Individual and community-level risk for COVID-19 mortality in the United States

Risk Groups and Psychosocial Factors for the Pandemic (COVID-19)

COVID-19, which started in 2019 and affected the whole world, has affected everyone at different intensities and in different ways. COVID-19, which is considered a pandemic, has turned into a major public health problem in terms of its consequences and has affected people biopsychosocially. However, people in risk groups may be affected more. This study was conducted to reveal the risk groups for the pandemic and to determine the psychosocial factors. Data were collected online using the relational screening model and snowball sampling methods. A Sociodemographic Information Form, COVID-19 Pandemic Psychosocial Impact Scale (C19-PPIS), and International Personality Inventory Short Form (IPISV) were sent online to 826 participants. Data were analysed using an independent sample t-test, a one-way ANOVA test, and the Pearson Correlation analysis. According to the results, young adults (X¯ = 2.77), women (X¯ = 2.79), singles (X¯ = 2.78), those who are unemployed (X¯ = 2.89), and those who had to change their home or city due to the pandemic (X¯ = 2.89) were more affected by the pandemic. Psychological support was the support system needed the most during the pandemic (X¯ = 3.04). In addition, a negative relationship was found between an extroverted personality and psychosocial impact from the pandemic (r = -0.148 and p < 0.01). A positive relationship was found between introversion (r = 0.183 and p < 0.01), agreeableness (r = 0.078 and p < 0.05), hostility (r = 0.094 and p < 0.01), disorganisation (r = 0.237 and p < 0.01), openness to development (r = 0.80 and p < 0.05), closed off to development (r = 0.070 and p < 0.05), emotional instability personality (r = 0.498 and p < 0.01), and psychosocial impact from the pandemic. This study has revealed important results regarding who has been most affected psychosocially by COVID-19. It is thought that the results obtained can guide state policies on what should be done in the field of preventive community mental health in another possible epidemic.

Assessing the impact of institutional mistrust on parental endorsement for COVID-19 vaccination among school communities in San Diego County, California

BACKGROUND

Institutional mistrust has weakened COVID-19 mitigation efforts. Assessing to what extent institutional mistrust impacts parental decision making is important in formulating structural efforts for improving future pandemic response. We hypothesized that institutional mistrust is associated with lower parental endorsement for COVID-19 vaccination.

METHODS

We distributed an online survey among parents from schools in areas with high levels of social vulnerability relative to the rest of San Diego County. We defined vaccination endorsement as having a child aged 5 years or older who received at least one COVID-19 vaccine dose or being very likely to vaccinate their child aged 6 months-4 years when eligible. Institutional mistrust reflected the level of confidence in institutions using an aggregate score from 11 to 44. We built a multivariable logistic regression model with potential confounding variables.

FINDINGS

Out of 290 parents in our sample, most were female (87.6%), reported their child as Hispanic/Latino (73.4%), and expressed vaccination endorsement (52.1%). For every one-point increase in mistrust score, there was an 8% reduction in the likelihood of participants endorsing vaccination for their child. Other statistically significant correlates that were positively associated with vaccination endorsement included parent vaccination status, child age, parent age, and Hispanic/Latino ethnicity.

CONCLUSION

Our study further demonstrates how institutional mistrust hinders public response during health emergencies. Our findings also highlight the importance of building confidence in institutions and its downstream effects on pandemic preparedness and public health. One way that institutions can improve their relationship with constituents is through building genuine partnerships with trusted community figures.

Perceived Impact of the COVID-19 Pandemic on Adults with Intellectual and Developmental Disability: A Qualitative Study

The current study examined the impact that the COVID-19 pandemic and resulting restrictions have had on individuals with intellectual and developmental disability. Semi-structured focus groups were conducted to collect data from participants who attended day programming by local community agency. Results indicate that the COVID-19 pandemic and its safety restrictions impacted participants' knowledge of the disease, programming and work, relationships, activities, and emotions in ways that were both similar to and different from other findings in other populations. Implications of these findings for research and practice are discussed.

The fallacy of global comparisons based on per capita measures

Media, social scientists and public health researchers often present comparisons across countries, and policy makers use such comparisons to take evidence-based action. For a meaningful comparison among countries, one often needs to normalize the measure for differences in population size. To address this issue, the first choice is usually to calculate per capita ratios. Such ratios, however, normalize the measure for differences in population size directly only under the highly restrictive assumption of a proportional increase of the measure with population size. Violation of this assumption frequently leads to misleading conclusions. We compare per capita ratios with an approach based on regression, a widely used statistical procedure that eliminates many of the problems with ratios and allows for straightforward data interpretation. It turns out that the per capita measures in three global datasets (gross domestic product, COVID-19-related mortality and CO2 production) systematically overestimate values in countries with small populations, while countries with large populations tend to have misleadingly low per capita ratios owing to the large denominators. Unfortunately, despite their biases, comparisons based on per capita ratios are still ubiquitous, and they are used for influential recommendations by various global institutions. Their continued use can cause significant damage when employed as evidence for policy actions and should therefore be replaced by a more scientifically substantiated and informative method, such as a regression-based approach.

Combination of rRT-PCR and Clinical Features to Predict Coronavirus Disease 2019 for Nosocomial Infection Control

Background

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), immediately became a pandemic. Therefore, nosocomial infection control is necessary to screen for patients with possible COVID-19.

Objective

This study aimed to investigate commonly measured clinical variables to predict COVID-19.

Methods

This cross-sectional study enrolled 1087 patients in the isolation ward of a university hospital. Conferences were organized to differentiate COVID-19 from non-COVID-19 cases, and multiple nucleic acid tests were mandatory when COVID-19 could not be excluded. Multivariate logistic regression models were employed to determine the clinical factors associated with COVID-19 at the time of hospitalization.

Results

Overall, 352 (32.4%) patients were diagnosed with COVID-19. The majority of the non-COVID-19 cases were predominantly caused by bacterial infections. Multivariate analysis indicated that COVID-19 was significantly associated with age, sex, body mass index, lactate dehydrogenase, C-reactive protein, and malignancy.

Conclusion

Some clinical factors are useful to predict patients with COVID-19 among those with symptoms similar to COVID-19. This study suggests that at least two real-time reverse-transcription polymerase chain reactions of SARS-CoV-2 are recommended to exclude COVID-19.

