bims-covirf Biomed News
on COVID19 risk factors
Issue of 2021‒01‒17
eight papers selected by
Catherine Rycroft
BresMed


  1. J Thorac Dis. 2020 Dec;12(12): 7429-7441
    Zhang T, Huang WS, Guan W, Hong Z, Gao J, Gao G, Wu G, Qin YY.
      Since December 2019, the pneumonia cases infected with 2019 novel coronavirus have appeared, posing a critical threat to global health. In this study, we performed a meta-analysis to discover the different clinical characteristics between severe and non-severe patients with COVID-19 to find the potential risk factors and predictors of this disease's severity, as well as to serve as a guidance for subsequent epidemic prevention and control work. PubMed, Cochrane Library, Medline, Embase and other databases were searched to collect studies on the difference of clinical characteristics of severe and non-severe patients. Meta-analysis was performed using RevMan 5.3 software, and the funnel plots could be made to evaluate the publication bias. P>0.05 means no statistical significance. Furthermore, a meta-regression analysis was performed by using Stata 15.0 to find the potential factors of the high degree of heterogeneity (I2>50%). Sixteen studies have been included, with 1,172 severe patients and 2,803 non-severe patients. Compared with non-severe patients, severe patients were more likely to have the symptoms of dyspnea, hemoptysis, and the complications of ARDS, shock, secondary infection, acute kidney injury, and acute cardiac injury. Interestingly, the former smokers were more prevalent in severe cases as compared to non-severe cases, but there was no difference between the two groups of 'current smokers'. Except for chronic liver disease and chronic kidney disease, the underlying comorbidities of hypertension, diabetes, cardiovascular disease, chronic obstructive pulmonary disease (COPD), malignancy, cerebrovascular disease, and HIV can make the disease worse. In terms of laboratory indicators, the decreased lymphocyte and platelet count, and the increased levels of white blood cell (WBC), D-dimer, creatine kinase, lactate dehydrogenase, procalcitonin, alanine aminotransferase, aspartate aminotransferase, and C-reactive protein were more prevalent in severe patients. Meta-regression analysis showed that patient age, gender, and proportion of severe cases did not significantly impact on the outcomes of any clinical indexes that showed high degree of heterogeneity in the meta-analysis. In conclusion, the severity of COVID-19 could be evaluated by, radiologic finding, some symptoms like dyspnea and hemoptysis, some laboratory indicators, and smoking history, especially the ex-smokers. Compared with non-severe patients, severe patients were more likely to have complications and comorbidities including hypertension, cardiovascular disease etc., which were the risk factors for the disease to be severer, but the chronic liver disease and chronic kidney disease were not associated the severity of COVID-19 in China.
    Keywords:  COVID-19; clinical characteristics; clinical features; meta-analysis; meta-regression
    DOI:  https://doi.org/10.21037/jtd-20-1743
  2. BMJ Open. 2021 01 11. 11(1): e044640
    Pijls BG, Jolani S, Atherley A, Derckx RT, Dijkstra JIR, Franssen GHL, Hendriks S, Richters A, Venemans-Jellema A, Zalpuri S, Zeegers MP.
      OBJECTIVE: We aimed to describe the associations of age and sex with the risk of COVID-19 in different severity stages ranging from infection to death.DESIGN: Systematic review and meta-analysis.
    DATA SOURCES: PubMed and Embase through 4 May 2020.
    STUDY SELECTION: We considered cohort and case-control studies that evaluated differences in age and sex on the risk of COVID-19 infection, disease severity, intensive care unit (ICU) admission and death.
    DATA EXTRACTION AND SYNTHESIS: We screened and included studies using standardised electronic data extraction forms and we pooled data from published studies and data acquired by contacting authors using random effects meta-analysis. We assessed the risk of bias using the Newcastle-Ottawa Scale.
    RESULTS: We screened 11.550 titles and included 59 studies comprising 36.470 patients in the analyses. The methodological quality of the included papers was high (8.2 out of 9). Men had a higher risk for infection with COVID-19 than women (relative risk (RR) 1.08, 95% CI 1.03 to 1.12). When infected, they also had a higher risk for severe COVID-19 disease (RR 1.18, 95% CI 1.10 to 1.27), a higher need for intensive care (RR 1.38, 95% CI 1.09 to 1.74) and a higher risk of death (RR 1.50, 95% CI 1.18 to 1.91). The analyses also showed that patients aged 70 years and above have a higher infection risk (RR 1.65, 95% CI 1.50 to 1.81), a higher risk for severe COVID-19 disease (RR 2.05, 95% CI 1.27 to 3.32), a higher need for intensive care (RR 2.70, 95% CI 1.59 to 4.60) and a higher risk of death once infected (RR 3.61, 95% CI 2.70 to 4.84) compared with patients younger than 70 years.
