Case-Fatality Risk Estimates for COVID-19 Calculated by Using a Lag Time for Fatality

Assessment and design of filters and masks against COVID-19 via modeling and simulations

This study aimed to evaluate and design masks against viruses, especially SARS-CoV-2 associated with COVID-19. A continuum filtration model was developed where the rate of particle deposition and "sticking" on the filter fibers is a critical term in the mass transfer, together with permeation velocity, filter porosity, tortuosity, and Brownian diffusion. CFD simulations of the airflow during respiration lead to the recommendation that the filter permeability should be above 4 × 10-11 m2 to direct the airflow for effectiveness against virus particles; otherwise, low filter permeabilities cause the unfiltered air to flow preferentially through the leak gaps between the mask and the headform. Different mask filters with microstructural and geometry data from the literature are assessed via filtration simulations for breathability and filtration efficiency. The results demonstrate that a surgical mask of 25% porosity, pore size of 150 µm and permeability of 4.4 × 10-11 m2 can achieve 100% minimum efficiency while demonstrating high breathability, complying with the criteria of FFP3, N95, and surgical Class II and IIR masks. Selected cotton and synthetic cloths as well as electrospun fiber layers are predicted to comply with FFP2, N95, and surgical mask Class II and IIR standards.

Patients Air Medical Transport During the COVID-19 Pandemic: A Retrospective Cohort Study

INTRODUCTION

Air medical transport during the coronavirus disease 2019 pandemic was essential for transferring critically ill patients. This study aimed to comparatively analyze air-transported patients with and without coronavirus disease 2019 according to their clinical condition and complications that occurred during the flight.

METHODS

This was a retrospective cohort study that analyzed the digital records of adult patients transported by fixed-wing aircraft from the interior of the state of Amazonas to the state capital Manaus, Brazil, from June 2019 to May 2021. Pearson's chi-squared, Fisher exact, and Wilcoxon-Mann-Whitney tests were applied (significance level of P < .05).

RESULTS

The sample consisted of 741 patients (60.59% men, median age 54 years). The incidence of complications during the flight was 7.28%, with emphasis on dyspnea, psychomotor agitation, and pain. There was a significant difference between patients with (n = 466) and without coronavirus disease 2019 (n = 275) regarding the variables age (P < .001), comorbidities (P < .001), body mass index (P < .001), impact (P < .001) and priority (P = .002) of the transfer, physiological severity (P < .001), use of vasoactive drugs when boarding the aircraft (P = .033), and occurrence of respiratory complications during air medical transport (P = .003).

DISCUSSION

Patients with coronavirus disease 2019 were older, had more comorbidities and were severely ill, and had higher body mass index, frequency of vasoactive drug use, and respiratory complications. Although there are minimal differences among these patients, the role that interhospital transfer plays in reducing burden on local, less well-equipped hospitals is a primary role of medical transport, particularly during pandemics.

Application of distributed lag models and spatial analysis for comparing the performance of the COVID-19 control decisions in European countries

Over the past three years, the COVID-19 outbreak has become a major worldwide problem, affecting the health systems and economies of countries. The mean delays, the expected time to observe the average effect of the number of new cases on the number of deaths, are gold times for decision-making regarding disease control and treatment facilities to reduce the fatality rate. The interest of the present study is estimating the mean delays and adjusted fatality rates of COVID-19 with the new application of Distributed Lag Models (DLM) and their spatial distributions. The daily cases and deaths data of COVID-19 for 39 European countries was obtained from two sources; the "European Centre for Disease Prevention and Control" and the "Our World in Data" database. The mean delay and the Adjusted Fatality Rate (AFR) for each country at three-time intervals; the first and subsequent peaks before and after vaccination were estimated by the Distributed Lag Models. The spatial analysis was applied to find the spatial correlation of the mean delays and adjusted fatality rates among European countries. In the three-time intervals, the first and the subsequent peaks before vaccination, and after vaccination, the median and interquartile range of the mean delays; and AFRs were: 1.1 (0.4, 3.2); 0.024 (0.016, 0.044), 9.2 (6.2, 12.40); 0.013 (0.005, 0.020) and 7.3 (4.4, 11.0); 0.001 (0.001, 0.005), respectively. In the subsequent peaks before vaccination, the mean delays considerably increased, and the AFRs decreased for most European countries. After vaccination, the AFRs decreased considerably. Except for the first peak, the spatial correlations of AFRs were not significant among neighboring countries. Consecutive outcomes will occur with delays in outbreaks of infectious disease. Also, the fatality rates for these outcomes should be adjusted on delays. Estimating the mean delays and adjusted fatality rates by Distributed lag Models and the spatial distributions of theme in outbreaks showed that prevention and medical policies after the first peak as well as vaccination were effective to reduce the fatality rate of COVID-19, but these effects were different between countries. These results recommended policymakers and governments assign prevention and medical resources more effectively.

The limits of human mobility traces to predict the spread of COVID-19: A transfer entropy approach

Mobile phone data have been widely used to model the spread of COVID-19; however, quantifying and comparing their predictive value across different settings is challenging. Their quality is affected by various factors and their relationship with epidemiological indicators varies over time. Here, we adopt a model-free approach based on transfer entropy to quantify the relationship between mobile phone-derived mobility metrics and COVID-19 cases and deaths in more than 200 European subnational regions. Using multiple data sources over a one-year period, we found that past knowledge of mobility does not systematically provide statistically significant information on COVID-19 spread. Our approach allows us to determine the best metric for predicting disease incidence in a particular location, at different spatial scales. Additionally, we identify geographic and demographic factors, such as users' coverage and commuting patterns, that explain the (non)observed relationship between mobility and epidemic patterns. Our work provides epidemiologists and public health officials with a general-not limited to COVID-19-framework to evaluate the usefulness of human mobility data in responding to epidemics.

Case fatality rate considering the lag time from the onset of COVID-19 infection to related death from 2020 to 2022 in Japan

Importance

On an ecological scale, the lag time between coronavirus disease 2019 (COVID-19) infection and related fatality has varied between epidemic waves and prefectures in Japan. The variability in lag time across areas of Japan during the seven distinct waves can help derive a more appropriate estimation of the weekly confirmed case fatality rate (CFR) of COVID-19.

Objective

To estimate the 7-day moving average CFR across area block levels in Japan from February 2020 to July 2022 using the lag time between COVID-19 infection and related fatality.

Main outcomes and measures

The 7-day moving average CFR of COVID-19 for area blocks in Japan considering the lag time between infection and death (total and subgroup analysis of elderly).

Results

Lag time was found to vary substantially among prefectures in Japan from the first wave to the seventh wave of the COVID-19 epidemic. The estimated 7-day moving average CFR based on the lag time reflects the Japanese COVID-19 pandemic and related policy interventions (e.g. vaccination of elderly people) rather than other standard CFR estimations.

Conclusions and relevance

The variation in estimated lag time across prefectures in Japan for different epidemic waves indicates that it is inadequate to use the clinical results of the period from the start of infection to death for evaluation of the ecological scale of the CFR. Moreover, the lag time between infection and related fatality was found to be either shorter or longer than the clinically reported period. This revealed that preliminary reports of CFR may be overestimated or underestimated, even if they consider the lag based on clinical reports.

Validating model output in the absence of ground truth data: A COVID-19 case study using the Simulator of Infectious Disease Dynamics in North Carolina (SIDD-NC) model

As the COVID-19 pandemic has progressed, various models have been developed to forecast changes in the outbreak and assess intervention strategies. In this study we validate the Simulator of Infectious Disease Dynamics in North Carolina (SIDD-NC) model against an ensemble of proxy-ground truth infections datasets. We assess the performance of SIDD-NC using Spearman Rank Correlation, RMSE, and percent RMSE at a state and county level. We conduct the analysis for the period of March 2020 through November 2020 as well as in shorter time increments to assess both the recreation of the pandemic curve as well as day-to-day transmission of SARS-CoV-2 within the population. We find that SIDD-NC performs well against the datasets in the ensemble, generating an estimate of infections that is robust both spatially and temporally.

Consequences and Perception of the COVID-19 Pandemic on Patients and Caregivers in an Austrian Memory Clinic Population One Year After Pandemic Onset

BACKGROUND

The COVID-19 pandemic was associated with high mortality and negative consequences for patients with Alzheimer's disease or dementia and their caregivers. Memory clinics play an important role in enabling early dementia diagnosis and providing support for patients and their caregivers.

OBJECTIVE

This study investigates the impact of the COVID-19 pandemic and its restrictions on patients of a memory clinic and their caregivers between March 2020 and March 2021.