Early indicators of bipolar risk in preschool offspring of parents with bipolar disorder

BACKGROUND

Offspring of parents with bipolar disorder (BD-I/II) are at increased risk to develop the disorder. Previous work indicates that bipolar spectrum disorder (BPSD) is often preceded by mood/anxiety symptoms. In school-age offspring of parents with BD, we previously built a risk calculator to predict BPSD onset, which generates person-level risk scores. Here, we test whether preschool symptoms predict school-age BPSD risk.

METHODS

We assessed 113 offspring of parents with BD 1-3 times during preschool years (2-5 years old) and then approximately every 2 years for a mean of 10.6 years. We used penalized (lasso) regression with linear mixed models to assess relationships between preschool mood, anxiety, and behavioral symptoms (parent-reported) and school-age predictors of BPSD onset (i.e., risk score, subthreshold manic symptoms, and mood lability), adjusting for demographics and parental symptomatology. Finally, we conducted survival analyses to assess associations between preschool symptoms and school-age onset of BPSD and mood disorder.

RESULTS

Of 113 preschool offspring, 33 developed new-onset mood disorder, including 19 with new-onset BPSD. Preschool irritability, sleep problems, and parental factors were lasso-selected predictors of school-age risk scores. After accounting for demographic and parental factors, preschool symptoms were no longer significant. Lasso regressions to predict mood lability and subthreshold manic symptoms yielded similar predictors (irritability, sleep problems, and parental affective lability), but preschool symptoms remained predictive even after adjusting for parental factors (ps < .005). Exploratory analyses indicated that preschool irritability univariately predicted new-onset BPSD (p = .02) and mood disorder (p = .02).

CONCLUSIONS

These results provide initial prospective evidence that, as early as preschool, youth who will develop elevated risk scores, mood lability, and subthreshold manic symptoms are already showing symptomatology; these preschool symptoms also predict new-onset BPSD. While replication of findings in larger samples is warranted, results point to the need for earlier assessment of risk and development of early interventions.

Understanding community level influences on the prevalence of SARS-CoV-2 infection in England: new insights from comparison over time and space

Understanding and monitoring the major influences on SARS-CoV-2 prevalence is essential to inform policy making and devise appropriate packages of non-pharmaceutical interventions. Through evaluating community level influences on the prevalence of SARS-CoV-2 infection and their spatio-temporal variations in England, this study aims to provide some insights into the most important risk parameters. We used spatial clusters developed in Jahanshahi and Jin (2021 Transportation 48, 1329-1359 (doi:10.1007/s11116-020-10098-9)) as geographical areas with distinct land use and travel patterns. We also segmented our data by time periods to control for changes in policies or development of the disease over the course of the pandemic. We then used multivariate linear regression to identify influences driving infections within the clusters and to compare the variations of those between the clusters. Our findings demonstrate the key roles that workplace and commuting modes have had on some of the sections of the working population after accounting for several interrelated influences including mobility and vaccination. We found communities of workers in care homes and warehouses and to a lesser extent textile and ready meal industries and those who rely more on public transport for commuting tend to carry a higher risk of infection across all residential area types and time periods.

Multimorbidity patterns in COVID-19 patients and their relationship with infection severity: MRisk-COVID study

BACKGROUND

Several chronic conditions have been identified as risk factors for severe COVID-19 infection, yet the implications of multimorbidity need to be explored. The objective of this study was to establish multimorbidity clusters from a cohort of COVID-19 patients and assess their relationship with infection severity/mortality.

METHODS

The MRisk-COVID Big Data study included 14 286 COVID-19 patients of the first wave in a Spanish region. The cohort was stratified by age and sex. Multimorbid individuals were subjected to a fuzzy c-means cluster analysis in order to identify multimorbidity clusters within each stratum. Bivariate analyses were performed to assess the relationship between severity/mortality and age, sex, and multimorbidity clusters.

RESULTS

Severe infection was reported in 9.5% (95% CI: 9.0-9.9) of the patients, and death occurred in 3.9% (95% CI: 3.6-4.2). We identified multimorbidity clusters related to severity/mortality in most age groups from 21 to 65 years. In males, the cluster with highest percentage of severity/mortality was Heart-liver-gastrointestinal (81-90 years, 34.1% severity, 29.5% mortality). In females, the clusters with the highest percentage of severity/mortality were Diabetes-cardiovascular (81-95 years, 22.5% severity) and Psychogeriatric (81-95 years, 16.0% mortality).

CONCLUSION

This study characterized several multimorbidity clusters in COVID-19 patients based on sex and age, some of which were found to be associated with higher rates of infection severity/mortality, particularly in younger individuals. Further research is encouraged to ascertain the role of specific multimorbidity patterns on infection prognosis and identify the most vulnerable morbidity profiles in the community.

TRIAL REGISTRATION

NCT04981249. Registered 4 August 2021 (retrospectively registered).

Longitudinal analyses using 18F-Fluorodeoxyglucose positron emission tomography with computed tomography as a measure of COVID-19 severity in the aged, young, and humanized ACE2 SARS-CoV-2 hamster models

This study compared disease progression of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) in three different models of golden hamsters: aged (≈60 weeks old) wild-type (WT), young (6 weeks old) WT, and adult (14-22 weeks old) hamsters expressing the human-angiotensin-converting enzyme 2 (hACE2) receptor. After intranasal (IN) exposure to the SARS-CoV-2 Washington isolate (WA01/2020), 2-deoxy-2-[fluorine-18]fluoro-D-glucose positron emission tomography with computed tomography (18F-FDG PET/CT) was used to monitor disease progression in near real time and animals were euthanized at pre-determined time points to directly compare imaging findings with other disease parameters associated with coronavirus disease 2019 (COVID-19). Consistent with histopathology, 18F-FDG-PET/CT demonstrated that aged WT hamsters exposed to 105 plaque forming units (PFU) developed more severe and protracted pneumonia than young WT hamsters exposed to the same (or lower) dose or hACE2 hamsters exposed to a uniformly lethal dose of virus. Specifically, aged WT hamsters presented with a severe interstitial pneumonia through 8 d post-exposure (PE), while pulmonary regeneration was observed in young WT hamsters at that time. hACE2 hamsters exposed to 100 or 10 PFU virus presented with a minimal to mild hemorrhagic pneumonia but succumbed to SARS-CoV-2-related meningoencephalitis by 6 d PE, suggesting that this model might allow assessment of SARS-CoV-2 infection on the central nervous system (CNS). Our group is the first to use (18F-FDG) PET/CT to differentiate respiratory disease severity ranging from mild to severe in three COVID-19 hamster models. The non-invasive, serial measure of disease progression provided by PET/CT makes it a valuable tool for animal model characterization.

