311 service requests as indicators of neighborhood distress and opioid use disorder

Abstract

Opioid use disorder and overdose deaths is a public health crisis in the United States, and there is increasing recognition that its etiology is rooted in part by social determinants such as poverty, isolation and social upheaval. Limiting research and policy interventions is the low temporal and spatial resolution of publicly available administrative data such as census data. We explore the use of municipal service requests (also known as “311” requests) as high resolution spatial and temporal indicators of neighborhood social distress and opioid misuse. We analyze the spatial associations between georeferenced opioid overdose event (OOE) data from emergency medical service responders and 311 service request data from the City of Columbus, OH, USA for the time period 2008–2017. We find 10 out of 21 types of 311 requests spatially associate with OOEs and also characterize neighborhoods with lower socio-economic status in the city, both consistently over time. We also demonstrate that the 311 indicators are capable of predicting OOE hotspots at the neighborhood-level: our results show code violation, public health, and street lighting were the top three accurate predictors with predictive accuracy as 0.92, 0.89 and 0.83, respectively. Since 311 requests are publicly available with high spatial and temporal resolution, they can be effective as opioid overdose surveillance indicators for basic research and applied policy.

Methodological approaches for the prediction of opioid use-related epidemics in the United States: a narrative review and cross-disciplinary call to action

“…29 Similarly, Li et al found that the location of 311 municipal service calls in Columbus, OH was associated with opioid overdose geographic hotspots. 33 A study conducted in Texas used a combination of data from emergency room visits, the Youth Risk Behavior Survey, poison control cases, and qualitative interviews to characterize a "cheese" heroin use outbreak. 31 Another study implemented in Kentucky used data from a combination of death certificates, overdose deaths from the State Medical Examiners' Office, emergency room visits, and prescription drug monitoring programs to detect potential opioid use clusters at state level.…”

“…Enhancing this information with epidemiological surveillance data, including poison call center data, as well as data from substance use treatment and harm reduction organizations can further minimize time to outbreak detection. 28,33 Substance use behavioral surveillance had until recently been grounded on routine population surveys such as the NSDUH, as well as opioid prescription and OUD treatment records.…”

A method to create a synthetic population with social networks for geographically-explicit agent-based models

“…Furthermore, when the 2020 US Census becomes available, we would like to extend our synthetic population dataset to comprise all the US population. Currently, the synthetic population did not comprise financial, employment or educational backgrounds, with such data, the synthetic population could be potentially used by an agent-based modeler wanting to explore issues such as residential location or public health issues (e.g., Jiang et al, 2021;Li et al, 2020). This could be remedied by adding additional variables to the agents from the census dataset.…”

STFTiS: Introducing a spatio‐temporal FTiS model to investigate the level of citizens' satisfaction of 311 non‐emergency services

“…Li et al. (2020) used an inhomogeneous cross‐K function to examine the pattern between 311 data and opioid overdose event data. Besides, they applied hotspot analysis (Getis‐Ord Gi* statistic) to determine the location of data clusters at the census block level.…”

“…The developed system includes heat maps to represent the intensity of reports at geographical points and the application of density-based and hierarchical clustering to visualize groupings of reports at different granularity levels. Li et al (2020) used an inhomogeneous cross-K function to examine the pattern between 311 data and opioid overdose event data. Besides, they applied hotspot analysis (Getis-Ord Gi* statistic) to determine the location of data clusters at the census block level.…”