A Latent Spatial Factor Approach for Synthesizing Opioid-Associated Deaths and Treatment Admissions in Ohio Counties

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.

A Dynamic Spatial Factor Model to Describe the Opioid Syndemic in Ohio

BACKGROUND

The opioid epidemic has been ongoing for over 20 years in the United States. As opioid misuse has shifted increasingly toward injection of illicitly produced opioids, it has been associated with HIV and hepatitis C transmission. These epidemics interact to form the opioid syndemic.

METHODS

We obtain annual county-level counts of opioid overdose deaths, treatment admissions for opioid misuse, and newly diagnosed cases of acute and chronic hepatitis C and newly diagnosed HIV from 2014 to 2019. Aligned with the conceptual framework of syndemics, we develop a dynamic spatial factor model to describe the opioid syndemic for counties in Ohio and estimate the complex synergies between each of the epidemics.

RESULTS

We estimate three latent factors characterizing variation of the syndemic across space and time. The first factor reflects overall burden and is greatest in southern Ohio. The second factor describes harms and is greatest in urban counties. The third factor highlights counties with higher than expected hepatitis C rates and lower than expected HIV rates, which suggests elevated localized risk for future HIV outbreaks.

CONCLUSIONS

Through the estimation of dynamic spatial factors, we are able to estimate the complex dependencies and characterize the synergy across outcomes that underlie the syndemic. The latent factors summarize shared variation across multiple spatial time series and provide new insights into the relationships between the epidemics within the syndemic. Our framework provides a coherent approach for synthesizing complex interactions and estimating underlying sources of variation that can be applied to other syndemics.

Spatial clustering of heroin-related overdose incidents: a case study in Cincinnati, Ohio

Background

Drug overdose is one of the top leading causes of accidental death in the U.S., largely due to the opioid epidemic. Although the opioid epidemic is a nationwide issue, it has not affected the nation uniformly.

Methods

We combined multiple data sources, including emergency medical service response, American Community Survey data, and health facilities datasets to analyze distributions of heroin-related overdose incidents in Cincinnati, Ohio at the census block group level. The Ripley’s K function and the local Moran’s I statistics were performed to examine geographic variation patterns in heroin-related overdose incidents within the study area. Then, conditional cluster maps were plotted to examine a relationship between heroin-related incident rates and sociodemographic characteristics of areas as well as the resources for opioid use disorder treatment.

Results

The global spatial analysis indicated that there was a clustered pattern of heroin-related overdose incident rates at every distance across the study area. The univariate local spatial analysis identified 7 hot spot clusters, 27 cold spot clusters, and 1 outlier cluster. Conditional cluster maps showed characteristics of neighborhoods with high heroin overdose rates, such as a higher crime rate, a high percentage of the male, a high poverty level, a lower education level, and a lower income level. The hot spots in the Southwest areas of Cincinnati had longer distances to opioid treatment programs and buprenorphine prescribing physicians than the median, while the hot spots in the South-Central areas of the city had shorter distances to those health resources.

Conclusions

Our study showed that the opioid epidemic disproportionately affected Cincinnati. Multi-phased spatial clustering models based on various data sources can be useful to identify areas that require more policy attention and targeted interventions to alleviate high heroin-related overdose rates.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-13557-3.

Estimating the burden of the opioid epidemic for adults and adolescents in Ohio counties

Quantifying the opioid epidemic at the local level is a challenging problem that has important consequences on resource allocation. Adults and adolescents may exhibit different spatial trends and require different interventions and resources so it is important to examine the problem for each age group. In Ohio, surveillance data are collected at the county level for each age group on measurable outcomes of the opioid epidemic, overdose deaths, and treatment admissions. However, our interest lies in quantifying the unmeasurable construct, representing the burden of the opioid epidemic, which drives rates of the outcomes. We propose jointly modeling adult and adolescent surveillance outcomes through a multivariate spatial factor model. A generalized spatial factor model within each age group quantifies a latent factor related to the number of opioid-associated treatment admissions and deaths. By assuming a multivariate conditional autoregressive model for the spatial factors of adults and adolescents, we allow the adolescent model to borrow strength from the adult model (and vice versa), improving estimation. We also incorporate county-level covariates to help explain spatial heterogeneity in each of the factors. We apply this approach to the state of Ohio and discuss the findings. Our framework provides a coherent approach for synthesizing information across multiple outcomes and age groups to better understand the spatial epidemiology of the opioid epidemic.

The Relationship Between Hepatitis C Virus Rates and Office-Based Buprenorphine Access in Ohio

Background

The United States is experiencing an epidemic of hepatitis C virus (HCV) infections due to injection drug use, primarily of opioids and predominantly in rural areas. Buprenorphine, a medication for opioid use disorder, may indirectly prevent HCV transmission. We assessed the relationship of HCV rates and office-based buprenorphine prescribing in Ohio.

