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Identifying long-term effects of SARS-CoV-2 and their association with social determinants of health in a cohort of over one million COVID-19 survivors

BACKGROUND: Despite an abundance of information on the risk factors of SARS-CoV-2, there have been few US-wide studies of long-term effects. In this paper we analyzed a large medical claims database of US based individuals to identify common long-term effects as well as their associations with vario...

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Autores principales: Mukherjee, Sumit, Kshirsagar, Meghana, Becker, Nicholas, Xu, Yixi, Weeks, William B., Patel, Shwetak, Ferres, Juan Lavista, Jackson, Michael L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9765366/
https://www.ncbi.nlm.nih.gov/pubmed/36539760
http://dx.doi.org/10.1186/s12889-022-14806-1
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author Mukherjee, Sumit
Kshirsagar, Meghana
Becker, Nicholas
Xu, Yixi
Weeks, William B.
Patel, Shwetak
Ferres, Juan Lavista
Jackson, Michael L.
author_facet Mukherjee, Sumit
Kshirsagar, Meghana
Becker, Nicholas
Xu, Yixi
Weeks, William B.
Patel, Shwetak
Ferres, Juan Lavista
Jackson, Michael L.
author_sort Mukherjee, Sumit
collection PubMed
description BACKGROUND: Despite an abundance of information on the risk factors of SARS-CoV-2, there have been few US-wide studies of long-term effects. In this paper we analyzed a large medical claims database of US based individuals to identify common long-term effects as well as their associations with various social and medical risk factors. METHODS: The medical claims database was obtained from a prominent US based claims data processing company, namely Change Healthcare. In addition to the claims data, the dataset also consisted of various social determinants of health such as race, income, education level and veteran status of the individuals. A self-controlled cohort design (SCCD) observational study was performed to identify ICD-10 codes whose proportion was significantly increased in the outcome period compared to the control period to identify significant long-term effects. A logistic regression-based association analysis was then performed between identified long-term effects and social determinants of health. RESULTS: Among the over 1.37 million COVID patients in our datasets we found 36 out of 1724 3-digit ICD-10 codes to be statistically significantly increased in the post-COVID period (p-value < 0.05). We also found one combination of ICD-10 codes, corresponding to ‘other anemias’ and ‘hypertension’, that was statistically significantly increased in the post-COVID period (p-value < 0.05). Our logistic regression-based association analysis with social determinants of health variables, after adjusting for comorbidities and prior conditions, showed that age and gender were significantly associated with the multiple long-term effects. Race was only associated with ‘other sepsis’, income was only associated with ‘Alopecia areata’ (autoimmune disease causing hair loss), while education level was only associated with ‘Maternal infectious and parasitic diseases’ (p-value < 0.05). CONCLUSION: We identified several long-term effects of SARS-CoV-2 through a self-controlled study on a cohort of over one million patients. Furthermore, we found that while age and gender are commonly associated with the long-term effects, other social determinants of health such as race, income and education levels have rare or no significant associations. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-022-14806-1.
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spelling pubmed-97653662022-12-21 Identifying long-term effects of SARS-CoV-2 and their association with social determinants of health in a cohort of over one million COVID-19 survivors Mukherjee, Sumit Kshirsagar, Meghana Becker, Nicholas Xu, Yixi Weeks, William B. Patel, Shwetak Ferres, Juan Lavista Jackson, Michael L. BMC Public Health Research BACKGROUND: Despite an abundance of information on the risk factors of SARS-CoV-2, there have been few US-wide studies of long-term effects. In this paper we analyzed a large medical claims database of US based individuals to identify common long-term effects as well as their associations with various social and medical risk factors. METHODS: The medical claims database was obtained from a prominent US based claims data processing company, namely Change Healthcare. In addition to the claims data, the dataset also consisted of various social determinants of health such as race, income, education level and veteran status of the individuals. A self-controlled cohort design (SCCD) observational study was performed to identify ICD-10 codes whose proportion was significantly increased in the outcome period compared to the control period to identify significant long-term effects. A logistic regression-based association analysis was then performed between identified long-term effects and social determinants of health. RESULTS: Among the over 1.37 million COVID patients in our datasets we found 36 out of 1724 3-digit ICD-10 codes to be statistically significantly increased in the post-COVID period (p-value < 0.05). We also found one combination of ICD-10 codes, corresponding to ‘other anemias’ and ‘hypertension’, that was statistically significantly increased in the post-COVID period (p-value < 0.05). Our logistic regression-based association analysis with social determinants of health variables, after adjusting for comorbidities and prior conditions, showed that age and gender were significantly associated with the multiple long-term effects. Race was only associated with ‘other sepsis’, income was only associated with ‘Alopecia areata’ (autoimmune disease causing hair loss), while education level was only associated with ‘Maternal infectious and parasitic diseases’ (p-value < 0.05). CONCLUSION: We identified several long-term effects of SARS-CoV-2 through a self-controlled study on a cohort of over one million patients. Furthermore, we found that while age and gender are commonly associated with the long-term effects, other social determinants of health such as race, income and education levels have rare or no significant associations. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-022-14806-1. BioMed Central 2022-12-20 /pmc/articles/PMC9765366/ /pubmed/36539760 http://dx.doi.org/10.1186/s12889-022-14806-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://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
Mukherjee, Sumit
Kshirsagar, Meghana
Becker, Nicholas
Xu, Yixi
Weeks, William B.
Patel, Shwetak
Ferres, Juan Lavista
Jackson, Michael L.
Identifying long-term effects of SARS-CoV-2 and their association with social determinants of health in a cohort of over one million COVID-19 survivors
title Identifying long-term effects of SARS-CoV-2 and their association with social determinants of health in a cohort of over one million COVID-19 survivors
title_full Identifying long-term effects of SARS-CoV-2 and their association with social determinants of health in a cohort of over one million COVID-19 survivors
title_fullStr Identifying long-term effects of SARS-CoV-2 and their association with social determinants of health in a cohort of over one million COVID-19 survivors
title_full_unstemmed Identifying long-term effects of SARS-CoV-2 and their association with social determinants of health in a cohort of over one million COVID-19 survivors
title_short Identifying long-term effects of SARS-CoV-2 and their association with social determinants of health in a cohort of over one million COVID-19 survivors
title_sort identifying long-term effects of sars-cov-2 and their association with social determinants of health in a cohort of over one million covid-19 survivors
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9765366/
https://www.ncbi.nlm.nih.gov/pubmed/36539760
http://dx.doi.org/10.1186/s12889-022-14806-1
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