https://issues-osu.github.io/ISSUESlab/publication/Select PublicationsHugo Blox Builder (https://hugoblox.com)en-usMon, 31 Aug 2026 00:00:00 +0000https://issues-osu.github.io/ISSUESlab/media/logo_hu2fedd529b9d9d7008e9f6968cd645fa1_199187_300x300_fit_lanczos_3.pnghttps://issues-osu.github.io/ISSUESlab/publication/https://issues-osu.github.io/ISSUESlab/publication/pregnancy-sle-lca/Mon, 31 Aug 2026 00:00:00 +0000https://issues-osu.github.io/ISSUESlab/publication/pregnancy-sle-lca/https://issues-osu.github.io/ISSUESlab/publication/racial-disparities-nonfatal-firearm-injuries/Wed, 17 Jun 2026 00:00:00 +0000https://issues-osu.github.io/ISSUESlab/publication/racial-disparities-nonfatal-firearm-injuries/<h3 id="plain-language-summary">Plain-Language Summary</h3> <p>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.</p> <p>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.</p>https://issues-osu.github.io/ISSUESlab/publication/ipv-symptoms/Mon, 01 Dec 2025 00:00:00 +0000https://issues-osu.github.io/ISSUESlab/publication/ipv-symptoms/https://issues-osu.github.io/ISSUESlab/publication/evaluating-child-fatality-reports-stm/Wed, 02 Jul 2025 00:00:00 +0000https://issues-osu.github.io/ISSUESlab/publication/evaluating-child-fatality-reports-stm/https://issues-osu.github.io/ISSUESlab/publication/can-jj/Wed, 01 Jan 2025 00:00:00 +0000https://issues-osu.github.io/ISSUESlab/publication/can-jj/<h3 id="plain-language-summary">Plain-Language Summary</h3> <p>This study examined the spatial overlap of child welfare and juvenile justice involvement using multivariate Bayesian spatial modeling. Results show a significant positive association between juvenile justice and child maltreatment referrals across neighborhoods. After adjusting for residential instability, immigrant concentration, and residential segregation, children in the least deprived 20% of neighborhoods were 95.2% and 55.5% less likely to be referred to child welfare or justice systems, respectively, compared to those in the most deprived 20%.</p>https://issues-osu.github.io/ISSUESlab/publication/random-forest-firearm-injury-chicago/Wed, 01 Jan 2025 00:00:00 +0000https://issues-osu.github.io/ISSUESlab/publication/random-forest-firearm-injury-chicago/<h3 id="plain-language-summary">Plain-Language Summary</h3> <p>This study describes the development and validation of a machine learning model (random forest) to predict the risk of firearm-related injuries across Chicago neighborhoods. By combining multi-source data, including local crime reports, socioeconomic vulnerabilities, and environmental exposures, the model maps out areas with the highest predicted risk.</p> <p>The model&rsquo;s predictions were validated against actual administrative records to verify its spatial accuracy. This predictive modeling framework shows how machine learning can help public health officials and community organizations anticipate high-risk spatial hot spots and deploy community-based interventions more effectively.</p>https://issues-osu.github.io/ISSUESlab/publication/greenview/Wed, 01 Jan 2025 00:00:00 +0000https://issues-osu.github.io/ISSUESlab/publication/greenview/<h3 id="plain-language-summary">Plain-Language Summary</h3> <p>This study examined how pedestrian-level green space relates to firearm-related and natural deaths in Cook County, Illinois, using a matched case-control design. Various greenness measures, including street-view green imagery, tree canopy coverage, and green space along walkable streets, were inversely associated with firearm homicide risk. Protective effects of tree canopy and localized greenery were stronger in less deprived neighborhoods, while neighborhood racial composition influenced the relationship between park proximity and homicide risk. Overall, street-level green space is linked to lower homicide odds and may offer greater protection in vulnerable communities.</p>https://issues-osu.github.io/ISSUESlab/publication/child-maltreatment-stm/Wed, 01 Jan 2025 00:00:00 +0000https://issues-osu.github.io/ISSUESlab/publication/child-maltreatment-stm/<h3 id="plain-language-summary">Plain-Language Summary</h3> <p>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.</p> <p>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.</p>https://issues-osu.github.io/ISSUESlab/publication/green-blue-suicide/Wed, 01 Jan 2025 00:00:00 +0000https://issues-osu.github.io/ISSUESlab/publication/green-blue-suicide/<h3 id="plain-language-summary">Plain-Language Summary</h3> <p>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.