) and opioid units prescribed on the day of discharge (OuPoD), as well as the effect of regional social deprivation on these outcomes.

RESULTS

At a 500-km radius from the surgery site, the Global Moran's I for PiDoD (2.71 × 10-3, variance = 1.67 × 10-6, P = 0.012) and OuPoD (2.19 × 10-3, SD = 1.87, variance = 1.66 × 10-6, P = 0.03) suggested significant spatial autocorrelation within each outcome. Local indicators of spatial autocorrelation, including local Moran's I, Local Indicator of Spatial Autocorrelation cluster maps, and Getis-Ord Gi* statistics, further demonstrated significant, specific regions of clustering both OuPoD and PiDoD. These spatial patterns were associated with spatial regions of area deprivation.

CONCLUSIONS

Our results suggest that the outcomes of pain intensity and opioid doses prescribed exhibit varying degrees of clustering of patient locations of residence, at both global and local levels. This indicates that a given patient's pain intensity on discharge is related to the pain intensity of nearby individuals. Similar interpretations exist for OuPoD, although the relative locations of hot spots of opioids dispensed in a geographic area appear to differ from those of hot spots of pain intensity on discharge.

Opioid Misuse and the Availability of Medical Marijuana Through Dispensaries

OBJECTIVE

In this study we assess whether changes in ZIP code-level density of medical marijuana facilities are related to changes in rates of opioid poisonings and opioid use disorder hospitalizations in California.

METHOD

A panel study using California hospital discharge data was conducted to assess the relationship between density of medical marijuana dispensaries and opioid poisonings and use disorder. There were 8,536 space-time units at the ZIP code level. Outcome measures included ZIP code counts of opioid poisonings and opioid use disorder; independent variables were local- and adjacent-area medical marijuana dispensaries and demographic and economic characteristics.

RESULTS

Independent of effects for covariates, densities of medical marijuana dispensaries were positively related to opioid use disorder (RR = 1.05, CI [1.03, 1.06]) and opioid poisonings (RR = 1.04, CI [1.02, 1.05]) in local areas, but negatively related to opioid misuse in spatially adjacent areas (RR = 0.91, CI [0.88, 0.94] for opioid use disorder, RR = 0.89, CI [0.86, 0.93] for opioid poisonings).

CONCLUSIONS

Although state-level studies suggest that more liberal marijuana policies may result in fewer opioid overdose deaths, our results within one state suggest that local availability of medical marijuana may not reduce those deaths. The relationship appears to be more complex, possibly based on socioeconomic conditions within and adjacent to areas with higher densities of medical marijuana dispensaries.

The spatio-temporal distribution of naloxone administration events in rural Ohio 2010-16

INTRODUCTION

In 2017, Ohio had the second highest rate of drug overdose deaths in the United States. Current opioid related epidemiologic literature has begun to uncover the environmental level influences on the opioid epidemic and how the end results may ultimately manifest over space and time. This work is still nascent however, with most clustering research conducted at a spatial unit such as county level, which (1) can obscure differences between urban and rural communities, (2) does not consider dynamics that cross county lines, and (3) is difficult to interpret directly into strategic and localized intervention efforts. We address this gap by describing, at the Census block level, the spatial-temporal clustering of opioid related events in rural Ohio.

METHODS

We use the outcome of the administration of naloxone emergency medical service (EMS) calls in rural Ohio Census blocks during 2010-16 in a Poisson model of spatial scan statistics.

RESULTS

We found that naloxone event clustering in rural Ohio in the recent decade was widely dispersed over time and space, with clusters that average 17 times the risk of having an event compared to areas outside the cluster. Many of the larger spatial clusters crossed administrative boundaries (i.e., county lines) suggesting that opioid misuse may be less responsive to county level policies than to other factors.

DISCUSSION

Timely identification of localized overdose event clustering can guide affected communities toward rapid interventions aimed at minimizing the morbidity and mortality resulting from contagious opioid misuse.

Individual and county-level variation in outcomes following non-fatal opioid-involved overdose

BACKGROUND

A lack of large-scale, individually linked data often has impeded efforts to disentangle individual-level variability in outcomes from area-level variability in studies of many diseases and conditions. This study investigated individual and county-level variability in outcomes following non-fatal overdose in a state-wide cohort of opioid overdose patients.

METHODS

Participants were 24 031 patients treated by emergency medical services or an emergency department for opioid-involved overdose in Indiana between 2014 and 2017. Outcomes included repeat non-fatal overdose, fatal overdose and death. County-level predictors included sociodemographic, socioeconomic and treatment availability indicators. Individual-level predictors included age, race, sex and repeat non-fatal opioid-involved overdose. Multilevel models examined outcomes following non-fatal overdose as a function of patient and county characteristics.

