Reviewing the Literature on the Impact of Gun Violence on Early Childhood Development.

PURPOSE OF REVIEW: To examine the impacts of gun violence on early childhood development including early childhood mental health, cognitive development, and the assessment and treatment of survivors. RECENT FINDINGS: The literature reflects that gun violence exposure is often associated with significant mental health outcomes including anxiety, post-traumatic stress, and depression in older youth. Historically, studies have focused on adolescents and their exposures to gun violence through proximity to gun violence within their communities, neighborhoods, and schools. However, the impacts of gun violence on young children are less known. Gun violence has significant impacts on mental health outcomes of youth aged 0-18. Few studies focus specifically on how gun violence impacts early childhood development. In light of the increase in youth gun violence over the past three decades with a significant uptick since the onset of the COVID-19 pandemic, continued efforts are needed to better understand how gun violence impacts early childhood development.

PI Prob: A risk prediction and clinical guidance system for evaluating patients with recurrent infections.

BACKGROUND: Primary immunodeficiency diseases represent an expanding set of heterogeneous conditions which are difficult to recognize clinically. Diagnostic rates outside of the newborn period have not changed appreciably. This concern underscores a need for novel methods of disease detection. OBJECTIVE: We built a Bayesian network to provide real-time risk assessment about primary immunodeficiency and to facilitate prescriptive analytics for initiating the most appropriate diagnostic work up. Our goal is to improve diagnostic rates for primary immunodeficiency and shorten time to diagnosis. We aimed to use readily available health record data and a small training dataset to prove utility in diagnosing patients with relatively rare features. METHODS: We extracted data from the Texas Children's Hospital electronic health record on a large population of primary immunodeficiency patients (n = 1762) and appropriately-matched set of controls (n = 1698). From the cohorts, clinically relevant prior probabilities were calculated enabling construction of a Bayesian network probabilistic model(PI Prob). Our model was constructed with clinical-immunology domain expertise, trained on a balanced cohort of 100 cases-controls and validated on an unseen balanced cohort of 150 cases-controls. Performance was measured by area under the receiver operator characteristic curve (AUROC). We also compared our network performance to classic machine learning model performance on the same dataset. RESULTS: PI Prob was accurate in classifying immunodeficiency patients from controls (AUROC = 0.945; p<0.0001) at a risk threshold of ≥6%. Additionally, the model was 89% accurate for categorizing validation cohort members into appropriate International Union of Immunological Societies diagnostic categories. Our network outperformed 3 other machine learning models and provides superior transparency with a prescriptive output element. CONCLUSION: Artificial intelligence methods can classify risk for primary immunodeficiency and guide management. PI Prob enables accurate, objective decision making about risk and guides the user towards the appropriate diagnostic evaluation for patients with recurrent infections. Probabilistic models can be trained with small datasets underscoring their utility for rare disease detection given appropriate domain expertise for feature selection and network construction.

Infection Phenotypes Among Patients with Primary Antibody Deficiency Mined from a US Patient Registry.

PURPOSE: Primary immunodeficiency disorders (PIDs) affect immune system development and/or function, increase infection susceptibility, and cause dysregulation or both. Recognition of PID requires assessment about the normal state of infection frequency and microbiology. To help clarify infection characteristics, we use data mined from the US Immunodeficiency Network (USIDNET) registry among primary antibody deficiency (PAD) patients before diagnosis. METHODS: We analyzed PAD patient data from the USIDNET registry prior to ultimate diagnosis. Our analysis included basic descriptive statistics for 8 major infection subtypes and significance testing for comparing infection rate by specific organisms across 7 distinct PAD subtypes. RESULTS: Of 2038 patients reviewed, 1259 (61.8%) had infections reported prior to diagnosis. Most (77.4%) had four or less reported infections prior to diagnosis; however, some suffered up to 16 infections. Infection patterns differed across the PAD subtypes. Patients with agammaglobulinemia differed significantly from patients with all other forms of PAD studied in at least one infection category, whereas patients with CVID differed from 3 other PAD categories in at least one infection category. Patterns of infections in patients with hypogammaglobulinemia, specific antibody deficiency, and transient hypogammaglobulinemia were less unique. For each of the infection types, bacteria were the most prevalent cause of disease. CONCLUSIONS: Our data shows that distinct subtypes of PAD display unique infection patterns. We also show that patients with agammaglobulinemia suffer more invasive infections and differ most significantly from all other forms of PAD studied. Our analysis has broad implications about infection surveillance, progression, and vulnerability by PAD subtype.

Visualizing and assessing US county-level COVID19 vulnerability.

BACKGROUND: Like most of the world, the United States' public health and economy are impacted by the COVID19 pandemic. However, discrete pandemic effects may not be fully realized on the macro-scale. With this perspective, our goal is to visualize spread of the pandemic and measure county-level features which may portend vulnerability. METHODS: We accessed the New York Times GitHub repository COVID19 data and 2018 United States Census data for all United States Counties. The disparate datasets were merged and filtered to allow for visualization and assessments about case fatality rate (CFR%) and associated demographic, ethnic and economic features. RESULTS: Our results suggest that county-level COVID19 fatality rates are related to advanced population age (P < .001) and less diversity as evidenced by higher proportion of Caucasians in High CFR% counties (P < .001). Also, lower CFR% counties had a greater proportion of the population reporting has having 2 or more races (P < .001). We noted no significant differences between High and Low CFR% counties with respect to mean income or poverty rate. CONCLUSIONS: Unique COVID19 impacts are realized at the county level. Use of public datasets, data science skills and information visualization can yield helpful insights to drive understanding about community-level vulnerability.