Shared community effects and the non-genetic maternal environment shape cortisol levels in wild chimpanzees

Mechanisms of inheritance remain poorly defined for many fitness-mediating traits, especially in long-lived animals with protracted development. Using 6,123 urinary samples from 170 wild chimpanzees, we examined the contributions of genetics, non-genetic maternal effects, and shared community effects on variation in cortisol levels, an established predictor of survival in long-lived primates. Despite evidence for consistent individual variation in cortisol levels across years, between-group effects were more influential and made an overwhelming contribution to variation in this trait. Focusing on within-group variation, non-genetic maternal effects accounted for 8% of the individual differences in average cortisol levels, significantly more than that attributable to genetic factors, which was indistinguishable from zero. These maternal effects are consistent with a primary role of a shared environment in shaping physiology. For chimpanzees, and perhaps other species with long life histories, community and maternal effects appear more relevant than genetic inheritance in shaping key physiological traits.

COVID-19 infection rate but not severity is associated with availability of greenness in the United States

Human exposure to greenness is associated with COVID-19 prevalence and severity, but most relevant research has focused on the relationships between greenness and COVID-19 infection rates. In contrast, relatively little is known about the associations between greenness and COVID-19 hospitalizations and deaths, which are important for risk assessment, resource allocation, and intervention strategies. Moreover, it is unclear whether greenness could help reduce health inequities by offering more benefits to disadvantaged populations. Here, we estimated the associations between availability of greenness (expressed as population-density-weighted normalized difference vegetation index) and COVID-19 outcomes across the urban-rural continuum gradient in the United States using generalized additive models with a negative binomial distribution. We aggregated individual COVID-19 records at the county level, which includes 3,040 counties for COVID-19 case infection rates, 1,397 counties for case hospitalization rates, and 1,305 counties for case fatality rates. Our area-level ecological study suggests that although availability of greenness shows null relationships with COVID-19 case hospitalization and fatality rates, COVID-19 infection rate is statistically significant and negatively associated with more greenness availability. When performing stratified analyses by different sociodemographic groups, availability of greenness shows stronger negative associations for men than for women, and for adults than for the elderly. This indicates that greenness might have greater health benefits for the former than the latter, and thus has limited effects for ameliorating COVID-19 related inequity. The revealed greenness-COVID-19 links across different space, time and sociodemographic groups provide working hypotheses for the targeted design of nature-based interventions and greening policies to benefit human well-being and reduce health inequity. This has important implications for the post-pandemic recovery and future public health crises.

Migrants' human rights and health protection during the COVID-19 pandemic in the Mediterranean Sea: what we have learnt from direct inspections in two Italian hotspots

This study aims to assess the situation of Italian hotspots for migrant reception during the COVID-19 pandemic, and specifically analyzing the situation of two hotspots located in the Sicily Region (Pozzallo harbor and Lampedusa Island), to identify critical issues. At the same time, we hypothesize solutions to guarantee the respect of human rights and suggest an operational protocol to be applied in similar situations, considering that the migration phenomenon is increasing and involving new geographical areas. Based on data obtained through the site inspections, the facilities of Pozzallo and Lampedusa exceeded their capacity to adequately contain the spread of the SARS-CoV-2 infection. Considering these findings, we suggest a practical workflow summarizing the main actions that should be applied to contain COVID-19, or other infectious disease, spreading in hotspots for migrants. The impact of the COVID-19 pandemic on migrants has received limited attention, although the migration phenomenon did not slow down during the pandemic period. Regarding the risk of spreading infectious diseases such as COVID-19, it is necessary that those countries who are most exposed to migration flows, such as Italy, plan dedicated strategies to minimize the possibility of transmission of SARS-CoV-2, using adequate protocols to monitor the possible insurgence of variants of interest (VOIs) or variants of concern (VOCs). Finally, it is important to state that these suggestions could be applied in any future pandemics.

Possible substance use disorders among first responders during the COVID-19 era: a quasi-experimental study of personal and residential vulnerability

Background: During the COVID-19 pandemic possible substance use disorders (SUD) were exacerbated from increased stress and isolation. Experiences of symptomology differ widely by occupations.Objectives: The objectives were to determine if there is a temporal relationship between COVID-19 vulnerability and possible SUDs among first responders, and to examine the association with neighborhood vulnerability.Methods: We conducted an analysis with two distinct cohorts dependent on time of entry: 1) First responders that began counseling prior to COVID-19 and 2) First responders that began counseling after the start of COVID-19. Data were collected at intake from first responders seeking mental health services between 2017 and 2021 at an organization in Dallas/Fort Worth, Texas. The study sample included 195 mostly male (75%) first responders (51% law enforcement officers; 49% emergency medical technicians/firefighters). Bivariate models tested unadjusted relationships between covariates and possible SUD. Adjusted models consisted of a two-level multivariable logistic regression models.Results: Nearly 40% (n = 77) screened positive for a possible SUD. Those beginning counseling after COVID-19 did not have higher odds of SUDs. For every unit increase in neighborhood Severe COVID-19 Health Risk Index at a first responder's residential location there was an increase in the odds of a possible SUD (AOR = 3.14, 95% CI: 1.47, 6.75).Conclusions: Our study highlights the degree to which personal and residential vulnerability to COVID-19 impacted first responders. The increased occupational stress of this population, and an established pattern of maladaptive coping, elucidates the need for preventative and clinical approaches to strengthen the resilience of this population.

Predictors of mortality among inpatients with COVID-19 infection in a tertiary referral center in the Philippines

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Mortality data were comparable to those of early reports relating to the wild-type SARS-CoV-2.

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Clinical and laboratory monitoring is critical during the 2nd to 3rd week of illness.

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Common and inexpensive laboratory tests may aid in the monitoring of patients.

