Revisiting the role of the urban environment in substance use: the case of analgesic overdose fatalities

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.

Higher unemployment benefits are associated with reduced drug overdose mortality in the United States before and during the COVID-19 pandemic

OBJECTIVES

Overdose mortality rates in the United States remain critical to population health. Economic , such as unemployment, are noted risk factors for drug overdoses. The COVID-19 pandemic exacerbated economic hardship; as a result, the US government enacted income protection programs in conjunction with existing unemployment insurance (UI) to dampen COVID-19-related economic consequences. We investigate whether UI, operationalized as the weekly benefit allowance (WBA) replacement rate, is negatively associated with drug-related overdoses.

METHODS

Data from the pooled 2014-2020 Detailed Restricted Mortality files for all counties from the Centers for Disease Control and Prevention, restricted to people ≥18 years of age, aggregated at the county-quarter level (n = 89,914). We included any fatal drug, opioid, and stimulant overdose. We modeled the association between WBA replacement rate (e.g., a greater proportion of weekly earnings replaced by UI) on each county-level age-adjusted mortality outcome using separate linear regression models during 2014-2020, pre-COVID (2014-2018), and post-COVID (2019-2020). We conducted sensitivity analyses using multi-level linear regression models.

RESULTS

Results indicated that a more robust WBA replacement rate any drug (Risk Difference [RD]: -0.06, 95 % Confidence Interval [CI]: -0.08, -0.05), opioid (RD: -0.04, 95 % CI: -0.06, -0.03), and stimulant (RD: -0.03, 95 % CI: -0.04, -0.02) across the entire study period (2014-2020). A more robust WBA replacement rate was associated with fewer fatal drug, opioid and stimulant overdoses in the pre-COVID-19 period and on fatal any drug and stimulant overdoses in the COVID-19 period.

CONCLUSIONS

Findings support the notion that income protection policies, such as robust UI, can have a supportive role in preventing fatal drug overdoses, calling for a broader discussion onthe role of the safety net programs to buffer drug-related harms.

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.

Social-spatial network structures among young urban and suburban persons who inject drugs in a large metropolitan area

BACKGROUND

Recent studies underscore the significance of adopting a syndemics approach to study opioid misuse, overdose, hepatitis C (HCV) and HIV infections, within the broader context of social and environmental contexts in already marginalized communities. Social interactions and spatial contexts are crucial structural factors that remain relatively underexplored. This study examines the intersections of social interactions and spatial contexts around injection drug use. More specifically, we investigate the experiences of different residential groups among young (aged 18-30) people who inject drugs (PWID) regarding their social interactions, travel behaviors, and locations connected to their risk behaviors. By doing so, we aim to achieve a more comprehensive understanding of the multidimensional risk environment, thereby facilitating the development of informed policies.

METHODS

We collected and examined data regarding young PWID's egocentric injection network and geographic activity spaces (i.e., where they reside, inject drugs, purchase drugs, and meet sex partners). Participants were stratified based on the location of all place(s) of residence in the past year i.e., urban, suburban, and transient (both urban and suburban) to i) elucidate geospatial concentration of risk activities within multidimensional risk environments based on kernel density estimates; and ii) examine spatialized social networks for each residential group.

RESULTS

Participants were mostly non-Hispanic white (59%); 42% were urban residents, 28% suburban, and 30% transient. We identified a spatial area with concentrated risky activities for each residential group on the West side of Chicago in Illinois where a large outdoor drug market area is located. The urban group (80%) reported a smaller concentrated area (14 census tracts) compared to the transient (93%) and suburban (91%) with 30 and 51 tracts, respectively. Compared to other areas in Chicago, the identified area had significantly higher neighborhood disadvantages. Significant differences were observed in social network structures and travel behaviors: suburban participants had the most homogenous network in terms of age and residence, transient participants had the largest network (degree) and more non-redundant connections, while the urban group had the shortest travel distance for all types of risk activities.

CONCLUSION

Distinct residential groups exhibit varying patterns of network interaction, travel behaviors, and geographical contexts related to their risk behaviors. Nonetheless, these groups share common concentrated risk activity spaces in a large outdoor urban drug market area, underscoring the significance of accounting for risk spaces and social networks in addressing syndemics within PWID populations.

The association between social fragmentation and deaths attributable to alcohol, drug use, and suicide: Longitudinal evidence from a population-based sample of Canadian adults

BACKGROUND

Social fragmentation has been theorized and empirically associated with suicide in prior research. However, less is known about whether social fragmentation is associated with deaths attributed to alcohol use or drug use. This research examined the association between social fragmentation and risk for deaths attributable to alcohol use, drug use, and suicide (collectively known as deaths of despair) among Canadian adults.

METHODS

A weighted sample representing 15,324,645 Canadians within 288 census divisions between 2006 and 2019 was used. Mortality data from the Canadian Vital Statistics Database (alcoholic liver disease, drug use, and suicide) was linked with census division socioeconomic data from the 2006 Canadian census using the Canadian Census Health and Environment Cohorts. Social fragmentation at the census division was created based on the Congdon Index. Cox-proportional hazard regression with survey weights and the sandwich estimator were used to account for clustering of individuals (level-1) nested within census divisions (level-2).

RESULTS

After adjusting for individual and census division confounders, social fragmentation was positively associated with all-cause mortality (HR = 1.04; 95% CI: 1.02, 1.07), suicide (HR = 1.09; 95%CI: 1.01, 1.18), drug overdose related mortality (HR = 1.13; 95%CI: 1.03, 1.24), and deaths of despair (HR = 1.10; 95% CI: 1.04, 1.16), and not significantly associated with alcohol related liver disease (HR = 1.06; 95% CI: 0.91, 1.23).

CONCLUSION

Social fragmentation is associated with an increased hazard of deaths of despair among Canadian adults. Efforts to improve social cohesion in areas that are highly socially fragmented need to be evaluated.

