Population Risk Factors for COVID-19 Mortality in 93 Countries

Infectious diseases and the role of needle biopsy post-mortem

Post-mortem examinations continue to play a crucial role in understanding the epidemiology and pathogenesis of infectious diseases. However, the perceived infection risk can preclude traditional, invasive, complete diagnostic autopsy. Post-mortem examination is especially important in emerging infectious diseases with potentially unknown infection risks, but rapid acquisition of good quality tissue samples is needed as part of the scientific and public health response. Needle biopsy post-mortem is a minimally invasive, rapid, closed-body autopsy technique that was originally developed to minimise the infection risk to practitioners. Since its inception, needle biopsy post-mortem has also been used as a technique to support complete diagnostic autopsy provision in poorly resourced regions and to facilitate post-mortem examinations in communities that might have religious or cultural objections to an invasive autopsy. This Review analyses the evolution and applicability of needle biopsy post-mortem in investigating endemic and emerging infectious diseases.

The impact of COVID-19 on "biological aging"

The global impact of the SARS-CoV-2 pandemic has been unprecedented, posing a significant public health challenge. Chronological age has been identified as a key determinant for severe outcomes associated with SARS-CoV-2 infection. Epigenetic age acceleration has previously been observed in various diseases including human immunodeficiency virus (HIV), Cytomegalovirus (CMV), cardiovascular diseases, and cancer. However, a comprehensive review of this topic is still missing in the field. In this review, we explore and summarize the research work focusing on biological aging markers, i.e., epigenetic age and telomere attrition in COVID-19 patients. From the reviewed articles, we identified a consistent pattern of epigenetic age dysregulation and shortened telomere length, revealing the impact of COVID-19 on epigenetic aging and telomere attrition.

Association of race/ethnicity and severe housing problems with COVID-19 deaths in the United States: Analysis of the first three waves

The objective of this study is to assess the associations of race/ethnicity and severe housing problems with COVID-19 death rates in the US throughout the first three waves of the COVID-19 pandemic in the US. We conducted a cross-sectional study using a negative binomial regression model to estimate factors associated with COVID-19 deaths in 3063 US counties between March 2020 and July 2021 by wave and pooled across all three waves. In Wave 1, counties with larger percentages of Black, Hispanic, American Indian and Alaska Native (AIAN), and Asian American and Pacific Islander (AAPI) residents experienced a greater risk of deaths per 100,000 residents of +22.82 (95% CI 15.09, 30.56), +7.50 (95% CI 1.74, 13.26), +13.52 (95% CI 8.07, 18.98), and +5.02 (95% CI 0.92, 9.12), respectively, relative to counties with larger White populations. By Wave 3, however, the mortality gap declined considerably in counties with large Black, AIAN and AAPI populations: +10.38 (95% CI 4.44, 16.32), +7.14 (95% CI 1.14, 13.15), and +3.72 (95% CI 0.81, 6.63), respectively. In contrast, the gap increased for counties with a large Hispanic population: +13 (95% CI 8.81, 17.20). Housing problems were an important predictor of COVID-19 deaths. However, while housing problems were associated with increased COVID-19 mortality in Wave 1, by Wave 3, they contributed to magnified mortality in counties with large racial/ethnic minority groups. Our study revealed that focusing on a wave-by-wave analysis is critical to better understand how the associations of race/ethnicity and housing conditions with deaths evolved throughout the first three COVID-19 waves in the US. COVID-19 mortality initially took hold in areas characterized by large racial/ethnic minority populations and poor housing conditions. Over time, as the virus spread to predominantly White counties, these disparities decreased substantially but remained sizable.

Risk perceptions and behaviors concerning rural tourism and economic-political drivers of COVID-19 policy in 2020

When COVID-19 was first introduced to the United States, state and local governments enacted a variety of policies intended to mitigate the virulence of the epidemic. At the time, the most effective measures to prevent the spread of COVID-19 included stay-at-home orders, closing of nonessential businesses, and mask mandates. Although it was well known that regions with high population density and cold climates were at the highest risk for disease spread, rural counties that are economically reliant on tourism were incentivized to enact fewer precautions against COVID-19. The uncertainty of the COVID-19 pandemic, the multiple policies to reduce transmission, and the changes in outdoor recreation behavior had a significant impact on rural tourism destinations and management of protected spaces. We utilize fine-scale incidence and demographic data to study the relationship between local economic and political concerns, COVID-19 mitigation measures, and the subsequent severity of outbreaks throughout the continental United States. We also present results from an online survey that measured travel behavior, health risk perceptions, knowledge and experience with COVID-19, and evaluation of destination attributes by 407 out-of-state visitors who traveled to Maine from 2020 to 2021. We synthesize this research to present a narrative on how perceptions of COVID-19 risk and public perceptions of rural tourism put certain communities at greater risk of illness throughout 2020. This research could inform future rural destination management and public health policies to help reduce negative socioeconomic, health and environmental impacts of pandemic-derived changes in travel and outdoor recreation behavior.

Unravelling the link between SARS-CoV-2 mutation frequencies, patient comorbidities, and structural dynamics

Genomic surveillance is crucial for tracking emergence and spread of novel variants of pathogens, such as SARS-CoV-2, to inform public health interventions and to enforce control measures. However, in some settings especially in low- and middle- income counties, where sequencing platforms are limited, only certain patients get to be selected for sequencing surveillance. Here, we show that patients with multiple comorbidities potentially harbour SARS-CoV-2 with higher mutation rates and thus deserve more attention for genomic surveillance. The relationship between the patient comorbidities, and type of amino acid mutations was assessed. Correlation analysis showed that there was a significant tendency for mutations to occur within the ORF1a region for patients with higher number of comorbidities. Frequency analysis of the amino acid substitution within ORF1a showed that nsp3 P822L of the PLpro protease was one of the highest occurring mutations. Using molecular dynamics, we simulated that the P822L mutation in PLpro represents a system with lower Root Mean Square Deviation (RMSD) fluctuations, and consistent Radius of gyration (Rg), Solvent Accessible Surface Area (SASA) values-indicate a much stabler protein than the wildtype. The outcome of this study will help determine the relationship between the clinical status of a patient and the mutations of the infecting SARS-CoV-2 virus.

The effect of the urban exposome on COVID-19 health outcomes: A systematic review and meta-analysis

BACKGROUND

The global severity of SARS-CoV-2 illness has been associated with various urban characteristics, including exposure to ambient air pollutants. This systematic review and meta-analysis aims to synthesize findings from ecological and non-ecological studies to investigate the impact of multiple urban-related features on a variety of COVID-19 health outcomes.

METHODS

On December 5, 2022, PubMed was searched to identify all types of observational studies that examined one or more urban exposome characteristics in relation to various COVID-19 health outcomes such as infection severity, the need for hospitalization, ICU admission, COVID pneumonia, and mortality.

RESULTS

A total of 38 non-ecological and 241 ecological studies were included in this review. Non-ecological studies highlighted the significant effects of population density, urbanization, and exposure to ambient air pollutants, particularly PM2.5. The meta-analyses revealed that a 1 μg/m3 increase in PM2.5 was associated with a higher likelihood of COVID-19 hospitalization (pooled OR 1.08 (95% CI:1.02-1.14)) and death (pooled OR 1.06 (95% CI:1.03-1.09)). Ecological studies, in addition to confirming the findings of non-ecological studies, also indicated that higher exposure to nitrogen dioxide (NO2), ozone (O3), sulphur dioxide (SO2), and carbon monoxide (CO), as well as lower ambient temperature, humidity, ultraviolet (UV) radiation, and less green and blue space exposure, were associated with increased COVID-19 morbidity and mortality.

CONCLUSION

This systematic review has identified several key vulnerability features related to urban areas in the context of the recent COVID-19 pandemic. The findings underscore the importance of improving policies related to urban exposures and implementing measures to protect individuals from these harmful environmental stressors.

Public health factors help explain cross country heterogeneity in excess death during the COVID19 pandemic

The COVID-19 pandemic has taken a devastating toll around the world. Since January 2020, the World Health Organization estimates 14.9 million excess deaths have occurred globally. Despite this grim number quantifying the deadly impact, the underlying factors contributing to COVID-19 deaths at the population level remain unclear. Prior studies indicate that demographic factors like proportion of population older than 65 and population health explain the cross-country difference in COVID-19 deaths. However, there has not been a comprehensive analysis including variables describing government policies and COVID-19 vaccination rate. Furthermore, prior studies focus on COVID-19 death rather than excess death to assess the impact of the pandemic. Through a robust statistical modeling framework, we analyze 80 countries and show that actionable public health efforts beyond just the factors intrinsic to each country are important for explaining the cross-country heterogeneity in excess death.

