# Enhancing Precision in Overdose Mortality Prediction through Data Linkage: A Heterogeneous Capture-Recapture Approach to Estimating Opioid Use Disorder Prevalence in Ohio

> **NIH NIH R01** · OHIO STATE UNIVERSITY · 2024 · $186,244

## Abstract

Project Summary
The opioid crisis in the United States has reached a critical stage, with unprecedented levels of illness and
death. In 2021, over 106,000 individuals died from drug overdoses, with opioids contributing to more than
80,000 of these deaths, accounting for 75% of all drug-related fatalities. Ohio has been particularly affected,
ranking fourth nationally in overdose death rates in 2019, and experiencing a shocking 1000% increase in
opioid-related deaths in Cuyahoga County from 2007 to 2016. By 2021, Ohio reported an opioid-related death
rate of 48.1 per 100,000 residents, significantly higher than the national average, and saw a notable surge in
opioid overdoses during the COVID-19 pandemic. Alongside the loss of life, Ohio faces increased risks of
Hepatitis C and HIV epidemics due to opioid misuse. Certain demographics, including the unemployed,
homeless, those with lower education levels, lacking social support and health insurance, and individuals in the
legal system, are disproportionately affected by opioid overdoses. Despite concerted efforts and dedicated
resources to combat opioid use disorder (OUD), a major challenge in addressing Ohio's opioid crisis remains
the lack of reliable estimates of OUD prevalence. This proposed study extends the work of the Opioid and
Substance Use Disorder Data Enclave (O-SUDDEn) project, which aims to improve overdose mortality
prediction by linking data. Building on this framework, the supplement will develop a model to estimate opioid
use disorder (OUD) prevalence in Ohio more accurately. This data integration will enhance existing predictive
models and inform targeted public health policies to address the opioid crisis. The study's central hypothesis is
that by refining prevalence estimation to reflect OUD patterns and associated risk factors, evidence-based
strategies and interventions can be implemented to reduce opioid-related fatalities and improve public health
outcomes in Ohio. The study aims to achieve the following objectives: Aim 1: Develop and Validate a Capture-
Recapture Approach Based on a Heterogeneity Model to Estimate the Prevalence of Opioid Use Disorder
(OUD). This involves creating a model that integrates observed and unobserved variations in opioid-related
outcomes to estimate the prevalence of OUD at the county level in Ohio. Aim 2: Assess the Impact of
Demographic and Socioeconomic Factors on the Estimated Prevalence of OUD. This aim aims to use the
refined prevalence estimates from Aim 1 to analyze how demographic and socioeconomic factors influence the
distribution of OUD across different geographical areas and populations.

## Key facts

- **NIH application ID:** 11118244
- **Project number:** 3R01DA057668-01S1
- **Recipient organization:** OHIO STATE UNIVERSITY
- **Principal Investigator:** Naleef Fareed
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $186,244
- **Award type:** 3
- **Project period:** 2022-09-30 → 2025-09-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11118244

## Citation

> US National Institutes of Health, RePORTER application 11118244, Enhancing Precision in Overdose Mortality Prediction through Data Linkage: A Heterogeneous Capture-Recapture Approach to Estimating Opioid Use Disorder Prevalence in Ohio (3R01DA057668-01S1). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/11118244. Licensed CC0.

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