First name
Christopher
Middle name
B
Last name
Forrest

Title

Does Living near Trees and Other Vegetation Affect the Contemporaneous Odds of Asthma Exacerbation among Pediatric Asthma Patients?

Year of Publication

2022

Date Published

2022 Apr 25

ISSN Number

1468-2869

Abstract

<p>Vegetation may influence asthma exacerbation through effects on aeroallergens, localized climates, air pollution, or children's behaviors and stress levels. We investigated the association between residential vegetation and asthma exacerbation by conducting a matched case-control study based on electronic health records of asthma patients, from the Children's Hospital of Philadelphia (CHOP). Our study included 17,639 exacerbation case events and 34,681 controls selected from non-exacerbation clinical visits for asthma, matched to cases by age, sex, race/ethnicity, public payment source, and residential proximity to the CHOP main campus ED and hospital. Overall greenness, tree canopy, grass/shrub cover, and impervious surface were assessed near children's homes (250&nbsp;m) using satellite imagery and high-resolution landcover data. We used generalized estimating equations to estimate odds ratios (OR) and 95% confidence intervals (CI) for associations between each vegetation/landcover measure and asthma exacerbation, with adjustment for seasonal and sociodemographic factors-for all cases, and for cases defined by diagnosis setting and exacerbation frequency. Lower odds of asthma exacerbation were observed in association with greater levels of tree canopy near the home, but only for children who experienced multiple exacerbations in a year (OR = 0.94 per 10.2% greater tree canopy coverage, 95% CI = 0.90-0.99). Our findings suggest possible protection for asthma patients from tree canopy, but differing results by case frequency suggest that potential benefits may be specific to certain subpopulations of asthmatic children.</p>

DOI

10.1007/s11524-022-00633-7

Alternate Title

J Urban Health

PMID

35467328

Title

Self-Reported Health Outcomes of Children and Youth with 10 Chronic Diseases.

Year of Publication

2022

Date Published

2022 Mar 02

ISSN Number

1097-6833

Abstract

<p><strong>OBJECTIVES: </strong>To identify pediatric patient-reported outcomes (PROs) that are associated with chronic conditions and to evaluate the effects of chronic disease activity on PROs.</p>

<p><strong>STUDY DESIGN: </strong>Participants 8-24 years-old and their parents were enrolled into 14 studies that evaluated PROMIS® PROs across 10 chronic conditions--asthma, atopic dermatitis, cancer, cancer survivors, chronic kidney disease, Crohn's disease, juvenile idiopathic arthritis, lupus, sickle cell disease, and type 1 diabetes mellitus. PRO scores were contrasted with the United States general population of children using nationally representative percentiles. PRO-specific coefficients of variation were computed to illustrate the degree of variation in scores within versus between conditions. Condition-specific measures of disease severity and Cohens d effect sizes were used to examine PRO scores by disease activity.</p>

<p><strong>RESULTS: </strong>Participants included 2,975 child respondents and 2,392 parent respondents who provided data for 3,409 unique children: 52% were 5-12 years-old, 52% female, 25% African-American/Black, and 14% Hispanic. Across all 10 chronic conditions, children reported more anxiety, fatigue, pain, and mobility restrictions than the general pediatric population. Variation in PRO scores within chronic disease cohorts was equivalent to variation within the general population, exceeding between-cohort variation by factors of 1.9 (mobility) to 5.7 (anxiety). Disease activity was consistently associated with poorer self-reported health, and these effects were weakest for peer relationships.</p>

<p><strong>CONCLUSIONS: </strong>Chronic conditions are associated with symptoms and functional status in children and adolescents across 10 different disorders. These findings highlight the need to complement conventional clinical evaluations with those obtained directly from patients themselves using PROs.</p>

DOI

10.1016/j.jpeds.2022.02.052

Alternate Title

J Pediatr

PMID

35247394

Title

Associations between high ambient temperatures and asthma exacerbation among children in Philadelphia, PA: a time series analysis.

