Pollen grading prediction scale for patients with Artemisia pollen allergy in China: A 3‐day moving predictive model
Abstract Background Artemisia pollen is the most prevalent outdoor aeroallergen causing respiratory allergies in Beijing, China. Pollen allergen concentrations have a direct impact on the quality of life of those suffering from allergies. Artemisia pollen deposition grading predictions can provide e...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-07-01
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| Series: | Clinical and Translational Allergy |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/clt2.12280 |
| _version_ | 1797773237534326784 |
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| author | Zhaoyin Yin Yuhui Ouyang Bing Dang Luo Zhang |
| author_facet | Zhaoyin Yin Yuhui Ouyang Bing Dang Luo Zhang |
| author_sort | Zhaoyin Yin |
| collection | DOAJ |
| description | Abstract Background Artemisia pollen is the most prevalent outdoor aeroallergen causing respiratory allergies in Beijing, China. Pollen allergen concentrations have a direct impact on the quality of life of those suffering from allergies. Artemisia pollen deposition grading predictions can provide early warning for the protection and treatment of patients as well as provide a scientific basis for allergen specific clinical immunotherapy. Objective To develop a model of Artemisia pollen grading to predict development in patients with pollen allergy. Methods Artemisia pollen data from four pollen monitoring stations in Beijing as well as the number of Artemisia pollen allergen serum specific immunoglobulin E positive cases in Beijing Tongren Hospital from 2014 to 2016 were used to develop a statistical model of pollen deposition and provide optimised threshold values. Results A logarithmic correlation existed between the number of patients with Artemisia pollen allergy and Artemisia pollen deposition, and the average pollen deposition for three consecutive days was most correlated with the number of allergic patients. Based on the threshold of the number of patients and the characteristics of Artemisia pollen, a five‐stage pollen deposition grading model was developed to predict the degree of pollen allergy. Conclusions Graded prediction of pollen deposition may help pollen allergic populations benefit from preventive interventions before onset. |
| first_indexed | 2024-03-12T22:02:29Z |
| format | Article |
| id | doaj.art-d1ff4abb65944d56a73bbe510af97212 |
| institution | Directory Open Access Journal |
| issn | 2045-7022 |
| language | English |
| last_indexed | 2024-03-12T22:02:29Z |
| publishDate | 2023-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | Clinical and Translational Allergy |
| spelling | doaj.art-d1ff4abb65944d56a73bbe510af972122023-07-25T04:32:34ZengWileyClinical and Translational Allergy2045-70222023-07-01137n/an/a10.1002/clt2.12280Pollen grading prediction scale for patients with Artemisia pollen allergy in China: A 3‐day moving predictive modelZhaoyin Yin0Yuhui Ouyang1Bing Dang2Luo Zhang3Institute of Urban Meteorology China Meteorological Administration Beijing ChinaDepartment of Allergy Beijing Tongren Hospital Capital Medical University Beijing ChinaBeijing Municipal Climate Center Beijing ChinaDepartment of Allergy Beijing Tongren Hospital Capital Medical University Beijing ChinaAbstract Background Artemisia pollen is the most prevalent outdoor aeroallergen causing respiratory allergies in Beijing, China. Pollen allergen concentrations have a direct impact on the quality of life of those suffering from allergies. Artemisia pollen deposition grading predictions can provide early warning for the protection and treatment of patients as well as provide a scientific basis for allergen specific clinical immunotherapy. Objective To develop a model of Artemisia pollen grading to predict development in patients with pollen allergy. Methods Artemisia pollen data from four pollen monitoring stations in Beijing as well as the number of Artemisia pollen allergen serum specific immunoglobulin E positive cases in Beijing Tongren Hospital from 2014 to 2016 were used to develop a statistical model of pollen deposition and provide optimised threshold values. Results A logarithmic correlation existed between the number of patients with Artemisia pollen allergy and Artemisia pollen deposition, and the average pollen deposition for three consecutive days was most correlated with the number of allergic patients. Based on the threshold of the number of patients and the characteristics of Artemisia pollen, a five‐stage pollen deposition grading model was developed to predict the degree of pollen allergy. Conclusions Graded prediction of pollen deposition may help pollen allergic populations benefit from preventive interventions before onset.https://doi.org/10.1002/clt2.12280Artemisia pollenBeijingChinapollen allergypollen deposition graded prediction |
| spellingShingle | Zhaoyin Yin Yuhui Ouyang Bing Dang Luo Zhang Pollen grading prediction scale for patients with Artemisia pollen allergy in China: A 3‐day moving predictive model Clinical and Translational Allergy Artemisia pollen Beijing China pollen allergy pollen deposition graded prediction |
| title | Pollen grading prediction scale for patients with Artemisia pollen allergy in China: A 3‐day moving predictive model |
| title_full | Pollen grading prediction scale for patients with Artemisia pollen allergy in China: A 3‐day moving predictive model |
| title_fullStr | Pollen grading prediction scale for patients with Artemisia pollen allergy in China: A 3‐day moving predictive model |
| title_full_unstemmed | Pollen grading prediction scale for patients with Artemisia pollen allergy in China: A 3‐day moving predictive model |
| title_short | Pollen grading prediction scale for patients with Artemisia pollen allergy in China: A 3‐day moving predictive model |
| title_sort | pollen grading prediction scale for patients with artemisia pollen allergy in china a 3 day moving predictive model |
| topic | Artemisia pollen Beijing China pollen allergy pollen deposition graded prediction |
| url | https://doi.org/10.1002/clt2.12280 |
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