Viruses and the risk of lung cancer: prevention and treatment

Lung cancer is the most common cancer in males and the second most common among females both in Europe and worldwide. Moreover, lung cancer is the leading cause of death due to cancer in males. The European region accounts for 23% of total cancer cases and 20% of cancer-related deaths. Relationships have been described between a number of infectious agents and cancers, but our knowledge of the role of viruses, both respiratory and systemic, in the pathogenesis of lung cancer is still rudimentary and has been poorly disseminated. In this chapter, we review the available evidence on the involvement of HPV, Epstein-Barr virus, HIV, cytomegalovirus and measles virus in the epidemiology and pathogenesis of lung cancer.Copyright © ERS 2021.

Epidemiology of Pemphigus.

Pemphigus is an epidemiologically heterogeneous group of autoimmune bullous diseases comprising pemphigus vulgaris (PV), pemphigus foliaceus, paraneoplastic pemphigus, IgA pemphigus, and pemphigus herpetiformis. Recently, our knowledge about the frequency of pemphigus, which is highly variable between different populations, has considerably expanded, and the first non-HLA genes associated with PV have been identified. In addition, a variety of comorbidities, including other autoimmune diseases, hematological malignancies, and psoriasis, have been described in this variant. Here, initial data about the impact of COVID-19 on this fragile patient population are discussed and perspectives for future epidemiological studies are outlined.

A systematic review and meta-analysis of the safety of anti-interleukin therapy in COVID-19.

Objective. To evaluate safety of anti-interleukin drugs used as a pathogenetic therapy of COVID-19 as assessed by risks of infectious complications. Materials and methods. A systematic review of publications related to safety assessment of anti-interleukin drugs recommended as pathogenetic therapy in COVID-19 patients in terms of incidence of serious adverse events and adverse events of "Infections and Invasions" class and a meta-analysis of the data were performed. Results. The meta-analysis included 16 randomized and 3 non-randomized studies. The hazard ratio of serious adverse events between the comparison groups was 0.93 [95% CI 0.85;1.01] (p = 0.1), the hazard ratio of adverse event of "Infections and Invasions" class was 0.9 [95% CI 0.8;1.02] (p = 0.09), showing no differences in the incidence of those events. Conclusions. This meta-analysis did not demonstrate statistically significant differences in the relative risks of serious adverse events and adverse events of "Infections and Invasions" class for the use of anti-interleukin drugs in COVID-19 patients.Copyright © 2022, Interregional Association for Clinical Microbiology and Antimicrobial Chemotherapy. All rights reserved.

Retrospective Evaluation of COVID-19 Therapeutics

The pandemic outbreak of COVID-19 created panic all over the world. As therapeutics that can effectively wipe out the virus and terminate transmission are not available, supportive therapeutics are the main clinical treatments for COVID-19. Repurposing available therapeutics from other viral infections is the primary surrogate in ameliorating and treating COVID-19. The therapeutics should be tailored individually by analyzing the severity of COVID-19, age, gender, comorbidities, and so on. We aim to investigate the effects of COVID-19 therapeutics and to search for laboratory parameters indicative of severity of illness. Multi-center collaboration and large cohort of patients will be required to evaluate therapeutics combinations in the future. This study is a single-center retrospective observational study of COVID-19 clinical data in China. Information on patients' treatment modalities, previous medical records, individual disease history, and clinical outcomes were considered to evaluate treatment efficacy. After screening, 2,844 patients are selected for the study. The result shows that treatment with TCM (Hazard Ratio (HR) 0.191 [95% Confidence Interval (CI), 0.14–0.25];p < 0.0001), antiviral therapy (HR 0.331 [95% CI 0.19–0.58];p = 0.000128), or Arbidol (HR 0.454 [95% CI 0.34–0.60];p < 0.0001) is associated with good prognostic of patients. Multivariate Cox regression analysis showed TCM treatment decreased the mortality hazard ratio by 69.4% (p < 0.0001). © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Approximate reciprocal relationship between two cause-specific hazard ratios in COVID-19 data with mutually exclusive events.

