# Alternate Emergency Over Dose Response in Chicago

> **NIH NIH R21** · UNIVERSITY OF CHICAGO · 2022 · $205,000

## Abstract

PROJECT ABSTRACT
Chicago is a national epicenter of opioid overdose (OD) and related harms. Opioid-related Emergency Service
Calls (ORESCs) are critical opportunities for service engagement and intervention. Nationally and in Chicago,
high mortality rates subsequent to non-fatal OD underscore that such opportunities are often missed. Chicago
will pilot two innovative responses to address these challenges. Alternate Immediate Response (AIR) will
provide assertive outreach and engagement, including connecting people with medication opioid use disorder
treatment and other related services. Alternate Immediate Response plus Follow-up (AIR-F) includes AIR,
along with 8 weeks of follow-up services. AIR response teams consisting of a community paramedic and a
peer support specialist/recovery coach will be deployed to provide assertive outreach services, including
developing treatment, safety, and follow-up plans, delivering brief interventions, and providing transport to
pertinent other services. AIR-F engagement will include linkages to follow-up services that promote treatment
retention, including case management, care coordination, and connections to community-based care and
treatment. We propose to use machine learning (ML) to develop a tool to identify individuals at highest risk of
OD and OD-related mortality in order to prioritize service delivery and follow-up services. In particular, we will
develop a random forest (RF) classifier that will combine data from the Chicago Department of Public Health,
the Office of Emergency Management and Communications, Chicago Police and Fire departments, Chicago
Office of Public Safety Administration, and Cook County Medical Examiner’s Office to create an integrated
dataset to trace emergency calls from origination to final disposition. We will also extract data from
unstructured text included in CFD ambulance data. By incorporating multiple large administrative datasets, the
tool will capitalize on diverse sources of “signal,” maximizing prediction accuracy. We will then use our
integrated data to predict individuals at highest risk of subsequent ORESCs, OD, arrest, and other adverse
outcomes. AIR/AIR-F staff will use these indicators along with other clinical data to allocate scarce follow-up
resources. We will use difference-in-differences estimation to compare post-intervention outcomes within the
service area on Chicago’s west side (Humboldt Park, West Garfield Park, and East Garfield Park) to that
observed in contiguous communities (Austin, North Lawndale, South Lawndale, Lower West Side, and West
Town) to gauge the population impact of AIR/AIR-F. Finally, we will conduct qualitative interviews with various
stakeholders to provide additional insight into the pilot and explore how Chicago can better serve at-risk
individuals. To help reduce the prevalence of ORESCs’ and associated mortality, all computer code developed
for this grant will be made available open-source. We will disseminate project findings...

## Key facts

- **NIH application ID:** 10425012
- **Project number:** 1R21DA055639-01
- **Recipient organization:** UNIVERSITY OF CHICAGO
- **Principal Investigator:** HAROLD Alexander POLLACK
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $205,000
- **Award type:** 1
- **Project period:** 2022-05-15 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10425012

## Citation

> US National Institutes of Health, RePORTER application 10425012, Alternate Emergency Over Dose Response in Chicago (1R21DA055639-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10425012. Licensed CC0.

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