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Design and analysis of outcomes following SARS-CoV-2 infection in veterans

BACKGROUND: Understanding how SARS-CoV-2 infection impacts long-term patient outcomes requires identification of comparable persons with and without infection. We report the design and implementation of a matching strategy employed by the Department of Veterans Affairs’ (VA) COVID-19 Observational R...

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Autores principales: Smith, Valerie A., Berkowitz, Theodore S. Z., Hebert, Paul, Wong, Edwin S., Niederhausen, Meike, Pura, John A., Berry, Kristin, Green, Pamela, Korpak, Anna, Fox, Alexandra, Baraff, Aaron, Hickok, Alex, Shahoumian, Troy A, Bohnert, Amy S.B., Hynes, Denise M., Boyko, Edward J., Ioannou, George N., Iwashyna, Theodore J., Bowling, C. Barrett, O’Hare, Ann M., Maciejewski, Matthew L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10071454/
https://www.ncbi.nlm.nih.gov/pubmed/37016340
http://dx.doi.org/10.1186/s12874-023-01882-z
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author Smith, Valerie A.
Berkowitz, Theodore S. Z.
Hebert, Paul
Wong, Edwin S.
Niederhausen, Meike
Pura, John A.
Berry, Kristin
Green, Pamela
Korpak, Anna
Fox, Alexandra
Baraff, Aaron
Hickok, Alex
Shahoumian, Troy A
Bohnert, Amy S.B.
Hynes, Denise M.
Boyko, Edward J.
Ioannou, George N.
Iwashyna, Theodore J.
Bowling, C. Barrett
O’Hare, Ann M.
Maciejewski, Matthew L.
author_facet Smith, Valerie A.
Berkowitz, Theodore S. Z.
Hebert, Paul
Wong, Edwin S.
Niederhausen, Meike
Pura, John A.
Berry, Kristin
Green, Pamela
Korpak, Anna
Fox, Alexandra
Baraff, Aaron
Hickok, Alex
Shahoumian, Troy A
Bohnert, Amy S.B.
Hynes, Denise M.
Boyko, Edward J.
Ioannou, George N.
Iwashyna, Theodore J.
Bowling, C. Barrett
O’Hare, Ann M.
Maciejewski, Matthew L.
author_sort Smith, Valerie A.
collection PubMed
description BACKGROUND: Understanding how SARS-CoV-2 infection impacts long-term patient outcomes requires identification of comparable persons with and without infection. We report the design and implementation of a matching strategy employed by the Department of Veterans Affairs’ (VA) COVID-19 Observational Research Collaboratory (CORC) to develop comparable cohorts of SARS-CoV-2 infected and uninfected persons for the purpose of inferring potential causative long-term adverse effects of SARS-CoV-2 infection in the Veteran population. METHODS: In a retrospective cohort study, we identified VA health care system patients who were and were not infected with SARS-CoV-2 on a rolling monthly basis. We generated matched cohorts within each month utilizing a combination of exact and time-varying propensity score matching based on electronic health record (EHR)-derived covariates that can be confounders or risk factors across a range of outcomes. RESULTS: From an initial pool of 126,689,864 person-months of observation, we generated final matched cohorts of 208,536 Veterans infected between March 2020-April 2021 and 3,014,091 uninfected Veterans. Matched cohorts were well-balanced on all 37 covariates used in matching after excluding patients for: no VA health care utilization; implausible age, weight, or height; living outside of the 50 states or Washington, D.C.; prior SARS-CoV-2 diagnosis per Medicare claims; or lack of a suitable match. Most Veterans in the matched cohort were male (88.3%), non-Hispanic (87.1%), white (67.2%), and living in urban areas (71.5%), with a mean age of 60.6, BMI of 31.3, Gagne comorbidity score of 1.4 and a mean of 2.3 CDC high-risk conditions. The most common diagnoses were hypertension (61.4%), diabetes (34.3%), major depression (32.2%), coronary heart disease (28.5%), PTSD (25.5%), anxiety (22.5%), and chronic kidney disease (22.5%). CONCLUSION: This successful creation of matched SARS-CoV-2 infected and uninfected patient cohorts from the largest integrated health system in the United States will support cohort studies of outcomes derived from EHRs and sample selection for qualitative interviews and patient surveys. These studies will increase our understanding of the long-term outcomes of Veterans who were infected with SARS-CoV-2. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-023-01882-z.
