Cargando…
Understanding comorbidities and health disparities related to COVID-19: a comprehensive study of 776 936 cases and 1 362 545 controls in the state of Indiana, USA
OBJECTIVE: To characterize COVID-19 patients in Indiana, United States, and to evaluate their demographics and comorbidities as risk factors to COVID-19 severity. MATERIALS AND METHODS: EHR data of 776 936 COVID-19 cases and 1 362 545 controls were collected from the COVID-19 Research Data Commons (...
Autores principales: | , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9893867/ https://www.ncbi.nlm.nih.gov/pubmed/36751466 http://dx.doi.org/10.1093/jamiaopen/ooad002 |
_version_ | 1784881615915188224 |
---|---|
author | Zidan, Nader Dey, Vishal Allen, Katie Price, John Zappone, Sarah Renee Hebert, Courtney Schleyer, Titus Ning, Xia |
author_facet | Zidan, Nader Dey, Vishal Allen, Katie Price, John Zappone, Sarah Renee Hebert, Courtney Schleyer, Titus Ning, Xia |
author_sort | Zidan, Nader |
collection | PubMed |
description | OBJECTIVE: To characterize COVID-19 patients in Indiana, United States, and to evaluate their demographics and comorbidities as risk factors to COVID-19 severity. MATERIALS AND METHODS: EHR data of 776 936 COVID-19 cases and 1 362 545 controls were collected from the COVID-19 Research Data Commons (CoRDaCo) in Indiana. Data regarding county population and per capita income were obtained from the US Census Bureau. Statistical analysis was conducted to determine the association of demographic and clinical variables with COVID-19 severity. Predictive analysis was conducted to evaluate the predictive power of CoRDaCo EHR data in determining COVID-19 severity. RESULTS: Chronic obstructive pulmonary disease, cardiovascular disease, and type 2 diabetes were found in 3.49%, 2.59%, and 4.76% of the COVID-19 patients, respectively. Such COVID-19 patients have significantly higher ICU admission rates of 10.23%, 14.33%, and 11.11%, respectively, compared to the entire COVID-19 patient population (1.94%). Furthermore, patients with these comorbidities have significantly higher mortality rates compared to the entire COVID-19 patient population. Health disparity analysis suggests potential health disparities among counties in Indiana. Predictive analysis achieved F1-scores of 0.8011 and 0.7072 for classifying COVID-19 cases versus controls and ICU versus non-ICU cases, respectively. DISCUSSION: Black population in Indiana was more adversely affected by COVID-19 than the White population. This is consistent to findings from existing studies. Our findings also indicate other health disparities in terms of demographic and economic factors. CONCLUSION: This study characterizes the relationship between comorbidities and COVID-19 outcomes with respect to ICU admission across a large COVID-19 patient population in Indiana. |
format | Online Article Text |
id | pubmed-9893867 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-98938672023-02-06 Understanding comorbidities and health disparities related to COVID-19: a comprehensive study of 776 936 cases and 1 362 545 controls in the state of Indiana, USA Zidan, Nader Dey, Vishal Allen, Katie Price, John Zappone, Sarah Renee Hebert, Courtney Schleyer, Titus Ning, Xia JAMIA Open Research and Applications OBJECTIVE: To characterize COVID-19 patients in Indiana, United States, and to evaluate their demographics and comorbidities as risk factors to COVID-19 severity. MATERIALS AND METHODS: EHR data of 776 936 COVID-19 cases and 1 362 545 controls were collected from the COVID-19 Research Data Commons (CoRDaCo) in Indiana. Data regarding county population and per capita income were obtained from the US Census Bureau. Statistical analysis was conducted to determine the association of demographic and clinical variables with COVID-19 severity. Predictive analysis was conducted to evaluate the predictive power of CoRDaCo EHR data in determining COVID-19 severity. RESULTS: Chronic obstructive pulmonary disease, cardiovascular disease, and type 2 diabetes were found in 3.49%, 2.59%, and 