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.

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.

Geo-spatial risk factor analysis for drug overdose death in South Florida from 2014 to 2019, and the independent contribution of social determinants of health

PURPOSE

The physical environment and social determinants of health have been shown to influence health behaviors including drug use and fatal drug overdose. The current research examines the effects of the built environment, social determinants of health measures and aggregated risk from the built environment at neighborhood-level on drug overdose death locations in Miami-Dade County, Florida.

METHODS

Risk Terrain Modeling (RTM) was used to assess the place features risk factors that significantly increase the risk of drug overdose death spatially in Miami-Dade County ZIP Code Tabulation Areas, Florida from 2014 to 2019. An aggregated neighborhood risk of fatal drug overdose measure was developed by averaging the risk per grid cell from the RTM within census block groups each year. Six logistic and zero-inflated regression models were built to examine the effects of three indices of incident-specific social determinants of health (IS-SDH) measures and aggregated risk measures separately, and simultaneously on drug overdose death locations each year.

RESULTS

Seven place features including parks, bus stops, restaurants and grocery stores were significantly related to the occurrence of fatal drug overdoses. When examined separately, one or more indices of the IS-SDH were significant covariates of drug overdose locations in some years. When examined simultaneously, the three indices of the IS-SDH and aggregated risk of fatal drug overdose measure could be all significant in certain years.

CONCLUSIONS

The patterns of high-risk areas and place features identified from the RTM related to drug overdose deaths may be used to inform the placement of treatment and prevention resources. A multi-factor approach that combines an aggregated neighborhood risk measure reflecting the risk from the built environment and the incident-specific social determinants of health measures can be used to identify the drug overdose death locations in certain years.

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.

Narrative minireview of the spatial epidemiology of substance use disorder in the United States: Who is at risk and where?

Drug overdose is the leading cause of death by injury in the United States. The incidence of substance use disorder (SUD) in the United States has increased steadily over the past two decades, becoming a major public health problem for the country. The drivers of the SUD epidemic in the United States have changed over time, characterized by an initial heroin outbreak between 1970 and 1999, followed by a painkiller outbreak, and finally by an ongoing synthetic opioid outbreak. The nature and sources of these abused substances reveal striking differences in the socioeconomic and behavioral factors that shape the drug epidemic. Moreover, the geospatial distribution of the SUD epidemic is not homogeneous. The United States has specific locations where vulnerable communities at high risk of SUD are concentrated, reaffirming the multifactorial socioeconomic nature of this epidemic. A better understanding of the SUD epidemic under a spatial epidemiology framework is necessary to determine the factors that have shaped its spread and how these patterns can be used to predict new outbreaks and create effective mitigation policies. This narrative minireview summarizes the current records of the spatial distribution of the SUD epidemic in the United States across different periods, revealing some spatiotemporal patterns that have preceded the occurrence of outbreaks. By analyzing the epidemic of SUD-related deaths, we also describe the epidemic behavior in areas with high incidence of cases. Finally, we describe public health interventions that can be effective for demographic groups, and we discuss future challenges in the study and control of the SUD epidemic in the country.

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.

Social-spatial network structures among young urban and suburban persons who inject drugs in a large metropolitan area

Background

It is estimated that there are 1.5% US adult population who inject drugs in 2018, with young adults aged 18-39 showing the highest prevalence. PWID are at a high risk of many blood-borne infections. Recent studies have highlight the importance of employing the syndemic approach to study opioid misuse, overdose, HCV and HIV, along with the social and environmental contexts where these interrelated epidemics occur in already marginalized communities. Social interactions and spatial contexts are important structural factors that are understudied.

Methods

Egocentric injection network and geographic activity spaces for young (aged 18-30) PWID and their injection, sexual, and social support network members (i.e., where reside, inject drugs, purchase drugs, and meet sex partners) were examined using baseline data from an ongoing longitudinal study (n=258). Participants were stratified based on the location of all place(s) of residence in the past year i.e., urban, suburban, and transient (both urban and suburban) to i) elucidate geospatial concentration of risk activities within multi-dimensional risk environments based on kernel density estimates; and ii) examine spatialized social networks for each residential group.

Results

Participants were mostly non-Hispanic white (59%); 42% were urban residents, 28% suburban, and 30% transient. We identified a spatial area with concentrated risky activities for each residence group on the West side of Chicago where a large outdoor drug market area is located. The urban group (80%) reported a smaller concentrated area (14 census tracts) compared to the transient (93%) and suburban (91%) with 30 and 51 tracts, respectively. Compared to other areas in Chicago, the identified area had significantly higher neighborhood disadvantages (e.g., higher poverty rate, p <0.001). Significant ( p <0.01 for all) differences were observed in social network structures: suburban had the most homogenous network in terms of age and residence, transient participants had the largest network (degree) and more non-redundant connections.

Conclusion

We identified concentrated risk activity spaces among PWID from urban, suburban, and transient groups in a large outdoor urban drug market area, which highlights the need for considering the role of risk spaces and social networks in addressing the syndemics in PWID populations.

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.

Sex Work Venue Disorder and HIV/STI Risk Among Female Sex Workers in Two México-US Border Cities: A Latent Class Analysis

Research increasingly recognizes the importance of social and built environments in shaping health, including risks for and outcomes related to HIV and sexually transmitted infections (STI), but research on sex work venues is limited. We use latent class analysis to identify patterns of sex work venue characteristics and factors associated with class membership in two México-US border cities. Among 603 female sex workers (FSW), three classes of sex work venues were identified: low, medium, and high disorder venues, characterized by level of violence, policing and drug activity. In multivariable analysis, risk exposures and outcomes varied by class, suggesting the need for place-based interventions that are tailored to specific venue profiles and that promote FSW health and safety in the workplace.

