Long-Term Coarse Particulate Matter Exposure Is Associated with Asthma among Children in Medicaid

Keet, Corinne A.; Keller, Jaime; Peng, Roger D.

doi:10.1164/rccm.201706-1267oc

Cited by 98 publications

(60 citation statements)

References 72 publications

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“…The questionnaire survey conducted in the United States and London did not observe significant associations between PM 2.5 concentrations and current asthma (OR: 1.43, 95% CI: 0.98‐2.10 with mean PM 2.5 concentration of 13.3 μg/m 3 ; and OR: 1.16, 95% CI: 0.82‐1.65 with mean PM 2.5 concentration of 13.7 μg/m 3 , respectively). Studies examining the effect of PM 2.5 on the prevalence of physician‐diagnosed asthma have determined that an increase of 10 μg/m 3 in the PM 2.5 concentration was associated with 10% and 18% increases in the prevalence of physician‐diagnosed asthma in China (with mean PM 2.5 concentration of 64 μg/m 3 ) and the United States (with mean PM 2.5 concentration of 9.8 μg/m 3 ), respectively. Exposure to PM 2.5 concentrations of 7.8 to 17.4 µg/m 3 was not significantly associated with the prevalence of physician‐diagnosed asthma .…”

Section: Discussionmentioning

confidence: 99%

Changes in the relationship between childhood asthma and ambient air pollution in Taiwan: Results from a nationwide survey repeated 5 years apart

Chen

Chuang

et al. 2018

Pediatric Allergy Immunology

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Background Childhood asthma is a common disease whose prevalence is changing. Shift in environmental exposure was one of the plausible explanations. This study investigated changes in the association between childhood asthma and ambient air pollution occurring over time. Method A nationwide questionnaire survey concerning respiratory illness and symptoms was administered to Taiwanese elementary and middle school students in 2011 and repeatedly in 2016‐2017. During the study period, the concentrations of ambient air pollutants were obtained from the Environmental Protection Administration (EPA) monitoring stations. Generalized estimating equation models were applied to examine the association between air pollution in the past year and the risk of current asthma. Results A total of 6346 children from the 2011 survey and 11 585 children from the 2016‐2017 survey attended schools located within a 1‐km radius of Taiwan EPA monitoring stations. The prevalence of childhood current asthma (children with physician‐diagnosed asthma and persistent asthma symptoms in the past year) increased from 7.5% to 9.6% during this period. The level of exposure to particulate matter with an aerodynamic diameter of ≤2.5 μm (PM2.5) in the past year was found to be associated with current asthma both in the 2011 survey (odds ratio (OR): 1.90, 95% confidence interval (CI): 1.41‐2.57) and in the 2016‐2017 survey (OR: 1.24, 95% CI: 1.04‐1.48). Conclusion Improved air quality has reduced the effect of PM2.5 on childhood asthma, but air quality remains a health concern in Taiwan.

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Section: Discussionmentioning

confidence: 99%

Changes in the relationship between childhood asthma and ambient air pollution in Taiwan: Results from a nationwide survey repeated 5 years apart

Chen

Chuang

et al. 2018

Pediatric Allergy Immunology

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“…The patterns of bias reduction (or increase) for a sequence of adjustment values m will differ by context and choice of link function, but the connection between adjustment basis, spatial scale, and overall interpretation remains the same as the linear case. For example, Keet et al (2018) used TPRS to adjust for large-scale confounding across the contiguous United States in an analysis of particulate matter and asthma-related outcomes. Automated selection could be done using extensions of information criteria such as QIC (Pan, 2001).…”

Section: Discussionmentioning

confidence: 99%

Selecting a Scale for Spatial Confounding Adjustment

Keller

Szpiro

2020

Journal of the Royal Statistical Society Series A: Statistics in Society

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Unmeasured, spatially-structured factors can confound associations between spatial environmental exposures and health outcomes. Adding flexible splines to a regression model is a simple approach for spatial confounding adjustment, but the spline degrees of freedom do not provide an easily interpretable spatial scale. We describe a method for quantifying the extent of spatial confounding adjustment in terms of the Euclidean distance at which variation is removed. We develop this approach for confounding adjustment with splines and using Fourier and wavelet filtering. We demonstrate differences in the spatial scales these bases can represent and provide a comparison of methods for selecting the amount of confounding adjustment. We find the best performance for selecting the amount of adjustment using an information criterion evaluated on an outcome model without exposure. We apply this method to spatial adjustment in an analysis of particulate matter and blood pressure in a cohort of United States women.