Year of Publication

2022

Date Published

2022 Mar 04

ISSN Number

1470-7926

Abstract

<p><strong>OBJECTIVES: </strong>High ambient temperatures may contribute to acute asthma exacerbation, a leading cause of morbidity in children. We quantified associations between hot-season ambient temperatures and asthma exacerbation in children ages 0-18 years in Philadelphia, PA.</p>

<p><strong>METHODS: </strong>We created a time series of daily counts of clinical encounters for asthma exacerbation at the Children's Hospital of Philadelphia linked with daily meteorological data, June-August of 2011-2016. We estimated associations between mean daily temperature (up to a 5-day lag) and asthma exacerbation using generalised quasi-Poisson distributed models, adjusted for seasonal and long-term trends, day of the week, mean relative humidity,and US holiday. In secondary analyses, we ran models with adjustment for aeroallergens, air pollutants and respiratory virus counts. We quantified overall associations, and estimates stratified by encounter location (outpatient, emergency department, inpatient), sociodemographics and comorbidities.</p>

<p><strong>RESULTS: </strong>The analysis included 7637 asthma exacerbation events. High mean daily temperatures that occurred 5 days before the index date were associated with higher rates of exacerbation (rate ratio (RR) comparing 33°C-13.1°C days: 1.37, 95% CI 1.04 to 1.82). Associations were most substantial for children ages 2 to &lt;5 years and for Hispanic and non-Hispanic black children. Adjustment for air pollutants, aeroallergens and respiratory virus counts did not substantially change RR estimates.</p>

<p><strong>CONCLUSIONS: </strong>This research contributes to evidence that ambient heat is associated with higher rates of asthma exacerbation in children. Further work is needed to explore the mechanisms underlying these associations.</p>

DOI

10.1136/oemed-2021-107823

Alternate Title

Occup Environ Med

PMID

35246484

Title

Using a Multi-Institutional Pediatric Learning Health System to Identify Systemic Lupus Erythematosus and Lupus Nephritis: Development and Validation of Computable Phenotypes.

Year of Publication

2021

Date Published

2021 Nov 03

ISSN Number

1555-905X

Abstract

<p><strong>BACKGROUND AND OBJECTIVES: </strong>Performing adequately powered clinical trials in pediatric diseases, such as SLE, is challenging. Improved recruitment strategies are needed for identifying patients.</p>

<p><strong>DESIGN, SETTING, PARTICIPANTS, &amp; MEASUREMENTS: </strong>Electronic health record algorithms were developed and tested to identify children with SLE both with and without lupus nephritis. We used single-center electronic health record data to develop computable phenotypes composed of diagnosis, medication, procedure, and utilization codes. These were evaluated iteratively against a manually assembled database of patients with SLE. The highest-performing phenotypes were then evaluated across institutions in PEDSnet, a national health care systems network of &gt;6.7 million children. Reviewers blinded to case status used standardized forms to review random samples of cases (=350) and noncases (=350).</p>

<p><strong>RESULTS: </strong>Final algorithms consisted of both utilization and diagnostic criteria. For both, utilization criteria included two or more in-person visits with nephrology or rheumatology and ≥60 days follow-up. SLE diagnostic criteria included absence of neonatal lupus, one or more hydroxychloroquine exposures, and either three or more qualifying diagnosis codes separated by ≥30 days or one or more diagnosis codes and one or more kidney biopsy procedure codes. Sensitivity was 100% (95% confidence interval [95% CI], 99 to 100), specificity was 92% (95% CI, 88 to 94), positive predictive value was 91% (95% CI, 87 to 94), and negative predictive value was 100% (95% CI, 99 to 100). Lupus nephritis diagnostic criteria included either three or more qualifying lupus nephritis diagnosis codes (or SLE codes on the same day as glomerular/kidney codes) separated by ≥30 days or one or more SLE diagnosis codes and one or more kidney biopsy procedure codes. Sensitivity was 90% (95% CI, 85 to 94), specificity was 93% (95% CI, 89 to 97), positive predictive value was 94% (95% CI, 89 to 97), and negative predictive value was 90% (95% CI, 84 to 94). Algorithms identified 1508 children with SLE at PEDSnet institutions (537 with lupus nephritis), 809 of whom were seen in the past 12 months.</p>