Drug overdoses, geographic trajectories, and the influence of built environment and neighborhood characteristics

Much research has analyzed the spatial patterns of drug overdose events and identified features of the environment associated with heightened overdose levels. Generally absent from the literature are studies that analyze how unique trajectories of overdoses vary over time. We address this gap in the literature through an analysis of drug overdoses occurring in Passaic County, New Jersey from 2015 through 2019. A group-based trajectory analysis classifies block groups according to their overdose trends. A mixed-effects panel negative binomial regression model then examines the built environment and neighborhood characteristics associated with overall overdose levels. Results indicate that Passaic County block groups can be classified across three groups based upon their overdose levels over the study period: low and stable, low with moderate increase, and elevated and increasing. While the largest effects were observed for concentrated disadvantage in the regression analysis, most variables positively associated with overdose levels were built environment measures.

City streetscapes and neighborhood characteristics of fatal opioid overdoses among people experiencing homelessness who use drugs in New York City, 2017-2019

BACKGROUND

While housing is a critical social determinant of nonprescription opioid overdose, little is known about how place impacts fatal overdose for people experiencing homelessness (PEH) who use drugs beyond the public versus private domains. This study investigated patterns of neighborhood environment features at locations of fatal overdoses among PEH.

METHODS

We remotely visited locations of opioid-involved fatal overdoses provided by the New York City Office of the Chief Medical Examiner, 2017-2019 (n=3276), with Google Street View and used systematic social observation to assess characteristics of the street block related to drug exposures. We cross-referenced home address with city shelters and supportive housing to identify PEH (n=503). We used the differences of K functions from the spatial point patterns and kernel ratio function maps to identify geographic clusters. We then used logistic regression to identify significant individual-, block-, and neighborhood-level covariates (neighborhood deprivation, segregation, population density).

RESULTS

Over half (55.9%, n=281) of fatal overdoses among PEH occurred in supportive housing or shelters, and 15.5% (n=78) occurred in public spaces (e.g., parks). Spatial analyses identified areas of significant concentrated fatal overdoses among PEH in Manhattan, the South Bronx, and Brooklyn. We identified several significant indicators of physical and social order and disorder associated with increased odds of experiencing homelessness at time of fatal overdose, including construction/renovation, graffiti, traffic calming features, and loitering.

CONCLUSION

Harm reduction services should be co-located in facilities that serve PEH and targeted to street blocks with indicators of physical and social disorder. While supportive housing is a crucial step in preventing fatal opioid overdoses among PEH, identifying neighborhoods for intervention services delivery and harm reduction outreach for PEH is necessary.

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 built environment on mental health: A COVID-19 lockdown perspective

Tackling mental health has become a priority for governments around the world because it influences not only individuals but also the whole society. As people spend a majority of their time (i.e., around 90%) in buildings, it is pivotal to understand the relationship between built environment and mental health, particularly during COVID-19 when people have experienced recurrent local and national lockdowns. Despite the demonstration by previous research that the design of the built environment can affect mental health, it is not clear if the same influence pattern remains when a 'black swan' event (e.g., COVID-19) occurs. To this end, we performed logistic regression and hierarchical regression analyses to examine the relationship between built environment and mental health utilising a data sample from the United Kingdom (UK) residents during the COVID-19 lockdown while considering their social demographics. Our results show that compared with depression and anxiety, people were more likely to feel stressed during the lockdown period. Furthermore, general house type, home workspace, and neighbourhood environment and amenity were identified to have significantly contributed to their mental health status. With the ensuing implications, this study represents one of the first to inform policymakers and built environment design professionals of how built environment should be designed to accommodate features that could mitigate mental health problems in any future crisis. As such, it contributes to the body of knowledge of built environment planning by considering mental health during the COVID-19 lockdown.

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.

Impact of a publicly funded pharmacy-dispensed naloxone program on fatal opioid overdose rates: A population-based study

BACKGROUND

Studies examining the impact of pharmacy-dispensed naloxone programs on fatal opioid overdose rates are lacking. We examined the impact of the publicly funded Ontario Naloxone Program for Pharmacies (ONPP), implemented in June 2016, on provincial rates of opioid overdose deaths.

METHODS

We conducted a population-based interrupted time-series study between July 1, 2012 and December 31, 2018. We considered a parsimonious model with terms for time, ONPP implementation, and time following the ONPP implementation. Models were adjusted for population characteristics, number of pharmacies and rate of naloxone distributed through non-pharmacy sites within provincial public health units.

RESULTS

In the parsimonious model, the ONPP was associated with a non-significant 9% reduction in the level of fatal opioid overdoses (rate ratio [RR] 0.91; 95% confidence interval [CI] 0.79-1.06), a finding that was most pronounced in regions in the lowest tertile of implementation (RR 0.75; 95% CI 0.62-0.91). Following multivariable adjustment, there was an increase in the level (RR 1.06; 95% CI 0.94-1.19) and slope change (RR 1.06; 95% CI 1.02-1.10) of fatal overdose rates.

CONCLUSION

The ONPP is insufficient as a single intervention to meaningfully reduce rates of fatal opioid overdoses during a period in which the cause of these deaths shifted from prescription opioids to highly potent fentanyl analogs. Access to additional harm reduction, treatment, and other interventions is necessary to prevent deaths and optimize the health of people who use drugs.

Spatial and neighborhood-level correlates of lay naloxone reversal events and service availability

BACKGROUND

The opioid epidemic in the United States continues to surge, reaching record deaths from opioid and fentanyl overdoses in 2020. This study analyzes spatial and neighborhood correlates of free naloxone distribution sites as well as overdose and naloxone reversal events in Baltimore, Maryland, which has one of the highest overdose rates in the country.