COVID-19 survival data presents a special situation where not only the time-to-event period is short, but also the two events or outcome types, death and release from hospital, are mutually exclusive, leading to two cause-specific hazard ratios (csHR d and csHR r ). The eventual mortality/release outcome is also analyzed by logistic regression to obtain odds-ratio (OR). We have the following three empirical observations: (1) The magnitude of OR is an upper limit of the csHR d : |log(OR)| ≥ |log(csHR d )|. This relationship between OR and HR might be understood from the definition of the two quantities; (2) csHR d and csHR r point in opposite directions: log(csHR d ) ⋅ log(csHR r ) < 0; This relation is a direct consequence of the nature of the two events; and (3) there is a tendency for a reciprocal relation between csHR d and csHR r : csHR d ∼ 1/csHR r . Though an approximate reciprocal trend between the two hazard ratios is in indication that the same factor causing faster death also lead to slow recovery by a similar mechanism, and vice versa, a quantitative relation between csHR d and csHR r in this context is not obvious. These results may help future analyses of data from COVID-19 or other similar diseases, in particular if the deceased patients are lacking, whereas surviving patients are abundant.

Antibody titer levels and the effect on subsequent SARS-CoV-2 infection in a large US-based cohort.

Despite a growing amount of data around the kinetics and durability of the antibody response induced by vaccination and previous infection, there is little understanding of whether or not a given quantitative level of antibodies correlates to protection against SARS-CoV-2 infection or reinfection. In this study, we examine SARS-CoV-2 anti-spike receptor binding domain (RBD) antibody titers and subsequent SARS-CoV-2 reverse transcription polymerase chain reaction (RT-PCR) tests in a large cohort of US-based patients. We analyzed antibody test results in a cohort of 22,204 individuals, 6.8% (n = 1,509) of whom eventually tested positive for SARS-CoV-2 RNA, suggesting infection or reinfection. Kaplan-Meier curves were plotted to understand the effect of various levels of anti-spike RBD antibody titers (classified into discrete ranges) on subsequent RT-PCR positivity rates. Statistical analyses included fitting a Cox proportional hazards model to estimate the age-, sex- and exposure-adjusted hazard ratios for S antibody titer, using zip-code positivity rates by week as a proxy for COVID-19 exposure. It was found that the best models of the temporally associated infection risk were those based on log antibody titer level (HR = 0.836 (p < 0.05)). When titers were binned, the hazard ratio associated with antibody titer >250 Binding Antibody Units (BAU) was 0.27 (p < 0.05, 95% CI [0.18, 0.41]), while the hazard ratio associated with previous infection was 0.20 (p < 0.05, 95% CI [0.10, 0.39]). Fisher exact odds ratio (OR) for Ab titers <250 BAU showed OR = 2.84 (p < 0.05; 95% CI: [2.30, 3.53]) for predicting the outcome of a subsequent PCR test. Antibody titer levels correlate with protection against subsequent SARS-CoV-2 infection or reinfection when examining a cohort of real-world patients who had the spike RBD antibody assay performed.

In the midst of a pandemic, more introverted individuals may have a mortality advantage.

Purpose: We investigated whether the relationship between extroversion and mortality changed during the COVID-19 pandemic. Methods: Midlife Americans were surveyed in 1995-96 with mortality follow-up through December 31, 2020. We used a Cox model to estimate age-specific mortality controlling for sex, race/ethnicity, the period trend in mortality, an indicator for the pandemic period (Mar-Dec 2020), extroversion, and an interaction between extroversion and the pandemic indicator. Results: Prior to the pandemic, extroversion was associated with somewhat lower mortality (HR = 0.93 per SD, 95% CI 0.88-0.97), but the relationship reversed during the pandemic. Extroversion was associated with greater pandemic-related excess mortality (HR = 1.29 per SD, 95% CI 1.002-1.67). That is, compared with persons who were more introverted, those who were highly extroverted suffered a bigger increase in mortality during the pandemic relative to pre-pandemic mortality levels. Conclusions: The slight mortality advantage enjoyed by more extroverted Americans prior to the pandemic disappeared during the first 10 months of the COVID-19 pandemic. We suspect that the mortality benefit of introversion during the pandemic is largely a result of reduced exposure to the risk of infection, but it may also derive in part from the ability of more introverted individuals to adapt more easily to reduced social interaction without engaging in self-destructive behavior (e.g., drug and alcohol abuse).

