The influence of air pollution on pollen allergy sufferers.

A multitude of consequences from global warming and environmental pollution can already be seen for nature and humans. The continuous burning of fossil fuels leads to rising temperatures and rising water levels causing extreme weather phenomena like heat waves and flooding. Increasing levels of air pollution also cause adverse health effects. This is especially important for pollen allergy sufferers because air pollution plays a central role in the interactions between pollen and humans. Today, pollen allergy sufferers are confronted with longer pollen seasons and pollen with potentially increased allergenicity. The effects for pollen allergy sufferers are an increased duration and severity of symptoms. New research results from the Medical University of Vienna prove that out of the most important air pollution parameters (particulate matter, nitrogen dioxide, sulfur dioxide, and ozone) especially ozone causes increased symptom severity in pollen allergy sufferers during the birch, grass, and ragweed pollen seasons.

Variability of grass pollen allergy symptoms throughout the season: Comparing symptom data profiles from the Patient's Hayfever Diary from 2014 to 2016 in Vienna (Austria).

BACKGROUND: Grass pollen allergy is the most widespread pollen allergy in the world. It still remains unknown in which aspects and in which extent symptoms from grass pollen allergy differ throughout the grass pollen season, although individual sensitization profiles of persons concerned are known for a long time. METHODS: The crowd-sourced symptom data of users of the Patient's Hayfever Diary were filtered for significant positive correlated users to grass pollen from Vienna (Austria) during the respective grass pollen seasons from 2014, 2015, and 2016. These symptom data were the foundation for 3 statistical approaches in order to examine different sections of the grass pollen season defined either by grass pollen data, phenology (grass species determination in the field), or symptom data itself. RESULTS: Results from all 3 approaches are similar and come to the same major conclusion. The symptom peak of most users is observed in the second section of the grass pollen season (70%), followed by the first section (20%), and with the least user numbers (10%) the third section. The profiles from single users entering data for all 3 years under study are robust and show a comparable behavior from year to year. CONCLUSION: Grass taxa such as Arrhenatherum, Festuca, and Lolium seem to induce the highest symptom severity in most users during the second section of the grass pollen season. Poa and Dactylis are the main triggers for the first section of the grass pollen season. The flower of Phleum und Cynodon is documented for the last section of the grass pollen season. Crowd-sourced symptom data is the prerequisite for personal pollen information to consider the individuality of grass pollen allergy sufferers. Phenological monitoring is needed to provide information on specific grass taxa of importance to allergic persons.

Impact of air pollution on symptom severity during the birch, grass and ragweed pollen period in Vienna, Austria: Importance of O3 in 2010-2018.

Air pollution is a threat to the general population, especially to pollen allergy sufferers in urban environments. Different air quality parameters have hitherto been examined which add to the burden of pollen allergy sufferers. Parameters such as NO2, SO2, PM10, PM2.5, and O3 are supposed to have additional impact, not only on health in general (increase in asthma, allergy sensitization frequency), but also on pollen grains (increase of allergenicity). However, it remains unknown if those air quality parameters increase symptom severity during the pollen season. We selected the birch, grass, and ragweed pollen seasons as different time periods throughout the year and analyzed the relationship of symptom data to pollen, air quality, and meteorological data (temperature, relative humidity) for the metropolis of Vienna (Austria). A linear regression model was computed based on different symptom data, and both pollen and air quality data were tested simultaneously. Ozone was positively and significantly associated with symptom scores in all three seasons, whereas this was only rarely the case with other pollutants. Therefore, only ozone was selected for further analysis in a model including meteorological parameters. In this model, effect estimates of ozone were attenuated but remained significant for the grass pollen season. The lack of significance in the other seasons may be attributed to the less numerous symptom data entries and the shorter duration of the pollen seasons for birch and ragweed. All other air quality parameters usually showed lower concentrations during the pollen seasons and displayed little variation. This might explain the lack of a clear signal. Our results suggest that today's allergic population is already affected by air quality (rising O3 levels). Air quality should be considered as well in pollen information and pollen allergy studies in general because of its increasing importance in the light of global warming.

Translating the Burden of Pollen Allergy Into Numbers Using Electronically Generated Symptom Data From the Patient's Hayfever Diary in Austria and Germany: 10-Year Observational Study.

