The ecology of overdose mortality in Philadelphia

Overdose as a complex contagion: modelling the community spread of overdose events following law enforcement efforts to disrupt the drug market

BACKGROUND

The opioid overdose mortality crisis in the USA is an ongoing public health epidemic. Ongoing law enforcement strategies to disrupt local unregulated drug markets can have an iatrogenic effect of increasing overdose by driving consumers towards new suppliers with unpredictable drug products of unknown potency.

METHODS

Cross-sectional study using point-level information on law enforcement opioid-related drug seizures from property room data, opioid-related non-fatal overdose events from emergency medical services and block group-level social determinants of health data from multiple sources. Using an endemic-epidemic spatiotemporal regression model, we estimated the degree to which exposure to drug supply disruptions triggers future overdose events within small space-time distances in Indianapolis, Indiana.

RESULTS

Neighbourhoods with more structural racism, economic deprivation or urban blight were associated with higher rates of non-fatal overdose. Exposure to an opioid-related drug seizure event had a significant and positive effect on the epidemic probability of non-fatal overdose. An opioid seizure that occurred within 250 m and 3 days, 250 m and 7 days, and 250 m and 14 days of an overdose event increased the risk of a new non-fatal overdose by 2.62 (rate ratio (RR)=2.62, 95% CI 1.87 to 3.67), 2.17 (RR=2.17, 95% CI 1.87 to 2.59) and 1.83 (RR=1.83, 95% CI 1.66 to 2.02), respectively. Similar spatiotemporal patterns were observed in a smaller spatial bandwidth.

CONCLUSIONS

Results demonstrated that overdoses exhibit a community spread process, which is exacerbated following law enforcement strategies to disrupt the unregulated drug market. We discuss decriminalisation and increasing resources that promote safer drug use to combat this public health crisis.

Examining the Relationship Between Local Governmental Expenditures on the Social Determinants of Health and County-Level Overdose Deaths, 2017-2020

CONTEXT

Prior studies have examined the social determinants of health (SDOH) characteristics of counties with elevated rates of overdose deaths. However, this research often ignores variation in upstream governmental investment in these SDOH categories.

OBJECTIVE

To examine the association of local governmental investments in SDOH with drug overdose mortality at the county level, controlling for demographic and socioeconomic characteristics.

DESIGN

Longitudinal analysis from 2017 to 2020.

SETTING

United States counties.

PARTICIPANTS

3126 counties.

MAIN OUTCOME AND MEASURES

The primary study outcome was county overdose death rates per 100 000 population taken from the Centers for Disease Control and Prevention. The main predictor of interest was the per capita average of local governmental expenditures in public health, social service, and education drawn from the US Census of Governments, categorized into quartiles. Covariates included county-level socioeconomic and demographic characteristics as well as county-level SUD services from the AHRQ SDOH Database.

RESULTS

In longitudinal multivariate linear regression models with state and year fixed effects, counties in the highest 3 quartiles of SDOH spending had significantly lower rates of overdose deaths compared with counties in the lowest quartile of SDOH spending; in particular, counties in quartile 2 had an average of 9.09 fewer overdose deaths per 100 000 population (95% CI: -12.58, -5.60), counties in quartile 3 had an average of 13.18 fewer deaths per 100 000 population (95% CI: -17.56; -8.80), and counties in quartile 4 had an average of 13.92 deaths per 100 000 population (95% CI: -19.16, -8.68).

CONCLUSIONS

Our findings demonstrate that higher levels of local governmental investments in SDOH were associated with significantly lower rates of overdose death in US counties after controlling for socioeconomic and demographic characteristics. Going forward, research should focus on determining causality in this relationship, the mechanisms through which this relationship occurs, as well as assess the impact of local public health expenditures allocated toward SUD prevention and treatment specifically on substance use outcomes at the local level.