Clinical outcomes of remdesivir-treated COVID-19 patients in South Korea.

OBJECTIVES: This study analyzed the clinical outcomes of remdesivir treatment in coronavirus disease 2019 (COVID-19) patients in South Korea. METHODS: This retrospective cohort study involved the secondary analysis of epidemiological data. Among patients diagnosed with COVID-19 from July 2, 2020 to March 23, 2021 (12 AM), 4,868 who received oxygen therapy and were released from isolation after receiving remdesivir treatment were assigned to the treatment group, and 6,068 patients who received oxygen therapy but not remdesivir were assigned to the untreated group. The study subjects included children under the age of 19. The general characteristics and severity were compared between the groups. Differences in the time to death and mortality were also compared. RESULTS: In the untreated group, the hazard ratio [HR] for mortality was 1.59 among patients aged ≥70 years and 2.32 in patients with severe disease in comparison to the treatment group. In a comparison of survival time among patients with severe disease aged ≥70 years, the HR for mortality before 50 days was 2.09 in the untreated group compared to the treatment group. CONCLUSION: Patients with remdesivir treatment showed better clinical outcomes in this study, but these results should be interpreted with caution since this study was not a fully controlled clinical trial.

COVID-19 Diagnosis and Risk of Death Among Adults With Cancer in Indiana: Retrospective Cohort Study.

BACKGROUND: Prior studies, generally conducted at single centers with small sample sizes, found that individuals with cancer experience more severe outcomes due to COVID-19, caused by SARS-CoV-2 infection. Although early examinations revealed greater risk of severe outcomes for patients with cancer, the magnitude of the increased risk remains unclear. Furthermore, prior studies were not typically performed using population-level data, especially those in the United States. Given robust prevention measures (eg, vaccines) are available for populations, examining the increased risk of patients with cancer due to SARS-CoV-2 infection using robust population-level analyses of electronic medical records is warranted. OBJECTIVE: The aim of this paper is to evaluate the association between SARS-CoV-2 infection and all-cause mortality among recently diagnosed adults with cancer. METHODS: We conducted a retrospective cohort study of newly diagnosed adults with cancer between January 1, 2019, and December 31, 2020, using electronic health records linked to a statewide SARS-CoV-2 testing database. The primary outcome was all-cause mortality. We used the Kaplan-Meier estimator to estimate survival during the COVID-19 period (January 15, 2020, to December 31, 2020). We further modeled SARS-CoV-2 infection as a time-dependent exposure (immortal time bias) in a multivariable Cox proportional hazards model adjusting for clinical and demographic variables to estimate the hazard ratios (HRs) among newly diagnosed adults with cancer. Sensitivity analyses were conducted using the above methods among individuals with cancer-staging information. RESULTS: During the study period, 41,924 adults were identified with newly diagnosed cancer, of which 2894 (6.9%) tested positive for SARS-CoV-2. The population consisted of White (n=32,867, 78.4%), Black (n=2671, 6.4%), Hispanic (n=832, 2.0%), and other (n=5554, 13.2%) racial backgrounds, with both male (n=21,354, 50.9%) and female (n=20,570, 49.1%) individuals. In the COVID-19 period analysis, after adjusting for age, sex, race or ethnicity, comorbidities, cancer type, and region, the risk of death increased by 91% (adjusted HR 1.91; 95% CI 1.76-2.09) compared to the pre-COVID-19 period (January 1, 2019, to January 14, 2020) after adjusting for other covariates. In the adjusted time-dependent analysis, SARS-CoV-2 infection was associated with an increase in all-cause mortality (adjusted HR 6.91; 95% CI 6.06-7.89). Mortality increased 2.5 times among adults aged 65 years and older (adjusted HR 2.74; 95% CI 2.26-3.31) compared to adults 18-44 years old, among male (adjusted HR 1.23; 95% CI 1.14-1.32) compared to female individuals, and those with ≥2 chronic conditions (adjusted HR 2.12; 95% CI 1.94-2.31) compared to those with no comorbidities. Risk of mortality was 9% higher in the rural population (adjusted HR 1.09; 95% CI 1.01-1.18) compared to adult urban residents. CONCLUSIONS: The findings highlight increased risk of death is associated with SARS-CoV-2 infection among patients with a recent diagnosis of cancer. Elevated risk underscores the importance of adhering to social distancing, mask adherence, vaccination, and regular testing among the adult cancer population.

