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.

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.

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.

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.

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.

Epigenome-wide association of neonatal methylation and trimester-specific prenatal PM2.5 exposure.

Exposure to particulate matter with an aerodynamic diameter smaller than 2.5 microns (PM2.5) can affect birth outcomes through physiological pathways such as inflammation. One potential way PM2.5 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 PM2.5 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 PM2.5 concentrations were estimated based on the mother's address of primary residence during pregnancy. PM2.5 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 PM2.5. 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 PM2.5 on DNAm. Further analysis with greater temporal resolution would be valuable to fully characterize the effect of PM2.5 on DNAm and child development.

Using Geomarkers and Sociodemographics to Inform Assessment of Caregiver Adversity and Resilience.

BACKGROUND AND OBJECTIVES: A high level of caregiver adverse childhood experiences (ACEs) and/or low resilience is associated with poor outcomes for both caregivers and their children after hospital discharge. It is unknown if sociodemographic or area-based measures (ie, "geomarkers") can inform the assessment of caregiver ACEs or resilience. Our objective was to determine if caregiver ACEs or resilience can be identified by using any combinations of sociodemographic measures, geomarkers, and/or caregiver-reported household characteristics. METHODS: Eligible participants for this cohort study were English-speaking caregivers of children hospitalized on a hospital medicine team. Caregivers completed the ACE questionnaire, Brief Resilience Scale, and strain surveys. Exposures included sociodemographic characteristics available in the electronic health record (EHR), geomarkers tied to a patient's geocoded home address, and household characteristics that are not present in the EHR (eg, income). Primary outcomes were a high caregiver ACE score (≥4) and/or a low BRS Score (<3). RESULTS: Of the 1272 included caregivers, 543 reported high ACE or low resilience, and 63 reported both. We developed the following regression models: sociodemographic variables in EHR (Model 1), EHR sociodemographics and geomarkers (Model 2), and EHR sociodemographics, geomarkers, and additional survey-reported household characteristics (Model 3). The ability of models to identify the presence of caregiver adversity was poor (all areas under receiver operating characteristics curves were <0.65). CONCLUSIONS: Models using EHR data, geomarkers, and household-level characteristics to identify caregiver adversity had limited utility. Directly asking questions to caregivers or integrating risk and strength assessments during pediatric hospitalization may be a better approach to identifying caregiver adversity.

Association of Primary Care Shortage Areas with Adverse Outcomes after Pediatric Liver Transplant.

OBJECTIVE: To characterize associations between living in primary care shortage areas and graft failure/death for children after liver transplantation. STUDY DESIGN: This was an observational study of all pediatric patients (aged <19 years) who received a liver transplant between January 1, 2005, and December 31, 2015 in the US, with follow-up through January 2019 (N = 5964). One hundred ninety-five patients whose home ZIP code could not be matched to primary care shortage area status were excluded. The primary outcome was a composite endpoint of graft failure or death. We used Cox proportional hazards to model the associations between health professional shortage area (HPSA) and graft failure/death. RESULTS: Children living in HPSAs had lower estimated graft survival rates at 10 years compared with those not in HPSAs (76% vs 80%; P < .001). In univariable analysis, residence in an HPSA was associated with a 22% higher hazard of graft failure/death than non-residence in an HPSA (hazard ratio [HR], 1.22; 95% CI, 1.09-1.36; P < .001). Black children from HPSAs had a 67% higher hazard of graft failure/death compared with those not in HPSAs (HR, 1.67; 95% CI, 1.29 to 2.16; P = .006); the effect of HPSA status was less pronounced for White children (HR, 1.11; 95% CI, 0.98-1.27; P = .10). CONCLUSIONS: Children living in primary care shortage areas are at increased risk of graft failure and death after liver transplant, and this risk is particularly salient for Black children. Future work to understand how living in these regions contributes to adverse outcomes may enable teams to mitigate this risk for all children with chronic illness.

Neighborhood Socioeconomic Deprivation and Health Care Utilization of Medically Complex Children.

