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• Ultrafine particles (UFP) cause respiratory health effects including exacerbation of existing asthma due to their size, composition, and respiratory deposition. • During a one-week sampling campaign, adolescents with and without asthma completed personal UFP monitoring. Respiratory symptoms were reported using ecological momentary assessment and validated questionnaires. Lung function was measured at the completion of the sampling following ATS/ERS criteria. • Median weekly exposure to ultrafine particles was not associated with lung function after covariate adjustment. • Median weekly UFP exposure was associated with an increased risk for respiratory symptoms among adolescents with asthma. An increasing amount of evidence suggests ultrafine particles (UFPs) are linked to adverse health effects, especially in those with chronic conditions such as asthma, due to their small size and physicochemical characteristics. Toxicological and experimental studies have demonstrated these properties, and the mechanisms by which they deposit and translocate in the body result in increased toxicity in comparison to other air pollutants. However, current epidemiological literature is limited due to exposure misclassification and thus identifying health outcomes associated with UFPs. The objective of this study was to investigate the association between weekly personal UFP exposure with lung function and respiratory symptoms in 117 asthmatic and non-asthmatic adolescents between 13 and 17 years of age in the Cincinnati area. Between 2017 and 2019, participants collected weekly UFP concentrations by sampling for 3 h a day in their home, school, and during transit. In addition, pulmonary function was evaluated at the end of the sampling week, and respiratory symptoms were logged on a mobile phone application. Multivariable linear regression and zero-inflated Poisson (ZIP) models were used to estimate the association between personal UFP and respiratory outcomes. The average median weekly UFP exposure of all participants was 4340 particles/cm3 (p/cc). Results of fully adjusted regression models revealed a negative association between UFPs and percent predicted forced expiratory volume/forced vital capacity ratio (%FEV 1 /FVC) (β:-0.03, 95% CI [-0.07, 0.02]). Prediction models estimated an association between UFPs and respiratory symptoms, which was greater in asthmatics compared to non-asthmatics. Our results indicate an interaction between asthma status and the likelihood of experiencing respiratory symptoms when exposed to UFPs, indicating an exacerbation of this chronic condition. More research is needed to determine the magnitude of the role UFPs play on respiratory health. [ABSTRACT FROM AUTHOR]

Background: Day-of-surgery cancellation (DoSC) represents a substantial wastage of hospital resources and can cause significant inconvenience to patients and families. Cancellation is reported to impact between 2% and 20% of the 50 million procedures performed annually in American hospitals. Up to 85% of cancellations may be amenable to the modification of patients' and families' behaviors. However, the factors underlying DoSC and the barriers experienced by families are not well understood.Objective: This study aims to conduct a geospatial analysis of patient-specific variables from electronic health records (EHRs) of Cincinnati Children's Hospital Medical Center (CCHMC) and of Texas Children's Hospital (TCH), as well as linked socioeconomic factors measured at the census tract level, to understand potential underlying contributors to disparities in DoSC rates across neighborhoods.Methods: The study population included pediatric patients who underwent scheduled surgeries at CCHMC and TCH. A 5-year data set was extracted from the CCHMC EHR, and addresses were geocoded. An equivalent set of data >5.7 years was extracted from the TCH EHR. Case-based data related to patients' health care use were aggregated at the census tract level. Community-level variables were extracted from the American Community Survey as surrogates for patients' socioeconomic and minority status as well as markers of the surrounding context. Leveraging the selected variables, we built spatial models to understand the variation in DoSC rates across census tracts. The findings were compared to those of the nonspatial regression and deep learning models. Model performance was evaluated from the root mean squared error (RMSE) using nested 10-fold cross-validation. Feature importance was evaluated by computing the increment of the RMSE when a single variable was shuffled within the data set.Results: Data collection yielded sets of 463 census tracts at CCHMC (DoSC rates 1.2%-12.5%) and 1024 census tracts at TCH (DoSC rates 3%-12.2%). For CCHMC, an L2-normalized generalized linear regression model achieved the best performance in predicting all-cause DoSC rate (RMSE 1.299%, 95% CI 1.21%-1.387%); however, its improvement over others was marginal. For TCH, an L2-normalized generalized linear regression model also performed best (RMSE 1.305%, 95% CI 1.257%-1.352%). All-cause DoSC rate at CCHMC was predicted most strongly by previous no show. As for community-level data, the proportion of African American inhabitants per census tract was consistently an important predictor. In the Texas area, the proportion of overcrowded households was salient to DoSC rate.Conclusions: Our findings suggest that geospatial analysis offers potential for use in targeting interventions for census tracts at a higher risk of cancellation. Our study also demonstrates the importance of home location, socioeconomic disadvantage, and racial minority status on the DoSC of children's surgery. The success of future efforts to reduce cancellation may benefit from taking social, economic, and cultural issues into account. [ABSTRACT FROM AUTHOR]

Objectives: To categorize newborn infants in Hamilton County, Ohio by late pregnancy fetal opioid exposure status and to assess their first-year healthcare utilization.Study Design: We used a population-based cohort of 41 136 live births from 2014-2017 and analyzed healthcare encounters in the first year of life from electronic health records. We prospectively assessed for the presence of opioids in maternal urine collected at delivery and for a diagnosis of newborn neonatal abstinence syndrome (NAS). At birth, infants were classified as unexposed to opioids, exposed to opioids and diagnosed with NAS, or subclinically exposed to opioids (exposure that did not result in NAS).Results: The prevalence of newborn opioid exposure was 37 per 1000 births. The duration of the hospital birth encounter was significantly longer for infants with subclinical exposure compared with unexposed infants (10% increase; 95% CI, 7%-13%). However, duration for infants with subclinical exposure was shorter compared to those with NAS. Neither subclinical exposure nor NAS was associated with total emergency department visits. Subclinical exposure was associated with increased odds of having at least 1 hospitalization in the first year. However, the total length of stay for hospitalizations was 82% that of the unexposed group (95% CI, 75%-89%). Infants with NAS had a 213% longer total length of stay compared with the unexposed group (95% CI, 191%-237%).Conclusions: Subclinical and overt opioid exposure among newborn infants was associated with increased first-year healthcare utilization. From 2014 to 2017, this cost the Hamilton County healthcare system an estimated $1 109 452 for longer birth encounters alone. [ABSTRACT FROM AUTHOR]

The article discusses research which assessed the changes in perceived knowledge, attitudes and awareness of study participants on air pollution after receiving their personal air monitoring results. Cited are the demographic characteristics of participants, the domains and questions included on the questionnaire, and the strengths and limitations of the study.

• We broadly assessed secondhand smoke exposure (SHSe) and pediatric CF lung function. • Pooled effect of SHSe on lung function was estimated with Bayesian meta-analysis. • Despite heterogeneity between studies, SHSe associated with a drop in lung function. Secondhand smoke exposure, an important environmental health factor in cystic fibrosis (CF), remains uniquely challenging to children with CF as they strive to maintain pulmonary function during early stages of growth and throughout adolescence. Despite various epidemiologic studies among CF populations, little has been done to coalesce estimates of the association between secondhand smoke exposure and lung function decline. A systematic review was performed using PRISMA guidelines. A Bayesian random-effects model was employed to estimate the association between secondhand smoke exposure and change in lung function (measured as FEV 1 % predicted). Quantitative synthesis of study estimates indicated that second-hand smoke exposure corresponded to a significant drop in FEV 1 (estimated decrease: -5.11% predicted; 95% CI: -7.20, -3.47). The estimate of between-study heterogeneity was 1.32% predicted (95% CI: 0.05, 4.26). There was moderate heterogeneity between the 6 analyzed studies that met review criteria (degree of heterogeneity: I 2 =61.9% [95% CI: 7.3–84.4%] and p = 0.022 from the frequentist method.) Our results quantify the impact at the pediatric population level and corroborate the assertion that secondhand smoke exposure negatively affects pulmonary function in children with CF. Findings highlight challenges and opportunities for future environmental health interventions in pediatric CF care. [ABSTRACT FROM AUTHOR]

Purpose: Quantify and examine the racial fairness of two widely used childhood asthma predictive precision medicine algorithms: the asthma predictive index (API) and the pediatric asthma risk score (PARS). Design: Apply the API and PARS and evaluate model performance overall and when stratified by race. Setting: Cincinnati, OH, USA. Subjects: A prospective birth cohort of 590 children with clinically measured asthma diagnosis by age seven. Measures: Model diagnostic criteria included sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Analysis: Significant differences in model performance between Black and white children were considered to be present if the P -value associated with a t-test based on 100 bootstrap replications was less than.05. Results: Compared to predictions for white children, predictions for Black children using the PARS had a higher sensitivity (.88 vs.57), lower specificity (.55 vs.83), higher PPV (.42 vs.33), but a similar NPV (.93 vs.93). Within the API and compared to predictions for white children, predictions for Black children had a higher sensitivity (.63 vs.53), similar specificity (.81 vs.80), higher PPV (.54 vs.28), and lower NPV (.86 vs.92). Conclusions: Overall, racial disparities in model diagnostic criteria were greatest for sensitivity and specificity in the PARS, but racial disparities existed in three of the four criteria for both the PARS and the API. [ABSTRACT FROM AUTHOR]

