Background. The transregional increase in pollen-associated allergies and their diversity have been scientifically proven. However, patchy pollen count measurement in many regions is a worldwide problem with few exceptions. Methods. This paper used data gathered from pollen count stations in Germany, Google queries using relevant allergological/biological keywords, and patient data from three German study centres collected in a prospective, double-blind, randomised, placebo-controlled, multicentre immunotherapy study to analyse a possible correlation between these data pools. Results. Overall, correlations between the patient-based, combined symptom medication score and Google data were stronger than those with the regionally measured pollen count data. The correlation of the Google data was especially strong in the groups of severe allergy sufferers. The results of the three-centre analyses show moderate to strong correlations with the Google keywords (up to >0.8 cross-correlation coefficient, ) in 10 out of 11 groups (three averaged patient cohorts and eight subgroups of severe allergy sufferers: high IgE class, high combined symptom medication score, and asthma). Conclusion. For countries with a good Internet infrastructure but no dense network of pollen traps, this could represent an alternative for determining pollen levels and, forecasting the pollen count for the next day. 1. Introduction Allergic rhinitis (AR), together with the comorbidity of asthma, ranks among the most frequent chronic diseases of our time [1, 2]. Its prevalence rates continue to rise and are thereby defining it as a global health problem. In several countries, the prevalence rate of AR in young adults is over 40% [2]. In the therapeutic ranking the avoidance of the allergen exposure comes first. An already established measurement supporting the avoidance of pollen exposure of affected persons suffering from seasonal AR is the measurement of the pollen count on the basis of pollen traps [3, 4]. This is especially drawing attention to the strength of the pollen count. Forecasting and providing the patients suffering from AR with these forecasts allow them to take measures to specifically avoid either strong or any pollen exposure. Possible measures could be an airing according to the time of the day and a postponement of outdoor activities. The warning of a high pollen count furthermore enables the patients to prepare themselves these days by taking along symptomatic medication especially for asthmatics. Data from clinical trials on AR must be interpreted in the context of
References
[1]
J. Bousquet, N. Khaltaev, A. A. Cruz et al., “Allergic Rhinitis and its Impact on Asthma (ARIA) 2008 update (in collaboration with the World Health Organization, GA2LEN and AllerGen),” Allergy, vol. 63, no. 86, pp. 8–160, 2008.
[2]
J. Bousquet, H. J. Schunemann, T. Zuberbier, et al., “Development and implementation of guidelines in allergic rhinitis—an ARIA-GA2LEN paper,” Allergy, vol. 65, no. 10, pp. 1212–1221, 2010.
[3]
E. Levetin and P. K. van de Water, “Pollen count forecasting,” Immunology and Allergy Clinics of North America, vol. 23, no. 3, pp. 423–442, 2003.
[4]
H. Moriguchi, M. Matsumoto, Y. Nishimoto, and K. Kuwada, “The development of a pollen information system for the improvement of QOL,” The Journal of Medical Investigation, vol. 48, no. 3-4, pp. 198–209, 2001.
[5]
N. Behbehani, N. Arifhodzic, M. Al-Mousawi et al., “The seasonal variation in allergic rhinitis and its correlation with outdoor allergens in Kuwait,” International Archives of Allergy and Immunology, vol. 133, no. 2, pp. 164–167, 2004.
[6]
L. Cecchi, M. Morabito, M. P. Domeneghetti, A. Crisci, M. Onorari, and S. Orlandini, “Long distance transport of ragweed pollen as a potential cause of allergy in central Italy,” Annals of Allergy, Asthma and Immunology, vol. 96, no. 1, pp. 86–91, 2006.
[7]
M. Sofiev, P. Siljamo, H. Ranta, and A. Rantio-Lehtim？ki, “Towards numerical forecasting of long-range air transport of birch pollen: theoretical considerations and a feasibility study,” International Journal of Biometeorology, vol. 50, no. 6, pp. 392–402, 2006.
[8]
P. K. van de Water, T. Keever, C. E. Main, and E. Levetin, “An assessment of predictive forecasting of Juniperus ashei pollen movement in the Southern Great Plains, USA,” International Journal of Biometeorology, vol. 48, no. 2, pp. 74–82, 2003.
[9]
S. F. Wood, “Review of hay fever. 1. Historical background and mechanisms,” Family Practice, vol. 3, no. 1, pp. 54–63, 1986.
[10]
A. Custovic, S. C. O. Taggart, H. C. Francis, M. D. Chapman, and A. Woodcock, “Exposure to house dust mite allergens and the clinical activity of asthma,” Journal of Allergy and Clinical Immunology, vol. 98, no. 1, pp. 64–72, 1996.
[11]
R. Mosges, M. Adrian, E. El Hassan, and V. Konig, “What Google(R) knows about the pollen season,” Allergy, vol. 66, no. 5, pp. 707–708, 2011.
[12]
N. Askitas and K. F. Zimmermann, “Google econometrics and unemployment forecasting,” Aplied Economics Quarterly, vol. 55, no. 2, pp. 107–120, 2009.
[13]
B. N. Breyer, S. Sen, D. S. Aaronson, M. L. Stoller, B. A. Erickson, and M. L. Eisenberg, “Use of google insights for search to track seasonal and geographic kidney stone incidence in the United States,” Urology, vol. 78, no. 2, pp. 267–271, 2011.
[14]
M. P. Connolly, M. Postma, and S. J. Silber, “What's on the mind of IVF consumers?” Reproductive BioMedicine Online, vol. 19, no. 6, pp. 767–769, 2009.
[15]
J. Ginsberg, M. H. Mohebbi, R. S. Patel, L. Brammer, M. S. Smolinski, and L. Brilliant, “Detecting influenza epidemics using search engine query data,” Nature, vol. 457, no. 7232, pp. 1012–1014, 2009.
[16]
C. T. Leffler, B. Davenport, and D. Chan, “Frequency and seasonal variation of ophthalmology-related internet searches,” Canadian Journal of Ophthalmology, vol. 45, no. 3, pp. 274–279, 2010.
[17]
J. A. Qasem, H. Nasrallah, B. N. Al-Khalaf et al., “Meteorological factors, aeroallergens and asthma-related visits in Kuwait: a 12-month retrospective study,” Annals of Saudi Medicine, vol. 28, no. 6, pp. 435–441, 2008.
[18]
K. Takasaki, K. Enatsu, H. Kumagami, and H. Takahashi, “Relationship between airborne pollen count and treatment outcome in Japanese cedar pollinosis patients,” European Archives of Oto-Rhino-Laryngology, vol. 266, no. 5, pp. 673–676, 2009.
[19]
E. R？der, M. Y. Berger, W. C. J. Hop, R. M. D. Bernsen, H. de Groot, and R. Gerth van Wijk, “Sublingual immunotherapy with grass pollen is not effective in symptomatic youngsters in primary care,” Journal of Allergy and Clinical Immunology, vol. 119, no. 4, pp. 892–898, 2007.
IDS, “Individuals using the Internet per 100 inhabitants, 2001–2011,” International Telecommunication Union, Geneva,Switzerland, 2011, http://www.itu.int/ITU-D/ict/statistics/.
[22]
E. L. Eisenstein, R. Collins, B. S. Cracknell et al., “Sensible approaches for reducing clinical trial costs,” Clinical Trials, vol. 5, no. 1, pp. 75–84, 2008.