Investigating COVID-19 transmission and mortality differences between indigenous and non-indigenous populations in Mexico.

OBJECTIVES: Indigenous populations have been disproportionately affected during pandemics. We investigated COVID-19 mortality estimates among indigenous and non-indigenous populations at national and sub-national levels in Mexico. METHODS: We obtained data from the Ministry of Health, Mexico, on 2,173,036 laboratory-confirmed RT-PCR positive COVID-19 cases and 238,803 deaths. We estimated mortality per 1000 person-weeks, mortality rate ratio (RR) among indigenous vs. non-indigenous groups, and hazard ratio (HR) for COVID-19 deaths across four waves of the pandemic, from February 2020 to March 2022. We also assessed differences in the reproduction number (Rt). RESULTS: The mortality rate among indigenous populations of Mexico was 68% higher than that of non-indigenous groups. Out of 32 federal entities, 23 exhibited higher mortality rates among indigenous groups (P < 0.05 in 13 entities). The fourth wave showed the highest RR (2.40). The crude HR was 1.67 (95% CI: 1.62, 1.72), which decreased to 1.08 (95% CI: 1.04, 1.11) after controlling for other covariates. During the intense fourth wave, the Rt among the two groups was comparable. CONCLUSION: Indigenous status is a significant risk factor for COVID-19 mortality in Mexico. Our findings may reflect disparities in non-pharmaceutical (e.g., handwashing and using facemasks), and COVID-19 vaccination interventions among indigenous and non-indigenous populations in Mexico.

Sex-adjusted approach to baseline variables demonstrated some improved predictive capabilities for disease severity and survival in patients with Coronavirus Disease 19.

INTRODUCTION: The study was focused on comparing crude and sex-adjusted hazard ratio calculated by the baseline variables which may have contributed to the severity of the disease course and fatal outcomes in Coronavirus Disease-19 (COVID-19) patients. METHOD: The study enrolled 150 eligible adult patients with confirmed SARS-CoV-2 infection. There were 61 (40.7%) male patients, and 89 (59.3%) female patients. Baseline information of patients was collected from patient medical records and surveys that the patients had completed on admission to the hospital. RESULTS: Considerable number of baseline variables stratified according to disease severity and outcomes showed different optimal cut-points (OCP) in men and women. Sex-adjusted baseline data categories such as age; BMI; systolic and diastolic blood pressure; peripheral RBC and platelet counts; haematocrit; percentage of neutrophils, lymphocytes, monocytes, and their ratio; percentage of eosinophils; titre of plasma IL-6, IL-8, IL-10, and IL-17; and CXCL10; and ratio of pro- and anti-inflammatory cytokines demonstrated significant impacts on the development of the severe stage and fatal outcomes by the mean hazard ratio in the Kaplan-Meier and Cox regression models. CONCLUSION: This study confirmed some improved predictive capabilities of the sex-adjusted approach in the analysis of the baseline predictive variables for severity and outcome of the COVID-19.

Reliably Assessing Duration of Protection for Coronavirus Disease 2019 Vaccines.

Decision making about vaccination and boosting schedules for coronavirus disease 2019 (COVID-19) hinges on reliable methods for evaluating the longevity of vaccine protection. We show that modeling of protection as a piecewise linear function of time since vaccination for the log hazard ratio of the vaccine effect provides more reliable estimates of vaccine effectiveness at the end of an observation period and also detects plateaus in protective effectiveness more reliably than the standard method of estimating a constant vaccine effect over each time period. This approach will be useful for analyzing data pertaining to COVID-19 vaccines and other vaccines for which rapid and reliable understanding of vaccine effectiveness over time is desired.

