Robust identification of environmental exposures and community characteristics predictive of rapid lung disease progression.

Environmental exposures and community characteristics have been linked to accelerated lung function decline in people with cystic fibrosis (CF), but geomarkers, the measurements of these exposures, have not been comprehensively evaluated in a single study. To determine which geomarkers have the greatest predictive potential for lung function decline and pulmonary exacerbation (PEx), a retrospective longitudinal cohort study was performed using novel Bayesian joint covariate selection methods, which were compared with respect to PEx predictive accuracy. Non-stationary Gaussian linear mixed effects models were fitted to data from 151 CF patients aged 6-20 receiving care at a CF Center in the midwestern US (2007-2017). The outcome was forced expiratory volume in 1 s of percent predicted (FEV1pp). Target functions were used to predict PEx from established criteria. Covariates included 11 routinely collected clinical/demographic characteristics and 45 geomarkers comprising 8 categories. Unique covariate selections via four Bayesian penalized regression models (elastic-net, adaptive lasso, ridge, and lasso) were evaluated at both 95 % and 90 % credible intervals (CIs). Resultant models included one to 6 geomarkers (air temperature, percentage of tertiary roads outside urban areas, percentage of impervious nonroad outside urban areas, fine atmospheric particulate matter, fraction achieving high school graduation, and motor vehicle theft) representing weather, impervious descriptor, air pollution, socioeconomic status, and crime categories. Adaptive lasso had the lowest information criteria. For PEx predictive accuracy, covariate selection from the 95 % CI elastic-net had the highest area under the receiver-operating characteristic curve (mean ± standard deviation; 0.780 ± 0.026) along with the 95 % CI ridge and lasso methods (0.780 ± 0.027). The 95 % CI elastic-net had the highest sensitivity (0.773 ± 0.083) while the 95 % CI adaptive lasso had the highest specificity (0.691 ± 0.087), suggesting the need for different geomarker sets depending on monitoring goals. Surveillance of certain geomarkers embedded in prediction algorithms can be used in real-time warning systems for PEx onset.

The Legacy of Redlining: Increasing Childhood Asthma Disparities Through Neighborhood Poverty.

RATIONALE: Identifying the root causes of racial disparities in childhood asthma is critical for health equity. OBJECTIVES: To determine if the 1930's racist policy of redlining 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 birth of participants from the Children's Respiratory and Environmental Workgroup birth cohort consortium into A, B, C, or D categories as defined by the Home Owners Loan Corporation (HOLC), 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 (SVI), 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 census 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 HOLC grade of D. In mediation analyses, residing in grade D tracts (aOR = 1.03 [95%CI 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 SVI 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.

Racial Fairness of Individual- and Community-Level Proxies of Socioeconomic Status Among Birthing Parent-Child Dyads.

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.

Development of a multimodal geomarker pipeline to assess the impact of social, economic, and environmental factors on pediatric health outcomes.

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.

Particulate Matter Exposure and Default Mode Network Equilibrium During Early Adolescence.

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 (PM2.5) 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 PM2.5 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 PM2.5 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 (O3, NO2). Results: A PM2.5 × time interaction was significant for within-network rsFC of the DMN such that higher PM2.5 concentrations were associated with a smaller increase in rsFC over time. Further, significant PM2.5 × time interactions were observed for between-network rsFC of the DMN and all three attention networks, with varied directionality. Conclusion: PM2.5 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.

Causal Mediation of Neighborhood-Level Pediatric Hospitalization Inequities.

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.

Social determinants of health predict health outcomes following pediatric allogeneic hematopoietic stem cell transplant.

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 (Mage  = 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.

Early-Life Exposure to Air Pollution and Childhood Asthma Cumulative Incidence in the ECHO CREW Consortium.

Importance: Exposure to outdoor air pollution contributes to childhood asthma development, but many studies lack the geographic, racial and ethnic, and socioeconomic diversity to evaluate susceptibility by individual-level and community-level contextual factors. Objective: To examine early life exposure to fine particulate matter (PM2.5) and nitrogen oxide (NO2) air pollution and asthma risk by early and middle childhood, and whether individual and community-level characteristics modify associations between air pollution exposure and asthma. Design, Setting, and Participants: This cohort study included children enrolled in cohorts participating in the Children's Respiratory and Environmental Workgroup consortium. The birth cohorts were located throughout the US, recruited between 1987 and 2007, and followed up through age 11 years. The survival analysis was adjusted for mother's education, parental asthma, smoking during pregnancy, child's race and ethnicity, sex, neighborhood characteristics, and cohort. Statistical analysis was performed from February 2022 to December 2023. Exposure: Early-life exposures to PM2.5 and NO2 according to participants' birth address. Main Outcomes and Measures: Caregiver report of physician-diagnosed asthma through early (age 4 years) and middle (age 11 years) childhood. Results: Among 5279 children included, 1659 (31.4%) were Black, 835 (15.8%) were Hispanic, 2555 (48.4%) where White, and 229 (4.3%) were other race or ethnicity; 2721 (51.5%) were male and 2596 (49.2%) were female; 1305 children (24.7%) had asthma by 11 years of age and 954 (18.1%) had asthma by 4 years of age. Mean values of pollutants over the first 3 years of life were associated with asthma incidence. A 1 IQR increase in NO2 (6.1 µg/m3) was associated with increased asthma incidence among children younger than 5 years (HR, 1.25 [95% CI, 1.03-1.52]) and children younger than 11 years (HR, 1.22 [95% CI, 1.04-1.44]). A 1 IQR increase in PM2.5 (3.4 µg/m3) was associated with increased asthma incidence among children younger than 5 years (HR, 1.31 [95% CI, 1.04-1.66]) and children younger than 11 years (OR, 1.23 [95% CI, 1.01-1.50]). Associations of PM2.5 or NO2 with asthma were increased when mothers had less than a high school diploma, among Black children, in communities with fewer child opportunities, and in census tracts with higher percentage Black population and population density; for example, there was a significantly higher association between PM2.5 and asthma incidence by younger than 5 years of age in Black children (HR, 1.60 [95% CI, 1.15-2.22]) compared with White children (HR, 1.17 [95% CI, 0.90-1.52]). Conclusions and Relevance: In this cohort study, early life air pollution was associated with increased asthma incidence by early and middle childhood, with higher risk among minoritized families living in urban communities characterized by fewer opportunities and resources and multiple environmental coexposures. Reducing asthma risk in the US requires air pollution regulation and reduction combined with greater environmental, educational, and health equity at the community level.

