bims-covirf Biomed News
on COVID19 risk factors
Issue of 2020‒11‒01
ten papers selected by
Catherine Rycroft
BresMed


  1. J Glob Health. 2020 Dec;10(2): 020503
    Khan MMA, Khan MN, Mustagir MG, Rana J, Islam MS, Kabir MI.
      Background: Coronavirus disease 2019 (COVID-19), the most hectic pandemic of the era, is increasing exponentially and taking thousands of lives worldwide. This study aimed to assess the prevalence of pre-existing comorbidities among COVID-19 patients and their mortality risks with each category of pre-existing comorbidity.Methods: To conduct this systematic review and meta-analysis, Medline, Web of Science, Scopus, and CINAHL databases were searched using pre-specified search strategies. Further searches were conducted using the reference list of the selected studies, renowned preprint servers (eg, medRxiv, bioRxiv, SSRN), and relevant journals' websites. Studies written in the English language included if those were conducted among COVID-19 patients with and without comorbidities and presented survivor vs non-survivor counts or hazard/odds of deaths or survivors with types of pre-existing comorbidities. Comorbidities reported in the selected studies were grouped into eight categories. The pooled likelihoods of deaths in each category were estimated using a fixed or random-effect model, based on the heterogeneity assessment. Publication bias was assessed by visual inspection of the funnel plot asymmetry and Egger's regression test. Trim and Fill method was used if there any publication bias was found.
    Results: A total of 41 studies included in this study comprised of 27 670 samples. The most common pre-existing comorbidities in COVID-19 patients were hypertension (39.5%), cardiovascular disease (12.4%), and diabetes (25.2%). The higher likelihood of deaths was found among COVID-19 patients who had pre-existing cardiovascular diseases (odds ratio (OR) = 3.42, 95% confidence interval (CI) = 2.86-4.09), immune and metabolic disorders (OR = 2.46, 95% CI = 2.03-2.85), respiratory diseases (OR = 1.94, 95% CI = 1.72-2.19), cerebrovascular diseases (OR = 4.12, 95% CI = 3.04-5.58), any types of cancers (OR = 2.22, 95% CI = 1.63-3.03), renal (OR = 3.02, 95% CI = 2.60-3.51), and liver diseases (OR = 2.35, 95% CI = 1.50-3.69).
    Conclusions: This study provides evidence that COVID-19 patients with pre-existing comorbidities had a higher likelihood of death. These findings could potentially help health care providers to sort out the most susceptible COVID-19 patients by comorbidities, take precautionary measures during hospitalization, assess susceptibility to death, and prioritize their treatment, which could potentially reduce the number of fatalities in COVID-19.
    DOI:  https://doi.org/10.7189/jogh.10.020503
  2. Sci Rep. 2020 Oct 30. 10(1): 18716
    An C, Lim H, Kim DW, Chang JH, Choi YJ, Kim SW.
      The rapid spread of COVID-19 has resulted in the shortage of medical resources, which necessitates accurate prognosis prediction to triage patients effectively. This study used the nationwide cohort of South Korea to develop a machine learning model to predict prognosis based on sociodemographic and medical information. Of 10,237 COVID-19 patients, 228 (2.2%) died, 7772 (75.9%) recovered, and 2237 (21.9%) were still in isolation or being treated at the last follow-up (April 16, 2020). The Cox proportional hazards regression analysis revealed that age > 70, male sex, moderate or severe disability, the presence of symptoms, nursing home residence, and comorbidities of diabetes mellitus (DM), chronic lung disease, or asthma were significantly associated with increased risk of mortality (p ≤ 0.047). For machine learning, the least absolute shrinkage and selection operator (LASSO), linear support vector machine (SVM), SVM with radial basis function kernel, random forest (RF), and k-nearest neighbors were tested. In prediction of mortality, LASSO and linear SVM demonstrated high sensitivities (90.7% [95% confidence interval: 83.3, 97.3] and 92.0% [85.9, 98.1], respectively) and specificities (91.4% [90.3, 92.5] and 91.8%, [90.7, 92.9], respectively) while maintaining high specificities > 90%, as well as high area under the receiver operating characteristics curves (0.963 [0.946, 0.979] and 0.962 [0.945, 0.979], respectively). The most significant predictors for LASSO included old age and preexisting DM or cancer; for RF they were old age, infection route (cluster infection or infection from personal contact), and underlying hypertension. The proposed prediction model may be helpful for the quick triage of patients without having to wait for the results of additional tests such as laboratory or radiologic studies, during a pandemic when limited medical resources must be wisely allocated without hesitation.