Benefits and challenges of geographic information systems (GIS) for data-driven outreach in black communities experiencing overdose disparities: results of a stakeholder focus group

BACKGROUND

Black individuals in the U.S. face increasing racial disparities in drug overdose related to social determinants of health, including place-based features. Mobile outreach efforts work to mitigate social determinants by servicing geographic areas with low drug treatment and overdose prevention access but are often limited by convenience-based targets. Geographic information systems (GIS) are often used to characterize and visualize the overdose crisis and could be translated to community to guide mobile outreach services. The current study examines the initial acceptability and appropriateness of GIS to facilitate data-driven outreach for reducing overdose inequities facing Black individuals.

METHODS

We convened a focus group of stakeholders (N = 8) in leadership roles at organizations conducting mobile outreach in predominantly Black neighborhoods of St. Louis, MO. Organizations represented provided adult mental health and substance use treatment or harm reduction services. Participants were prompted to discuss current outreach strategies and provided feedback on preliminary GIS-derived maps displaying regional overdose epidemiology. A reflexive approach to thematic analysis was used to extract themes.

RESULTS

Four themes were identified that contextualize the acceptability and utility of an overdose visualization tool to mobile service providers in Black communities. They were: 1) importance of considering broader community context; 2) potential for awareness, engagement, and community collaboration; 3) ensuring data relevance to the affected community; and 4) data manipulation and validity concerns.

CONCLUSIONS

There are several perceived benefits of using GIS to map overdose among mobile providers serving Black communities that are overburdened by the overdose crisis but under resourced. Perceived potential benefits included informing location-based targets for services as well as improving awareness of the overdose crisis and facilitating collaboration, advocacy, and resource allocation. However, as GIS-enabled visualization of drug overdose grows in science, public health, and community settings, stakeholders must consider concerns undermining community trust and benefits, particularly for Black communities facing historical inequities and ongoing disparities.

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.

Social and economic determinants of drug overdose deaths: a systematic review of spatial relationships

PURPOSE

To synthesize the available evidence on the extent to which area-level socioeconomic conditions are associated with drug overdose deaths in the United States.

METHODS

We performed a systematic review (in MEDLINE, EMBASE, PsychINFO, Web of Science, EconLit) for papers published prior to July 2022. Eligible studies quantitatively estimated the association between an area-level measure of socioeconomic conditions and drug overdose deaths in the US, and were published in English. We assessed study quality using the Effective Public Health Practice Project Quality Assessment Tool. The protocol was preregistered at Prospero (CRD42019121317).

RESULTS

We identified 28 studies that estimated area-level effects of socioeconomic conditions on drug overdose deaths in the US. Studies were scored as having moderate to serious risk of bias attributed to both confounding and in analysis. Socioeconomic conditions and drug overdose death rates were moderately associated, and this was a consistent finding across a large number of measures and differences in study designs (e.g., cross-sectional versus longitudinal), years of data analyzed, and primary unit of analysis (e.g., ZIP code, county, state).

CONCLUSIONS

This review highlights the evidence for area-level socioeconomic conditions are an important factor underlying the geospatial distribution of drug overdose deaths in the US and the need to understand the mechanisms underlying these associations to inform future policy recommendations. The current evidence base suggests that, at least in the United States, employment, income, and poverty interventions may be effective targets for preventing drug overdose mortality rates.

Radical hospitality: Innovative programming to build community and meet the needs of people who use drugs at a government-sanctioned overdose prevention site in San Francisco, California

BACKGROUND

The Tenderloin Center (TLC), a multi-service center where people could receive or be connected to basic needs, behavioral health care, housing, and medical services, was open in San Francisco for 46 weeks in 2022. Within a week of operation, services expanded to include an overdose prevention site (OPS), also known as safe consumption site. OPSs have operated internationally for over three decades, but government-sanctioned OPSs have only recently been implemented in the United States. We used ethnographic methods to understand the ways in which a sanctioned OPS, situated in a multi-service center, impacts the lives of people who use drugs (PWUD).

METHODS

We conducted participant observation and in-depth interviews June-December 2022. Extensive field notes and 39 in-depth interviews with 24 TLC guests and 15 TLC staff were analyzed using an inductive analysis approach. Interviewees were asked detailed questions about their experiences using and working at the TLC.

