Dr. Keith Warren | ISSUES | The Ohio State University

Dr. Keith Warren

Wed, 04 Nov 2026 10:45:00 +0000

Dr. Keith Warren is an Associate Professor at the College of Social Work and a Faculty Affiliate in the ISSUES Lab. His research focuses on the application of complexity theory and social systems to therapeutic communities and substance use recovery.

An assessment of opioid overdose mortality risk across the urban–rural continuum: Integrating satellite-derived and socioeconomic indicators

Wed, 04 Nov 2026 10:45:00 +0000

Session: Built environment and community-based approaches to advancing health equity
Program: Public Health Education and Health Promotion
Date: Wednesday, November 4, 2026 | 10:45 AM – 11:00 AM

Detecting Hidden Mechanisms of Abuse-Related Injury in Children: Structured Topic Modeling of Clinical Death Reviews and Legal Opinions

Mon, 24 Aug 2026 00:00:00 +0000

Presented at the ISPCAN World Congress on Child Abuse and Neglect, Melbourne, Australia, August 2426, 2026.

Full title: Detecting Hidden Mechanisms of Abuse-Related Injury in Children: Structured Topic Modeling of Clinical Death Reviews and Legal Opinions Triangulating Multidisciplinary Child Fatality Reviews with Factual and Procedural Narratives from Case Law

Abstract

Existing child maltreatment surveillance systems miss or misclassify between 8090% of maltreatment deaths, and CPS records undercount fatalities by 5576%. Unstructured narrative text clinical death reviews, court opinions, and social work records accounts for 8090% of all organizational data produced, yet it is rarely leveraged for injury surveillance.

This presentation introduces a two-study AI + Structured Topic Modeling (STM) pipeline that integrates two distinct narrative corpora to recover latent, abuse-related injury mechanisms that structured administrative codes routinely obscure.

Study Design

	Study 1: CFRT Death Reviews	Study 2: CourtListener Case Law
Source	Colorado Child Fatality Review Team reports	Appellate opinions retrieved from CourtListener
Documents	223 child-level narrative records	751 opinions → 335 confirmed cases from Ohio
Text	Two free-text fields per report (clinical + administrative)	Facts + procedural history
LLM Extraction	GPT-4o-mini: caregiver behavior, injury mechanism, setting, diagnosis	GPT-4o-mini: 9 structured fields including perpetrator, victim type, legal outcome
STM Models	Risk & Contributing Factors; Circumstances	Injury Mechanism; Long-Term Consequences
Covariate	Incident year (temporal prevalence)	Judicial ruling (affirmed / reversed)

Research Questions

Can large language models transform unstructured child fatality narratives into standardized semantic features caregiver behavior, injury mechanism, setting, diagnosis?
What latent themes emerge, and what typology of risk and incident circumstances do they suggest?
Do the recovered themes reveal recurring patterns of head and neck trauma consistent with abuse-related injury?
How does the prevalence of these themes shift across incident years (20202025)?

Key Findings

Both corpora converge on craniofacial injury as the dominant mechanism routinely undercounted by ICD codes.
Strangulation emerged as a critical lethality predictor; soft-tissue damage can surface 23 days post-injury, requiring clinician and court training on delayed sequelae.
Records document long-term neurological harm (cerebral palsy, pituitary damage, blindness) in children often years before clinical follow-up.
Children represent a structurally distinct injury profile in both datasets, warranting specialized clinical and legal protocols.
“Accidental” injuries (falls, environmental contact) are frequently embedded in an abuse context screening for mechanism, not just diagnosis, is essential.
Appellate opinions and death reviews function as injury-surveillance sources beyond structured administrative fields.

Methodology

The pipeline applies:

PDF parsing (pdftools) and regex isolation of narrative fields
GPT-4o-mini for LLM-based structured field extraction
Structured Topic Modeling (STM) with incident year or judicial ruling as a prevalence covariate
BART zero-shot classification as an independent supervised-style validation check
FREX word scoring (frequency � exclusivity) for topic labeling

Lab Members

This presentation was developed by the ISSUES Lab team at The Ohio State University:

Dr. Gia E. Barboza-Salerno (Founding Director), Dr. Henriikka Weir, Malcolm McCarthy, Dr. Karla Shockley McCarthy, Dr. Amy Watson-Grace, Dr. Keith Warren, Esenia Cassidy, and Olivia McLucas with collaborators Scottye Cash and Suzanne Perkins.

📄 Presentation

Download the presentation slides here .