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Clinical pathways can be adapted to local data, especially in resource-poor settings.

Objectives

The aim of this study was to determine the predictors of mortality and describe laboratory trends among adults with confirmed COVID-19.

Methods

The medical records of adult patients admitted to a referral hospital with COVID-19 were retrospectively reviewed. Demographic and clinical characteristics, and laboratory parameters, were compared between survivors and non-survivors. Predictors of mortality were determined by multivariate analysis. Mean laboratory values were plotted across illness duration.

Results

Of 1215 patients, 203 (16.7%) had mild, 488 (40.2%) moderate, 183 (15.1%) severe, and 341 (28.1%) critical COVID-19 on admission. In-hospital mortality was 18.2% (0% mild, 6.1% moderate, 15.8% severe, 47.5% critical). Predictors of mortality were age ≥ 60 years, COPD, qSOFA score ≥ 2, WBC > 10 × 10⁹/L, absolute lymphocyte count < 1000, neutrophil ≥ 70%, PaO₂/FiO₂ ratio ≤ 200, eGFR < 90 mL/min/1.73 m², LDH > 600 U/L, and CRP > 12 mg/L. Non-survivors exhibited an increase in LDH and decreases in PaO₂/FiO₂ ratio and eGFR during the 2nd–3rd week of illness.

Conclusion

The overall mortality rate was high. Predictors of mortality were similar to those of other reports globally. Marked inflammation and worsening pulmonary and renal function were evident among non-survivors by the 2nd–3rd week of illness.

Differential self-reported COVID-19 impacts among U.S. secondary teachers by race/ethnicity

The COVID-19 pandemic created drastic changes for public education in the United States, including the role and responsibilities of educators. This study explores the self-reported psycho-social implications of COVID-19 among U.S. secondary teachers who are white, Black, Indigenous, and people of color. Using a national survey (n = 1,478) fielded between October 2020 and March 2021, we capture teachers' self-reported level of concern, life change, impact on thinking, and impact on teaching ability due to the COVID-19 pandemic. While teachers who are Black, Indigenous, and people of color report higher levels of concern and daily life change stemming from COVID-19, they report lower impacts on their teaching ability relative to their white peers. These findings are consistent with racial/ethnic disparities in COVID-19 case rates and mortality and highlight the resiliency of the U.S. secondary teachers who are Black, Indigenous, and people of color.

Adverse effects of COVID-19 mRNA vaccines: the spike hypothesis

Vaccination is a major tool for mitigating the coronavirus disease 2019 (COVID-19) pandemic, and mRNA vaccines are central to the ongoing vaccination campaign that is undoubtedly saving thousands of lives. However, adverse effects (AEs) following vaccination have been noted which may relate to a proinflammatory action of the lipid nanoparticles used or the delivered mRNA (i.e., the vaccine formulation), as well as to the unique nature, expression pattern, binding profile, and proinflammatory effects of the produced antigens - spike (S) protein and/or its subunits/peptide fragments - in human tissues or organs. Current knowledge on this topic originates mostly from cell-based assays or from model organisms; further research on the cellular/molecular basis of the mRNA vaccine-induced AEs will therefore promise safety, maintain trust, and direct health policies.

Clinical characteristics and risk factors for COVID-19 infection and disease severity: A nationwide observational study in Estonia

BACKGROUND

COVID-19 pandemic has led to overloading of health systems all over the world. For reliable risk stratification, knowledge on factors predisposing to SARS-CoV-2 infection and to severe COVID-19 disease course is needed for decision-making at the individual, provider, and government levels. Data to identify these factors should be easily obtainable.

METHODS AND FINDINGS

Retrospective cohort study of nationwide e-health databases in Estonia. We used longitudinal health records from 66,295 people tested positive for SARS-CoV-2 RNA from 26 February 2020 to 28 February 2021 and 254,958 randomly selected controls from the reference population with no known history of SARS-CoV-2 infection or clinical COVID-19 diagnosis (case to control ratio 1:4) to predict risk factors of infection and severe course of COVID-19. We analysed sociodemographic and health characteristics of study participants. The SARS-CoV-2 infection risk was slightly higher among women, and was higher among those with comorbid conditions or obesity. Dementia (RRR 3.77, 95%CI 3.30⎼4.31), renal disease (RRR 1.88, 95%CI 1.56⎼2.26), and cerebrovascular disease (RRR 1.81, 95%CI 1.64⎼2.00) increased the risk of infection. Of all SARS-CoV-2 infected people, 92% had a non-severe disease course, 4.8% severe disease (requiring hospitalisation), 1.7% critical disease (needing intensive care), and 1.5% died. Male sex, increasing age and comorbid burden contributed significantly to more severe COVID-19, and the strength of association for male sex increased with the increasing severity of COVID-19 outcome. The strongest contributors to critical illness (expressed as RRR with 95% CI) were renal disease (7.71, 4.71⎼12.62), the history of previous myocardial infarction (3.54, 2.49⎼5.02) and obesity (3.56, 2.82⎼4.49). The strongest contributors to a lethal outcome were renal disease (6.48, 3.74⎼11.23), cancer (3.81, 3.06⎼4.75), liver disease (3.51, 1.36⎼9.02) and cerebrovascular disease (3.00, 2.31⎼3.89).

CONCLUSIONS

We found divergent effect of age and gender on infection risk and severity of COVID-19. Age and gender did not contribute substantially to infection risk, but did so for the risk of severe disease Co-morbid health conditions, especially those affecting renin-angiotensin system, had an impact on both the risk of infection and severe disease course. Age and male sex had the most significant impact on the risk of severe COVID-19. Taking into account the role of ACE2 receptors in the pathogenesis of SARS-CoV-2 infection, as well as its modulating action on the renin-angiotensin system in cardiovascular and renal diseases, further research is needed to investigate the influence of hormonal status on ACE2 expression in different tissues, which may be the basis for the development of COVID-19 therapies.

