Identifying impacts of air pollution on subacute asthma symptoms using digital medication sensors.

Details

• Personal Author:
  Balmes J ; Barrett MA ; Combs V ; Gondalia R ; Henderson K ; Hogg C ; Jerrett M ; Kaye L ; Moyer SS ; Renda AM ; Simrall G ; Smith T ; Su JG ; Sublett J ; Tarini P ; Van Sickle D ; Wojcik O
• Description:
  Background: Objective tracking of asthma medication use and exposure in real-time and space has not been feasible previously. Exposure assessments have typically been tied to residential locations, which ignore exposure within patterns of daily activities. Methods: We investigated the associations of exposure to multiple air pollutants, derived from nearest air quality monitors, with space-time asthma rescue inhaler use captured by digital sensors, in Jefferson County, Kentucky. A generalized linear mixed model, capable of accounting for repeated measures, over-dispersion and excessive zeros, was used in our analysis. A secondary analysis was done through the random forest machine learning technique. Results: The 1039 participants enrolled were 63.4% female, 77.3% adult (>=18) and 46.8% White. Digital sensors monitored the time and location of over 286 980 asthma rescue medication uses and associated air pollution exposures over 193 697 patient-days, creating a rich spatiotemporal dataset of over 10 905 240 data elements. In the generalized linear mixed model, an interquartile range (IQR) increase in pollutant exposure was associated with a mean rescue medication use increase per person per day of 0.201 [95% confidence interval (CI): 0.189-0.214], 0.153 (95% CI: 0.136-0.171), 0.131 (95% CI: 0.115-0.147) and 0.113 (95% CI: 0.097-0.129), for sulphur dioxide (SO2), nitrogen dioxide (NO2), fine particulate matter (PM2.5) and ozone (O3), respectively. Similar effect sizes were identified with the random forest model. Time-lagged exposure effects of 0-3 days were observed. Conclusions: Daily exposure to multiple pollutants was associated with increases in daily asthma rescue medication use for same day and lagged exposures up to 3 days. Associations were consistent when evaluated with the random forest modelling approach. [Description provided by NIOSH]
• Subjects:
  Air Pollutants, Occupational Air Pollution Asthma Asthma, Occupational Environmental Exposure Environmental Monitoring Environmental Pollution Health Care Facilities Workforce And Services Occupational Health Respiratory System Respiratory Tract Diseases Safety

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• Keywords:
  Asthma; Air Pollution; Air Pollutants; Environmental Pollution; Medical Monitoring; Sensors; Respiratory Diseases; Environmental Monitoring; Models; Author Keywords: Asthma; Short-acting Beta Agonist; Mobile Health; Digital Sensor; Environmental Trigger
• ISSN:
  0300-5771
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  California ; Kentucky ; New Jersey ; OSHA Region 10 ; OSHA Region 2 ; OSHA Region 4 ; OSHA Region 5 ; OSHA Region 9 ; Washington ; Wisconsin
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  213-224
• Volume:
  51
• Issue:
  1
• NIOSHTIC Number:
  nn:20064781
• Citation:
  Int J Epidemiol 2022 Feb; 51(1):213-224
• Contact Point Address:
  Jason G Su, Division of Environmental Health Sciences, School of Public Health, 2121 Berkeley Way West, University of California at Berkeley, Berkeley, CA 94720-7360
• Email:
  jasonsu@berkeley.edu
• CAS Registry Number:
  Nitrogen dioxide (CAS RN 10102-44-0) ; Ozone (CAS RN 10028-15-6) ; Sulfur dioxide (CAS RN 7446-09-5)
• Federal Fiscal Year:
  2022
• Performing Organization:
  University of Washington
• Peer Reviewed:
  True
• Start Date:
  20050701
• Source Full Name:
  International Journal of Epidemiology
• End Date:
  20250630
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:f833a400a14e993327a35f6e2837f615d439f81ed68c1aa663a982f682ed619acb3c40dd541ed5703c8b6795b3d17d3771b47e43ffef627c851632f5d0e371ee
• Download URL:
  https://stacks.cdc.gov/view/cdc/249235/cdc_249235_DS1.pdf
• File Type:
  [PDF - 960.18 KB ]