757Using smartphone technology to characterise associations between respiratory symptoms and pollen

Autor: Iain S. Koolhof, David M. J. S. Bowman, Sharon L. Campbell, Nick Cooling, Fay H. Johnston, Grant J. Williamson, Penelope J. Jones, Antonio Gasparrini, Amanda J. Wheeler, Christopher Lucani
Rok vydání: 2021
Předmět:
Zdroj: International Journal of Epidemiology. 50
ISSN: 1464-3685
0300-5771
DOI: 10.1093/ije/dyab168.325
Popis: Background Pollen is a well-established trigger of asthma and allergic rhinoconjunctivitis, yet key gaps in our understanding remain. These include knowledge of concentration thresholds for symptoms, exposure-response associations through time, and the potential for interactions with other environmental stressors such as air pollution. Smartphone technology offers an opportunity to address these challenges using large datasets that capture individual symptoms in real time. Methods We analysed 44,820 symptom reports logged by 2,272 users of the AirRater app over four years to evaluate associations between daily respiratory symptoms and atmospheric concentrations of pollen in Tasmania, Australia. We used case time series, a novel methodology developed for app-sourced data. We adjusted for seasonality and meteorology and tested for interactions with particulate pollution (PM2.5). Results There was a non-linear association between pollen concentrations and respiratory symptoms for up to three days following exposure. Risk ratios (RR) were greatest on the same day, for total pollen increased steeply to a RR of 1.31 (95% CI: 1.26-1.37) at a concentration of 50 grains/m3 before plateauing. Associations with individual pollen taxa showed similar non-linear trends. There was an interaction with PM2.5, with effect estimates significantly higher when PM2.5 was >50 µg/m3 (p for interaction < 0.001). Conclusions The association between respiratory symptoms and airborne pollen was non-linear, greatest in magnitude on the day of exposure, and synergistic with air pollution. Key messages Smartphone symptom tracking offers a useful means of assessing dose-response relationships in environmental epidemiology.
Databáze: OpenAIRE
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