METHODS

Using data from a randomized clinical trial on HIV prevention among people using substances in Baltimore, Maryland, as well as demographic data from the US Census Bureau, we conducted: (1) exploratory spatial visualizations of census tracts' minimum distance to naloxone distribution sites, (2) univariable Wilcoxon rank-sum tests to compare census tracts on demographic metrics, and (3) bivariable and multivariable negative binomial regression models to assess associations between census tract characteristics and naloxone reversal events.

RESULTS

Valid geographic data were provided for 518 overdose events involving either fentanyl or heroin in this study. Of these, 190 (37%) attempted naloxone reversal events were reported. Exploratory spatial visualization techniques suggest that most distribution sites are appropriately located near populations at high risk of overdose, but study findings also identify areas where drug use and overdoses occur that are located farther from distribution sites. In multivariable analyses, naloxone administration was significantly and inversely associated with distance to the nearest distribution site (incidence rate ratio (IRR)=0.72 per 1000m increase, 95% CI 0.59-0.89, p=0.002).

CONCLUSION

Study findings emphasize the correlation between proximity to naloxone sites and utilization of resources, highlighting that physical proximity to harm reduction resources may contribute to uptake. Results further underscore that research on service accessibility and utilization must consider the spatial distribution of health services.

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.

Neighborhood disadvantage and prescription drug misuse in low-income urban mothers

BACKGROUND

Prescription drug misuse remains a persistent problem in the United States. Residents living in disadvantaged neighborhoods are at greater risk of substance abuse such as alcohol, tobacco, or drugs. However, whether neighborhood disadvantage affects prescription drug misuse remains underexplored.

METHODS

This study uses data on 3444 mothers from the Fragile Families and Child Wellbeing Study to examine the role of neighborhood disadvantage in prescription drug misuse. In addition, we examine whether social support and neighborhood collective efficacy (social cohesion and social control) explain this relationship. The analysis uses multivariate logistic regression and delineated between the following neighborhoods: affluent (3% poverty), low poverty (3-10%), moderate poverty (10-20%), and high poverty neighborhoods (20% or more).

RESULTS

Mothers living in moderately poor neighborhoods were more than twice as likely (odds = 2.17, 95% CI: 1.43-3.27) to misuse prescription drugs than mothers living in neighborhoods with high poverty. Mothers living in neighborhoods with high poverty did not have a statistically significant difference in prescription drug misuse than those living in affluent or low poverty neighborhoods. Social support and neighborhood collective efficacy did not explain these associations. The association between moderate poverty and prescription drug misuse was mostly direct and there was no indirect association.

CONCLUSION

The study highlights the higher risk of prescription drug misuse among mothers living in neighborhoods with moderate poverty. Interventions aimed at reducing opioid misuse should focus on demographic groups that are more vulnerable such as low-income mothers living in disadvantaged neighborhoods.

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.

Ensemble learning to predict opioid-related overdose using statewide prescription drug monitoring program and hospital discharge data in the state of Tennessee

Objective

To develop and validate algorithms for predicting 30-day fatal and nonfatal opioid-related overdose using statewide data sources including prescription drug monitoring program data, Hospital Discharge Data System data, and Tennessee (TN) vital records. Current overdose prevention efforts in TN rely on descriptive and retrospective analyses without prognostication.

Materials and Methods

Study data included 3 041 668 TN patients with 71 479 191 controlled substance prescriptions from 2012 to 2017. Statewide data and socioeconomic indicators were used to train, ensemble, and calibrate 10 nonparametric “weak learner” models. Validation was performed using area under the receiver operating curve (AUROC), area under the precision recall curve, risk concentration, and Spiegelhalter z-test statistic.

Results

Within 30 days, 2574 fatal overdoses occurred after 4912 prescriptions (0.0069%) and 8455 nonfatal overdoses occurred after 19 460 prescriptions (0.027%). Discrimination and calibration improved after ensembling (AUROC: 0.79–0.83; Spiegelhalter P value: 0–.12). Risk concentration captured 47–52% of cases in the top quantiles of predicted probabilities.

Discussion

Partitioning and ensembling enabled all study data to be used given computational limits and helped mediate case imbalance. Predicting risk at the prescription level can aggregate risk to the patient, provider, pharmacy, county, and regional levels. Implementing these models into Tennessee Department of Health systems might enable more granular risk quantification. Prospective validation with more recent data is needed.

Conclusion

Predicting opioid-related overdose risk at statewide scales remains difficult and models like these, which required a partnership between an academic institution and state health agency to develop, may complement traditional epidemiological methods of risk identification and inform public health decisions.

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.

Socioeconomic correlates of incident and fatal opioid overdose among Swedish people with opioid use disorder

Background

Opioid overdose (OD) and opioid OD death are major health threats to people with opioid use disorder (OUD). Socioeconomic factors are underexplored potential determinants of opioid OD. In this study, we assessed socioeconomic and other factors and their associations with incident and fatal opioid OD, in a cohort consisting of 22,079 individuals with OUD.

Methods

We performed a retrospective, longitudinal study based on Swedish national register data for the period January 2005–December 2017. We used Cox proportional hazard models to investigate the risk of incident and fatal opioid OD as a function of several individual, parental and neighborhood covariates.

Results

Univariate analysis showed that several covariates were associated with incident and fatal opioid OD. In the multivariate analysis, incident opioid OD was associated with educational attainment (Hazard ratio [HR] 0.96; 95% confidence interval [CI] 0.94–0.97), having received social welfare (HR 1.31; 95% CI 1.22–1.39), and criminal conviction (HR 1.53; 95% CI 1.42–1.65). Fatal opioid OD was also associated with criminal conviction (HR 1.93; 95% CI 1.61–2.32).