RESULTS

10.9% (n=2612) of patients had a repeat non-fatal overdose, 2.4% (n=580) died of drug overdose and 9.2% (n=2217) died overall. Patients with a repeat overdose were over three times more likely to die of drug-related causes (OR=3.68, 99.9% CI 2.62 to 5.17, p<0.001). County-level effects were limited primarily to treatment availability indicators. Higher rates of buprenorphine treatment providers were associated with lower rates of mortality (OR=0.82, 95% CI 0.68 to 0.97, p=0.024), but the opposite trend was found for naltrexone treatment providers (OR=1.20, 95% CI 1.03 to 1.39, p=0.021). Cross-level interactions showed higher rates of Black deaths relative to White deaths in counties with high rates of naltrexone providers (OR=1.73, 95% CI 1.09 to 2.73, p=0.019).

CONCLUSION

Although patient-level differences account for most variability in opioid-related outcomes, treatment availability may contribute to county-level differences, necessitating multifaceted approaches for the treatment and prevention of opioid abuse.

Compositional and contextual factors associated with drug overdose deaths in the United States

Background: In 2017, the US Department of Health and Human Services declared the Opioid epidemic a public health emergency. In the US, emergency rooms treat more than 1,000 people each day for drug overdose, and 115 of them die. This study examines compositional and contextual factors associated with drug overdose deaths rates in the US. Methods: Local spatial autocorrelation statistics were used to estimate hot spot areas to identify census tracts with high risk of drug overdose death. Logistic regressions investigated the relationship between drug overdose death rates and various compositional and contextual variables across census tracks. Results: The adjusted logistic model shows that compositional variables: depression (OR = 2.47 [2.37-2.58]), poor mental health (OR = 1.71 [1.63-1.79]), median age 1.41 (1.36-1.47) and the percentage of people with a high school diploma (OR = 1.30 [1.24-1.35]) were positively associated with the rate of drug overdose deaths. On the other hand, contextual variables: the percentage having health insurance (OR = 0.66 [0.64-0.69]), the Theil's H index (OR = 0.69 [0.66-0.71]), population density (OR = 0.80 [0.77-0.84]), poverty (OR = 0.90 [0.86-0.95]), and median household income (OR = 0.91[0.86-0.96]) were negatively associated with drug overdose deaths. Discussion: The analysis reveals a consistently strong association between compositional mental health factors and census tract-level death rates from drug overdose.

Drivers of opioid use in Appalachian Pennsylvania: Cross-cutting social and community-level factors

BACKGROUND

Four Appalachian states including Pennsylvania (PA) have the highest drug overdose rates in the country, calling for better understanding of the social and economic drivers of opioid use in the region. Using key informant interviews, we explored the social and community drivers of opioid use in a non-urban Appalachian Pennsylvania community.

METHODS

In 2017, we conducted qualitative interviews with 20 key stakeholders from a case community selected using the results from quantitative spatial models of hospitalizations for opioid use disorders. In small town located 10 miles outside Pittsburgh, PA, we asked participants to share their perceptions of contextual factors that influence opioid use among residents. We then used qualitative thematic analysis to organize and generate the results.

RESULTS

Participants identified several contextual factors that influence opioid use among residents. Three cross-cutting thematic topics emerged: 1) acceptance and denial of use through familial and peer influences, community environments, and social norms; 2) impacts of economic shifts and community leadership on availability of programs and opportunities; and 3) the role of coping within economic disadvantage and social depression.

CONCLUSION

Uncovering multi-level, contextual drivers of opioid use can benefit the development of future public health interventions. These results suggest that social and community-level measures of structural deprivation, acceptance and/or denial of the opioid epidemic, community engagement and development, social support, and social depression are important for future research and programmatic efforts in the Appalachian region.

Strengths and weaknesses of existing data sources to support research to address the opioids crisis

Better opioid prescribing practices, promoting effective opioid use disorder treatment, improving naloxone access, and enhancing public health surveillance are strategies central to reducing opioid-related morbidity and mortality. Successfully advancing and evaluating these strategies requires leveraging and linking existing secondary data sources. We conducted a scoping study in Fall 2017 at RAND, including a literature search (updated in December 2018) complemented by semi-structured interviews with policymakers and researchers, to identify data sources and linking strategies commonly used in opioid studies, describe data source strengths and limitations, and highlight opportunities to use data to address high-priority public health research questions. We identified 306 articles, published between 2005 and 2018, that conducted secondary analyses of existing data to examine one or more public health strategies. Multiple secondary data sources, available at national, state, and local levels, support such research, with substantial breadth in data availability, data contents, and the data's ability to support multi-level analyses over time. Interviewees identified opportunities to expand existing capabilities through systematic enhancements, including greater support to states for creating and facilitating data use, as well as key data challenges, such as data availability lags and difficulties matching individual-level data over time or across datasets. Multiple secondary data sources exist that can be used to examine the impact of public health approaches to addressing the opioid crisis. Greater data access, improved usability for research purposes, and data element standardization can enhance their value, as can improved data availability timeliness and better data comparability across jurisdictions.

