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A Bayesian framework for estimating the risk ratio of hospitalization for people with comorbidity infected by SARS-CoV-2 virus.
J Am Med Inform Assoc. 2021 03 01; 28(3):472-476.JAMIA

Abstract

OBJECTIVE

Estimating the hospitalization risk for people with comorbidities infected by the SARS-CoV-2 virus is important for developing public health policies and guidance. Traditional biostatistical methods for risk estimations require: (i) the number of infected people who were not hospitalized, which may be severely undercounted since many infected people were not tested; (ii) comorbidity information for people not hospitalized, which may not always be readily available. We aim to overcome these limitations by developing a Bayesian approach to estimate the risk ratio of hospitalization for COVID-19 patients with comorbidities.

MATERIALS AND METHODS

We derived a Bayesian approach to estimate the posterior distribution of the risk ratio using the observed frequency of comorbidities in COVID-19 patients in hospitals and the prevalence of comorbidities in the general population. We applied our approach to 2 large-scale datasets in the United States: 2491 patients in the COVID-NET, and 5700 patients in New York hospitals.

RESULTS

Our results consistently indicated that cardiovascular diseases carried the highest hospitalization risk for COVID-19 patients, followed by diabetes, chronic respiratory disease, hypertension, and obesity, respectively.

DISCUSSION

Our approach only needs (i) the number of hospitalized COVID-19 patients and their comorbidity information, which can be reliably obtained using hospital records, and (ii) the prevalence of the comorbidity of interest in the general population, which is regularly documented by public health agencies for common medical conditions.

CONCLUSION

We developed a novel Bayesian approach to estimate the hospitalization risk for people with comorbidities infected with the SARS-CoV-2 virus.

Authors+Show Affiliations

Department of Medicine, Stritch School of Medicine, Loyola University Chicago, Maywood, Illinois, USA.Department of Medicine, Stritch School of Medicine, Loyola University Chicago, Maywood, Illinois, USA. Center for Biomedical Informatics, Stritch School of Medicine, Loyola University Chicago, Maywood, Illinois, USA.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

32986795

Citation

Gao, Xiang, and Qunfeng Dong. "A Bayesian Framework for Estimating the Risk Ratio of Hospitalization for People With Comorbidity Infected By SARS-CoV-2 Virus." Journal of the American Medical Informatics Association : JAMIA, vol. 28, no. 3, 2021, pp. 472-476.
Gao X, Dong Q. A Bayesian framework for estimating the risk ratio of hospitalization for people with comorbidity infected by SARS-CoV-2 virus. J Am Med Inform Assoc. 2021;28(3):472-476.
Gao, X., & Dong, Q. (2021). A Bayesian framework for estimating the risk ratio of hospitalization for people with comorbidity infected by SARS-CoV-2 virus. Journal of the American Medical Informatics Association : JAMIA, 28(3), 472-476. https://doi.org/10.1093/jamia/ocaa246
Gao X, Dong Q. A Bayesian Framework for Estimating the Risk Ratio of Hospitalization for People With Comorbidity Infected By SARS-CoV-2 Virus. J Am Med Inform Assoc. 2021 03 1;28(3):472-476. PubMed PMID: 32986795.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - A Bayesian framework for estimating the risk ratio of hospitalization for people with comorbidity infected by SARS-CoV-2 virus. AU - Gao,Xiang, AU - Dong,Qunfeng, PY - 2020/08/04/received PY - 2020/09/02/revised PY - 2020/09/17/accepted PY - 2020/9/29/pubmed PY - 2021/3/17/medline PY - 2020/9/28/entrez KW - Bayesian KW - COVID-19 KW - SARS-CoV-2 KW - comorbidity KW - hospitalization KW - risk ratio SP - 472 EP - 476 JF - Journal of the American Medical Informatics Association : JAMIA JO - J Am Med Inform Assoc VL - 28 IS - 3 N2 - OBJECTIVE: Estimating the hospitalization risk for people with comorbidities infected by the SARS-CoV-2 virus is important for developing public health policies and guidance. Traditional biostatistical methods for risk estimations require: (i) the number of infected people who were not hospitalized, which may be severely undercounted since many infected people were not tested; (ii) comorbidity information for people not hospitalized, which may not always be readily available. We aim to overcome these limitations by developing a Bayesian approach to estimate the risk ratio of hospitalization for COVID-19 patients with comorbidities. MATERIALS AND METHODS: We derived a Bayesian approach to estimate the posterior distribution of the risk ratio using the observed frequency of comorbidities in COVID-19 patients in hospitals and the prevalence of comorbidities in the general population. We applied our approach to 2 large-scale datasets in the United States: 2491 patients in the COVID-NET, and 5700 patients in New York hospitals. RESULTS: Our results consistently indicated that cardiovascular diseases carried the highest hospitalization risk for COVID-19 patients, followed by diabetes, chronic respiratory disease, hypertension, and obesity, respectively. DISCUSSION: Our approach only needs (i) the number of hospitalized COVID-19 patients and their comorbidity information, which can be reliably obtained using hospital records, and (ii) the prevalence of the comorbidity of interest in the general population, which is regularly documented by public health agencies for common medical conditions. CONCLUSION: We developed a novel Bayesian approach to estimate the hospitalization risk for people with comorbidities infected with the SARS-CoV-2 virus. SN - 1527-974X UR - https://www.unboundmedicine.com/medline/citation/32986795/A_Bayesian_framework_for_estimating_the_risk_ratio_of_hospitalization_for_people_with_comorbidity_infected_by_SARS_CoV_2_virus_ L2 - https://academic.oup.com/jamia/article-lookup/doi/10.1093/jamia/ocaa246 DB - PRIME DP - Unbound Medicine ER -