METHODS

We conducted a prospective, single-center, questionnaire-based, observational study to assess consequences and perception of the COVID-19 pandemic on emotion, cognitive function, social living, areas of care, and information retrieval.

RESULTS

Results of 255 participants' (mean age 76.78, SD 8.9; 12% cognitively intact, 33% mild cognitive impairment, 55% dementia) and 203 caregivers' COVID-19 questionnaires (valid response rate 71%) could be included in the study. Participants reported a prevalence of psychological symptoms associated with the pandemic between 3-20%. Caregivers living outside compared to those living with the participant reported higher rates of new onset or worsening of neuropsychiatric symptoms in participants since pandemic onset. Patients with dementia showed the lowest use of digital communication before (15.7%) and after (17.1%) pandemic onset in the diagnostic groups.

CONCLUSION

The COVID-19 pandemic frequently led to social isolation and reduced cognitive stimulation due to restrictions in elderly persons with cognitive deficits resulting in negative effects on emotional and social levels. We hypothesize that the implementation and sensitization with digital communication in clinical routine could provide a useful tool to counteract these negative effects.

High potential of technology to face new respiratory viruses: mechanical ventilation devices for effective healthcare to next pandemic emergencies

Some countries in the presence of unforeseen Coronavirus Disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), have experienced lower total deaths, though higher numbers of COVID-19 related infections. Results here suggest that one of the explanations is the critical role of ventilator technology in clinical health environment to cope with the initial stage of COVID-19 pandemic crisis. Statistical evidence shows that a large number of ventilators or breathing devices in countries (26.76 units per 100,000 inhabitants) is associated with a fatality rate of 1.44% (December 2020), whereas a higher fatality rate given by 2.46% is in nations with lower numbers of ventilator devices (10.38 average units per 100,000 people). These findings suggest that a large number of medical ventilators in clinical setting has a high potential for more efficient healthcare and improves the effective preparedness of crisis management to cope with new respiratory pandemic diseases in society. Hence, a forward-thinking and technology-oriented strategy in healthcare sector, based on investments in high-tech ventilator devices and other new medical technologies, can help clinicians deliver effective care and reduce negative effects of present and future respiratory infectious diseases, in particular when new drugs and appropriate treatments are missing in clinical environment to face unknown respiratory viral agents .

COVID-19 hospitalization and mortality in community-dwelling racially and ethnically diverse persons living with dementia

BACKGROUND

Community-dwelling persons living with dementia (PLWD) are vulnerable to COVID-19 infection, severity, and mortality due to the high prevalence of comorbidities, reliance on caregivers, and potential inability to employ risk reduction measures, among other factors.

METHODS

We used a retrospective cohort of Medicare Fee-For-Service beneficiaries enrolled from January 2018 to September 2020 (n = 13,068,583), a comparison cohort from January 2019 to April 2021 (n = 13,250,297), and logistic regression to estimate the effect of dementia on COVID-19 hospitalization and mortality in community-dwelling older persons.

RESULTS

COVID-19 diagnoses were higher among persons living with dementia (PLWD) than those without dementia. Conditional on COVID-19 in the 2020 cohort, White PLWD were at higher risk of hospitalization compared to White persons without dementia (aOR 1.31, 95% CI: 1.26-1.36) and marginal for Black PLWD (aOR 1.10, 95% CI: 1.01-1.20), no significant differences were found within other racial/ethnic groups. PLWD were 1.8 times (aOR 1.78, 95% CI: 1.72-1.84) more likely to die within 30 days of COVID-19 on average. Within racial/ethnic groups, the estimate for White PLWD, compared with White persons without dementia, was highest (aOR 2.01, 95% CI: 1.92-2.10), followed by Black PLWD (aOR 1.55, 95% CI: 1.41-1.70), and smallest among Hispanic PLWD (aOR 1.37, 95% CI: 1.24-1.50). PLWD hospitalized with COVID-19 were 1.6 times (aOR 1.59, 95% CI: 1.52-1.67) more likely to die within 30 days than similar persons without dementia. Estimates from the 2021 cohort, when vaccines were available to older persons, were similar to those in 2020.

CONCLUSIONS

Community-dwelling PLWD experienced worse outcomes after a COVID-19 diagnosis than their counterparts without dementia. Results demonstrating higher mortality, but not hospitalization rates, for all races/ethnicities except White PLWD suggest there may have been differential care/treatment that point to potential health care system inequities that persisted into 2021. Understanding the mechanisms underlying these differences may improve ongoing care for community-dwelling PLWD.

Examining Impacts of COVID-19-Related Stay-At-Home Orders through a Two-Way Random Effects Model

The COVID-19 pandemic has had far-reaching impacts on public health and safety, economics, and the transportation system. To reduce the spread of this disease, federal and local governments around the world have introduced stay-at-home orders and other restrictions on travel to "non-essential" businesses to implement social distancing. Preliminary evidence suggests substantial variability in the impacts of these orders in the United States, both across states and over time. This study examines this issue using daily county-level vehicle miles traveled (VMT) data for the 48 continental U.S. states and the District of Columbia. A two-way random effects model is estimated to assess changes in VMT from March 1 to June 30, 2020 as compared with baseline January travel levels. The implementation of stay-at-home orders was associated with a 56.4 percent reduction in VMT on average. However, this effect was shown to dissipate over time, which may be attributable to "quarantine fatigue." In the absence of full shelter-in-place orders, travel was also reduced where restrictions on select businesses were introduced. For example, restrictions on entertainment, indoor dining, and indoor recreational activities corresponded to reductions in VMT of 3 to 4 percent while restrictions on retail and personal care facilities showed 13 percent lower traffic levels. VMT was also shown to vary based on the number of COVID case reports, as well as with respect to other characteristics, including median household income, political leanings, and how rural the county was in nature.

Impact of COVID-19 pneumonia on interstitial lung disease: semi-quantitative evaluation with computed tomography

OBJECTIVES

To evaluate the CT scores and fibrotic pattern changes in interstitial lung disease (ILD) patients, with and without previous COVID-19 pneumonia.

METHODS

Patients with ILD (idiopathic pulmonary fibrosis (IPF) and connective tissue disease-associated ILD (CTD-ILD)) were retrospectively enrolled in the study which consisted of patients who had COVID-19 pneumonia while the control group had not. All patients had two CT scans, initial and follow-up, which were evaluated semi-quantitatively for severity, extent, and total CT scores, fibrosis patterns, and traction bronchiectasis.

RESULTS

A total of 102 patients (pneumonia group n = 48; control group n = 54) were enrolled in the study. For both groups, baseline characteristics were similar and CT scores were increased. While there was a 4.5 ± 4.6 point change in the total CT score of the COVID-19 group, there was a 1.2 ± 2.7 point change in the control group (p < 0.001). In the IPF subgroup, the change in total CT score was 7.0 points (95% CI: 4.1 to 9.9) in the COVID-19 group and 2.1 points (95% CI: 0.8 to 3.4) in the control group. Seven patients (14.6%) in the COVID-19 group progressed to a higher fibrosis pattern, but none in the control group.

CONCLUSIONS

Semi-quantitative chest CT scores in ILD patients demonstrated a significant increase after having COVID-19 pneumonia compared to ILD patients who had not had COVID-19 pneumonia. The increase in CT scores was more prominent in the IPF subgroup. There was also a worsening in the fibrosis pattern in the COVID-19 group.

KEY POINTS

• The impact of COVID-19 pneumonia on existing interstitial lung diseases and fibrosis is unclear. • COVID-19 pneumonia may worsen existing interstitial lung involvement with direct lung damage and indirect inflammatory effect. • COVID-19 pneumonia may affect existing lung fibrosis by triggering inflammatory pathways.

A deconvolution approach to modelling surges in COVID-19 cases and deaths

The COVID-19 pandemic continues to emphasize the importance of epidemiological modelling in guiding timely and systematic responses to public health threats. Nonetheless, the predictive qualities of these models remain limited by their underlying assumptions of the factors and determinants shaping national and regional disease landscapes. Here, we introduce epidemiological feature detection, a novel latent variable mixture modelling approach to extracting and parameterizing distinct and localized features of real-world trends in daily COVID-19 cases and deaths. In this approach, we combine methods of peak deconvolution that are commonly used in spectroscopy with the susceptible-infected-recovered-deceased model of disease transmission. We analyze the second wave of the COVID-19 pandemic in Israel, Canada, and Germany and find that the lag time between reported cases and deaths, which we term case-death latency, is closely correlated with adjusted case fatality rates across these countries. Our findings illustrate the spatiotemporal variability of both these disease metrics within and between different disease landscapes. They also highlight the complex relationship between case-death latency, adjusted case fatality rate, and COVID-19 management across various degrees of decentralized governments and administrative structures, which provides a retrospective framework for responding to future pandemics and disease outbreaks.

