ABSTRACT
OBJECTIVES: Estimating the isolated effect of coronavirus disease 2019 (COVID-19) on the risk of mortality is challenging. We aimed to determine whether COVID-19 was associated with high rates of mortality independently of age, sex and underlying disorders. METHODS: A population-based, matched, case-control study of adults insured by Clalit Health Services was performed. Cases were defined as patients who died of all causes between July and December 2020. Each case was matched in a ratio of 1:1 with a living control based on age, sex and co-morbidities. An unconditional logistic regression analysis was performed to identify independent risk factors for mortality. RESULTS: A total of 2874 patients who died were successfully matched with 2874 living controls. The prevalence of COVID-19 was higher among the patients who died than among the controls (13.5% [387/2874] vs. 4% [115/2874], respectively; OR, 3.73; 95% CI, 3.01-4.63; p < 0.001). A significantly increased odds of mortality was also observed in patients with COVID-19 without underlying diseases (OR, 3.67; 95% CI, 2.58-5.23) and in patients with COVID-19 and underlying diseases (OR, 3.77; 95% CI, 2.87-4.94). A multi-variate logistic analysis showed that COVID-19 (OR, 2.01; 95% CI, 1.07-3.77), low socio-economic status (OR, 1.36; 95% CI, 1.02-1.82), dementia (OR, 2.50; 95% CI, 2.10-3.01), smoking (OR, 1.35; 95% CI, 1.13-1.63) and an interaction variable of age >80 years and COVID-19 (OR, 2.27; 95% CI, 1.14-4.54) were independent risk factors for mortality, whereas influenza vaccination and high body mass index were associated with lower rates of mortality. CONCLUSION: Testing positive for COVID-19 increased the risk of death three folds, regardless of underlying disorders. These results emphasize the effect of COVID-19 on mortality during the early period of the COVID-19 outbreak, when no vaccines or effective therapeutics were available.
ABSTRACT
BACKGROUND: Long COVID has become a burden on healthcare systems worldwide. Research into the etiology and risk factors has been impeded by observing all diverse manifestations as part of a single entity. We aimed to determine patterns of symptoms in convalescing COVID-19 patients. METHODS: Symptomatic patients were recruited from four countries. Data were collected regarding demographics, comorbidities, acute disease and persistent symptoms. Factor analysis was performed to elucidate symptom patterns. Associations of the patterns with patients' characteristics, features of acute disease and effect on daily life were sought. RESULTS: We included 1027 symptomatic post-COVID individuals in the analysis. The majority of participants were graded as having a non-severe acute COVID-19 (N = 763, 74.3%). We identified six patterns of symptoms: cognitive, pain-syndrome, pulmonary, cardiac, anosmia-dysgeusia and headache. The cognitive pattern was the major symptoms pattern, explaining 26.2% of the variance; the other patterns each explained 6.5-9.5% of the variance. The cognitive pattern was higher in patients who were outpatients during the acute disease. The pain-syndrome pattern was associated with acute disease severity, higher in women and increased with age. The pulmonary pattern was associated with prior lung disease and severe acute disease. Only two of the patterns (cognitive and cardiac) were associated with failure to return to pre-COVID occupational and physical activity status. CONCLUSION: Long COVID diverse symptoms can be grouped into six unique patterns. Using these patterns in future research may improve our understanding of pathophysiology and risk factors of persistent COVID, provide homogenous terminology for clinical research, and direct therapeutic interventions.