Conclusion

Individuals with low education and receipt of social welfare had higher risks of incident opioid OD and individuals with criminal conviction were identified as a risk group for both incident and fatal opioid OD. Our findings should raise attention among health prevention policy makers in general, and among decision-makers within the criminal justice system and social services in particular.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13011-021-00409-3.

Evaluation of Policy Effectiveness by Mathematical Modeling for the Opioid Crisis with Spatial Study and Trend Analysis

The current opioid epidemic in the US presents a great problem which calls for policy supervision and regulation. In this work, the opioid cases of five states were used for trend analysis and modeling for the estimation of potential policy effects. An evaluation model was established to analyze the severity of the opioid abuse based on the entropy weight method (EWM) and rank sum ratio (RSR). Four indexes were defined to estimate the spatial distribution of development and spread of the opioid crisis. Thirteen counties with the most severe opioid abuse in five states were determined using the EWM-RSR model and those indexes. Additionally, a forecast of the development of opioid abuse was given based on an autoregressive (AR) model. The RSR values of the thirteen counties would increase to the range between 0.951 and 1.226. Furthermore, the least absolute shrinkage and selection operator (LASSO) method was adopted. The previous indexes were modified, incorporating the comprehensive socioeconomic effects. The optimal penalty term was found to facilitate the stability and reliability of the model. By using the comprehensive model, it was found that three factors—VC112, VC114, VC115—related to disabled people have a great influence on the development of opioid abuse. The simulated policies were performed in the model to decrease the values of the indicators by 10%–50%. The corresponding RSR values can decline to the range between 0.564 and 0.606. Adopting policies that benefit the disabled population should inhibit the trend of opioid abuse.

Alcohol outlets, drug paraphernalia sales, and neighborhood drug overdose

BACKGROUND

Alcohol outlets have been associated with various forms of injury and may contribute to neighborhood disparities in drug overdose. Few studies have examined the associations between alcohol outlets and drug overdose. This study investigated whether alcohol outlets were associated with the neighborhood drug overdose rate and whether the sale of drug paraphernalia contributes to this association.

METHODS

A cross-sectional ecological spatial analysis was conducted within census block groups in Baltimore City (n = 653). Outcomes were counts of EMS calls for any drug overdose in 2015 (n = 3,856). Exposures of interest were counts of alcohol outlets licensed for off-premise and on-premise consumption and the proportion of off-premise outlets selling drug paraphernalia (e.g., blunt wrappers, baggies, pipes). Negative binomial regression was used to assess the relationship between outlet count and overdose rate, and if paraphernalia sales altered this relationship, controlling for other neighborhood factors. Spatial autocorrelation was assessed and regression inference adjusted accordingly.

RESULTS

Each additional off-premise alcohol outlet was associated with a 16.6% increase in the neighborhood overdose rate (IRR=1.17, 95%CI=(1.11, 1.23)), adjusted for other neighborhood variables. On-premise alcohol outlets were not significantly associated with overdose rate when adjusting for off-premise alcohol outlets (IRR=1.01, 95% CI=(0.97, 1.06)). The proportion of off-premise outlets that sold drug paraphernalia was negatively associated with overdose rate (IRR=0.55, 95% CI=(0.41, 0.74)) and did not alter the relationship between off-premise outlets and overdose.

CONCLUSION

This study provides preliminary public health evidence for informing policy decisions about alcohol outlet licensing and zoning. Alcohol outlets could be potential community partners for harm reduction strategies such as health communication in identifying overdose symptoms or Good Samaritan Laws.

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.

Stakeholders' Perceptions of Factors Influencing the Use of Take-Home-Naloxone

Background and Aims: Opioid associated death and overdose is a growing burden in societies all over the world. In recent years, legislative changes have increased access to naloxone in the take-home setting for use by patients with a substance use disorder and bystanders, to prevent opioid overdose deaths. However, few studies have explored the factors influencing the uptake by its multiple stakeholders. The aim of this scoping review was to explore the factors influencing the use of take-home naloxone from the perspectives of different stakeholders. Methods: A scoping review methodology was adopted with a systematic search of databases EMBASE, MEDLINE and PubMed. A variation of the search words “naloxone”, “opioid” and “overdose” were used in each database. The articles were screened according to the predetermined inclusion/exclusion criteria and categorized based on their key perspective or target population. Results: The initial database search yielded a total of 1483 articles. After a series of screening processes, 51 articles were included for analysis. Two key stakeholder perspectives emerged: patients and bystanders (n = 36), and healthcare professionals (n = 15). Within the patient and bystander group, a strong consensus arose that there were positive outcomes from increased access to take-home naloxone and relevant training programs. Despite these positive outcomes, some healthcare professionals were concerned that take-home naloxone would encourage high-risk opioid use. Conclusion: Take-home naloxone is slowly being introduced into community practice, with a sense of enthusiasm from patients and bystanders. There are still a number of barriers that need to be addressed from healthcare professionals’ perspective. Future research should be aimed at emergency care professionals outside of the US, who are most experienced with naloxone and its potential impact on the community.

Geospatial Variations and Neighborhood Deprivation in Drug-Related Admissions and Overdoses

Drug overdoses are a national and global epidemic. However, while overdoses are inextricably linked to social, demographic, and geographical determinants, geospatial patterns of drug-related admissions and overdoses at the neighborhood level remain poorly studied. The objective of this paper is to investigate spatial distributions of patients admitted for drug-related admissions and overdoses from a large, urban, tertiary care center using electronic health record data. Additionally, these spatial distributions were adjusted for a validated socioeconomic index called the Area Deprivation Index (ADI). We showed spatial heterogeneity in patients admitted for opioid, amphetamine, and psychostimulant-related diagnoses and overdoses. While ADI was associated with drug-related admissions, it did not correct for spatial variations and could not account alone for this spatial heterogeneity.

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.

