Application of a Time-Stratified Case-Crossover Design to Explore the Effects of Air Pollution and Season on Childhood Asthma Hospitalization in Cities of Differing Urban Patterns: Big Data Analytics of Government Open Data

Abstract

Few studies have assessed the lagged effects of levels of different urban city air pollutants and seasons on asthma hospitalization in children. This study used big data analysis to explore the effects of daily changes in air pollution and season on childhood asthma hospitalization from 2001 to 2010 in Taipei and Kaohsiung City, Taiwan. A time-stratified case-crossover study and conditional logistic regression analysis were employed to identify associations between the risk of hospitalization due to asthma in children and the levels of air pollutants (PM2.5, PM10, O3, SO2, and NO2) in the days preceding hospitalization. During the study period, 2900 children in Taipei and 1337 in Kaohsiung aged ≤15 years were hospitalized due to asthma for the first time. The results indicated that the levels of air pollutants were significantly associated with the risk of asthma hospitalization in children, and seasonal effects were observed. High levels of air pollution in Kaohsiung had greater effects than in Taipei after adjusting for seasonal variation. The most important factor was O3 in spring in Taipei. In children aged 0–6 years, asthma was associated with O3 in Taipei and SO2 in Kaohsiung, after controlling for the daily mean temperature and relative humidity.

Contribution of Satellite-Derived Aerosol Optical Depth PM2.5 Bayesian Concentration Surfaces to Respiratory-Cardiovascular Chronic Disease Hospitalizations in Baltimore, Maryland

“…Although Weber et al [16] did not find warm-cold season differences, others have found season differences in the contribution of PM 2.5 to respiratory-cardiovascular chronic disease hospital events [12,59,[74][75][76][77][78][79]. Warm vs. cold season differences were evaluated in this study.…”

Framework for Big Data integration in e-government

“…The article reflected the results of an investigation into the interiors of buildings in the city of Beijing. In the same context, there were several other studies focused on air pollution monitoring by Big Data analysis techniques [4,5,[12][13][14][15][16][17][18]. Some focused specifically on vehicular congestion [19][20], while others examined the adverse effects that occur in humans, such as infertility [21].…”

PAGE Study: Summary of a Study Protocol to Estimate the Prevalence of Severe Asthma in Spain Using Big Data Methods

“…The big data concept refers to the processing of large volumes of data by developing mathematical algorithms, in order to establish relationships between data (structured, unstructured and semi-structured) and to determine behavioral patterns that predict trends for improving decision-making. Analysis of big data using traditional methods would take too long and would be very expensive to upload them to a relational database for analysis, and few studies have analyzed this technology in patients with asthma [19][20][21][22][23][24][25][26]. Savana is able to analyze and interpret the plain free text contained in electronic medical records, regardless of the electronic system in which they operate, in order to conduct predictive analyses without the use of classical statistical methods, and to compare them with the descriptive analysis of prevalence and the prospective analysis of the course of patients in the first part of the study, which will be considered the gold standard against which the tool will be compared.…”