    CONCLUSIONS: Meta-analyses on 59 studies comprising 36.470 patients showed that men and patients aged 70 and above have a higher risk for COVID-19 infection, severe disease, ICU admission and death.
    PROSPERO REGISTRATION NUMBER: CRD42020180085.
    Keywords:  COVID-19; epidemiology; infectious diseases
    DOI:  https://doi.org/10.1136/bmjopen-2020-044640
  3. Acta Anaesthesiol Scand. 2021 Jan 12.
    Ahlström B, Frithiof R, Hultström M, Larsson IM, Strandberg G, Lipcsey M.
      BACKGROUND: Several studies have recently addressed factors associated with severe Coronavirus disease 2019 (COVID-19); however, some medications and comorbidities have yet to be evaluated in a large matched cohort. We therefore explored the role of relevant comorbidities and medications in relation to the risk of intensive care unit (ICU) admission and mortality.METHODS: All ICU COVID-19 patients in Sweden until 27 May 2020 were matched to population controls on age and sex to assess the risk of ICU admission. Cases were identified, comorbidities and medications were retrieved from high-quality registries. Three conditional logistic regression models were used for risk of ICU admission and three Cox proportional hazards models for risk of ICU mortality, one with comorbidities, one with medications and finally with both models combined, respectively.
    RESULTS: We included 1981 patients and 7924 controls. Hypertension, type 2 diabetes mellitus, chronic renal failure, asthma, obesity, being a solid organ transplant recipient and immunosuppressant medications were independent risk factors of ICU admission and oral anticoagulants were protective. Stroke, asthma, chronic obstructive pulmonary disease and treatment with renin-angiotensin-aldosterone inhibitors (RAASi) were independent risk factors of ICU mortality in the pre-specified primary analyses; treatment with statins was protective. However, after adjusting for the use of continuous renal replacement therapy, RAASi were no longer an independent risk factor.
    CONCLUSION: In our cohort oral anticoagulants were protective of ICU admission and statins was protective of ICU death. Several comorbidities and ongoing RAASi treatment were independent risk factors of ICU admission and ICU mortality.
    DOI:  https://doi.org/10.1111/aas.13781
  4. JMIR Public Health Surveill. 2021 01 12. 7(1): e22794
    Laires PA, Dias S, Gama A, Moniz M, Pedro AR, Soares P, Aguiar P, Nunes C.
      BACKGROUND: COVID-19, a viral respiratory disease first reported in December 2019, quickly became a threat to global public health. Further understanding of the epidemiology of the SARS-CoV-2 virus and the risk perception of the community may better inform targeted interventions to reduce the impact and spread of COVID-19.OBJECTIVE: In this study, we aimed to examine the association between chronic diseases and serious outcomes following COVID-19 infection, and to explore its influence on people's self-perception of risk for worse COVID-19 outcomes.
    METHODS: This study draws data from two databases: (1) the nationwide database of all confirmed COVID-19 cases in Portugal, extracted on April 28, 2020 (n=20,293); and (2) the community-based COVID-19 Barometer survey, which contains data on health status, perceptions, and behaviors during the first wave of COVID-19 (n=171,087). We assessed the association between relevant chronic diseases (ie, respiratory, cardiovascular, and renal diseases; diabetes; and cancer) and death and intensive care unit (ICU) admission following COVID-19 infection. We identified determinants of self-perception of risk for severe COVID-19 outcomes using logistic regression models.
    RESULTS: Respiratory, cardiovascular, and renal diseases were associated with mortality and ICU admission among patients hospitalized due to COVID-19 infection (odds ratio [OR] 1.48, 95% CI 1.11-1.98; OR 3.39, 95% CI 1.80-6.40; and OR 2.25, 95% CI 1.66-3.06, respectively). Diabetes and cancer were associated with serious outcomes only when considering the full sample of COVID-19-infected cases in the country (OR 1.30, 95% CI 1.03-1.64; and OR 1.40, 95% CI 1.03-1.89, respectively). Older age and male sex were both associated with mortality and ICU admission. The perception of risk for severe COVID-19 disease in the study population was 23.9% (n=40,890). This was markedly higher for older adults (n=5235, 46.4%), those with at least one chronic disease (n=17,647, 51.6%), or those in both of these categories (n=3212, 67.7%). All included diseases were associated with self-perceptions of high risk in this population.