RESULTS

TLC guests and staff described an atmosphere where radical hospitality-welcoming guests with extraordinary warmth, generosity, and unconditional acceptance-was central to the culture. We found that the co-location of an OPS within a multi-service agency (1) allowed for the culture of radical hospitality to flourish, (2) yielded a convenient one-stop shop model, (3) created a space for community building, and (4) offered safety and respite to guests.

CONCLUSIONS

The co-location of an OPS within a multi-service drop-in center is an important example of how such an organization can build positive sociality among PWUD while protecting autonomy and reducing overdose mortality. Overdose response and reversal is an act of relational accountability in which friends, peers, and even strangers intervene to protect and revive one another. This powerful intervention was operationalized as an anti-oppressive, horizontal activity through radical hospitality with a built environment that allowed PWUD to be both social and safe.

Concentrated disadvantage and functional disability: a longitudinal neighbourhood analysis in 100 US cities

BACKGROUND

Socioeconomic disadvantage related to poverty, unemployment and social disinvestment contributes to significant disparities in community health in the USA. Yet, there remains limited ecological research on the relationship between neighbourhood disadvantage and functional disability. Much of the work in this area has focused on elderly populations without attention to variation across age and sex groups.

METHODS

Using a longitudinal dataset of almost 16 000 neighbourhoods, we examine the relationship between neighbourhood disadvantage and functional disability. Leveraging a series of cross-lagged panel models, we account for reciprocal dynamics and a range of pertinent covariates while assessing differences across age- and sex-specific groups.

RESULTS

Accounting for reciprocal effects, we found that the association between concentrated disadvantage and functional disability varies across age and sex groups. Concentrated disadvantage is most consistently associated with increased functional disability among boys (5-17 years), young men (18-34 years) and middle-aged men (35-64 years). Similar associations are found among girls (5-17 years) and middle-aged women (35-64 years).

CONCLUSION

Local neighbourhood economic conditions are significantly associated with functional disability among relatively young populations of males and females. Exposure to neighbourhood disadvantage and deprivation may accelerate disablement processes and shift the age curve of disability risk.

Creation and Validation of a New Socio-built Environment Index Measure of Opioid Overdose Risk for Use in Both Non-urban and Urban Settings

A great deal of literature has examined features of the physical built environment as predictors of opioid overdose and other substance use-related outcomes. Other literature suggests that social characteristics of settings are important predictors of substance use outcomes. However, there is a dearth of literature simultaneously measuring both physical and social characteristics of settings in an effort to better predict opioid overdose. There is also a dearth of literature examining built environment as a predictor of overdose in non-urban settings. The present study presents a novel socio-built environment index measure of opioid overdose risk comprised of indicators measuring both social and physical characteristics of settings - and developed for use in both urban and non-urban settings - and assesses its validity among 565 urban, suburban, and rural New Jersey municipalities. We found that this novel measure had good convergent validity, based on significant positive associations with a social vulnerability index and crime rates, and significant negative associations with a municipal revitalization index and high school graduation rates. The index measure had good discriminant validity, based on lack of association with three different racial isolation indices. Finally, our index measure had good health outcome-based criterion validity, based on significant positive associations with recent overdose mortality. There were no major differences between rural, suburban, and urban municipalities in validity analysis findings. This promising new socio-built environment risk index measure could improve ability to target and allocate resources to settings with the greatest risk, in order to improve their impact on overdose outcomes.

Examining Increasing Racial Inequities in Opioid Overdose Deaths: a Spatiotemporal Analysis of Black and White Decedents in St. Louis, Missouri, 2011-2021