Machine learning-based mortality prediction models for smoker COVID-19 patients

BACKGROUND

The large number of SARS-Cov-2 cases during the COVID-19 global pandemic has burdened healthcare systems and created a shortage of resources and services. In recent years, mortality prediction models have shown a potential in alleviating this issue; however, these models are susceptible to biases in specific subpopulations with different risks of mortality, such as patients with prior history of smoking. The current study aims to develop a machine learning-based mortality prediction model for COVID-19 patients that have a history of smoking in the Iranian population.

METHODS

A retrospective study was conducted across six medical centers between 18 and 2020 and 15 March 2022, comprised of 678 CT scans and laboratory-confirmed COVID-19 patients that had a history of smoking. Multiple machine learning models were developed using 10-fold cross-validation. The target variable was in-hospital mortality and input features included patient demographics, levels of care, vital signs, medications, and comorbidities. Two sets of models were developed for at-admission and post-admission predictions. Subsequently, the top five prediction models were selected from at-admission models and post-admission models and their probabilities were calibrated.

RESULTS

The in-hospital mortality rate for smoker COVID-19 patients was 20.1%. For "at admission" models, the best-calibrated model was XGBoost which yielded an accuracy of 87.5% and F₁ score of 86.2%. For the "post-admission" models, XGBoost also outperformed the rest with an accuracy of 90.5% and F₁ score of 89.9%. Active smoking was among the most important features in patients' mortality prediction.

CONCLUSION

Our machine learning-based mortality prediction models have the potential to be adapted for improving the management of smoker COVID-19 patients and predicting patients' chance of survival.

Risk Factors for Sudden Death Within 2 Days After Diagnosis of COVID-19 in Korea

BACKGROUND

We aimed to analyze the risk factors for sudden death after diagnosis of coronavirus disease 2019 (COVID-19) in South Korea and to provide evidence for informing prevention and control interventions for patients at risk of sudden death.

METHODS

We included 30,302 COVID-19 related deaths registered in the patient management information system (Central Disease Control Headquarters) between January 1, 2021, and December 15, 2022. We collected their epidemiological data recorded by the reporting city, province, or country. We performed multivariate logistic regression analysis to identify risk factors for sudden death after diagnosis of COVID-19.

RESULTS

Among the 30,302 deaths, there were 7,258 (24.0%) and 23,044 (76.0%) sudden and non-sudden deaths, respectively. Sudden death means a person who died within 2 days of diagnosis and who did not receive inpatient treatment. Underlying condition, vaccination status, and place of death were significantly associated with the survival period in all age groups. Moreover, region, sex, and prescription were significantly associated with the survival period only in certain age groups. However, reinfection was not significantly associated with the survival period in any age group.

CONCLUSION

To our knowledge, this is the first study on the risk factors for sudden death after a diagnosis of COVID-19, which included age, underlying condition, vaccination status, and place of death. Additionally, individuals aged < 60 years without an underlying condition were at high risk for sudden death. However, this group has relatively low interest in health, as can be seen from the high non-vaccination rate (16.1% of the general population vs. 61.6% of the corresponding group). Therefore, there is a possibility for the presence of an uncontrolled underlying disease in this population. In addition, many sudden deaths occurred due to delayed hospital visits to continue economic activities even after the onset of COVID-19 symptoms (7 days overall vs. 10 days average for the group). In conclusion, 'continued interest in health' is a key factor in avoiding sudden death in the economically active group (under 60 years of age).

The Association of the Levels of High-Density Lipoprotein and Apolipoprotein A1 with SARS-CoV-2 Infection and COVID-19 Severity: An Analysis of the N3C Database

This study analyzed data from the National COVID Cohort Collaborative (N3C) database to investigate whether high-density lipoprotein (HDL) and its major protein component, apolipoprotein A1 (apoA1), are associated with severe COVID-19 sequelae, specifically acute kidney injury (AKI) and severe COVID-19 disease as defined by the infection resulting in hospitalization, extracorporeal membrane oxygenation (ECMO), invasive ventilation, or death. Our study included a total of 1,415,302 subjects with HDL values and 3589 subjects with apoA1 values. Higher levels of both HDL and apoA1 were associated with a lower incidence of infection as well as a lower incidence of severe disease. Higher HDL levels were also associated with a lower incidence of developing AKI. Most comorbidities were negatively correlated with SARS-CoV-2 infection, presumably due to the behavioral changes that occurred as a result of the precautions taken by individuals with underlying comorbidities. The presence of comorbidities, however, was associated with developing severe COVID-19 disease and AKI. African American and Hispanic populations experienced worse outcomes, including a higher incidence of infection and the development of severe disease, as well as AKI. Smoking and being male were associated with a lower incidence of infection, while they were risk factors for the development of severe disease and AKI. The results on cholesterol and diabetes drugs warrant further research, given that the database included multiple drugs in each category impeding for analysis of specific medications. Despite the current limitations in the N3C data, this study is the first to investigate the roles of HDL and apoA1 on the outcomes of COVID-19 using the US population data.

Frequent hospital presenters' use of health information during COVID-19: results of a cross-sectional survey

BACKGROUND

High-frequency hospital users often present with chronic and complex health conditions and are at increased risk of serious morbidity and mortality if they contract COVID-19. Understanding where high-frequency hospital users are sourcing their information, whether they understand what they find, and how they apply the information to prevent the spread of COVID-19 is essential for health authorities to be able to target communication approaches.

METHODS

Cross-sectional survey of 200 frequent hospital users (115 with limited English proficiency) informed by the WHO's "Rapid, simple, flexible behavioral insights on COVID-19". Outcome measures were source of, and trust in information, and knowledge of symptoms, preventive strategies, restrictions, and identification of misinformation.

RESULTS

The most frequently cited source of information was television (n = 144, 72%) followed by the internet (n = 84, 42%). One in four television users sought their information from overseas news outlets from their country of origin, while for those using the internet, 56% relied on Facebook and other forms of social media including YouTube and WeChat. Overall, 41.2% of those surveyed had inadequate knowledge about symptoms, 35.8% had inadequate knowledge about preventative strategies, 30.2% had inadequate knowledge about government-imposed restrictions, and 69% believed in misinformation. Half of the respondents (50%) trusted all information, and only one in five (20%) were uncertain or untrusting. English-speaking participants were almost three times more likely to have adequate knowledge about symptoms (OR 2.69, 95%CI 1.47;4.91) and imposed restrictions (OR 2.10 95%CI 1.06; 4.19), and 11 times more likely to recognize misinformation (OR 11.52 95%CI 5.39; 24.60) than those with limited English.

CONCLUSION

Within this population of high-frequency hospital users with complex and chronic conditions, many were sourcing their information from less trustworthy or locally relevant sources, including social media and overseas news outlets. Despite this, at least half were trusting all the information that they found. Speaking a language other than English was a much greater risk factor for having inadequate knowledge about COVID-19 and believing in misinformation. Health authorities must look for methods to engage diverse communities, and tailor health messaging and education in order to reduce disparities in health outcomes.

Country-level predictors of COVID-19 mortality

This study aimed to identify country-level predictors of COVID-19 mortality, after controlling for diverse potential factors, and utilizing current worldwide mortality data. COVID-19 deaths, as well as geographic, demographic, socioeconomic, healthcare, population health, and pandemic-related variables, were obtained for 152 countries. Continuous variables were examined with Spearman's correlation, categorical variables with ANOVA or Welch's Heteroscedastic F Test, and country-level independent predictors of COVID-19 mortality identified by weighted generalized additive models. This study identified independent mortality predictors in six limited models, comprising groups of related variables. However, in the full model, only WHO region, percent of population ≥ 65 years, Corruption Perception Index, hospital beds/100,000 population, and COVID-19 cases/100,000 population were predictive of mortality, with model accounting for 80.7% of variance. These findings suggest areas for focused intervention in the event of similar future public health emergencies, including prioritization of the elderly, optimizing healthcare capacity, and improving deficient health sector-related governance.

Clinical Disease Characteristics and Treatment Trajectories Associated with Mortality among COVID-19 Patients in Punjab, Pakistan

BACKGROUND

Data on Pakistani COVID-19 patient mortality predictors is limited. It is essential to comprehend the relationship between disease characteristics, medications used, and mortality for better patient outcomes.

METHODS

The medical records of confirmed cases in the Lahore and Sargodha districts were examined using a two-stage cluster sampling from March 2021 to March 2022. Demographics, signs and symptoms, laboratory findings, and pharmacological medications as mortality indicators were noted and analyzed.

RESULTS

A total of 288 deaths occurred out of the 1000 cases. Death rates were higher for males and people over 40. Most of those who were mechanically ventilated perished (OR: 124.2). Dyspnea, fever, and cough were common symptoms, with a significant association amid SpO2 < 95% (OR: 3.2), RR > 20 breaths/min (OR: 2.5), and mortality. Patients with renal (OR: 2.3) or liver failure (OR: 1.5) were at risk. Raised C-reactive protein (OR: 2.9) and D-dimer levels were the indicators of mortality (OR: 1.6). The most prescribed drugs were antibiotics, (77.9%), corticosteroids (54.8%), anticoagulants (34%), tocilizumab (20.3%), and ivermectin (9.2%).

