bims-covirf 2020-10-25 papers

Issue of 2020‒10‒25
six papers selected by
Catherine Rycroft
BresMed

Iran J Public Health. 2020 Jul;49(7): 1211-1221

Factors Associated with Mortality in COVID-19 Patients: A Systematic Review and Meta-Analysis.

Mojtaba Sepandi, Maryam Taghdir, Yousef Alimohamadi, Sima Afrashteh, Hadiseh Hosamirudsari.

Background: The current study aimed to identify effective factors on the death among COVID-19 patients.Methods: All articles published in the period Jan 1, 2020, to Mar 23, 2020, written in English and reporting factors associated with COVID-19 mortality were reviewed. The random-effects model with 95% CI was used to calculate the pooled Odds Ratio (OR) and Hazard Ratio (HR). Data were analyzed using Stata ver.11.0.
Results: The older age OR: 1.21(1.10-1.33) and male gender OR: 1.41(1.04-1.89) were most prone to death due to COVID-19. The Comorbidity with some chronic diseases such as Diabetes type2 OR: 2.42(1.06-5.52), Hypertension OR: 2.54(1.21-5.32), Kidney disorder OR: 2.61(1.22-5.60), Respiratory disorder 3.09 (1.39-6.88) and Heart diseases OR: 4.37 (1.13-16.90) can increase the risk of COVID19 mortality.
Conclusion: Infection with COVID-19 is associated with substantial mortality mainly in older patients with comorbidities. We found the significant effect of age, gender and comorbidities such as Diabetes Mellitus, Hypertension, Kidney disorders and Heart diseases on the risk of death in patients with COVID-19. The factors associated with mortality found in this research can help to recognize patients with COVID-19 who are at higher risk of a poor prognosis. Monitoring these factors can serve to give early warning for the appropriate interventions.

Keywords: COVID-19; Comorbidities; Coronavirus disease 2019; Mortality; SARS-CoV-2

DOI: https://doi.org/10.18502/ijph.v49i7.3574

Hum Genomics. 2020 Oct 22. 14(1): 40

Human genetic factors associated with susceptibility to SARS-CoV-2 infection and COVID-19 disease severity.

Cleo Anastassopoulou, Zoi Gkizarioti, George P Patrinos, Athanasios Tsakris.

BACKGROUND: The emergence of the novel coronavirus in Wuhan, Hubei Province, China, in December 2019 marked the synchronization of the world to a peculiar clock that is counting infected cases and deaths instead of hours and minutes. The pandemic, highly transmissible severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has indeed caused considerable morbidity and mortality and drastically changed our everyday lives. As we continue to become acquainted with the seventh coronavirus known to infect our species, a number of its characteristics keep surprising us. Among those is the wide spectrum of clinical manifestations of the resulting coronavirus disease 2019 (COVID-19), which ranges from asymptomatic or mildly symptomatic infections to severe pneumonia, respiratory failure, and death.MAIN BODY: Data, now from patient populations, are beginning to accumulate on human genetic factors that may contribute to the observed diversified disease severity. Therefore, we deemed it prudent to review the associations between specific human genetic variants and clinical disease severity or susceptibility to infection that have been reported in the literature to date (at the time of writing this article in early August 2020 with updates in mid-September). With this work, we hope (i) to assist the fast-paced biomedical research efforts to combat the virus by critically summarizing current knowledge on the potential role of host genetics, and (ii) to help guide current genetics and genomics research towards candidate gene variants that warrant further investigation in larger studies. We found that determinants of differing severity of COVID-19 predominantly include components of the immune response to the virus, while determinants of differing susceptibility to SARS-CoV-2 mostly entail genes related to the initial stages of infection (i.e., binding of the cell surface receptor and entry).
CONCLUSION: Elucidating the genetic determinants of COVID-19 severity and susceptibility to SARS-CoV-2 infection would allow for the stratification of individuals according to risk so that those at high risk would be prioritized for immunization, for example, if or when safe and effective vaccines are developed. Our enhanced understanding of the underlying biological mechanisms could also guide personalized therapeutics. Such knowledge is already beginning to provide clues that help explain, at least in part, current epidemiologic observations regarding the typically more severe or benign disease course in older males and children, respectively.

