BACKGROUND: Host biomarkers predict disease severity in adults with community-acquired pneumonia (CAP). We evaluated the association of the white blood cell (WBC) count, absolute neutrophil count (ANC), C-reactive protein (CRP), and procalcitonin with the development of severe outcomes in children with CAP. METHODS:We performed a prospective cohort study of children 3 months to 18 years of age with CAP in the emergency department. The primary outcome was disease severity: mild (discharged from the hospital), mild-moderate (hospitalized but not moderate-severe or severe), moderate-severe (eg, hospitalized with receipt of intravenous fluids, supplemental oxygen, complicated pneumonia), and severe (eg, intensive care, vasoactive infusions, chest drainage, severe sepsis). Outcomes were examined within the cohort with suspected CAP and in a subset with radiographic CAP.RESULTS: Of 477 children, there were no statistical differences in the median WBC count, ANC, CRP, or procalcitonin across severity categories. No biomarker had adequate discriminatory ability between severe and nonsevere disease (area under the curve [AUC]: 0.53-0.6 for suspected CAP and 0.59-0.64 for radiographic CAP). In analyses adjusted for age, antibiotic use, fever duration, and viral pathogen detection, CRP was associated with moderate-severe disease (odds ratio 1.12; 95% confidence interval, 1.0-1.25). CRP and procalcitonin revealed good discrimination of children with empyema requiring chest drainage (AUC: 0.83) and sepsis with vasoactive infusions (CRP AUC: 0.74; procalcitonin AUC: 0.78), although prevalence of these outcomes was low.CONCLUSIONS: WBC count, ANC, CRP, and procalcitonin are generally not useful to discriminate nonsevere from severe disease in children with CAP, although CRP and procalcitonin may have some utility in predicting the most severe outcomes. WHAT'S KNOWN ON THIS SUBJECT: Prognostic tools are limited for children with community-acquired pneumonia (CAP). Host biomarkers, including C-reactive protein (CRP) and procalcitonin, have been shown to be associated with severe clinical outcomes in adults with CAP. Data in children are limited.WHAT THIS STUDY ADDS: White blood cell count, CRP, and procalcitonin are generally not useful to discriminate overall disease severity in children with CAP. CRP and procalcitonin may have utility in predicting the most severe outcomes, but research is necessary to validate these findings.

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 prior to 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 MVLI indicates worse adherence behavior; values ≥2.5 are predictive of late allograft rejection.Findings: There was a 5% increase in MLVI for each 0.1 increase in deprivation index (95%CI: −1%, 11%, p=0.08). Roughly 24% of participants from the most deprived quartile had an MLVI

BACKGROUND: Disparities in health service use have been described across a range of sociodemographic factors. Patterns of PICU use have not been thoroughly assessed. METHODS: This was a population-level, retrospective analysis of admissions to the Cincinnati Children's Hospital Medical Center PICU between 2011 and 2016. Residential addresses of patients were geocoded and spatially joined to census tracts. Pediatric patients were eligible for inclusion if they resided within Hamilton County, Ohio. PICU admission and bed-day rates were calculated by using numerators of admissions and bed days, respectively, over a denominator of tract child population. Relationships between tract-level PICU use and child poverty were assessed by using Spearman's r and analysis of variance. Analyses were event based; children admitted multiple times were counted as discrete admissions. RESULTS: There were 4071 included admissions involving 3129 unique children contributing a total of 12 297 PICU bed days. Child poverty was positively associated with PICU admission rates (r = 0.59; P , .001) and bed-day rates (r = 0.47; P , .001). When tracts were grouped into quintiles based on child poverty rates, the PICU bed-day rate ranged from 23.4 days per 1000 children in the lowest poverty quintile to 81.9 days in the highest poverty quintile (P , .001). CONCLUSIONS: The association between poverty and poor health outcomes includes pediatric intensive care use. This association exists for children who grow up in poverty and around poverty. Future efforts should characterize the interplay between patient-and neighborhoodlevel risk factors and explore neighborhood-level interventions to improve child health. WHAT'S KNOWN ON THIS SUBJECT: Poverty adversely affects health. The health impacts of socioeconomic status and poverty occur at the individual and community levels. Socioeconomic disparities in PICU use have not been as robustly assessed compared with other medical disciplines. WHAT THIS STUDY ADDS: Socioeconomic disparities extend to pediatric critical illness. Neighborhood poverty affects children' s need for intensive care. We argue that this association represents more than an aggregate of individual risk factors, and the interplay of individual and community demographics merits further investigation.

