Health effects of air pollution on respiratory symptoms: A longitudinal study using digital health sensors.

Publisher: Elsevier Science Country of Publication: Netherlands NLM ID: 7807270 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-6750 (Electronic) Linking ISSN: 01604120 NLM ISO Abbreviation: Environ Int Subsets: MEDLINE

Publication: : Amsterdam : Elsevier Science
Original Publication: Oxford; Elmsford, N. Y., Pergamon Press.

Air Pollution*/adverse effects ; Air Pollution*/statistics & numerical data ; Particulate Matter*/analysis ; Particulate Matter*/adverse effects ; Air Pollutants*/analysis ; Air Pollutants*/adverse effects ; Ozone*/analysis ; Ozone*/adverse effects; Humans ; California/epidemiology ; Longitudinal Studies ; Environmental Exposure/adverse effects ; Environmental Exposure/statistics & numerical data ; Asthma/epidemiology ; Asthma/chemically induced ; Male ; Nitrogen Dioxide/analysis ; Nitrogen Dioxide/adverse effects ; Pulmonary Disease, Chronic Obstructive/epidemiology ; Female ; Middle Aged ; Environmental Monitoring/methods ; Aged ; Adult ; Digital Health

Previous studies of air pollution and respiratory disease often relied on aggregated or lagged acute respiratory disease outcome measures, such as emergency department (ED) visits or hospitalizations, which may lack temporal and spatial resolution. This study investigated the association between daily air pollution exposure and respiratory symptoms among participants with asthma and chronic obstructive pulmonary disease (COPD), using a unique dataset passively collected by digital sensors monitoring inhaled medication use. The aggregated dataset comprised 456,779 short-acting beta-agonist (SABA) puffs across 3,386 people with asthma or COPD, between 2012 and 2019, across the state of California. Each rescue use was assigned space-time air pollution values of nitrogen dioxide (NO 2 ), fine particulate matter with diameter ≤ 2.5 µm (PM 2.5 ) and ozone (O 3 ), derived from highly spatially resolved air pollution surfaces generated for the state of California. Statistical analyses were conducted using linear mixed models and random forest machine learning. Results indicate that daily air pollution exposure is positively associated with an increase in daily SABA use, for individual pollutants and simultaneous exposure to multiple pollutants. The advanced linear mixed model found that a 10-ppb increase in NO 2 , a 10 μg m ^-3 increase in PM 2.5 , and a 30-ppb increase in O 3 were respectively associated with incidence rate ratios of SABA use of 1.025 (95 % CI: 1.013-1.038), 1.054 (95 % CI: 1.041-1.068), and 1.161 (95 % CI: 1.127-1.233), equivalent to a respective 2.5 %, 5.4 % and 16 % increase in SABA puffs over the mean. The random forest machine learning approach showed similar results. This study highlights the potential of digital health sensors to provide valuable insights into the daily health impacts of environmental exposures, offering a novel approach to epidemiological research that goes beyond residential address. Further investigation is warranted to explore potential causal relationships and to inform public health strategies for respiratory disease management.
Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: All co-authors affirm the absence of any conflict of interest related to this research, with the exception of Meredith Barrett and Vy Vuong, who are employees of ResMed (to which Propeller Health is affiliated) and receive salary and stock.
(Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Keywords: Air pollution; Digital sensors; Environmental epidemiology; Inhaler use; Land use regression; Respiratory symptoms

0 (Particulate Matter)
0 (Air Pollutants)
66H7ZZK23N (Ozone)
S7G510RUBH (Nitrogen Dioxide)

Date Created: 20240614 Date Completed: 20240617 Latest Revision: 20240726

20240727

10.1016/j.envint.2024.108810

38875815