Significant inequities exist in the rates of pediatric firearm-related injuries. This study shows that a child’s neighborhood context profoundly impacts their risk of firearm injury, with black children and children from disadvantaged neighborhoods bearing the greatest burden.

Background:The extent to which environmental exposures and community characteristics of the built environment collectively predict rapid lung function decline, during adolescence and early adulthood in cystic fibrosis (CF), has not been examined.Objective: To identify built environment characteristics predictive of rapid CF lung function decline.Methods: We performed a retrospective, single-center, longitudinal cohort study (n = 173 individuals with CF aged 6-20 years, 2012(n = 173 individuals with CF aged 6-20 years, -2017. We used a stochastic model to predict lung function, measured as forced expiratory volume in 1 s (FEV 1 ) of % predicted. Traditional demographic/clinical characteristics were evaluated as predictors. Built environmental predictors included exposure to elemental carbon attributable to traffic sources (ECAT), neighborhood material deprivation (poverty, education, housing, and healthcare access), greenspace near the home, and residential drivetime to the CF center. Measurements and Main Results:The final model, which included ECAT, material deprivation index, and greenspace, alongside traditional demographic/clinical predictors, significantly improved fit and prediction, compared with only demographic/clinical predictors (Likelihood Ratio Test statistic: 26.78, p < 0.0001; the difference in Akaike Information Criterion: 15). An increase of 0.1 μg/m 3 of ECAT was associated with 0.104% predicted/yr (95% confidence interval: 0.024, 0.183) more rapid decline. Although not statistically significant, material deprivation was similarly associated (0.1-unit increase corresponded to additional decline of 0.103%

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Motivation: DeGAUSS is a software application for geocoding and geomarker assessment that circumvents barriers related to cost and reproducibility presented by conventional approaches. Most importantly, DeGAUSS safeguards protected health information (PHI), such as a mailing address. Implementation: DeGAUSS is implemented as a family of software containers with R code, geospatial software libraries, and geographic data. The containers are available in online repositories and accessed by users through the command line. It is free and open-source software under continuous development. General Features: DeGAUSS operates locally, ensuring that PHI is not passed over the internet, and reproducibly, which allows for easier operation in multi-site studies. In addition to geocoding, software containers are available for many geomarkers commonly utilized in epidemiological studies, including data related to the census, meteorology, land cover, greenspace, roadways, access to care, and air pollution. Availability: This software is freely available via &ldquo;GitHub Container Registry&rdquo; at https://degauss.org/.