CONCLUSIONS

Older males having breathing difficulties or signs of organ failure with raised C-reactive protein or D-dimer levels had high mortality. Antivirals, corticosteroids, tocilizumab, and ivermectin had better outcomes; antivirals were associated with lower mortality risk.

A united approach to promoting healthy living behaviours and associated health outcomes: a global call for policymakers and decisionmakers

Chronic disease pandemics have challenged societies and public health throughout history and remain ever-present. Despite increased knowledge, awareness and advancements in medicine, technology, and global initiatives the state of global health is declining. The coronavirus disease 2019 (COVID-19) pandemic has compounded the current perilous state of global health, and the long-term impact is yet to be realised. A coordinated global infrastructure could add substantial benefits to public health and yield prominent and consistent policy resulting in impactful change. To achieve global impact, research priorities that address multi-disciplinary social, environmental, and clinical must be supported by unified approaches that maximise public health. We present a call to action for established public health organisations and governments globally to consider the lessons from the COVID-19 pandemic and unite with true collaborative efforts to address current, longstanding, and growing challenges to public health.

Predicting the mortality of patients with Covid-19: A machine learning approach

Background and Aims

Infection with Covid-19 disease can lead to mortality in a short time. Early prediction of the mortality during an epidemic disease can save patients' lives through taking timely and necessary care interventions. Therefore, predicting the mortality of patients with Covid-19 using machine learning techniques can be effective in reducing mortality rate in Covid-19. The aim of this study is to compare four machine-learning algorithm for predicting mortality in Covid-19 disease.

Methods

The data of this study were collected from hospitalized patients with COVID-19 in five hospitals settings in Tehran (Iran). Database contained 4120 records, about 25% of which belonged to patients who died due to Covid-19. Each record contained 38 variables. Four machine-learning techniques, including random forest (RF), regression logistic (RL), gradient boosting tree (GBT), and support vector machine (SVM) were used in modeling.

Results

GBT model presented higher performance compared to other models (accuracy 70%, sensitivity 77%, specificity 69%, and the ROC area under the curve 0.857). RF, RL, and SVM models with the ROC area under curve 0.836, 0.818, and 0.794 were in the second and third places.

Conclusion

Considering the combination of multiple influential factors affecting death Covid-19 can help in early prediction and providing a better care plan. In addition, using different modeling on data can be useful for physician in providing appropriate care.

Alzheimer's disease and COVID-19: Interactions, intrinsic linkages, and the role of immunoinflammatory responses in this process

Alzheimer's disease (AD) and COVID-19 share many common risk factors, such as advanced age, complications, APOE genotype, etc. Epidemiological studies have also confirmed the internal relationship between the two diseases. For example, studies have found that AD patients are more likely to suffer from COVID-19, and after infection with COVID-19, AD also has a much higher risk of death than other chronic diseases, and what's more interesting is that the risk of developing AD in the future is significantly higher after infection with COVID-19. Therefore, this review gives a detailed introduction to the internal relationship between Alzheimer's disease and COVID-19 from the perspectives of epidemiology, susceptibility and mortality. At the same time, we focused on the important role of inflammation and immune responses in promoting the onset and death of AD from COVID-19.

Race, employment, and the pandemic: An exploration of covariate explanations of COVID-19 case fatality rate variance

We derive a simple asymptotic approximation for the long-run case fatality rate of COVID-19 (alpha and delta variants) and show that these estimations are highly correlated to the interaction between US State median age and projected US unemployment rate (Adj. r2 = 60%). We contrast this to the high level of correlation between point (instantaneous) estimates of per state case fatality rates and the interaction of median age, population density and current unemployment rates (Adj. r2 = 50.2%). To determine whether this is caused by a "race effect," we then analyze unemployment, race, median age and population density across US states and show that adding the interaction of African American population and unemployment explains 53.5% of the variance in COVID case fatality rates for the alpha and delta variants when considering instantaneous case fatality rate. Interestingly, when the asymptotic case fatality rate is used, the dependence on the African American population disappears, which is consistent with the fact that in the long-run COVID does not discriminate on race, but may discriminate on access to medical care which is highly correlated to employment in the US. The results provide further evidence of the impact inequality can have on case fatality rates in COVID-19 and the impact complex social, health and economic factors can have on patient survival.

Risk perception and conspiracy theory endorsement predict compliance with COVID-19 public health measures

Public health measures such as spatial distancing and physical hygiene have been found effective in mitigating the spread of the coronavirus. However, there is considerable variability in individual compliance with such public health measures and factors contributing to these interindividual differences are currently still understudied. The present study set out to determine the role of risk perception and conspiracy theory endorsement on compliance with COVID-19 public health measures and explored variations in these associations across participant age and the developmental status of a country, leveraging a large multi-national data set (N = 45,772) across 66 countries/territories, collected via online survey during the early phase of the COVID-19 pandemic (between April and May 2020). Human Development Index (HDI), developed by the United Nations Development Program, was used as a proxy of a country's achievement in key dimensions of human development. Overall, higher risk perception was associated with greater compliance, particularly in individuals with greater conspiracy theory endorsement. Specifically, people from more developed countries who perceived themselves less at risk but showed stronger conspiracy theory endorsement reported the lowest compliance with COVID-19 public health measures. Findings from this study advance understanding of the interplay between risk perception and conspiracy theory endorsement in their effect on compliance with COVID-19 public health measures, under consideration of both individual-level and country-level demographic variables and have potential to inform the design of tailored interventions to fight the current and future global pandemics.

Determinants of COVID-19 cases and deaths in OECD countries

Aim

This research aims to examine the effects of variables that can affect COVID-19 deaths and cases in Organisation for Economic Co-operation and Development (OECD) countries during the years 2020 (first wave), 2021 (vaccine available), and 2022 (vaccine available and Omicron variant appeared).

Material and method

The factors that are thought to affect the case and death rates in 37 OECD countries were examined by multiple linear regression analysis using SPSS 22. The dependent variables were the COVID-19 deaths and cases per 10,000 (in 2020, 2021, and 2022); the independent variables were universal health coverage, physicians, nurses, intensive care beds, hospital beds, non-communicable diseases mortality per 100,000 people, population over 65 years of age, out-of-pocket expenditure, private expenditure, and health expenditure per capita and percent of % GDP.

Results

It was determined that the non-communicable diseases mortality is the relatively important variable COVID-19 cases and deaths in 2020 and 2021. After controlling for the scores of other variables, according to the ß coefficients, a one-unit increase in the number of physicians variable increases COVID-19 cases by 1.14 units in 2022; a one-unit increase in the universal coverage variable decreases COVID-19 deaths by 0.33 units in 2022.

Conclusion

The results of this research provide evidence that the effects of the COVID-19 outbreak have changed between 2020, the first wave of the epidemic, 2021, when the vaccine is available, and 2022, when both the vaccine is available and the Omicron variant is seen. With the increase in vaccination in 2022, the impact of non-communicable diseases mortality on the number of COVID-19 cases has decreased.

Evaluation of the Effect of Underlying Diseases on Mortality of COVID-19 Patients: A Study of 19,985 Cases

Background: The outbreak of a new coronavirus in China in 2019 (COVID-19) caused a global health crisis. Objectives: This study was performed to investigate the effect of different underlying diseases on mortality in patients with COVID-19. Methods: This retrospective cohort study was performed on COVID-19 patients admitted to the Shahid Rahimi and Sohada-ye Ashayer teaching hospitals in Khorramabad, Iran, from 2019 to 2021. Data on disease severity, clinical manifestations, mortality, and underlying disorders were collected and analyzed using the SPSS software version 22 at a 95% confidence interval and 0.05 significance level. Results: The study included 9653 men (48%) and 10332 women (52%). Patients with chronic kidney diseases, cancer, chronic obstructive pulmonary disease, hypertension, cardiovascular disease, and diabetes were at higher mortality risk than those without these underlying diseases, respectively. However, there was no significant relationship between asthma and mortality. Also, age > 50 years, male gender, oxygen saturation < 93 on admission, and symptoms lasting ≤ 5 days were associated with increased mortality. Conclusions: Since patients with underlying diseases are at higher mortality risk, they should precisely follow the advice provided by health authorities and receive a complete COVID-19 vaccination series.

Association of greenness with COVID-19 deaths in India: An ecological study at district level

BACKGROUND

The world has witnessed a colossal death toll due to the novel coronavirus disease-2019 (COVID-19). A few environmental epidemiology studies have identified association of environmental factors (air pollution, greenness, temperature, etc.) with COVID-19 incidence and mortality, particularly in developed countries. India, being one of the most severely affected countries by the pandemic, still has a dearth of research exploring the linkages of environment and COVID-19 pandemic.

OBJECTIVES

We evaluate whether district-level greenness exposure is associated with a reduced risk of COVID-19 deaths in India.