    DOI:  https://doi.org/10.1038/s41598-020-75767-2
  3. J Biomed Res. 2020 Sep 30. 1-9
    Pan W, Miyazaki Y, Tsumura H, Miyazaki E, Yang W.
      Many studies have investigated causes of COVID-19 and explored safety measures for preventing COVID-19 infections. Unfortunately, these studies fell short to address disparities in health status and resources among decentralized communities in the United States. In this study, we utilized an advanced modeling technique to examine complex associations of county-level health factors with COVID-19 mortality for all 3141 counties in the United States. Our results indicated that counties with more uninsured people, more housing problems, more urbanized areas, and longer commute are more likely to have higher COVID-19 mortality. Based on the nationwide population-based data, this study also echoed prior research that used local data, and confirmed that county-level sociodemographic factors, such as more Black, Hispanic, and older subpopulations, are attributed to high risk of COVID-19 mortality. We hope that these findings will help set up priorities on high risk communities and subpopulations in future for fighting the novel virus.
    Keywords:  COVID-19; health disparity; health factors; hierarchical generalized linear model; mortality
    DOI:  https://doi.org/10.7555/JBR.34.20200129
  4. J Glob Health. 2020 Dec;10(2): 020506
    Patel U, Malik P, Mehta D, Shah D, Kelkar R, Pinto C, Suprun M, Dhamoon M, Hennig N, Sacks H.
      Background: Coronavirus disease-2019 (COVID-19), a pandemic that brought the whole world to a standstill, has led to financial and health care burden. We aimed to evaluate epidemiological characteristics, needs of resources, outcomes, and global burden of the disease.Methods: Systematic review was performed searching PubMed from December 1, 2019, to March 25, 2020, for full-text observational studies that described epidemiological characteristics, following MOOSE protocol. Global data were collected from the JHU-Corona Virus Resource Center, WHO-COVID-2019 situation reports, KFF.org, and Worldometers.info until March 31, 2020. The prevalence percentages were calculated. The global data were plotted in excel to calculate case fatality rate (CFR), predicted CFR, COVID-19 specific mortality rate, and doubling time for cases and deaths. CFR was predicted using Pearson correlation, regression models, and coefficient of determination.
    Results: From 21 studies of 2747 patients, 8.4% of patients died, 20.4% recovered, 15.4% were admitted to ICU and 14.9% required ventilation. COVID-19 was more prevalent in patients with hypertension (19.3%), smoking (11.3%), diabetes mellitus (10%), and cardiovascular diseases (7.4%). Common complications were pneumonia (82%), cardiac complications (26.4%), acute respiratory distress syndrome (15.7%), secondary infection (11.2%), and septic shock (4.3%). Though CFR and COVID-19 specific death rates are dynamic, they were consistently high for Italy, Spain, and Iran. Polynomial growth models were best fit for all countries for predicting CFR. Though many interventions have been implemented, stern measures like nationwide lockdown and school closure occurred after very high infection rates (>10cases per 100 000population) prevailed. Given the trend of government measures and decline of new cases in China and South Korea, most countries will reach the peak between April 1-20, if interventions are followed.
