Aims: To investigate for the first time the short-term effects of airborne pollen counts on general practitioner (GP) consultations for asthma attacks in the Greater Paris area between 2003-2007.
Methods: Counts were available for common pollens (Betula, Cupressa, Fraxinus and Poaceae). Weekly data on GP visits for asthma attacks were obtained from the French GP Sentinel Network. A quasi-Poisson regression with generalised additive models was implemented. Short-term effects of pollen counts were assessed using single and multi-pollen models after adjustment for air pollution and influenza.
Results: A mean weekly incidence rate of 25.4 cases of asthma attacks per 100,000 inhabitants was estimated during the study period. The strongest significant association between asthma attacks and pollen counts was registered for grass (Poaceae) in the same week of asthma attacks, with a slight reduction of the effect observed in the multi-pollen model. Adjusted relative risk for Poaceae was 1.54 (95% CI: 1.33-1.79) with an inter-quartile range increase of 17.6 grains/m3 during the pollen season.
Conclusions: For the first time, a significant short-term association was observed between Poaceae pollen counts and consultations for asthma attacks as seen by GPs. These findings need to be confirmed by more consistent time-series and investigations on a daily basis.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6602237 | PMC |
http://dx.doi.org/10.4104/pcrj.2010.00027 | DOI Listing |
IntroductionAsthma attacks are set off by triggers such as pollutants from the environment, respiratory viruses, physical activity and allergens. The aim of this research is to create a machine learning model using data from mobile health technology to predict and appropriately warn a patient to avoid such triggers.MethodsLightweight machine learning models, XGBoost, Random Forest, and LightGBM were trained and tested on cleaned asthma data with a 70-30 train-test split.
View Article and Find Full Text PDFBMC Med Inform Decis Mak
January 2025
Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Electronic health records (EHRs) provide a rich source of observational patient data that can be explored to infer underlying causal relationships. These causal relationships can be applied to augment medical decision-making or suggest hypotheses for healthcare research. In this study, we explored a large-scale EHR dataset on patients with asthma or related conditions (N = 14,937).
View Article and Find Full Text PDFWorld Allergy Organ J
January 2025
Institute of Life Science, Chongqing Medical University, Chongqing, China.
Background: Allergic rhinitis (AR) is a common chronic respiratory disease that can lead to the development of various other conditions. Although genetic risk loci associated with AR have been reported, the connections between these loci and AR comorbidities or other diseases remain unclear.
Methods: This study conducted a phenome-wide association study (PheWAS) using known AR risk loci to explore the impact of known AR risk variants on a broad spectrum of phenotypes.
Zhonghua Yi Xue Za Zhi
January 2025
Department of Respiratory and Critical Care Medicine, Nanfang Hospital, Southern Medical University, Guangzhou510515, China.
To investigate the characteristics of type 2 inflammation in patients with nocturnal asthma, and analyze the improvement of asthma symptoms after the use of inhaled corticosteroids (ICS) combined with different long-acting bronchodilators. Data of 231 asthma patients who first visited the Respiratory and Critical Care Medical Clinic of Nanfang Hospital of Southern Medical University from January 2020 to June 2023 and had positive bronchodilator tests (BDT), were retrospectively analyzed. These patients were divided into nocturnal asthma group and non-nocturnal asthma group based on the presence or absence of nocturnal symptoms.
View Article and Find Full Text PDFImmunotherapy
January 2025
kThoraxklinik Heidelberg, Heidelberg, Germany; lIKF Pneumologie, Mainz, Germany.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!