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

New paper:  Li, Y., Miller, H.J., Root, E.D., Hyder, A. and Liu, D. “Understanding the role of urban social and physical environment in opioid overdose events using found geospatial data,” Health and Place, 75, 102792.

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

Opioid treatment deserts

The latest outcome from our opioid overdose mapping project: we find disparities across neighborhoods and racial groups in access to opioid treatment providers:

Hyder A, Lee J, Dundon A, Southerland LT, All D, Hammond G, and Miller, H.J. (2021) Opioid Treatment Deserts: Concept development and application in a US Midwestern urban county. PLoS ONE 16(5): e0250324. https://doi.org/10.1371/journal.pone.0250324

Abstract

Objectives.  An Opioid Treatment Desert is an area with limited accessibility to medication-assisted treatment and recovery facilities for Opioid Use Disorder. We explored the concept of Opioid Treatment Deserts including racial differences in potential spatial accessibility and applied it to one Midwestern urban county using high resolution spatiotemporal data.
Methods

We obtained individual-level data from one Emergency Medical Services (EMS) agency (Columbus Fire Department) in Franklin County, Ohio. Opioid overdose events were based on EMS runs where naloxone was administered from 1/1/2013 to 12/31/2017. Potential spatial accessibility was measured as the time (in minutes) it would take an individual, who may decide to seek treatment after an opioid overdose, to travel from where they had the overdose event, which was a proxy measure of their residential location, to the nearest opioid use disorder (OUD) treatment provider that provided medically-assisted treatment (MAT). We estimated accessibility measures overall, by race and by four types of treatment providers (any type of MAT for OUD, Buprenorphine, Methadone, or Naltrexone). Areas were classified as an Opioid Treatment Desert if the estimate travel time to treatment provider (any type of MAT for OUD) was greater than a given threshold. We performed sensitivity analysis using a range of threshold values based on multiple modes of transportation (car and public transit) and using only EMS runs to home/residential location types.

Results. A total of 6,929 geocoded opioid overdose events based on data from EMS agencies were used in the final analysis. Most events occurred among 26–35 years old (34%), identified as White adults (56%) and male (62%). Median travel times and interquartile range (IQR) to closest treatment provider by car and public transit was 2 minutes (IQR: 3 minutes) and 17 minutes (IQR: 17 minutes), respectively. Several neighborhoods in the study area had limited accessibility to OUD treatment facilities and were classified as Opioid Treatment Deserts. Travel time by public transit for most treatment provider types and by car for Methadone-based treatment was significantly different between individuals who were identified as Black adults and White adults based on their race.

Conclusions.  Disparities in access to opioid treatment exist at the sub-county level in specific neighborhoods and across racial groups in Columbus, Ohio and can be quantified and visualized using local public safety data (e.g., EMS runs). Identification of Opioid Treatment Deserts can aid multiple stakeholders better plan and allocate resources for more equitable access to MAT for OUD and, therefore, reduce the burden of the opioid epidemic while making better use of real-time public safety data to address a public health epidemic that has turned into a public safety crisis.

The impacts of COVID-19 pandemic on public transit demand in the United States

New paper: Liu L, Miller HJ, Scheff J (2020) The impacts of COVID-19 pandemic on public transit demand in the United States. PLOS ONE 15(11):e0242476. https://doi.org/10.1371/journal.pone.0242476

Abstract

The COVID-19 pandemic and related restrictions led to major transit demand decline for many public transit systems in the United States. This paper is a systematic analysis of the dynamics and dimensions of this unprecedented decline. Using transit demand data derived from a widely used transit navigation app, we fit logistic functions to model the decline in daily demand and derive key parameters: base value, the apparent minimal level of demand and cliff and base points, representing the initial date when transit demand decline began and the final date when the decline rate attenuated. Regression analyses reveal that communities with higher proportions of essential workers, vulnerable populations (African American, Hispanic, Female, and people over 45 years old), and more coronavirus Google searches tend to maintain higher levels of minimal demand during COVID-19. Approximately half of the agencies experienced their decline before the local spread of COVID-19 likely began; most of these are in the US Midwest. Almost no transit systems finished their decline periods before local community spread. We also compare hourly demand profiles for each system before and during COVID-19 using ordinary Procrustes distance analysis. The results show substantial departures from typical weekday hourly demand profiles. Our results provide insights into public transit as an essential service during a pandemic.

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