    Conclusions: A collective approach undertaken by a responsible government, wise strategy implementation and a receptive population may help contain the spread of COVID-19 outbreak. Close monitoring of predictive models of such indicators in the highly affected countries would help to evaluate the potential fatality if the second wave of pandemic occurs. The future studies should be focused on identifying accurate indicators to mitigate the effect of underestimation or overestimation of COVID-19 burden.
    DOI:  https://doi.org/10.7189/jogh.10.020506
  5. Eur Heart J Qual Care Clin Outcomes. 2020 Oct 27. pii: qcaa081. [Epub ahead of print]
    Phelps M, Christensen DM, Gerds T, Fosbøl E, Torp-Pedersen C, Schou M, Køber L, Kragholm K, Andersson C, Biering-Sørensen T, Christensen HC, Andersen MP, Gislason G.
      BACKGROUND: Pre-existing cardiovascular diseases (CVDs) have been proposed to identify patients at higher risk of adverse COVID-19 outcomes, but existing evidence is conflicting. Thus, it is unclear whether pre-existing CVDs are independently important predictors for severe COVID-19.METHODS AND RESULTS: In a nationwide Danish cohort of hospital-screened COVID-19 patients aged > =40, we investigated if pre-existing CVDs predict the 30-day risk of (1) composite outcome of severe COVID-19 and (2) all-cause mortality. We estimated 30-day risks using a Cox regression model including age, sex, each CVD comorbidity, COPD-asthma, diabetes, and chronic kidney disease. To illustrate CVD comorbidities' importance, we evaluated the predicted risks of death and severe infection, for each sex, along ages 40 - 85. 4,090 COVID-19 hospital-screened patients were observed as of August 26, 2020; 22.1% had ≥ 1 CVD, 23.7% had severe infection within 30 days and 12.6% died. Predicted risks of both outcomes at age 75 among men with single CVD comorbidities did not differ in clinically meaningful amounts compared to men with no comorbidities risks for the composite outcome of severe infection; women with heart failure (28.2%; 95% CI 21.1%-37.0%) or atrial fibrillation (30.0%; 95% CI: 24.2%-36.9%) showed modest increases compared to women with no comorbidities (24.0%; 95% CI: 21.4%-26.9%).
    CONCLUSIONS: The results showing only modest effects of CVDs on increased risks of poor COVID-19 outcomes are important in allowing public health authorities and clinicians to provide more tailored guidance to cardiovascular patients, who have heretofore been grouped together as high-risk due to their disease status.
    DOI:  https://doi.org/10.1093/ehjqcco/qcaa081
  6. Geriatr Gerontol Int. 2020 Oct 27.
    de Souza CD, de Arruda Magalhães AJ, Lima AJ, Nunes DN, de Fátima Machado Soares É, de Castro Silva L, Santos LG, Dos Santos Cardoso VI, Nobre YV, do Carmo RF.
      AIM: Older adults are the main risk group for coronavirus disease 2019 (COVID-19). This study aimed to describe the clinical manifestations and factors associated with mortality from COVID-19 among older adults in Brazil.METHODS: A cross-sectional observational study was carried out with data from 9807 cases of COVID-19 among older adults in the state of Alagoas, Brazil. We determined the case fatality rate between age groups and clinical factors associated with mortality.
    RESULTS: A total of 52.5% (n = 5145) were women, and with an average age of 70.21 ± 8.37 years. The fatality rate was 11.9%, with a higher rate in men (14.4%) compared with women (9.8%). The fatality rate increased with age. The most common manifestations were fever (n = 4926; 50.2%), cough (n = 5737; 58.5%), headache (n = 1980; 20.2%) and fatigue (n = 2022; 20.6%). The most prevalent comorbidities were diabetes (n = 1528; 5.6%), cardiovascular disease (n = 1528; 15.6%) and systemic arterial hypertension (n = 597; 6.1%). The factors associated with mortality were male sex (OR 1.54), age ≥75 years (OR 2.40), dyspnea (OR 2.92), diabetes (OR 2.33), hypertension (OR 1.53) and chronic kidney disease (OR 2.02).