Effects of strict containment policies on COVID-19 pandemic crisis: lessons to cope with next pandemic impacts

The goal of the study here is to analyze and assess whether strict containment policies to cope with Coronavirus Disease 2019 (COVID-19) pandemic crisis are effective interventions to reduce high numbers of infections and deaths. A homogenous sample of 31 countries is categorized in two sets: countries with high or low strictness of public policy to cope with COVID-19 pandemic crisis. The findings here suggest that countries with a low intensity of strictness have average confirmed cases and fatality rates related to COVID-19 lower than countries with high strictness in containment policies (confirmed cases are 24.69% vs. 26.06% and fatality rates are 74.33% vs. 76.38%, respectively, in countries with low and high strictness of COVID-19 public policies of containment). What this study adds is that high levels of strict restriction policies may not be useful measures of control in containing the spread and negative impact of pandemics similar to COVID-19 and additionally a high strictness in containment policies generates substantial social and economic costs. These findings can be explained with manifold socioeconomic and environmental factors that support transmission dynamics and circulation of COVID-19 pandemic. Hence, high levels of strictness in public policy (and also a high share of administering new vaccines) seem to have low effectiveness to stop pandemics similar to COVID-19 driven by mutant viral agents. These results here suggest that the design of effective health policies for prevention and preparedness of future pandemics should be underpinned in a good governance of countries and adoption of new technology, rather than strict and generalized health polices having ambiguous effects of containment in society.

What Is the Place of Intermediate Care Unit in Patients with COVID-19? A Single Center Experience

Introduction

COVID-19 pandemic has led to an increased rate of intensive care unit (ICU) stays. Intermediate care units (IMCUs) are a useful resource for the management of patients with severe COVID-19 that do not require ICU admission. In this research, we aimed to determine survival outcomes and parameters predicting mortality in patients who have been admitted to IMCU.

Materials and Methods

Patients who were admitted to IMCU between April 2019 and January 2021 were analyzed retrospectively. Sociodemographics, clinical characteristics, and blood parameters on admission were compared between the patients who died in IMCU and the others. Blood parameters at discharge were compared between survived and deceased individuals. Survival analysis was performed via Kaplan-Meier analysis. Blood parameters predicting mortality were determined by univariate and multivariate Cox regression analysis.

Results

A total of 140 patients were included within the scope of this study. The median age was 72.5 years, and 77 (55%) of them were male and 63 (45%) of them were female. A total of 37 (26.4%) patients deceased in IMCU, and 40 patients (28.5%) were transferred to ICU. Higher platelet count (HR 3.454; 95% CI 1.383-8.625; p=0.008), procalcitonin levels (HR 3.083; 95% CI 1.158-8.206; p=0.024), and lower oxygen saturation (HR 4.121; 95% CI 2.018-8.414; p < 0.001) were associated with an increased risk of mortality in IMCU. At discharge from IMCU, higher procalcitonin levels (HR 2.809; 95% CI 1.216-6.487; p=0.016), lower platelet count (HR 2.269; 95% CI 1.012-5.085; p=0.047), and noninvasive mechanic ventilation requirement (HR 2.363; 95% CI 1.201-4.651; p=0.013) were associated with an increased risk of mortality. Median OS was found as 41 days. The overall survival rate was found 40% while the IMCU survival rate was 73.6%.

Conclusions

IMCU seems to have a positive effect on survival in patients with severe COVID-19 infection. Close monitoring of these parameters and early intervention may improve survival rates and outcomes.

Evolution of the lethality due to SARS-CoV-2 in Spain according to age group and sex

The emergence of SARS-CoV-2 in China in December 2019 has posed a major challenge to health systems in all countries around the world. One of the most relevant epidemiological measures to consider during the course of a pandemic is the proportion of cases that eventually die from the disease (case fatality ratio, CFR). Monitoring the evolution of this indicator is of paramount importance because it allows for the assessment of both variations in the lethality of the virus and the effectiveness of the control measures implemented by health authorities. One of the problems with estimating the CFR in practice is that the available data only show daily or weekly counts of new cases and deaths; there is no information on when each deceased patient was infected and therefore it is not possible to know exactly how many cases there were at the time the patient became infected. Various approaches have been proposed for calculating the CFR by correcting for the time lag between infection and death. In this paper, we present a novel methodology to perform a non-parametric estimation of the evolution of the CFR by initially identifying an optimal time lag between infections and deaths. The goodness of this procedure is assessed by means of a simulation study and the method is applied to the estimation of the CFR in Spain in the period from July 2020 to March 2022.

Wave after wave: determining the temporal lag in Covid-19 infections and deaths using spatial panel data from Germany

The Covid-19 pandemic requires a continuous evaluation of whether current policies and measures taken are sufficient to protect vulnerable populations. One quantitative indicator of policy effectiveness and pandemic severity is the case fatality ratio, which relies on the lagged number of infections relative to current deaths. The appropriate length of the time lag to be used, however, is heavily debated. In this article, I contribute to this debate by determining the temporal lag between the number of infections and deaths using daily panel data from Germany’s 16 federal states. To account for the dynamic spatial spread of the virus, I rely on different spatial econometric models that allow not only to consider the infections in a given state but also spillover effects through infections in neighboring federal states. My results suggest that a wave of infections within a given state is followed by increasing death rates 12 days later. Yet, if the number of infections in other states rises, the number of death cases within that given state subsequently decreases. The results of this article contribute to the better understanding of the dynamic spatio-temporal spread of the virus in Germany, which is indispensable for the design of effective policy responses.

Time-series comparison of COVID-19 case fatality rates across 21 countries with adjustment for multiple covariates

OBJECTIVES

Although it is widely used as a measure for mortality, the case fatality rate (CFR) ofcoronavirus disease 2019 (COVID-19) can vary over time and fluctuate for many reasons otherthan viral characteristics. To compare the CFRs of different countries in equal measure, weestimated comparable CFRs after adjusting for multiple covariates and examined the mainfactors that contributed to variability in the CFRs among 21 countries.

METHODS

For statistical analysis, time-series cross-sectional data were collected from OurWorld in Data, CoVariants.org, and GISAID. Biweekly CFRs of COVID-19 were estimated bypooled generalized linear squares regression models for the panel data. Covariates includedthe predominant virus variant, reproduction rate, vaccination, national economic status,hospital beds, diabetes prevalence, and population share of individuals older than age 65. Intotal, 21 countries were eligible for analysis.

RESULTS

Adjustment for covariates reduced variation in the CFRs of COVID-19 across countriesand over time. Regression results showed that the dominant spread of the Omicron variant,reproduction rate, and vaccination were associated with lower country-level CFRs, whereasage, the extreme poverty rate, and diabetes prevalence were associated with higher countrylevel CFRs.

CONCLUSION

A direct comparison of crude CFRs among countries may be fallacious, especiallyin a cross-sectional analysis. Our study presents an adjusted comparison of CFRs over timefor a more proper comparison. In addition, our findings suggest that comparing CFRs amongdifferent countries without considering their context, such as the epidemic phase, medicalcapacity, surveillance strategy, and socio-demographic traits, should be avoided.

The challenge of estimating the direct and indirect effects of COVID-19 interventions - Toward an integrated economic and epidemiological approach

Decisions on public health measures to contain a pandemic are often based on parameters such as expected disease burden and additional mortality due to the pandemic. Both pandemics and non-pharmaceutical interventions to fight pandemics, however, produce economic, social, and medical costs. The costs are, for example, caused by changes in access to healthcare, social distancing, and restrictions on economic activity. These factors indirectly influence health outcomes in the short- and long-term perspective. In a narrative review based on targeted literature searches, we develop a comprehensive perspective on the concepts available as well as the challenges of estimating the overall disease burden and the direct and indirect effects of COVID-19 interventions from both epidemiological and economic perspectives, particularly during the early part of a pandemic. We review the literature and discuss relevant components that need to be included when estimating the direct and indirect effects of the COVID-19 pandemic. The review presents data sources and different forms of death counts, and discusses empirical findings on direct and indirect effects of the pandemic and interventions on disease burden as well as the distribution of health risks.