Clinical Characteristics and Outcomes of Patients With SARS-CoV-2 Reinfection.

Objective: To examine the clinical characteristics, risk of hospitalization, and mortality of patients diagnosed with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) reinfection. Patients and Methods: We retrospectively reviewed all patients with SARS-CoV-2 reinfection at all Mayo Clinic sites between May 23, 2020, and June 30, 2021 (the period before the emergence of the Delta variant in the United States). The reinfection was defined as a positive SARS-CoV-2 test more than or equal to 90 days after initial infection or 45-89 days after with symptomatic second episode. Vaccination status was classified as fully vaccinated, first dose, and unvaccinated. Comparative analysis of baseline characteristics and comorbidities was performed by hospitalization and vaccination status. The survival analysis of the hospitalized patients was performed using Cox proportional hazard regression. Results: Among the 554 patients reinfected with SARS-CoV-2, 59 (10.6%) were pediatric, and 495 (89.4%) were adults. The median age was 13.9 years (interquartile range, 8.5-16.5 years) for the pediatric and 50.2 years (interquartile range, 28.4-65.6 years) for the adult population. Among the adult patients, the majority were unvaccinated (83.4%, n=413), and the duration to reinfection from initial infection was the longest in the fully vaccinated group (P<.001). Forty-two (75%) out of 56 patients were seropositive within 7 days of reinfection. In hospitalized adult patients, Charlson Comorbidity Index was an independent risk factor for mortality (adjusted hazard ratio, 0.35; 95% CI, 0.19-0.51). Conclusion: In this study, most adult patients with SARS-CoV-2 reinfection were unvaccinated. Furthermore, the duration to reinfection was longest in fully vaccinated individuals. Seropositivity was common among adult patients.

Risk factors for impaired pulmonary diffusion function in convalescent COVID-19 patients: A systematic review and meta-analysis.

Background: The long-term prognosis of COVID-19 survivors remains poorly understood. It is evidenced that the lung is the main damaged organ in COVID-19 survivors, most notably in impairment of pulmonary diffusion function. Hence, we conducted a meta-analysis of the potential risk factors for impaired diffusing capacity for carbon monoxide (DLCO) in convalescent COVID-19 patients. Methods: We performed a systematic search of PubMed, Web of Science, Embase, and Ovid databases for relevant studies from inception until January 7, 2022, limited to papers involving human subjects. Studies were reviewed for methodological quality. Fix-effects and random-effects models were used to pool results. Heterogeneity was assessed using I2. The publication bias was assessed using the Egger's test. PROSPERO registration: CRD42021265377. Findings: A total of eighteen qualified articles were identified and included in the systematic review, and twelve studies were included in the meta-analysis. Our results showed that female (OR: 4.011; 95% CI: 2.928-5.495), altered chest computerized tomography (CT) (OR: 3.002; 95% CI: 1.319-6.835), age (OR: 1.018; 95% CI: 1.007-1.030), higher D-dimer levels (OR: 1.012; 95% CI: 1.001-1.023) and urea nitrogen (OR: 1.004;95% CI: 1.002-1.007) were identified as risk factors for impaired DLCO. Interpretation: Pulmonary diffusion capacity was the most common impaired lung function in recovered patients with COVID-19. Several risk factors, such as female, altered chest CT, older age, higher D-dimer levels and urea nitrogen are associated with impairment of DLCO. Raising awareness and implementing interventions for possible modifiable risk factors may be valuable for pulmonary rehabilitation. Funding: This work was financially supported by Emergency Key Program of Guangzhou Laboratory (EKPG21-29, EKPG21-31), Incubation Program of National Science Foundation for Distinguished Young Scholars by Guangzhou Medical University (GMU2020-207).

Characteristics and Factors Associated With Mortality in Patients With Coronavirus Disease 2019 and Pneumothorax.