The third wave of the opioid overdose crisis-defined by the proliferation of illicit fentanyl and its analogs-has not only led to record numbers of overdose deaths but also to unprecedented racial inequities in overdose deaths impacting Black Americans. Despite this racialized shift in opioid availability, little research has examined how the spatial epidemiology of opioid overdose death has also shifted. The current study examines the differential geography of OOD by race and time (i.e., pre-fentanyl versus fentanyl era) in St. Louis, Missouri. Data included decedent records from the local medical examiners suspected to involve opioid overdose (N = 4420). Analyses included calculating spatial descriptive analyses and conducting hotspot analyses (i.e., Gettis-Ord Gi*) stratified by race (Black versus White) and time (2011-2015 versus 2016-2021). Results indicated that fentanyl era overdose deaths were more densely clustered than pre-fentanyl era deaths, particularly those among Black decedents. Although hotspots of overdose death were racially distinct pre-fentanyl, they substantially overlapped in the fentanyl era, with both Black and White deaths clustering in predominantly Black neighborhoods. Racial differences were observed in substances involved in cause of death and other overdose characteristics. The third wave of the opioid crisis appears to involve a geographic shift from areas where White individuals live to those where Black individuals live. Findings demonstrate racial differences in the epidemiology of overdose deaths that point to built environment determinants for future examination. Policy interventions targeting high-deprivation communities are needed to reduce the burden of opioid overdose on Black communities.

Understanding the differential effect of local socio-economic conditions on the relation between prescription opioid supply and drug overdose deaths in US counties

BACKGROUND AND AIMS

Both local socio-economic conditions and prescription opioid supply are associated with drug overdose deaths, which exhibit substantial geographical heterogeneity across the United States. We measured whether the associations of prescription opioid supply with drug overdose deaths vary by local socio-economic conditions.

DESIGN

Ecological county-level study, including 3109 US counties between 2006 and 2019 (n = 43 526 county-years) using annual mortality data.

SETTING

United States.

CASES

A total of 711 447 drug overdose deaths.

MEASUREMENTS

We modeled overdose counts using Bayesian hierarchical Poisson models, estimating associations between four types of drug overdose deaths (deaths involving any drugs, any opioid, prescription opioids only and heroin), prescription opioid supply and five socio-economic indicators: unemployment, poverty rate, income inequality, Rey index (components include mean household income, % high school graduates, % blue-collar workers and unemployment rate), and American human development index (HDI; an indicator of community wellbeing).

FINDINGS

Drug overdose deaths and all substance-specific overdose deaths were higher in counties with higher income inequality [adjusted odds ratios (aORs) = 1.09-1.13], Rey index (aORs = 1.15-1.21) and prescription opioid supply (aORs = 1.14-1.21), and lower in counties with higher HDI scores (aORs = 0.75-0.92). Poverty rate, income inequality and HDI scores were found to modify the effect of prescription opioid supply on heroin overdose deaths. The plot of the interactions showed that when disadvantage is high, increasing prescription opioid supply does not increase heroin overdose deaths. The less disadvantage there is, indicated by lower poverty rates, higher HDI scores and lower income inequality, the greater the effect of increasing prescription opioid supply relative to population size on heroin overdose deaths in US counties.

CONCLUSIONS

In the United States, prescription opioid supply is associated with higher drug overdose deaths; associations are stronger in counties with less disadvantage and less income inequality, but only for heroin overdose deaths.

"The new normal has become a nonstop crisis": a qualitative study of burnout among Philadelphia's harm reduction and substance use disorder treatment workers during the COVID-19 pandemic

BACKGROUND

The COVID-19 pandemic worsened the ongoing overdose crisis in the United States (US) and caused significant mental health strain and burnout among health care workers (HCW). Harm reduction, overdose prevention, and substance use disorder (SUD) workers may be especially impacted due to underfunding, resources shortages, and chaotic working environments. Existing research on HCW burnout primarily focuses on licensed HCWs in traditional environments and fails to account for the unique experiences of harm reduction workers, community organizers, and SUD treatment clinicians.

METHODS

We conducted a qualitative secondary analysis descriptive study of 30 Philadelphia-based harm reduction workers, community organizers, and SUD treatment clinicians about their experiences working in their roles during the COVID-19 pandemic in July-August 2020. Our analysis was guided by Shanafelt and Noseworthy's model of key drivers of burnout and engagement. We aimed to assess the applicability of this model to the experiences of SUD and harm reduction workers in non-traditional settings.

