IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i16p8486-d612436.html
   My bibliography  Save this article

COVID-19-Associated Mortality in US Veterans with and without SARS-CoV-2 Infection

Author

Listed:
  • Ayako Suzuki

    (Division of Gastroenterology, Duke University, Durham, NC 27710, USA
    Division of Gastroenterology, Durham VA Medical Center, Durham, NC 27705, USA)

  • Jimmy T. Efird

    (VA Cooperative Studies Program Epidemiology Center, Durham VA Health Care System, Durham, NC 27705, USA)

  • Thomas S. Redding

    (VA Cooperative Studies Program Epidemiology Center, Durham VA Health Care System, Durham, NC 27705, USA)

  • Andrew D. Thompson

    (VA Cooperative Studies Program Epidemiology Center, Durham VA Health Care System, Durham, NC 27705, USA)

  • Ashlyn M. Press

    (VA Cooperative Studies Program Epidemiology Center, Durham VA Health Care System, Durham, NC 27705, USA)

  • Christina D. Williams

    (VA Cooperative Studies Program Epidemiology Center, Durham VA Health Care System, Durham, NC 27705, USA
    Department of Medicine, Duke University, Durham, NC 27710, USA
    Duke Cancer Institute, Duke University School of Medicine, Duke University Health Care System, Durham, NC 27710, USA)

  • Christopher J. Hostler

    (Division of Infectious Diseases, Duke University School of Medicine, Durham, NC 27710, USA
    Infectious Diseases Section, Durham VA Health Care System, Durham, NC 27705, USA)

  • Christine M. Hunt

    (Division of Gastroenterology, Duke University, Durham, NC 27710, USA
    Division of Gastroenterology, Durham VA Medical Center, Durham, NC 27705, USA
    VA Cooperative Studies Program Epidemiology Center, Durham VA Health Care System, Durham, NC 27705, USA)

Abstract

Background: We performed an observational Veterans Health Administration cohort analysis to assess how risk factors affect 30-day mortality in SARS-CoV-2-infected subjects relative to those uninfected. While the risk factors for coronavirus disease 2019 (COVID-19) have been extensively studied, these have been seldom compared with uninfected referents. Methods: We analyzed 341,166 White/Black male veterans tested for SARS-CoV-2 from March 1 to September 10, 2020. The relative risk of 30-day mortality was computed for age, race, ethnicity, BMI, smoking status, and alcohol use disorder in infected and uninfected subjects separately. The difference in relative risk was then evaluated between infected and uninfected subjects. All the analyses were performed considering clinical confounders. Results: In this cohort, 7% were SARS-CoV-2-positive. Age >60 and overweight/obesity were associated with a dose-related increased mortality risk among infected patients relative to those uninfected. In contrast, relative to never smoking, current smoking was associated with a decreased mortality among infected and an increased mortality in uninfected, yielding a reduced mortality risk among infected relative to uninfected. Alcohol use disorder was also associated with decreased mortality risk in infected relative to the uninfected. Conclusions: Age, BMI, smoking, and alcohol use disorder affect 30-day mortality in SARS-CoV-2-infected subjects differently from uninfected referents. Advanced age and overweight/obesity were associated with increased mortality risk among infected men, while current smoking and alcohol use disorder were associated with lower mortality risk among infected men, when compared with those uninfected.