OBJECTIVES: To assess the association between neighborhood socioeconomic deprivation and health care utilization in a cohort of children with medical complexity (CMC). METHODS: Cross-sectional study of children aged <18 years receiving care in our institution's patient-centered medical home (PCMH) for CMC in 2016 to 2017. Home addresses were assigned to census tracts and a tract-level measure of socioeconomic deprivation (Deprivation Index with range 0-1, higher numbers represent greater deprivation). Health care utilization outcomes included emergency department visits, hospitalizations, inpatient bed days, and missed PCMH clinic appointments. To evaluate the independent association between area-level socioeconomic deprivation and utilization outcomes, multivariable Poisson and linear regression models were used to control for demographic and clinical covariates. RESULTS: The 512 included CMC lived in neighborhoods with varying degrees of socioeconomic deprivation (median 0.32, interquartile range 0.26-0.42, full range 0.12-0.82). There was no association between area-level deprivation and emergency department visits (adjusted risk ratio [aRR] 0.98; 95% confidence interval [CI]: 0.93 to 1.04), hospitalizations (aRR 0.97; 95% CI: 0.92 to 1.01), or inpatient bed-days (aRR 1.00, 95% CI: 0.80 to 1.27). However, there was a 13% relative increase in the missed clinic visit rate for every 0.1 unit increase in Deprivation Index (95% CI: 8%-18%). CONCLUSIONS: A child's socioeconomic context is associated with their adherence to PCMH visits. Our PCMH for CMC includes children living in neighborhoods with a range of socioeconomic deprivation and may blunt effects from harmful social determinants. Incorporating knowledge of the socioeconomic context of where CMC and their families live is crucial to ensure equitable health outcomes.

Pediatric anxiety and daily fine particulate matter: A longitudinal study.

Daily variations in ambient fine particulate matter (PM2.5) could contribute to the morbidity of anxiety disorders in children and adolescents, but has not yet been studied longitudinally at a daily level. We tested this association using repeated weekly measures of anxiety symptom severity in a group of 23 adolescents with generalized anxiety disorder. After estimating ambient PM2.5 concentrations using a validated model, we found that increased concentrations were significantly associated with increased anxiety symptom severity and frequency two, three, and four days later. PM2.5 may be a novel, modifiable exposure that could inform population level interventions to decrease psychiatric morbidity.

Socioeconomic and Racial Disparities in Diabetic Ketoacidosis Admissions in Youth With Type 1 Diabetes.

OBJECTIVE: We sought to determine whether census tract poverty, race, and insurance status were associated with the likelihood and severity of diabetic ketoacidosis (DKA) hospitalization among youth with type 1 diabetes (T1D). METHODS: We conducted a retrospective population-based cohort study using Cincinnati Children's Hospital electronic medical record (EMR) data from January 1, 2011, to December 31, 2017, for T1D patients ≤18 years old. The primary outcome was admission for DKA. Secondary outcomes included DKA severity, defined by initial pH and bicarbonate, and length of stay. Exposures were the poverty rate for the youth's home census tract, parent-reported race, and insurance status. We used multivariable logistic regression to analyze effects on odds of admission. RESULTS: We identified 439 patients with T1D; 152 were hospitalized. The cohort was 48% female, 25% Black, and 36% publicly insured; the median age was 14 years. For every 10% increase in a youth's census tract poverty rate, the adjusted odds of admission increased by 22% (95% CI, 1.03-1.47). Public insurance status was associated with DKA admission (adjusted odds ratio [AOR], 2.71, 95% CI, 1.62-4.55) while race was not. There were no clinically meaningful differences in pH or bicarbonate by census tract poverty, race, or insurance status; however, Black patients experienced differences in care (eg, longer length of stay). CONCLUSION: Youth with T1D living in high poverty areas and on public insurance were significantly more likely to be admitted for DKA. Severity upon presentation was similar across exposures. Understanding contextual mechanisms by which disparities emerge will inform changes aimed at equitably improving care.

Seasonality, mediation and comparison (SMAC) methods to identify influences on lung function decline.

This study develops a comprehensive method to assess seasonal influences on a longitudinal marker and compare estimates between cohorts. The method extends existing approaches by (i) combining a sine-cosine model of seasonality with a specialized covariance function for modeling longitudinal correlation; (ii) performing mediation analysis on a seasonality model. An example dataset and R code are provided. The bundle of methods is referred to as seasonality, mediation and comparison (SMAC). The case study described utilizes lung function as the marker observed on a cystic fibrosis cohort but SMAC can be used to evaluate other markers and in other disease contexts. Key aspects of customization are as follows.•This study introduces a novel seasonality model to fit trajectories of lung function decline and demonstrates how to compare this model to a conventional model in this context.•Steps required for mediation analyses in the seasonality model are shown.•The necessary calculations to compare seasonality models between cohorts, based on estimation coefficients, are derived in the study.

Identifying sensitive windows of airborne lead exposure associated with behavioral outcomes at age 12.