Geographic variation in the provision of naloxone by pharmacies in Ontario, Canada: A population-based small area variation analysis

BACKGROUND

Regional variation in pharmacy-dispensed naloxone rates could create access disparities that undermine the effectiveness of this approach. We explored individual and public health unit (PHU)-level determinants of regional variation in naloxone distribution through the Ontario Naloxone Program for Pharmacies.

METHODS

We conducted a population-based study between April 1, 2017 and March 31, 2018. We calculated age- and sex-standardized pharmacy-dispensed naloxone rates for the 35 Ontario PHUs, and identified determinants of these rates using generalized estimating equations negative binomial regression.

RESULTS

The age- and sex-standardized pharmacy-dispensed naloxone rate in Ontario was 5.5 (range 1.8-11.6) kits per 1000 population. Variables associated with higher naloxone dispensing rates included opioid use disorder history [rate ratio (RR) 2.27; 95% confidence interval (CI) 1.75-2.96], opioid agonist therapy (RR 11.17; 95% CI 7.15-17.44), and PHU opioid overdose rate (RR 1.09 per 10 deaths; 95% CI 1.06-1.13). Pharmacy-dispensed naloxone rates were lower in rural areas (RR 0.83; 95% CI 0.73-0.94) and among individuals dispensed one (RR 0.72; 95% CI 0.65-0.79), two to five (RR 0.67; 95% CI 0.54-0.84) or 6-10 (RR 0.92; 95% CI 0.74-1.14) opioids in the prior year relative to those receiving no opioids.

CONCLUSION

Pharmacy-dispensed naloxone programs are important components of a public health response to the opioid overdose crisis. We found considerable variation in pharmacy-dispensed naloxone rates that could limit program effectiveness, particularly in rural settings with limited access to health and harm reduction services..

The ecology of overdose mortality in Philadelphia

Fatal drug overdose represents a significant public health threat in Philadelphia, but substantial variation exists across its communities. This study uses negative binomial longitudinal regression to model ZIP code overdose fatalities over a seven-year period. Model covariates indicate that structural inequality, police arrest activity, and features of the built environment are associated with increased mortality across ZIP codes. Additionally, fatalities are spatially concentrated in select geographies of the city. These findings emphasize the pertinence of community ecological features in the production of stratified within-city health outcomes, and inform the geographic distribution of harm reduction interventions.

Socioeconomic marginalization and opioid-related overdose: A systematic review

BACKGROUND

Socioeconomic marginalization (SEM) is an important but under-explored determinant of opioid overdose with important implications for health equity and associated public policy initiatives. This systematic review synthesizes evidence on the role of SEM in both fatal and non-fatal overdose among people who use opioids.

METHODS

Studies published between January 1, 2000 and March 31, 2018 were identified through searching electronic databases, citations, and by contacting experts. The titles, abstracts, citation information, and descriptor terms of citations were screened by two team members. Data were synthesized using the lumping technique.

RESULTS

A total of 37 studies met inclusion criteria and were included in the review, with 34 of 37 finding a significant association between at least one socioeconomic factor and overdose. The included studies contained variables related to eight socioeconomic factors: criminal justice system involvement, income, employment, social support, health insurance, housing/homelessness, education, and composite measures of socio-economic status. Most studies found associations in the hypothesized direction, whereby increased SEM was associated with a higher rate or increased likelihood of the overdose outcome measured. The review revealed an underdeveloped evidence base.

CONCLUSIONS

Nearly all reviewed studies found a connection between a socioeconomic variable and overdose, but more research is needed with an explicit focus on SEM, using robust and nuanced measures that capture multiple dimensions of disadvantage, and collect data over time to better inform decision making around opioid overdose.

Power of the Peer and Parent: Gender Differences, Norms, and Nonmedical Prescription Opioid Use Among Adolescents in South Central Kentucky

This study examined risk factors of nonmedical prescription opioid use (NMPOU) among adolescents and how risk factors differ by gender. In the fall of 2017, adolescents attending 6th through 12th grades across 44 schools in 10 south central Kentucky counties were invited to participate in an anonymous, school-based survey. A total of 11,761 adolescents completed the survey. Logistic regression was conducted to examine the association between NMPOU and constructs of the Theory of Reasoned Action (i.e., attitudes and subjective norms), descriptive norms (i.e., peer use), and parental control of prescription medications in the home. There were 297 (2.7%) adolescents who reported NMPOU in the past 12 months. In the adjusted multivariate logistic regression model, for both males and females, the adolescents who perceived that more of their peers engaged in NMPOU were significantly more likely to endorse NMPOU, whereas male and female adolescents who perceived their peers disapproved of use were significantly less likely to report NMPOU. Parent disapproval was significantly associated with decreased NMPOU for females only. Moderated regression analyses revealed that gender moderated the relationship between parental disapproval and NMPOU. We found that during adolescence, NMPOU is influenced by peer norms for both genders and parental norms for females. These results indicate that prevention efforts should focus on changing adolescents' peer and parental norms related to NMPOU.

Exposure to Medicines in the Family Medicine Cabinet: Is It a Harbinger of Later Opioid Dependence?

Background: Despite research on prescription opioids and dependence being a national priority, little is known about the association between several potential adolescent risk factors and later opioid dependence among those who use opioids non-medically. Objectives: To investigate the association between lifetime opioid dependence and adolescent self-perceived health, health beliefs (thinking there was a pill for everything), health behaviors (onset of alcohol use before 15, onset of prescription opioid use before 15) and parental health practices (having opioids in the family medicine cabinet at age 14, parental suggestions to take pills when sick). Methods: A sample of 343 community members who non-medically used prescription opioids in the past 12 months were recruited for the Prescription Drug Misuse, Abuse, and Dependence Study and retrospectively assessed for adolescent risk factors of lifetime opioid dependence (DSM-IV). Results: Logistic regression revealed the strongest predictor of lifetime opioid dependence was having a prescription opioid in the family medicine cabinet at age 14. Those who grew up believing there was a pill for everything and those who initiated alcohol use before 15 were 1.83 and 1.78 times as likely, respectively, to meet dependence criteria than their counterparts. Demographics and other adolescent predictors were not associated with opioid dependence. Conclusions: Findings suggest several adolescent exposures can be targeted to reduce opioid dependence. Through their behavior, parents can reduce their teens' risk for opioid dependence.

