ABSTRACT
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.
ABSTRACT
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.
ABSTRACT
OBJECTIVE: We explored longitudinal trends in sociodemographic characteristics, reported symptoms, laboratory findings, pharmacological and non-pharmacological treatment, comorbidities, and 30-day in-hospital mortality among hospitalized patients with coronavirus disease 2019 (COVID-19). METHODS: This retrospective cohort study included patients diagnosed with COVID-19 in the United States Veterans Health Administration between 03/01/20 and 08/31/20 and followed until 09/30/20. We focused our analysis on patients that were subsequently hospitalized, and categorized them into groups based on the month of hospitalization. We summarized our findings through descriptive statistics. We used Cuzick's Trend Test to examine any differences in the distribution of our study variables across the six months. RESULTS: During our study period, we identified 43,267 patients with COVID-19. A total of 8,240 patients were hospitalized, and 13.1% (N = 1,081) died within 30 days of admission. Hospitalizations increased over time, but the proportion of patients that died consistently declined from 24.8% (N = 221/890) in March to 8.0% (N = 111/1,396) in August. Patients hospitalized in March compared to August were younger on average, mostly black, urban-dwelling, febrile and dyspneic. They also had a higher frequency of baseline comorbidities, including hypertension and diabetes, and were more likely to present with abnormal laboratory findings including low lymphocyte counts and elevated creatinine. Lastly, there was a decline from March to August in receipt of mechanical ventilation (31.4% to 13.1%) and hydroxychloroquine (55.3% to <1.0%), while treatment with dexamethasone (3.7% to 52.4%) and remdesivir (1.1% to 38.9%) increased. CONCLUSION: Among hospitalized patients with COVID-19, we observed a trend towards decreased disease severity and mortality over time.