Dr. Karla Shockley McCarthy | ISSUES | The Ohio State University

GIS Day 2025

Wed, 22 Oct 2025 00:00:00 +0000

Congratulations to our undergraduates Oliva McLucas and Malcolm McCarthy for their abstracts accepted to the 2025 GIS Day at The Ohio State University on November 19, 2025.

Olivia McLucas: Introducing a Spatial Adverse Childhood Environments Index using Geographical Information Systems We extend the cumulative Adverse Childhood Experiences (ACEs) framework to the neighborhood scale with spACEs (Spatial Adverse Childhood Environments), an index of structural disadvantage in childhood. The spACES index is constructed from county-level measures in the 1990 Census, including poverty, educational attainment, employment structure, housing burden, single-parent prevalence, and racial residential segregation, which reflect the environments in which the Opportunity Atlas birth cohorts (children born 1978 to 1985) were raised. The Opportunity Atlas links childhood places of residence to adult outcomes, capturing household income rank, educational attainment, incarceration, and family formation (2010-2015), as well as credit outcomes (2020). Using GIS, we visualize geographic variation in spACEs and assess its association with long-term adult outcomes. We propose spACEs as a revised measure of childhood structural adversity that captures the enduring impact of neighborhood disadvantage across the life course.

McCarthy, M.: Mapping Traumatic Brain Injury using Geographic Information Systems Traumatic brain injury (TBI) is a critical public health problem linked to numerous mechanisms, including unintentional injuries such as falls and motor vehicle crashes and intentional injuries such as assaults. Despite the magnitude of this burden, no research has systematically examined the geographic distribution of TBI hospitalizations and their county-level correlates. We utilize county-level hospitalization data from the Washington State Department of Health, filtered to include all TBI-related admissions by year. These records are linked to the Area Deprivation Index (ADI) for the period 2016–2023, encompassing domains of economic hardship and inequality, educational attainment, and financial strength. The merged dataset enables the analysis of spatial and temporal patterns of TBI hospitalization risk and tests whether county-level social and economic disadvantage is associated with higher rates of TBI hospitalization. Using GIS visualization and regression models, we demonstrate how community structural conditions shape the distribution of TBI and inform strategies for prevention.

The presentations will be made available after the conference.

Enhancing Brain Injury Surveillance Using Large Language Models and Topic Modeling

Sun, 19 Oct 2025 00:00:00 +0000

Our presentation, Enhancing Brain Injury Surveillance Using Large Language Models and Topic Modeling, was delivered by Dr. Shockley McCarthy at the AI for BI Conference at The Ohio State University on October 17, 2025.

This work introduces an AI-driven framework for improving the detection and contextual understanding of brain injury cases using unstructured medical and administrative text. By applying large language models (LLMs) and structured topic modeling, we extract interpretable patterns from injury narratives and ICD-coded data to identify mechanisms of injury, intent, and underreported cases of intimate partner violence (IPV)–related brain injury.

Integrating methods from public health, law, and computational linguistics, our approach aims to enhance the precision of injury surveillance systems and inform equitable prevention and policy strategies addressing violence-related traumatic brain injury (TBI).

Download the presentation here .

Dr. Karla Shockley McCarthy

Tue, 03 Nov 2026 14:45:00 +0000

Dr. Karla Shockley, PhD, LMSW, TRCC, FSW, is a Principal Investigator and Senior Research Consultant at the Ohio Colleges of Medicine Government Resource Center, where she leads applied health services and population health research. Her work integrates collaborative team-based design and implementation of mixed methods studies with independent contributions in survey development, data analysis, and dissemination across qualitative and quantitative domains. She focuses on translating complex data into actionable insights that inform public health policy and practice. Dr. Shockley’s research also emphasizes trauma-responsive care and systems—with a particular focus on family violence and brain injury, occupational well-being, and systems equity. She applies a transdisciplinary lens, drawing from social work, education, and geospatial science, to examine how structural and environmental conditions shape outcomes for children, families, and frontline professionals.

Dr. Shockley’s scholarship includes peer-reviewed publications in journals such as Child Abuse & Neglect, Journal of Family Violence, Social Science & Medicine, and BMC Public Health. Her research employs advanced methodologies, including mixed methods design, structural equation modeling, GIS, process evaluation, measure development and validation, and implementation science. She has led and contributed to studies on child maltreatment, intimate partner violence, opioid misuse, and suicidality among youth, using both primary and secondary data sources—often using large administrative datasets and spatial analysis—to uncover patterns of harm and resilience. Her work has been presented at national and international conferences, including the Society for Social Work and Research, the Council on Social Work Education, and the American Public Health Association.