BACKGROUND: Pollen allergies affect a significant proportion of the population globally. At present, Web-based tools such as pollen diaries and mobile apps allow for easy and fast documentation of allergic symptoms via the internet. OBJECTIVE: This study aimed to characterize the users of the Patient's Hayfever Diary (PHD), a Web-based platform and mobile app, to apply different symptom score calculations for comparison, and to evaluate the contribution of organs and medications to the total score for the first time. METHODS: The PHD users were filtered with regard to their location in Austria and Germany, significant positive correlation to the respective pollen type (birch/grass), and at least 15 entries in the respective season. Furthermore, 4 different symptom score calculation methods were applied to the datasets from 2009 until 2018, of which 2 were raw symptom scores and 2 were symptom load index (normalized) calculations. Pearson correlation coefficients were calculated pairwise for these 4 symptom score calculations. RESULTS: Users were mostly male and belonged to the age groups of 21 to 40 years or >40 years. User numbers have increased in the last 5 years, especially when mobile apps were made available. The Pearson correlation coefficients showed a significant linear relationship above 0.9 among the 4 symptom score datasets and thus indicated no significant difference between the different methods of symptom score calculation. The nose contributed the most to the symptom score and determined about 40% of the score. CONCLUSIONS: The exact method of calculation of the symptom score is not critical. All computation methods show the same behavior (increase/decrease during the season). Therefore, the symptom load index is a useful computation method in all fields exploring pollen allergy, and Web-based diaries are a globally applicable tool to monitor the effect of pollen on human health via electronically generated symptom data.

The evaluation of pollen concentrations with statistical and computational methods on rooftop and on ground level in Vienna - How to include daily crowd-sourced symptom data.

BACKGROUND: It is recommended to position pollen monitoring stations on rooftop level to assure a large catchment area and to gain data that are representative for a regional scale. Herein, an investigation of the representativeness of pollen concentrations was performed for 20 pollen types in the pollen seasons 2015-2016 in Vienna for rooftop and ground level and was compared with weather data and for the first time with symptom data. METHODS: The complete data set was analyzed with various statistical methods including Spearmen correlation, ANOVA, Kolmogorov-Smirnov test and logistic regression calculation: Odds ratio and Yule's Q values. Computational intelligence methods, namely Self Organizing Maps (SOMs) were employed that are capable of describing similarities and interdependencies in an effective way taking into account the U-matrix as well. The Random Forest algorithm was selected for modeling symptom data. RESULTS: The investigation of the representativeness of pollen concentrations on rooftop and ground level concerns the progress of the season, the peak occurrences and absolute quantities. Most taxa examined showed similar patterns (e.g. Betula), while others showed differences in pollen concentrations exposure on different heights (e.g. the Poaceae family). Maximum temperature, mean temperature and humidity showed the highest influence among the weather parameters and daily pollen concentrations for the majority of taxa in both traps. CONCLUSION: The rooftop trap was identified as the more adequate one when compared with the local symptom data. Results show that symptom data correlate more with pollen concentrations measured on rooftop than with those measured on ground level.

The connection of pollen concentrations and crowd-sourced symptom data: new insights from daily and seasonal symptom load index data from 2013 to 2017 in Vienna.

BACKGROUND: Online pollen diaries and mobile applications nowadays allow easy and fast documentation of pollen allergy symptoms. Such crowd-sourced symptom data provides insights into the development and the onset of a pollen allergy. Hitherto studies of the symptom load index (SLI) showed a discrepancy between the SLI and the total pollen amount of a season, but did not analyze the daily data. METHODS: The Patient's Hayfever Diary (PHD) was used as data pool for symptom data. Symptom data of Vienna (Austria) was chosen as a large and local sample size within the study period of 2013 until 2017. The city was divided into three different areas based on equal population densities and different environmental factors. Correlation factors, regression lines, locally weighted smoothing (LOESS) curves and line plots were calculated to examine the data. RESULTS: Daily SLI and pollen concentration data correlates well and the progress of the SLI within a pollen season is mirrored by the pollen concentrations. The LOESS curves do not deviate much from the regression line and support the linearity of the symptom-pollen correlation on a daily basis. Seasonal SLI data does not follow the same pattern as the respective seasonal pollen indices. Results did not vary in the three areas within Vienna or when compared with the Eastern region of Austria showing no significant spatial variation of the SLI. DISCUSSION: Results indicate a linear relationship of the SLI and pollen concentrations/seasonal polllen index (SPIn) on a daily basis for both in general and throughout the season, but not on a seasonal basis. These findings clarify the frequent misinterpretation of the SLI as index that is tightly connected to pollen concentrations, but reflects as well the seasonal variation of the burden of pollen allergy sufferers. CONCLUSION: More than just the seasonal pollen index has to be considered when the SLI of a selected pollen season has to be explained. Cross-reactivity to other pollen types, allergen content and air pollution could play a considerable role. The similar behavior of the SLI in Vienna and a whole region indicate the feasibility of a possible symptom forecast in future and justifies the use of a single pollen monitoring station within a city of the size of Vienna.

The grass pollen season 2015: a proof of concept multi-approach study in three different European cities.