Associations between indoor relative humidity and global COVID-19 outcomes

Globally, the spread and severity of COVID-19 have been distinctly non-uniform. Seasonality was suggested as a contributor to regional variability, but the relationship between weather and COVID-19 remains unclear and the focus of attention has been on outdoor conditions. Because humans spend most of their time indoors and because most transmission occurs indoors, we here, instead, investigate the hypothesis that indoor climate-particularly indoor relative humidity (RH)-may be the more relevant modulator of outbreaks. To study this association, we combined population-based COVID-19 statistics and meteorological measurements from 121 countries. We rigorously processed epidemiological data to reduce bias, then developed and experimentally validated a computational workflow to estimate indoor conditions based on outdoor weather data and standard indoor comfort conditions. Our comprehensive analysis shows robust and systematic relationships between regional outbreaks and indoor RH. In particular, we found intermediate RH (40-60%) to be robustly associated with better COVID-19 outbreak outcomes (versus RH < 40% or >60%). Together, these results suggest that indoor conditions, particularly indoor RH, modulate the spread and severity of COVID-19 outbreaks.

Impact of COVID-19 on the Mode of Presentation and Stage at Diagnosis of Colorectal Cancer

AIM

This study compares the stage at the presentation of patients with colorectal cancer (CRC) before and after introducing COVID-19 restrictions and the mode of presentation.

METHODS

This is a retrospective cohort study comparing the incidence of CRC, TNM stage and mode of presentation in the pre-COVID and COVID cohorts at a single UK Trust. All patients discussed at the CRC multidisciplinary team (MDT) from March 2017 to March 2021 were included and split into two cohorts; the pre-COVID group from 01/03/2017 to 29/02/2020 and the COVID group from 01/03/2020 to 28/02/2021. Percentages were used for descriptive statistics. Student's t-test was used for the comparison of demographic variables. Chi-squared test was used for the difference analysis for the categorical data, such as TNM and mode of presentation. P value ≤0.05 was significant.

RESULTS

In total, 1373 patients were diagnosed with CRC during the period from March 2017 to March 2021. The pre-COVID group (2017-2020) included 1104 CRC patients, compared to 269 patients in the COVID one (2020-2021). The mean age was higher in the pre-COVID group (p = 0.001). There was a statistically significant increase in the proportion of cases presenting with T4 disease (p = 0.023) and metastatic disease (p = 0.032) in the COVID group compared to the pre-COVID group. There was also a significant increase in the rate of emergency presentations (p < 0.0001).

CONCLUSION

We observed a statistically significant increase in rates of locally advanced (T4) and metastatic (distant) CRC in patients presenting after introducing the COVID-19 lockdown. There was also an increase in emergency presentations. There was no observed difference in nodal status. This may reflect disruption to cancer diagnostic services and the reluctance of patients to access medical care during a pandemic, particularly the elderly.

WHO working definition of vitality capacity for healthy longevity monitoring

Intrinsic capacity, a crucial concept in healthy ageing, is defined by WHO as "the composite of all the physical and mental capacities that an individual can draw on at any point in time". Vitality capacity is considered the underlying physiological determinant of intrinsic capacity. To advance the measurement and monitoring of vitality capacity, a working group of WHO staff members and twenty experts representing six WHO regions was convened to discuss and clarify the attributes of vitality capacity and to develop a clear working definition of the concept. Potential biomarkers to measure vitality capacity were identified, and the following consensual working definition was developed: vitality capacity is a physiological state (due to normal or accelerated biological ageing processes) resulting from the interaction between multiple physiological systems, reflected in (the level of) energy and metabolism, neuromuscular function, and immune and stress response functions of the body.

Safety of maintaining elective and emergency surgery during the COVID-19 pandemic with the introduction of a Protected Elective Surgical Unit (PESU): A cross-specialty evaluation of 30-day outcomes in 9,925 patients undergoing surgery in a University Health Board

Background

The COVID-19 pandemic has caused unprecedented health care challenges mandating surgical service reconfiguration. Within our hospital, emergency and elective streams were separated and self-contained Protected Elective Surgical Units were developed to mitigate against infection-related morbidity. Aims of this study were to determine the risk of COVID-19 transmission and mortality and whether the development of Protected Elective Surgical Units can result in significant reduction in risk.

Methods

A retrospective observational study of consecutive patients from 18 specialties undergoing elective or emergency surgery under general, spinal, or epidural anaesthetic over a 12-month study period was undertaken. Primary outcome measures were 30-day postoperative COVID-19 transmission rate and mortality. Secondary adjusted analyses were performed to ascertain hospital and Protected Elective Surgical Unit transmission rates.

Results

Between 15 March 2020 and 14 March 2021, 9,925 patients underwent surgery: 6,464 (65.1%) elective, 5,116 (51.5%) female, and median age 57 (39–70). A total of 69.5% of all procedures were performed in Protected Elective Surgical Units. Overall, 30-day postoperative COVID-19 transmission was 2.8% (3.4% emergency vs 1.2% elective P < .001). Protected Elective Surgical Unit postoperative transmission was significantly lower than non–Protected Elective Surgical Unit (0.42% vs 3.2% P < .001), with an adjusted likely in-hospital Protected Elective Surgical Unit transmission of 0.04%. The 30-day all-cause mortality was 1.7% and was 14.6% in COVID-19–positive patients. COVID-19 infection, age > 70, male sex, American Society of Anesthesiologists grade > 2, and emergency surgery were all independently associated with mortality.

Conclusion

This study has demonstrated that Protected Elective Surgical Units can facilitate high-volume elective surgical services throughout peaks of the COVID-19 pandemic while minimising viral transmission and mortality. However, mortality risk associated with perioperative COVID-19 infection remains high.

Evaluating a contact tracing course: How universities can develop the public health workforce

OBJECTIVES

To describe the participants of a university-based COVID-19 contact tracing course and determine whether the course changed knowledge, attitudes, and intention to participate in contact tracing.

PARTICIPANTS

Faculty, staff, and students were eligible.

METHODS

Surveys evaluated the impact of the course on participant intentions to engage in contact tracing. Logistic regression identified characteristics associated with increased likelihood of participating in contact tracing.

RESULTS

Nearly 800 individuals participated, of whom 26.2% identified as Hispanic/Latino and 14.0% as Black. Nearly half (48.8%) planned to conduct contact tracing. While attitudes did not change, knowledge improved (67.9% vs. 93.8% scores on assessments; p < 0.001). Younger participants and Black individuals were more more likely to be confident that they would participate in contact tracing.

CONCLUSIONS

Course completion was associated with increased knowledge. Participants were racially and ethnically diverse, highlighting how universities can partner with health departments to develop workforces that reflect local communities.

Household factors and the risk of severe COVID-like illness early in the U.S. pandemic

OBJECTIVE

To investigate the role of children in the home and household crowding as risk factors for severe COVID-19 disease.

METHODS

We used interview data from 6,831 U.S. adults screened for the Communities, Households and SARS/CoV-2 Epidemiology (CHASING) COVID Cohort Study in April 2020.

RESULTS

In logistic regression models, the adjusted odds ratio [aOR] of hospitalization due to COVID-19 for having (versus not having) children in the home was 10.5 (95% CI:5.7-19.1) among study participants living in multi-unit dwellings and 2.2 (95% CI:1.2-6.5) among those living in single unit dwellings. Among participants living in multi-unit dwellings, the aOR for COVID-19 hospitalization among participants with more than 4 persons in their household (versus 1 person) was 2.5 (95% CI:1.0-6.1), and 0.8 (95% CI:0.15-4.1) among those living in single unit dwellings.

CONCLUSION

Early in the US SARS-CoV-2 pandemic, certain household exposures likely increased the risk of both SARS-CoV-2 acquisition and the risk of severe COVID-19 disease.

Influenza complications: Reporting cardiac events through a hospital surveillance system

BACKGROUND AND AIMS

The aim of this study was to determine the complications of influenza in all adults hospitalized with this disease and, specifically, to analyze the characteristics of post-infection cardiovascular events.

METHODS

This work is observational descriptive study of adults hospitalized with influenza during the 2017-2018 and 2018-2019 seasons using the specific registry of a tertiary hospital. The complications analyzed were pneumonia, multiple organ dysfunction syndrome, septic shock, acute respiratory distress syndrome, and cardiac events.

RESULTS

A total of 928 adults with influenza required hospitalization and 303 (32.7%) presented with one or more complications. A post-influenza cardiac event occurred in 2.5% of patients; they had a higher probability of ICU admission and higher mortality. Influenza vaccination was a protective factor for cardiac events (OR 0.32; 95%CI 0.13-0.83).

CONCLUSIONS

Influenza can lead to important complications in hospitalized patients. The high mortality rate associated with post-infection cardiac events means that preventive measures, including annual influenza vaccination, need to be emphasized.

High Number and Specific Comorbidities Could Impact the Immune Response in COVID-19 Patients

Background

Cellular immunodeficiency and comorbidities are common in COVID-19 patients.