Keywords: Allelic variation; COVID-19; Clinical outcome; Coronavirus; Genetic predisposition; Genotype; Polymorphisms; SARS-CoV-2; TLR7; X chromosome

DOI: https://doi.org/10.1186/s40246-020-00290-4

Sci Total Environ. 2020 Oct 13. pii: S0048-9697(20)36339-7. [Epub ahead of print] 142810

Identifying novel factors associated with COVID-19 transmission and fatality using the machine learning approach.

Mengyuan Li, Zhilan Zhang, Wenxiu Cao, Yijing Liu, Beibei Du, Canping Chen, Qian Liu, Md Nazim Uddin, Shanmei Jiang, Cai Chen, Yue Zhang, Xiaosheng Wang.

The COVID-19 virus has infected more than 38 million people and resulted in more than one million deaths worldwide as of October 14, 2020. By using the logistic regression model, we identified novel critical factors associated with COVID19 cases, death, and case fatality rates in 154 countries and in the 50 U.S. states. Among numerous factors associated with COVID-19 risk, economic inequality enhanced the risk of COVID-19 transmission. The per capita hospital beds correlated negatively with COVID-19 deaths. Blood types B and AB were protective factors for COVID-19 risk, while blood type A was a risk factor. The prevalence of HIV and influenza and pneumonia was associated with reduced COVID-19 risk. Increased intake of vegetables, edible oil, protein, vitamin D, and vitamin K was associated with reduced COVID-19 risk, while increased intake of alcohol was associated with increased COVID-19 risk. Other factors included age, sex, temperature, humidity, social distancing, smoking, health investment, urbanization level, and race. High temperature is a more compelling factor mitigating COVID-19 transmission than low temperature. Our comprehensive identification of the factors affecting COVID-19 transmission and fatality may provide new insights into the COVID-19 pandemic and advise effective strategies for preventing and migrating COVID-19 spread.

Keywords: COVID-19 fatality; COVID-19 transmission; Machine learning; Protective factor; Risk factor

DOI: https://doi.org/10.1016/j.scitotenv.2020.142810

PLoS One. 2020 ;15(10): e0241265

Prevalence of underlying diseases in died cases of COVID-19: A systematic review and meta-analysis.

Fatemeh Javanmardi, Abdolkhalegh Keshavarzi, Ali Akbari, Amir Emami, Neda Pirbonyeh.

INTRODUCTION: Underlying disease have a critical role in vulnerability of populations for a greater morbidity and mortality when they suffer from COVID-19. The aim of current study is evaluating the prevalence of underlying disease in died people with COVID-19.METHODS: The current study have been conducted according to PRISMA guideline. International database including PubMed, Scopus, Web of Science, Cochrane and google scholar were searched for relevant studies up to 1 June. All relevant articles that reported underlying disease in died cases of COVID-19 were included in the analysis.
RESULTS: After screening and excluding duplicated and irrelevant studies, 32 articles included in the analysis. The most prevalent comorbidities were hypertension, diabetes, cardiovascular disease, liver disease, lung disease, malignancy, cerebrovascular disease, COPD and asthma. Among all reported underlying disease, highest and lowest prevalence was related to hypertension and asthma which were estimated 46% (37% - 55%) and 3% (2%- 6%), respectively.
CONCLUSION: In summary, underlying disease have a critical role in poor outcomes, severity of disease and high mortality rate of COVID-19 cases. Patients with hypertension, cardiovascular disease and diabetes should be carefully monitored and be aware of health protocols.

DOI: https://doi.org/10.1371/journal.pone.0241265

JAMA Netw Open. 2020 10 01. 3(10): e2025197

Characteristics Associated With Racial/Ethnic Disparities in COVID-19 Outcomes in an Academic Health Care System.

Tian Gu, Jasmine A Mack, Maxwell Salvatore, Swaraaj Prabhu Sankar, Thomas S Valley, Karandeep Singh, Brahmajee K Nallamothu, Sachin Kheterpal, Lynda Lisabeth, Lars G Fritsche, Bhramar Mukherjee.