Her research interests span healthcare and education systems quality and access, child and family resilience, trauma-informed interventions, financial policy and inclusion, violence in families, and the role of relationships and belongingness in promoting well-being. She is particularly focused on how identity and structural inequities intersect to influence life course outcomes and the development of data-driven community-centered solutions. Dr. Shockley brings over 25 years as an educator, seven years as a licensed mental health professional, and six years as a certified financial social worker. She also serves as a research affiliate for the Center for Brain Injury Recovery & Discovery, where she collaborates on initiatives addressing the intersection of domestic violence and brain injury.

Karla Shockley is the Co-Director of the ISSUES Lab.

Certifications

Licensed Social Worker - Ohio, 2018
Trauma-Responsive Care, 2020
Financial Social Worker, 2019
Nurtured Heart Approach Advanced Trainer, 2018
Secondary Social Studies Educator - Pennsylvania & Ohio, 1996

Awards and honours

Picker Gold Challenge Grant, The Arnold P. Gold Foundation, 2023
Enterprise Research Grant Award, The Ohio State University Office of Diversity and Inclusion, 2022
PhD Research Seed Grant, The Ohio State University College of Social Work, 2022
Institute on Teaching and Mentoring Scholarship, The Ohio State University College of Social Work, 2023
Graduate Associate Performance Award, The Ohio State University, 2021
Life Paths Promising Scholar Award - Honorable Mention, Life Paths Research Center, 2020

Disability-related depression inequities are driven by accessibility deficits: Evidence from crowdsourced sidewalk data and machine learning

Tue, 03 Nov 2026 14:45:00 +0000

Session: Mobility, Transit, and Built Environments for Disabled People
Program: Disability
Date: Monday, November 2, 2026 | 2:45 PM – 3:00 PM

Extreme heat and suicide risk among older adults: A longitudinal time-series analysis in an urban U.S. setting

Mon, 02 Nov 2026 12:30:00 +0000

Session: Climate, Culture, and Life-Course Contexts in Mental Health
Program: Mental Health
Date: Monday, November 2, 2026 | 12:30 PM – 1:30 PM

Improving surveillance of IPV-related head and neck injuries using AI-enhanced ED narratives

Mon, 02 Nov 2026 10:30:00 +0000

Session: Injury Epidemiology, Trauma Systems, and Health Care Utilization Posters
Program: Injury Control and Emergency Health Services
Date: Monday, November 2, 2026 | 10:30 AM – 11:30 AM

Identifying place-specific mortality mechanisms by integrating cause-of-death narratives and neighborhood typologies using machine learning

Mon, 02 Nov 2026 09:15:00 +0000

Session: Epidemiology Section: Conceptualization and Measurement
Program: Epidemiology
Date: Monday, November 2, 2026 | 9:15 AM – 9:30 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 Malcolm McCarthy for Presenting at the American Psychological Association 2026 Annual Convention

Fri, 07 Aug 2026 00:00:00 +0000

We are thrilled to congratulate Malcolm McCarthy, an undergraduate psychology student in the ISSUES Lab, for presenting his first-authored research poster at the American Psychological Association (APA) 2026 Annual Convention, held August 6-8, 2026 at the Walter E. Washington Convention Center in Washington, DC!

Malcolm’s poster, titled:

“How Multiple Environmental Factors Influence Mental Health Outcomes”

explores how neighborhood-level environmental conditions including greenspace exposure and suicide death concentrations shape mental health and suicide risk across census tracts in Cook County, Illinois. The study integrates spatial data and statistical modeling to disentangle the complex relationships between depression burden, environmental predictors, and suicide mortality.

Key Findings

Neighborhoods with higher depression burden were not the same neighborhoods with higher suicide mortality, highlighting that these are distinct spatial phenomena.
Higher greenspace predicted greater depression while also being associated with reduced suicide incidence.
Walkability and greenspace together influenced the co-location of depression and suicidality.
Suicide locations were closer to greenspace boundaries than random control points, and suicide risk increased within 1 km of nearby greenspace.

