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Household presentation of influenza and acute respiratory illnesses to a primary care sentinel network: retrospective database studies (2013–2018)

BACKGROUND: Direct observation of the household spread of influenza and respiratory infections is limited; much of our understanding comes from mathematical models. The study aims to determine household incidence of influenza-like illness (ILI), lower (LRTI) and upper (URTI) respiratory infections w...

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Autores principales: de Lusignan, Simon, Sherlock, Julian, Akinyemi, Oluwafunmi, Pebody, Richard, Elliot, Alex, Byford, Rachel, Yonova, Ivelina, Zambon, Maria, Joy, Mark
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7677442/
https://www.ncbi.nlm.nih.gov/pubmed/33218318
http://dx.doi.org/10.1186/s12889-020-09790-3
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author de Lusignan, Simon
Sherlock, Julian
Akinyemi, Oluwafunmi
Pebody, Richard
Elliot, Alex
Byford, Rachel
Yonova, Ivelina
Zambon, Maria
Joy, Mark
author_facet de Lusignan, Simon
Sherlock, Julian
Akinyemi, Oluwafunmi
Pebody, Richard
Elliot, Alex
Byford, Rachel
Yonova, Ivelina
Zambon, Maria
Joy, Mark
author_sort de Lusignan, Simon
collection PubMed
description BACKGROUND: Direct observation of the household spread of influenza and respiratory infections is limited; much of our understanding comes from mathematical models. The study aims to determine household incidence of influenza-like illness (ILI), lower (LRTI) and upper (URTI) respiratory infections within a primary care routine data and identify factors associated with the diseases’ incidence. METHODS: We conducted two five-year retrospective analyses of influenza-like illness (ILI), lower (LRTI) and upper (URTI) respiratory infections using the England Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) primary care sentinel network database; a cross-sectional study reporting incident rate ratio (IRR) from a negative binomial model and a retrospective cohort study, using a shared gamma frailty survival model, reporting hazard ratios (HR). We reported the following household characteristics: children < 5 years old, each extra household member, gender, ethnicity (reference white), chronic disease, pregnancy, and rurality. RESULTS: The IRR where there was a child < 5 years were 1·62 (1·38–1·89, p < 0·0001), 2·40 (2.04–2.83, p < 0·0001) and 4·46 (3.79–5.255, p < 0·0001) for ILI, LRTI and URTI respectively. IRR also increased with household size, rurality and presentations and by female gender, compared to male. Household incidence of URTI and LRTI changed little between years whereas influenza did and were greater in years with lower vaccine effectiveness. The HR where there was a child < 5 years were 2·34 (95%CI 1·88–2·90, p < 0·0001), 2·97 (95%CI 2·76–3·2, p < 0·0001) and 10·32 (95%CI 10.04–10.62, p < 0·0001) for ILI, LRTI and URTI respectively. HR were increased with female gender, rurality, and increasing household size. CONCLUSIONS: Patterns of household incidence can be measured from routine data and may provide insights for the modelling of disease transmission and public health policy. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-020-09790-3.
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spelling pubmed-76774422020-11-20 Household presentation of influenza and acute respiratory illnesses to a primary care sentinel network: retrospective database studies (2013–2018) de Lusignan, Simon Sherlock, Julian Akinyemi, Oluwafunmi Pebody, Richard Elliot, Alex Byford, Rachel Yonova, Ivelina Zambon, Maria Joy, Mark BMC Public Health Research Article BACKGROUND: Direct observation of the household spread of influenza and respiratory infections is limited; much of our understanding comes from mathematical models. The study aims to determine household incidence of influenza-like illness (ILI), lower (LRTI) and upper (URTI) respiratory infections within a primary care routine data and identify factors associated with the diseases’ incidence. METHODS: We conducted two five-year retrospective analyses of influenza-like illness (ILI), lower (LRTI) and upper (URTI) respiratory infections using the England Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) primary care sentinel network database; a cross-sectional study reporting incident rate ratio (IRR) from a negative binomial model and a retrospective cohort study, using a shared gamma frailty survival model, reporting hazard ratios (HR). We reported the following household characteristics: children < 5 years old, each extra household member, gender, ethnicity (reference white), chronic disease, pregnancy, and rurality. RESULTS: The IRR where there was a child < 5 years were 1·62 (1·38–1·89, p < 0·0001), 2·40 (2.04–2.83, p < 0·0001) and 4·46 (3.79–5.255, p < 0·0001) for ILI, LRTI and URTI respectively. IRR also increased with household size, rurality and presentations and by female gender, compared to male. Household incidence of URTI and LRTI changed little between years whereas influenza did and were greater in years with lower vaccine effectiveness. The HR where there was a child < 5 years were 2·34 (95%CI 1·88–2·90, p < 0·0001), 2·97 (95%CI 2·76–3·2, p < 0·0001) and 10·32 (95%CI 10.04–10.62, p < 0·0001) for ILI, LRTI and URTI respectively. HR were increased with female gender, rurality, and increasing household size. CONCLUSIONS: Patterns of household incidence can be measured from routine data and may provide insights for the modelling of disease transmission and public health policy. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-020-09790-3. BioMed Central 2020-11-20 /pmc/articles/PMC7677442/ /pubmed/33218318 http://dx.doi.org/10.1186/s12889-020-09790-3 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
de Lusignan, Simon
Sherlock, Julian
Akinyemi, Oluwafunmi
Pebody, Richard
Elliot, Alex
Byford, Rachel
Yonova, Ivelina
Zambon, Maria
Joy, Mark
Household presentation of influenza and acute respiratory illnesses to a primary care sentinel network: retrospective database studies (2013–2018)
title Household presentation of influenza and acute respiratory illnesses to a primary care sentinel network: retrospective database studies (2013–2018)
title_full Household presentation of influenza and acute respiratory illnesses to a primary care sentinel network: retrospective database studies (2013–2018)
title_fullStr Household presentation of influenza and acute respiratory illnesses to a primary care sentinel network: retrospective database studies (2013–2018)
title_full_unstemmed Household presentation of influenza and acute respiratory illnesses to a primary care sentinel network: retrospective database studies (2013–2018)
title_short Household presentation of influenza and acute respiratory illnesses to a primary care sentinel network: retrospective database studies (2013–2018)
title_sort household presentation of influenza and acute respiratory illnesses to a primary care sentinel network: retrospective database studies (2013–2018)
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7677442/
https://www.ncbi.nlm.nih.gov/pubmed/33218318
http://dx.doi.org/10.1186/s12889-020-09790-3
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