Importance: Black patients are overrepresented in the number of COVID-19 infections, hospitalizations, and deaths in the US. Reasons for this disparity may be due to underlying comorbidities or sociodemographic factors that require further exploration.Objective: To systematically determine patient characteristics associated with racial/ethnic disparities in COVID-19 outcomes.
Design, Setting, and Participants: This retrospective cohort study used comparative groups of patients tested or treated for COVID-19 at the University of Michigan from March 10, 2020, to April 22, 2020, with an outcome update through July 28, 2020. A group of randomly selected untested individuals were included for comparison. Examined factors included race/ethnicity, age, smoking, alcohol consumption, comorbidities, body mass index (BMI; calculated as weight in kilograms divided by height in meters squared), and residential-level socioeconomic characteristics.
Exposure: In-house polymerase chain reaction (PCR) tests, commercial antibody tests, nasopharynx or oropharynx PCR deployed by the Michigan Department of Health and Human Services and reverse transcription-PCR tests performed in external labs.
Main Outcomes and Measures: The main outcomes were being tested for COVID-19, having test results positive for COVID-19 or being diagnosed with COVID-19, being hospitalized for COVID-19, requiring intensive care unit (ICU) admission for COVID-19, and COVID-19-related mortality (including inpatient and outpatient). Medical comorbidities were defined from the International Classification of Diseases, Ninth Revision, and International Classification of Diseases, Tenth Revision, codes and were aggregated into a comorbidity score. Associations with COVID-19 outcomes were examined using odds ratios (ORs).
Results: Of 5698 patients tested for COVID-19 (mean [SD] age, 47.4 [20.9] years; 2167 [38.0%] men; mean [SD] BMI, 30.0 [8.0]), most were non-Hispanic White (3740 patients [65.6%]) or non-Hispanic Black (1058 patients [18.6%]). The comparison group included 7168 individuals who were not tested (mean [SD] age, 43.1 [24.1] years; 3257 [45.4%] men; mean [SD] BMI, 28.5 [7.1]). Among 1139 patients diagnosed with COVID-19, 492 (43.2%) were White and 442 (38.8%) were Black; 523 (45.9%) were hospitalized, 283 (24.7%) were admitted to the ICU, and 88 (7.7%) died. Adjusting for age, sex, socioeconomic status, and comorbidity score, Black patients were more likely to be hospitalized compared with White patients (OR, 1.72 [95% CI, 1.15-2.58]; P = .009). In addition to older age, male sex, and obesity, living in densely populated areas was associated with increased risk of hospitalization (OR, 1.10 [95% CI, 1.01-1.19]; P = .02). In the overall population, higher risk of hospitalization was also observed in patients with preexisting type 2 diabetes (OR, 1.82 [95% CI, 1.25-2.64]; P = .02) and kidney disease (OR, 2.87 [95% CI, 1.87-4.42]; P < .001). Compared with White patients, obesity was associated with higher risk of having test results positive for COVID-19 among Black patients (White: OR, 1.37 [95% CI, 1.01-1.84]; P = .04. Black: OR, 3.11 [95% CI, 1.64-5.90]; P < .001; P for interaction = .02). Having any cancer was associated with higher risk of positive COVID-19 test results for Black patients (OR, 1.82 [95% CI, 1.19-2.78]; P = .005) but not White patients (OR, 1.08 [95% CI, 0.84-1.40]; P = .53; P for interaction = .04). Overall comorbidity burden was associated with higher risk of hospitalization in White patients (OR, 1.30 [95% CI, 1.11-1.53]; P = .001) but not in Black patients (OR, 0.99 [95% CI, 0.83-1.17]; P = .88; P for interaction = .02), as was type 2 diabetes (White: OR, 2.59 [95% CI, 1.49-4.48]; P < .001; Black: OR, 1.17 [95% CI, 0.66-2.06]; P = .59; P for interaction = .046). No statistically significant racial differences were found in ICU admission and mortality based on adjusted analysis.
Conclusions and Relevance: These findings suggest that preexisting type 2 diabetes or kidney diseases and living in high-population density areas were associated with higher risk for COVID-19 hospitalization. Associations of risk factors with COVID-19 outcomes differed by race.

DOI: https://doi.org/10.1001/jamanetworkopen.2020.25197