Background:Acute exposure to ambient particulate matter <2.5μm in aerodynamic diameter (PM2.5) has been associated with adult psychiatric exacerbations but has not been studied in children.Objectives:Our objectives were to estimate the association between acute exposures to ambient PM2.5 and psychiatric emergency department (ED) utilization and to determine if it is modified by community deprivation.Methods:We used a time-stratified case-crossover design to analyze all pediatric, psychiatric ED encounters at Cincinnati Children’s Hospital Medical Center in Cincinnati, Ohio, from 2011 to 2015 (n=13,176). Conditional logistic regression models adjusted for temperature, humidity, and holiday effects were used to estimate the odds ratio (OR) for a psychiatric ED visit 0–3 d after ambient PM2.5 exposures, estimated at residential addresses using a spatiotemporal model.Results:A 10-μg/m3 increase in PM2.5 was associated with a significant increase in any psychiatric ED utilization 1 [OR=1.07 (95% CI: 1.02, 1.12)] and 2 [OR=1.05 (95% CI: 1.00, 1.10)] d later. When stratified by visit reason, associations were significant for ED visits related to adjustment disorder {e.g., 1-d lag [OR=1.24 (95% CI: 1.02, 1.52)] and suicidality 1-d lag [OR=1.44 (95% CI: 1.03, 2.02)]}. There were significant differences according to community deprivation, with some lags showing stronger associations among children in high versus low deprivation areas for ED visits for anxiety {1-d lag [OR=1.39 (95% CI: 0.96, 2.01) vs. 0.85 (95% CI: 0.62, 1.17)] and suicidality same day [OR=1.98 (95% CI: 1.22, 3.23) vs. 0.93 (95% CI: 0.60, 1.45)]}. In contrast, for some lags, associations with ED visits for adjustment disorder were weaker for children in high-deprivation areas {1-d lag [OR=1.00 (95% CI: 0.76, 1.33) vs. 1.50 (95% CI: 1.16, 1.93)]}.Discussion:These findings warrant additional research to confirm the associations in other populations. https://doi.org/10.1289/EHP4815

Background and Objectives: Nonalcoholic fatty liver disease (NAFLD) is linked to obesity. Obesity is associated with lower socioeconomic status (SES). An independent link between pediatric NAFLD and SES has not been elucidated. The objective of this study was to evaluate the distribution of socioeconomic deprivation, measured using an area-level proxy, in pediatric patients with known NAFLD and to determine whether deprivation is associated with liver disease severity. Methods: Retrospective study of patients <21 years with NAFLD, followed from 2009 to 2018. The patients’ addresses were mapped to census tracts, which were then linked to the community deprivation index (CDI; range 0--1, higher values indicating higher deprivation, calculated from six SES-related variables available publicly in US Census databases). Results: Two cohorts were evaluated; 1 with MRI (magnetic resonance imaging) and/or MRE (magnetic resonance elastography) findings indicative of NAFLD (n = 334), and another with biopsy-confirmed NAFLD (n = 245). In the MRI and histology cohorts, the majority were boys (66%), non-Hispanic (77%–78%), severely obese (79%–80%), and publicly insured (55%–56%, respectively). The median CDI for both groups was 0.36 (range 0.15–0.85). In both cohorts, patients living above the median CDI were more likely to be younger at initial presentation, time of MRI, and time of liver biopsy. MRI-measured fat fraction and liver stiffness, as well as histologic characteristics were not different between the high- and low-deprivation groups. Conclusions: Children with NAFLD were found across the spectrum of deprivation. Although children from more deprived neighborhoods present at a younger age, they exhibit the same degree of NAFLD severity as their peers from less deprived areas.