Background: While benefits of greenness to health have been reported, findings specific to child respiratory health are inconsistent.Methods: We utilized a prospective birth cohort followed from birth to age 7 years (n = 617). Residential surrounding greenness was quantified via Normalized Difference Vegetation Index (NDVI) within 200, 400, and 800 m distances from geocoded home addresses at birth, age 7 years, and across childhood. Respiratory health outcomes were assessed at age 7 years, including asthma and lung function [percent predicted forced expiratory volume in the first second (%FEV1), percent predicted forced vital capacity (%FVC), and percent predicted ratio of forced expiratory volume in the first second to forced vital capacity (%FEV1/FVC)]. We assessed associations using linear and logistic regression models adjusted for community deprivation, household income, and traffic-related air pollution. We tested for effect measure modification by atopic status.Results: We noted evidence of positive confounding as inverse associations were attenuated upon adjustment in the multivariable models. We found evidence of effect measure modification of NDVI and asthma within 400 m at age 7 years by atopic status (p = 0.04), whereby children sensitized to common allergens were more likely to develop asthma as exposure to greenness increased (OR = 1.3, 95% CI: 0.9, 2.0) versus children not sensitized to common allergens (OR = 0.8, 95% CI: 0.5, 1.2). We found consistently positive associations between NDVI and %FEV1 and %FVC which similarly evidenced positive confounding upon adjustment. In the adjusted regression models, NDVI at 7 years of age was associated with %FEV1 (200 m: β = 2.1, 95% CI: 0.1, 3.3; 400 m: β = 1.6, 95% CI: 0.3, 2.9) and %FVC (200 m: β = 1.8, 95% CI: 0.7, 3.0; 400 m: β = 1.6, 95% CI: 0.3, 2.8; 800 m: β = 1.5, 95% CI: 0.1, 2.8). Adjusted results for %FEV1/FVC were non-significant except exposure at birth in the 400 m buffer (β = 0.81, 95% CI: 0.1, 1.5). We found no evidence of effect measure modification of NDVI by atopic status for objective measures of lung function.Conclusion: Sensitivity to allergens may modify the effect of greenness on risk for asthma in children but greenness is likely beneficial for concurrent lung function regardless of allergic status. [ABSTRACT FROM AUTHOR]

Evidence on the relationship between exposure to greenness and adolescent mental health is limited. The purpose of this study was to examine the association between greenness throughout childhood and mental health at age 12 years. We assessed greenness using the satellite-based measure of Normalized Difference Vegetation Index (NDVI) within 200m, 400m, and 800m of home address at birth, age 12 years, and across childhood (averaged for each year from birth to age 12) among the Cincinnati Childhood Allergy and Air Pollution Study (CCAAPS) cohort. Self-reported symptoms of anxiety and depression were assessed at age 12 years using the Spence Children's Anxiety Scale (SCAS) and Children's Depression Inventory 2 (CDI 2), respectively. Associations were estimated using linear regression, adjusting for covariates including traffic-related air pollution, neurological hazard exposure, blood lead level, household income, and community deprivation. In adjusted models, NDVI was largely not associated with self-reported anxiety and depression symptoms, except for the SCAS separation anxiety subscale at 400m and 800m (0.1 unit increase mean NDVI 400m: β = −0.97, 95% CI: −1.86, −0.07; 800m: β = −1.33, 95% CI: −2.32, −0.34). While we found no direct relationship between greenness and overall symptoms of anxiety and depression in adolescents upon adjustment for relevant covariates at the 200m distance, greenness may lesson symptoms of separation anxiety within 400m and 800m distance from the home address at age 12 years. Future research should examine mechanisms for these relationships at the community- and individual-level. • Participants of a birth cohort self-reported anxiety and depression at age 12 years. • We used NDVI to estimate greenness surrounding the home in three buffer distances. • We examined associations in three time-periods: birth, age 12, and across childhood. • Greenness may decrease symptoms of separation anxiety in adolescents. [ABSTRACT FROM AUTHOR]

Objectives Vermiculite ore containing Libby amphibole asbestos (LAA) was mined in Libby, MT, from the 1920s-1990. Recreational and residential areas in Libby were contaminated with LAA. This objective of this study was to characterize childhood exposure to LAA and investigate its association with respiratory health during young adulthood. Methods Young adults who resided in Libby prior to age 18 completed a health and activity questionnaire, pulmonary function testing, chest x-ray and HRCT scan. LAA exposure was estimated based on participant report of engaging in activities with potential LAA exposure. Quantitative LAA estimates for activities were derived from sampling data and literature reports. Results A total of 312 participants (mean age 25.1 years) were enrolled and reported respiratory symptoms in the past 12 months including pleuritic chest pain (23%), regular cough (17%), shortness of breath (18%), and wheezing or whistling in the chest (18%). Cumulative LAA exposure was significantly associated with shortness of breath (aOR = 1.12, 95% CI 1.01–1.25 per doubling of exposure). Engaging in recreational activities near Rainy Creek Road (near the former mine site) and the number of instances heating vermiculite ore to make it expand or pop were also significantly associated with respiratory symptoms. LAA exposure was not associated with pulmonary function or pleural or interstitial changes on either chest x-ray or HRCT. Conclusions Pleural or interstitial changes on x-ray or HRCT were not observed among this cohort of young adults. However, childhood exposure to LAA was significantly associated with respiratory symptoms during young adulthood. Pleuritic chest pain, in particular, has been identified as an early symptom associated with LAA exposure and therefore warrants continued follow-up given findings of progressive disease in other LAA exposed populations. [ABSTRACT FROM AUTHOR]

Autism Spectrum Disorder (ASD) affects about 1 in 44 children and environmental exposures may contribute to disease onset. Air pollution has been associated with adverse neurobehavioral outcomes, yet little research has examined its association with autistic-like behaviors. Therefore, our objective was to examine the association between exposure to air pollution, including NO 2 and PM 2.5 , during pregnancy and the first year of life to ASD-like behaviors during childhood. Participants (n = 435) enrolled in the Cincinnati Childhood Allergy and Air Pollution Study and the Health Outcomes and Measures of the Environment Study were included in the analysis. Daily exposures to NO 2 and PM 2.5 at the residential addresses of participants were estimated using validated spatiotemporal models and averaged to obtain prenatal and first year exposure estimates. ASD-like behaviors were assessed via the Social Responsiveness Scale (SRS) questionnaire at age 12. Linear regression models adjusting for confounders were applied to estimate the association between pollutants and SRS scores. After adjusting for covariates, the association between NO 2 and PM 2.5 and SRS scores remained positive but were no longer statistically significant. Prenatal and first year exposure to NO 2 were associated with total SRS T-scores with an estimated 0.4 point increase (95% CI: −0.7, 1.6) per 5.2 ppb increase in NO 2 exposure and 0.7 point (95% CI: −0.3, 1.6) per 4.2 ppb increase in NO 2 exposure, respectively. For PM 2.5 , a 2.6 μg/m3 increase in prenatal exposure was associated with a 0.1 point increase (95% CI: −1.1, 1.4) in SRS Total T-scores and a 1.3 μg/m3 increase first year of life was associated with a 1 point increase (95% CI: −0.2, 2.3). In summary, exposure to NO 2 and PM 2.5 during pregnancy and the first year of life were not significantly associated with higher autistic-like behaviors measured with SRS scores after adjustment of covariates. Additional research is warranted given prior studies suggesting air pollution contributes to ASD. [ABSTRACT FROM AUTHOR]

Context: Area-level predictive models are commonly used to screen children for blood lead levels (BLLs) greater than the Center for Disease Control and Prevention (CDC) blood lead reference value (BLRV) of 3.5 µg/dL., Objectives: To increase screening accuracy and precision by creating a parcel-level model incorporating housing characteristics to predict parcels where children are at high risk., Design: Residential addresses associated with child blood lead tests were linked to neighborhood- and parcel-level characteristics and proximity to lead sources. Regression forests were fit using different predictor combinations and compared using cross-validated accuracy and decile-based agreement across all residential parcels., Setting: Hamilton County, Ohio, United States., Participants: Children less than 6 years of age with blood lead tests between January 2020 and April 2023., Main Outcome Measure: Cross-validated model accuracy and decile-based agreement across residential parcels., Results: 27,782 tests were matched to a residential parcel. Regression forests using Parcel + Source (70.8% AUC) and Neighborhood + Parcel + Source predictors (70.3% AUC) had the highest cross-validated accuracy for predicting BLLs >3.5 µg/dL. Parcel-level predictions revealed heterogeneity of risk across parcels within the same tract., Conclusions: Parcel characteristics improved the accuracy of predicting locations of children with BLLs >3.5 µg/dL and can help identify children at high risk living in low-risk areas. A parcel-level identification of housing-based lead hazards could guide and support action to prevent pediatric lead exposure., (Copyright © 2025 Wolters Kluwer Health, Inc. All rights reserved.)