Correlation between country-level numbers of COVID-19 cases and mortalities, and country-level characteristics: A global study

Background: Not much is known about correlations between country-level characteristics and country-level numbers of COVID-19 cases and mortalities. Methods: Using data from the World Health Organization and other international organisations, we summarised country-level COVID-19 case and mortality counts per 100,000 population, and COVID-19 case fatality rate from January 2020 to August 2021. We conducted adjusted linear regression analysis to assess relationships between these counts/rate and certain country-level characteristics. We reported adjusted regression coefficients, β and associated 95% confidence intervals. Results: There was a positive correlation between the number of cases and country-level male/female ratio, and positive correlations between the numbers of cases and mortalities and country-level proportion of 60+-year-olds, universal health coverage index of service coverage (UHC) and tourism. Country economic status correlated negatively with the numbers of cases and mortalities. COVID-19 case fatality rate was highest in Peru, South American region (9.2%), and lowest in Singapore, Western Pacific region (0.1%). A negative correlation was observed between case fatality rate and country-level male/female ratio, population density and economic status. These observations remained mostly among mid-/low-income countries, particularly a positive correlation between the number of cases and male/female ratio and proportion of 60+-year-olds. Conclusions: Various country-level characteristics such as male/female ratio, proportion of older adults, country economic status, UHC and tourism appear to be correlated with the country-level number of COVID-19 cases and/or mortalities. Consideration of these characteristics may be necessary when designing country-level COVID-19 epidemiological studies and in comparing COVID-19 data between countries.

Higher education responses to COVID-19 in the United States: Evidence for the impacts of university policy

With a dataset of testing and case counts from over 1,400 institutions of higher education (IHEs) in the United States, we analyze the number of infections and deaths from SARS-CoV-2 in the counties surrounding these IHEs during the Fall 2020 semester (August to December, 2020). We find that counties with IHEs that remained primarily online experienced fewer cases and deaths during the Fall 2020 semester; whereas before and after the semester, these two groups had almost identical COVID-19 incidence. Additionally, we see fewer cases and deaths in counties with IHEs that reported conducting any on-campus testing compared to those that reported none. To perform these two comparisons, we used a matching procedure designed to create well-balanced groups of counties that are aligned as much as possible along age, race, income, population, and urban/rural categories-demographic variables that have been shown to be correlated with COVID-19 outcomes. We conclude with a case study of IHEs in Massachusetts-a state with especially high detail in our dataset-which further highlights the importance of IHE-affiliated testing for the broader community. The results in this work suggest that campus testing can itself be thought of as a mitigation policy and that allocating additional resources to IHEs to support efforts to regularly test students and staff would be beneficial to mitigating the spread of COVID-19 in a pre-vaccine environment.

Factors Associated With COVID-19 Death in the United States: Cohort Study

BACKGROUND

Since the initial COVID-19 cases were identified in the United States in February 2020, the United States has experienced a high incidence of the disease. Understanding the risk factors for severe outcomes identifies the most vulnerable populations and helps in decision-making.

OBJECTIVE

This study aims to assess the factors associated with COVID-19-related deaths from a large, national, individual-level data set.

METHODS

A cohort study was conducted using data from the Optum de-identified COVID-19 electronic health record (EHR) data set; 1,271,033 adult participants were observed from February 1, 2020, to August 31, 2020, until their deaths due to COVID-19, deaths due to other reasons, or the end of the study. Cox proportional hazards models were constructed to evaluate the risks for each patient characteristic.

RESULTS

A total of 1,271,033 participants (age: mean 52.6, SD 17.9 years; male: 507,574/1,271,033, 39.93%) were included in the study, and 3315 (0.26%) deaths were attributed to COVID-19. Factors associated with COVID-19-related death included older age (80 vs 50-59 years old: hazard ratio [HR] 13.28, 95% CI 11.46-15.39), male sex (HR 1.68, 95% CI 1.57-1.80), obesity (BMI 40 vs <30 kg/m2: HR 1.71, 95% CI 1.50-1.96), race (Hispanic White, African American, Asian vs non-Hispanic White: HR 2.46, 95% CI 2.01-3.02; HR 2.27, 95% CI 2.06-2.50; HR 2.06, 95% CI 1.65-2.57), region (South, Northeast, Midwest vs West: HR 1.62, 95% CI 1.33-1.98; HR 2.50, 95% CI 2.06-3.03; HR 1.35, 95% CI 1.11-1.64), chronic respiratory disease (HR 1.21, 95% CI 1.12-1.32), cardiac disease (HR 1.10, 95% CI 1.01-1.19), diabetes (HR 1.92, 95% CI 1.75-2.10), recent diagnosis of lung cancer (HR 1.70, 95% CI 1.14-2.55), severely reduced kidney function (HR 1.92, 95% CI 1.69-2.19), stroke or dementia (HR 1.25, 95% CI 1.15-1.36), other neurological diseases (HR 1.77, 95% CI 1.59-1.98), organ transplant (HR 1.35, 95% CI 1.09-1.67), and other immunosuppressive conditions (HR 1.21, 95% CI 1.01-1.46).

CONCLUSIONS

This is one of the largest national cohort studies in the United States; we identified several patient characteristics associated with COVID-19-related deaths, and the results can serve as the basis for policy making. The study also offered directions for future studies, including the effect of other socioeconomic factors on the increased risk for minority groups.

Assessing community-level COVID-19 infection risk through three-generational household concentration in Nebraska, U.S.: An approach for COVID-19 prevention

The three-generational household was a focal point of concern for school and community the Coronavirus Disease 2019 (COVID-19) transmission. The current study, using small area data and household variables, reported an approach to neighborhood-level COVID-19 mitigation for school reopening and communities returning to normalcy. The study started with an age-stratified Poisson regression to examine the association between the proportion of three-generational households and COVID-19 infection rates based on data from 74 census tracts in Lancaster County, Nebraska, U.S. from March 5, 2020 to August 22, 2020, followed by mapping the model-based risk score by census tract in the study area. We explored the feasibility of using COVID-19 infection rates and vaccination rates to inform decision-making on school opening from March 5, 2020 to February 3, 2021. The overall infection rate increased by 3% for every unit increased in the percentage of three-generational households after controlling for other covariates in the model. The census tracts were classified into low-, medium-, and high-priority neighborhoods for potential community-based interventions, such as targeted messages for household hygiene and isolation strategies.