RESULTS

We deductively coded our data in alignment with Shanafelt and Noseworthy's key drivers of burnout and engagement: (1) workload and job demands, (2) meaning in work, (3) control and flexibility, (4) work-life integration, (5) organizational culture and values, (6) efficiency and resources and (7) social support and community at work. While Shanafelt and Noseworthy's model broadly encompassed the experiences of our participants, it did not fully account for their concerns about safety at work, lack of control over the work environment, and experiences of task-shifting.

CONCLUSIONS

Burnout among healthcare providers is receiving increasing attention nationally. Much of this coverage and the existing research have focused on workers in traditional healthcare spaces and often do not consider the experiences of community-based SUD treatment, overdose prevention, and harm reduction providers. Our findings indicate a gap in existing frameworks for burnout and a need for models that encompass the full range of the harm reduction, overdose prevention, and SUD treatment workforce. As the US overdose crisis continues, it is vital that we address and mitigate experiences of burnout among harm reduction workers, community organizers, and SUD treatment clinicians to protect their wellbeing and to ensure the sustainability of their invaluable work.

County-Level Economic Changes and Drug Mortality in the United States: Evidence from the Great Recession

We aimed at examining whether county-level economic changes were associated with changes in county-level drug mortality rates since the Great Recession and whether the association is equally distributed across major sociodemographic subgroups. Using the Centers for Disease Control and Prevention's Wide-Ranging Online Data for Epidemiologic Research (2004-2019), combined with census data, we conducted fixed effects analyses by including county-level economic changes as primary exposures and county-level drug-related mortality rates (per 100,000 people) from 2004-2007 (i.e., prior to the recession) to 2008-2011, 2012-2015, and 2016-2019 as an outcome variable based on 1833 counties. Our findings showed that drug mortality rates increased from 13.9 (2004-2007) to 16.0 (2008-2011), 18.0 (2012-2015), and 23.0 (2016-2019). Counties experiencing smaller median household income growth during and/or after the recession were associated with greater increase in drug mortality than counties experiencing larger median household income growth among the total population and all sociodemographic subgroups. Counties experiencing larger increases in unemployment rates and percentage of vacant housing units were associated with greater increase in drug mortality than counties experiencing smaller or no increase in unemployment rates and percentage of vacant housing units among certain sociodemographic subgroups. Findings suggest the importance of local economic contexts in understanding drug mortality risk since the recession. Drug overdose prevention polices need to be formulated by taking local economic changes following a major recession into consideration.

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.

Understanding the role of urban social and physical environment in opioid overdose events using found geospatial data

Opioid use disorder is a serious public health crisis in the United States. Manifestations such as opioid overdose events (OOEs) vary within and across communities and there is growing evidence that this variation is partially rooted in community-level social and economic conditions. The lack of high spatial resolution, timely data has hampered research into the associations between OOEs and social and physical environments. We explore the use of non-traditional, "found" geospatial data collected for other purposes as indicators of urban social-environmental conditions and their relationships with OOEs at the neighborhood level. We evaluate the use of Google Street View images and non-emergency "311" service requests, along with US Census data as indicators of social and physical conditions in community neighborhoods. We estimate negative binomial regression models with OOE data from first responders in Columbus, Ohio, USA between January 1, 2016, and December 31, 2017. Higher numbers of OOEs were positively associated with service request indicators of neighborhood physical and social disorder and street view imagery rated as boring or depressing based on a pre-trained random forest regression model. Perceived safety, wealth, and liveliness measures from the street view imagery were negatively associated with risk of an OOE. Age group 50-64 was positively associated with risk of an OOE but age 35-49 was negative. White population, percentage of individuals living in poverty, and percentage of vacant housing units were also found significantly positive however, median income and percentage of people with a bachelor's degree or higher were found negative. Our result shows neighborhood social and physical environment characteristics are associated with likelihood of OOEs. Our study adds to the scientific evidence that the opioid epidemic crisis is partially rooted in social inequality, distress and underinvestment. It also shows the previously underutilized data sources hold promise for providing insights into this complex problem to help inform the development of population-level interventions and harm reduction policies.