The purpose of this study was to describe and evaluate the implementation of the Chicago Parent Program (CPP) in a community setting. Included are details of the co-designed implementation by academic and community partners, and parenting and child behavior outcomes. Two hundred eighty caregivers of children aged 2-5 years participated in the CPP in seven community sites. Measures of parenting and child behavior were administered at pre-intervention, post-intervention, and 3-month follow-up. Caregivers reported broad improvements in parenting and child behavior from pre-intervention through 3-month follow-up. No effects were found for attendance, COVID-19 exposure, and remote versus in-person delivery. The CPP was successfully implemented in a community setting and demonstrated significant changes in parenting and child behavior., (© 2025 Wiley Periodicals LLC.)

As the COVID-19 pandemic progressed, reliable, accessible, and equitable community-based testing strategies were sought that did not flood already overburdened hospitals and emergency departments. In Hamilton County, Ohio, home to ~800 000 people across urban, suburban, and rural areas, we sought to develop and optimize an accessible, equitable county-wide COVID-19 testing program. Using Coronavirus Aid, Relief, and Economic Security Act funding, multidisciplinary, multiorganization partners created the test and protect program to deliver safe, reliable testing in neighborhoods and organizations needing it most. Our approach involved: (1) use of geospatial analytics to identify testing locations positioned to optimize access; (2) community engagement to ensure sites were in trusted places; and (3) tracking of data over time to facilitate ongoing improvement. Between August 2020 and December 2021, more than 65 000 tests were completed for nearly 46 000 individuals at community-based testing sites. These methods could have application beyond COVID-19 and our region., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Rationale: Identifying the root causes of racial disparities in childhood asthma is critical for health equity. Objectives: To determine whether the racist policy of redlining in the 1930s led to present-day disparities in childhood asthma by increasing community-level poverty and decreasing neighborhood socioeconomic position (SEP). Methods: We categorized census tracts at the birth address of participants from the Children's Respiratory and Environmental Workgroup birth cohort consortium into categories A, B, C, and D as defined by the Home Owners Loan Corporation, with D being the highest perceived risk. Surrogates of present-day neighborhood-level SEP were determined for each tract, including the percentage of low-income households, the CDC's Social Vulnerability Index, and other tract-level variables. We performed causal mediation analysis, which, under the assumption of no unmeasured confounding, estimates the direct and mediated pathways by which redlining may cause asthma disparities through tract-level mediators adjusting for individual-level covariates. Measurements and Main Results: Of 4,849 children, the cumulative incidence of asthma through age 11 was 26.6%, and 13.2% resided in census tracts with a Home Owners Loan Corporation grade of D. In mediation analyses, residing in Grade-D tracts (adjusted odds ratio = 1.03 [95% confidence interval = 1.01, 1.05]) was significantly associated with childhood asthma, with 79% of this increased risk mediated by percentage of low-income households; results were similar for the Social Vulnerability Index and other tract-level variables. Conclusions: The historical structural racist policy of redlining led to present-day asthma disparities in part through decreased neighborhood SEP. Policies aimed at reversing the effects of structural racism should be considered to create more just, equitable, and healthy communities.

Current methods for identifying temporal windows of effect for time-varying exposures in omics settings can control false discovery rates at the biomarker-level but cannot efficiently screen for timing-specific effects in high dimensions. Current approaches leverage separate models for site screening and identification of susceptible time windows, which miss associations that vary over time. We introduce the epigenome-wide distributed lag model (EWDLM), a novel approach that combines traditional false discovery rate methods with the distributed lag model (DLM) to screen for timing-specific effects in high dimensional settings. This is accomplished by marginalizing DLM effect estimates over time and correcting for multiple comparisons. In a simulation investigating timing-specific effects of ambient air pollution during pregnancy on DNA methylation across the epigenome at age 12 years, EWDLM achieved an increased sensitivity for associations limited to specific periods of time compared to traditional two-stage approaches. In a real-world EWDLM analysis, 353 CpG sites at which DNAm measured at age 12 was significantly associated with PM2.5 exposure during pregnancy were identified. EWDLM is a novel method that provides an efficient and sensitive way to screen epigenomic datasets for associations with exposures localized to specific time periods., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Air pollution exposure has been associated with adverse cognitive and mental health outcomes in children, adolescents, and adults, although youth may be particularly susceptible given ongoing brain development. However, the neurodevelopmental mechanisms underlying the associations among air pollution, cognition, and mental health remain unclear. We examined the impact of particulate matter (PM 2.5 ) on resting-state functional connectivity (rsFC) of the default mode network (DMN) and three key attention networks: dorsal attention, ventral attention, and cingulo-opercular. Methods: Longitudinal changes in rsFC within/between networks were assessed from baseline (9-10 years) to the 2-year follow-up (11-12 years) in 10,072 youth ( M ± SD = 9.93 + 0.63 years; 49% female) from the Adolescent Brain Cognitive Development (ABCD ® ) study. Annual ambient PM 2.5 concentrations from the 2016 calendar year were estimated using hybrid ensemble spatiotemporal models. RsFC was estimated using functional neuroimaging. Linear mixed models were used to test associations between PM 2.5 and change in rsFC over time while adjusting for relevant covariates (e.g., age, sex, race/ethnicity, parental education, and family income) and other air pollutants (O 3 , NO 2 ). Results: A PM 2.5 × time interaction was significant for within-network rsFC of the DMN such that higher PM 2.5 concentrations were associated with a smaller increase in rsFC over time. Further, significant PM 2.5 × time interactions were observed for between-network rsFC of the DMN and all three attention networks, with varied directionality. Conclusion: PM 2.5 exposure was associated with alterations in the development and equilibrium of the DMN-a network implicated in self-referential processing-and anticorrelated attention networks, which may impact trajectories of cognitive and mental health symptoms across adolescence.

Background: Socioeconomic status and pollution exposure have been described as risk factors for poor survival in patients with non-small cell lung cancer (NSCLC). However, the relationship between these factors is complex and inadequately studied. This study aimed to evaluate the relationship between environmental and social factors and their impact on survival after NSCLC resection., Methods: A prospective database for all patients with NSCLC who underwent primary resection from 2006 to 2021 was analyzed. Ambient fine particulate matter (air pollution smaller than 2.5 μm [PM 2.5 ]), greenness, and deprivation index (a measure of neighborhood-level material deprivation composed of 6 factors) were linked to individual patients by geocoding their residential address., Results: A total of 661 patients who underwent pulmonary resection for NSCLC were evaluated. Black patients had increased levels of community deprivation compared with White patients; however, there was no difference in PM 2.5 exposure or overall survival between races. Increased PM 2.5 exposure was an independent predictor of worse survival on univariable and multivariable analysis (hazard ratio, 1.06; P  = .003)., Conclusions: Increased PM 2.5 exposure is associated with worse overall survival in resected NSCLC and was a more significant factor than race and material deprivation in this population. Interventions to reduce environmental air pollution could improve lung cancer survival.

Background: While precision medicine algorithms can be used to improve health outcomes, concerns have been raised about racial equity and unintentional harm from encoded biases. In this study, we evaluated the fairness of using common individual- and community-level proxies of pediatric socioeconomic status (SES) such as insurance status and community deprivation index often utilized in precision medicine algorithms., Methods: Using 2012-2021 vital records obtained from the Ohio Department of Health, we geocoded and matched each residential birth address to a census tract to obtain community deprivation index. We then conducted sensitivity and specificity analyses to determine the degree of match between deprivation index, insurance status, and birthing parent education level for all, Black, and White children to assess if there were differences based on race., Results: We found that community deprivation index and insurance status fail to accurately represent individual SES, either alone or in combination. We found that deprivation index had a sensitivity of 61.2% and specificity of 74.1%, while insurance status had a higher sensitivity of 91.6% but lower specificity of 60.1%. Furthermore, these inconsistencies were race-based across all proxies evaluated, with greater sensitivities for Black children but greater specificities for White children., Conclusion: This may explain some of the racial disparities present in precision medicine algorithms that utilize SES proxies. Future studies should examine how to mitigate the biases introduced by using SES proxies, potentially by incorporating additional data on housing conditions., (© 2024. The Author(s).)