Suggested Citation

  • Ayako Suzuki & Jimmy T. Efird & Thomas S. Redding & Andrew D. Thompson & Ashlyn M. Press & Christina D. Williams & Christopher J. Hostler & Christine M. Hunt, 2021. "COVID-19-Associated Mortality in US Veterans with and without SARS-CoV-2 Infection," IJERPH, MDPI, vol. 18(16), pages 1-13, August.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:16:p:8486-:d:612436
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/16/8486/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/18/16/8486/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hansen, Bruce E., 2016. "Efficient shrinkage in parametric models," Journal of Econometrics, Elsevier, vol. 190(1), pages 115-132.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kitagawa, Toru & Muris, Chris, 2016. "Model averaging in semiparametric estimation of treatment effects," Journal of Econometrics, Elsevier, vol. 193(1), pages 271-289.
    2. Stéphane Bonhomme & Martin Weidner, 2020. "Minimizing Sensitivity to Model Misspecification," CeMMAP working papers CWP37/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Stéphane Bonhomme & Martin Weidner, 2022. "Minimizing sensitivity to model misspecification," Quantitative Economics, Econometric Society, vol. 13(3), pages 907-954, July.
    4. Minsu Chang & Francis J. DiTraglia, 2020. "A Generalized Focused Information Criterion for GMM," Papers 2011.07085, arXiv.org.
    5. Ali Mehrabani & Aman Ullah, 2022. "Weighted Average Estimation in Panel Data," Working Papers 202209, University of California at Riverside, Department of Economics, revised Apr 2022.
    6. De Luca, Giuseppe & Magnus, Jan R. & Peracchi, Franco, 2018. "Weighted-average least squares estimation of generalized linear models," Journal of Econometrics, Elsevier, vol. 204(1), pages 1-17.
    7. Edvard Bakhitov, 2020. "Frequentist Shrinkage under Inequality Constraints," Papers 2001.10586, arXiv.org.
    8. Phillip Heiler & Jana Mareckova, 2019. "Shrinkage for Categorical Regressors," Papers 1901.01898, arXiv.org.
    9. Timothy B. Armstrong & Michal Kolesár & Mikkel Plagborg‐Møller, 2022. "Robust Empirical Bayes Confidence Intervals," Econometrica, Econometric Society, vol. 90(6), pages 2567-2602, November.
    10. St'ephane Bonhomme & Martin Weidner, 2018. "Minimizing Sensitivity to Model Misspecification," Papers 1807.02161, arXiv.org, revised Oct 2021.
    11. Isaiah Andrews & Matthew Gentzkow & Jesse M. Shapiro, 2020. "On the Informativeness of Descriptive Statistics for Structural Estimates," Econometrica, Econometric Society, vol. 88(6), pages 2231-2258, November.
    12. repec:hum:wpaper:sfb649dp2017-016 is not listed on IDEAS
    13. Tryphonides, Andreas, 2017. "Conditional moment restrictions and the role of density information in estimated structural models," SFB 649 Discussion Papers 2017-016, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    14. Keisuke Hirano & Jack R. Porter, 2023. "Asymptotic Representations for Sequential Decisions, Adaptive Experiments, and Batched Bandits," Papers 2302.03117, arXiv.org.
    15. Cheng Chou & Ruoyao Shi, 2020. "Utilizing Two Types of Survey Data to Enhance the Accuracy of Labor Supply Elasticity Estimation," Working Papers 202018, University of California at Riverside, Department of Economics.
    16. Shahnaz Parsaeian, 2024. "Stein-like Common Correlated Effects Estimation under Structural Breaks," Econometrics, MDPI, vol. 12(2), pages 1-23, April.
    17. Shakhawat Hossain & Shahedul A. Khan, 2020. "Shrinkage estimation of the exponentiated Weibull regression model for time‐to‐event data," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 74(4), pages 592-610, November.
    18. Tae‐Hwy Lee & Shahnaz Parsaeian & Aman Ullah, 2022. "Optimal forecast under structural breaks," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 965-987, August.
    19. Ruoyao Shi & Zhipeng Liao, 2018. "An Averaging GMM Estimator Robust to Misspecification," Working Papers 201803, University of California at Riverside, Department of Economics.
    20. Hao Hao & Bai Huang & Tae-hwy Lee, 2024. "Model averaging estimation of panel data models with many instruments and boosting," Journal of Applied Statistics, Taylor & Francis Journals, vol. 51(1), pages 53-69, January.
    21. McCloskey, Adam, 2017. "Bonferroni-based size-correction for nonstandard testing problems," Journal of Econometrics, Elsevier, vol. 200(1), pages 17-35.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:18:y:2021:i:16:p:8486-:d:612436. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.