bims-covirf Biomed News
on COVID19 risk factors
Issue of 2021‒02‒21
six papers selected by
Catherine Rycroft

  1. PLoS One. 2021 ;16(2): e0247205
    Dite GS, Murphy NM, Allman R.
      Up to 30% of people who test positive to SARS-CoV-2 will develop severe COVID-19 and require hospitalisation. Age, gender, and comorbidities are known to be risk factors for severe COVID-19 but are generally considered independently without accurate knowledge of the magnitude of their effect on risk, potentially resulting in incorrect risk estimation. There is an urgent need for accurate prediction of the risk of severe COVID-19 for use in workplaces and healthcare settings, and for individual risk management. Clinical risk factors and a panel of 64 single-nucleotide polymorphisms were identified from published data. We used logistic regression to develop a model for severe COVID-19 in 1,582 UK Biobank participants aged 50 years and over who tested positive for the SARS-CoV-2 virus: 1,018 with severe disease and 564 without severe disease. Model discrimination was assessed using the area under the receiver operating characteristic curve (AUC). A model incorporating the SNP score and clinical risk factors (AUC = 0.786; 95% confidence interval = 0.763 to 0.808) had 111% better discrimination of disease severity than a model with just age and gender (AUC = 0.635; 95% confidence interval = 0.607 to 0.662). The effects of age and gender are attenuated by the other risk factors, suggesting that it is those risk factors-not age and gender-that confer risk of severe disease. In the whole UK Biobank, most are at low or only slightly elevated risk, but one-third are at two-fold or more increased risk. We have developed a model that enables accurate prediction of severe COVID-19. Continuing to rely on age and gender alone (or only clinical factors) to determine risk of severe COVID-19 will unnecessarily classify healthy older people as being at high risk and will fail to accurately quantify the increased risk for younger people with comorbidities.
  2. PLoS One. 2021 ;16(2): e0246190
    Fathi M, Vakili K, Sayehmiri F, Mohamadkhani A, Hajiesmaeili M, Rezaei-Tavirani M, Eilami O.
      BACKGROUND AND OBJECTIVES: With the increase in the number of COVID-19 infections, the global health apparatus is facing insufficient resources. The main objective of the current study is to provide additional data regarding the clinical characteristics of the patients diagnosed with COVID-19, and in particular to analyze the factors associated with disease severity, lack of improvement, and mortality.METHODS: 102 studies were included in the present meta-analysis, all of which were published before September 24, 2020. The studies were found by searching a number of databases, including Scopus, MEDLINE, Web of Science, and Embase. We performed a thorough search from early February until September 24. The selected papers were evaluated and analyzed using Stata software application version 14.
    RESULTS: Ultimately, 102 papers were selected for this meta- analysis, covering 121,437 infected patients. The mean age of the patients was 58.42 years. The results indicate a prevalence of 79.26% for fever (95% CI: 74.98-83.26; I2 = 97.35%), 60.70% for cough (95% CI: 56.91-64.43; I2 = 94.98%), 33.21% for fatigue or myalgia (95% CI: 28.86-37.70; I2 = 96.12%), 31.30% for dyspnea (95% CI: 26.14-36.69; I2 = 97.67%), and 10.65% for diarrhea (95% CI: 8.26-13.27; I2 = 94.20%). The prevalence for the most common comorbidities was 28.30% for hypertension (95% CI: 23.66-33.18; I2 = 99.58%), 14.29% for diabetes (95% CI: 11.88-16.87; I2 = 99.10%), 12.30% for cardiovascular diseases (95% CI: 9.59-15.27; I2 = 99.33%), and 5.19% for chronic kidney disease (95% CI: 3.95-6.58; I2 = 96.42%).
    CONCLUSIONS: We evaluated the prevalence of some of the most important comorbidities in COVID-19 patients, indicating that some underlying disorders, including hypertension, diabetes, cardiovascular diseases, and chronic kidney disease, can be considered as risk factors for patients with COVID-19 infection. Furthermore, the results show that an elderly male with underlying diseases is more likely to have severe COVID-19.
  3. Clin Infect Dis. 2021 Feb 20. pii: ciab154. [Epub ahead of print]
    Dai CL, Kornilov SA, Roper RT, Cohen-Cline H, Jade K, Smith B, Heath JR, Diaz G, Goldman JD, Magis AT, Hadlock JJ.
      BACKGROUND: Data on the characteristics of COVID-19 patients disaggregated by race/ethnicity remain limited. We evaluated the sociodemographic and clinical characteristics of patients across racial/ethnic groups and assessed their associations with COVID-19 outcomes.METHODS: This retrospective cohort study examined 629,953 patients tested for SARS-CoV-2 in a large health system spanning California, Oregon, and Washington between March 1 and December 31, 2020. Sociodemographic and clinical characteristics were obtained from electronic health records. Odds of SARS-CoV-2 infection, COVID-19 hospitalization, and in-hospital death were assessed with multivariate logistic regression.
    RESULTS: 570,298 patients with known race/ethnicity were tested for SARS-CoV-2, of whom 27.8% were non-White minorities. 54,645 individuals tested positive, with minorities representing 50.1%. Hispanics represented 34.3% of infections but only 13.4% of tests. While generally younger than White patients, Hispanics had higher rates of diabetes but fewer other comorbidities. 8,536 patients were hospitalized and 1,246 died, of whom 56.1% and 54.4% were non-White, respectively. Racial/ethnic distributions of outcomes across the health system tracked with state-level statistics. Increased odds of testing positive and hospitalization were associated with all minority races/ethnicities. Hispanic patients also exhibited increased morbidity, and Hispanic race/ethnicity was associated with in-hospital mortality (OR: 1.39 [95% CI: 1.14-1.70]).