Aim

The purpose of this study was to investigate comorbidities impacting on the cellular immunity in COVID-19 patients.

Methods

The research objects included 55 healthy controls and 718 COVID-19 patients who divided into the control group and the COVID-19 group, respectively. Those in the COVID-19 group were divided into subgroups on the basis of the number and types of comorbidities present. Lymphocyte itself and its subsets were compared between the control group and the COVID-19 group, the groups with comorbidities based on the different number and types of comorbidities, and the relationship between the lymphocyte counts and subsets with the number and types of comorbidities was investigated.

Results

Compared with the control group, the lymphocyte counts and T cell subsets were significantly increased in the groups with comorbidities, but both B and NK cell subsets were significantly decreased in the no comorbidity group and in most of the groups with comorbidities (all P<0.05). In the three comorbidities group, the lymphocyte counts and T cell subsets were all significantly decreased, but the CD56+ percentage was obviously increased (all P<0.05). The number of comorbidities was negatively correlated with the lymphocyte counts and the T and NK cell subsets. A negative correlation also existed between cancer and both the lymphocyte counts and the T cell subsets, between chronic hepatitis B and the lymphocyte counts, and between chronic kidney disease and the CD3+ counts. A positive correlation existed between nonalcoholic fatty liver disease (NAFLD) disease and both lymphocyte and CD3+ counts. The risk factors were number of comorbidities for the lymphocyte count, CD3+CD4+ and CD3+CD8+ percentages, NAFLD for the lymphocyte and CD3+ counts, cardiovascular diseases for CD3+CD4+ and CD3+CD8+ percentages, diabetes mellitus for the CD3+CD8+ percentage, and cancer for the CD3+ percentage, respectively.

Conclusions

High numbers of comorbidities and specific comorbidities could impact the immune response of COVID-19 patients. This study provides a reference for clinicians in the identification of suitable and timely immunotherapy for COVID-19 patients.

Clinical Trial Registry

https://www.chictr.org.cn/enindex.aspx, identifier ChiCTR2000034563.

Cross-sectional survey of changes in knowledge, attitudes and practice of mask use in Sydney and Melbourne during the 2020 COVID-19 pandemic

OBJECTIVES

Since mask uptake and the timing of mask use has the potential to influence the control of the COVID-19 pandemic, this study aimed to assess the changes in knowledge toward mask use in Sydney and Melbourne, Australia, during the 2020 COVID-19 pandemic.

DESIGN

An observational study, using a cross-sectional survey, was distributed to adults in Sydney and Melbourne, Australia, during July-August 2020 (survey 1) and September 2020 (survey 2), during the COVID-19 pandemic in Australia.

SETTING AND PARTICIPANTS

Participants aged 18 years or older and living in either Sydney or Melbourne.

PRIMARY AND SECONDARY OUTCOME MEASURES

Demographics, risk measures, COVID-19 severity and perception, mask attitude and uptake were determined in this study.

RESULTS

A total of 700 participants completed the survey. In both Sydney and Melbourne, a consistent decrease was reported in almost all risk-mitigation behaviours between March 2020 and July 2020 and again between March 2020 and September 2020. However, mask use and personal protective equipment use increased in both Sydney and Melbourne from March 2020 to September 2020. There was no significant difference in mask use during the pandemic between the two cities across both timepoints (1.24 (95% CI 0.99 to 1.22; p=0.072)). Perceived severity and perceived susceptibility of COVID-19 infection were significantly associated with mask uptake. Trust in information on COVID-19 from both national (1.77 (95% CI 1.29 to 2.44); p<0.000)) and state (1.62 (95% CI 1.19 to 2.22); p=0.003)) government was a predictor of mask use across both surveys.

CONCLUSION

Sydney and Melbourne both had high levels of reported mask wearing during July 2020 and September 2020, consistent with the second wave and mask mandates in Victoria, and cluster outbreaks in Sydney at the time. High rates of mask compliance may be explained by high trust levels in information from national and state government, mask mandates, risk perceptions, current outbreaks and the perceived level of risk of COVID-19 infection at the time.

Severe Acute Respiratory Syndrome Coronavirus 2 Incidence and Risk Factors in a National, Community-Based Prospective Cohort of US Adults

BACKGROUND

Prospective cohort studies of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) incidence complement case-based surveillance and cross-sectional seroprevalence surveys.

METHODS

We estimated the incidence of SARS-CoV-2 infection in a national cohort of 6738 US adults, enrolled in March-August 2020. Using Poisson models, we examined the association of social distancing and a composite epidemiologic risk score with seroconversion. The risk score was created using least absolute shrinkage selection operator (LASSO) regression to identify factors predictive of seroconversion. The selected factors were household crowding, confirmed case in household, indoor dining, gathering with groups of ≥10, and no masking in gyms or salons.

RESULTS

Among 4510 individuals with ≥1 serologic test, 323 (7.3% [95% confidence interval (CI), 6.5%-8.1%]) seroconverted by January 2021. Among 3422 participants seronegative in May-September 2020 and retested from November 2020 to January 2021, 161 seroconverted over 1646 person-years of follow-up (9.8 per 100 person-years [95% CI, 8.3-11.5]). The seroincidence rate was lower among women compared with men (incidence rate ratio [IRR], 0.69 [95% CI, .50-.94]) and higher among Hispanic (2.09 [1.41-3.05]) than white non-Hispanic participants. In adjusted models, participants who reported social distancing with people they did not know (IRR for always vs never social distancing, 0.42 [95% CI, .20-1.0]) and with people they knew (IRR for always vs never, 0.64 [.39-1.06]; IRR for sometimes vs never, 0.60 [.38-.96]) had lower seroconversion risk. Seroconversion risk increased with epidemiologic risk score (IRR for medium vs low score, 1.68 [95% CI, 1.03-2.81]; IRR for high vs low score, 3.49 [2.26-5.58]). Only 29% of those who seroconverted reported isolating, and only 19% were asked about contacts.

CONCLUSIONS

Modifiable risk factors and poor reach of public health strategies drove SARS-CoV-2 transmission across the United States.

Multimodal Benefits of Exercise in Patients With Multiple Sclerosis and COVID-19

Multiple sclerosis (MS) is a demyelinating disease characterized by plaque formation and neuroinflammation. The plaques can present in various locations, causing a variety of clinical symptoms in patients with MS. Coronavirus disease-2019 (COVID-19) is also associated with systemic inflammation and a cytokine storm which can cause plaque formation in several areas of the brain. These concurring events could exacerbate the disease burden of MS. We review the neuro-invasive properties of SARS-CoV-2 and the possible pathways for the entry of the virus into the central nervous system (CNS). Complications due to this viral infection are similar to those occurring in patients with MS. Conditions related to MS which make patients more susceptible to viral infection include inflammatory status, blood-brain barrier (BBB) permeability, function of CNS cells, and plaque formation. There are also psychoneurological and mood disorders associated with both MS and COVID-19 infections. Finally, we discuss the effects of exercise on peripheral and central inflammation, BBB integrity, glia and neural cells, and remyelination. We conclude that moderate exercise training prior or after infection with SARS-CoV-2 can produce health benefits in patients with MS patients, including reduced mortality and improved physical and mental health of patients with MS.

Association of anti-contagion policies with the spread of COVID-19 in United States

BACKGROUND

The outbreak of a novel coronavirus, known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), or COVID-19, raised worldwide concern. The present study investigates the association between anti-contagion policies and the spread of COVID-19 across the United States.

DESIGN AND METHODS

We selected the most frequently implemented COVID-19 anti-contagion policies in all the U.S. states issued from 29 February 2020. Accordingly, we modified an epidemiological model and combined it with a comprehensive statistical analysis to evaluate the policies' individual and overall likely impact.

RESULTS

For the first time, a novel index, evaluates the associations between policy implementation and COVID-19 spread at both statewide and national levels. Our results indicate that governmental policies requiring mask use, businesses social distancing, and quarantining travelers may be most effective for controlling COVID-19 spread. Simultaneously, widespread orders like school closure and safer-at-home that can be particularly disruptive to the economy and social fabric of society may be unnecessary given their lack of association with reducing infection.

CONCLUSIONS

The absence of any COVID-19 vaccines during the first several months of its pandemic necessitated using governmental policies to help stop the spread of this disease. Our index showed the association between implemented policies and COVID-19 spread, highlighting the specific policies with the greatest association - mandatory quarantine upon entering a state, businesses implementing social distancing, and mandatory mask use - and those with less association like school closure and safer-at-home orders. This study provided evidence to inform policy choices for the current global crisis and future pandemics.