Pharmacies and features of the built environment associated with opioid overdose: A geospatial comparison of rural and urban regions in Alabama, USA

BACKGROUND

Elements of the physical environment have been shown to influence health behaviors including drug use and overdose mortality. Throughout the opioid epidemic in the United States, rural regions have been disproportionately affected by opioid overdose. Although the relationship between the urban built environment and opioid overdose has been established, little is known as to how trends may differ in rural areas.

METHODS

Risk terrain modeling was used as a spatial analytical approach to assess environmental features that significantly increase the risk of opioid overdose in Jefferson County, Alabama. Spatial risk assessments were conducted for urban and rural regions as well as for the county as a whole. Criminogenic, opioid-related, and community variables were included and compared across spatial risk models.

RESULTS

The geographic context, rural or urban, influenced the relationship between environmental features and opioid overdose. In rural areas, community features such as bus stops and public schools were related to the occurrence of opioid overdose. In urban areas, inpatient treatment centers, transitional living facilities, express loan establishments, and liquor vendors were significantly related to the locations of opioid overdose.

CONCLUSION

Risk terrain modeling can be used to locate high-risk areas for opioid overdose while identifying factors that are contributing to the risk of events occurring in communities. The patterns of overdose risk differ in rural and urban contexts and may be used to inform the placement of treatment and prevention resources.

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.

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 Geographic Distribution of Fentanyl-Involved Overdose Deaths in Cook County, Illinois

Objectives. To contrast the geographic distribution of fentanyl-involved and non-fentanyl-involved fatal overdoses between 2014 and 2018 in Cook County, Illinois.Methods. We conducted a spatial analysis using locations of fentanyl-involved fatal overdoses (n = 1433) compared with nonfentanyl opioid and polydrug fatal overdoses (n = 1838) collected through the Cook County Medical Examiner's Office from 2014 to 2018. We also used logistic regression to test significant individual- and neighborhood-level covariates.Results. Fentanyl overdoses geographically clustered more than nonfentanyl overdoses, and this difference was statistically significant. One area in particular showed significantly elevated risk for fentanyl overdoses (P < .05) located in 2 specific neighborhoods of Chicago. The odds of a fentanyl-involved overdose were significantly increased for men, Blacks, Latinos/as, and younger individuals. Neighborhood deprivation score was the only significant neighborhood-level predictor (odds ratio = 1.11; 95% confidence interval = 1.07, 1.17).Conclusions. Fentanyl-involved fatal overdoses follow a distinct geographic distribution associated with resource deprivation in neighborhoods where they occur. This suggests an evolving bifurcated drug market, with drug markets in resource-deprived neighborhoods disproportionately likely to include fentanyl.

The epidemiology of opioid overdose in Flint and Genesee County, Michigan: Implications for public health practice and intervention

As the opioid epidemic continues to worsen throughout the United States, researchers and practitioners require additional tools to help in efforts to address use and prevent overdose. Although opioids are increasingly of concern to all racial and socioeconomic groups, specific geographic regions and sub-populations remain more burdened by overdoses than others. The example of Flint, Michigan, is used to contextualize the landscape of opioid overdose death and understand geographic and demographic variation in risk. Kernel density analysis and spatial joins in ArcGIS were used to map opioid overdose death clusters, treatment availability, and neighborhood-level conditions to uncover factors related to overdose death. Spatial analysis revealed three geographic clusters in opioid overdose death in Flint. These neighborhoods tended to be somewhat poorer but also significantly Whiter than the average Flint neighborhood. Alternatively, opioid overdose death clusters did not occur in predominately African-American neighborhoods. As well, treatment sites were not coincident with the location of overdose death clusters, suggesting a potential need for geographically-targeted interventions. Of the 47 treatment sites, only 29 offered medication-assisted treatment, and expansion of these programs may therefore be warranted. This work is of great importance to ongoing prevention and treatment efforts in Flint, but also to other communities with a need for better tools to monitor and intervene in the opioid epidemic.

Characterizing the Rural Opioid Use Environment in Kentucky Using Google Earth: Virtual Audit

Background

The opioid epidemic has ravaged rural communities in the United States. Despite extensive literature relating the physical environment to substance use in urban areas, little is known about the role of physical environment on the opioid epidemic in rural areas.

Objective

This study aimed to examine the reliability of Google Earth to collect data on the physical environment related to substance use in rural areas.

Methods

Systematic virtual audits were performed in 5 rural Kentucky counties using Google Earth between 2017 and 2018 to capture land use, health care facilities, entertainment venues, and businesses. In-person audits were performed for a subset of the census blocks.

Results

We captured 533 features, most of which were images taken before 2015 (71.8%, 383/533). Reliability between the virtual audits and the gold standard was high for health care facilities (>83%), entertainment venues (>95%), and businesses (>61%) but was poor for land use features (>18%). Reliability between the virtual audit and in-person audit was high for health care facilities (83%) and entertainment venues (62%) but was poor for land use (0%) and businesses (12.5%).

Conclusions

Poor reliability for land use features may reflect difficulty characterizing features that require judgment or natural changes in the environment that are not reflective of the Google Earth imagery because it was captured several years before the audit was performed. Virtual Google Earth audits were an efficient way to collect rich neighborhood data that are generally not available from other sources. However, these audits should use caution when the images in the observation area are dated.