Objectives: We sought to create a computational pipeline for attaching geomarkers, contextual or geographic measures that influence or predict health, to electronic health records at scale, including developing a tool for matching addresses to parcels to assess the impact of housing characteristics on pediatric health., Materials and Methods: We created a geomarker pipeline to link residential addresses from hospital admissions at Cincinnati Children's Hospital Medical Center (CCHMC) between July 2016 and June 2022 to place-based data. Linkage methods included by date of admission, geocoding to census tract, street range geocoding, and probabilistic address matching. We assessed 4 methods for probabilistic address matching., Results: We characterized 124 244 hospitalizations experienced by 69 842 children admitted to CCHMC. Of the 55 684 hospitalizations with residential addresses in Hamilton County, Ohio, all were matched to 7 temporal geomarkers, 97% were matched to 79 census tract-level geomarkers and 13 point-level geomarkers, and 75% were matched to 16 parcel-level geomarkers. Parcel-level geomarkers were linked using our exact address matching tool developed using the best-performing linkage method., Discussion: Our multimodal geomarker pipeline provides a reproducible framework for attaching place-based data to health data while maintaining data privacy. This framework can be applied to other populations and in other regions. We also created a tool for address matching that democratizes parcel-level data to advance precision population health efforts., Conclusion: We created an open framework for multimodal geomarker assessment by harmonizing and linking a set of over 100 geomarkers to hospitalization data, enabling assessment of links between geomarkers and hospital admissions., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Introduction: Patients with cystic fibrosis (CF) experience frequent episodes of acute decline in lung function called pulmonary exacerbations (PEx). An existing clinical and place-based precision medicine algorithm that accurately predicts PEx could include racial and ethnic biases in clinical and geospatial training data, leading to unintentional exacerbation of health inequities., Methods: We estimated receiver operating characteristic curves based on predictions from a nonstationary Gaussian stochastic process model for PEx within 3, 6, and 12 months among 26,392 individuals aged 6 years and above (2003-2017) from the US CF Foundation Patient Registry. We screened predictors to identify reasons for discriminatory model performance., Results: The precision medicine algorithm performed worse predicting a PEx among Black patients when compared with White patients or to patients of another race for all three prediction horizons. There was little to no difference in prediction accuracies among Hispanic and non-Hispanic patients for the same prediction horizons. Differences in F508del, smoking households, secondhand smoke exposure, primary and secondary road densities, distance and drive time to the CF center, and average number of clinical evaluations were key factors associated with race., Conclusions: Racial differences in prediction accuracies from our PEx precision medicine algorithm exist. Misclassification of future PEx was attributable to several underlying factors that correspond to race: CF mutation, location where the patient lives, and clinical awareness. Associations of our proxies with race for CF-related health outcomes can lead to systemic racism in data collection and in prediction accuracies from precision medicine algorithms constructed from it., Competing Interests: None., (© The Author(s) 2024.)

Background and Objectives: Population-wide racial inequities in child health outcomes are well documented. Less is known about causal pathways linking inequities and social, economic, and environmental exposures. Here, we sought to estimate the total inequities in population-level hospitalization rates and determine how much is mediated by place-based exposures and community characteristics., Methods: We employed a population-wide, neighborhood-level study that included youth <18 years hospitalized between July 1, 2016 and June 30, 2022. We defined a causal directed acyclic graph a priori to estimate the mediating pathways by which marginalized population composition causes census tract-level hospitalization rates. We used negative binomial regression models to estimate hospitalization rate inequities and how much of these inequities were mediated indirectly through place-based social, economic, and environmental exposures., Results: We analyzed 50 719 hospitalizations experienced by 28 390 patients. We calculated census tract-level hospitalization rates per 1000 children, which ranged from 10.9 to 143.0 (median 45.1; interquartile range 34.5 to 60.1) across included tracts. For every 10% increase in the marginalized population, the tract-level hospitalization rate increased by 6.2% (95% confidence interval: 4.5 to 8.0). After adjustment for tract-level community material deprivation, crime risk, English usage, housing tenure, family composition, hospital access, greenspace, traffic-related air pollution, and housing conditions, no inequity remained (0.2%, 95% confidence interval: -2.2 to 2.7). Results differed when considering subsets of asthma, type 1 diabetes, sickle cell anemia, and psychiatric disorders., Conclusions: Our findings provide additional evidence supporting structural racism as a significant root cause of inequities in child health outcomes, including outcomes at the population level., (Copyright © 2024 by the American Academy of Pediatrics.)

Background: Pediatric hematopoietic stem cell transplantation (HCT) is an intensive medical procedure that places substantial financial and logistical burdens on families and is associated with significant health risks, such as graft-versus-host disease (GVHD), and infections. The influence of the social determinants of health (SDoH) on outcomes following pediatric HCT is understudied. This study aimed to examine whether SDoH predicts outcomes following pediatric HCT., Procedure: Data were collected from 84 children who received HCT (M age  = 5.8 years, SD = 3.7) and their primary caregiver. Detailed demographic information was collected from caregivers at baseline, and child health information was extracted from the electronic medical records. Multivariate logistic regression was used to examine the association between SDoH and health outcomes within a 24-month period following pediatric HCT., Results: After controlling for malignancy as reason for transplant and donor type, lower family income predicted the incidence of chronic GVHD. Neighborhood deprivation, total family income, public health insurance, caregiver relationship status, caregiver educational attainment, and perceived family financial difficulties did not predict acute GVHD or the number of infections., Conclusions: Total family income is a simple family indicator of SDoH that predicts chronic GVHD after pediatric allogeneic HCT. These findings provide further support for the importance of screening of child and family SDoH risks to ensure that fundamental needs can be met to mitigate potential health disparities for up to 2 years following pediatric HCT., (© 2024 Wiley Periodicals LLC.)

Background: Cystic fibrosis (CF) is a genetic disease but is greatly impacted by non-genetic (social/environmental and stochastic) influences. Some people with CF experience rapid decline, a precipitous drop in lung function relative to patient- and/or center-level norms. Those who experience rapid decline in early adulthood, compared to adolescence, typically exhibit less severe clinical disease but greater loss of lung function. The extent to which timing and degree of rapid decline are informed by social and environmental determinants of health (geomarkers) is unknown., Methods: A longitudinal cohort study was performed (24,228 patients, aged 6-21 years) using the U.S. CF Foundation Patient Registry. Geomarkers at the ZIP Code Tabulation Area level measured air pollution/respiratory hazards, greenspace, crime, and socioeconomic deprivation. A composite score quantifying social-environmental adversity was created and used in covariate-adjusted functional principal component analysis, which was applied to cluster longitudinal lung function trajectories., Results: Social-environmental phenotyping yielded three primary phenotypes that corresponded to early, middle, and late timing of peak decline in lung function over age. Geographic differences were related to distinct cultural and socioeconomic regions. Extent of peak decline, estimated as forced expiratory volume in 1 s of % predicted/year, ranged from 2.8 to 4.1 % predicted/year depending on social-environmental adversity. Middle decliners with increased social-environmental adversity experienced rapid decline 14.2 months earlier than their counterparts with lower social-environmental adversity, while timing was similar within other phenotypes. Early and middle decliners experienced mortality peaks during early adolescence and adulthood, respectively., Conclusion: While early decliners had the most severe CF lung disease, middle and late decliners lost more lung function. Higher social-environmental adversity associated with increased risk of rapid decline and mortality during young adulthood among middle decliners. This sub-phenotype may benefit from enhanced lung-function monitoring and personalized secondary environmental health interventions to mitigate chemical and non-chemical stressors., Competing Interests: Declaration of Competing Interest Author RDS serves on the Cystic Fibrosis Foundation Patient Registry Committee. The remaining authors have no conflicts of interest to report.