Trends in outcomes following COVID-19 symptom onset in Milan: a cohort study

BACKGROUND

For people with symptomatic COVID-19, the relative risks of hospital admission, death without hospital admission and recovery without admission, and the times to those events, are not well understood. We describe how these quantities varied with individual characteristics, and through the first wave of the pandemic, in Milan, Italy.

METHODS

A cohort study of 27 598 people with known COVID-19 symptom onset date in Milan, Italy, testing positive between February and June 2020 and followed up until 17 July 2020. The probabilities of different events, and the times to events, were estimated using a mixture multistate model.

RESULTS

The risk of death without hospital admission was higher in March and April (for non-care home residents, 6%-8% compared with 2%-3% in other months) and substantially higher for care home residents (22%-29% in March). For all groups, the probabilities of hospitalisation decreased from February to June. The probabilities of hospitalisation also increased with age, and were higher for men, substantially lower for healthcare workers and care home residents, and higher for people with comorbidities. Times to hospitalisation and confirmed recovery also decreased throughout the first wave. Combining these results with our previously developed model for events following hospitalisation, the overall symptomatic case fatality risk was 15.8% (15.4%-16.2%).

CONCLUSIONS

The highest risks of death before hospital admission coincided with periods of severe burden on the healthcare system in Lombardy. Outcomes for care home residents were particularly poor. Outcomes improved as the first wave waned, community healthcare resources were reinforced and testing became more widely available.

Impact of the Timing of Stay-at-Home Orders and Mobility Reductions on First-Wave COVID-19 Deaths in US Counties

As severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission continues to evolve, understanding the contribution of location-specific variations in nonpharmaceutical interventions and behaviors to disease transmission during the initial epidemic wave will be key for future control strategies. We offer a rigorous statistical analysis of the relative effectiveness of the timing of both official stay-at-home orders and population mobility reductions during the initial stage of the US coronavirus disease 2019 (COVID-19) epidemic. We used a Bayesian hierarchical regression to fit county-level mortality data from the first case on January 21, 2020, through April 20, 2020, and quantify associations between the timing of stay-at-home orders and population mobility with epidemic control. We found that among 882 counties with an early local epidemic, a 10-day delay in the enactment of stay-at-home orders would have been associated with 14,700 additional deaths by April 20 (95% credible interval: 9,100, 21,500), whereas shifting orders 10 days earlier would have been associated with nearly 15,700 fewer lives lost (95% credible interval: 11,350, 18,950). Analogous estimates are available for reductions in mobility-which typically occurred before stay-at-home orders-and are also stratified by county urbanicity, showing significant heterogeneity. Results underscore the importance of timely policy and behavioral action for early-stage epidemic control.

Physical, Emotional, and Social Pain During COVID-19 Pandemic-Related Social Isolation

The socio-emotional condition during the COVID-19 pandemic subsidises the (re)modulation of interactive neural circuits underlying risk assessment behaviour at the physical, emotional, and social levels. Experiences of social isolation, exclusion, or affective loss are generally considered some of the most “painful” things that people endure. The threats of social disconnection are processed by some of the same neural structures that process basic threats to survival. The lack of social connection can be “painful” due to an overlap in the neural circuitry responsible for both physical and emotional pain related to feelings of social rejection. Indeed, many of us go to great lengths to avoid situations that may engender these experiences. Accordingly, this work focuses on pandemic times; the somatisation mentioned above seeks the interconnection and/or interdependence between neural systems related to emotional and cognitive processes such that a person involved in an aversive social environment becomes aware of himself, others, and the threatening situation experienced and takes steps to avoid daily psychological and neuropsychiatric effects. Social distancing during isolation evokes the formation of social distress, increasing the intensity of learned fear that people acquire, consequently enhancing emotional and social pain.

Optimal levels of vaccination to reduce COVID-19 infected individuals and deaths: A global analysis

Coronavirus disease 2019 (COVID-19) continues to be a pandemic threat that is generating a constant state of alert in manifold countries. One of the strategies of defense against infectious diseases (e.g., COVID-19) is the vaccinations that decrease the numbers of infected individuals and deaths. In this context, the optimal level of vaccination for COVID-19 is a basic point to control this pandemic crisis in society. The study here,-using data of doses of vaccines administered per 100 inhabitants, confirmed cases and case fatality ratio of COVID-19 between countries (N=192) from March to May 2021,- clarifies the optimal levels of vaccination for reducing the number of infected individuals and, consequently, the numbers of deaths at global level. Findings reveal that the average level of administering about 80 doses of vaccines per 100 inhabitants between countries can sustain a reduction of confirmed cases and number of deaths. In addition, results suggest that an intensive vaccination campaign in the initial phase of pandemic wave leads to a lower optimal level of doses administered per 100 inhabitants (roughly 47 doses of vaccines administered) for reducing infected individuals; however, the growth of pandemic wave (in May, 2021) moves up the optimal level of vaccines to about 90 doses for reducing the numbers of COVID-19 related infected individuals. All these results here could aid policymakers to prepare optimal strategies directed to a rapid COVID-19 vaccination rollout, before the takeoff of pandemic wave, to lessen negative effects of pandemic crisis on environment and socioeconomic systems.

The global case fatality rate of coronavirus disease 2019 by continents and national income: A meta-analysis

The aim of this study is to provide a more accurate representation of COVID‐19's case fatality rate (CFR) by performing meta‐analyses by continents and income, and by comparing the result with pooled estimates. We used multiple worldwide data sources on COVID‐19 for every country reporting COVID‐19 cases. On the basis of data, we performed random and fixed meta‐analyses for CFR of COVID‐19 by continents and income according to each individual calendar date. CFR was estimated based on the different geographical regions and levels of income using three models: pooled estimates, fixed‐ and random‐model. In Asia, all three types of CFR initially remained approximately between 2.0% and 3.0%. In the case of pooled estimates and the fixed model results, CFR increased to 4.0%, by then gradually decreasing, while in the case of random‐model, CFR remained under 2.0%. Similarly, in Europe, initially, the two types of CFR peaked at 9.0% and 10.0%, respectively. The random‐model results showed an increase near 5.0%. In high‐income countries, pooled estimates and fixed‐model showed gradually increasing trends with a final pooled estimates and random‐model reached about 8.0% and 4.0%, respectively. In middle‐income, the pooled estimates and fixed‐model have gradually increased reaching up to 4.5%. in low‐income countries, CFRs remained similar between 1.5% and 3.0%. Our study emphasizes that COVID‐19 CFR is not a fixed or static value. Rather, it is a dynamic estimate that changes with time, population, socioeconomic factors, and the mitigatory efforts of individual countries.

Europe showed the highest COVID‐19 case fatality rate (CFR) until mid‐October 2020, and North America and South America follows.

Asia showed the highest CFR since the first confirmed case of COVID‐19 emerged, however, it had shown a declining tendency since March 2020.

CFRs in high‐income countries showed an explosive increase compared with those in low‐income countries, which can be interpreted as due to the under‐reporting of mortality cases from COVID‐19.

Not Only High Number and Specific Comorbidities but Also Age Are Closely Related to Progression and Poor Prognosis in Patients With COVID-19

Background: Some patients with comorbidities and rapid disease progression have a poor prognosis.

Aim: We aimed to investigate the characteristics of comorbidities and their relationship with disease progression and outcomes of COVID-19 patients.

Methods: A total of 718 COVID-19 patients were divided into five clinical type groups and eight age-interval groups. The characteristics of comorbidities were compared between the different clinical type groups and between the different age-interval groups, and their relationships with disease progression and outcomes of COVID-19 patients were assessed.

Results: Approximately 91.23% (655/718) of COVID-19 patients were younger than 60 years old. Approximately 64.76% (465/718) had one or more comorbidities, and common comorbidities included non-alcoholic fatty liver disease (NAFLD), hyperlipidaemia, hypertension, diabetes mellitus (DM), chronic hepatitis B (CHB), hyperuricaemia, and gout. COVID-19 patients with comorbidities were older, especially those with chronic obstructive pulmonary disease (COPD) and cardiovascular disease (CVD). Hypertension, DM, COPD, chronic kidney disease (CKD) and CVD were mainly found in severe COVID-19 patients. According to spearman correlation analysis the number of comorbidities was correlated positively with disease severity, the number of comorbidities and NAFLD were correlated positively with virus negative conversion time, hypertension, CKD and CVD were primarily associated with those who died, and the above-mentioned correlation existed independently of age. Risk factors included age, the number of comorbidities and hyperlipidaemia for disease severity, the number of comorbidities, hyperlipidaemia, NAFLD and COPD for the virus negative conversion time, and the number of comorbidities and CKD for prognosis. Number of comorbidities and age played a predictive role in disease progression and outcomes.

