bims-covirf Biomed News
on COVID19 risk factors
Issue of 2020‒11‒15
seven papers selected by
Catherine Rycroft

  1. Diabetes Metab Syndr. 2020 Oct 28. pii: S1871-4021(20)30410-0. [Epub ahead of print]14(6): 2103-2109
    Tuty Kuswardhani RA, Henrina J, Pranata R, Anthonius Lim M, Lawrensia S, Suastika K.
      BACKGROUND AND AIMS: The ongoing COVID-19 pandemic is disproportionately affecting patients with comorbidities. Therefore, thorough comorbidities assessment can help establish risk stratification of patients with COVID-19, upon hospital admission. Charlson Comorbidity Index (CCI) is a validated, simple, and readily applicable method of estimating the risk of death from comorbid disease and has been widely used as a predictor of long-term prognosis and survival.METHODS: We performed a systematic review and meta-analysis of CCI score and a composite of poor outcomes through several databases.
    RESULTS: Compared to a CCI score of 0, a CCI score of 1-2 and CCI score of ≥3 was prognostically associated with mortality and associated with a composite of poor outcomes. Per point increase of CCI score also increased mortality risk by 16%. Moreover, a higher mean CCI score also significantly associated with mortality and disease severity.
    CONCLUSION: CCI score should be utilized for risk stratifications of hospitalized COVID-19 patients.
    Keywords:  COVID-19; Charlson comorbidity index; Mechanical ventilation; Mortality; Severity
  2. PLoS One. 2020 ;15(11): e0242182
    Oetjens MT, Luo JZ, Chang A, Leader JB, Hartzel DN, Moore BS, Strande NT, Kirchner HL, Ledbetter DH, Justice AE, Carey DJ, Mirshahi T.
      BACKGROUND: Empirical data on conditions that increase risk of coronavirus disease 2019 (COVID-19) progression are needed to identify high risk individuals. We performed a comprehensive quantitative assessment of pre-existing clinical phenotypes associated with COVID-19-related hospitalization.METHODS: Phenome-wide association study (PheWAS) of SARS-CoV-2-positive patients from an integrated health system (Geisinger) with system-level outpatient/inpatient COVID-19 testing capacity and retrospective electronic health record (EHR) data to assess pre-COVID-19 pandemic clinical phenotypes associated with hospital admission (hospitalization).
    RESULTS: Of 12,971 individuals tested for SARS-CoV-2 with sufficient pre-COVID-19 pandemic EHR data at Geisinger, 1604 were SARS-CoV-2 positive and 354 required hospitalization. We identified 21 clinical phenotypes in 5 disease categories meeting phenome-wide significance (P<1.60x10-4), including: six kidney phenotypes, e.g. end stage renal disease or stage 5 CKD (OR = 11.07, p = 1.96x10-8), six cardiovascular phenotypes, e.g. congestive heart failure (OR = 3.8, p = 3.24x10-5), five respiratory phenotypes, e.g. chronic airway obstruction (OR = 2.54, p = 3.71x10-5), and three metabolic phenotypes, e.g. type 2 diabetes (OR = 1.80, p = 7.51x10-5). Additional analyses defining CKD based on estimated glomerular filtration rate, confirmed high risk of hospitalization associated with pre-existing stage 4 CKD (OR 2.90, 95% CI: 1.47, 5.74), stage 5 CKD/dialysis (OR 8.83, 95% CI: 2.76, 28.27), and kidney transplant (OR 14.98, 95% CI: 2.77, 80.8) but not stage 3 CKD (OR 1.03, 95% CI: 0.71, 1.48).
    CONCLUSIONS: This study provides quantitative estimates of the contribution of pre-existing clinical phenotypes to COVID-19 hospitalization and highlights kidney disorders as the strongest factors associated with hospitalization in an integrated US healthcare system.
  3. Public Health. 2020 Sep 30. pii: S0033-3506(20)30421-2. [Epub ahead of print]189 66-72
    Prado-Galbarro FJ, Sanchez-Piedra C, Gamiño-Arroyo AE, Cruz-Cruz C.
      OBJECTIVES: This study aimed to evaluate the association of chronic diseases and indigenous ethnicity on the poor prognosis of outpatients with coronavirus disease 2019 (COVID-19) and hospitalised patients in Mexico.STUDY DESIGN: The study design is an observational study of consecutive COVID-19 cases that were treated in Mexican healthcare units and hospitals between February 27 and April 27, 2020.
    METHODS: Epidemiological, clinical and sociodemographic data were analysed from outpatients and hospitalised patients. Cox regression models were used to analyse the risk of mortality after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection.
    RESULTS: In total, 15,529 patients with COVID-19 were characterised; 62.6% of patients were aged older than 40 years, 57.8% were men and 1.4% were of indigenous ethnicity. A high proportion had a history of diabetes (18.4%), hypertension (21.9%) and obesity (20.9%). Among hospitalised patients, 11.2% received health care in the intensive care unit. Advanced age, male sex, indigenous ethnicity and having a history of chronic diseases, such as hypertension, diabetes and obesity, were significantly associated with a high risk of death after SARS-CoV-2 infection. Diabetes and obesity were the comorbidities most highly associated with death through the models used in this study. Moreover, living in Mexico City and Mexico State (where there is easy access to medical services) and walking (rather than driving or getting public transport) were negatively associated with mortality after SARS-CoV-2 infection.
