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

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.