    CONCLUSION: Major healthcare disparities were evident, especially among Hispanics who tested positive at a higher rate, required excess hospitalization and mechanical ventilation, and had higher odds of in-hospital mortality despite younger age. Targeted, culturally-responsive interventions and equitable vaccine development and distribution are needed to address the increased risk of poorer COVID-19 outcomes among minority populations.
    Keywords:  COVID-19; SARS-CoV-2; health disparity; public health; race/ethnicity
  4. J Clin Exp Hepatol. 2021 Feb 08.
    Atmosudigdo IS, Pranata R, Lim MA, Henrina J, Yonas E, Vania R, Radi B.
      Objective: This systematic review and meta-analysis aimed to evaluate whether dyslipidemia affects the mortality and severity of COVID-19, we also aimed to evaluate whether other comorbidities influence the association.Methods: A systematic literature search using PubMed, Embase, and EuropePMC was performed on 8 October 2020. This study's main outcome is a poor composite outcome, comprising of mortality and severe COVID-19.
    Results: There were 9 studies with 3,663 patients. The prevalence of dyslipidemia in this pooled analysis was 18% (4%-32%). Dyslipidemia was associated with increased composite poor outcome (RR 1.39 [1.02, 1.88], p=0.010; I2: 56.7%, p=0.018). Subgroup analysis showed that dyslipidemia was associated with severe COVID-19 (RR 1.39 [1.03, 1.87], p=0.008; I2: 57.4%, p=0.029). Meta-regression showed that the association between dyslipidemia and poor outcome varies by age (coefficient: -0.04, p=0.033), male gender (coefficient: -0.03, p=0.042), and hypertension (coefficient: -0.02, p=0.033), but not diabetes (coefficient: -0.24, p=0.135) and cardiovascular diseases (coefficient: -0.01, p=0.506). Inverted funnel-plot was relatively symmetrical. Egger's test indicates that the pooled analysis was not statistically significant for small-study effects (p=0.206).
    Conclusion: Dyslipidemia potentially increases mortality and severity of COVID-19. The association was stronger in patients with older age, male, and hypertension.
    PROSPERO Registration Number: CRD42020213491.
    Keywords:  ACE2, Angiotensin Converting Enzyme 2; BMI, Body Mass Index; COVID-19; COVID-19, Coronavirus Disease 2019; CVD, Cardiovascular Diseases; HDL, high-density lipoprotein; LDL, low-density lipoprotein; MOOSE, Meta-analysis of Observational Studies in Epidemiology; NOS, Newcastle Ottawa Scale; RR, Risk Ratio; TG, Triglycerides; WHO, World Health Organization; coronavirus; dyslipidemia; hyperlipidemia; prognosis; vLDL, very-low-density lipoprotein
  5. Public Health. 2021 Jan 15. pii: S0033-3506(21)00016-0. [Epub ahead of print]192 15-20
    Peres IT, Bastos LSL, Gelli JGM, Marchesi JF, Dantas LF, Antunes BBP, Maçaira PM, Baião FA, Hamacher S, Bozza FA.
      OBJECTIVES: The coronavirus disease 2019 (COVID-19) pandemic has highlighted inequalities in access to healthcare systems, increasing racial disparities and worsening health outcomes in these populations. This study analysed the association between sociodemographic characteristics and COVID-19 in-hospital mortality in Brazil.STUDY DESIGN: A retrospective analysis was conducted on quantitative reverse transcription polymerase chain reaction-confirmed hospitalised adult patients with COVID-19 with a defined outcome (i.e. hospital discharge or death) in Brazil. Data were retrieved from the national surveillance system database (SIVEP-Gripe) between February 16 and August 8, 2020.
    METHODS: Clinical characteristics, sociodemographic variables, use of hospital resources and outcomes of hospitalised adult patients with COVID-19, stratified by self-reported race, were investigated. The primary outcome was in-hospital mortality. The association between self-reported race and in-hospital mortality, after adjusting for clinical characteristics and comorbidities, was evaluated using a logistic regression model.
    RESULTS: During the study period, Brazil had 3,018,397 confirmed COVID-19 cases and 100,648 deaths. The study population included 228,196 COVID-19-positive adult in-hospital patients with a defined outcome; the median age was 61 years, 57% were men, 35% (79,914) self-reported as Black/Brown and 35.4% (80,853) self-reported as White. The total in-hospital mortality was 37% (85,171/228,196). Black/Brown patients showed higher in-hospital mortality than White patients (42% vs 37%, respectively), were admitted less frequently to the intensive care unit (ICU) (32% vs 36%, respectively) and used more invasive mechanical ventilation (21% vs 19%, respectively), especially outside the ICU (17% vs 11%, respectively). Black/Brown race was independently associated with high in-hospital mortality after adjusting for sex, age, level of education, region of residence and comorbidities (odds ratio = 1.15; 95% confidence interval = 1.09-1.22).
    CONCLUSIONS: Among hospitalised Brazilian adults with COVID-19, Black/Brown patients showed higher in-hospital mortality, less frequently used hospital resources and had potentially more severe conditions than White patients. Racial disparities in health outcomes and access to health care highlight the need to actively implement strategies to reduce inequities caused by the wider health determinants, ultimately leading to a sustainable change in the health system.
    Keywords:  COVID-19; In-hospital mortality; Sociodemographic factors