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spelling pubmed-100714542023-04-04 Design and analysis of outcomes following SARS-CoV-2 infection in veterans Smith, Valerie A. Berkowitz, Theodore S. Z. Hebert, Paul Wong, Edwin S. Niederhausen, Meike Pura, John A. Berry, Kristin Green, Pamela Korpak, Anna Fox, Alexandra Baraff, Aaron Hickok, Alex Shahoumian, Troy A Bohnert, Amy S.B. Hynes, Denise M. Boyko, Edward J. Ioannou, George N. Iwashyna, Theodore J. Bowling, C. Barrett O’Hare, Ann M. Maciejewski, Matthew L. BMC Med Res Methodol Research BACKGROUND: Understanding how SARS-CoV-2 infection impacts long-term patient outcomes requires identification of comparable persons with and without infection. We report the design and implementation of a matching strategy employed by the Department of Veterans Affairs’ (VA) COVID-19 Observational Research Collaboratory (CORC) to develop comparable cohorts of SARS-CoV-2 infected and uninfected persons for the purpose of inferring potential causative long-term adverse effects of SARS-CoV-2 infection in the Veteran population. METHODS: In a retrospective cohort study, we identified VA health care system patients who were and were not infected with SARS-CoV-2 on a rolling monthly basis. We generated matched cohorts within each month utilizing a combination of exact and time-varying propensity score matching based on electronic health record (EHR)-derived covariates that can be confounders or risk factors across a range of outcomes. RESULTS: From an initial pool of 126,689,864 person-months of observation, we generated final matched cohorts of 208,536 Veterans infected between March 2020-April 2021 and 3,014,091 uninfected Veterans. Matched cohorts were well-balanced on all 37 covariates used in matching after excluding patients for: no VA health care utilization; implausible age, weight, or height; living outside of the 50 states or Washington, D.C.; prior SARS-CoV-2 diagnosis per Medicare claims; or lack of a suitable match. Most Veterans in the matched cohort were male (88.3%), non-Hispanic (87.1%), white (67.2%), and living in urban areas (71.5%), with a mean age of 60.6, BMI of 31.3, Gagne comorbidity score of 1.4 and a mean of 2.3 CDC high-risk conditions. The most common diagnoses were hypertension (61.4%), diabetes (34.3%), major depression (32.2%), coronary heart disease (28.5%), PTSD (25.5%), anxiety (22.5%), and chronic kidney disease (22.5%). CONCLUSION: This successful creation of matched SARS-CoV-2 infected and uninfected patient cohorts from the largest integrated health system in the United States will support cohort studies of outcomes derived from EHRs and sample selection for qualitative interviews and patient surveys. These studies will increase our understanding of the long-term outcomes of Veterans who were infected with SARS-CoV-2. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-023-01882-z. BioMed Central 2023-04-04 /pmc/articles/PMC10071454/ /pubmed/37016340 http://dx.doi.org/10.1186/s12874-023-01882-z Text en © This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2023, corrected publication 2023 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
Smith, Valerie A.
Berkowitz, Theodore S. Z.
Hebert, Paul
Wong, Edwin S.
Niederhausen, Meike
Pura, John A.
Berry, Kristin
Green, Pamela
Korpak, Anna
Fox, Alexandra
Baraff, Aaron
Hickok, Alex
Shahoumian, Troy A
Bohnert, Amy S.B.
Hynes, Denise M.
Boyko, Edward J.
Ioannou, George N.
Iwashyna, Theodore J.
Bowling, C. Barrett
O’Hare, Ann M.
Maciejewski, Matthew L.
Design and analysis of outcomes following SARS-CoV-2 infection in veterans
title Design and analysis of outcomes following SARS-CoV-2 infection in veterans
title_full Design and analysis of outcomes following SARS-CoV-2 infection in veterans
title_fullStr Design and analysis of outcomes following SARS-CoV-2 infection in veterans
title_full_unstemmed Design and analysis of outcomes following SARS-CoV-2 infection in veterans
title_short Design and analysis of outcomes following SARS-CoV-2 infection in veterans
title_sort design and analysis of outcomes following sars-cov-2 infection in veterans
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10071454/
https://www.ncbi.nlm.nih.gov/pubmed/37016340
http://dx.doi.org/10.1186/s12874-023-01882-z
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