Assessing Spatial Relationships between Prescription Drugs, Race, and Overdose in New York State from 2013 to 2015

Over the last decade, New York State has experienced one of the greatest increases in opioid overdose deaths in the United States, particularly from heroin and synthetic opioids. This study investigated spatial patterns in the distribution of county-level rates of overdose deaths in New York State and associations between prescriptions for opioid pain relievers, race, and overdose deaths from 2013-2015. Global and local Moran's I tests for spatial autocorrelation examined Bayesian smoothed rates of overdose for clusters of counties with high and low rates of overdose mortality. Getis Ord* analyses identified local hotspots of high and low clusters of overdose. Model performance indicators selected the best-fitting spatial regression model to examine associations between prescriptions for opioid pain relievers, race/ethnicity (non-Hispanic White, Black, and Hispanic) after adjusting for spatial dependence in the data. Socio-demographic characteristics of clusters were examined. Findings suggest rates of opioid overdose deaths are clustered in New York. Rates of prescription opioids were associated with rates of overdose from any opioid, prescription pain relievers, and synthetic opioids. Greater populations of African Americans were associated with greater rates of heroin overdose death rates. Findings from this study inform public health opioid overdose prevention interventions and policies.

Increased Risk of Opioid Overdose Death Following Cold Weather: A Case-Crossover Study

BACKGROUND

The United States is in the midst of an opioid overdose crisis. Little is known about the role of environmental factors in increasing risk of fatal opioid overdose.

METHODS

We conducted a case-crossover analysis of 3,275 opioid overdose deaths recorded in Connecticut and Rhode Island in 2014-2017. We compared the mean ambient temperature on the day of death, as well as average temperature up to 14 days before death, to referent periods matched on year, month, and day of week.

RESULTS

Low average temperatures over the 3-7 days before death were associated with higher odds of fatal opioid overdose. Relative to 11°C, an average temperature of 0°C over the 7 days before death was associated with a 30% higher odds of death (odds ratio: 1.3; 95% confidence interval, 1.1, 1.5).

CONCLUSIONS

Low average temperature may be associated with higher risk of death due to opioid overdose.

Poverty matters: Cannabis use among people with serious mental illness: Findings from the United States survey on drug use and health, 2015

BACKGROUND:

Little is known about the influence of contextual characteristics on comorbid substance use and serious mental illness (SMI).

AIMS:

To explore the role of poverty on comorbid SMI and cannabis use.

METHODS:

We used data from the 2015 National Survey on Drug Use and Health, considering those in poverty, with income under 100% of the US poverty threshold.

RESULTS:

People in poverty were more likely to suffer from concurrent SMI and cannabis use (3.07%, 95% confidence interval (CI):1.84%; 5.07%), even controlling for gender, age, tobacco and alcohol use (odds ratio (OR) = 2.77, 95% CI: 1.27; 6.03, p = .010).

CONCLUSION:

The magnitude of the association between SMI and cannabis use is influenced by poverty status. More research on potential mediators like income inequality and impoverished social capital is needed.

The effects of changes to the built environment on the mental health and well-being of adults: Systematic review

There is increasing interest in the influence of place on health, and the need to distinguish between environmental and individual level factors. For environmental-level factors, current evidence tends to show associations through cross-sectional and uncontrolled longitudinal analyses rather than through more robust study designs that can provide stronger causal evidence. We restricted this systematic review to randomised (or cluster) randomised controlled trials and controlled before-and-after studies of changes to the built environment. Date of search was December 2016. We identified 14 studies. No evidence was found of an effect on mental health from 'urban regeneration' and 'improving green infrastructure' studies. Beneficial effects on quality-of-life outcomes from 'improving green infrastructure' were found in two studies. One 'improving green infrastructure' study reported an improvement in social isolation. Risk-of-bias assessment indicated robust data from only four studies. Overall, evidence for the impact of built environment interventions on mental health and quality-of-life is weak. Future research requires more robust study designs and interdisciplinary research involving public health, planning and urban design experts.

Socioeconomic and geographical disparities in prescription and illicit opioid-related overdose deaths in Orange County, California, from 2010-2014

Background: Reports indicate a geographic effect of socioeconomic inequalities on the occurrence of opioid-related fatal overdoses. This study aims to (1) estimate the rates of opioid-related overdoses, (2) estimate the association of benzodiazepine co-ingestion with opioid-related deaths, (3) estimate associations between socioeconomic indicators and opioid-related deaths, and (4) map the distribution of fatal overdoses, in Orange County (OC), California. Methods: An ecologic study was conducted of all opioid- related deaths (1205 total) from 2010 to 2014 obtained from the OC Coroner Division database (1065 OC residents, 55 nonresidents, 85 OC homeless) (analyzed 2016-2017). Rates of opioid overdose, benzodiazepine co-ingestion prevalence, and associations with socioeconomic status (SES; education, poverty, median income) using ZIP code analysis in the residential and homeless communities were calculated. Results: Of 1205 deaths, 904 involved prescription-type opioids, 223 involved heroin, 39 involved both, and 39 not stated; 973 were classified unintentional overdoses, 180 suicides, and 52 undetermined; 49% of cases involved benzodiazepines. Prescription-type opioid and heroin death rates for residents were 5.4/ 100,000 person-years (95% confidence interval [CI]: 5.0-5.8) and 1.2/100,000 person-years (95% CI: 1.0-1.4), respectively. Males, age group 45-54, and Caucasian race had the highest rate (13.6/100,000) of opioid mortality. The highest death rates were seen in homeless adults, at 136/100,000 person-years for prescription-type opioids (95% CI: 99.0-185.5) and 156/100,000 person-years for heroin (95% CI: 116.8-209.5). Conclusions: The burden of prescription-type opioid-related deaths in OC affects all demographics and levels of SES; there is a disproportionately high rate of opioid-related deaths in the OC homeless population.