Do levels of airborne grass pollen influence asthma hospital admissions?

Bircan Erbas, Jiun-Horng Chang, Shyamali Dharmage, Eng Kok Ong, Rob J Hyndman, Ed Newbigin, Michael Abramson
(2007) Clinical and Experimental Allergy 37(11), 1641-1647

DOI

Background: The effects of environmental factors and ambient concentrations of grass pollen on allergic asthma are yet to be established.

Objective: We sought to estimate the independent effects of grass pollen concentrations in the air over Melbourne on asthma hospital admissions, for the 1992-1993 pollen season.

Methods: Daily grass pollen concentrations were monitored over a 24 hr period at three stations in Melbourne. The outcome variable was defined as all-age asthma hospital admissions with ICD9-493 codes. The ambient air pollutants were average daily measures of Ozone (O3), Nitrogen Dioxide (NO2) and Sulfur Dioxide (SO2), and the airborne particle index (API) representing fine particulate pollution. Semi-parametric Poisson Regression models were used to estimate these effects, adjusted for air temperature, humidity, wind speed, rainfall, day-of-the-week effects and seasonal variation.

Results: Grass pollen was a strong independent nonlinear predictor of asthma hospital admissions in a multi-pollutant model (p=0.01). Our data suggests that grass pollen had an increasing effect on asthma hospital admissions up to a threshold of 30 grains per m3, and that the effect remains stable thereafter.

Conclusion/Clinical Relevance of the Results: Our findings suggest that grass pollen levels influence asthma hospital admissions. High grass pollen days, currently defined as more than 50 grains per m3, are days when most sensitive individuals will experience allergic symptoms. However, some asthmatic patients may be at a significant risk even when airborne grass pollen levels are below this level. Patients with pollen allergies and asthma would be advised to stay indoors or take additional preventive medication at lower ambient concentrations.

Keywords: asthma, ambient pollen, air pollution, statistical models, nonlinearity.