<p><strong>CONCLUSIONS: </strong>Electronic health record-based algorithms for SLE and lupus nephritis demonstrated excellent classification accuracy across PEDSnet institutions.</p>

DOI

10.2215/CJN.07810621

Alternate Title

Clin J Am Soc Nephrol

PMID

34732529

Title

Tympanostomy Tubes or Medical Management for Recurrent Acute Otitis Media.

Year of Publication

2021

Number of Pages

860-861

Date Published

2021 Aug 26

ISSN Number

1533-4406

DOI

10.1056/NEJMc2109725

Alternate Title

N Engl J Med

PMID

34437792

Title

Prediction of early childhood obesity with machine learning and electronic health record data.

Year of Publication

2021

Number of Pages

104454

Date Published

2021 Apr 09

ISSN Number

1872-8243

Abstract

<p><strong>OBJECTIVE: </strong>This study compares seven machine learning models developed to predict childhood obesity from age &gt; 2 to ≤ 7 years using Electronic Healthcare Record (EHR) data up to age 2 years.</p>

<p><strong>MATERIALS AND METHODS: </strong>EHR data from of 860,510 patients with 11,194,579 healthcare encounters were obtained from the Children's Hospital of Philadelphia. After applying stringent quality control to remove implausible growth values and including only individuals with all recommended wellness visits by age 7 years, 27,203 (50.78 % male) patients remained for model development. Seven machine learning models were developed to predict obesity incidence as defined by the Centers for Disease Control and Prevention (age/sex adjusted BMI&gt;95th percentile). Model performance was evaluated by multiple standard classifier metrics and the differences among seven models were compared using the Cochran's Q test and post-hoc pairwise testing.</p>

<p><strong>RESULTS: </strong>XGBoost yielded 0.81 (0.001) AUC, which outperformed all other models. It also achieved statistically significant better performance than all other models on standard classifier metrics (sensitivity fixed at 80 %): precision 30.90 % (0.22 %), F1-socre 44.60 % (0.26 %), accuracy 66.14 % (0.41 %), and specificity 63.27 % (0.41 %).</p>

<p><strong>DISCUSSION AND CONCLUSION: </strong>Early childhood obesity prediction models were developed from the largest cohort reported to date. Relative to prior research, our models generalize to include males and females in a single model and extend the time frame for obesity incidence prediction to 7 years of age. The presented machine learning model development workflow can be adapted to various EHR-based studies and may be valuable for developing other clinical prediction models.</p>

DOI

10.1016/j.ijmedinf.2021.104454

Alternate Title

Int J Med Inform

PMID

33866231

Title

Effects of Ambient Air Pollution on Childhood Asthma Exacerbation in the Philadelphia Metropolitan Region, 2011 - 2014.

