BACKGROUND: There is increasing interest in the daily pollen count, with pollen-sensitive individuals using it to determine medication use and researchers relying on it for commencing clinical drug trials and assessing drug efficacy according to allergen exposure. Counts are often expressed qualitatively as low, medium, and high, and often only 1 pollen trap is used for an entire region. OBJECTIVES: To examine the spatial variability in the pollen count in Sydney, Australia, and to compare discrepancies among low-, medium-, and high-count days at 3 sites separated by a maximum of 30 km. METHODS: Three sites in western Sydney were sampled using Burkard traps. Data from the 3 sites were used to compare vegetation differences, possible effects of some meteorological parameters, and discrepancies among sites in low-, medium-, and high-count days. RESULTS: Total pollen counts during the spring months were 14,382 grains/m3 at Homebush, 11,584 grains/m3 at Eastern Creek, and 9,269 grains/m3 at Nepean. The only significant correlation between differences in meteorological parameters and differences in pollen counts was the Homebush-Nepean differences in rainfall and pollen counts. Comparison between low- and high-count days among the 3 sites revealed a discordance rate of 8% to 17%. CONCLUSIONS: For informing the public about pollen counts, the count from 1 trap is a reasonable estimation in a 30-km region; however, the discrepancies among 3 trap sites would have a significant impact on the performance of a clinical trial where enrollment was determined by a low or high count. Therefore, for clinical studies, data collection must be local and applicable to the study population.
BACKGROUND: There is increasing interest in the daily pollen count, with pollen-sensitive individuals using it to determine medication use and researchers relying on it for commencing clinical drug trials and assessing drug efficacy according to allergen exposure. Counts are often expressed qualitatively as low, medium, and high, and often only 1 pollen trap is used for an entire region. OBJECTIVES: To examine the spatial variability in the pollen count in Sydney, Australia, and to compare discrepancies among low-, medium-, and high-count days at 3 sites separated by a maximum of 30 km. METHODS: Three sites in western Sydney were sampled using Burkard traps. Data from the 3 sites were used to compare vegetation differences, possible effects of some meteorological parameters, and discrepancies among sites in low-, medium-, and high-count days. RESULTS: Total pollen counts during the spring months were 14,382 grains/m3 at Homebush, 11,584 grains/m3 at Eastern Creek, and 9,269 grains/m3 at Nepean. The only significant correlation between differences in meteorological parameters and differences in pollen counts was the Homebush-Nepean differences in rainfall and pollen counts. Comparison between low- and high-count days among the 3 sites revealed a discordance rate of 8% to 17%. CONCLUSIONS: For informing the public about pollen counts, the count from 1 trap is a reasonable estimation in a 30-km region; however, the discrepancies among 3 trap sites would have a significant impact on the performance of a clinical trial where enrollment was determined by a low or high count. Therefore, for clinical studies, data collection must be local and applicable to the study population.
Authors: Kate R Weinberger; Patrick L Kinney; Guy S Robinson; Daniel Sheehan; Iyad Kheirbek; Thomas D Matte; Gina S Lovasi Journal: J Expo Sci Environ Epidemiol Date: 2016-12-21 Impact factor: 5.563
Authors: Paul J Beggs; Constance H Katelaris; Danielle Medek; Fay H Johnston; Pamela K Burton; Bradley Campbell; Alison K Jaggard; Don Vicendese; David M J S Bowman; Ian Godwin; Alfredo R Huete; Bircan Erbas; Brett J Green; Rewi M Newnham; Ed Newbigin; Simon G Haberle; Janet M Davies Journal: Aust N Z J Public Health Date: 2015-02 Impact factor: 2.939
Authors: Isabell Müller-Germann; Bernhard Vogel; Heike Vogel; Andreas Pauling; Janine Fröhlich-Nowoisky; Ulrich Pöschl; Viviane R Després Journal: PLoS One Date: 2015-10-22 Impact factor: 3.240
Authors: Ariane Guilbert; Bianca Cox; Nicolas Bruffaerts; Lucie Hoebeke; Ann Packeu; Marijke Hendrickx; Koen De Cremer; Sandrine Bladt; Olivier Brasseur; An Van Nieuwenhuyse Journal: Environ Health Date: 2018-04-11 Impact factor: 5.984