Difference in symptom severity between early and late grass pollen season in patients with seasonal allergic rhinitis

<p>Abstract</p> <p>Background</p> <p>For the development of forecasts for seasonal allergic rhinitis symptoms, it is essential to understand the relationship between grass pollen concentrations and the symptoms of grass pollen allergic patients.</p> <p>Objective</p> <p>The aim of this study was to delineate this relationship between seasonal allergic rhinitis symptoms and grass pollen concentrations in the Netherlands.</p> <p>Methods</p> <p>Grass pollen allergic patients (n = 80 [2007] - 84 [2008]) were enrolled into the study. They were asked to enter their seasonal allergic rhinitis symptoms (runny nose, sneezing, blocked nose, post nasal drip, and eye symptoms) daily on a scale from 0 to 3 to the study centre either by short message service (SMS) or by internet from May-July 2007 and April-July 2008. Daily pollen counts were used to define the early and the late grass pollen season as the period 'before and during' respectively 'after' the first grass pollen peak (more than 150 pollen/m³).</p> <p>Results</p> <p>At similar grass pollen concentrations, the daily mean of the individual maximum symptom scores reported in the early season were higher as compared to that reported in the late season [differences of -0.41 (2007) and -0.30 (2008)]. This difference could not be explained by medication use by the patients nor by co-sensitization to birch.</p> <p>Conclusions</p> <p>We conclude that seasonal allergic rhinitis symptoms at similar grass pollen concentrations are more severe in the early flowering season as compared to those in the late flowering season. This finding is not only relevant for development of forecasts for seasonal allergic rhinitis symptoms but also for understanding symptom development and planning and analysis of clinical studies.</p

Constructing a 7-day Ahead Forecast Model for Grass Pollen at North London, United Kingdom

A number of media outlets now issue medium-range (~7 day) weather forecasts on a regular basis. It is therefore logical that aerobiologists should attempt to produce medium-range forecasts for allergenic pollen that cover the same time period as the weather forecasts. The objective of this study is to construct a medium-range (< 7 day) forecast model for grass pollen at north London. The forecast models were produced using regression analysis based on grass pollen and meteorological data from 1990-1999 and tested on data from 2000 and 2002. The modelling process was improved by dividing the grass pollen season into three periods; the pre-peak, peak and post peak periods of grass pollen release. The forecast consisted of five regression models. Two simple linear regression models predicting the start and end date of the peak period, and three multiple regression models forecasting daily average grass pollen counts in the pre-peak, peak and post-peak periods. Overall the forecast models achieved 62% accuracy in 2000 and 47% in 2002, reflecting the fact that the 2002 grass pollen season was of a higher magnitude than any of the other seasons included in the analysis. This study has the potential to make a notable contribution to the field of aerobiology. Winter averages of the North Atlantic Oscillation were used to predict certain characteristics of the grass pollen season, which presents an important advance in aerobiological work. The ability to predict allergenic pollen counts for a period between five and seven days will benefit allergy sufferers. Furthermore, medium-range forecasts for allergenic pollen will be of assistance to the medical profession, including allergists planning treatment and physicians scheduling clinical trials

A 30-Day-Ahead Forecast Model for Grass Pollen in North London, United Kingdom

A 30-day ahead forecast method has been developed for grass pollen at north London. The total period of the grass pollen season is covered by eight multiple regression models, each covering a 10-day period running consecutively from 21st May to 8th August. This means that three models were used for each 30-day forecast. The forecast models were produced using grass pollen and environmental data from 1961-1999 and tested on data from 2000 and 2002. Model accuracy was judged in two ways: the number of times the forecast model was able to successfully predict the severity (relative to the 1961-1999 dataset as a whole) of grass pollen counts in each of the eight forecast periods on a scale of one to four; and the number of times the forecast model was able to predict whether grass pollen counts were higher or lower than the mean. The models achieved 62.5% accuracy in both assessment years when predicting the relative severity of grass pollen counts on a scale of one to four, which equates to six of the eight 10-day periods being forecast correctly. The models attained 87.5% and 100% accuracy in 2000 and 2002 respectively when predicting whether grass pollen counts would be higher or lower than the mean. Attempting to predict pollen counts during distinct 10-day periods throughout the grass pollen season is a novel approach. The models also employed original methodology in the use of winter averages of the North Atlantic Oscillation to forecast 10-day means of allergenic pollen counts