Pollen exposure and hospitalization due to asthma exacerbations: daily time series in a European city.

Exposure to pollen can contribute to increased hospital admissions for asthma exacerbation. This study applied an ecological time series analysis to examine associations between atmospheric concentrations of different pollen types and the risk of hospitalization for asthma in London from 2005 to 2011. The analysis examined short-term associations between daily pollen counts and hospital admissions in the presence of seasonal and long-term patterns, and allowed for time lags between exposure and admission. Models were adjusted for temperature, precipitation, humidity, day of week, and air pollutants. Analyses revealed an association between daily counts (continuous) of grass pollen and adult hospital admissions for asthma in London, with a 4-5-day lag. When grass pollen concentrations were categorized into Met Office pollen 'alert' levels, 'very high' days (vs. 'low') were associated with increased admissions 2-5 days later, peaking at an incidence rate ratio of 1.46 (95%, CI 1.20-1.78) at 3 days. Increased admissions were also associated with 'high' versus 'low' pollen days at a 3-day lag. Results from tree pollen models were inconclusive and likely to have been affected by the shorter pollen seasons and consequent limited number of observation days with higher tree pollen concentrations. Future reductions in asthma hospitalizations may be achieved by better understanding of environmental risks, informing improved alert systems and supporting patients to take preventive measures

Environmental DNA reveals links between abundance and composition of airborne grass pollen and respiratory health

This is the final version. Available on open access from Elsevier via the DOI in this recordData and Code Availability Statement: Data collected using qPCR is archived and on NERC EIDC [https://doi.org/10.5285/28208be4-0163-45e6-912c-2db205126925]. Standard pollen monitoring ‘count’ data were sourced from the MEDMI database, with the exception of data from Bangor which were produced as part of the present study and are available on request. Prescribing datasets are publicly available, as are weather, air pollution, deprivation (IMD) and rural-urban category data. Hospital episode statistics (HES) datasets are sensitive, individual-level health data, which are subject to strict privacy regulations and are not publicly available. The study did not generate any unique codeGrass (Poaceae) pollen is the most important outdoor aeroallergen, exacerbating a range of respiratory conditions, including allergic asthma and rhinitis (‘hay fever’). Understanding the relationships between respiratory diseases and airborne grass pollen with view to improving forecasting has broad public health and socioeconomic relevance. It is estimated that there are over 400 million people with allergic rhinitis and over 300 million with asthma, globally, often comorbidly . In the UK, allergic asthma has an annual cost of around US$ 2.8 billion (2017). The relative contributions of the >11,000 (worldwide) grass species to respiratory health have been unresolved, as grass pollen cannot be readily discriminated using standard microscopy. Instead, here we used novel environmental DNA (eDNA) sampling and quantitative PCR (qPCR) , to measure the relative abundances of airborne pollen from common grass species, during two grass pollen seasons (2016 and 2017), across the UK. We quantitatively demonstrate discrete spatiotemporal patterns in airborne grass pollen assemblages. Using a series of generalised additive models (GAMs), we explore the relationship between the incidences of airborne pollen and severe asthma exacerbations (sub-weekly) and prescribing rates of drugs for respiratory allergies (monthly). Our results indicate that a subset of grass species may have disproportionate influence on these population-scale respiratory health responses during peak grass pollen concentrations. The work demonstrates the need for sensitive and detailed biomonitoring of harmful aeroallergens in order to investigate and mitigate their impacts on human health.Natural Environment Research Council (NERC)National Institute for Health Research (NIHR)Public Health EnglandUniversity of ExeterUniversity College LondonMet Offic

Temperate airborne grass pollen defined by spatio-temporal shifts in community composition

This is the author accepted manuscript. The final version is available from Nature Research via the DOI in this record.Grass pollen is the world’s most harmful outdoor aeroallergen. However, it is unknown how airborne pollen assemblages change across time and space. Human sensitivity varies between different species of grass that flower at different times, but it is not known whether temporal turnover in species composition match terrestrial flowering or whether species richness steadily accumulates over the grass pollen season. Here, using targeted, high-throughput sequencing, we demonstrate that all grass genera displayed discrete, temporally restricted peaks of incidence, which varied with latitude and longitude throughout Great Britain, revealing that the taxonomic composition of grass pollen exposure changes substantially across the grass pollen season.Natural Environment Research CouncilBiotechnology and Biological Sciences Research Council (BBSRC

Predicting the severity of the grass pollen season and the effect of climate change in Northwest Europe

Allergic rhinitis is an inflammation in the nose caused by overreaction of the immune system to allergens in the air. Managing allergic rhinitis symptoms is challenging and requires timely intervention. The following are major questions often posed by those with allergic rhinitis: How should I prepare for the forthcoming season? How will the season's severity develop over the years? No country yet provides clear guidance addressing these questions. We propose two previously unexplored approaches for forecasting the severity of the grass pollen season on the basis of statistical and mechanistic models. The results suggest annual severity is largely governed by preseasonal meteorological conditions. The mechanistic model suggests climate change will increase the season severity by up to 60%, in line with experimental chamber studies. These models can be used as forecasting tools for advising individuals with hay fever and health care professionals how to prepare for the grass pollen season