METHODS

We used average normalized difference vegetation index (NDVI) from January to March 2019, derived by Oceansat-2 satellite, to represent district-level greenness exposure. COVID-19 death counts were obtained through May 1, 2021 (around the peak of the second wave) from an open portal: covid19india.org. We used hierarchical generalized negative binomial regressions to check the associations of greenness with COVID-19 death counts. Analyses were adjusted for air pollution (PM_2.5), temperature, rainfall, population density, proportion of older adults (50 years and above), sex ratio over age 50, proportions of rural population, household overcrowding, materially deprived households, health facilities, and secondary school education.

RESULTS

Our analyses found a significant association between greenness and reduced risk of COVID-19 deaths. Compared to the districts with the lowest NDVI (quintile 1), districts within quintiles 3, 4, and 5 have respectively, around 32% [MRR = 0.68 (95% CI: 0.51, 0.88)], 39% [MRR = 0.61 (95% CI: 0.46, 0.80)], and 47% [MRR = 0.53 (95% CI: 0.40, 0.71)] reduced risk of COVID-19 deaths. The association remains consistent for analyses restricted to districts with a rather good overall death registration (>80%).

CONCLUSION

Though cause-of-death statistics are limited, we confirm that exposure to greenness was associated with reduced district-level COVID-19 deaths in India. However, material deprivation and air pollution modify this association.

Age-specific SARS-CoV-2 infection fatality rates derived from serological data vary with income and income inequality

The ongoing COVID-19 pandemic has killed at least 1.1 million people in the United States and over 6.7 million globally. Accurately estimating the age-specific infection fatality rate (IFR) of SARS-CoV-2 for different populations is crucial for assessing and understanding the impact of COVID-19 and for appropriately allocating vaccines and treatments to at-risk groups. We estimated age-specific IFRs of wild-type SARS-CoV-2 using published seroprevalence, case, and death data from New York City (NYC) from March to May 2020, using a Bayesian framework that accounted for delays between key epidemiological events. IFRs increased 3-4-fold with every 20 years of age, from 0.06% in individuals between 18-45 years old to 4.7% in individuals over 75. We then compared IFRs in NYC to several city- and country-wide estimates including England, Switzerland (Geneva), Sweden (Stockholm), Belgium, Mexico, and Brazil, as well as a global estimate. IFRs in NYC were higher for individuals younger than 65 years old than most other populations, but similar for older individuals. IFRs for age groups less than 65 decreased with income and increased with income inequality measured using the Gini index. These results demonstrate that the age-specific fatality of COVID-19 differs among developed countries and raises questions about factors underlying these differences, including underlying health conditions and healthcare access.

Is Economic Growth Good for Population Health? A Critical Review

A large multidisciplinary literature discusses the relationship between economic growth and population health. The idea that economic growth is good for societies has inspired extensive academic debate, but conclusions have been mixed. To help shed light on the subject, this paper focuses on opportunities for consensus in this large literature. Much scholarship finds that the health-growth relationship varies according to (1) which aspect of "health" is under consideration, (2) shape (e.g., positive linear or logarithmic), (3) issues of timing (e.g., growth over the short or long term), (4) a focus on health inequalities as opposed to population averages, and (5) multivariable relationships with additional factors. After reflecting upon these findings, I propose that economic growth promotes health in some respects, for some countries, and in conjunction with other life-supporting priorities, but does not by itself improve population health generally speaking. I then argue there is already wide, interdisciplinary consensus to support this stance. Moreover, policies focusing exclusively on economic growth threaten harm to both population health and growth, which is to say that political dynamics are also implicated. Yet multivariable approaches can help clarify the bigger picture of how growth relates to health. For moving this literature forward, the best opportunities may involve the simultaneous analysis of multiple factors. The recognition of consensus around these issues would be welcome, and timely.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42650-023-00072-y.

Dietary, comorbidity, and geo-economic data fusion for explainable COVID-19 mortality prediction

Many factors significantly influence the outcomes of infectious diseases such as COVID-19. A significant focus needs to be put on dietary habits as environmental factors since it has been deemed that imbalanced diets contribute to chronic diseases. However, not enough effort has been made in order to assess these relations. So far, studies in the field have shown that comorbid conditions influence the severity of COVID-19 symptoms in infected patients. Furthermore, COVID-19 has exhibited seasonal patterns in its spread; therefore, considering weather-related factors in the analysis of the mortality rates might introduce a more relevant explanation of the disease's progression. In this work, we provide an explainable analysis of the global risk factors for COVID-19 mortality on a national scale, considering dietary habits fused with data on past comorbidity prevalence and environmental factors such as seasonally averaged temperature geolocation, economic and development indices, undernourished and obesity rates. The innovation in this paper lies in the explainability of the obtained results and is equally essential in the data fusion methods and the broad context considered in the analysis. Apart from a country's age and gender distribution, which has already been proven to influence COVID-19 mortality rates, our empirical analysis shows that countries with imbalanced dietary habits generally tend to have higher COVID-19 mortality predictions. Ultimately, we show that the fusion of the dietary data set with the geo-economic variables provides more accurate modeling of the country-wise COVID-19 mortality rates with respect to considering only dietary habits, proving the hypothesis that fusing factors from different contexts contribute to a better descriptive analysis of the COVID-19 mortality rates.

Effects of the built environment and human factors on the spread of COVID-19: A systematic literature review

Soon after its emergence, COVID-19 became a global problem. While different types of vaccines and treatments are now available, still non-pharmacological policies play a critical role in managing the pandemic. The literature is enriched enough to provide comprehensive, practical, and scientific insights to better deal with the pandemic. This research aims to find out how the built environment and human factors have affected the transmission of COVID-19 on different scales, including country, state, county, city, and urban district. This is done through a systematic literature review of papers indexed on the Web of Science and Scopus. Initially, these databases returned 4264 papers, and after different stages of screening, we found 166 relevant papers and reviewed them. The empirical papers that had at least one case study and analyzed the effects of at least one built environment factor on the spread of COVID-19 were selected. Results showed that the driving forces can be divided into seven main categories: density, land use, transportation and mobility, housing conditions, demographic factors, socio-economic factors, and health-related factors. We found that among other things, overcrowding, public transport use, proximity to public spaces, the share of health and services workers, levels of poverty, and the share of minorities and vulnerable populations are major predictors of the spread of the pandemic. As the most studied factor, density was associated with mixed results on different scales, but about 58 % of the papers reported that it is linked with a higher number of cases. This study provides insights for policymakers and academics to better understand the dynamic roles of the non-pharmacological driving forces of COVID-19 at different levels.

Analysis of Genetic Variants Associated with COVID-19 Outcome Highlights Different Distributions among Populations

The clinical spectrum of SARS-CoV-2 infection ranges from asymptomatic status to mild infections, to severe disease and death. In this context, the identification of specific susceptibility factors is crucial to detect people at the higher risk of severe disease and improve the outcome of COVID-19 treatment. Several studies identified genetic variants conferring higher risk of SARS-CoV-2 infection and COVID-19 severity. The present study explored their genetic distribution among different populations (AFR, EAS, EUR and SAS). As a result, the obtained data support the existence of a genetic basis for the observed variability among populations, in terms of SARS-CoV-2 infection and disease outcomes. The comparison of ORs distribution for genetic risk of infection as well as for disease outcome shows that each population presents its own characteristics. These data suggest that each country could benefit from a population-wide risk assessment, aimed to personalize the national vaccine programs and the preventative measures as well as the allocation of resources and the access to proper therapeutic interventions. Moreover, the host genetics should be further investigated in order to realize personalized medicine protocols tailored to improve the management of patients suffering from COVID-19.

Comorbidities, sociodemographic factors, and determinants of health on COVID-19 fatalities in the United States

Previous studies have evaluated comorbidities and sociodemographic factors individually or by type but not comprehensively. This study aims to analyze the influence of a wide variety of factors in a single study to better understand the big picture of their effects on case-fatalities. This cross-sectional study used county-level comorbidities, social determinants of health such as income and race, measures of preventive healthcare, age, education level, average household size, population density, and political voting patterns were all evaluated on a national and regional basis. Analysis was performed through Generalized Additive Models and adjusted by the COVID-19 Community Vulnerability Index (CCVI). Effect estimates of COVID-19 fatality rates for risk factors such as comorbidities, sociodemographic factors and determinant of health. Factors associated with reducing COVID-19 fatality rates were mostly sociodemographic factors such as age, education and income, and preventive health measures. Obesity, minimal leisurely activity, binge drinking, and higher rates of individuals taking high blood pressure medication were associated with increased case fatality rate in a county. Political leaning influenced case case-fatality rates. Regional trends showed contrasting effects where larger household size was protective in the Midwest, yet harmful in Northeast. Notably, higher rates of respiratory comorbidities such as asthma and chronic obstructive pulmonary disease (COPD) diagnosis were associated with reduced case-fatality rates in the Northeast. Increased rates of chronic kidney disease (CKD) within counties were often the strongest predictor of increased case-fatality rates for several regions. Our findings highlight the importance of considering the full context when evaluating contributing factors to case-fatality rates. The spectrum of factors identified in this study must be analyzed in the context of one another and not in isolation.

