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Mil Med ; 2021 Oct 20.
Article in English | MEDLINE | ID: covidwho-1475819


OBJECTIVES: We explored factors related to testing positive for severe acute respiratory coronavirus 2 (SARS-CoV-2) to identify populations most at risk for this airborne pathogen. METHODS: Data were abstracted from the medical record database of the U.S. Department of Veterans Affairs and from public sources. Veterans testing positive were matched in a 1:4 ratio to those at a similar timepoint and local disease burden who remained negative between March 1, 2020, and December 31, 2020. Multivariable logistic regression was used to calculate odds ratios for the association of each potential risk factor with a positive test result. RESULTS: A total of 24,843 veterans who tested positive for SARS-CoV-2 were matched with 99,324 controls. Cases and controls were similar in age, sex, ethnicity, and rurality, but cases were more likely to be Black, reside in low-income counties, and suffer from dementia. Multivariable analysis demonstrated highest risk for Black veterans, those with dementia or diabetes, and those living in nursing homes or high-poverty areas. Veterans living in counties likely to be more adherent to public health guidelines were at the lowest risk. CONCLUSIONS: Our results are similar to those from studies of other populations and add to that work by accounting for several important proxies for risk. In particular, this work has implications for the value of infection control measures at the population level in helping to stem widespread outbreaks of this type.

Mil Med ; 2021 Oct 06.
Article in English | MEDLINE | ID: covidwho-1455331


INTRODUCTION: Early identification of patients with coronavirus disease 2019 (COVID-19) who are at risk for hospitalization may help to mitigate disease burden by allowing healthcare systems to conduct sufficient resource and logistical planning in the event of case surges. We sought to develop and validate a clinical risk score that uses readily accessible information at testing to predict individualized 30-day hospitalization risk following COVID-19 diagnosis. METHODS: We assembled a retrospective cohort of U.S. Veterans Health Administration patients (age ≥ 18 years) diagnosed with COVID-19 between March 1, 2020, and December 31, 2020. We screened patient characteristics using Least Absolute Shrinkage and Selection Operator logistic regression and constructed the risk score using characteristics identified as most predictive for hospitalization. Patients diagnosed before November 1, 2020, comprised the development cohort, while those diagnosed on or after November 1, 2020, comprised the validation cohort. We assessed risk score discrimination by calculating the area under the receiver operating characteristic (AUROC) curve and calibration using the Hosmer-Lemeshow (HL) goodness-of-fit test. This study was approved by the Veteran's Institutional Review Board of Northern New England at the White River Junction Veterans Affairs Medical Center (Reference no.:1473972-1). RESULTS: The development and validation cohorts comprised 11,473 and 12,970 patients, of whom 4,465 (38.9%) and 3,669 (28.3%) were hospitalized, respectively. The independent predictors for hospitalization included in the risk score were increasing age, male sex, non-white race, Hispanic ethnicity, homelessness, nursing home/long-term care residence, unemployed or retired status, fever, fatigue, diarrhea, nausea, cough, diabetes, chronic kidney disease, hypertension, and chronic obstructive pulmonary disease. Model discrimination and calibration was good for the development (AUROC = 0.80; HL P-value = .05) and validation (AUROC = 0.80; HL P-value = .31) cohorts. CONCLUSIONS: The prediction tool developed in this study demonstrated that it could identify patients with COVID-19 who are at risk for hospitalization. This could potentially inform clinicians and policymakers of patients who may benefit most from early treatment interventions and help healthcare systems anticipate capacity surges.