Cystic fibrosis (CF) is a progressive, genetic disease characterized by frequent, prolonged drops in lung function. Accurately predicting rapid underlying lung‐function decline is essential for clinical decision support and timely intervention. Determining whether an individual is experiencing a period of rapid decline is complicated due to its heterogeneous timing and extent, and error component of the measured lung function. We construct individualized predictive probabilities for “nowcasting” rapid decline. We assume each patient's true longitudinal lung function, S(t), follows a nonlinear, nonstationary stochastic process, and accommodate between‐patient heterogeneity through random effects. Corresponding lung‐function decline at time t is defined as the rate of change, S′(t). We predict S′(t) conditional on observed covariate and measurement history by modeling a measured lung function as a noisy version of S(t). The method is applied to data on 30 879 US CF Registry patients. Results are contrasted with a currently employed decision rule using single‐center data on 212 individuals. Rapid decline is identified earlier using predictive probabilities than the center's currently employed decision rule (mean difference: 0.65 years; 95% confidence interval (CI): 0.41, 0.89). We constructed a bootstrapping algorithm to obtain CIs for predictive probabilities. We illustrate real‐time implementation with R Shiny. Predictive accuracy is investigated using empirical simulations, which suggest this approach more accurately detects peak decline, compared with a uniform threshold of rapid decline. Median area under the ROC curve estimates (Q1‐Q3) were 0.817 (0.814‐0.822) and 0.745 (0.741‐0.747), respectively, implying reasonable accuracy for both. This article demonstrates how individualized rate of change estimates can be coupled with probabilistic predictive inference and implementation for a useful medical‐monitoring approach.

Background Proadrenomedullin (proADM), a vasodilatory peptide with antimicrobial and anti-inflammatory properties, predicts severe outcomes in adults with community-acquired pneumonia (CAP) to a greater degree than C-reactive protein and procalcitonin. We evaluated the ability of proADM to predict disease severity across a range of clinical outcomes in children with suspected CAP. Methods We performed a prospective cohort study of children 3 months to 18 years with CAP in the emergency department (ED). Disease severity was defined as: mild (discharged home), mild-moderate (hospitalized but not moderate-severe or severe), moderate-severe (e.g., hospitalized with supplemental oxygen, broadening of antibiotics, complicated pneumonia), and severe (e.g., vasoactive infusions, chest drainage, severe sepsis). Outcomes were examined using proportional odds logistic regression within the cohort with suspected CAP and in a subset with radiographic CAP. Results Among 369 children, median proADM increased with disease severity [mild: median 0.53 nmol/L (IQR:0.43, 0.73), mild-moderate: 0.56 nmol/L (IQR:0.45, 0.71), moderate-severe: 0.61 nmol/L (IQR:0.47, 0.77), severe: 0.70 nmol/L (IQR:0.55, 1.04) (p=.002)]. ProADM was significantly associated with increased odds of developing severe outcomes (suspected CAP odds ratio (OR) 1.68 [95% CI, 1.2, 2.36], radiographic CAP OR 2.11 [95% CI, 1.36, 3.38]) adjusted for age, fever duration, antibiotic use, and pathogen. ProADM had an area under the ROC curve (AUC) of 0.64 (95%CI, 0.56,0.72) in those with suspected CAP and AUC 0.77 (95% CI, 0.68,0.87) in radiographic CAP. Conclusions ProADM was associated with severe disease and discriminated moderately well children who developed severe disease from those who did not, particularly in radiographic CAP.

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.