Year of Publication

2021

Number of Pages

110955

Date Published

2021 Mar 04

ISSN Number

1096-0953

Abstract

<p>Fine particulate matter (PM) and ozone (O) air pollutants are known risk factors for asthma exacerbation. We studied the association of these air pollutants with pediatric asthma exacerbation in the Philadelphia metropolitan region, and evaluated potential effect modification by children's characteristics (e.g., race/ethnicity, atopic conditions) and environmental factors (e.g., neighborhood tree canopy, meteorological factors, aeroallergens). We conducted a time-stratified case-crossover study of 54,632 pediatric (age ≤18 years) asthma exacerbation cases occurring from 2011-2014, identified through electronic health records (EHR) of the Children's Hospital of Philadelphia (CHOP) health system. We applied conditional logistic regression to estimate associations between air pollution and asthma exacerbation, using daily census-tract level pollutant concentrations estimated from the EPA Fused Air Quality Surface Using Downscaling (FAQSD) files. The associations were estimated within warm (Apr - Sep) and cold (Oct - Mar) months for unlagged exposure and for cumulative effects up to 5 days after exposure, with adjustment for temperature, relative humidity, and holidays. We found small increases in odds of asthma exacerbation with higher pollutant concentrations, with positive associations (OR, comparing concentrations of 75 to 25 percentile) observed for PM during both warm (1.03, 95% CI: 0.98 - 1.08) and cold months (1.05, 95% CI: 1.02 - 1.07), and for O during cold months (1.08, 95% CI: 1.02 - 1.14). The exposure-response relationship with PM during the cold months was essentially linear, whereas thresholds of effect were observed for the other associations at low-medium pollutant concentrations. Results were robust to multi-pollutant modeling and adjustment for additional covariates. We found no effect modification by most children's characteristics, while effect sizes were higher on days with detected tree and grass pollens during warm months. Our results suggest that even small decreases in pollutant concentrations could potentially reduce risk of childhood asthma exacerbation - an important finding, given the high burden of childhood asthma and known disparities in asthma control.</p>

DOI

10.1016/j.envres.2021.110955

Alternate Title

Environ Res

PMID

33676951

Title

Ambient daily pollen levels in association with asthma exacerbation among children in Philadelphia, Pennsylvania.

Year of Publication

2020

Number of Pages

106138

Date Published

2020 Sep 19

ISSN Number

1873-6750

Abstract

<p>Pollen from trees, grasses, and weeds can trigger asthma exacerbation in sensitized individuals. However, there are gaps in knowledge about the effects, such as the relative risks from different plant taxa and threshold levels of effect. We aimed to describe the local association between pollen and asthma exacerbation among children in the City of Philadelphia, and to evaluate whether effects are modified by children's characteristics and clinical factors (e.g., child's age, race/ethnicity, comorbidities). We conducted a time-stratified case-crossover study of pediatric (age &lt;18 years) asthma exacerbation, with cases identified through electronic health records (EHR) of the Children's Hospital of Philadelphia (CHOP) health system from March through October in the years 2011-2016. Daily pollen counts were obtained from the local National Allergy Bureau certified pollen counter. We applied conditional logistic regression to estimate odds ratios (OR) and 95% confidence intervals (CI) for the association between the pollen level (vs. none detected) and odds of asthma exacerbation, adjusting for temperature, relative humidity, and holidays. We estimated same-day exposure effects, as well as effects from exposure lagged by up to 5 days. There were 35,040 asthma exacerbation events during the study period, with the majority occurring among black, non-Hispanic children (81.8%) and boys (60.4%). We found increased odds of asthma exacerbation among Philadelphia children in association with tree pollen, both for total tree pollen and most individual tree types. Increased odds from total tree pollen were observed at the lowest levels studied (≤5 grains/m, unlagged, OR&nbsp;=&nbsp;1.06, 95% CI: 1.02, 1.10), and exhibited a positive exposure-response pattern of effect; tree pollen levels above 1000 grains/m (unlagged) were associated with 64% increased odds of asthma exacerbation (95% CI: 1.45, 1.84). Grass pollen was associated with asthma exacerbation only at levels above the 99 percentile (52 grains/m), which occurred, on average, two days per year during the study period (with 2-day lag, OR&nbsp;=&nbsp;1.38, 95% CI: 1.19, 1.60). There was an inverse association (reduced asthma exacerbation) with ragweed pollen that was consistent across analyses. Pollen from other weeds was associated with increased odds of asthma exacerbation, without a clear exposure-response pattern (2-day lag, significant increases ranging from 8% to 19%). Increased odds from tree pollen and weeds (other than ragweed) were higher among children with allergic rhinitis. While there are known benefits from urban vegetation for human health, there are risks as well. It is important to note, however, that pollen is released during a limited time frame each year, and advisories informed by local data can enable susceptible individuals to avoid outdoor exposure on high-risk days.</p>

DOI

10.1016/j.envint.2020.106138

Alternate Title

Environ Int

PMID

32961469

Title

Heavy precipitation and asthma exacerbation risk among children: A case-crossover study using electronic health records linked with geospatial data.

