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Development of influenza-associated disease burden pyramid in Shanghai, China, 2010–2017: a Bayesian modelling study
OBJECTIVES: Negative estimates can be produced when statistical modelling techniques are applied to estimate morbidity and mortality attributable to influenza. Based on the prior knowledge that influenza viruses are hazardous pathogens and have adverse health outcomes of respiratory and circulatory...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8438833/ https://www.ncbi.nlm.nih.gov/pubmed/34497077 http://dx.doi.org/10.1136/bmjopen-2020-047526 |
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author | Li, Jing Wang, Chunfang Ruan, Luanqi Jin, Shan Ye, Chuchu Yu, Huiting Zhu, Weiping Wang, Xiling |
author_facet | Li, Jing Wang, Chunfang Ruan, Luanqi Jin, Shan Ye, Chuchu Yu, Huiting Zhu, Weiping Wang, Xiling |
author_sort | Li, Jing |
collection | PubMed |
description | OBJECTIVES: Negative estimates can be produced when statistical modelling techniques are applied to estimate morbidity and mortality attributable to influenza. Based on the prior knowledge that influenza viruses are hazardous pathogens and have adverse health outcomes of respiratory and circulatory disease (R&C), we developed an improved model incorporating Bayes’ theorem to estimate the disease burden of influenza in Shanghai, China, from 2010 to 2017. DESIGN: A modelling study using aggregated data from administrative systems on weekly R&C mortality and hospitalisation, influenza surveillance and meteorological data. We constrained the regression coefficients for influenza activity to be positive by truncating the prior distributions at zero. SETTING: Shanghai, China. PARTICIPANTS: People registered with R&C deaths (450 298) and hospitalisations (2621 787, from 1 July 2013), and with influenza-like illness (ILI) outpatient visits (342 149) between 4 January 2010 and 31 December 2017. PRIMARY OUTCOME MEASURES: Influenza-associated disease burden (mortality, hospitalisation and outpatient visit rates) and clinical severity (outpatient–mortality, outpatient–hospitalisation and hospitalisation–mortality risks). RESULTS: Influenza was associated with an annual average of 15.49 (95% credibility interval (CrI) 9.06–22.06) excess R&C deaths, 100.65 (95% CrI 48.79–156.78) excess R&C hospitalisations and 914.95 (95% CrI 798.51–1023.66) excess ILI outpatient visits per 100 000 population in Shanghai. 97.23% and 80.24% excess R&C deaths and hospitalisations occurred in people aged ≥65 years. More than half of excess morbidity and mortality were associated with influenza A(H3N2) virus, and its severities were 1.65-fold to 3.54-fold and 1.47-fold to 2.16-fold higher than that for influenza A(H1N1) and B viruses, respectively. CONCLUSIONS: The proposed Bayesian approach with reasonable prior information improved estimates of influenza-associated disease burden. Influenza A(H3N2) virus was generally associated with higher morbidity and mortality, and was relatively more severe compared with influenza A(H1N1) and B viruses. Targeted influenza prevention and control strategies for the elderly in Shanghai may substantially reduce the disease burden. |
format | Online Article Text |
id | pubmed-8438833 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-84388332021-09-24 Development of influenza-associated disease burden pyramid in Shanghai, China, 2010–2017: a Bayesian modelling study Li, Jing Wang, Chunfang Ruan, Luanqi Jin, Shan Ye, Chuchu Yu, Huiting Zhu, Weiping Wang, Xiling BMJ Open Infectious Diseases OBJECTIVES: Negative estimates can be produced when statistical modelling techniques are applied to estimate morbidity and mortality attributable to influenza. Based on the prior knowledge that influenza viruses are hazardous pathogens and have adverse health outcomes of respiratory and circulatory disease (R&C), we developed an improved model incorporating Bayes’ theorem to estimate the disease burden of influenza in Shanghai, China, from 2010 to 2017. DESIGN: A modelling study using aggregated data from administrative systems on weekly R&C mortality and hospitalisation, influenza surveillance and meteorological data. We constrained the regression coefficients for influenza activity to be positive by truncating the prior distributions at zero. SETTING: Shanghai, China. PARTICIPANTS: People registered with R&C deaths (450 298) and hospitalisations (2621 787, from 1 July 2013), and with influenza-like illness (ILI) outpatient visits (342 149) between 4 January 2010 and 31 December 2017. PRIMARY OUTCOME MEASURES: Influenza-associated disease burden (mortality, hospitalisation and outpatient visit rates) and clinical severity (outpatient–mortality, outpatient–hospitalisation and hospitalisation–mortality risks). RESULTS: Influenza was associated with an annual average of 15.49 (95% credibility interval (CrI) 9.06–22.06) excess R&C deaths, 100.65 (95% CrI 48.79–156.78) excess R&C hospitalisations and 914.95 (95% CrI 798.51–1023.66) excess ILI outpatient visits per 100 000 population in Shanghai. 97.23% and 80.24% excess R&C deaths and hospitalisations occurred in people aged ≥65 years. More than half of excess morbidity and mortality were associated with influenza A(H3N2) virus, and its severities were 1.65-fold to 3.54-fold and 1.47-fold to 2.16-fold higher than that for influenza A(H1N1) and B viruses, respectively. CONCLUSIONS: The proposed Bayesian approach with reasonable prior information improved estimates of influenza-associated disease burden. Influenza A(H3N2) virus was generally associated with higher morbidity and mortality, and was relatively more severe compared with influenza A(H1N1) and B viruses. Targeted influenza prevention and control strategies for the elderly in Shanghai may substantially reduce the disease burden. BMJ Publishing Group 2021-09-08 /pmc/articles/PMC8438833/ /pubmed/34497077 http://dx.doi.org/10.1136/bmjopen-2020-047526 Text en © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) . |
spellingShingle | Infectious Diseases Li, Jing Wang, Chunfang Ruan, Luanqi Jin, Shan Ye, Chuchu Yu, Huiting Zhu, Weiping Wang, Xiling Development of influenza-associated disease burden pyramid in Shanghai, China, 2010–2017: a Bayesian modelling study |
title | Development of influenza-associated disease burden pyramid in Shanghai, China, 2010–2017: a Bayesian modelling study |
title_full | Development of influenza-associated disease burden pyramid in Shanghai, China, 2010–2017: a Bayesian modelling study |
title_fullStr | Development of influenza-associated disease burden pyramid in Shanghai, China, 2010–2017: a Bayesian modelling study |
title_full_unstemmed | Development of influenza-associated disease burden pyramid in Shanghai, China, 2010–2017: a Bayesian modelling study |
title_short | Development of influenza-associated disease burden pyramid in Shanghai, China, 2010–2017: a Bayesian modelling study |
title_sort | development of influenza-associated disease burden pyramid in shanghai, china, 2010–2017: a bayesian modelling study |
topic | Infectious Diseases |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8438833/ https://www.ncbi.nlm.nih.gov/pubmed/34497077 http://dx.doi.org/10.1136/bmjopen-2020-047526 |
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