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Prior morbidity and sickness absence with Covid-19 among wholesale and retail blue-collar workers
BACKGROUND: Cancer, cardiovascular disease, hypertension, diabetes, chronic kidney disorders, chronic lung disorders, neurological disorders, obesity, immunocompromisation, and schizophrenia or bipolar disorders were found to be risk factors for severe Covid-19. The aim was to determine associations...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10596797/ http://dx.doi.org/10.1093/eurpub/ckad160.1304 |
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author | Farrants, K Alexanderson, K |
author_facet | Farrants, K Alexanderson, K |
author_sort | Farrants, K |
collection | PubMed |
description | BACKGROUND: Cancer, cardiovascular disease, hypertension, diabetes, chronic kidney disorders, chronic lung disorders, neurological disorders, obesity, immunocompromisation, and schizophrenia or bipolar disorders were found to be risk factors for severe Covid-19. The aim was to determine associations between having such diagnoses and future sickness absence (SA) due to Covid-19 or related diagnoses in 2020. METHODS: A prospective population-based cohort study of all 299,268 blue-collar workers (48% women) in Sweden in 2018, using linked microdata from four nationwide registers covering 2018-2020. People with any of the above diagnoses in 2018 or 2019 were identified using microdata from specialised in- and outpatient healthcare and prescribed drugs. Logistic regression was used to calculate the odds ratio (OR) and 95% confidence interval (CI) of a new SA spell >14 days due to diagnosed Covid-19 or one of the diagnoses used by the Social Insurance Agency for suspected Covid-19 in 2020, separately for each diagnosis group and a global estimate for all risk factors together, adjusted for sociodemographic characteristics. RESULTS: In total, 38,422 individuals had at least one of the diagnoses that was a risk factor for severe Covid-19. Of them, 284 individuals had SA due to Covid-19 or related diagnoses (0.7%) in 2020, while 1210 of those with no such risk factor for Covid-19 had SA due to Covid-19 or related diagnoses (0.5%). The adjusted OR of SA due to Covid-19 or related diagnoses was 1.37 (95% CI 1.20-1.56) for those who had any risk factors for severe Covid-19. The group of diagnoses with the highest adjusted OR for SA in Covid-19-related diagnoses was lung disease (2.23; 95% CI 1.93-2.56). CONCLUSIONS: While only a few of those with prior morbidity that was a risk factor for severe Covid-19 had SA due to Covid-19 or related diagnoses in 2020, they had a higher risk than those without such prior morbidity. KEY MESSAGES: • Having prior morbidity with a diagnosis identified as a risk factor for severe Covid-19 was associated with a higher risk of having SA due to Covid-19 or related diagnoses in 2020. • Prior lung disease was the diagnosis group with the highest risk of SA due to Covid-19 or related diagnoses. |
format | Online Article Text |
id | pubmed-10596797 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-105967972023-10-25 Prior morbidity and sickness absence with Covid-19 among wholesale and retail blue-collar workers Farrants, K Alexanderson, K Eur J Public Health Poster Displays BACKGROUND: Cancer, cardiovascular disease, hypertension, diabetes, chronic kidney disorders, chronic lung disorders, neurological disorders, obesity, immunocompromisation, and schizophrenia or bipolar disorders were found to be risk factors for severe Covid-19. The aim was to determine associations between having such diagnoses and future sickness absence (SA) due to Covid-19 or related diagnoses in 2020. METHODS: A prospective population-based cohort study of all 299,268 blue-collar workers (48% women) in Sweden in 2018, using linked microdata from four nationwide registers covering 2018-2020. People with any of the above diagnoses in 2018 or 2019 were identified using microdata from specialised in- and outpatient healthcare and prescribed drugs. Logistic regression was used to calculate the odds ratio (OR) and 95% confidence interval (CI) of a new SA spell >14 days due to diagnosed Covid-19 or one of the diagnoses used by the Social Insurance Agency for suspected Covid-19 in 2020, separately for each diagnosis group and a global estimate for all risk factors together, adjusted for sociodemographic characteristics. RESULTS: In total, 38,422 individuals had at least one of the diagnoses that was a risk factor for severe Covid-19. Of them, 284 individuals had SA due to Covid-19 or related diagnoses (0.7%) in 2020, while 1210 of those with no such risk factor for Covid-19 had SA due to Covid-19 or related diagnoses (0.5%). The adjusted OR of SA due to Covid-19 or related diagnoses was 1.37 (95% CI 1.20-1.56) for those who had any risk factors for severe Covid-19. The group of diagnoses with the highest adjusted OR for SA in Covid-19-related diagnoses was lung disease (2.23; 95% CI 1.93-2.56). CONCLUSIONS: While only a few of those with prior morbidity that was a risk factor for severe Covid-19 had SA due to Covid-19 or related diagnoses in 2020, they had a higher risk than those without such prior morbidity. KEY MESSAGES: • Having prior morbidity with a diagnosis identified as a risk factor for severe Covid-19 was associated with a higher risk of having SA due to Covid-19 or related diagnoses in 2020. • Prior lung disease was the diagnosis group with the highest risk of SA due to Covid-19 or related diagnoses. Oxford University Press 2023-10-24 /pmc/articles/PMC10596797/ http://dx.doi.org/10.1093/eurpub/ckad160.1304 Text en © The Author(s) 2023. Published by Oxford University Press on behalf of the European Public Health Association. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Poster Displays Farrants, K Alexanderson, K Prior morbidity and sickness absence with Covid-19 among wholesale and retail blue-collar workers |
title | Prior morbidity and sickness absence with Covid-19 among wholesale and retail blue-collar workers |
title_full | Prior morbidity and sickness absence with Covid-19 among wholesale and retail blue-collar workers |
title_fullStr | Prior morbidity and sickness absence with Covid-19 among wholesale and retail blue-collar workers |
title_full_unstemmed | Prior morbidity and sickness absence with Covid-19 among wholesale and retail blue-collar workers |
title_short | Prior morbidity and sickness absence with Covid-19 among wholesale and retail blue-collar workers |
title_sort | prior morbidity and sickness absence with covid-19 among wholesale and retail blue-collar workers |
topic | Poster Displays |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10596797/ http://dx.doi.org/10.1093/eurpub/ckad160.1304 |
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