Social-environmental phenotypes of rapid cystic fibrosis lung disease progression in adolescents and young adults living in the United States.

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.

Impact of Environmental Exposures on Lung Cancer in Patients Who Never Smoked.

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.

High Resolution and Spatiotemporal Place-Based Computable Exposures at Scale.

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.

Racial Disparities in Pediatric Inflammatory Bowel Disease Care: Differences in Outcomes and Health Service Utilization Between Black and White Children.

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.

Lung function and secondhand smoke exposure among children with cystic fibrosis: A Bayesian meta-analysis.

BACKGROUND: 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. METHODS: 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 FEV1% predicted). RESULTS: Quantitative synthesis of study estimates indicated that second-hand smoke exposure corresponded to a significant drop in FEV1 (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: I2=61.9% [95% CI: 7.3-84.4%] and p = 0.022 from the frequentist method.) CONCLUSIONS: 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.

Participant engagement to develop report-back materials for personal air monitoring.

Background: Studies that measure environmental exposures in biological samples frequently provide participants their results. In contrast, studies using personal air monitors do not typically provide participants their monitoring results. The objective of this study was to engage adolescents who completed personal air sampling and their caregivers to develop understandable and actionable report-back documents containing the results of their personal air sampling. Methods: Adolescents and their caregivers who previously completed personal air sampling participated in focus groups to guide the development of report-back materials. We conducted thematic analyses of focus group data to guide the design of the report-back document and convened experts in community engagement, reporting study results, and human subjects research to provide feedback. Final revisions to the report-back document were made based on follow-up focus group feedback. Results: Focus groups identified critical components of an air-monitoring report-back document to include an overview of the pollutant being measured, a comparison of individual personal sampling data to the overall study population, a guide to interpreting results, visualization of individual data, and additional information on pollution sources, health risks, and exposure reduction strategies. Participants also indicated their desire to receive study results in an electronic and interactive format. The final report-back document was electronic and included background information, participants' results presented using interactive maps and figures, and additional material regarding pollution sources. Conclusion: Studies using personal air monitoring technology should provide research participants their results in an understandable and meaningful way to empower participants with increased knowledge to guide exposure reduction strategies.

Air pollution exposure and social responsiveness in childhood: The cincinnati combined childhood cohorts.

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 NO2 and PM2.5, 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 NO2 and PM2.5 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 NO2 and PM2.5 and SRS scores remained positive but were no longer statistically significant. Prenatal and first year exposure to NO2 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 NO2 exposure and 0.7 point (95% CI: -0.3, 1.6) per 4.2 ppb increase in NO2 exposure, respectively. For PM2.5, 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 NO2 and PM2.5 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.

Built environment factors predictive of early rapid lung function decline in cystic fibrosis.

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, 2012-2017). We used a stochastic model to predict lung function, measured as forced expiratory volume in 1 s (FEV1 ) 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/m3 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% predicted/year [-0.113, 0.319]). High-risk regional areas of rapid decline and age-related heterogeneity were identified from prediction mapping. CONCLUSION: Traffic-related air pollution exposure is an important predictor of rapid pulmonary decline that, coupled with community-level material deprivation and routinely collected demographic/clinical characteristics, enhance CF prognostication and enable personalized environmental health interventions.

Children from disadvantaged neighborhoods experience disproportionate injury from interpersonal violence.

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.

Racial Fairness in Precision Medicine: Pediatric Asthma Prediction Algorithms.

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.

Use Of Area-Based Socioeconomic Deprivation Indices: A Scoping Review And Qualitative Analysis.

There is considerable interest among researchers, clinicians, and policy makers in understanding the impact of place on health. In this scoping review and qualitative analysis, we sought to assess area-level socioeconomic deprivation indices used in public health and health outcomes research in the US. We conducted a systematic scoping review to identify area-level socioeconomic deprivation indices commonly used in the US since 2015. We then qualitatively compared the indices based on the input-variable domains, data sources, index creation characteristics, index accessibility, the geography over which the index is applied, and the nature of the output measure or measures. We identified fifteen commonly used indices of area-level socioeconomic deprivation. There were notable differences in the characteristics of each index, particularly in how they define socioeconomic deprivation based on input-variable domains, the geography over which they are applied, and their output measures. These characteristics can help guide future index selection and application in clinical care, research, and policy decisions.