Trends that will shape us: Transportation

On April 7, I participated in a panel discussion at the Columbus Metropolitan Club; topic: Trends that Will Shape Us: Transportation. Other guests include Jack Marchbanks (Director, Ohio Department of Transportation) and Kevin Chambers (Managing Director – Logistics, Distribution and Supply Chain, JobsOhio).

It was an interesting and lively conversation: spanning public transit, the impact of COVID on cities, social equity, infrastructure, freight and logistics.  Check it out!

Link to recording

 

 

Columbus 10TV News – Ohio State study: 311 statistics can predict opiate overdose hotspots

I recently appeared on Columbus ⁦10TV news to discuss our research on ‘311’ service requests to the city as indicators of social distress and opioid use disorder. ⁦ It is a good segment that conveys the message well: 311 data can help us understand the social and neighborhood distress that underlies bad outcomes like opioid use disorder.

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

New paper: Li, Y., Hyder, A., Southerland, L.T., Hammond, G., Porr, A. and Miller, H.J. “311 service requests as indicators of neighborhood distress and opioid use disorder,” Scientific Reports, 10, 19579.

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.

Media