Excess Mortality during the COVID-19 Pandemic in Cities of Chile: Magnitude, Inequalities, and Urban Determinants

We estimated excess mortality in Chilean cities during the COVID-19 pandemic and its association with city-level factors. We used mortality, and social and built environment data from the SALURBAL study for 21 Chilean cities, composed of 81 municipalities or "comunas", grouped in 4 macroregions. We estimated excess mortality by comparing deaths from January 2020 up to June 2021 vs 2016-2019, using a generalized additive model. We estimated a total of 21,699 (95%CI 21,693 to 21,704) excess deaths across the 21 cities. Overall relative excess mortality was highest in the Metropolitan (Santiago) and the North regions (28.9% and 22.2%, respectively), followed by the South and Center regions (17.6% and 14.1%). At the city-level, the highest relative excess mortality was found in the Northern cities of Calama and Iquique (around 40%). Cities with higher residential overcrowding had higher excess mortality. In Santiago, capital of Chile, municipalities with higher educational attainment had lower relative excess mortality. These results provide insight into the heterogeneous impact of COVID-19 in Chile, which has served as a magnifier of preexisting urban health inequalities, exhibiting different impacts between and within cities. Delving into these findings could help prioritize strategies addressed to prevent deaths in more vulnerable communities.

Socio-cultural Correlates of the COVID-19 Outcomes

While basically all countries have been hit by the COVID-19 pandemic, the impact has varied in large degrees among countries. In the present study, national differences in six COVID-19 indicators (COVID-19 deaths per capita, excess mortality, change in GDP per capita, vaccination rate, stringency index, and overall impact of the pandemic) were studied in relation to socio-economic and Hofstede's cultural dimensions by using the latest data available. The results differed to some degree from the studies conducted in the earlier stage of the pandemic. COVID-19 deaths per capita were predicted by Uncertainty Avoidance (UA) and Indulgence (IVR); excess mortality by UA; the impact of pandemics by Power Distance (PDI), Long-term Orientation (LTOWS) and IVR; change in GDP per capita by PDI; vaccination rate by Individualism and UA; and Stringency Index by LTOWS. In addition to further clarifying the role of cultural dimensions in the pandemic, three conclusions can be drawn. First, the pandemic reached different countries at different times, which is reflected in the results. The conclusion about the role of socio-economic and cultural factors can be drawn only after the pandemic. Second, cultural dimensions were related to COVID-19 measures only when socio-economic indicators were not considered but lost their significance when socio-economic variables were entered into the models. Cultural dimensions influence the outcome variables via socio-economic factors. Third, earlier studies have focused mainly on COVID-19 deaths. The impact of the COVID-19 pandemic is a complex phenomenon and cannot be reduced to the death rate.

Analysis of potential risk factors associated with COVID-19 and hospitalization

Coronavirus disease 2019 (COVID-19) was found to cause complications in certain groups of people, leading to hospitalization. Several factors have been linked to this, such as gender, age, comorbidity, and race. Understanding the precise reasons for the COVID-19-induced complications might help in designing strategies to minimize hospitalization. A retrospective, cross-sectional observational study was conducted for patients in a COVID-19-designated specialty hospital after obtaining ethical clearance. Patients' demographic and clinical characteristics, such as age, gender, race, vaccinated status, complications, comorbidities, and medications, were retrieved from the hospital medical database. The data were statistically analyzed to determine the association between the predictors and the outcomes of COVID-19. An odds ratio (both unadjusted and adjusted) analysis was carried out to determine the risk factors for hospitalization [non-intensive care (non-ICU) and intensive care (ICU)] due to COVID-19. The data from the study indicated that the majority of patients hospitalized due to COVID-19 were male (>55%), aged > 60 years (>40%), married (>80%), and unvaccinated (>71%). The common symptoms, complications, comorbidities, and medications were fever, pneumonia, hypertension, and prednisolone, respectively. Male gender, patients older than 60 years, unemployed, unvaccinated, complicated, and comorbid patients had an odds ratio of more than 2 and were found to be significantly (p < 0.05) higher in ICU admission. In addition, administration of prednisolone and remdesivir was found to significantly reduce (p < 0.05) the odds ratio in ICU patients. The analysis of the data suggested that male gender, age above 60 years, and unvaccinated with comorbidities increased the complications and resulted in hospitalization, including ICU admission. Hypertension and type 2 diabetes associated with obesity as metabolic syndrome could be considered one of the major risk factors. Preventive strategies need to be directed toward these risk factors to reduce the complications, as well as hospitalization to defeat the COVID-19 pandemic.

Predicting COVID-19 Infections in Eswatini Using the Maximum Likelihood Estimation Method

COVID-19 country spikes have been reported at varying temporal scales as a result of differences in the disease-driving factors. Factors affecting case load and mortality rates have varied between countries and regions. We investigated the association between socio-economic, weather, demographic and health variables with the reported cases of COVID-19 in Eswatini using the maximum likelihood estimation method for count data. A generalized Poisson regression (GPR) model was fitted with the data comprising 15 covariates to predict COVID-19 risk in the whole of Eswatini. The results show that the variables that were key determinants in the spread of the disease were those that included the proportion of elderly above 55 years at 98% (95% CI: 97–99%) and the proportion of youth below the age of 35 years at 8% (95% CI: 1.7–38%) with a pseudo R-square of 0.72. However, in the early phase of the virus when cases were fewer, results from the Poisson regression showed that household size, household density and poverty index were associated with reported COVID-19 cases in the country. We then produced a disease-risk map of predicted COVID-19 in Eswatini using variables that were selected by the regression model at a 5% significance level. The map could be used by the country to plan and prioritize health interventions against COVID-19. The identified areas of high risk may be further investigated to find out the risk amplifiers and assess what could be done to prevent them.

Insights for Alzheimer's disease pharmacotherapy and current clinical trials

Over the years, the scientific community has sought improvements in the life quality of patients diagnosed with Alzheimer's disease (AD). Synaptic loss and neuronal death observed in the regions responsible for cognitive functions represent an irreversible progressive disease that is clinically characterized by impaired cognitive and functional abilities, along with behavioral symptoms. Currently, image and body fluid biomarkers can provide early dementia diagnostic, being it the best way to slow the disease's progression. The first signs of AD development are still complex, the existence of individual genetic and phenotypic characteristics about the disease makes it difficult to standardize studies on the subject. The answer seems to be related between Aβ and tau proteins. Aβ deposition in the medial parietal cortex appears to be the initial stage of AD, but it does not have a strong correlation with neurodegeneration. The strongest link between symptoms occurs with tau aggregation, which antecede Aβ deposits in the medial temporal lobe, however, the protein can be found in cognitively healthy older people. The answer to the question may lie in some catalytic effect between both proteins. Amid so many doubts, Aducanumab was approved, which raised controversies and results intense debate in the scientific field. Abnormal singling of some blood biomarkers produced by adipocytes under high lipogenesis, such as TNFα, leptin, and interleukin-6, demonstrate to be linked to neuroinflammation worsens, diabetes, and also severe cases of COVID-19, howsoever, under higher lipolysis, seem to have therapeutic anti-inflammatory effects in the brain, which has increasingly contributed to the understanding of AD. In addition, the relationship of severe clinical complications caused by Sars-CoV-2 viral infection and AD, go beyond the term "risk group" and may be related to the development of dementia long-term. Thus, this review summarized the current emerging pharmacotherapies, alternative treatments, and nanotechnology applied in clinical trials, discussing relevant points that may contribute to a more accurate look.

How different COVID-19 recovery paths affect human health, environmental sustainability, and food affordability: a modelling study

BACKGROUND

The COVID-19 pandemic arrived at a time of faltering global poverty reduction and increasing levels of diet-related diseases, both of which have a strong link to poor outcomes for those with COVID-19. Governments responded to the pandemic by placing unprecedented restrictions on internal and external movements, which have resulted in an economic contraction. In response to the economic shock, G20 governments have committed to providing US$14 trillion stimuli to support economic recovery. We aimed to assess the impact of different COVID-19 recovery paths on human health, environmental sustainability, and food sustainability.

METHODS

We used LandSyMM, a global gridded land use change model, to analyse the impact of recovery paths from COVID-19. The paths were illustrated by four scenarios that represent different pandemic severities (including a single or recurrent pandemic) and alternate modes of recovery, including a transition of food demand towards healthier diets that result in changes to the food system: (1) solidarity and celery, (2) nothing new, (3) fries and fragmentation, and (4) best laid plans. For each scenario, we modelled the economic shocks of the pandemic and the impact of policy measures to promote healthier diets in the years after the COVID-19 pandemic, including the supply of and demand for food, environmental outcomes, and human health outcomes. The four scenarios use established future population growth and economic development projections derived from the Shared Socioeconomic Pathways 2. We quantified the outcomes from more societally cooperative pandemic responses that result in reduced trade barriers and improved technological development against less cooperative responses.

