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
© 2022. The Author(s)..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:22 |
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| Enthalten in: |
BMC public health - 22(2022), 1 vom: 20. Dez., Seite 2394 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Mukherjee, Sumit [VerfasserIn] |
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| Links: |
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| Themen: |
Infectious diseases |
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| Anmerkungen: |
Date Completed 22.12.2022 Date Revised 23.12.2022 published: Electronic Citation Status MEDLINE |
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| doi: |
10.1186/s12889-022-14806-1 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM350513996 |
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| 100 | 1 | |a Mukherjee, Sumit |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a 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 |
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| 500 | |a Date Revised 23.12.2022 | ||
| 500 | |a published: Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2022. The Author(s). | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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) | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Observational Study | |
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Infectious diseases | |
| 650 | 4 | |a Medical claims | |
| 650 | 4 | |a Observational study | |
| 650 | 4 | |a SARS-CoV-2COVID-19 Long-term effects | |
| 650 | 4 | |a Social determinants of health | |
| 700 | 1 | |a Kshirsagar, Meghana |e verfasserin |4 aut | |
| 700 | 1 | |a Becker, Nicholas |e verfasserin |4 aut | |
| 700 | 1 | |a Xu, Yixi |e verfasserin |4 aut | |
| 700 | 1 | |a Weeks, William B |e verfasserin |4 aut | |
| 700 | 1 | |a Patel, Shwetak |e verfasserin |4 aut | |
| 700 | 1 | |a Ferres, Juan Lavista |e verfasserin |4 aut | |
| 700 | 1 | |a Jackson, Michael L |e verfasserin |4 aut | |
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