Background: Despite the rising incidence of lung cancer in patients who never smoked, environmental risk factors such as ambient air pollution in this group are poorly described. Our objective was to identify the relationship of environmental exposures with lung cancer in patients who never smoked., Methods: A prospectively collected database was reviewed for all patients with non-small cell lung carcinoma (NSCLC) who underwent resection from 2006 to 2021. Environmental exposures were estimated using the geocoded home address of patients. Logistic regression was used to determine the association of clinical and environmental variables with smoking status. Kaplan-Meier and Cox proportional hazards analyses were used to assess survival., Results: A total of 665 patients underwent resection for NSCLC, of which 67 (10.1%) were patients who never smoked and 598 (89.9%) were current/former smokers. Patients who never smoked were more likely of white race (p = 0.001) and had well-differentiated tumors with carcinoid or adenocarcinoma histology (p < 0.001). Environmental exposures were similar between groups, but patients who never smoked had less community material deprivation (p = 0.002) measured by household income, education, health insurance, and vacancies. They had improved overall survival (p = 0.012) but equivalent cancer recurrence (p = 0.818) as those who smoked. In univariable Cox analyses, fine particulate matter (HR: 1.447 [95% CI 1.197-1.750], p < 0.001), distance to nearest major roadway (HR: 1.067 [1.024-1.111], p = 0.002), and greenspace (HR: 0.253 [0.087-0.737], p = 0.012) were associated with overall survival in patients who never smoked., Conclusions: Lung cancer patients who never smoked have unique clinical and pathologic characteristics, including higher socioeconomic status. Interventions to reduce environmental exposures may improve lung cancer survival in this population., (© 2023. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Objective: To describe racial inequities in pediatric inflammatory bowel disease care and explore potential drivers., Methods: We undertook a single-center, comparative cohort study of newly diagnosed Black and non-Hispanic White patients with inflammatory bowel disease, aged <21 years, from January 2013 through 2020. Primary outcome was corticosteroid-free remission (CSFR) at 1 year. Other longitudinal outcomes included sustained CSFR, time to anti-tumor necrosis factor therapy, and evaluation of health service utilization., Results: Among 519 children (89% White, 11% Black), 73% presented with Crohn's disease and 27% with ulcerative colitis. Disease phenotype did not differ by race. More patients from Black families had public insurance (58% vs 30%, P < .001). Black patients were less likely to achieve CSFR 1-year post diagnosis (OR: 0.52, 95% CI:0.3-0.9) and less likely to achieve sustained CSFR (OR: 0.48, 95% CI: 0.25-0.92). When adjusted by insurance type, differences by race to 1-year CSFR were no longer significant (aOR: 0.58; 95% CI: 0.33, 1.04; P = .07). Black patients were more likely to transition from remission to a worsened state, and less likely to transition to remission. We found no differences in biologic therapy utilization or surgical outcomes by race. Black patients had fewer gastroenterology clinic visits and 2-fold increased odds for emergency department visits., Conclusions: We observed no differences by race in phenotypic presentation and medication usage. Black patients had half the odds of achieving clinical remission, but a degree of this was mediated by insurance status. Understanding the cause of such differences will require further exploration of social determinants of health., Competing Interests: Declaration of Competing Interest The authors declare no conflicts of interest., (Published by Elsevier Inc.)

Place-based exposures, termed "geomarkers", are powerful determinants of health but are often understudied because of a lack of open data and integration tools. Existing DeGAUSS (Decentralized Geomarker Assessment for Multisite Studies) software has been successfully implemented in multi-site studies, ensuring reproducibility and protection of health information. However, DeGAUSS relies on transporting geomarker data, which is not feasible for high-resolution spatiotemporal data too large to store locally or download over the internet. We expanded the DeGAUSS framework for high-resolution spatiotemporal geomarkers. Our approach stores data subsets based on coarsened location and year in an online repository, and appropriate subsets are downloaded to complete exposure assessment locally using exact date and location. We created and validated two free and open-source DeGAUSS containers for estimation of high-resolution, daily ambient air pollutant exposures, transforming published exposure assessment models into computable exposures for geomarker assessment at scale., (©2023 AMIA - All rights reserved.)

Background: Disparities in pediatric injury have been widely documented and are driven, in part, by differential exposures to social determinants of health (SDH). Here, we hypothesized that neighborhood socioeconomic deprivation and specific sociodemographic characteristics would be associated with interpersonal violence-related injury admission., Methods: We conducted a retrospective cohort study of all patients ≤16 years, residing in Hamilton County, admitted to our level 1 pediatric trauma center. Residential addresses were geocoded to link admissions with a census tract-level socioeconomic deprivation index. Admissions were categorized as resulting from interpersonal violence or not - based on a mechanism of injury (MOI) of abuse or assault. The percentage of interpersonal violence-related injury admissions was compared across patient demographics and neighborhood deprivation index tertiles. These factors were then evaluated with multivariable regression analysis., Results: Interpersonal violence accounted for 6.2% (394 of 6324) of all injury-related admissions. Interpersonal violence-related injury admission was associated with older age, male sex, Black race, public insurance, and living in tertiles of census tracts with higher socioeconomic deprivation. Those living in the most deprived tertile experienced 62.2% of all interpersonal violence-related injury admissions but only 36.9% of non-violence related injury admissions (p < 0.001). After adjustment, insurance and neighborhood deprivation accounted for much of the increase in interpersonal violence-related admissions for Black compared to White children., Conclusions: Children from higher deprivation neighborhoods, who are also disproportionately Black and publicly insured, experience a higher burden of interpersonal violence-related injury admissions. Level of evidence Level III., Competing Interests: Declaration of Competing Interest The authors have no conflicts of interest relevant to this article to disclose., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Background: Racial disparities in childhood asthma outcomes result from a complex interplay of individual- and neighborhood-level factors., Objectives: We sought to examine racial disparities in asthma-related emergency department (ED) visits between African American (AA) and European American (EA) children., Methods: This is a retrospective study of patients younger than 18 years who visited the ED at Cincinnati Children's for asthma from 2009 to 2018. The outcome was number of ED visits during a year. We assessed 11 social, economic, and environmental variables. Mediation and mixed-effects analyses were used to assess relationships between race, mediators, and number of ED visits., Results: A total of 31,114 children (46.1% AA, 53.9% EA) had 186,779 asthma-related ED visits. AA children had more visits per year than EA children (2.23 vs 2.15; P < .001). Medicaid insurance was associated with a 7% increase in rate of ED visits compared with commercial insurance (1.07; 95% CI, 1.03-1.1). Neighborhood socioeconomic deprivation was associated with an increased rate of ED visits in AA but not in EA children. Area-level particulate matter with diameter less than 2.5 μm, pollen, and outdoor mold were associated with an increased rate of ED visits for both AA and EA children (all P < .001). Associations between race and number of ED visits were mediated by insurance, area-level deprivation, particulate matter with diameter less than 2.5 μm, and outdoor mold (all P < .001), altogether accounting for 55% of the effect of race on ED visits. Race was not associated with number of ED visits (P = .796) after accounting for mediators., Conclusions: Racial disparities in asthma-related ED visits are mediated by social, economic, and environmental factors, which may be amenable to interventions aimed at improving outcomes and eliminating inequities., (Copyright © 2022 American Academy of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.)

Accumulating data suggest that air pollution increases the risk of internalizing psychopathology, including anxiety and depressive disorders. Moreover, the link between air pollution and poor mental health may relate to neurostructural and neurofunctional changes. We systematically reviewed the MEDLINE database in September 2021 for original articles reporting effects of air pollution on 1) internalizing symptoms and behaviors (anxiety or depression) and 2) frontolimbic brain regions (i.e., hippocampus, amygdala, prefrontal cortex). One hundred and eleven articles on mental health (76% human, 24% animals) and 92 on brain structure and function (11% human, 86% animals) were identified. For literature search 1, the most common pollutants examined were PM 2.5 (64.9%), NO 2 (37.8%), and PM 10 (33.3%). For literature search 2, the most common pollutants examined were PM 2.5 (32.6%), O 3 (26.1%) and Diesel Exhaust Particles (DEP) (26.1%). The majority of studies (73%) reported higher internalizing symptoms and behaviors with higher air pollution exposure. Air pollution was consistently associated (95% of articles reported significant findings) with neurostructural and neurofunctional effects (e.g., increased inflammation and oxidative stress, changes to neurotransmitters and neuromodulators and their metabolites) within multiple brain regions (24% of articles), or within the hippocampus (66%), PFC (7%), and amygdala (1%). For both literature searches, the most studied exposure time frames were adulthood (48% and 59% for literature searches 1 and 2, respectively) and the prenatal period (26% and 27% for literature searches 1 and 2, respectively). Forty-three percent and 29% of studies assessed more than one exposure window in literature search 1 and 2, respectively. The extant literature suggests that air pollution is associated with increased depressive and anxiety symptoms and behaviors, and alterations in brain regions implicated in risk of psychopathology. However, there are several gaps in the literature, including: limited studies examining the neural consequences of air pollution in humans. Further, a comprehensive developmental approach is needed to examine windows of susceptibility to exposure and track the emergence of psychopathology following air pollution exposure., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Exposure to particulate matter with an aerodynamic diameter smaller than 2.5 microns (PM 2.5 ) can affect birth outcomes through physiological pathways such as inflammation. One potential way PM 2.5 affects physiology could be through altering DNA methylation (DNAm). Considering that exposures during specific windows of gestation may have unique effects on DNAm, we hypothesized a timing-specific association between PM 2.5 exposure during pregnancy and DNAm in the neonatal epithelial-cell epigenome., Methods: After collecting salivary samples from a cohort of 91 neonates, DNAm was assessed at over 850,000 cytosine-guanine dinucleotide (CpG) methylation sites on the epigenome using the MethylationEPIC array. Daily ambient PM 2.5 concentrations were estimated based on the mother's address of primary residence during pregnancy. PM 2.5 was averaged over the first two trimesters, separately and combined, and tested for association with DNAm through an epigenome-wide association (EWA) analysis. For each EWA, false discovery rate (FDR)-corrected P < 0.05 constituted a significant finding and every CpG site with uncorrected P < 0.0001 was selected to undergo pathway and network analysis to identify molecular functions enriched by them., Results: Our analysis showed that cg18705808 was associated with the combined average of PM 2.5 . Pathway and network analysis revealed little similarity between the first two trimesters. Previous studies reported that TMEM184A , the gene regulated by cg18705808, has a putative role in inflammatory pathways., Conclusions: The differences in pathway and network analyses could potentially indicate trimester-specific effects of PM 2.5 on DNAm. Further analysis with greater temporal resolution would be valuable to fully characterize the effect of PM 2.5 on DNAm and child development., Competing Interests: The authors declare that they have no conflicts of interest with regard to the content of this report. The results reported herein correspond to specific aims of grant R56MD013006 to investigator A.T.F. and K.B. from the National Institute of Minority Health and Health Disparities. T.B.M. was supported by the National Institutes of Health (NIH) R01 HL132344 and R01 HG011411 grants. This work was also supported by grants from the Cincinnati Children’s Hospital Medical Center including Academic Research Committee and Trustee awards to K.B. and A.T.F. Sponsorships or competing interests that may be relevant to content are disclosed at the end of the article., (Copyright © 2022 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of The Environmental Epidemiology. All rights reserved.)