Conclusion: Not only high number and specific comorbidities but also age are closely related to progression and poor prognosis in patients with COVID-19. These findings provide a reference for clinicians to focus on not only the number and specific comorbidities but also age in COVID-19 patients to predict disease progression and prognosis.

Clinical Trial Registry: Chinese Clinical Trial Register ChiCTR2000034563.

Cost-effectiveness analysis of COVID-19 vaccination in Poland

INTRODUCTION

The aim of our study was to assess the potential value of the Comirnaty vaccine (BNT162b2) in Poland. A model was used to estimate patient events, direct medical costs, utilities, and cost-effectiveness for 1 year with and without implementation of the vaccine.

MATERIAL AND METHODS

The Markov model with 1-week cycles was used to estimate patient events, direct medical costs, utilities, and cost-effectiveness for 1 year with and without implementing the Comirnaty vaccine in Poland. The incremental cost per quality-adjusted life-year (QALY) gained vs. no vaccine was calculated for the general population and selected age-groups. All costs are reported in PLN (average exchange rate in 2020: 1 EUR = 4.44 PLN).

RESULTS

In the base case analysis the incremental cost per QALY gained associated with vaccinating the whole population is 6249 PLN. For individuals aged 60-69 years and > 80 years vaccination is less costly and more effective than no vaccination. The incremental cost per QALY gained when vaccinating individuals aged 40-49 and 30-39 years is 28,135 PLN and 67,823 PLN, respectively. In the general population and in younger subpopulations the incremental cost-effectiveness ratio is most sensitive to the vaccine effectiveness, vaccine price, and SARS-CoV-2 infection rates.

CONCLUSIONS

When prioritization is required due to supply constraints, vaccination of the elderly is justified because it gives the highest number of QALY gained and generates savings for the health care system. Continual updates of the model concerning vaccine real-life effectiveness and epidemic course are required to refine the prioritisation scheme in the future.

The Epidemiology of COVID-19 in Malaysia

BACKGROUND

COVID-19 has rapidly spread across the globe. Critical to the control of COVID-19 is the characterisation of its epidemiology. Despite this, there has been a paucity of evidence from many parts of the world, including Malaysia. We aim to describe the epidemiology of COVID-19 in Malaysia to inform prevention and control policies better.

METHODS

Malaysian COVID-19 data was extracted from 16 March 2020 up to 31 May 2021. We estimated the following epidemiological indicators: 7-day incidence rates, 7-day mortality rates, case fatality rates, test positive ratios, testing rates and the time-varying reproduction number (Rt).

FINDINGS

Between 16 March 2020 and 31 May 2021, Malaysia has reported 571,901 cases and 2,796 deaths. Malaysia's average 7-day incidence rate was 26•6 reported infections per 100,000 population (95% CI: 17•8, 38•1). The average test positive ratio and testing rate were 4•3% (95% CI: 1•6, 10•2) and 0•8 tests per 1,000 population (95% CI: <0•1, 3•7), respectively. The case fatality rates (CFR) was 0•6% (95% CI: <0•1, 3•7). Among the 2,796 cases who died, 87•3% were ≥ 50 years.

INTERPRETATION

The public health response was successful in the suppression of COVID-19 transmission or the first half of 2020. However, a state election and outbreaks in institutionalised populations have been the catalyst for more significant community propagation. This rising community transmission has continued in 2021, leading to increased incidence and strained healthcare systems. Calibrating NPI based on epidemiological indicators remain critical for us to live with the virus. (243 words).

FUNDING

This study is part of the COVID-19 Epidemiological Analysis and Strategies (CEASe) Project with funding from the Ministry of Science, Technology and Innovation (UM.0000245/HGA.GV).

Meta-analysis and adjusted estimation of COVID-19 case fatality risk in India and its association with the underlying comorbidities

Management of coronavirus disease 2019 (COVID-19) in India is a top government priority. However, there is a lack of COVID-19 adjusted case fatality risk (aCFR) estimates and information on states with high aCFR. Data on COVID-19 cases and deaths in the first pandemic wave and 17 state-specific geodemographic, socio-economic, health and comorbidity-related factors were collected. State-specific aCFRs were estimated, using a 13-day lag for fatality. To estimate country-level aCFR in the first wave, state estimates were meta-analysed based on inverse-variance weighting and aCFR as either a fixed- or random-effect. Multiple correspondence analyses, followed by univariable logistic regression, were conducted to understand the association between aCFR and geodemographic, health and social indicators. Based on health indicators, states likely to report a higher aCFR were identified. Using random- and fixed-effects models, cumulative aCFRs in the first pandemic wave on 27 July 2020 in India were 1.42% (95% CI 1.19%-1.70%) and 2.97% (95% CI 2.94%-3.00%), respectively. At the end of the first wave, as of 15 February 2021, a cumulative aCFR of 1.18% (95% CI 0.99%-1.41%) using random and 1.64% (95% CI 1.64%-1.65%) using fixed-effects models was estimated. Based on high heterogeneity among states, we inferred that the random-effects model likely provided more accurate estimates of the aCFR for India. The aCFR was grouped with the incidence of diabetes, hypertension, cardiovascular diseases and acute respiratory infections in the first and second dimensions of multiple correspondence analyses. Univariable logistic regression confirmed associations between the aCFR and the proportion of urban population, and between aCFR and the number of persons diagnosed with diabetes, hypertension, cardiovascular diseases and stroke per 10,000 population that had visited NCD (Non-communicable disease) clinics. Incidence of pneumonia was also associated with COVID-19 aCFR. Based on predictor variables, we categorised 10, 17 and one Indian state(s) expected to have a high, medium and low aCFR risk, respectively. The current study demonstrated the value of using meta-analysis to estimate aCFR. To decrease COVID-19 associated fatalities, states estimated to have a high aCFR must take steps to reduce co-morbidities.

[Immunosenescence, viral infections and nutrition: A narrative review of scientific available evidence]

Aging of the immune system, or immunosenescence, alters the viral immune response in the elderly, especially when frailty exists. Research findings have demonstrated an imbalance in pro- and anti-inflammatory mechanisms, reduced production and diversification of T lymphocytes, and an alteration in immunovigilance and antibody synthesis. In this context, nutrition has a role in combating sarcopenia and frailty. Some food components that contribute to immune-competence are protein, vitamin D, n-3 fatty acids, antioxidant vitamins (vitamins C and E), zinc, selenium and iron. In times of a pandemic, nutritional recommendations for immune-competence in the elderly should be based on clinical studies. In this article, immunosenescence and its relationship to nutrition are addressed, including interventions studied in the context of the COVID-19 pandemic.

In Vitro Replication Inhibitory Activity of Xanthorrhizol against Severe Acute Respiratory Syndrome Coronavirus 2

In spite of the large number of repositioned drugs and direct-acting antivirals in clinical trials for the management of the ongoing COVID-19 pandemic, there are few cost-effective therapeutic options for severe acute respiratory syndrome (SARS) coronavirus 2 (SCoV2) infection. In this paper, we show that xanthorrhizol (XNT), a bisabolane-type sesquiterpenoid compound isolated from the Curcuma xanthorrhizza Roxb., a ginger-line plant of the family Zingiberaceae, displays a potent antiviral efficacy in vitro against SCoV2 and other related coronaviruses, including SARS-CoV-1 (SCoV1) and a common cold-causing human coronavirus. XNT reduced infectious SCoV2 titer by ~3-log₁₀ at 20 μM and interfered with the replication of the SCoV1 subgenomic replicon, while it had no significant antiviral effects against hepatitis C virus and noroviruses. Further, XNT exerted similar antiviral functions against SCoV2 variants, such as a GH clade strain and a delta strain currently predominant worldwide. Neither SCoV2 entry into cells nor the enzymatic activity of viral RNA polymerase (Nsp12), RNA helicase (Nsp13), or the 3CL main protease (Nsp5) was inhibited by XNT. While its CoV replication inhibitory mechanism remains elusive, our results demonstrate that the traditional folk medicine XNT could be a promising antiviral candidate that inhibits a broad range of SCoV2 variants of concern and other related CoVs.

Estimating the SARS-CoV2 infections detection rate and cumulative incidence in the World Health Organization African Region ten months into the pandemic

As of 03 January 2021, the WHO African region is the least affected by the coronavirus disease-2019 (COVID-19) pandemic, accounting for only 2.4% of cases and deaths reported globally. However, concerns abound about whether the number of cases and deaths reported from the region reflect the true burden of the disease and how the monitoring of the pandemic trajectory can inform response measures.