Policy Implications

The research points to actionable environmental design strategies such as accessible greenspace, improved walkability, and targeted community support services as levers for reducing mental health disparities in urban areas.

Co-authored with Dr. Gia Barboza-Salerno and Dr. Karla Shockley-McCarthy, Malcolm’s presentation was part of the Society for the Psychological Study of Social Issues (SPSSI) poster session (Division 9).

📄 Download the Poster

This is an outstanding achievement for an undergraduate researcher. Malcolm’s presentation reflects the caliber of work being done in the ISSUES Lab and his dedication to evidence-based science in service of community wellbeing. We are incredibly proud of you, Malcolm!

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

Detecting Intimate Partner Violence Injury Mechanisms Using LLMs and LDA

Wed, 29 Apr 2026 15:45:00 -0500

Dr. Gia Barboza-Salerno, Olivia McLucas, and Dr. Karla Shockley will present their research at the 2026 Symposium on Data Science & Statistics (SDSS) in Milwaukee, Wisconsin.

Presentation Details

Title: Automated Detection of Intimate Partner Violence Injury Mechanisms from Emergency Department Narratives Using Large Language Models and Latent Dirichlet Allocation

Session: AI and LLM Applications I
Date: Tuesday, April 29, 2026
Time: 3:45 - 5:15 PM CT
Format: 25-minute refereed presentation
Location: Milwaukee, Wisconsin

Registration: Register for SDSS 2026
Registration Deadline: March 25, 2026

Abstract

We developed an AI pipeline designed to overcome limitations in existing surveillance systems by distinguishing the underlying mechanisms of Traumatic Brain Injury (TBI) in intimate partner violence (IPV). The primary goal was to apply a Large Language Model (LLM) to extract and classify behavioral and contextual information from unstructured free-text narratives within the National Electronic Injury Surveillance System (NEISS).

The pipeline first cleans the narratives and uses the LLM to convert the text into six targeted structured semantic representations (e.g., action, cause, diagnosis), which are then quantified using Latent Dirichlet Allocation (LDA) topic modeling. Our results can be used to enhance injury surveillance to identify IPV in emergency department settings.

Introduction

Head, face, and neck injuries are common IPV-related injuries and frequently involve TBI, yet NEISS diagnostic codes lack the behavioral detail needed to differentiate mechanisms of harm. Although NEISS narratives contain brief descriptions of arguments, aggression, object use, and environmental context, they have been underutilized in surveillance research and rarely analyzed at scale.

As a result, diagnostic codes collapse distinct injury pathways—including accidental falls, object impacts, impulsive actions during conflict, and direct assaults—into the same categories, obscuring meaningful variation in risk and limiting clinical screening and prevention efforts.

Our method fills this gap by applying AI-based extraction and topic modeling to detect behavioral and situational information embedded in NEISS narratives, producing a more nuanced understanding of IPV-related injury mechanisms.

Methods

Analytical Framework

We developed an AI pipeline to extract and classify behavioral and contextual information from unstructured NEISS narratives related to IPV:

Text Preprocessing: Used regular expressions to flag domestic-violence–related incidents, expand abbreviations, clean shorthand, and isolate IPV cases
Custom Prompting: Generated prompts by merging cleaned narratives with structured NEISS variables
LLM Extraction: Transformed narratives into six semantic fields (action, cause, diagnosis, mechanism, object, location)
Topic Modeling: Applied LDA to identify latent themes within each semantic field
Typology Classification: Integrated regex-based typology and LDA clusters to produce quantitative injury pathways

Text Processing and Prompting

We used vectorized regular expressions to:

Flag violence-related narratives
Expand abbreviations and clean shorthand
Isolate IPV cases by identifying relationship and conflict terms
Exclude clearly non-IPV incidents

After preprocessing, we generated custom prompts by merging each cleaned narrative with structured NEISS variables to produce context-rich descriptions of the event.

LLM Extraction and Topic Modeling

The LLM transformed cleaned narratives into six structured semantic representations:

Action: What happened (e.g., verbal disputes, punching, striking objects)
Cause: Why it happened (e.g., intoxication, falls, collisions)
Diagnosis: Medical outcome (e.g., fractures, contusions, lacerations)
How: Mechanism of injury (e.g., physical struggle, impulsive acts)
What: Objects involved (e.g., walls, windows, doors, fists)
Where: Location (e.g., home, public place, bathroom)

LDA topic modeling systematically discovered and quantified overarching themes across all responses, resulting in quantifiable dimensions like interpersonal conflict or blunt impact injuries.

