# Large Data Spatiotemporal Modeling of Optimal Combinations of Interventions to Reduce Opioid Harm in the United States

> **NIH NIH R01** · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · 2024 · $640,348

## Abstract

The goal of this project is to prevent and reduce deaths and injuries due to opioids in the United States by
determining the best combination of state and local harm reduction and drug paraphernalia laws needed to
reduce overdose rates and other opioid-related harms. To do this, we will: 1) conduct original review of laws on
relevant harm reduction and drug paraphernalia laws in the 836 municipalities with >50,000 people and
associated counties; 2) conduct biannual surveys on implementation of harm reduction laws and drug
paraphernalia laws by law enforcement; 3) create an extensive national dataset by merging data on state and
local harm reduction and drug paraphernalia laws; implementation of laws by law enforcement; EMS and fatality
data; and information on local harm reduction resources, and socioeconomic indicators; 4) use the merged
dataset to determine which combinations of state and local laws have resulted in the biggest decreases in
overdoses and related harms; and (5) determine which local characteristics enhanced those effective
combinations of policies. Overdose deaths in the United States increased more than six-fold since 2001, and
now account for more loss of life than high blood pressure, AIDS, and pneumonia. States, cities, and counties
are combating this epidemic by passing laws to reduce overdoses, and by investing in access to harm reduction
services. But these efforts are often undertaken in isolation and without considering how the different state and
local laws interact or how local factors like enforcement of laws and access to harm reduction services influence
their effectiveness. This project will help answer those questions by using large data and powerful analytics to
bring together all the evidence on this complicated topic. At the end of the project, we will be able to anwer the
following questions: What combinations of state and local harm reduction and drug paraphernalia laws most
effectively prevent and reduce opioid deaths and injuries in the United States? And how can we best support
local efforts to ensure that those effective combinations have the greatest impact?

## Key facts

- **NIH application ID:** 10864986
- **Project number:** 5R01DA051509-03
- **Recipient organization:** NEW YORK UNIVERSITY SCHOOL OF MEDICINE
- **Principal Investigator:** Magdalena Cerda
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $640,348
- **Award type:** 5
- **Project period:** 2022-09-30 → 2027-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10864986

## Citation

> US National Institutes of Health, RePORTER application 10864986, Large Data Spatiotemporal Modeling of Optimal Combinations of Interventions to Reduce Opioid Harm in the United States (5R01DA051509-03). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10864986. Licensed CC0.

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