Methods

We conducted an ecological study of the county-level (n = 88) relationship between HCV case rates and office-based buprenorphine prescribing in Ohio. We fit adjusted negative binomial models between the county-level acute and total HCV incidence rates during 2013–2017 and 1) the number of patients in each county that could be served by office-based buprenorphine (prescribing capacity) and 2) the number served by office-based buprenorphine (prescribing frequency) from January–March, 2018.

Results

For each 10% increase in acute HCV rate, office-based buprenorphine prescribing capacity differed by 1% (95% CI: –1%, 3%). For each 10% increase in total HCV rate, office-based buprenorphine prescribing capacity was 12% (95% CI: 7%, 17%) higher. For each 10% increase in acute HCV rate, office-based buprenorphine prescribing frequency was 1% (95% CI: –1%, 3%) higher. For each 10% increase in total HCV rate, office-based buprenorphine prescribing frequency was 14% (95% CI: 7%, 20%) higher.

Conclusions

Rural counties in Ohio have less office-based buprenorphine and higher acute HCV rates versus urban counties, but a similar relationship between office-based buprenorphine prescribing and HCV case rates. To adequately prevent and control HCV rates, certain rural counties may need more office-based buprenorphine prescribing in areas with high HCV case rates.

A multivariate spatio-temporal model of the opioid epidemic in Ohio: A factor model approach

Opioid misuse is a significant public health issue and a national epidemic with a high prevalence of associated morbidity and mortality. The epidemic is particularly severe in Ohio which has some of the highest overdose rates in the country. It is important to understand spatial and temporal trends of the opioid epidemic to learn more about areas that are most affected and to inform potential community interventions and resource allocation. We propose a multivariate spatio-temporal model to leverage existing surveillance measures, opioid-associated deaths and treatment admissions, to learn about the underlying epidemic for counties in Ohio. We do this using a temporally varying spatial factor that synthesizes information from both counts to estimate common underlying risk which we interpret as the burden of the epidemic. We demonstrate the use of this model with county-level data from 2007-2018 in Ohio. Through our model estimates, we identify counties with above and below average burden and examine how those regions have shifted over time given overall statewide trends. Specifically, we highlight the sustained above average burden of the opioid epidemic on southern Ohio throughout the 12 years examined.

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.

Suspected heroin-related overdoses incidents in Cincinnati, Ohio: A spatiotemporal analysis

BACKGROUND

Opioid misuse and deaths are increasing in the United States. In 2017, Ohio had the second highest overdose rates in the US, with the city of Cincinnati experiencing a 50% rise in opioid overdoses since 2015. Understanding the temporal and geographic variation in overdose emergencies may help guide public policy responses to the opioid epidemic.

METHODS AND FINDINGS

We used a publicly available data set of suspected heroin-related emergency calls (n = 6,246) to map overdose incidents to 280 census block groups in Cincinnati between August 1, 2015, and January 30, 2019. We used a Bayesian space-time Poisson regression model to examine the relationship between demographic and environmental characteristics and the number of calls within block groups. Higher numbers of heroin-related incidents were found to be associated with features of the built environment, including the proportion of parks (relative risk [RR] = 2.233; 95% credible interval [CI]: [1.075-4.643]), commercial (RR = 13.200; 95% CI: [4.584-38.169]), manufacturing (RR = 4.775; 95% CI: [1.958-11.683]), and downtown development zones (RR = 11.362; 95% CI: [3.796-34.015]). The number of suspected heroin-related emergency calls was also positively associated with the proportion of male population, the population aged 35-49 years, and distance to pharmacies and was negatively associated with the proportion aged 18-24 years, the proportion of the population with a bachelor's degree or higher, median household income, the number of fast food restaurants, distance to hospitals, and distance to opioid treatment programs. Significant spatial and temporal heterogeneity in the risks of incidents remained after adjusting for covariates. Limitations of this study include lack of information about the nature of incidents after dispatch, which may differ from the initial classification of being related to heroin, and lack of information on local policy changes and interventions.

CONCLUSIONS

We identified areas with high numbers of reported heroin-related incidents and features of the built environment and demographic characteristics that are associated with these events in the city of Cincinnati. Publicly available information about opiate overdoses, combined with data on spatiotemporal risk factors, may help municipalities plan, implement, and target harm-reduction measures. In the US, more work is necessary to improve data availability in other cities and states and the compatibility of data from different sources in order to adequately measure and monitor the risk of overdose and inform health policies.