Objective: To describe the incidence, clinical characteristics, and factors associated with mortality in patients hospitalized for coronavirus disease 2019 (COVID-19) in whom pneumothorax developed. Patients and Methods: This study was a retrospective analysis conducted using a large administrative database of adult patients hospitalized for COVID-19 in the United States from February 1, 2020, to June 10, 2021. We characterized the clinical features of patients in whom pneumothorax developed and the factors associated with mortality and stratified pneumothorax by the timing of the initiation of invasive mechanical ventilation (IMV) and by the time of hospital admission (early versus late). Results: A total of 811,065 adult patients had a positive test result for severe acute respiratory syndrome coronavirus 2, of whom 103,858 (12.8%) were hospitalized. Pneumothorax occurred in 1915 patients (0.24% overall and 1.84% among hospitalized patients). Over time, the use of steroids and remdesivir increased, whereas the use of IMV, pneumothorax rates, and mortality decreased. The clinical characteristics associated with pneumothorax were male sex; the receipt of IMV; and treatment with steroids, remdesivir, or convalescent plasma. Most patients with pneumothorax received IMV, but pneumothorax developed before the initiation of IMV and/or early during hospitalization in majority. Multivariable analysis revealed that pneumothorax increased the risk of death (adjusted hazard ratio [aHR], 1.15; 95% CI, 1.06-1.24). In patients who did not receive IMV, pneumothorax led to nearly twice the mortality (aHR, 1.99; 95% CI, 1.56-2.54). Increased mortality was also noted when pneumothorax occurred before IMV (aHR, 1.37; 95% CI, 1.11-1.69) and within 7 days of hospital admission (aHR, 1.60; 95% CI, 1.29-1.98). Conclusion: The overall incidence of pneumothorax in patients hospitalized for COVID-19 was low. Pneumothorax is an independent risk factor for death.

In-hospital mortality and severe outcomes after hospital discharge due to COVID-19: A prospective multicenter study from Brazil.

Background: We evaluated in-hospital mortality and outcomes incidence after hospital discharge due to COVID-19 in a Brazilian multicenter cohort. Methods: This prospective multicenter study (RECOVER-SUS, NCT04807699) included COVID-19 patients hospitalized in public tertiary hospitals in Brazil from June 2020 to March 2021. Clinical assessment and blood samples were performed at hospital admission, with post-hospital discharge remote visits. Hospitalized participants were followed-up until March 31, 2021. The outcomes were in-hospital mortality and incidence of rehospitalization or death after hospital discharge. Kaplan-Meier curves and Cox proportional-hazard models were performed. Findings: 1589 participants [54.5% male, age=62 (IQR 50-70) years; BMI=28.4 (IQR,24.9-32.9) Kg/m² and 51.9% with diabetes] were included. A total of 429 individuals [27.0% (95%CI,24.8-29.2)] died during hospitalization (median time 14 (IQR,9-24) days). Older age [vs<40 years; age=60-69 years-aHR=1.89 (95%CI,1.08-3.32); age=70-79 years-aHR=2.52 (95%CI,1.42-4.45); age≥80-aHR=2.90 (95%CI 1.54-5.47)]; noninvasive or mechanical ventilation at admission [vs facial-mask or none; aHR=1.69 (95%CI 1.30-2.19)]; SAPS-III score≥57 [vs<57; aHR=1.47 (95%CI 1.13-1.92)] and SOFA score≥10 [vs <10; aHR=1.51 (95%CI 1.08-2.10)] were independently associated with in-hospital mortality. A total of 65 individuals [6.7% (95%CI 5.3-8.4)] had a rehospitalization or death [rate=323 (95%CI 250-417) per 1000 person-years] in a median time of 52 (range 1-280) days post-hospital discharge. Age ≥ 60 years [vs <60, aHR=2.13 (95%CI 1.15-3.94)] and SAPS-III ≥57 at admission [vs <57, aHR=2.37 (95%CI 1.22-4.59)] were independently associated with rehospitalization or death after hospital discharge. Interpretation: High in-hospital mortality rates due to COVID-19 were observed and elderly people remained at high risk of rehospitalization and death after hospital discharge. Funding: Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) and Programa INOVA-FIOCRUZ.