Background: Disparities in pediatric injury are widely documented and partly driven by differential exposures to social determinants of health (SDH). Here, we examine associations between neighborhood-level SDH and pediatric firearm-related injury admissions as a step to defining specific targets for interventions to prevent injury., Methods: We conducted a retrospective review of patients 16 years or younger admitted to our Level I pediatric trauma center (2010-2019) after a firearm-related injury. We extracted patients' demographic characteristics and intent of injury. We geocoded home addresses to enable quantification of injury-related admissions at the neighborhood (census tract) level. Our population-level exposure variable was a socioeconomic deprivation index for each census tract., Results: Of 15,686 injury-related admissions, 140 were for firearm-related injuries (median age, 14 years; interquartile range, 11-15 years). Patients with firearm-related injuries were 75% male and 64% Black; 66% had public insurance. Nearly half (47%) of firearm-related injuries were a result of assault, 32% were unintentional, and 6% were self-inflicted; 9% died. At the neighborhood level, the distribution of firearm-related injuries significantly differed by deprivation quintile ( p < 0.05). Children from the highest deprivation quintile experienced 25% of injuries of all types, 57% of firearm-related injuries, and 70% of all firearm-related injuries from assault. They had an overall risk of firearm-related injury 30 times that of children from the lowest deprivation quintile., Conclusion: Increased neighborhood socioeconomic deprivation is associated with more firearm-related injuries requiring hospitalization, at rates far higher than injury-related admissions overall. Addressing neighborhood-level SDH may help prevent pediatric firearm-related injury., Level of Evidence: Prognostic and epidemiological, Level III., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)

One in 10 people die awaiting transplantation from donor shortage. Only half of Americans register as organ donors. In this cross-sectional study, we evaluated population-level associations of neighborhood socioeconomic deprivation and racial segregation on organ donor registration rates. We analyzed state identification card demographic and organ donor registration data from 5 states to estimate the association between a neighborhood socioeconomic deprivation index (range [0, 1]; higher values indicate more deprivation) and a racial index of concentration at the extreme (ICE) (range [-1, 1]; lower values indicate predominantly black neighborhoods, higher values indicate predominantly white neighborhoods) on organ donor registration rates within a specified geography (census tract or ZIP code tabulation area [ZCTA]). Among 26 720 738 registrants, 32% of the sample were registered organ donors. At the census tract level, with each 0.1 decrease in the deprivation index, the organ donor registration rate increased by 6.8% (95% confidence interval [CI]: 6.6%, 7.0%). With each 0.1 increase in the racial ICE, the rate increased by 1.5% (95% CI: 1.5%, 1.6%). These associations held true at the ZCTA level. Areas with less socioeconomic deprivation and a higher concentration of white residents have higher organ donor registration rates. Public health initiatives should consider neighborhood context and novel data sources in designing optimal intervention strategies., (© 2020 The American Society of Transplantation and the American Society of Transplant Surgeons.)

Background: Despite steady gains in life expectancy, individuals with cystic fibrosis (CF) lung disease still experience rapid pulmonary decline throughout their clinical course, which can ultimately end in respiratory failure. Point-of-care tools for accurate and timely information regarding the risk of rapid decline is essential for clinical decision support., Objective: This study aims to translate a novel algorithm for earlier, more accurate prediction of rapid lung function decline in patients with CF into an interactive web-based application that can be integrated within electronic health record systems, via collaborative development with clinicians., Methods: Longitudinal clinical history, lung function measurements, and time-invariant characteristics were obtained for 30,879 patients with CF who were followed in the US Cystic Fibrosis Foundation Patient Registry (2003-2015). We iteratively developed the application using the R Shiny framework and by conducting a qualitative study with care provider focus groups (N=17)., Results: A clinical conceptual model and 4 themes were identified through coded feedback from application users: (1) ambiguity in rapid decline, (2) clinical utility, (3) clinical significance, and (4) specific suggested revisions. These themes were used to revise our application to the currently released version, available online for exploration. This study has advanced the application's potential prognostic utility for monitoring individuals with CF lung disease. Further application development will incorporate additional clinical characteristics requested by the users and also a more modular layout that can be useful for care provider and family interactions., Conclusions: Our framework for creating an interactive and visual analytics platform enables generalized development of applications to synthesize, model, and translate electronic health data, thereby enhancing clinical decision support and improving care and health outcomes for chronic diseases and disorders. A prospective implementation study is necessary to evaluate this tool's effectiveness regarding increased communication, enhanced shared decision-making, and improved clinical outcomes for patients with CF., (©Christopher Wolfe, Teresa Pestian, Emrah Gecili, Weiji Su, Ruth H Keogh, John P Pestian, Michael Seid, Peter J Diggle, Assem Ziady, John Paul Clancy, Daniel H Grossoehme, Rhonda D Szczesniak, Cole Brokamp. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 16.12.2020.)

Background: Neighborhood socioeconomic deprivation is associated with adverse health outcomes. We sought to determine if neighborhood socioeconomic deprivation was associated with adherence to immunosuppressive medications after liver transplantation., Methods: We conducted a secondary analysis of a multicenter, prospective cohort of children enrolled in the medication adherence in children who had a liver transplant study (enrollment 2010-2013). Participants (N = 271) received a liver transplant ≥1 year before enrollment and were subsequently treated with tacrolimus. The primary exposure, connected to geocoded participant home addresses, was a neighborhood socioeconomic deprivation index (range 0-1, higher indicates more deprivation). The primary outcome was the medication level variability index (MLVI), a surrogate measure of adherence to immunosuppression in pediatric liver transplant recipients. Higher MLVI indicates worse adherence behavior; values ≥2.5 are predictive of late allograft rejection., Results: There was a 5% increase in MLVI for each 0.1 increase in deprivation index (95% confidence interval, -1% to 11%; P = 0.08). Roughly 24% of participants from the most deprived quartile had an MLVI ≥2.5 compared with 12% in the remaining 3 quartiles (P = 0.018). Black children were more likely to have high MLVI even after adjusting for deprivation (adjusted odds ratio 4.0 95% confidence interval, 1.7-10.6)., Conclusions: This is the first study to evaluate associations between neighborhood socioeconomic deprivation and an objective surrogate measure of medication adherence in children posttransplant. These findings suggest that neighborhood context may be an important consideration when assessing adherence. Differential rates of medication adherence may partly explain links between neighborhood factors and adverse health outcomes following pediatric liver transplantation.

Background: The British Thoracic Society (BTS) guideline for pediatric community-acquired pneumonia (CAP) outlines severity criteria to guide clinical decision-making. Our objective was to examine the predictive performance of the criteria on the need for hospitalization (NFH) and disposition., Methods: This was a retrospective cohort study of children 3 months-18 years of age diagnosed with CAP in an urban, pediatric emergency department (ED) in the United States from September 2014 to August 2015. Children with chronic medical conditions, recent ED visits, and ED transfers were excluded. The main outcomes were interventions or diagnoses that necessitate hospitalization (ie, NFH) and disposition (eg, admit vs. discharge). Test characteristics, stratified by age, were calculated for each outcome., Results: Of 518 eligible children, 56.6% (n = 293) were discharged from the ED with 372 children meeting at least 1 BTS criterion. Overall BTS criteria were specific but not sensitive for NFH nor for disposition. For children <1 year of age sensitive criteria included not feeding and temperature for NFH and tachycardia, cyanosis and not feeding for disposition. For children ≥1 year of age, tachycardia had a sensitivity of >0.60 for both outcomes. The areas under the receiver operator characteristic curves for predicting any BTS criteria was 0.57 for NFH and 0.84 for disposition., Conclusions: The BTS CAP severity criteria had fair to excellent ability to predict NFH and disposition, respectively. Although specific, the low sensitivity and poor discriminatory ability for NFH of these criteria suggest a need for improved prognostic tools for children with CAP.