Congratulations to Keith Warren and Gia Barboza-Salerno for Completing the Agent-Based Modeling Certificate

Sun, 09 Aug 2026 00:00:00 +0000

We are proud to announce that Keith Warren and Gia Barboza-Salerno have successfully completed the Santa Fe Institute’s Introduction to Agent-Based Modeling certificate course through abm.complexityexplorer.org! 🎓

Agent-Based Modeling is a powerful computational method for simulating the actions and interactions of autonomous agents including individuals, households, and organizations in order to understand how macro-level patterns emerge from micro-level behaviors. ABM is particularly well-suited for studying complex social systems such as neighborhood dynamics, health behavior spread, child welfare intervention cascades, and community-level resilience.

The addition of ABM to the ISSUES Lab’s methodological toolkit opens exciting new avenues for our research on structural inequality, environmental justice, and social determinants of health.

Congratulations to Keith and Gia on this outstanding achievement! We look forward to seeing how these new computational skills enrich the lab’s work.

Racial and Ethnic Disparities in Non-Fatal Self-Inflicted Firearm Injuries Among Adults in the United States, 2000-2021

Wed, 17 Jun 2026 00:00:00 +0000

Plain-Language Summary

This study analyzes nonfatal, self-inflicted firearm injuries in the United States over a 22-year period (2000-2021) using the National Electronic Injury Surveillance System (NEISS-FISS). Because structured database codes often fail to capture the context or intent behind a self-inflicted discharge, the research team used text mining and a machine learning (LASSO) classification model to analyze the unstructured clinical narratives written by emergency department staff.

The results show that while 58% of visits were likely unintentional discharges (accidents), 10% were probable suicide attempts. The narrative text mining revealed distinct, racially disparate patterns of injury: Non-Hispanic Black patients suffered the highest proportion of lower-extremity injuries (51%), while Hispanic patients had the highest rates of craniofacial injuries (26%) and a significant association with alcohol involvement (increasing from 12% in coded data to 20% in narrative data). This highlights the value of using natural language processing to analyze emergency department notes for better injury prevention and public health surveillance.

New Publication: Racial and Ethnic Disparities in Non-Fatal Self-Inflicted Firearm Injuries

Mon, 15 Jun 2026 00:00:00 +0000

Abstract

Nonfatal self-inflicted firearm injuries represent an important and understudied public health concern in the United States. We analyzed nationally representative data from the National Electronic Injury Surveillance System–Firearm Injury Surveillance Study (NEISS-FISS), 2000–2021, to characterize nonfatal self-inflicted firearm injuries among U.S. adults aged 18–64 years. Emergency department (ED) narratives were combined using text mining and an elastic net (LASSO) classification model to augment structured surveillance data. Among 7,362 unweighted cases (representing approximately 260,602 ED visits, 58% were classified as probable unintentional discharges, whereas approximately 10% were probable suicide attempts. Among cases with complete race and ethnicity data, Non-Hispanic White (NHW; 69.2%), Non-Hispanic Black (NHB; 23.2%), and Hispanic (7.6%) individuals comprised the primary analytic groups. Narrative analysis substantially increased detection of contextual factors, particularly alcohol involvement among Hispanic patients (12.0% to 20.1%). NHB patients had the highest proportion of lower-extremity injuries (51%), while Hispanic patients had the highest prevalence of craniofacial injuries (26%). The LASSO model effectively distinguished terms associated with probable suicide attempts from unintentional firearm discharges based on narrative language. Findings highlight limitations of structured intent coding within NEISS-FISS and demonstrate the value of narrative for improving characterization of firearm injury circumstances, revealing distinct racial and ethnic group patterns, to inform more tailored prevention strategies.

Plain-Language Summary

Ohio Recovery Housing Conference 2026

Wed, 15 Apr 2026 13:00:00 +0000

Dr. Keith Warren, Dr. Gia Barboza-Salerno, and Taylor Harrington will present at the 2026 Ohio Recovery Housing Conference during Breakout Session D (1:00 - 2:15 PM) on Wednesday, April 15, 2026.

This presentation will share findings from the lab’s ongoing research on recovery housing, examining the role of recovery housing in supporting individuals with substance use disorder, particularly in underserved and high-risk communities.

Conference Details:

Date: April 15, 2026
Time: 1:00 - 2:15 PM
Session: Breakout Session D
Registration: Register here
Full Schedule: View conference agenda

The Land Cover Paradox

Wed, 01 Jan 2025 00:00:00 +0000

Plain-Language Summary

This study explores the associations between developed open space, tree canopy, blue space, and land cover diversity and suicide risk across Chicago neighborhoods. Using Bayesian spatial Poisson models at the Census Block Group level, the research reveals a land cover paradox: while developed open spaces and blue spaces are associated with lower suicide rates overall, the protective effects are context-dependent and are not uniformly experienced across urban neighborhoods. The findings emphasize that suicide prevention efforts should incorporate neighborhood-level environmental factors and structural disadvantages rather than focusing solely on individual risk profiles.