eCite Digital Repository

Characterising non-linear associations between airborne pollen counts and respiratory symptoms from the AirRater smartphone app in Tasmania, Australia: A case time series approach


Jones, PJ and Koolhof, IS and Wheeler, AJ and Williamson, GJ and Lucani, C and Campbell, SL and Bowman, DMJS and Cooling, N and Gasparrini, A and Johnston, FH, Characterising non-linear associations between airborne pollen counts and respiratory symptoms from the AirRater smartphone app in Tasmania, Australia: A case time series approach, Environmental Research Article 111484. ISSN 0013-9351 (2021) [Refereed Article]

Copyright Statement

2021 Published by Elsevier Inc.

DOI: doi:10.1016/j.envres.2021.111484


Pollen is a well-established trigger of asthma and allergic rhinitis, yet concentration-response relationships, lagged effects, and interactions with other environmental factors remain poorly understood. Smartphone technology offers an opportunity to address these challenges using large, multi-year datasets that capture individual symptoms and exposures in real time. We aimed to characterise associations between six pollen types and respiratory symptoms logged by users of the AirRater smartphone app in Tasmania, Australia. We analysed 44,820 symptom reports logged by 2272 AirRater app users in Tasmania over four years (2015-2019). With these data we evaluated associations between daily respiratory symptoms and atmospheric pollen concentrations. We implemented Poisson regression models, using the case time series approach designed for app-sourced data. We assessed potentially non-linear and lagged associations with (a) total pollen and (b) six individual pollen taxa. We adjusted for seasonality and meteorology and tested for interactions with particulate air pollution (PM2.5). We found evidence of non-linear associations between total pollen and respiratory symptoms for up to three days following exposure. For total pollen, the same-day relative risk (RR) increased to 1.31 (95% CI: 1.261.37) at a concentration of 50 grains/m3 before plateauing. Associations with individual pollen taxa were also non-linear with some diversity in shapes. For all pollen taxa the same-day RR was highest. The interaction between total pollen and PM2.5 was positive, with risks associated with pollen significantly higher in the presence of high concentrations of PM2.5. Our results support a non-linear response between airborne pollen and respiratory symptoms. The association was strongest on the day of exposure and synergistic with particulate air pollution. The associations found with Dodonaea and Myrtaceae highlight the need to further investigate the role of Australian native pollen types in allergic respiratory disease.

Item Details

Item Type:Refereed Article
Keywords:pollen, air pollution, m-health, asthma, allergic rhinitis, particulate pollution
Research Division:Health Sciences
Research Group:Epidemiology
Research Field:Environmental epidemiology
Objective Division:Health
Objective Group:Clinical health
Objective Field:Prevention of human diseases and conditions
UTAS Author:Jones, PJ (Dr Penelope Jones)
UTAS Author:Koolhof, IS (Mr Iain Koolhof)
UTAS Author:Wheeler, AJ (Dr Amanda Wheeler)
UTAS Author:Williamson, GJ (Dr Grant Williamson)
UTAS Author:Lucani, C (Mr Christopher Lucani)
UTAS Author:Campbell, SL (Dr Sharon Campbell)
UTAS Author:Bowman, DMJS (Professor David Bowman)
UTAS Author:Cooling, N (Dr Nick Cooling)
UTAS Author:Johnston, FH (Professor Fay Johnston)
ID Code:144786
Year Published:2021
Web of Science® Times Cited:7
Deposited By:Office of the School of Natural Sciences
Deposited On:2021-06-10
Last Modified:2022-08-25

Repository Staff Only: item control page