Literature DB >> 31254082

Time-series analysis of satellite-derived fine particulate matter pollution and asthma morbidity in Jackson, MS.

Howard H Chang1, Anqi Pan1, David J Lary2, Lance A Waller1, Lei Zhang3, Bruce T Brackin4, Richard W Finley5, Fazlay S Faruque6.   

Abstract

In order to examine associations between asthma morbidity and local ambient air pollution in an area with relatively low levels of pollution, we conducted a time-series analysis of asthma hospital admissions and fine particulate matter pollution (PM2.5) in and around Jackson, MS, for the period 2003 to 2011. Daily patient-level records were obtained from the Mississippi State Department of Health (MSDH) Asthma Surveillance System. Patient geolocations were aggregated into a grid with 0.1° × 0.1° resolution within the Jackson Metropolitan Statistical Area. Daily PM2.5 concentrations were estimated via machine-learning algorithms with remotely sensed aerosol optical depth and other associated parameters as inputs. Controlling for long-term temporal trends and meteorology, we estimated a 7.2% (95% confidence interval 1.7-13.1%) increase in daily all-age asthma emergency room admissions per 10 μg/m3 increase in the 3-day average of PM2.5 levels (current day and two prior days). Stratified analyses reveal significant associations between asthma and 3-day average PM2.5 for males and blacks. Our results contribute to the current epidemiologic evidence on the association between acute ambient air pollution exposure and asthma morbidity, even in an area characterized by relatively good air quality.

Entities:  

Keywords:  Asthma; Hospital admission; PM2.5; Remote sensing; Time-series

Mesh:

Substances:

Year:  2019        PMID: 31254082     DOI: 10.1007/s10661-019-7421-4

Source DB:  PubMed          Journal:  Environ Monit Assess        ISSN: 0167-6369            Impact factor:   2.513


  7 in total

1.  Geospatial technology in environmental health applications.

Authors:  Fazlay S Faruque
Journal:  Environ Monit Assess       Date:  2019-06-28       Impact factor: 2.513

2.  The association between asthma emergency department visits and satellite-derived PM2.5 in Lima, Peru.

Authors:  Bryan N Vu; Vilma Tapia; Stefanie Ebelt; Gustavo F Gonzales; Yang Liu; Kyle Steenland
Journal:  Environ Res       Date:  2021-05-04       Impact factor: 8.431

3.  Machine learning-driven identification of early-life air toxic combinations associated with childhood asthma outcomes.

Authors:  Yan-Chak Li; Hsiao-Hsien Leon Hsu; Yoojin Chun; Po-Hsiang Chiu; Zoe Arditi; Luz Claudio; Gaurav Pandey; Supinda Bunyavanich
Journal:  J Clin Invest       Date:  2021-11-15       Impact factor: 19.456

4.  Global and Geographically and Temporally Weighted Regression Models for Modeling PM2.5 in Heilongjiang, China from 2015 to 2018.

Authors:  Qingbin Wei; Lianjun Zhang; Wenbiao Duan; Zhen Zhen
Journal:  Int J Environ Res Public Health       Date:  2019-12-14       Impact factor: 3.390

5.  Using Machine Learning for the Calibration of Airborne Particulate Sensors.

Authors:  Lakitha O H Wijeratne; Daniel R Kiv; Adam R Aker; Shawhin Talebi; David J Lary
Journal:  Sensors (Basel)       Date:  2019-12-23       Impact factor: 3.576

6.  Effects of particulate matter on hospital admissions for respiratory diseases: an ecological study based on 12.5 years of time series data in Shanghai.

Authors:  Wenjia Peng; Hao Li; Li Peng; Ying Wang; Weibing Wang
Journal:  Environ Health       Date:  2022-01-13       Impact factor: 5.984

7.  Using Bayesian time-stratified case-crossover models to examine associations between air pollution and "asthma seasons" in a low air pollution environment.

Authors:  Matthew Bozigar; Andrew B Lawson; John L Pearce; Erik R Svendsen; John E Vena
Journal:  PLoS One       Date:  2021-12-08       Impact factor: 3.240

  7 in total

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