    CONCLUSIONS: The profile and the risk factors evidenced show the need to adopt mechanisms to protect the elderly population.
    Keywords:  COVID-19; elderly; geriatric patients; mortality; risk factors
    DOI:  https://doi.org/10.1111/ggi.14061
  7. BMJ. 2020 10 28. 371 m3582
    Shah ASV, Wood R, Gribben C, Caldwell D, Bishop J, Weir A, Kennedy S, Reid M, Smith-Palmer A, Goldberg D, McMenamin J, Fischbacher C, Robertson C, Hutchinson S, McKeigue P, Colhoun H, McAllister DA.
      OBJECTIVE: To assess the risk of hospital admission for coronavirus disease 2019 (covid-19) among patient facing and non-patient facing healthcare workers and their household members.DESIGN: Nationwide linkage cohort study.
    SETTING: Scotland, UK, 1 March to 6 June 2020.
    PARTICIPANTS: Healthcare workers aged 18-65 years, their households, and other members of the general population.
    MAIN OUTCOME MEASURE: Admission to hospital with covid-19.
    RESULTS: The cohort comprised 158 445 healthcare workers, most of them (90 733; 57.3%) being patient facing, and 229 905 household members. Of all hospital admissions for covid-19 in the working age population (18-65 year olds), 17.2% (360/2097) were in healthcare workers or their households. After adjustment for age, sex, ethnicity, socioeconomic deprivation, and comorbidity, the risk of admission due to covid-19 in non-patient facing healthcare workers and their households was similar to the risk in the general population (hazard ratio 0.81 (95% confidence interval 0.52 to 1.26) and 0.86 (0.49 to 1.51), respectively). In models adjusting for the same covariates, however, patient facing healthcare workers, compared with non-patient facing healthcare workers, were at higher risk (hazard ratio 3.30, 2.13 to 5.13), as were household members of patient facing healthcare workers (1.79, 1.10 to 2.91). After sub-division of patient facing healthcare workers into those who worked in "front door," intensive care, and non-intensive care aerosol generating settings and other, those in front door roles were at higher risk (hazard ratio 2.09, 1.49 to 2.94). For most patient facing healthcare workers and their households, the estimated absolute risk of hospital admission with covid-19 was less than 0.5%, but it was 1% and above in older men with comorbidity.
    CONCLUSIONS: Healthcare workers and their households contributed a sixth of covid-19 cases admitted to hospital. Although the absolute risk of admission was low overall, patient facing healthcare workers and their household members had threefold and twofold increased risks of admission with covid-19.
    DOI:  https://doi.org/10.1136/bmj.m3582
  8. Nat Commun. 2020 Oct 30. 11(1): 5493
    Fajnzylber J, Regan J, Coxen K, Corry H, Wong C, Rosenthal A, Worrall D, Giguel F, Piechocka-Trocha A, Atyeo C, Fischinger S, Chan A, Flaherty KT, Hall K, Dougan M, Ryan ET, Gillespie E, Chishti R, Li Y, Jilg N, Hanidziar D, Baron RM, Baden L, Tsibris AM, Armstrong KA, Kuritzkes DR, Alter G, Walker BD, Yu X, Li JZ, .
      The relationship between SARS-CoV-2 viral load and risk of disease progression remains largely undefined in coronavirus disease 2019 (COVID-19). Here, we quantify SARS-CoV-2 viral load from participants with a diverse range of COVID-19 disease severity, including those requiring hospitalization, outpatients with mild disease, and individuals with resolved infection. We detected SARS-CoV-2 plasma RNA in 27% of hospitalized participants, and 13% of outpatients diagnosed with COVID-19. Amongst the participants hospitalized with COVID-19, we report that a higher prevalence of detectable SARS-CoV-2 plasma viral load is associated with worse respiratory disease severity, lower absolute lymphocyte counts, and increased markers of inflammation, including C-reactive protein and IL-6. SARS-CoV-2 viral loads, especially plasma viremia, are associated with increased risk of mortality. Our data show that SARS-CoV-2 viral loads may aid in the risk stratification of patients with COVID-19, and therefore its role in disease pathogenesis should be further explored.