4.76% of the COVID-19 patients, respectively. Such COVID-19 patients have significantly higher ICU admission rates of 10.23%, 14.33%, and 11.11%, respectively, compared to the entire COVID-19 patient population (1.94%). Furthermore, patients with these comorbidities have significantly higher mortality rates compared to the entire COVID-19 patient population. Health disparity analysis suggests potential health disparities among counties in Indiana. Predictive analysis achieved F1-scores of 0.8011 and 0.7072 for classifying COVID-19 cases versus controls and ICU versus non-ICU cases, respectively. DISCUSSION: Black population in Indiana was more adversely affected by COVID-19 than the White population. This is consistent to findings from existing studies. Our findings also indicate other health disparities in terms of demographic and economic factors. CONCLUSION: This study characterizes the relationship between comorbidities and COVID-19 outcomes with respect to ICU admission across a large COVID-19 patient population in Indiana. Oxford University Press 2023-02-02 /pmc/articles/PMC9893867/ /pubmed/36751466 http://dx.doi.org/10.1093/jamiaopen/ooad002 Text en © The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics 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 | Research and Applications Zidan, Nader Dey, Vishal Allen, Katie Price, John Zappone, Sarah Renee Hebert, Courtney Schleyer, Titus Ning, Xia Understanding comorbidities and health disparities related to COVID-19: a comprehensive study of 776 936 cases and 1 362 545 controls in the state of Indiana, USA |
title | Understanding comorbidities and health disparities related to COVID-19: a comprehensive study of 776 936 cases and 1 362 545 controls in the state of Indiana, USA |
title_full | Understanding comorbidities and health disparities related to COVID-19: a comprehensive study of 776 936 cases and 1 362 545 controls in the state of Indiana, USA |
title_fullStr | Understanding comorbidities and health disparities related to COVID-19: a comprehensive study of 776 936 cases and 1 362 545 controls in the state of Indiana, USA |
title_full_unstemmed | Understanding comorbidities and health disparities related to COVID-19: a comprehensive study of 776 936 cases and 1 362 545 controls in the state of Indiana, USA |
title_short | Understanding comorbidities and health disparities related to COVID-19: a comprehensive study of 776 936 cases and 1 362 545 controls in the state of Indiana, USA |
title_sort | understanding comorbidities and health disparities related to covid-19: a comprehensive study of 776 936 cases and 1 362 545 controls in the state of indiana, usa |
topic | Research and Applications |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9893867/ https://www.ncbi.nlm.nih.gov/pubmed/36751466 http://dx.doi.org/10.1093/jamiaopen/ooad002 |
work_keys_str_mv | AT zidannader understandingcomorbiditiesandhealthdisparitiesrelatedtocovid19acomprehensivestudyof776936casesand1362545controlsinthestateofindianausa AT deyvishal understandingcomorbiditiesandhealthdisparitiesrelatedtocovid19acomprehensivestudyof776936casesand1362545controlsinthestateofindianausa AT allenkatie understandingcomorbiditiesandhealthdisparitiesrelatedtocovid19acomprehensivestudyof776936casesand1362545controlsinthestateofindianausa AT pricejohn understandingcomorbiditiesandhealthdisparitiesrelatedtocovid19acomprehensivestudyof776936casesand1362545controlsinthestateofindianausa AT zapponesarahrenee understandingcomorbiditiesandhealthdisparitiesrelatedtocovid19acomprehensivestudyof776936casesand1362545controlsinthestateofindianausa AT hebertcourtney understandingcomorbiditiesandhealthdisparitiesrelatedtocovid19acomprehensivestudyof776936casesand1362545controlsinthestateofindianausa AT schleyertitus understandingcomorbiditiesandhealthdisparitiesrelatedtocovid19acomprehensivestudyof776936casesand1362545controlsinthestateofindianausa AT ningxia understandingcomorbiditiesandhealthdisparitiesrelatedtocovid19acomprehensivestudyof776936casesand1362545controlsinthestateofindianausa |