The leading neighborhood-level predictors of drug overdose: A mixed machine learning and spatial approach

BACKGROUND

Drug overdose is a leading cause of unintentional death in the United States and has contributed significantly to a decline in life expectancy during recent years. To combat this health issue, this study aims to identify the leading neighborhood-level predictors of drug overdose and develop a model to predict areas at the highest risk of drug overdose using geographic information systems and machine learning (ML) techniques.

METHOD

Neighborhood-level (block group) predictors were grouped into three domains: socio-demographic factors, drug use variables, and protective resources. We explored different ML algorithms, accounting for spatial dependency, to identify leading predictors in each domain. Using geographically weighted regression and the best-performing ML algorithm, we combined the output prediction of three domains to produce a final ensemble model. The model performance was validated using classification evaluation metrics, spatial cross-validation, and spatial autocorrelation testing.

RESULTS

The variables contributing most to the predictive model included the proportion of households with food stamps, households with an annual income below $35,000, opioid prescription rate, smoking accessories expenditures, and accessibility to opioid treatment programs and hospitals. Compared to the error estimated from normal cross-validation, the generalized error of the model did not increase considerably in spatial cross-validation. The ensemble model using ML outperformed the GWR method.

CONCLUSION

This study identified strong neighborhood-level predictors that place a community at risk of experiencing drug overdoses, as well as protective factors. Our findings may shed light on several specific avenues for targeted intervention in neighborhoods at risk for high drug overdose burdens.

Barriers to effective care: Specialty drug treatment in Philadelphia

INTRODUCTION

In Philadelphia, the poorest big city in the United States, an estimated 60,000 people misuse opioids and more than 3500 have died of overdose in the past three years. In 2019, fentanyl was detected in 76% of drug-related deaths and 94% of opioid-involved deaths. While much attention has been directed at the public face of the city's drug problem, more than 75% of drug deaths in 2017 took place in a private residence.

METHOD

Based on qualitative research to understand the vulnerabilities of this hidden population of drug users, we interviewed kin of 35 people who had died of opioid overdose in 2017 to learn whether their loved one had interacted with any social services or harm-reduction interventions.

RESULTS

In our demographically and geographically representative sample of decedents, we found that while most had received treatment at least once, many faced barriers to getting treatment when they needed it, including barriers related to stigma, structural racism, gender inequities, bureaucracy, insurance requirements, and cost.

CONCLUSION

We argue that these barriers place an undue burden on people with substance use disorder and their kin during particularly fraught moments of heightened vulnerability. The failure of state and federal policies, practices, and infrastructure to address these barriers, and the failure to require that evidence-based care be provided during treatment have deleterious effects on people affected by the opioid epidemic in the United States.

Exploring the Influence of Drug Trafficking Gangs on Overdose Deaths in the Largest Narcotics Market in the Eastern United States

Research has found that drug markets tend to cluster in space, potentially because of the profit that can be made when customers are drawn to areas with multiple suppliers. But few studies have examined how these clusters of drug markets—which have been termed “agglomeration economies”—may be related to accidental overdose deaths, and in particular, the spatial distribution of mortality from overdose. Focusing on a large neighborhood in Philadelphia, Pennsylvania, known for its open-air drug markets, this study examines whether deaths from accidental drug overdose are clustered around street corners controlled by drug trafficking gangs. This study incorporates theoretically-informed social and physical environmental characteristics of street corner units into the models predicting overdose deaths. Given a number of environmental changes relevant to drug use locations was taking place in the focal neighborhood during the analysis period, the authors first employ a novel concentration metric—the Rare Event Concentration Coefficient—to assess clustering of overdose deaths annually between 2015 and 2019. The results of these models reveal that overdose deaths became less clustered over time and that the density was considerably lower after 2017. Hence, the predictive models in this study are focused on the two-year period between 2018 and 2019. Results from spatial econometric regression models find strong support for the association between corner drug markets and accidental overdose deaths. In addition, a number of sociostructural factors, such as concentrated disadvantage, and physical environmental factors, particularly blighted housing, are associated with a higher rate of overdose deaths. Implications from this study highlight the need for efforts that strategically coordinate law enforcement, social service provision and reductions in housing blight targeted to particular geographies.