Regulation of MHC Class I Expression in Lung Epithelial Cells during Inflammation

Lung infections are a perennial leading cause of death worldwide. The lung epithelium comprises three main cell types: alveolar type I (AT1), alveolar type II (AT2), and bronchiolar cells. Constitutively, these three cell types express extremely low amounts of surface MHC class I (MHC I) molecules, that is, <1% of levels found on medullary thymic epithelial cells (ECs). We report that inhalation of the TLR4 ligand LPS upregulates cell surface MHC I by ∼25-fold on the three subtypes of mouse lung ECs. This upregulation is dependent on Nlrc5, Stat1, and Stat2 and caused by a concerted production of the three IFN families. It is nevertheless hampered, particularly in AT1 cells, by the limited expression of genes instrumental in the peptide loading of MHC I molecules. Genes involved in production and response to cytokines and chemokines were selectively induced in AT1 cells. However, discrete gene subsets were selectively downregulated in AT2 or bronchiolar cells following LPS inhalation. Genes downregulated in AT2 cells were linked to cell differentiation and cell proliferation, and those repressed in bronchiolar cells were primarily involved in cilium function. Our study shows a delicate balance between the expression of transcripts maintaining lung epithelium integrity and transcripts involved in Ag presentation in primary lung ECs.

Gout and the COVID-19 pandemic

PURPOSE OF REVIEW

This review gives an overview of recently published articles on COVID-19 and gout.

RECENT FINDINGS

People with gout are likely to be at an increased risk of poor outcomes after COVID-19 infection due to comorbid cardiometabolic conditions. The effects of chronic hyperuricemia on trained immunity, and the hyperinflammatory state induced by gout itself may also play a role. Frequent courses of glucocorticoids for gout flares may be associated with adverse outcomes after COVID-19 infection and reduced immunogenicity to the COVID-19 vaccination. Similarities between the pathophysiology of gout flares and the dysregulated inflammatory response of severe COVID-19 have been identified. Medications used in the treatment of gout, including colchicine and interleukin-1 inhibitors, have shown promise in the treatment of COVID-19 in clinical trials. Overall, the COVID-19 pandemic has had a negative impact on gout care, with patients reporting more difficulty with disease control, accessing medications and healthcare, and poorer quality of life.

SUMMARY

The COVID-19 pandemic has created many challenges for people with gout. At present, there is a lack of guidance on the management of gout during the pandemic and paucity of research assessing outcomes of COVID-19 infection in people with gout.

microRNAs and Inflammatory Immune Response in SARS-CoV-2 Infection: A Narrative Review

The current SARS-CoV-2 pandemic has emerged as an international challenge with strong medical and socioeconomic impact. The spectrum of clinical manifestations of SARS-CoV-2 is wide, covering asymptomatic or mild cases up to severe and life-threatening complications. Critical courses of SARS-CoV-2 infection are thought to be driven by the so-called “cytokine storm”, derived from an excessive immune response that induces the release of proinflammatory cytokines and chemokines. In recent years, non-coding RNAs (ncRNAs) emerged as potential diagnostic and therapeutic biomarkers in both inflammatory and infectious diseases. Therefore, the identification of SARS-CoV-2 miRNAs and host miRNAs is an important research topic, investigating the host–virus crosstalk in COVID-19 infection, trying to answer the pressing question of whether miRNA-based therapeutics can be employed to tackle SARS-CoV-2 complications. In this review, we aimed to directly address ncRNA role in SARS-CoV-2-immune system crosstalk upon COVID-19 infection, particularly focusing on inflammatory pathways and cytokine storm syndromes.

COVID-19 Mortality in Europe, by Latitude and Obesity Status: A Geo-Spatial Analysis in 40 Countries

On 30 January 2020, the World Health Organization (WHO) declared the current novel coronavirus disease 2019 (COVID-19) as a public health emergency of international concern and later characterized it as a pandemic. New data show that excess body mass and vitamin D deficiency might be related to the disease severity and mortality. The aim of this study was to evaluate whether latitude, as a proxy of sunlight exposure and Vitamin D synthesis, and prevalent obesity among European populations, is related to COVID-19 spread and severity. European COVID-19 data (incidence and fatality), including information on the prevalence of obesity, social distancing, and others were obtained by the "Our World in Data" website on 17 April 2021. Adjusted analysis showed that higher COVID-19 incidence and fatality were pictured in countries being in higher latitude, both during the whole period, as well as, during the time period 1 November 2020-31 March 2021. Higher incidence and fatality of COVID-19 were observed where the prevalence of overweight/obesity was higher during the whole time period, whereas during the time period 1 November 2020-31 March 2021, only COVID-19 incidence was higher but not a fatality. The present results provide insights for targeted interventions and preventive strategies against COVID-19.

Impact of Dementia on Mortality Due to Coronavirus Disease 2019: Propensity-Score-Matching Study

BACKGROUND AND PURPOSE

Patients with dementia are particularly vulnerable to coronavirus disease 2019 (COVID-19) because they tend to be older and often have concomitant diseases. Previous studies have investigated the impact of dementia on COVID-19 outcomes, but the evidence is not robust for Asian populations. We aimed to determine the relationship between dementia and COVID-19 outcomes using data from a large-scale nationwide public database.

METHODS

Data on patients with COVID-19 who were released from quarantine between January 1, 2020 and April 30, 2020, published by the Korea Disease Control and Prevention Agency, were divided into two groups based on the dementia status. Propensity-score matching was used to adjust for multiple confounders between the dementia and no-dementia groups. Binary, ordinal logistic regression and multivariate Cox proportional-hazards models were used to compare mortality, quarantine duration, and clinical deterioration according to the dementia status in the two groups.

RESULTS

Males and older individuals (age ≥60 years) constituted 41.5% and 32.9%, respectively, of the 5,299 patients. The prevalence of dementia was 4.2%, and 4.5% of the participants died during hospitalization. In multivariate analysis, dementia was significantly associated with increased mortality (odds ratio [OR]=2.80, 95% confidence interval [CI]=1.60-4.60), longer duration of quarantine (hazard ratio=1.69, 95% CI=1.16-2.45), and larger shift to a worse clinical severity (common OR=1.74, 95% CI=1.18-2.61).

CONCLUSIONS

After adjusting for important clinical predictors, dementia was associated with increased in-hospital mortality, duration of quarantine, and clinical deterioration during hospitalization in COVID-19 patients.