FINDINGS

Repeated pandemic shocks (the fries and fragmentation and best laid plans scenarios) reduce the ability of the lowest income countries to ensure food security. A post-pandemic recovery that includes dietary transition towards the consumption of less meat and more fruits and vegetables (the solidarity and celery scenario) could prevent 2583 premature deaths per million in 2060, whereas recovery paths that are focused on economic recovery (the fries and fragmentation scenario) could trigger an additional 778 deaths per million in 2060. The transition of dietary preferences towards healthier diets (the solidarity and celery scenario) also reduces nitrogen fertiliser use by 40 million tonnes and irrigation water by 400 km³ compared with no dietary change in 2060 (the nothing new scenario). Finally, the scenario with dietary transition increases the affordability of the average diet.

INTERPRETATION

The economic impact of the COVID-19 pandemic is most visible in low-income countries, where a reduction in growth projections makes a greater difference to the affordability of a basic diet. A change in dietary preferences is most impactful in reducing mortality and the burden of disease when income levels are high. At lower income, a transition towards lower meat consumption reduces undernourishment and diet-related mortality.

FUNDING

The Global Food Security's Resilience of the UK Food System Programme project, with support from the Biotechnology and Biological Sciences Research Council, Economic and Social Research Council, Natural Environment Research Council, and the Scottish Government.

Does educational stress mediate the relationship between intolerance of uncertainty and academic life satisfaction in teenagers during the COVID-19 pandemic?

The present study aims to investigate the mediator role of educational stress in the relationship between intolerance to uncertainty and academic life satisfaction among teenagers. The sample consisted of 257 female and 202 male high school students with an average age of 16.03 (SD = 1.21) continuing their education in the spring semester of the 2020-2021 academic year in Turkey. The data were collected via an online survey. Analyses revealed that intolerance of uncertainty directly and indirectly via educational stress affects the academic life satisfaction of teenagers. Educational stress partially mediates the relationship. It was also found that the full mediation model has a good fit with the data. The academic life satisfaction of teenagers was harmed by their tendencies in tolerating the uncertainties they have been facing during the COVID-19 pandemic and elevated levels of educational stress.

Contagious COVID-19 policies: Policy diffusion during times of crisis

The COVID-19 crisis demanded rapid, widespread policy action. In response, nations turned to different forms of social distancing policies to reduce the spread of the virus. These policies were implemented globally, proving as contagious as the virus they are meant to prevent. Yet, variation in their implementation invites questions as to how and why countries adopt social distancing policies, and whether the causal mechanisms driving these policy adoptions are based on internal resources and problem conditions or other external factors such as conditions in other countries. We leverage daily changes in international social distancing policies to understand the impacts of problem characteristics, institutional and economic context, and peer effects on social distancing policy adoption. Using fixed-effects models on an international panel of daily data from 2020, we find that peer effects, particularly mimicry of geographic neighbors, political peers, and language agnates drive policy diffusion and shape countries' policy choices.

Mortality due to COVID-19 in Spain and its association with environmental factors and determinants of health

BACKGROUND

The objective of this study was to identify which air pollutants, atmospheric variables and health determinants could influence COVID-19 mortality in Spain. This study used information from 41 of the 52 provinces in Spain (from Feb. 1, to May 31, 2021). Generalized Linear Models (GLM) with Poisson link were carried out for the provinces, using the Rate of Mortality due to COVID-19 (CM) per 1,000,000 inhabitants as dependent variables, and average daily concentrations of PM₁₀ and NO₂ as independent variables. Meteorological variables included maximum daily temperature (Tmax) and average daily absolute humidity (HA). The GLM model controlled for trend, seasonalities and the autoregressive character of the series. Days with lags were established. The relative risk (RR) was calculated by increases of 10 g/m³ in PM₁₀ and NO₂ and by 1 ℃ in the case of Tmax and 1 g/m³ in the case of HA. Later, a linear regression was carried out that included the social determinants of health.

RESULTS

Statistically significant associations were found between PM₁₀, NO₂ and the CM. These associations had a positive value. In the case of temperature and humidity, the associations had a negative value. PM₁₀ being the variable that showed greater association, with the CM followed of NO₂ in the majority of provinces. Anyone of the health determinants considered, could explain the differential geographic behavior.

CONCLUSIONS

The role of PM₁₀ is worth highlighting, as the chemical air pollutant for which there was a greater number of provinces in which it was associated with CM. The role of the meteorological variables-temperature and HA-was much less compared to that of the air pollutants. None of the social determinants we proposed could explain the heterogeneous geographical distribution identified in this study.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1186/s12302-022-00617-z.

Comparative Optimism, Self-Superiority, Egocentric Impact Perception and Health Information Seeking: A COVID-19 Study

We examined perceived self-other differences (self-uniqueness) in appraisals of one’s risk of an infectious disease (COVID-19), one’s adherence to behavioural precautionary measures against the disease, and the impact of these measures on one’s life. We also examined the relationship of self-uniqueness with information seeking and trust in sources of information about the disease. We administered an online survey to a community sample (N = 8696) of Dutch-speaking individuals, mainly in Belgium and The Netherlands, during the first lockdown (late April-Mid June 2020). As a group, participants reported that they were less likely to get infected or infect others or to suffer severe outcomes than average (unrealistic optimism) and that they adhered better than average to behavioural precautionary measures (illusory superiority). Except for participants below 25, who reported that they were affected more than average by these measures (egocentric impact bias), participants also generally reported that they were less affected than average (allocentric impact bias). Individual differences in self-uniqueness were associated with differences in the number of information sources being used and trust on these sources. Higher comparative optimism for infection, self-superiority, and allocentric impact perception were associated with information being sought from fewer sources; higher self-superiority and egocentric impact perception were associated with lower trust. We discuss implications for health communication.

The efficiency of COVID cases to COVID policies: a robust conditional approach

This paper examines the efficiency of four major COVID-19 social distancing policies: (i) shelter-in-place orders (SIPO), (ii) non-essential business closures, (iii) mandatory quarantine for travelers, and (iv) bans on large gatherings. Results suggest that the average US state is highly inefficient in producing the fraction of the population that does not have COVID-19 without social distancing policies put in place. We find that having any of the four major social distancing policies increases conditional efficiency by 9.7 (9.5) percentage points in the first 100 days (full sample). This corresponds to 57 (172) fewer total COVID-19 cases per 100,000 population in the first 100 days (full sample). We also find that population density accounts for a majority of unconditional state inefficiency. Evidence suggests considerable heterogeneity in conditional efficiency improvement, indicating that no uniform national social distancing policy would have been more effective; more effective strategies would have been to target more densely populated areas. Conditional efficiency regressions suggest that bans on large gatherings were the most effective policies, with SIPOs and non-essential business closures having smaller impacts. States that implemented social distancing policies except mandatory quarantine for traveler policies were highly effective for the first 100 days, but had less effectiveness over the full sample. There is also preliminary evidence that premature revocations of social distancing policies reduced conditional efficiency, leading to COVID-19 case spikes.

COVID-19 and challenges of assistive technology use in Iran

PURPOSE

Assistive technology users may encounter challenges and inequality in having an access to health information and care during the emergency or in a crisis time. This issue seems to be understudied in most developing countries. The aim of this study was to explore the challenges faced by Iranian people with disabilities faced during the COVID-19 pandemic as far as the use of assistive technology is concerned.

METHOD

A thematic analysis approach was employed to collect and analyse the data. We interviewed 10, 12 and 20 participants with physical, visual, and hearing disability, respectively during the pandemic between May to July 2020. A six-step thematic analysis method was used to identify categories and main themes.

RESULTS

The results revealed that people with disability were faced with some challenges in accessing information or receiving it on time during the emergency time. The lack of clear information may increase uncertainty about providing, using or maintaining assistive products. With no clear information or instruction, increased fear of infection, as well as the lack of necessary infrastructure for using available online applications, people with a disability had to rely more on others and seemed to feel disempowered.

CONCLUSION

Assistive technology (AT) users may not receive enough care and attention during health crisis, nor may be included in crisis management programs. Actions to create preparedness plans to meet the needs of AT users in possible future crisis seem to be necessary.IMPLICATIONS FOR REHABILITATIONAssistive technology users' voice and needs should be given priority in crisis management programs.Web accessibility barriers and information accessibility challenges need more research attention in order to create effective and timely information dissemination programs.There seems to be a research gap about AT users during health crisis, and more research in this area is needed.