    CONCLUSIONS: Our results demonstrate the association between some prevalent chronic diseases and increased risk of worse COVID-19 outcomes. It also brings forth a greater understanding of the community's risk perceptions of serious COVID-19 disease. Hence, this study may aid health authorities to better adapt measures to the real needs of the population and to identify vulnerable individuals requiring further education and awareness of preventive measures.
    Keywords:  COVID-19; association; chronic disease; morbidity; outcome; perception; risk; risk factors
    DOI:  https://doi.org/10.2196/22794
  5. Rev Bras Epidemiol. 2021 ;pii: S1415-790X2020000100212. [Epub ahead of print]23 e200106
    Galvão MHR, Roncalli AG.
      OBJECTIVE: To perform a survival analysis of individuals diagnosed with COVID-19 identified by health information systems, analyzing the factors associated with the highest risk of death.METHODS: Survival analysis of individuals notified with COVID-19 in Rio Grande do Norte State using data from the Health Information Systems for the surveillance of cases of and deaths from COVID-19. The dependent variable was the period until the outcome occurrence. The independent variables were sex, self-reported skin color, age group, residence in the capital, and the presence of comorbidities. For data analysis the Kaplan-Meyer method and Cox-time-dependent Regression Model for multivariate analysis were used, with the covariable "period since the event notification recorded in days".
    RESULTS: Highest risk of death were observed in individuals aged 80 or older (HR = 8.06; p < 0.001), male (HR = 1.45, p < 0.001), non-white skin color (HR = 1.13; p < 0.033) or with no information (HR = 1.29; p < 0.001), with comorbidities (HR = 10.44; p < 0.001) or presence of comorbidities not reported (HR = 10.87; p < 0.001).
    CONCLUSION: The highest risk of occurrence of deaths from COVID-19 was observed in older adults, especially those over 80, patients who have comorbidities, men, and of non-white skin color. From the identification of the profile of patients with a higher risk of death with the identification by the health system, specific strategies of health care must be taken to prevent the evolution to death in these cases.
    DOI:  https://doi.org/10.1590/1980-549720200106
  6. PLoS Med. 2021 01;18(1): e1003490
    Seligman B, Ferranna M, Bloom DE.
      BACKGROUND: The COVID-19 epidemic in the United States is widespread, with more than 200,000 deaths reported as of September 23, 2020. While ecological studies show higher burdens of COVID-19 mortality in areas with higher rates of poverty, little is known about social determinants of COVID-19 mortality at the individual level.METHODS AND FINDINGS: We estimated the proportions of COVID-19 deaths by age, sex, race/ethnicity, and comorbid conditions using their reported univariate proportions among COVID-19 deaths and correlations among these variables in the general population from the 2017-2018 National Health and Nutrition Examination Survey (NHANES). We used these proportions to randomly sample individuals from NHANES. We analyzed the distributions of COVID-19 deaths by race/ethnicity, income, education level, and veteran status. We analyzed the association of these characteristics with mortality by logistic regression. Summary demographics of deaths include mean age 71.6 years, 45.9% female, and 45.1% non-Hispanic white. We found that disproportionate deaths occurred among individuals with nonwhite race/ethnicity (54.8% of deaths, 95% CI 49.0%-59.6%, p < 0.001), individuals with income below the median (67.5%, 95% CI 63.4%-71.5%, p < 0.001), individuals with less than a high school level of education (25.6%, 95% CI 23.4% -27.9%, p < 0.001), and veterans (19.5%, 95% CI 15.8%-23.4%, p < 0.001). Except for veteran status, these characteristics are significantly associated with COVID-19 mortality in multiple logistic regression. Limitations include the lack of institutionalized people in the sample (e.g., nursing home residents and incarcerated persons), the need to use comorbidity data collected from outside the US, and the assumption of the same correlations among variables for the noninstitutionalized population and COVID-19 decedents.
    CONCLUSIONS: Substantial inequalities in COVID-19 mortality are likely, with disproportionate burdens falling on those who are of racial/ethnic minorities, are poor, have less education, and are veterans. Healthcare systems must ensure adequate access to these groups. Public health measures should specifically reach these groups, and data on social determinants should be systematically collected from people with COVID-19.