Year of Publication

2020

Number of Pages

109714

Date Published

2020 Jun 04

ISSN Number

1096-0953

Abstract

<p>Extreme precipitation events may be an important environmental trigger for asthma exacerbations in children. We used a time stratified case-crossover design and data from a large electronic health record database at the Children's Hospital of Philadelphia (CHOP) to estimate associations of daily heavy precipitation (defined as&nbsp;&gt;&nbsp;95th percentile of the summertime distribution) with asthma exacerbation among children. We defined control days as those falling on the same day of the week within the same month and year as the case. We restricted our primary analyses to the summer months in years 2011-2016 and used conditional logistic regression models to estimate associations between heavy precipitation and acute asthma exacerbations in both outpatient (primary care, specialty care, and emergency department) and inpatient settings. We investigated numerous individual-level (e.g., age, sex, eczema diagnosis) and environmental measures (e.g., greenspace, particulate matter) as potential effect modifiers. The analysis include 13,483 asthma exacerbations in 10,434 children. Odds of asthma exacerbation were 11% higher on heavy precipitation vs. no precipitation days (95% CI: 1.02-1.21). There was little evidence of effect modification by most measures. These results suggest that heavy summertime precipitation events may contribute to asthma exacerbations. Further research using larger datasets from other health systems is needed to confirm these results, and to explore underlying mechanisms.</p>

DOI

10.1016/j.envres.2020.109714

Alternate Title

Environ. Res.

PMID

32559685

Title

Variability in Diagnosed Asthma in Young Children in a Large Pediatric Primary Care Network.

Year of Publication

2020

Date Published

2020 Feb 07

ISSN Number

1876-2867

Abstract

<p><strong>OBJECTIVES: </strong>Our objectives were to (1) quantify the frequency of wheezing episodes and asthma diagnosis in young children in a large pediatric primary care network and (2) assess the variability in practice-level asthma diagnosis, accounting for common asthma risk factors and comorbidities. We hypothesized that significant variability in practice-level asthma diagnosis rates would remain after adjusting for associated predictors.</p>

<p><strong>METHODS: </strong>We generated a retrospective longitudinal birth cohort of children who visited one of 31 pediatric primary care practices within the first 6 months of life from 1/2005-12/2016. Children were observed for up to 8 years or until the end of the observation window. We used multivariable discrete time survival models to evaluate predictors of asthma diagnosis by 3-month age intervals. We compared unadjusted and adjusted proportions of children diagnosed with asthma by practice.</p>

<p><strong>RESULTS: </strong>Of the 161,502 children in the cohort, 34,578 children (21%) received at least one asthma diagnosis. In multivariable modeling, male gender, minority race/ethnicity, gestational age &lt;34 weeks, allergic rhinitis, food allergy, and prior wheezing episodes were associated with asthma diagnosis. After adjusting for variation in these predictors across practices, the cumulative incidence of asthma diagnosis by practice by age 6 years ranged from 11-47% (interquartile range (IQR): 24-29%).</p>

<p><strong>CONCLUSIONS: </strong>Across pediatric primary care practices, adjusted incidence of asthma diagnosis by age 6 years ranged widely, though variation gauged by the IQR was more modest. Potential sources of practice-level variation, such as differing diagnosis thresholds and labeling of different wheezing phenotypes as "asthma", should be further investigated.</p>

DOI

10.1016/j.acap.2020.02.003

Alternate Title

Acad Pediatr

PMID

32044466

WATCH THIS PAGE

Subscription is not available for this page.