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Longitudinal Asthma Patterns in Italian Adult General Population Samples: Host and Environmental Risk Factors

Background: Asthma patterns are not well established in epidemiological studies. Aim: To assess asthma patterns and risk factors in an adult general population sample. Methods: In total, 452 individuals reporting asthma symptoms/diagnosis in previous surveys participated in the AGAVE survey (2011–20...

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Autores principales: Maio, Sara, Baldacci, Sandra, Simoni, Marzia, Angino, Anna, La Grutta, Stefania, Muggeo, Vito, Fasola, Salvatore, Viegi, Giovanni
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7696248/
https://www.ncbi.nlm.nih.gov/pubmed/33187300
http://dx.doi.org/10.3390/jcm9113632
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author Maio, Sara
Baldacci, Sandra
Simoni, Marzia
Angino, Anna
La Grutta, Stefania
Muggeo, Vito
Fasola, Salvatore
Viegi, Giovanni
author_facet Maio, Sara
Baldacci, Sandra
Simoni, Marzia
Angino, Anna
La Grutta, Stefania
Muggeo, Vito
Fasola, Salvatore
Viegi, Giovanni
author_sort Maio, Sara
collection PubMed
description Background: Asthma patterns are not well established in epidemiological studies. Aim: To assess asthma patterns and risk factors in an adult general population sample. Methods: In total, 452 individuals reporting asthma symptoms/diagnosis in previous surveys participated in the AGAVE survey (2011–2014). Latent transition analysis (LTA) was performed to detect baseline and 12-month follow-up asthma phenotypes and longitudinal patterns. Risk factors associated with longitudinal patterns were assessed through multinomial logistic regression. Results: LTA detected four longitudinal patterns: persistent asthma diagnosis with symptoms, 27.2%; persistent asthma diagnosis without symptoms, 4.6%; persistent asthma symptoms without diagnosis, 44.0%; and ex -asthma, 24.1%. The longitudinal patterns were differently associated with asthma comorbidities. Persistent asthma diagnosis with symptoms showed associations with passive smoke (OR 2.64, 95% CI 1.10–6.33) and traffic exposure (OR 1.86, 95% CI 1.02–3.38), while persistent asthma symptoms (without diagnosis) with passive smoke (OR 3.28, 95% CI 1.41–7.66) and active smoke (OR 6.24, 95% CI 2.68–14.51). Conclusions: LTA identified three cross-sectional phenotypes and their four longitudinal patterns in a real-life setting. The results highlight the necessity of a careful monitoring of exposure to active/passive smoke and vehicular traffic, possible determinants of occurrence of asthma symptoms (with or without diagnosis). Such information could help affected patients and physicians in prevention and management strategies.
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spelling pubmed-76962482020-11-29 Longitudinal Asthma Patterns in Italian Adult General Population Samples: Host and Environmental Risk Factors Maio, Sara Baldacci, Sandra Simoni, Marzia Angino, Anna La Grutta, Stefania Muggeo, Vito Fasola, Salvatore Viegi, Giovanni J Clin Med Article Background: Asthma patterns are not well established in epidemiological studies. Aim: To assess asthma patterns and risk factors in an adult general population sample. Methods: In total, 452 individuals reporting asthma symptoms/diagnosis in previous surveys participated in the AGAVE survey (2011–2014). Latent transition analysis (LTA) was performed to detect baseline and 12-month follow-up asthma phenotypes and longitudinal patterns. Risk factors associated with longitudinal patterns were assessed through multinomial logistic regression. Results: LTA detected four longitudinal patterns: persistent asthma diagnosis with symptoms, 27.2%; persistent asthma diagnosis without symptoms, 4.6%; persistent asthma symptoms without diagnosis, 44.0%; and ex -asthma, 24.1%. The longitudinal patterns were differently associated with asthma comorbidities. Persistent asthma diagnosis with symptoms showed associations with passive smoke (OR 2.64, 95% CI 1.10–6.33) and traffic exposure (OR 1.86, 95% CI 1.02–3.38), while persistent asthma symptoms (without diagnosis) with passive smoke (OR 3.28, 95% CI 1.41–7.66) and active smoke (OR 6.24, 95% CI 2.68–14.51). Conclusions: LTA identified three cross-sectional phenotypes and their four longitudinal patterns in a real-life setting. The results highlight the necessity of a careful monitoring of exposure to active/passive smoke and vehicular traffic, possible determinants of occurrence of asthma symptoms (with or without diagnosis). Such information could help affected patients and physicians in prevention and management strategies. MDPI 2020-11-11 /pmc/articles/PMC7696248/ /pubmed/33187300 http://dx.doi.org/10.3390/jcm9113632 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Maio, Sara
Baldacci, Sandra
Simoni, Marzia
Angino, Anna
La Grutta, Stefania
Muggeo, Vito
Fasola, Salvatore
Viegi, Giovanni
Longitudinal Asthma Patterns in Italian Adult General Population Samples: Host and Environmental Risk Factors
title Longitudinal Asthma Patterns in Italian Adult General Population Samples: Host and Environmental Risk Factors
title_full Longitudinal Asthma Patterns in Italian Adult General Population Samples: Host and Environmental Risk Factors
title_fullStr Longitudinal Asthma Patterns in Italian Adult General Population Samples: Host and Environmental Risk Factors
title_full_unstemmed Longitudinal Asthma Patterns in Italian Adult General Population Samples: Host and Environmental Risk Factors
title_short Longitudinal Asthma Patterns in Italian Adult General Population Samples: Host and Environmental Risk Factors
title_sort longitudinal asthma patterns in italian adult general population samples: host and environmental risk factors
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7696248/
https://www.ncbi.nlm.nih.gov/pubmed/33187300
http://dx.doi.org/10.3390/jcm9113632
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