    DOI:  https://doi.org/10.1038/s41467-020-19057-5
  9. J Epidemiol Community Health. 2020 Oct 29. pii: jech-2020-215280. [Epub ahead of print]
    Do DP, Frank R.
      BACKGROUND: The disproportionate burden of the COVID-19 pandemic on racial/ethnic minority communities has revealed glaring inequities. However, multivariate empirical studies investigating its determinants are still limited. We document variation in COVID-19 case and death rates across different racial/ethnic neighbourhoods in New York City (NYC), the initial epicentre of the U.S. coronavirus outbreak, and conduct a multivariate ecological analysis investigating how various neighbourhood characteristics might explain any observed disparities.METHODS: Using ZIP-code-level COVID-19 case and death data from the NYC Department of Health, demographic and socioeconomic data from the American Community Survey and health data from the Centers for Disease Control's 500 Cities Project, we estimated a series of negative binomial regression models to assess the relationship between neighbourhood racial/ethnic composition (majority non-Hispanic White, majority Black, majority Hispanic and Other-type), neighbourhood poverty, affluence, proportion of essential workers, proportion with pre-existing health conditions and neighbourhood COVID-19 case and death rates.
    RESULTS: COVID-19 case and death rates for majority Black, Hispanic and Other-type minority communities are between 24% and 110% higher than those in majority White communities. Elevated case rates are completely accounted for by the larger presence of essential workers in minority communities but excess deaths in Black neighbourhoods remain unexplained in the final model.
    CONCLUSIONS: The unequal COVID-19 case burden borne by NYC's minority communities is closely tied to their representation among the ranks of essential workers. Higher levels of pre-existing health conditions are not a sufficient explanation for the elevated mortality burden observed in Black communities.
    Keywords:  Epidemics; Ethnicity; Health inequalities; Neighbourhood/place; Social inequalities
    DOI:  https://doi.org/10.1136/jech-2020-215280
  10. PRiMER. 2020 ;4 15
    Long JD, Ward CA, Khorasani-Zadeh A.
      Introduction: Obesity has been declared a major risk factor for morbidity and mortality in COVID-19 patients. In this rapid review, we provide an overview of recently-published papers with clinical and epidemiological relevance on this topic.Methods: As part of a weekly COVID-19 data mining meeting, we conducted a literature review regarding the role of obesity in COVID-19 outcomes, particularly in young patients with COVID-19. We utilized the PubMed, Upstate Medical University Health Sciences Library, Google Scholar, and LitCovid databases to identify the articles.
    Results: Our group identified seven relevant publications (four retrospective case series and three reviews).
    Conclusion: Our group's review of this topic illustrates that obesity is a common comorbidity in hospitalized COVID-19 patients. Obesity is associated with an increased likelihood of intermittent mandatory ventilation within the first 10 days of hospitalization and a higher risk of admission to acute or critical hospital care, including in patients aged less than 60 years, with one study showing it to be a greater risk factor than cardiovascular or pulmonary conditions for critical COVID-19 illness. There are some indications that moderate-intensity exercise may be beneficial for promoting a healthy immune system in patients with and without obesity. Given these findings, hospitals should ensure their staff are prepared and their facilities are adequately equipped to provide high-quality care to patients with obesity (PWO) hospitalized with COVID-19. Family medicine and primary care physicians are encouraged to counsel their PWO about their increased risk for morbidity and mortality during this pandemic.
    DOI:  https://doi.org/10.22454/PRiMER.2020.104798