COVID-19 in Patients with Glomerular Disease: Follow-Up Results from the IRoc-GN International Registry

Background

The acute and long-term effects of severe acute respiratory syndrome coronavirus 2 infection in individuals with GN are still unclear. To address this relevant issue, we created the International Registry of COVID-19 infection in GN.

Methods

We collected serial information on kidney-related and -unrelated outcomes from 125 GN patients (63 hospitalized and 62 outpatients) and 83 non-GN hospitalized patients with coronavirus disease 2019 (COVID-19) and a median follow-up period of 6.4 (interquartile range 2.3-9.6) months after diagnosis. We used logistic regression for the analyses of clinical outcomes and linear mixed models for the longitudinal analyses of eGFR. All multiple regression models were adjusted for age, sex, ethnicity, and renin-angiotensin-aldosterone system inhibitor use.

Results

After adjustment for pre-COVID-19 eGFR and other confounders, mortality and AKI did not differ between GN patients and controls (adjusted odds ratio for AKI=1.28; 95% confidence interval [CI], 0.46 to 3.60; P=0.64). The main predictor of AKI was pre-COVID-19 eGFR (adjusted odds ratio per 1 SD unit decrease in eGFR=3.04; 95% CI, 1.76 to 5.28; P<0.001). GN patients developing AKI were less likely to recover pre-COVID-19 eGFR compared with controls (adjusted 6-month post-COVID-19 eGFR=0.41; 95% CI, 0.25 to 0.56; times pre-COVID-19 eGFR). Shorter duration of GN diagnosis, higher pre-COVID-19 proteinuria, and diagnosis of focal segmental glomerulosclerosis or minimal change disease were associated with a lower post-COVID-19 eGFR.

Conclusions

Pre-COVID-19 eGFR is the main risk factor for AKI regardless of GN diagnosis. However, GN patients are at higher risk of impaired eGFR recovery after COVID-19-associated AKI. These patients (especially those with high baseline proteinuria or a diagnosis of focal segmental glomerulosclerosis or minimal change disease) should be closely monitored not only during the acute phases of COVID-19 but also after its resolution.

Using Statewide Electronic Health Record and Influenza Vaccination Data to Plan and Prioritize COVID-19 Vaccine Outreach and Communications in Wisconsin Communities

The University of Wisconsin Neighborhood Health Partnerships Program used electronic health record and influenza vaccination data to estimate COVID-19 relative mortality risk and potential barriers to vaccination in Wisconsin ZIP Code Tabulation Areas. Data visualization revealed four groupings to use in planning and prioritizing vaccine outreach and communication based on ZIP Code Tabulation Area characteristics. The program provided data, visualization, and guidance to health systems, health departments, nonprofits, and others to support planning targeted outreach approaches to increase COVID-19 vaccination uptake. (Am J Public Health. 2021;111(12):2111-2114. https://doi.org/10.2105/AJPH.2021.306524).

Measuring the COVID-19 Mortality Burden in the United States : A Microsimulation Study

BACKGROUND

Fully assessing the mortality burden of the COVID-19 pandemic requires measuring years of life lost (YLLs) and accounting for quality-of-life differences.

OBJECTIVE

To measure YLLs and quality-adjusted life-years (QALYs) lost from the COVID-19 pandemic, by age, sex, race/ethnicity, and comorbidity.

DESIGN

State-transition microsimulation model.

DATA SOURCES

Health and Retirement Study, Panel Study of Income Dynamics, data on excess deaths from the Centers for Disease Control and Prevention, and nursing home death counts from the Centers for Medicare & Medicaid Services.

TARGET POPULATION

U.S. population aged 25 years and older.

TIME HORIZON

Lifetime.

PERSPECTIVE

Individual.

INTERVENTION

COVID-19 pandemic through 13 March 2021.

OUTCOME MEASURES

YLLs and QALYs lost per 10 000 persons in the population. The estimates account for the age, sex, and race/ethnicity of decedents, along with obesity, smoking behavior, lung disease, heart disease, diabetes, cancer, stroke, hypertension, dementia, and nursing home residence.

RESULTS OF BASE-CASE ANALYSIS

The COVID-19 pandemic resulted in 6.62 million QALYs lost (9.08 million YLLs) through 13 March 2021, with 3.6 million (54%) lost by those aged 25 to 64 years. The greatest toll was on Black and Hispanic communities, especially among men aged 65 years or older, who lost 1138 and 1371 QALYs, respectively, per 10 000 persons. Absent the pandemic, 38% of decedents would have had average or above-average life expectancies for their subgroup defined by age, sex, and race/ethnicity.

RESULTS OF SENSITIVITY ANALYSIS

Accounting for uncertainty in risk factors for death from COVID-19 yielded similar results.

LIMITATION

Estimates may vary depending on assumptions about mortality and quality-of-life projections.

CONCLUSION

Beyond excess deaths alone, the COVID-19 pandemic imposed a greater life expectancy burden on persons aged 25 to 64 years, including those with average or above-average life expectancies, and a disproportionate burden on Black and Hispanic communities.

PRIMARY FUNDING SOURCE

National Institute on Aging.

Data-Driven Decision Making and Proactive Citizen-Scientist Communication: A Cross-Sectional Study on COVID-19 Vaccination Adherence

Due to the severe impact of COVID-19 on public health, rollout of the vaccines must be large-scale. Current solutions are not intended to promote an active collaboration between communities and public health researchers. We aimed to develop a digital platform for communication between scientists and the general population, and to use it for an exploratory study on factors associated with vaccination readiness. The digital platform was developed in Latvia and was equipped with dynamic consent management. During a period of six weeks 467 participants were enrolled in the population-based cross-sectional exploratory study using this platform. We assessed demographics, COVID-19-related behavioral and personal factors, and reasons for vaccination. Logistic regression models adjusted for the level of education, anxiety, factors affecting the motivation to vaccinate, and risk of infection/severe disease were built to investigate their association with vaccination readiness. In the fully adjusted multiple logistic regression model, factors associated with vaccination readiness were anxiety (odds ratio, OR = 3.09 [95% confidence interval 1.88; 5.09]), feelings of social responsibility (OR = 1.61 [1.16; 2.22]), and trust in pharmaceutical companies (OR = 1.53 [1.03; 2.27]). The assessment of a large number of participants in a six-week period show the potential of a digital platform to create a data-driven dialogue on vaccination readiness.