Safety and Feasibility of Lung Cancer Surgery under the COVID-19 Circumstance

Simple Summary

The global coronavirus disease 2019 (COVID-19) pandemic has drastically changed the current practice of medicine worldwide. As more clinical data is collected and processed, we are beginning to have an understanding of which patients are more at risk for severe complications of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. Preliminary data has shown that patients with lung cancer are disproportionally affected by the current COVID-19 pandemic. Furthermore, studies have shown that lung cancer patients are also significantly more likely to be admitted to the ICU and need mechanical ventilation. A specific subset of patients that are even more at risk for severe COVID-19 are those that require lung cancer surgery. To minimize the risk of SARS-CoV-2 infections in patients undergoing surgery, new treatment guidelines and preventive measures are necessary. In this review, we summarize the latest evidence regarding recommendations for patients undergoing lung cancer surgery in the COVID-19 era.

Abstract

The current coronavirus disease 2019 (COVID-19) pandemic has forced healthcare providers worldwide to adapt their practices. Our understanding of the effects of COVID-19 has increased exponentially since the beginning of the pandemic. Data from large-scale, international registries has provided more insight regarding risk factors for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and has allowed us to delineate specific subgroups of patients that have higher risks for severe complications. One particular subset of patients that have significantly higher risks of SARS-CoV-2 infection with higher morbidity and mortality rates are those that require surgical treatment for lung cancer. Earlier studies have shown that COVID-19 infections in patients that underwent lung cancer surgery is associated with higher rates of respiratory failure and mortality. However, deferral of cancer treatments is associated with increased mortality as well. This creates difficult situations in which healthcare providers are forced to weigh the benefits of surgical treatment against the possibility of SARS-CoV-2 infections. A number of oncological and surgical organizations have proposed treatment guidelines and recommendations for patients planned for lung cancer surgery. In this review, we summarize the latest data and recommendations for patients undergoing lung cancer surgery in the COVID-19 circumstance.

Low performance of a SARS-CoV-2 point-of-care lateral flow immunoassay in symptomatic children during the pandemic

OBJECTIVE

to evaluate the accuracy of an antibody point-of-care lateral flow immunoassay (LFI - Wondfo Biotech Co., Guangzhou, China) in a pediatric population.

METHODS

children and adolescents (2 months to 18 years) with signs and symptoms suggestive of acute SARS-CoV-2 infection were prospectively investigated with nasopharyngeal RT-PCR and LFI at the emergency room. RT-PCR was performed at baseline, and LFI at the same time or scheduled for those with less than 7 days of the clinical picture. Overall accuracy, sensitivity and specificity were assessed, as well as according to the onset of symptoms (7-13 or ≥14 days) at the time of the LFI test.

RESULTS

In 175 children included, RT-PCR and LFI were positive in 51 (29.14%) and 36 (20.57%), respectively. The overall sensitivity, specificity, positive and negative predictive value was 70.6% (95%CI 56.2-82.5), 96.8% (95%CI 91.9-99.1), 90.0% (95%CI 77.2-96.0), and 88.9% (95%CI 83.9-92.5), respectively. At 7-13 and ≥14 days after the onset of symptoms, sensitivity was 60.0% (95%CI 26.2-87.8) and 73.2% (95%CI 57.1-85.8) and specificity was 97.9% (95%CI 88.7-99.9) and 96.1% (95%CI 89.0-99.2), respectively.

CONCLUSION

Despite its high specificity, in the present study the sensitivity of LFI in children was lower (around 70%) than most reports in adults. Although a positive result is informative, a negative LFI test cannot rule out COVID-19 in children.

Obstructive lung diseases burden and COVID-19 in developing countries: a perspective

PURPOSE OF REVIEW

Asthma and chronic obstructive pulmonary disease (COPD) are widely prevalent disorders, and important contributors to morbidity and mortality, in both developing and developed countries. It is conjectured that these obstructive lung diseases may have had more deleterious effects in developing nations during the 2019 coronavirus disease (COVID-19) pandemic. We provide an evidence-based perspective on the relationship between asthma/COPD prevalence and COVID-19 burden, and the impact of comorbid asthma/COPD on selected COVID-19 outcomes and healthcare utilization, with special reference to developing countries.

RECENT FINDINGS

Developing countries with higher COPD (but not asthma) prevalence appear to have higher COVID-19 related mortality. Patients with asthma (but not COPD) in developing countries may be less likely to acquire COVID-19. Published literature suggests that the overall impact of comorbid asthma or COPD on adverse COVID-19 outcomes may be broadly similar between developed and developing nations.

SUMMARY

There is paucity of information on interaction between asthma/COPD and COVID-19 in developing countries. Limited data suggest minor differences between developed and developing nations. In view of inadequacies in healthcare preparedness and delivery in several developing countries, there is a need to generate quality evidence to assess impact of obstructive lung diseases and COVID-19 on each other.

Suicidal Ideation during the COVID-19 Pandemic among A Large-Scale Iranian Sample: The Roles of Generalized Trust, Insomnia, and Fear of COVID-19

The novel 2019 coronavirus disease (COVID-19) is still not under control globally. The pandemic has caused mental health issues among many different cohorts and suicidal ideation in relation to COVID-19 has been reported in a number of recent studies. Therefore, the present study proposed a model to explain the associations between generalized trust, fear of COVID-19, insomnia, and suicidal ideation during the COVID-19 pandemic among a large-scale Iranian sample. Utilizing cluster sampling with multistage stratification, residents from Qazvin province in Iran were invited to participate in the present study. Adults aged over 18 years (n = 10,843; 6751 [62.3%] females) completed ‘paper–and-pencil’ questionnaires with the assistance of a trained research assistant. Structural equation modeling (SEM) was applied to understand the associations between generalized trust, fear of COVID-19, insomnia, and suicidal ideation. Slightly over one-fifth of the participants (n = 2252; 20.8%) reported suicidal ideation. Moreover, the SEM results indicated that generalized trust was indirectly associated with suicidal ideation via fear of COVID-19 and insomnia. Furthermore, generalized trust was not directly associated with suicidal ideation. The proposed model was invariant across gender groups, age groups, and participants residing in different areas (i.e., urban vs. rural). Generalized trust might reduce individuals’ suicidal ideation during the COVID-19 pandemic period via reduced levels of fear of COVID-19 and insomnia. Healthcare providers and policymakers may want to assist individuals in developing their generalized trust, reducing fear of COVID-19, and improving insomnia problems to avoid possible suicidal behaviors.

The impact of coronavirus disease 2019 (COVID-19) on older adults with an intellectual disability during the first wave of the pandemic in Ireland

Background: People with intellectual disability have increased risk of exposure to and adverse outcomes from coronavirus disease 2019 (COVID-19).They also face challenges to mental health and well-being from COVID-19-related social restrictions and service closures. Methods: Data from a supplemental COVID-19 survey from the Intellectual Disability Supplement to the Irish Longitudinal Study on Ageing (IDS-TILDA) (n=710) was used to assess outcomes from the first infection wave of COVID-19 among adults with intellectual disability aged 40+ years in Ireland. Data was gathered on testing, for symptoms and outcomes; procedures to manage COVID-19; and both stress/anxiety and positive experiences during the pandemic. Demographic and health-related data from the main IDS-TILDA dataset was included in analyses. Results: High rates were identified of health conditions associated with poorer COVID-19 outcomes, including overweight/obesity (66.6%, n=365), high cholesterol (38.6%, n=274) and cardiovascular disease (33.7%, n=239). Over half (53.5%, n=380) reported emotional, nervous or psychiatric disorders. Almost two-thirds (62.4%, n=443) were tested for COVID-19, with 10% (n=71) reporting symptoms and 2.5% (n=11) testing positive. There were no instances of COVID-19 related mortality. Common symptoms included fatigue, fever, and cough. Some participants (7.8%, n=55) moved from their usual home, most often to isolate (n=31) or relocate to a family home (n=11). Three-quarters (78.7%) of those who were symptomatic or who tested positive had plans to manage self-isolation and two-thirds were able to comply with guidelines. Over half (55%, n=383) reported some COVID-19 related stress/anxiety; and a similar proportion reported positive aspects during this period (58%, n=381). Conclusions: Our data suggests that people with intellectual disability avoided the worst impacts of COVID-19 during the first infection wave in Ireland. Nevertheless, participants' health profiles suggest that this population remains at high risk for adverse infection outcomes. Repeated measures are needed to track health and well-being outcomes across multiple infection waves.

A global country-level analysis of the relationship between obesity and COVID-19 cases and mortality

AIM

To assess the association of country-level obesity prevalence with COVID-19 case and mortality rates, to evaluate the impact of obesity prevalence on worldwide variation.

METHODS

Data on COVID-19 prevalence and mortality, country-specific governmental actions, socioeconomic, demographic, and healthcare capacity factors were extracted from publicly available sources. Multivariable negative binomial regression was used to assess the independent association of obesity with COVID-19 case and mortality rates.

