Characterising non-linear associations between airborne pollen counts and respiratory symptoms from the AirRater smartphone app in Tasmania, Australia : A case time series approach
Journal article
Jones, Penelope J., Koolhof, Iain S., Wheeler, Amanda J., Williamson, Grant J., Lucani, Christopher, Campbell, Sharon L., Bowman, David J. M. S., Cooling, Nick, Gasparrini, Antonio and Johnston, Fay H.. (2021). 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. 200, p. Article 111484. https://doi.org/10.1016/j.envres.2021.111484
Authors | Jones, Penelope J., Koolhof, Iain S., Wheeler, Amanda J., Williamson, Grant J., Lucani, Christopher, Campbell, Sharon L., Bowman, David J. M. S., Cooling, Nick, Gasparrini, Antonio and Johnston, Fay H. |
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Abstract | 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 analyzed 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: 1.26–1.37) 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. |
Keywords | pollen; air pollution; m-health; asthma; allergic rhinitis |
Year | 2021 |
Journal | Environmental Research |
Journal citation | 200, p. Article 111484 |
Publisher | Elsevier Inc. |
ISSN | 0013-9351 |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.envres.2021.111484 |
Scopus EID | 2-s2.0-85107682870 |
Open access | Published as ‘gold’ (paid) open access |
Research or scholarly | Research |
Page range | 1-11 |
Publisher's version | License File Access Level Open |
Output status | Published |
Publication dates | |
Online | 08 Jun 2021 |
Publication process dates | |
Accepted | 02 Jun 2021 |
Deposited | 09 Nov 2021 |
https://acuresearchbank.acu.edu.au/item/8x05w/characterising-non-linear-associations-between-airborne-pollen-counts-and-respiratory-symptoms-from-the-airrater-smartphone-app-in-tasmania-australia-a-case-time-series-approach
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Publisher's version
OA_Jones_2021_Characterising_non_linear_associations_between_airborne.pdf | |
License: CC BY-NC-ND 4.0 | |
File access level: Open |
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