# Opioid and SUD Data Enclave (O-SUDDEn): Bringing real-time data to the opioid crisis

> **NIH NIH R01** · OHIO STATE UNIVERSITY · 2022 · $2,053,649

## Abstract

ABSTRACT
The widespread availability of synthetic opioids (fentanyl) has fueled the rapidly rising rates of unintentional over
dose (OD) fatalities. Policy makers, state and local agencies, and investigators have focused on the Ohio
experience as a bellwether for the experiences of other states because of the representativeness of Ohio’s
demographics. The lack of timely geospatially-linked longitudinal data sources has impeded the ability of
communities and state agencies to pivot allocation of resources to regions where they are needed most. Limited
integration of environmental risk factors such as sociodemographic characteristics fail to support identification of
new targets for intervention or new approaches to emerging threats from changes in local drug supply. We
believe that agile data systems and informatics tools that can be used to demonstrate the utility of predictive
analytics and machine intelligence approaches on how to enable data-driven decision making will ultimately
prove translatable across the country and diverse localities. Our proposed Opioid and Substance Use Disorder
Data Enclave (O-SUDDEn) will provide a novel and transformative approach to support rigorous and
reproducible research on the opioid and substance use crises. O-SUDDEn will fill several existing gaps in data
infrastructure and prediction models that include machine learning, geospatial analyses, and community context.
The specific aims for O-SUDDEn are as follows: Aim 1: Data linkage with establishment of O-SUDDEn. We
will develop a geospatially-sensitive, individual-level secure data lake that integrates multiple disparate data
sources that meets the requirement of a coded-limited set under HIPAA. New data sources include real-time
individual-level longitudinal data from urine drug testing (UDT) and community contextual data based on the
Ohio Opportunity Index, an area-level social determinants of health developed and used by our team. We will
develop query and use tools for data harmonization and integration, prepare and release data sets for
dissemination and secondary data analyses; and facilitate secondary use of related administrative data to
generate evidence that informs targeted opioid interventions. Aim 2: Develop Predictive Models and
Surveillance algorithms. Geospatial and machine learning will be used to model the contribution of opioid,
cocaine, and stimulant use on OD, OD death and opioid use disorder/substance use disorder (OUD/SUD);
temporal relationship of real time data including UDT and demographic and contextual variables to identify high-
risk populations and subpopulations or geographic locales. We will validate model performances and predictive
power and then disseminate surveillance and forecasting algorithms through the O-SUDDEn portal for end-user
access. Aim 3: Deploy a human-centered platform and actional informatics. Applying co-design principles
with a human-centered work group consisting of end-user stakeholders (community, resea...

## Key facts

- **NIH application ID:** 10590246
- **Project number:** 1R01DA057668-01
- **Recipient organization:** OHIO STATE UNIVERSITY
- **Principal Investigator:** Naleef Fareed
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $2,053,649
- **Award type:** 1
- **Project period:** 2022-09-30 → 2025-09-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10590246

## Citation

> US National Institutes of Health, RePORTER application 10590246, Opioid and SUD Data Enclave (O-SUDDEn): Bringing real-time data to the opioid crisis (1R01DA057668-01). Retrieved via AI Analytics 2026-06-01 from https://api.ai-analytics.org/grant/nih/10590246. Licensed CC0.

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