    CONCLUSIONS: Diabetes, hypertension and obesity combined with older age, male sex and indigenous ethnicity increase the risk of death after SARS-CoV-2 infection in the Mexican population. It is recommended that the incidence of COVID-19 is monitored in indigenous communities, and access to health services is increased nationwide.
    Keywords:  COVID-19; Chronic diseases; Risk factors; SARS-CoV-2; Vulnerable populations
  4. J Racial Ethn Health Disparities. 2020 Nov 09.
    Ojinnaka CO, Adepoju OE, Burgess AV, Woodard L.
      BACKGROUND: Texas ranks 2nd in the count of COVID cases. Pre-existing disparities in healthcare may be intersecting with COVID-19 outcomes.OBJECTIVES: To explore the relationship between county-level race/ethnic composition and COVID-19 mortality in the state of Texas and determine whether county-level health factors, healthcare access measures, and other demographic characteristics explain this relationship.
    METHODS: This retrospective study uses county-level case and fatality data obtained from the Texas Department of State Health Services and merged with the 2020 Robert Wood Johnson foundation (RWJF) county health rankings data. The outcome variables were fatalities per 100,000 population. A two-part/hurdle model examined (1) the probability of having a COVID-19 fatality and (2) fatalities per 100,000 population in counties with 1+ fatalities. For both parts of the hurdle model, we examined the impacts of racial and ethnic composition, adjusting for county characteristics and health factors.
    RESULTS: The odds of having a COVID-19 fatality decreased with a unit increase in the rate of primary care physicians in a county (OR = 0.93; 95% CI = 0.89, 0.99). In the second part of the model, there was a statistically significant increase in COVID-19 fatalities/100,000 population with every 1 % increase in the proportion of Hispanics (β = 5.41; p = 0.03) and African Americans (β = 5.08; p value = 0.04).
    CONCLUSION: Counties with higher rates of minorities, specifically Hispanics and African Americans, have a higher COVID-19 fatality burden. Targeted interventions are needed to raise awareness of preventive measures in these communities.
    Keywords:  COVID-19 mortality; County-level factors; Healthcare access; Racial disparities
  5. J Racial Ethn Health Disparities. 2020 Nov 12.
    Raharja A, Tamara A, Kok LT.
      OBJECTIVES: This article evaluates if ethnicity is an independent poor prognostic factor in COVID-19 disease.METHODS: MEDLINE, EMBASE, Cochrane, WHO COVID-19 databases from inception to 15/06/2020 and medRxiv. No language restriction. Newcastle-Ottawa Scale (NOS) and GRADE framework were utilised to assess the risk of bias and certainty of evidence. PROSPERO CRD42020188421.
    RESULTS: Seventy-two articles (59 cohort studies with 17,950,989 participants, 13 ecological studies; 54 US-based, 15 UK-based; 41 peer-reviewed) were included for systematic review and 45 for meta-analyses. Risk of bias was low: median NOS 7 of 9 (interquartile range 6-8). Compared to White ethnicity, unadjusted all-cause mortality was similar in Black (RR: 0.96 [95% CI: 0.83-1.08]) and Asian (RR: 0.99 [0.85-1.16]) but reduced in Hispanic ethnicity (RR: 0.69 [0.57-0.84]). Age- and sex-adjusted risks were significantly elevated for Black (HR: 1.38 [1.09-1.75]) and Asian (HR: 1.42 [1.15-1.75]), but not for Hispanic (RR: 1.14 [0.93-1.40]). Further adjusting for comorbidities attenuated these associations to non-significance: Black (HR: 0.95 [0.72-1.25]); Asian (HR: 1.17 [0.84-1.63]); Hispanic (HR: 0.94 [0.63-1.44]). Subgroup analyses showed a trend towards greater disparity in outcomes for UK ethnic minorities, especially hospitalisation risk.
    CONCLUSIONS: This review could not confirm a certain ethnicity as an independent poor prognostic factor for COVID-19. Racial disparities in COVID-19 outcomes may be partially attributed to higher comorbidity rates in certain ethnicity.
    Keywords:  Acute kidney injury; COVID-19; Ethnicity; Hospitalisation; Intubation; Mortality
  6. Cureus. 2020 Oct 07. 12(10): e10837
    Salah HM, Sharma T, Mehta J.
      Introduction Studies have reported conflicting results regarding the effect of smoking on outcome in coronavirus disease 2019 (COVID-19) patients, but the results have been conflicting. In this meta-analysis, we systematically examined the association between smoking and mortality in COVID-19. Methods PubMed database was searched to look for relevant articles. Inclusion criteria were as follows: (1) cohort studies or case series studies; (2) study population included individuals with a confirmed COVID-19 infection; (3) the status of smoking was reported, regardless if it was current or in the past; and (4) mortality among smokers was reported in the study or could be calculated and compared to non-smokers. Mortality rates were pooled using a random effects model. Risk ratio (RR) and its 95% confidence interval (CI) were also calculated using the same model. Another meta-analysis was then performed to assess the difference in mortality between current and former smokers. Results Ten studies with a total of 11,189 patients were included. Mortality among smokers was 29.4% compared to 17.0% among non-smokers. RR was 2.07 (95% CI: 1.59, 2.69). Based on analysis of four studies (532 patients), there was no difference in mortality risk between current and former smokers (RR: 1.03; 95% CI: 0.75, 1.40). Conclusions Smoking, current or past, is associated with higher mortality in COVID-19 patients. Mortality among current smokers was about 50% greater than former smokers, but the difference was not statistically significant.
    Keywords:  covid-19; mortality; sars-cov-2; smoking