Despite reported health benefits of urban greenspace (gs), the epidemiological evidence is less clear for allergic disease. To address a limitation of previous research, we examined the associations of medium- and high-resolution residential gs measures and tree and/or grass canopies with allergic outcomes for children enrolled in the longitudinal cincinnati childhood allergy and air pollution study (ccaaps). We estimated residential gs based on 400 m radial buffers around participant addresses (n = 478) using the normalized differential vegetation index (ndvi) and land cover-derived urban greenspace (ugs) (tree and grass coverage, combined and separate) at 30 m and 1.5-2.5 m resolution, respectively. Associations between outdoor aeroallergen sensitization and allergic rhinitis at age 7 and residential gs measures at different exposure windows were examined using multivariable logistic regression models. A 10% increase in ugs-derived grass coverage was associated with an increased risk of sensitization to grass pollens (adjusted odds ratio [aor]: 1.27; 95% confidence interval = 1.02-1.58). For each 10% increase in ugs-derived tree canopy coverage, nonstatistically significant decreased odds were found for grass pollen sensitization, tree pollen sensitization, and sensitization to either (aor range = 0.87-0.94). Results similar in magnitude to ugs-tree canopy coverage were detected for ndvi and allergic sensitizations. High-resolution (down to 1.5 m) gs measures of grass- and tree-covered areas showed associations in opposite directions for different allergy outcomes. These data suggest that measures strongly correlated with tree canopy (e.g., ndvi) may be insufficient to detect health effects associated with proximity to different types of vegetation or help elucidate mechanisms related to specific gs exposure pathways., (Published by Elsevier B.V.)

Epidemiologic studies have found air pollution to be causally linked to respiratory health including the exacerbation and development of childhood asthma. Accurately characterizing exposure is paramount in these studies to ensure valid estimates of health effects. Here, we provide a brief overview of the evolution of air pollution exposure assessment ranging from the use of ground-based, single-site air monitoring stations for population-level estimates to recent advances in spatiotemporal models, which use advanced machine learning algorithms and satellite-based data to accurately estimate individual-level daily exposures at high spatial resolutions. In addition, we review recent advances in sensor technology that enable the use of personal monitoring in epidemiologic studies, long-considered the "holy grail" of air pollution exposure assessment. Finally, we highlight key advantages and uses of each approach including the generalizability and public health relevance of air pollution models and the accuracy of personal monitors that are useful to guide personalized prevention strategies. Investigators and clinicians interested in the effects of air pollution on allergic disease and asthma should carefully consider the pros and cons of each approach to guide their application in research and practice., (Copyright © 2019 American Academy of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.)

Objective: To assess the relationship between greenspace exposure and childhood internalizing and externalizing behaviors., Study Design: We analyzed data from the Cincinnati Childhood Allergy and Air Pollution Study, an ongoing prospective birth cohort. Greenspace exposure was estimated based on children's addresses using normalized difference vegetation index (NDVI) images. Neurobehavioral outcomes were assessed using the Behavioral Assessment System for Children, Second Edition. Regression models adjusted for neighborhood deprivation, maternal education, race, and sex assessed the risk for problematic internalizing and externalizing behaviors at residential greenspace buffers of 200, 400, and 800 m., Results: There were 562 and 313 children in our age 7- and 12-year analyses, respectively. At age 7 years, a 0.1-unit increase in NDVI was associated with decreased conduct scores (β = -1.10, 95% CI [-2.14, -0.06], 200 m). At age 12 years, a 0.1-unit increase in NDVI was associated with a decrease in anxiety scores (β = -1.83, 95% CI [-3.44, -0.22], 800 m), decreased depression scores (β = -1.36, 95% CI [-2.61, -0.12], 200 m), and decreased somatization scores (β = -1.83, 95% CI [-3.22, -0.44], 200 m)., Conclusions: This study provides evidence that increased exposure to residential greenspace is associated with reduced youth's problematic internal and external behaviors, measured by Behavioral Assessment System for Children, Second Edition, at ages 7 and 12 years. Improved understanding of this mechanism could allow for implementation of neighborhood-level approaches for reducing the risk for childhood behavioral problems., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Purpose: The purpose of the study was to conduct an individual-level analysis of hospital utilization during the first year of life to test the hypothesis that community material deprivation increases health care utilization., Methods: We used a population-based perinatal data repository based on linkage of electronic health records from regional delivery hospitals to subsequent hospital utilization at the region's only dedicated children's hospital. Zero-inflated Poisson and Cox proportional hazards regression models were used to quantify the causal role of a census tract-based deprivation index on the total number, length, and time until hospital utilization during the first year of life., Results: After adjusting for any neonatal intensive care unit admission, chronic complex conditions, race and ethnicity, insurance status, birth season, and very low birth weight, we found that a 10% increase in the deprivation index caused a 1.032-fold increase (95% confidence interval (CI), [1.025-1.040]) in post initial hospitalization length of stay, a 1.011-fold increase (95% CI, [1.002-1.021]) in number of post initial hospital encounters, and 1.022-fold increase (95% CI, [1.009-1.035]) in hazard for hospitalization utilization during the first year of life., Conclusions: Interventions designed to reduce material deprivation and income inequalities could significantly reduce infant hospital utilization., (Copyright © 2018 Elsevier Inc. All rights reserved.)

The clinical course of cystic fibrosis (CF) lung disease is marked by acute drops of lung function, defined clinically as rapid decline. As such, lung function is monitored routinely through pulmonary function testing, producing hundreds of measurements over the lifespan of an individual patient. Point-of-care technologies aimed at improving detection of rapid decline have been limited. Our aim in this early translational study is to develop and translate a predictive algorithm into a prototype prognostic tool for improved detection of rapid decline. The predictive algorithm was developed, validated and checked for 6-month, 1-year, and 2-year forecast accuracies using data on demographic and clinical characteristics from 30 879 patients aged 6 years and older who were followed in the U.S. Cystic Fibrosis Foundation Patient Registry from 2003 to 2015. Predictions of rapid decline based on the algorithm were compared to a detection algorithm currently being used at a CF center with 212 patients who received care between 2012-2017. The algorithm was translated into a prototype web application using RShiny, which resulted from an iterative development and refinement based on clinician feedback. The study showed that the algorithm had excellent predictive accuracy and earlier detection of rapid decline, compared to the current approach, and yielded a prototype platform with the potential to serve as a viable point-of-care tool. Future work includes implementation of this clinical prototype, which will be evaluated prospectively under real-world settings, with the aim of improving the pre-visit planning process for CF point of care. Likely extensions to other point-of-care settings are discussed.

Background: The Pediatric Infectious Diseases Society (PIDS)-Infectious Diseases Society of America (IDSA) guideline for community-acquired pneumonia (CAP) recommends intensive care unit (ICU) admission or continuous monitoring for children meeting severity criteria. Our objective was to validate these criteria., Methods: This was a retrospective cohort study of children aged 3 months-18 years diagnosed with CAP in a pediatric emergency department (ED) from September 2014 through August 2015. Children with chronic conditions and recent ED visits were excluded. The primary predictor was the PIDS-IDSA severity criteria. Outcomes included disposition, and interventions and diagnoses that necessitated hospitalization (ie, need for hospitalization [NFH])., Results: Of 518 children, 56.6% were discharged; 54.3% of discharged patients and 80.8% of those hospitalized for less than 24 hours were classified as severe. Of those admitted, 10.7% did not meet severity criteria; 69.5% met PIDS-IDSA severity criteria. Of those children, 73.1% did not demonstrate NFH. The areas under the receiver operator characteristic curves (AUC) for PIDS-IDSA major criteria were 0.63 and 0.51 for predicting disposition and NFH, respectively. For PIDS-IDSA minor criteria, the AUC was 0.81 and 0.56 for predicting disposition and NFH, respectively. The sensitivity, specificity, and likelihood ratios (LR)+ and LR- of the PIDS-IDSA criteria were 89%, 46%, 1.65, and 0.23 for disposition and 95%, 16%, 1.13, and 0.31 for NFH., Conclusions: More than half of children classified as severe by PIDS-IDSA criteria were not hospitalized. The PIDS-IDSA CAP severity criteria have only fair ability to predict the need for hospitalization. New predictive tools specifically for children are required to improve clinical decision making.