Data & Results

Quantification of Contextual Dimensions (LDA Topics)

Topic modeling produced interpretable themes across all semantic dimensions:

Action: verbal disputes, play fighting, punching, striking objects
Cause: intoxication, falls, collisions with walls, windows, or furniture
Diagnosis: fractures, contusions, lacerations, closed head injury
How: physical struggle, impulsive acts, heated altercations
What: walls, windows, doors, bricks, glass, fists
Where: home, public place, outside environment, bathrooms

Identified Head/Neck Injury Typologies

Narrative- and topic-derived clusters aligned with six major typologies:

Typology	%	Representative Narrative
Accidental fall or slip	38.6%	“Slipped and fell hitting head on carpet.”
Self-inflicted harm	19.9%	“Banging head against wall during argument.”
Physical assault	25.1%	“Stabbed in neck with kitchen knife.”
Blunt object impact	9.9%	“Head-butted wall; forehead hematoma.”
Environmental	2.3%	“Pepper spray during fight.”
Vehicle-related impact	1.7%	“Swerved and crashed; head/neck pain.”

Word Cloud Visualization

The figure above shows word-cloud representations of dominant latent topics extracted from LLM-generated semantic dimensions (Cause, Diagnosis, How). Each cloud visualizes high-frequency terms within a topic cluster, highlighting recurrent mechanisms of injury across IPV-related emergency department visits.

Key patterns identified:

Heavy concentration of head trauma in incidents involving walls, floors, or doors
High rates of head contact in intoxication-related falls
Frequent head or neck injury during impulsive self-directed actions

Discussion & Conclusions

Combining regex-based classification with LLM extraction and LDA provides a detailed mapping of how, where, and why brain injuries occur during IPV-related incidents.

Key Findings

Blurred Boundaries: Many injuries categorized as “accidental” or “environmental” occur in the context of relationship conflict, revealing blurred boundaries between unintentional and interpersonal harm
Enhanced Surveillance: These methods improve surveillance and detection of IPV-related injuries in emergency departments by recovering behavioral information that structured diagnostic codes cannot capture
Mechanism Identification: The narrative-based approach identifies specific mechanisms that contribute to injury, whereas structured codes often make it difficult to distinguish between distinct behavioral pathways

Future Work

Expanding prompts to capture richer contextual cues
Validating typologies with larger datasets
Examining variation across sociodemographic and clinical subgroups

About SDSS

The Symposium on Data Science & Statistics is a premier conference bringing together researchers, practitioners, and students working at the intersection of statistics, data science, and computational methods. The 2026 symposium takes place April 28 - May 1 in Milwaukee, Wisconsin.

Keywords: Traumatic brain injury, intimate partner violence, surveillance, Natural Language Processing, topic modeling, emergency medicine, LLM, LDA

Emotional and Physical Symptoms Following Intimate Partner Violence Victimization in the United States: Implications for Law and Public Health Policy

Mon, 01 Dec 2025 00:00:00 +0000

Living Here Predicts How You’ll Die: Linking Death Narratives to Neighborhood Economic Disinvestment with Unsupervised Machine Learning

Wed, 19 Nov 2025 12:00:00 +0000

Intimate Partner Violence & Brain Injury in U.S. Adults: Findings from the Medical Expenditure Panel Survey, 2021

Wed, 27 Aug 2025 00:00:00 +0000

Overview

National survey data show that adults who screen positive for intimate partner violence (IPV) experience more injuries and face greater medical costs than those who do not. Nine million screened IPV-positive using the HITS screening tool, and survivors were significantly more likely to utilize emergency rooms and hospitals for treating their injuries. Head and brain injuries were much more frequent among IPV survivors, and the average cost of treating these injuries was nearly twice as high compared to adults who did not screen positive for IPV.

Key Messages

Approximately 9 million adults screened positive for IPV in 2021.
IPV-positive adults had 3–4× higher ER and inpatient injury rates than IPV-negative adults.
Adults who screened positive for IPV experienced more than twice the rate of brain-injury healthcare events compared to those who screened negative.
Average per-capita spending on brain-injury care was about twice as high among IPV-positive adults.
Inpatient TBI events are less common than ER events but far more costly; counts and spending are higher among IPV-positive adults.
Poverty, social stressors, depression, and chronic conditions are significantly associated with IPV screen positivity.