RESULTS

Across 168 countries for which data were available, higher obesity prevalence was associated with increased COVID-19 mortality and prevalence rates. For every 1% increase in obesity prevalence, the mortality rate was increased by 8.3% (incidence rate ratio [IRR] 1.083, 95% confidence interval [CI] 1.048-1.119; P < 0.001) and the case rate was higher by 6.6% (IRR 1.066, 95% CI 1.035-1.099; P < 0.001). Additionally, higher median population age, greater female ratio, higher Human Development Index (HDI), lower population density, and lower hospital bed availability were all significantly associated with higher COVID-19 mortality rate. In addition, stricter governmental actions, higher HDI and lower mean annual temperature were significantly associated with higher COVID-19 case rate.

CONCLUSION

These findings demonstrate that obesity prevalence is a significant and potentially modifiable risk factor of increased COVID-19 national caseload and mortality. Future research to study whether weight loss improves COVID-19 outcomes is urgently required.

A Rapid Realist Review of Effective Mental Health Interventions for Individuals with Chronic Physical Health Conditions during the COVID-19 Pandemic Using a Systems-Level Mental Health Promotion Framework

The 2020 global outbreak of COVID-19 exposed and heightened threats to mental health across societies. Research has indicated that individuals with chronic physical health conditions are at high risk for suffering from severe COVID-19 illness and from the adverse consequences of public health responses to COVID-19, such as social isolation. This paper reports on the findings of a rapid realist review conducted alongside a scoping review to explore contextual factors and underlying mechanisms or drivers associated with effective mental health interventions within and across macro-meso-micro systems levels for individuals with chronic physical health conditions. This rapid realist review extracted 14 qualified studies across 11 countries and identified four key mechanisms from COVID-19 literature-trust, social connectedness, accountability, and resilience. These mechanisms are discussed in relation to contextual factors and outcomes reported in the COVID literature. Realist reviews include iterative searches to refine their program theories and context-mechanism-outcome explanations. A purposive search of pre-COVID realist reviews on the study topic was undertaken, looking for evidence of the robustness of these mechanisms. There were differences in some of the pre-COVID mechanisms due to contextual factors. Importantly, an additional mechanism-power-sharing-was highlighted in the pre-COVID literature, but absent in the COVID literature. Pre-existing realist reviews were used to identify potential substantive theories and models associated with key mechanisms. Based on the overall findings, implications are provided for mental health promotion policy, practice, and research.

Incidence and effects of deep vein thrombosis on the outcome of patients with coronavirus disease 2019 infection

Background

Deep vein thrombosis (DVT) has been reported to occur at different rates in patients with coronavirus disease 2019 (COVID-19). Limited data exist regarding comparisons with non–COVID-19 patients with similar characteristics. Our objective was to compare the rates of DVT in patients with and without COVID-19 and to determine the effect of DVT on the outcomes.

Methods

We performed a retrospective, observational cohort study at a single-institution, level 1 trauma center comparing patients with and without COVID-19. The 573 non–COVID-19 patients (age, 61 ± 17 years; 44.9% male) had been treated from March 20, 2019 to June 30, 2019, and the 213 COVID-19 patients (age, 61 ± 16 years; 61.0% male) had been treated during the same interval in 2020. Standard prophylactic anticoagulation therapy consisted of 5000 U of heparin three times daily for the medical patients without COVID-19 who were not in the intensive care unit (ICU). The ICU, surgical, and trauma patients without COVID-19 had received 40 mg of enoxaparin daily (not adjusted to weight). The patients with COVID-19 had also received enoxaparin 40 mg daily (also not adjusted to weight), regardless of whether treated in the ICU. The two primary outcomes were the rate of deep vein thrombosis (DVT) in the COVID-19 group vs that in the historic control and the effect of DVT on mortality. The subgroup analyses included patients with adult respiratory distress syndrome (ARDS), pulmonary embolism (PE), and intensive care unit patients (ICU).

Results

The rate of DVT and PE for the non–COVID-19 patients was 12.4% (71 of 573) and 3.3% (19 of 573) compared with 33.8% (72 of 213) and 7.0% (15 of 213) for the COVID-19 patients, respectively. Unprovoked PE had developed in 10 of 15 COVID-19 patients (66.7%) compared with 8 of 497 non–COVID-19 patients (1.6%). The 60 COVID-19 patients with ARDS had had an incidence of DVT of 46.7% (n = 28). In contrast, the incidence of DVT for the 153 non–COVID-19 patients with ARDS was 28.8% (n = 44; P = .01). The COVID-19 patients requiring the ICU had had an increased rate of DVT (39 of 90; 43.3%) compared with the non–COVID-19 patients (33 of 123; 33.3%; P = .01). The risk factors for mortality included age, DVT, multiple organ failure syndrome, and prolonged ventilatory support with the following odd ratios: 1.030 (95% confidence interval [CI], 1.002-1.058), 2.847 (95% CI, 1.356-5.5979), 4.438 (95% CI, 1.973-9.985), and 5.321 (95% CI, 1.973-14.082), respectively.

Conclusions

The incidence of DVT for COVID-19 patients receiving standard-dose prophylactic anticoagulation that was not weight adjusted was high, especially for ICU patients. DVT is one of the factors contributing to increased mortality. These results suggest a reevaluation is necessary of the present standard-dose thromboprophylaxis for patients with COVID-19.

Predictors of COVID-19 epidemics in countries of the World Health Organization African Region

Countries of the World Health Organization (WHO) African Region have experienced a wide range of coronavirus disease 2019 (COVID-19) epidemics. This study aimed to identify predictors of the timing of the first COVID-19 case and the per capita mortality in WHO African Region countries during the first and second pandemic waves and to test for associations with the preparedness of health systems and government pandemic responses. Using a region-wide, country-based observational study, we found that the first case was detected earlier in countries with more urban populations, higher international connectivity and greater COVID-19 test capacity but later in island nations. Predictors of a high first wave per capita mortality rate included a more urban population, higher pre-pandemic international connectivity and a higher prevalence of HIV. Countries rated as better prepared and having more resilient health systems were worst affected by the disease, the imposition of restrictions or both, making any benefit of more stringent countermeasures difficult to detect. Predictors for the second wave were similar to the first. Second wave per capita mortality could be predicted from that of the first wave. The COVID-19 pandemic highlights unanticipated vulnerabilities to infectious disease in Africa that should be taken into account in future pandemic preparedness planning.

Job loss and lower healthcare utilisation due to COVID-19 among older adults across 27 European countries

BACKGROUND

Older adults are at greater risk for becoming severely ill from COVID-19; however, the impact of the pandemic on their economic activity and non-COVID-19-related healthcare utilisation is not well understood. The aim of this study was to examine the prevalence and predictors of COVID-19-related unemployment and healthcare utilisation in a sample of older adults across 27 European countries.

METHODS

We used data from the Survey of Health, Ageing and Retirement in Europe COVID-19 Survey, collected between June and August 2020. Participants (n=52 061) reported whether they lost a job, forwent medical treatment and whether their appointment was postponed due to COVID-19. Three-level models were estimated for each outcome to test the effects of individual, household and country-level characteristics.

RESULTS

The mean prevalence of reported job loss, and forgone and postponed medical care was 19%, 12% and 26%, respectively. Job loss was associated with female sex, lower education and household income, and older age in women. For example, the OR of job loss, comparing primary versus tertiary (college) education, was 1.89 (95% CI 1.59 to 2.26). Forgone and postponed medical care was associated with older age in men, female sex and higher education. At the country level, postponed medical care was associated with more stringent governmental anti-COVID measures.

CONCLUSION

Job loss and lower healthcare utilisation for non-COVID-19-related reasons were common among older adults and were associated with several sociodemographic characteristics. Job loss appeared to disproportionally affect already economically vulnerable individuals, raising concerns about the exacerbation of social inequalities.

Prognostic significance of temporal changes of lipid profile in COVID-19 patients

Background

COVID-19 is a multisystemic disease that affects many organs and has metabolic effects.

Aims

This study aims to investigate the effect of the temporal changes of lipid levels on the prognosis during the course of the disease.

Study design

Retrospective cross-sectional study.

Methods

For this single-center study, data of patients who were treated for COVID-19 were collected. Fasting lipid parameters including total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglyceride (TG) levels were collected within 24 h of hospitalization. For investigation of temoral changes in lipid parameters, the results of the same parameters in the one-year period before COVID-19 were collected from medical records. A total number of 324 eligible COVID-19 patients were included in this study. The association of changes of lipid parameters with COVID-19 symptom severity and in-hospital mortality were investigated.

Results

The mean age of the severe group (n = 139) was 65.4 ± 15.5 years, and 60% were male. TC, LDL-C and HDL-C levels were significantly lower compared to pre-COVID measurements in the study population. Multiple linear regression analysis determined age, acute kidney injury, hs-Troponin, D-dimer, temporal changes in TC, and TG levels were determined as independent predictors for the development of COVID-19 mortality.

Conclusion

Our findings showed that temporal changes in lipid parameters before and after COVID-19 may be associated with mortality and in-hospital adverse outcomes.