In localities with combined sewer systems, combined sewer overflow (CSO) events frequently occur following high precipitation and can result in the release of untreated sewage and industrial wastewater into surface waters. We hypothesized that either direct contact with or proximity to aerosolized CSO effluent would increase the risk for childhood emergency department (ED) visits for asthma, gastrointestinal (GI) illnesses, and skin and soft tissue infections (SSTIs) in Cincinnati, OH, USA. ED visits for 2010-2014 due to GI diseases, asthma, and SSTIs were extracted from the Cincinnati Children's Hospital Medical Center electronic health records. The location and timing of CSO events were obtained from the Metropolitan Sewer District (MSD) of Greater Cincinnati. ED visits with a residential address within 500m of a CSO site were used in a case-control crossover study with two bi-directional control periods. Conditional logistic regression models were used to estimate the risk of an ED visit associated with a CSO event at lag periods of 0 to 7days. Statistically significant elevated risks for GI-related ED visits was observed two (OR: 1.16 [95% CI 1.04,1.30]) days after CSO events. CSO events were not significantly associated with asthma- or SSTI-related ED visits, but show similar trends. Our findings suggest an increased risk for GI-related ED visits following CSO events among children who reside near CSO sites., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Slowing cystic fibrosis (CF) lung disease progression is crucial to survival, but point-of-care technologies aimed at early detection-and possibly prevention-of rapid lung function decline are limited. This proof-of-principle study leverages a rich national patient registry and follow-up data on a local CF cohort to build an algorithm and prototype prognostic tool aimed at early detection of rapid lung function decline. The algorithm was developed using a novel longitudinal analysis of lung function (measured as forced expiratory volume in 1 s of % predicted, FEV1). Covariates included clinical and demographic characteristics selected from the registry based on information criterion. Preliminary assessment of algorithm performance suggested excellent predictive accuracy and earlier detection of rapid decline than standard of care being applied at a local center. Graphical displays were presented and evaluated for clinical utility. Predictions from the algorithms and chosen graphical displays were translated into a prototype web application using RShiny and underwent iterative development based on clinician feedback. This paper suggests that the algorithm and its translation could offer a means for earlier detection and treatment of rapid decline, providing clinicians with a viable point-of-care technology to intervene prior to irreversible lung damage.

Exposure assessment for elemental components of particulate matter (PM) using land use modeling is a complex problem due to the high spatial and temporal variations in pollutant concentrations at the local scale. Land use regression (LUR) models may fail to capture complex interactions and non-linear relationships between pollutant concentrations and land use variables. The increasing availability of big spatial data and machine learning methods present an opportunity for improvement in PM exposure assessment models. In this manuscript, our objective was to develop a novel land use random forest (LURF) model and compare its accuracy and precision to a LUR model for elemental components of PM in the urban city of Cincinnati, Ohio. PM smaller than 2.5 μm (PM2.5) and eleven elemental components were measured at 24 sampling stations from the Cincinnati Childhood Allergy and Air Pollution Study (CCAAPS). Over 50 different predictors associated with transportation, physical features, community socioeconomic characteristics, greenspace, land cover, and emission point sources were used to construct LUR and LURF models. Cross validation was used to quantify and compare model performance. LURF and LUR models were created for aluminum (Al), copper (Cu), iron (Fe), potassium (K), manganese (Mn), nickel (Ni), lead (Pb), sulfur (S), silicon (Si), vanadium (V), zinc (Zn), and total PM2.5 in the CCAAPS study area. LURF utilized a more diverse and greater number of predictors than LUR and LURF models for Al, K, Mn, Pb, Si, Zn, TRAP, and PM2.5 all showed a decrease in fractional predictive error of at least 5% compared to their LUR models. LURF models for Al, Cu, Fe, K, Mn, Pb, Si, Zn, TRAP, and PM2.5 all had a cross validated fractional predictive error less than 30%. Furthermore, LUR models showed a differential exposure assessment bias and had a higher prediction error variance. Random forest and other machine learning methods may provide more accurate exposure assessment.

The complex mixture of chemicals and elements that constitute particulate matter (PM*) varies by season and geographic location because source contributors differ over time and place. The composition of PM having an aerodynamic diameter < 2.5 μm (PM2.5) is hypothesized to be responsible, in part, for its toxicity. Epidemiologic studies have identified specific components and sources of PM2.5 that are associated with adverse health outcomes. The majority of these studies use measures of outdoor concentrations obtained from one or a few central monitoring sites as a surrogate for measures of personal exposure. Personal PM2.5 (and its elemental composition), however, may be different from the PM2.5 measured at stationary outdoor sites. The objectives of this study were (1) to describe the relationships between the concentrations of various elements in indoor, outdoor, and personal PM2.5 samples, (2) to identify groups of individuals with similar exposures to mixtures of elements in personal PM2.5 and to examine personal and home characteristics of these groups, and (3) to evaluate whether concentrations of elements from outdoor PM2.5 samples are appropriate surrogates for personal exposure to PM2.5 and its elements and whether indoor PM2.5 concentrations and information about home characteristics improve the prediction of personal exposure. The objectives of the study were addressed using data collected as part of the Relationships of Indoor, Outdoor, and Personal Air (RIOPA) study. The RIOPA study has previously measured the mass concentrations of PM2.5 and its elemental constituents during 48-hour concurrent indoor, outdoor (directly outside the home), and personal samplings in three urban areas (Los Angeles, California; Houston, Texas; and Elizabeth, New Jersey). The resulting data and information about personal and home characteristics (including air-conditioning use, nearby emission sources, time spent indoors, census-tract geography, air-exchange rates, and other information) for each RIOPA participant were downloaded from the RIOPA study database. We performed three sets of analyses to address the study aims. First, we conducted descriptive analyses to describe the relationships between elemental concentrations in the concurrently gathered indoor, outdoor, and personal air samples. We assessed the correlation between personal exposure and indoor concentrations as well as personal exposure and outdoor concentrations of each element and calculated ratios between them. In addition, we performed principal component analysis (PCA) and calculated principal component scores (PCSs) to examine the heterogeneity of the elemental composition and then tested whether the mixture of elements in indoor, outdoor, and personal PM2.5 was significantly different within each study site and across study sites. Secondly, we performed model-based clustering analysis to group RIOPA participants with similar exposures to mixtures of elements in personal PM2.5. We examined the association between cluster membership and the concentrations of elements in indoor and outdoor PM2.5 samples and personal and home characteristics. Finally, we developed a series of linear regression models and random forest models to examine the association between personal exposure to elements in PM2.5 and (1) outdoor measurements, (2) outdoor and indoor measurements, and (3) outdoor and indoor measurements and home characteristics. As we developed each model, the improvement in prediction of personal exposure when including additional information was assessed. Personal exposures to PM2.5 and to most elements were significantly correlated with both indoor and outdoor concentrations, although concentrations in personal samples frequently exceeded those of indoor and outdoor samples. In general, for most PM2.5 elements indoor concentrations were more highly correlated with personal exposure than were outdoor concentrations. PCA showed that the mixture of elements in indoor, outdoor, and personal PM2.5 varied significantly across sample types within each study site and also across study sites within each sample type. Using model-based clustering, we identified seven clusters of RIOPA participants whose personal PM2.5 samples had similar patterns of elemental composition. Using this approach, subsets of RIOPA participants were identified whose personal exposures to PM2.5 (and its elements) were significantly higher than their indoor and outdoor concentrations (and vice versa). The results of linear and random forest regression models were consistent with our correlation analyses and demonstrated that (1) indoor concentrations were more significantly associated with personal exposure than were outdoor concentrations and (2) participant reports of time spent at their home significantly modified many of the associations between indoor and personal concentrations. In linear regression models, the inclusion of indoor concentrations significantly improved the prediction of personal exposures to Ba, Ca, Cl, Cu, K, Sn, Sr, V, and Zn compared with the use of outdoor elemental concentrations alone. Including additional information on personal and home characteristics improved the prediction for only one element, Pb. Our results support the use of outdoor monitoring sites as surrogates of personal exposure for a limited number of individual elements associated with long-range transport and with a few local or indoor sources. Based on our PCA and clustering analyses, we concluded that the overall elemental composition of PM2.5 obtained at outdoor monitoring sites may not accurately represent the elemental composition of personal PM2.5. Although the data used in these analyses compared outdoor PM2.5 composition collected at the home with indoor and personal samples, our results imply that studies examining the complete elemental composition of PM2.5 should be cautious about using data from central outdoor monitoring sites because of the potential for exposure misclassification. The inclusion of personal and home characteristics only marginally improved the prediction of personal exposure for a small number of elements in PM2.5. We concluded that the additional cost and burden of indoor and personal sampling may be justified for studies examining elements because neither outdoor monitoring nor questionnaire data on home and personal characteristics were able to represent adequately the overall elemental composition of personal PM2.5.