Despite the precipitous decline of airborne lead concentrations following the removal of lead in gasoline, lead is still detectable in ambient air in most urban areas. Few studies, however, have examined the health effects of contemporary airborne lead concentrations in children. METHODS: We estimated monthly air lead exposure among 263 children (Cincinnati Childhood Allergy and Air Pollution Study; Cincinnati, OH; 2001-2005) using temporally scaled predictions from a validated land use model and assessed neurobehavioral outcomes at age 12 years using the parent-completed Behavioral Assessment System for Children, 2nd edition. We used distributed lag models to estimate the effect of airborne lead exposure on behavioral outcomes while adjusting for potential confounding by maternal education, community-level deprivation, blood lead concentrations, greenspace, and traffic related air pollution. RESULTS: We identified sensitive windows during mid- and late childhood for increased anxiety and atypicality scores, whereas sensitive windows for increased aggression and attention problems were identified immediately following birth. The strongest effect was at age 12, where a 1 ng/m3 increase in airborne lead exposure was associated with a 3.1-point (95% confidence interval: 0.4, 5.7) increase in anxiety scores. No sensitive windows were identified for depression, somatization, conduct problems, hyperactivity, or withdrawal behaviors. CONCLUSIONS: We observed associations between exposure to airborne lead concentrations and poor behavioral outcomes at concentrations 10 times lower than the National Ambient Air Quality Standards set by the US Environmental Protection Agency.

Neighborhood socioeconomic deprivation, racial segregation, and organ donation across 5 states.

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.

Seasonal variation of lung function in cystic fibrosis: longitudinal modeling to compare a Midwest US cohort to international populations.

Characterizing seasonal trend in lung function in individuals with chronic lung disease may lead to timelier treatment of acute respiratory symptoms and more precise distinction between seasonal exposures and variability. Limited research has been conducted to assess localized seasonal fluctuation in lung function decline in individuals with cystic fibrosis (CF) in context with routinely collected demographic and clinical data. We conducted a longitudinal cohort study of 253 individuals aged 6-22 years with CF receiving care at a pediatric Midwestern US CF center with median (range) of follow-up time of 4.7 (0-9.95) years, implementing two distinct models to estimate seasonality effects. The outcome, lung function, was measured as percent-predicted of forced expiratory volume in 1 second (FEV1). Both models showed that older age, being male, using Medicaid insurance and having Pseudomonas aeruginosa infection corresponded to accelerated FEV1 decline. A sine wave model for seasonality had better fit to the data, compared to a linear model with categories for seasonality. Compared to international cohorts, seasonal fluctuations occurred earlier and with greater volatility, even after adjustment for ambient temperature. Average lung function peaked in February and dipped in August, and FEV1 fluctuation was 0.81 % predicted (95% CI: 0.52 to 1.1). Adjusting for temperature shifted the peak and dip to March and September, respectively, and decreased FEV1 fluctuation to 0.45 % predicted (95% CI: 0.08 to 0.82). Understanding localized seasonal variation and its impact on lung function may allow researchers to perform precision public health for lung diseases and disorders at the point-of-care level.

Characterization of Arabidopsis thaliana regrowth patterns suggests a trade-off between undamaged fitness and damage tolerance.

Herbivory is a fundamental type of plant-animal interaction that presents substantial selection pressure on plants to replace lost tissues and to prevent subsequent losses in fitness. Apical herbivory, which entails removal or damage to the apical meristem, causes a change in plant architecture by disrupting the balance of hormones produced in part by the apical meristem. Therefore, for an annual semelparous plant, the ability to preserve reproductive success following damage (i.e., to tolerate damage) is largely dependent on the plant's pre-damage investment into fitness and its regrowth pattern following damage. Using multiple regression analyses, we assessed the relationship of developmental and architectural traits of experimentally damaged plants relative to undamaged plants of 33 Arabidopsis thaliana genotypes that display a wide range of undamaged fitness and damage tolerance. Our analyses revealed evidence for an evolutionary bet-hedging strategy within a subset of genotypes to presumably maximize fitness under natural herbivory-genotypes with the greatest seed production when undamaged exhibited a significant reduction in seed yield when damaged, while genotypes with low undamaged seed production were the only genotypes whose seed yield increased when damaged. Patterns of endopolyploidy paralleled those of seed production, such that the increase in whole-plant ploidy by genome re-replication during growth/regrowth contributes to undamaged fitness, damage tolerance, and their trade-off. Overall, this study provides the first large-scale characterization of A. thaliana regrowth patterns and suggests that investment into fitness and endopolyploidy when undamaged may come at a cost to tolerance ability once damaged.