Multivariate spatiotemporal modeling of drug- and alcohol-poisoning deaths in New York City, 2009-2014

Drug- and alcohol-poisoning deaths remain current public health problems. Studies to date have typically focused on individual-level predictors of drug overdose deaths, and there remains a limited understanding of the spatiotemporal patterns and predictors of the joint outcomes. We use a hierarchical Bayesian spatiotemporal multivariate Poisson regression model on data from (N = 167) ZIP-codes between 2009 and 2014 in New York City to examine the spatiotemporal patterns of the joint occurrence of drug (opioids) and alcohol-poisoning deaths, and the covariates associated with each outcome. Results indicate that rates of both outcomes were highly positively correlated across ZIP-codes (cross-correlation: 0.57, 95% credible interval (CrI): 0.29, 0.77). ZIP-codes with a higher prevalence of heavy drinking had higher alcohol-poisoning deaths (relative risk (RR):1.63, 95% CrI: 1.26, 2.05) and drug-poisoning deaths (RR: 1.29, 95% CrI: 1.03, 1.59). These spatial patterns may guide public health planners to target specific areas to address these co-occurring epidemics.

The impact of opioid use disorder on levels of educational attainment: Perceived benefits and consequences

BACKGROUND/AIMS

Adolescents and young adults have the highest rates of opioid use, and despite shared risk-factors such as mental health and social pressures, there is little information on the relationship between education and opioid use disorder. In this study, we sought to assess differences in educational attainment between opioid users and the general population, and the impact of opioid use on one's education.

METHODS

Patients (N = 14,349) entering opioid treatment programs across the United States were surveyed on educational attainment from 2010 to 2018. Data were compared to estimates from an annual survey administered by the US Census. A follow-up sub-set of opioid users (N = 240) was interviewed to add context and expand on the structured survey.

RESULTS

Nearly one-third (32.2%) of the US population is estimated to have earned a bachelor's/advanced degree, compared to just 7.8% of treatment-seeking opioid users. Over half of follow-up respondents (57.5%) reported initiating regular opioid use while attending an educational institution. The majority (68.0%) also indicated opioids negatively impacted their education, with drug-seeking behavior prioritized over attendance and academic performance. Perceived benefits included cognitive enhancement and therapeutic value for anxiety/depression.

CONCLUSIONS

Our data suggest that opioid users achieve lower levels of education, which may result in both individual and economic costs. Prevention programs need to not only include education-specific risk factors (e.g., social norms), but underlying precipitators such as mental health, stigma, and the myth of risk-free cognitive enhancement. Opioid specific services should be available and disseminated to student populations, including certification of campus physicians to provide buprenorphine maintenance.

Prescription drug monitoring programs operational characteristics and fatal heroin poisoning

BACKGROUND

Prescription drug monitoring programs (PDMP), by reducing access to prescribed opioids (POs), may contribute to a policy environment in which some people with opioid dependence are at increased risk for transitioning from POs to heroin/other illegal opioids. This study examines how PDMP adoption and changes in the characteristics of PDMPs over time contribute to changes in fatal heroin poisoning in counties within states from 2002 to 2016.

METHODS

Latent transition analysis to classify PDMPs into latent classes (Cooperative, Proactive, and Weak) for each state and year, across three intervals (1999-2004, 2005-2009, 2010-2016). We examined the association between probability of PDMP latent class membership and the rate of county-level heroin poisoning death.

RESULTS

After adjustment for potential county-level confounders and co-occurring policy changes, adoption of a PDMP was significantly associated with increased heroin poisoning rates (22% increase by third year post-adoption). Findings varied by PDMP type. From 2010-2016, states with Cooperative PDMPs (those more likely to share data with other states, to require more frequent reporting, and include more drug schedules) had 19% higher heroin poisoning rates than states with Weak PDMPs (adjusted rate ratio [ARR] = 1.19; 95% CI = 1.14, 1.25). States with Proactive PDMPs (those more likely to report outlying prescribing and dispensing and provide broader access to law enforcement) had 6% lower heroin poisoning rates than states with No/Weak PDMPs (ARR = 0.94; 95% CI = 0.90, 0.98).

CONCLUSION

There is a consistent, positive association between state PDMP adoption and heroin poisoning mortality. However, this varies by PDMP type, with Proactive PDMPs associated with a small reduction in heroin poisoning deaths. This raises questions about the potential for PDMPs to support efforts to decrease heroin overdose risk, particularly by using proactive alerts to identify patients in need of treatment for opioid use disorder. Future research on mechanisms explaining the reduction in heroin poisonings after enactment of Proactive PDMPs is merited.