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“…This paper is motivated by a study of ambient PM exposure and asthma in a cohort of children enrolled in Medicaid. Keet, Keller, and Peng () investigated the potential relationship between asthma morbidity and long‐term exposure to fine and coarse PM in 7,810,025 children of ages 5 to 20 enrolled in Medicaid for the entire 2009–2010 period. Fine PM (PM 2.5 ) refers to airborne particles with median diameter 2.5 μm or less, whereas coarse PM (PM 10 − 2.5 ) are particles with median diameter between 2.5 μm and 10 μm.…”

Section: Motivating Example: Pm and Asthma In Childrenmentioning

confidence: 99%

“…Because less than one third of the counties in the United States contain a PM monitor (see Figure ), Keet et al () fit a national spatial model for PM 2.5 and PM 10 to predict the two‐year average PM concentration in each ZCTA and greatly increase the size of the cohort available for analysis. They estimated that 1‐μg/m 3 differences in PM 2.5 and in PM 10 − 2.5 were associated with rate ratios of 1.072 and 1.036, respectively, for hospital admissions due to asthma.…”

Section: Motivating Example: Pm and Asthma In Childrenmentioning

confidence: 99%

“…We apply these prediction error model selection approaches to models for the 2009–2010 average concentration of PM 2.5 , PM 10 , and PM 10 − 2.5 . We use the same set of monitoring data as in Keet et al () but fit prediction models that differ from the model used in that manuscript. In particular, we include modeled PM 2.5 values from CMAQ as a prediction covariate and perform model selection using

{\overset{M}{true}}_{1}

.…”

Section: Application To Annual Average Pmmentioning

confidence: 99%

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Error in estimating area‐level air pollution exposures for epidemiology

Keller

Peng

2019

Environmetrics

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In air pollution epidemiology, measurements of relevant exposure concentrations are typically made at point locations, resulting in spatial misalignment between the exposure data and health outcomes aggregated at the area level. To obtain values that match the spatial units of the health data, observations can be averaged directly or prediction models developed. We present a framework for evaluating the error in aggregating exposure concentrations to the area unit. We present estimators of mean squared error that can be used for model selection. We find that exposure prediction models, even when misspecified, outperform monitor averages in settings with realistic numbers of monitors and that important reductions in error of the health effect estimate can be obtained when restricting to areas with a monitor. In an analysis of long‐term particulate matter concentrations across the United States, we estimated the error of the prediction model approach to be less than that of the monitor averaging approach on average across counties. We present health effect estimates about particulate matter exposure and pediatric asthma morbidity in the Medicaid population using each approach. Our findings support the use of a prediction model for estimating area‐wide averages, even when restricting to areas that contain a monitor.

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Long-Term Coarse Particulate Matter Exposure Is Associated with Asthma among Children in Medicaid

Abstract: Among children enrolled in Medicaid, exposure to higher average coarse PM levels is associated with increased asthma prevalence and morbidity. These results suggest the need for direct monitoring of coarse PM and reconsideration of limits on long-term average coarse PM pollution levels.

Cited by 98 publications

References 72 publications

Changes in the relationship between childhood asthma and ambient air pollution in Taiwan: Results from a nationwide survey repeated 5 years apart

Changes in the relationship between childhood asthma and ambient air pollution in Taiwan: Results from a nationwide survey repeated 5 years apart

Selecting a Scale for Spatial Confounding Adjustment

Error in estimating area‐level air pollution exposures for epidemiology

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