Application of data science methods to identify school and home risk factors for asthma and allergy-related symptoms among children in New York

Xinlei Deng; George Thurston; Wangjian Zhang; Ian Ryan; Connie Jiang; Haider Khwaja; Xiaobo Romeiko; Tia Marks; Bo Ye; Yanji Qu; Shao Lin

doi:10.1016/j.scitotenv.2020.144746

Application of data science methods to identify school and home risk factors for asthma and allergy-related symptoms among children in New York

Sci Total Environ. 2021 May 20:770:144746. doi: 10.1016/j.scitotenv.2020.144746. Epub 2021 Jan 23.

Authors

Xinlei Deng¹, George Thurston², Wangjian Zhang¹, Ian Ryan¹, Connie Jiang³, Haider Khwaja⁴, Xiaobo Romeiko¹, Tia Marks¹, Bo Ye⁵, Yanji Qu⁶, Shao Lin⁷

Affiliations

¹ Department of Environmental Health Sciences, University at Albany, State University of New York, Rensselaer, NY 12144, USA.
² Department of Environmental Medicine, School of Medicine, New York University, New York, NY 12144, USA.
³ Department of Statistics, College of Arts and Sciences, University of Virginia, Charlottesville, VA 22904, USA.
⁴ Department of Environmental Health Sciences, University at Albany, State University of New York, Rensselaer, NY 12144, USA; Wadsworth Center, New York State Department of Health, Albany, NY 12201, USA.
⁵ Department of Epidemiology and Biostatistics, University at Albany, State University of New York, Rensselaer, NY 12144, USA.
⁶ Guangdong Cardiovascular Institute, WHO Collaborating Center for Research and Training in Cardiovascular Diseases, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China.
⁷ Department of Environmental Health Sciences, University at Albany, State University of New York, Rensselaer, NY 12144, USA; Department of Epidemiology and Biostatistics, University at Albany, State University of New York, Rensselaer, NY 12144, USA. Electronic address: slin@albany.edu.

PMID: 33736384
DOI: 10.1016/j.scitotenv.2020.144746

Abstract

Objectives: Few studies have comprehensively assessed multiple environmental exposures affecting children's health. This study applied machine-learning methods to evaluate how indoor environmental conditions at home and school contribute to asthma and allergy-related symptoms.

Methods: We randomly selected 10 public schools representing different socioeconomic statuses in New York State (2017-2019) and distributed questionnaires to students to collect health status and home-and school-environmental exposures. Indoor air quality was measured at school, and ambient particle exposures (PM_2.5 and components) were measured using real-time personal monitors for 48 h. We used random forest model to identify the most important risk factors for asthma and allergy-related symptoms, and decision tree for visualizing the inter-relationships among the multiple risk factors with the health outcomes.

Results: The top contributing factors identified for asthma were family rhinitis history (relative importance: 10.40%), plant pollen trigger (5.48%); bedroom carpet (3.58%); environmental tobacco smoke (ETS) trigger symptom (2.98%); and ETS exposure (2.56%). For allergy-related symptoms, plant pollen trigger (10.88%), higher paternal education (7.33%), bedroom carpet (5.28%), family rhinitis history (4.78%), and higher maternal education (4.25%) were the strongest contributing factors. Conversely, primary heating with hot water radiator was negatively (-6.86%) associated with asthma symptoms. Younger children (<9 years old) with family history of rhinitis and carpeting in the bedroom were the prominent combined risk factors for asthma. Children jointly exposed to pollen, solvents, and carpeting in their home tended to have greater risks of allergy-related symptoms, even without family history of rhinitis.

Conclusion: Family rhinitis history, bedroom carpet, and pollen triggers were the most important risk factors for both asthma and allergy-related symptoms. Our new findings included that hot-water radiator was related to reduced asthma symptoms, and the combination of young age, rhinitis history, and bedroom carpeting was related to increased asthma symptoms. Further studies are needed to confirm our findings.

Keywords: Allergy; Asthma; Home and school environment; Machine-learning; Risk factors.

MeSH terms

Air Pollution, Indoor*
Asthma* / epidemiology
Child
Data Science
Environmental Exposure
Humans
New York / epidemiology
Risk Factors
Schools