Cryptocurrencies and Tokens Lifetime Analysis from 2009 to 2021

The success of Bitcoin has spurred emergence of countless alternative coins with some of them shutting down only few weeks after their inception, thus disappearing with millions of dollars collected from enthusiast investors through initial coin offering (ICO) process. This has led investors from the general population to the institutional ones, to become skeptical in venturing in the cryptocurrency market, adding to its highly volatile characteristic. It is then of vital interest to investigate the life span of available coins and tokens, and to evaluate their level of survivability. This will make investors more knowledgeable and hence build their confidence in hazarding in the cryptocurrency market. Survival analysis approach is well suited to provide the needed information. In this study, we discuss the survival outcomes of coins and tokens from the first release of a cryptocurrency in 2009. Non-parametric methods of time-to-event analysis namely Aalen Additive Hazards Model (AAHM) trough counting and martingale processes, Cox Proportional Hazard Model (CPHM) are based on six covariates of interest. Proportional hazards assumption (PHA) is checked by assessing the Kaplan-Meier estimates of survival functions at the levels of each covariate. The results in different regression models display significant and non-significant covariates, relative risks and standard errors. Among the results, it was found that cryptocurrencies under standalone blockchain were at a relatively higher risk of collapsing. It was also found that the 2013–2017 cryptocurrencies release was at a high risk as compared to 2009–2013 release and that cryptocurrencies for which headquarters are known had the relatively better survival outcomes. This provides clear indicators to watch out for while selecting the coins or tokens in which to invest.

Alcohol intake and mortality risk of COVID-19, pneumonia, and other infectious diseases: An analysis of 437191 UK biobank participants.

This study aims to investigate the association between alcohol consumption and COVID-19, infectious diseases, and pneumonia mortality. This is a prospective analysis of 437,191 UK Biobank participants (age 56.3 years, 54% female). The main exposure was self-reported alcohol consumption. In addition to never and previous drinkers, we applied quartiles-based and UK guidelines-based criteria to divide current drinkers by weekly consumption into four groups. Outcomes included COVID-19, infectious diseases, and pneumonia mortality, obtained from the national death registries until May 2020. After an 11-year follow-up, compared to never drinkers, previous drinkers had higher mortality risks of infectious diseases and pneumonia (adjusted HR: 1.29 [95% CI 1.06-1.57] and 1.35 [1.07-1.70], respectively), but not COVID-19. There was a curvilinear association of alcohol consumption with infectious diseases and pneumonia mortality. Drinking within-guidelines (<14 UK units/wk) and amounts up to double the recommendation (14 to < 28 UK units/wk) was associated with the lowest mortality risks of infectious diseases (0.70 [0.59-0.83] and 0.70 [0.59-0.83], respectively) and pneumonia (0.71 [0.58-0.87] and 0.72 [0.58-0.88], respectively). Alcohol consumption was associated with lower risks of COVID-19 mortality (e.g., drinking within-guidelines: 0.53 [0.33-0.86]). Drinkers reporting multiples of the recommended alcohol drinking amounts did not have higher mortality risks of COVID-19 and other infectious diseases than never drinkers. Despite the well-established unfavorable effects on general health, we found no deleterious associations between alcohol consumption and the risk of infectious diseases, including COVID-19. Future research with other study designs is needed to confirm the causality.

Survival analysis: A primer for the clinician scientists.

Survival analysis is a collection of statistical procedures employed on time-to-event data. The outcome variable of interest is time until an event occurs. Conventionally, it dealt with death as the event, but it can handle any event occurring in an individual like disease, relapse from remission, and recovery. Survival data describe the length of time from a time of origin to an endpoint of interest. By time, we mean years, months, weeks, or days from the beginning of being enrolled in the study. The major limitation of time-to-event data is the possibility of an event not occurring in all the subjects during a specific study period. In addition, some of the study subjects may leave the study prematurely. Such situations lead to what is called censored observations as complete information is not available for these subjects. Life table and Kaplan-Meier techniques are employed to obtain the descriptive measures of survival times. The main objectives of survival analysis include analysis of patterns of time-to-event data, evaluating reasons why data may be censored, comparing the survival curves, and assessing the relationship of explanatory variables to survival time. Survival analysis also offers different regression models that accommodate any number of covariates (categorical or continuous) and produces adjusted hazard ratios for individual factor.