Policy Relevance & Recommendations

Standardized protocols: Implement routine protocols so that IPV-positive patients with head injury are screened for brain injury, and patients with head injury are also screened for IPV.
Integrated referral pathways: Establish clear referral systems across emergency, primary, and specialty care so that when screening protocols are triggered, patients are automatically connected to appropriate follow-up services.
Follow-up for delayed symptoms: Incorporate temporal follow-up in care plans, recognizing that brain injury symptoms may appear or worsen over time.
Accessibility and appropriateness: Ensure screening and follow-up services are accessible, widely available, and tailored to the needs of IPV survivors.
Holistic screening: Extend screening beyond identifying head injury to include social and health needs that may magnify injury effects and increase vulnerability to future IPV.
Care coordination: Utilize medical social workers and interdisciplinary teams to integrate medical, psychosocial, and safety needs into a comprehensive care response.

Methodology

This brief analyzes nationally representative data from the Medical Expenditure Panel Survey—Household Component (MEPS-HC) 2021. Adults (age ≥18) were classified as IPV screen-positive if they scored ≥10 on the Hurt, Insult, Threaten, Scream (HITS) scale, a validated four-item screening tool for intimate partner violence.

Injury outcomes were identified using ICD-10-CM codes and Clinical Classifications Software Refined (CCSR) groupings from the MEPS Conditions file, with condition–event linkages used to assign emergency department (ER), inpatient, outpatient, office-based, home health, and prescription care to specific injuries. Brain injuries were defined as traumatic brain injury (ICD-10 S06) and other head injuries (S00–S09).

We estimated:

Injury and brain-injury events per 100 adults,
Per-capita spending (in 2021 USD), and
Weighted counts of injury-linked encounters.

Analyses applied MEPS survey weights (PERWT21F) and accounted for the complex design (VARSTR, VARPSU). We used survey-weighted generalized linear models (svyglm) to generate adjusted comparisons standardized over age, sex, and race/ethnicity. Risk ratios were estimated for sociodemographic factors, health-related social needs, and chronic health conditions associated with IPV screen positivity.

Suggested Citation

Barboza-Salerno, G. E., Shockley McCarthy, K., Watson-Grace, A., Harrington, T., McCarthy, M. A., Peremen, N., & McLucas, O. (2025). Intimate Partner Violence & Brain Injury in U.S. Adults: Findings from the Medical Expenditure Panel Survey, 2021. Policy brief. The Ohio State University, ISSUE Lab (Investigating Spatial Structures in Urban Environments).

Download

Download the full brief for more information

Structured Topic Modeling of Child Fatal Injury Narratives Using Machine Learning with County-Level Demographic Indicators to Strengthen Prevention Strategies

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

Plain-Language Summary

This study applies machine learning and natural language processing to analyze child fatality reports in Pennsylvania from 2016 to 2023. Unstructured text narratives from child death investigations were combined with census data to identify recurring themes and environmental factors surrounding child neglect and traumatic injuries.

Using structured topic modeling, the researchers identified 11 distinct circumstances of death, including substance misuse, sleep-related deaths, firearms, and supervisory neglect. The analysis showed that counties with higher poverty levels and larger non-White populations experienced a higher prevalence of firearm-related and severe traumatic injuries. The findings demonstrate how combining clinical narratives with machine learning can strengthen fatality surveillance and help construct more effective, targeted prevention programs.

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.

Utilizing Artificial Intelligence and Geospatial Analysis to Examine the Urban Built Environment, Social Vulnerability, and Child Neglect in Los Angeles

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

Plain-Language Summary

This study uses artificial intelligence and computer vision to analyze street-level imagery from Google Street View across 141 census block groups in Los Angeles, California. By measuring features of the built and natural environment, the research team constructed neighborhood indices for natural surveillance, lighting, land-use mix, and greenness.

The findings show that higher quality built environments, specifically better lighting, visibility (natural surveillance), and green space, are associated with lower rates of neighborhood child neglect, even when controlling for area-level deprivation and poverty. This research demonstrates how AI street-level audits can provide actionable data for city planners, social workers, and policymakers to design community-based prevention strategies that protect vulnerable children.