arXiv subjects

Iris Horng

Publications and source records attributed to Iris Horng.

4 recordsLinked to original sources

arXiv2026

Effects of Repetitive Low-Level Blast Exposure on Mental Health in Veterans

Links between blast exposure and mild traumatic brain injury (mTBI), as well as other negative physical and mental effects are well established. However, the effects of low-level blast exposure, which does not rise to the level of a mTBI diagnosis, are less well-documented. Low-level blast exposure may initiate or accelerate neurodegenerative changes, leading to accelerated age-related cognitive decline or overt neuropathological diagnoses such as Chronic Traumatic Encephalopathy (CTE), in addition to other non-neurodegenerative long-term harmful effects, such as clinical depression. This study analyzed the effect of low-level blast exposure through a survey of military veterans who served in the U.S. military as mortarmen (n = 42). Survey participants responded to screening assessments measuring depression, PTSD, anxiety, and alcohol use disorder. Through a matched pair study design, surveyed mortarmen were compared to two control groups consisting of veterans without a history of mTBI exposure (n = 283) and veterans who had suffered at least one mTBI exposure (n = 1264). We found that surveyed mortarmen reported statistically significant higher depression (as measured by PHQ-9) than both groups of controls. Even after accounting for Post-Traumatic Stress disorder (PTSD) and unhealthy alcohol use, mortarmen still reported higher depression, suggesting that PTSD was not the sole factor responsible for worse mental health symptoms. These results highlight the importance of more comprehensive study of cumulative blast overpressure and its relationship with mental health.

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arXiv2026

The Role of Measured Covariates in Assessing Sensitivity to Unmeasured Confounding

Sensitivity analysis is widely used to assess the robustness of causal conclusions in observational studies, yet its interaction with the structure of measured covariates is often overlooked. When latent confounders cannot be directly adjusted for and are instead controlled using proxy variables, strong associations between exposure and measured proxies can amplify sensitivity to residual confounding. We formalize this phenomenon in linear regression settings by showing that a simple ratio involving the exposure model coefficient and residual exposure variance provides an observable measure of this increased sensitivity. Applying our framework to smoking and lung cancer, we document how growing socioeconomic stratification in smoking behavior over time leads to heightened sensitivity to unmeasured confounding in more recent data. These results highlight the importance of multicollinearity when interpreting sensitivity analyses based on proxy adjustment.

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arXiv2025

Probabilistic Record Linkage of Two Gun Violence Data Sets

Objective: Gun violence is a serious public health problem in the United States. The Gun Violence Archive (GVA) provides detailed geographic information, while the National Violent Death Reporting System (NVDRS) offers demographic, socioeconomic, and narrative data on gun homicides. We developed and tested a method for merging datasets to inform analysis and strategies to reduce gun violence rates in the United States. Methods: After preprocessing the data, we used a probabilistic record linkage program to link records from the GVA (n = 36,245) with records from the NVDRS (n = 30,592). We evaluated sensitivity (the false match rate) by using a manual approach. Results: The linkage returned 27,420 matches of gun violence incidents from the GVA and NVDRS datasets. Because of restricted details accessible from GVA online records, only 942 of these matched records could be manually evaluated. Our framework achieved a 90.12% (849 of 942 accuracy rate in linking GVA incidents with corresponding NVDRS records. Practice Implications: Electronic linkage of gun violence data from 2 sources is feasible and can be used to increase the utility of the datasets.

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arXiv2024

Predictive Modeling of Lower-Level English Club Soccer Using Crowd-Sourced Player Valuations

In this research, we examine the capabilities of different mathematical models to accurately predict various levels of the English football pyramid. Existing work has largely focused on top-level play in European leagues; however, our work analyzes teams throughout the entire English Football League system. We modeled team performance using weighted Colley and Massey ranking methods which incorporate player valuations from the widely-used website Transfermarkt to predict game outcomes. Our initial analysis found that lower leagues are more difficult to forecast in general. Yet, after removing dominant outlier teams from the analysis, we found that top leagues were just as difficult to predict as lower leagues. We also extended our findings using data from multiple German and Scottish leagues. Finally, we discuss reasons to doubt attributing Transfermarkt's predictive value to wisdom of the crowd.

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