title: Development and validation of a 5-day-ahead hay fever forecast for patients with grass-pollen-induced allergic rhinitis.
abstract: One-third of the Dutch population suffers from allergic rhinitis, including hay fever. In this study, a 5-day-ahead hay fever forecast was developed and validated for grass pollen allergic patients in the Netherlands. Using multiple regression analysis, a two-step pollen and hay fever symptom prediction model was developed using actual and forecasted weather parameters, grass pollen data and patient symptom diaries. Therefore, 80 patients with a grass pollen allergy rated the severity of their hay fever symptoms during the grass pollen season in 2007 and 2008. First, a grass pollen forecast model was developed using the following predictors: (1) daily means of grass pollen counts of the previous 10 years; (2) grass pollen counts of the previous 2-week period of the current year; and (3) maximum, minimum and mean temperature (R (2)=0.76). The second modeling step concerned the forecasting of hay fever symptom severity and included the following predictors: (1) forecasted grass pollen counts; (2) day number of the year; (3) moving average of the grass pollen counts of the previous 2 week-periods; and (4) maximum and mean temperatures (R (2)=0.81). Since the daily hay fever forecast is reported in three categories (low-, medium- and high symptom risk), we assessed the agreement between the observed and the 1- to 5-day-ahead predicted risk categories by kappa, which ranged from 65 % to 77 %. These results indicate that a model based on forecasted temperature and grass pollen counts performs well in predicting symptoms of hay fever up to 5 days ahead.
authors.affiliation: Department of Pulmonology, Leiden University Medical Center, PO Box 9600, Albinusdreef 2, 2300RC, Leiden, The Netherlands, l.a.de_weger@lumc.nl.
authors.forename: Letty A
authors.initials: LA
authors.lastname: de Weger
authors.forename: Thijs
authors.initials: T
authors.lastname: Beerthuizen
authors.forename: Pieter S
authors.initials: PS
authors.lastname: Hiemstra
authors.forename: Jacob K
authors.initials: JK
authors.lastname: Sont
mesh_terms: D000293:Adolescent; D000328:Adult; D000485:Allergens / Q000276:immunology; D005544:Forecasting / Q000379:methods*; D006801:Humans; D008875:Middle Aged; D008962:Models, Theoretical; D009426:Netherlands; D006109:Poaceae / Q000276:immunology*; D011058:Pollen / Q000276:immunology*; D012044:Regression Analysis; D015203:Reproducibility of Results; D006255:Rhinitis, Allergic, Seasonal / Q000209:etiology / Q000276:immunology* / Q000517:prevention & control*; D013696:Temperature; D055815:Young Adult
chemical_list: D000485:Allergens