</p>https://issues-osu.github.io/ISSUESlab/publication/ai-geoanalysis-child-neglect-la/Wed, 01 Jan 2025 00:00:00 +0000https://issues-osu.github.io/ISSUESlab/publication/ai-geoanalysis-child-neglect-la/<h3 id="plain-language-summary">Plain-Language Summary</h3> <p>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.</p> <p>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.</p>https://issues-osu.github.io/ISSUESlab/publication/mlposter/Thu, 01 Aug 2024 00:00:00 +0000https://issues-osu.github.io/ISSUESlab/publication/mlposter/<h3 id="plain-language-summary">Plain-Language Summary</h3> <p>This research presentation examines the interplay between the built, natural, and social environments in predicting the risk of child abuse and neglect (CAN). By utilizing machine learning algorithms, the study categorizes environmental determinants of child well-being into key dimensions including urban design, environmental contaminants, and neighborhood socioeconomic conditions.</p> <p>The study demonstrates that characteristics of the physical and natural environment, such as neighborhood walkability, lighting, and greenness, interact with social vulnerabilities to shape family stress and safety. This work highlights the value of using computational models to translate physical streetscape characteristics into policy-relevant indicators for child maltreatment prevention.</p>https://issues-osu.github.io/ISSUESlab/publication/multilevel-model-service-disposition/Tue, 18 Jun 2024 00:00:00 +0000https://issues-osu.github.io/ISSUESlab/publication/multilevel-model-service-disposition/<h3 id="plain-language-summary">Plain-Language Summary</h3> <p>This study investigates how individual case characteristics and county-level social vulnerabilities influence the decisions made by child protective services (CPS) investigators when recommending services for families. Analyzing over 12,000 cases across New Mexico, the study looks at factors such as child age, abuse type, prior history, and county-level deprivation (e.g., housing and transportation deficits).</p> <p>The results show that county-level characteristics significantly influence whether a family receives support services. Even when families present with similar risk levels, those living in counties with higher structural vulnerabilities are subject to different service disposition outcomes. The findings highlight the need for standardized decision-making tools to ensure equitable treatment and service distribution across different geographic regions.</p>https://issues-osu.github.io/ISSUESlab/publication/gunvio/Mon, 01 Jul 2013 00:00:00 +0000https://issues-osu.github.io/ISSUESlab/publication/gunvio/<h3 id="plain-language-summary">Plain-Language Summary</h3> <p>This study evaluates the exposure of school children to gun violence along their walkable routes to and from school in Englewood, Chicago. Using acoustic sensor detection technology (ShotSpotter) alongside realistic network routing models, the research quantifies both direct and indirect gunfire exposure during daily school commutes.</p> <p>The findings show that children walking to school face frequent and geographically concentrated gunfire exposure, which varies significantly depending on specific walking routes and neighborhood social vulnerability. This work demonstrates the power of integrating spatial routing algorithms with acoustic gunshot data to guide school safety policies, active transit planning, and targeted community policing.</p>https://issues-osu.github.io/ISSUESlab/publication/jjandcan/Mon, 01 Jul 2013 00:00:00 +0000https://issues-osu.github.io/ISSUESlab/publication/jjandcan/<h3 id="plain-language-summary">Plain-Language Summary</h3> <p>This study examines the geographic and temporal patterns of child abuse and neglect referrals across New Mexico census tracts over a 9-year period. By utilizing Bayesian spatial models, the researchers identified areas with persistently high risk and examined how social and housing vulnerabilities affect maltreatment rates.</p> <p>The results demonstrate that neighborhood deprivation, housing insecurity, and residential instability are strong predictors of substantiated child abuse and neglect. The study highlights the utility of using space-time models to pinpoint communities that require enhanced support services, enabling child welfare agencies to allocate resources more equitably and proactively.</p>