    DOI:  https://doi.org/10.1371/journal.pmed.1003490
  7. BMC Infect Dis. 2021 Jan 09. 21(1): 40
    Vaughan L, Veruttipong D, Shaw JG, Levy N, Edwards L, Winget M.
      BACKGROUND: COVID-19 studies are primarily from the inpatient setting, skewing towards severe disease. Race and comorbidities predict hospitalization, however, ambulatory presentation of milder COVID-19 disease and characteristics associated with progression to severe disease is not well-understood.METHODS: We conducted a retrospective chart review including all COVID-19 positive cases from Stanford Health Care (SHC) in March 2020 to assess demographics, comorbidities and symptoms in relationship to: 1) their access point of testing (outpatient, inpatient, and emergency room (ER)) and 2) development of severe disease.
    RESULTS: Two hundred fifty-seven patients tested positive: 127 (49%), 96 (37%), and 34 (13%) at outpatient, ER and inpatient, respectively. Overall, 61% were age < 55; age > 75 was rarer in outpatient setting (11%) than ER (14%) or inpatient (24%). Most patients presented with cough (86%), fever/chills (76%), or fatigue (63%). 65% of inpatients reported shortness of breath compared to 30-32% of outpatients and ER patients. Ethnic/minority patients had a significantly higher risk of developing severe disease (Asian OR = 4.8 [1.6-14.2], Hispanic OR = 3.6 [1.1-11.9]). Medicare-insured patients were marginally more likely (OR = 4.0 [0.9-17.8]). Other factors associated with developing severe disease included kidney disease (OR = 6.1 [1.0-38.1]), cardiovascular disease (OR = 4.7 [1.0-22.1], shortness of breath (OR = 5.4 [2.3-12.6]) and GI symptoms (OR = 3.3 [1.4-7.7]; hypertension without concomitant CVD or kidney disease was marginally significant (OR = 2.3 [0.8-6.5]).
    CONCLUSIONS: Early widespread symptomatic testing for COVID-19 in Silicon Valley included many less severely ill patients. Thorough manual review of symptomatology reconfirms the heterogeneity of COVID-19 symptoms, and challenges in using clinical characteristics to predict decline. We re-demonstrate that socio-demographics are consistently associated with severity.
    Keywords:  COVID-19; Comorbidities; Race; Socio-demographics; Symptoms
    DOI:  https://doi.org/10.1186/s12879-021-05764-x
  8. Med J Islam Repub Iran. 2020 ;34 133
    Khorrami Z, Nili S, Sharifi H, Eybpoosh S, Shokoohi M.
      Background: Coronavirus Disease 2019 (COVID-19) has resulted in a considerable number of deaths worldwide. This ecological study aimed to explore the relationship between COVID-19 hospitalization and mortality with smoking, obesity, and underlying conditions in Iran. Methods: Provincial-level COVID-19 data were obtained from the official reports. Two outcomes were assessed: the total number of hospitalizations and deaths. Data on underlying health conditions, cigarette smoking, and obesity were obtained from national surveys. Negative binomial regression was used to report incident rate (IRR) ratios. Results: As of April 22, 2020, a total number of 43 950 lab-confirmed COVID-19 hospitalizations and 5391confirmed COVID-19 deaths were officially reported. Adjusting for underdetection to cover the number of clinically-confirmed COVID-19 cases, a total of 76 962 additional hospitalizations (ie, total lab- and clinically-confirmed hospitalizations = 120 912; 175% increase) and 7558 additional deaths (ie, total lab- and clinically-confirmed deaths = 12 949; 140% increase) were estimated during the same period. Provinces with a higher prevalence of obesity (IRR: 2.75, 95% CI: 1.49, 5.10), cigarette smoking (1.81; 95% CI: 1.01, 3.27), hypertension (1.88; 95% CI: 1.03, 3.44), and diabetes mellitus (1.74; 95% CI: 0.96, 3.16) had a higher likelihood of COVID-19 death rates. Conclusion: Inequality in COVID-19 hospitalization and mortality was observed in provinces whose populations had underlying diseases, in particular, obesity, cigarette smoking, hypertension, and diabetes.
    Keywords:  COVID-19; Ecological study; Hospitalization; Iran; Mortality; Underlying conditions
    DOI:  https://doi.org/10.34171/mjiri.34.133