We retrospectively estimated four key epidemiological parameters (the total number of cases, the number of missed cases, the detection rate and the cumulative incidence) using the COVID-19 prevalence calculator tool developed by Resolve to Save Lives. We used cumulative cases and deaths reported during the period 25 February to 31 December 2020 for each WHO Member State in the region as well as population data to estimate the four parameters of interest. The estimated number of confirmed cases in 42 countries out of 47 of the WHO African region included in this study was 13 947 631 [95% confidence interval (CI): 13 334 620–14 635 502] against 1 889 512 cases reported, representing 13.5% of overall detection rate (range: 4.2% in Chad, 43.9% in Guinea). The cumulative incidence of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) was estimated at 1.38% (95% CI: 1.31%–1.44%), with South Africa the highest [14.5% (95% CI: 13.9%–15.2%)] and Mauritius [0.1% (95% CI: 0.099%–0.11%)] the lowest. The low detection rate found in most countries of the WHO African region suggests the need to strengthen SARS-CoV-2 testing capacities and adjusting testing strategies.

Epidemiological model for India's COVID-19 pandemic

This study aims to analyse the effectiveness of the lockdown measure taken to control the transmission of COVID-19 in India by examining the peak of the epidemic pre and post the adoption of stringent lockdown from 25 March 2020. Susceptible-exposed-infectious-recovered (SEIR) model has been developed to trace the peak of the outbreak. The study suggests that with the implementation of lockdown the peak of epidemic in India has delayed by two and a half month. Before lockdown peak was examined in end of May 2020 but post lockdown, it is expected to arrive in mid-August 2020. Thus lockdown measures has delayed the arrival of peak of epidemic which would be helpful in preparing the healthcare system in advance, to tackle worst situation if arises in future.

Leveraging weather data for forecasting cases-to-mortality rates due to COVID-19

There are several recent publications criticizing the failure of COVID-19 forecasting models, with swinging over predictions and underpredictions, which have made it difficult for decision and policy making. Observing the failures of several COVID-19 forecasting models and the alarming spread of the virus, we seek to use some stable response for forecasting COVID-19, viz., ratios of COVID-19 cases to mortalities, rather than COVID-19 cases or fatalities. A trend of low COVID-19 cases-to-mortality ratios calls for urgent attention: the need for vaccines, for instance. Studies have shown that there are influences of weather parameters on COVID-19; and COVID-19 may have come to stay and could manifest a seasonal outbreak profile similar to other infectious respiratory diseases. In this paper, the influences of some weather, geographical, economic and demographic covariates were evaluated on COVID-19 response based on a series of Granger-causality tests. The effect of four weather parameters, viz., temperature, rainfall, solar irradiation and relative humidity, on daily COVID-19 cases-to-mortality ratios of 36 countries from 5 continents of the world were determined through regression analysis. Regression studies show that these four weather factors impact ratios of COVID-19 cases-to-mortality differently. The most impactful factor is temperature which is positively correlated with COVID-19 cases-to-mortality responses in 24 out of 36 countries. Temperature minimally affects COVID-19 cases-to-mortality ratios in the tropical countries. The most influential weather factor - temperature - was incorporated in training random forest and deep learning models for forecasting the cases-to-mortality rate of COVID-19 in clusters of countries in the world with similar weather conditions. Evaluation of trained forecasting models incorporating temperature features show better performance compared to a similar set of models trained without temperature features. This implies that COVID-19 forecasting models will predict more accurately if temperature features are factored in, especially for temperate countries.

Tracking SARS-CoV-2: Novel Trends and Diagnostic Strategies

The COVID-19 pandemic has had an enormous impact on economies and health systems globally, therefore a top priority is the development of increasingly better diagnostic and surveillance alternatives to slow down the spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In order to establish massive testing and contact tracing policies, it is crucial to have a clear view of the diagnostic options available and their principal advantages and drawbacks. Although classical molecular methods such as RT-qPCR are broadly used, diagnostic alternatives based on technologies such as LAMP, antigen, serological testing, or the application of novel technologies such as CRISPR-Cas for diagnostics, are also discussed. The present review also discusses the most important automation strategies employed to increase testing capability. Several serological-based diagnostic kits are presented, as well as novel nanotechnology-based diagnostic methods. In summary, this review provides a clear diagnostic landscape of the most relevant tools to track COVID-19.

SARS-CoV-2 incidence and risk factors in a national, community-based prospective cohort of U.S. adults

BACKGROUND

Epidemiologic risk factors for incident SARS-CoV-2 infection as determined via prospective cohort studies greatly augment and complement information from case-based surveillance and cross-sectional seroprevalence surveys.

METHODS

We estimated the incidence of SARS-CoV-2 infection and risk factors in a well-characterized, national prospective cohort of 6,738 U.S. adults, enrolled March-August 2020, a subset of whom (n=4,510) underwent repeat serologic testing between May 2020 and January 2021. We examined the crude associations of sociodemographic factors, epidemiologic risk factors, and county-level community transmission with the incidence of seroconversion. In multivariable Poisson models we examined the association of social distancing and a composite score of several epidemiologic risk factors with the rate of seroconversion.

FINDINGS

Among the 4,510 individuals with at least one serologic test, 323 (7.3%, 95% confidence interval [CI] 6.5%-8.1%) seroconverted by January 2021. Among 3,422 participants seronegative in May-September 2020 and tested during November 2020-January 2021, we observed 161 seroconversions over 1,646 person-years of follow-up (incidence rate of 9.8 per 100 person-years [95%CI 8.3-11.5]). In adjusted models, participants who reported always or sometimes social distancing with people they knew (IRRalways vs. never 0.43, 95%CI 0.21-1.0; IRRsometimes vs. never 0.47, 95%CI 0.22-1.2) and people they did not know (IRRalways vs. never 0.64, 95%CI 0.39-1.1; IRRsometimes vs. never 0.60, 95%CI 0.38-0.97) had lower rates of seroconversion. The rate of seroconversion increased across tertiles of the composite score of epidemiologic risk (IRRmedium vs. low 1.5, 95%CI 0.92-2.4; IRRhigh vs. low 3.0, 95%CI 2.0-4.6). Among the 161 observed seroconversions, 28% reported no symptoms of COVID-like illness (i.e., were asymptomatic), and 27% reported a positive SARS-CoV-2 diagnostic test. Ultimately, only 29% reported isolating and 19% were asked about contacts.

INTERPRETATION

Modifiable epidemiologic risk factors and poor reach of public health strategies drove SARS-CoV-2 transmission across the U.S during May 2020-January 2021.

FUNDING

U.S. National Institutes of Allergy and Infectious Diseases (NIAID).

Flatten the curve: Empirical evidence on how non-pharmaceutical interventions substituted pharmaceutical treatments during COVID-19 pandemic

During the outbreak of the COVID-19 pandemic, Non-Pharmaceutical and Pharmaceutical treatments were alternative strategies for governments to intervene. Though many of these intervention methods proved to be effective to stop the spread of COVID-19, i.e., lockdown and curfew, they also posed risk to the economy; in such a scenario, an analysis on how to strike a balance becomes urgent. Our research leverages the mobility big data from the University of Maryland COVID-19 Impact Analysis Platform and employs the Generalized Additive Model (GAM), to understand how the social demographic variables, NPTs (Non-Pharmaceutical Treatments) and PTs (Pharmaceutical Treatments) affect the New Death Rate (NDR) at county-level. We also portray the mutual and interactive effects of NPTs and PTs on NDR. Our results show that there exists a specific usage rate of PTs where its marginal effect starts to suppress the NDR growth, and this specific rate can be reduced through implementing the NPTs.

The effects of fear of COVID-19, loneliness, and resilience on the quality of life in older adults living in a nursing home

This study aimed to evaluate the fear of COVID-19, loneliness, resilience, and quality of life levels in older adults in a nursing home during the pandemic, and the effects of these variables and descriptive characteristics on their quality of life. Data were collected using a participant information form, the Mini Mental State Exam (MMSE), the Brief Resilience Scale, the Fear of COVID-19 Scale (FCV-19S), the Loneliness Scale for Elderly (LSE), and the World Health Organization Quality of Life-BREF Turkish Version (WHOQOL-BREF-TR). Regarding the WHOQOL-BREF-TR scale, being male, history of chronic disease, MMSE score, and the changes in sleep pattern significantly affected the physical dimension (R²=0.353, p<0.01) while age, and MMSE and LSE scores significantly affected the psychological dimension (R²=0.364, p<0.01). Also, the MMSE, FCV-19S, and LSE scores significantly affected the social relations dimension (R²=0.234, p<0.01) while MMSE, FCV-19S, and LSE scores significantly affected the environmental dimension (R²=0.351, p<0.01).