BACKGROUND: Grasses release the most widespread aeroallergens with considerable sensitization rates, while different species produce several pollen concentration peaks throughout the season. This study analyzed the prevalence of grass species in three different European city areas and compared the flowering period of these species with daily pollen concentrations and the symptom loads of grass pollen allergy sufferers. METHODS: The most prevalent grass species in Vienna (Austria), Berlin (Germany) and Turku (Finland) were studied and examined by use of three different approaches: phenology, pollen monitoring and symptom load evaluation. A mobile pollen exposure chamber was employed to observe reaction patterns of grass pollen allergy sufferers to three common grass species evaluated in this study versus placebo. RESULTS: Common meadow grass (Poa pratensis) and the fescue grass species (Festuca spp.) are important contributors within the grass pollen season. The pollination period of orchard grass (Dactylis glomerata) and false-oat grass (Arrhenatherum elatius) indicated a greater importance in Berlin and Vienna, whereas a broader spectrum of grass species contributed in Turku to the main pollen season. The standardized provocation induced a nasal symptom load, reduction in nasal flow and increased secretion, in contrary to the placebo control group in grass pollen allergic subjects. CONCLUSION: The phenological observations, pollen measurements and symptom data evaluation provided unique insights into the contribution of multiple grass species in different European regions. All investigated grass species in the provocation induced rhinitis symptoms of comparable significance, with some degree of variation in symptom patterns.

The grass pollen season 2014 in Vienna: A pilot study combining phenology, aerobiology and symptom data.

BACKGROUND: Grasses (Poaceae) are one of the largest plant families and are distributed worldwide. Grass pollen allergy is one of the most important pollen allergies affecting large parts of the population worldwide. The grass pollen season itself is special since it is caused by the flowering of various grass species that present unique profiles of allergenicity, which assumingly plays a significant role and impact on grass pollen sensitization profiles for the allergy sufferer. The aim of this study, conducted in Vienna, 2014, was to analyze the possible contribution of prevalent grass species to the grass pollen season and to the symptom load of grass allergy sufferers. METHODS: This was the first study that combines phenological observations (i.e. grass species and their flowering periods) with aerobiological measurements (i.e. daily grass pollen concentrations) in concert with allergic symptoms from local users of the Patient's Hayfever Diary (symptom load index calculation). RESULTS: Both the pollen concentrations and the symptom levels were higher in the first half of the main grass pollen season and therefore show the highest impact on pollen allergy sufferers. Of important note were the following species that are widely distributed in Vienna: Kentucky bluegrass (Poa pratensis), orchard grass (Dactylis glomerata), false oat-grass (Arrhenatherum elatius), fescue grass (Festuca sp.) and perennial rye-grass (Lolium perenne). CONCLUSION: Monitoring different grass species provided evidence for varying contribution in pollination across the main grass pollen season and highlighted the significance this impact may have on pollen allergy sufferers.

Envisioning the future in symptom science.

Symptom assessment and management continues to be a priority issue for nursing science and practice. However, as the complexity of symptom etiology and expression becomes clear, new approaches and technologies are needed to better address biology and context, common data sources need to be built and shared, and addressing the impact of empirical findings on health policy becomes essential. In this article, we provide a forum to reflect on the future direction of symptom science, with the goal of stimulating further dialogue and improving outcomes for patients and families around the world and for years to come.

Development of a telehealthcare decision support system for patients discharged from the hospital.

OBJECTIVE: This article presents the development of a telehealthcare decision support system (TDSS) for patients discharged from the hospital, where symptom data are important indications of the recovery progress for patients. Symptom data are difficult to quantify in a telehealthcare application scenario because the observations and perceptions on symptoms by the patient themselves are subjective. In the TDSS, both symptom data from patients and clinical histories from the hospital information system are collected. Machine learning algorithms are used to build a predictive model for classifying patients according to their symptom data and clinical histories, to provide a degree of urgency for the patient to return to the hospital. MATERIALS AND METHODS: During a 1-year period, 1,467 patient cases were collected. Symptom data and clinical histories were preprocessed into 49 parameters for machine learning. The training data of patients were validated manually with their actual clinical histories of returning to the hospital. The performances of predictive models trained by five different machine learning algorithms were evaluated and compared. RESULTS: The Bayesian network algorithm had the best performance among the machine learning algorithms tested in this application scenario and was selected to be implemented in the TDSS. On the 1,467 patient cases collected, its precision in 10-fold cross-validation was 79.3%. The most important six parameters were also selected from the 49 parameters by feature selection. The performance of correct prediction by the TDSS is comparable to that by the nursing team at the call center. CONCLUSIONS: The TDSS provides a degree of urgency for patients to return to the hospital and thereby assists the telehealthcare nursing team in making such decisions. The performance of the TDSS is expected to improve as more cases of patient data are collected and input into the TDSS. The TDSS has been implemented in one of the largest commercialized telehealthcare practices in Taiwan administered by Min-Sheng General Hospital.