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This study identified the meteorological parameters that influence PM 2.5 concentrations in the Greater Cincinnati area by employing principal components analysis and multi-variable regression. Meteorological and PM 2.5 data were collected over several years to derive statistical relationships about the seasonal variability of meteorological parameters and quantify their influence on PM 2.5 . We studied the effect of meteorological parameters by seasons and by k-means clustering. The results show that outdoor temperature (OT), planetary boundary height (HPBL) and visibility (VIS) have the strongest effect on PM 2.5 . The distribution of PM 2.5 concentrations in each cluster and season was evaluated using the Kolmogorov–Smirnov test with data fitting using the lognormal and gamma distributions. To our observation, we found the PM 2.5 concentration fits the gamma distribution marginally better than the lognormal distribution. [ABSTRACT FROM AUTHOR]

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. Disease severity was defined as mild (discharged home), mild–moderate (hospitalized but not moderate–severe or severe), moderate–severe (eg, hospitalized with supplemental oxygen, broadening of antibiotics, complicated pneumonia), and severe (eg, 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 [IQR], 0.53 [0.43–0.73]; mild–moderate: 0.56 [0.45–0.71]; moderate–severe: 0.61 [0.47–0.77]; severe: 0.70 [0.55–1.04] nmol/L) (P  = .002). ProADM was significantly associated with increased odds of developing severe outcomes (suspected CAP: 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 AUC of 0.64 (95% CI,.56–.72) in those with suspected CAP and an AUC of 0.77 (95% CI,.68–.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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: Pediatric emergency medical services (EMS) utilization is costly and resource intensive; significant variation exists across large-scale geographies. Less is known about variation at smaller geographic levels where factors including lack of transportation, low health literacy, and decreased access to medical homes may be more relevant. Our objective was to determine whether pediatric EMS utilization varied across Hamilton County, Ohio, census tracts and whether such utilization was associated with socioeconomic deprivation. Methods: This was a retrospective analysis of children living in Hamilton County, Ohio, transported by EMS to the Cincinnati Children's emergency department between July 1, 2014, and July 31, 2016. Participants' addresses were assigned to census tracts and an EMS utilization rate and deprivation index were calculated for each. Pearson's correlation coefficients evaluated relationships between tract-level EMS utilization and deprivation. Tract-level deprivation was used as a predictor in patient-level evaluations of acuity. Results: During the study period, there were 4,877 pediatric EMS transports from 219 of the 222 county census tracts. The county EMS utilization rate during the study period was 2.4 transports per 100 children (range 0.2-11). EMS utilization rates were positively correlated with increasing deprivation (r = 0.72, 95% confidence interval [CI], 0.65-0.77). Deprivation was associated with lower illness severity at triage, fewer transports resulting in resuscitation suite use, and fewer transports resulting in hospitalizations (all p < 0.05). Conclusions: EMS utilization varied substantially across census tracts in Hamilton County, Ohio. A deeper understanding into why certain socioeconomically deprived areas contribute to disproportionately high rates of EMS utilization could support development of targeted interventions to improve use. [ABSTRACT FROM AUTHOR]

Exposure to traffic-related air pollution (TRAP) has been implicated in asthma development, persistence, and exacerbation. This exposure is highly significant as large segments of the global population resides in zones that are most impacted by TRAP and schools are often located in high TRAP exposure areas. Recent findings shed new light on the epigenetic mechanisms by which exposure to traffic pollution may contribute to the development and persistence of asthma. In order to delineate TRAP induced effects on the epigenome, utilization of newly available innovative methods to assess and quantify traffic pollution will be needed to accurately quantify exposure. This review will summarize the most recent findings in each of these areas. Although there is considerable evidence that TRAP plays a role in asthma, heterogeneity in both the definitions of TRAP exposure and asthma outcomes has led to confusion in the field. Novel information regarding molecular characterization of asthma phenotypes, TRAP exposure assessment methods, and epigenetics are revolutionizing the field. Application of these new findings will accelerate the field and the development of new strategies for interventions to combat TRAP-induced asthma. [ABSTRACT FROM AUTHOR]