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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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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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. |
format | Online Article Text |
id | pubmed-9765366 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
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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