Targeting COVID Vaccine Hesitancy in Rural Communities in Tennessee: Implications for Extending the COVID-19 Pandemic in the South

Approximately 40% of Tennesseans are vaccinated fully, due mainly to higher vaccination levels within urban counties. Significantly lower rates are observed in rural counties. Surveys suggest COVID-19 vaccine hesitancy is entrenched mostly among individuals identifying as white, rural, Republican, and evangelical Christian. Rural counties represent 70 of the total 95 counties in Tennessee, and vaccine hesitancy signifies an immediate public health crisis likely to extend the COVID-19 pandemic. Tennessee is a microcosm of the pandemic’s condition in the Southern U.S. Unvaccinated communities are the greatest contributors of new COVID-19 infections, hospitalizations, and deaths. Rural Tennesseans have a long history of cultural conservatism, poor health literacy, and distrust of government and medical establishments and are more susceptible to misinformation and conspiracy theories. Development of novel strategies to increase vaccine acceptance is essential. Here, I examine the basis of COVID-19 following SARS-CoV-2 infection and summarize the pandemic’s extent in the South, current vaccination rates and efforts across Tennessee, and underlying factors contributing to vaccine hesitancy. Finally, I discuss specific strategies to combat COVID-19 vaccine hesitancy. We must develop novel strategies that go beyond financial incentives, proven ineffective toward vaccinations. Successful strategies for vaccine acceptance of rural Tennesseans could increase acceptance among unvaccinated rural U.S. populations.

Comprehensive evaluation of COVID-19 patient short- and long-term outcomes: Disparities in healthcare utilization and post-hospitalization outcomes

BACKGROUND

Understanding risk factors for short- and long-term COVID-19 outcomes have implications for current guidelines and practice. We study whether early identified risk factors for COVID-19 persist one year later and through varying disease progression trajectories.

METHODS

This was a retrospective study of 6,731 COVID-19 patients presenting to Michigan Medicine between March 10, 2020 and March 10, 2021. We describe disease progression trajectories from diagnosis to potential hospital admission, discharge, readmission, or death. Outcomes pertained to all patients: rate of medical encounters, hospitalization-free survival, and overall survival, and hospitalized patients: discharge versus in-hospital death and readmission. Risk factors included patient age, sex, race, body mass index, and 29 comorbidity conditions.

RESULTS

Younger, non-Black patients utilized healthcare resources at higher rates, while older, male, and Black patients had higher rates of hospitalization and mortality. Diabetes with complications, coagulopathy, fluid and electrolyte disorders, and blood loss anemia were risk factors for these outcomes. Diabetes with complications, coagulopathy, fluid and electrolyte disorders, and blood loss were associated with lower discharge and higher inpatient mortality rates.

CONCLUSIONS

This study found differences in healthcare utilization and adverse COVID-19 outcomes, as well as differing risk factors for short- and long-term outcomes throughout disease progression. These findings may inform providers in emergency departments or critical care settings of treatment priorities, empower healthcare stakeholders with effective disease management strategies, and aid health policy makers in optimizing allocations of medical resources.

Characteristics, Comorbidities, and Data Gaps for Coronavirus Disease Deaths, Tennessee, USA

As of March 2021, coronavirus disease (COVID-19) had led to >500,000 deaths in the United States, and the state of Tennessee had the fifth highest number of cases per capita. We reviewed the Tennessee Department of Health COVID-19 surveillance and chart-abstraction data during March 15‒August 15, 2020. Patients who died from COVID-19 were more likely to be older, male, and Black and to have underlying conditions (hereafter comorbidities) than case-patients who survived. We found 30.4% of surviving case-patients and 20.3% of deceased patients had no comorbidity information recorded. Chart-abstraction captured a higher proportion of deceased case-patients with >1 comorbidity (96.3%) compared with standard surveillance deaths (79.0%). Chart-abstraction detected higher rates of each comorbidity except for diabetes, which had similar rates among standard surveillance and chart-abstraction. Investing in public health data collection infrastructure will be beneficial for the COVID-19 pandemic and future disease outbreaks.

The effects of Alzheimer's and Parkinson's disease on 28-day mortality of COVID-19

We compared the prognosis of inpatients with a known diagnosis of Alzheimer's or Parkinson's disease who have COVID-19 infection with other hospitalized patients with COVID-19. Our cohort study started in October 2020 and ended in May 2021 and included inpatients with COVID-19 infection who were admitted to hospitals. From a total of 67,871 patients with a confirmed diagnosis of COVID-19, a sample of 3732 individuals were selected of which 363 had Alzheimer's, and 259 had Parkinson's disease. All patients had both positive RT-PCR test and positive chest CT for COVID-19. The outcome was dead within 28 days of admission and the predictors were a large number of demographic and clinical features, and comorbidities recorded at patients’ bedside. Mortality were 37.5%, 35.1%, and 29.5% in patients with Alzheimer's disease, Parkinson's disease; and in other patients, respectively. The hazard ratio for Alzheimer's disease was 1.27 (95% CI, 1.06–1.53, p = 0.010) and for Parkinson's disease was 1.17 (95% CI, 0.94–1.46, p = 0.171). Age was a predictor of mortality, hazard ratio = 1.04 (95% CI, 1.03–1.05, p < 0.001). Patients with Alzheimer's disease and COVID-19 infection were older and more likely to have a loss of consciousness on admission (both p ≤ 0.001). We concluded that inpatients with Alzheimer's disease have an increased risk for 28-day mortality from COVID-19 and healthcare settings should be ready to provide critical care for them such as early intubation and immediate O₂ therapy. However, Parkinson's disease does not significantly predict higher mortality of COVID-19.

Risk Factors of Mortality for Patients Receiving Venovenous Extracorporeal Membrane Oxygenation for COVID-19 Acute Respiratory Distress Syndrome

Background: Venovenous extracorporeal membrane oxygenation (VV-ECMO) for select adults with severe acute respiratory distress syndrome (ARDS) cause by coronavirus disease 2019 (COVID-19) infection is a guideline-supported therapy with associated hospital survival of 62%-74%, similar to expected survival with VV-ECMO for other indications. However, ECMO is a resource-heavy intervention, and these patients often require long ECMO runs and prolonged intensive care unit (ICU) care. Identifying factors associated with mortality in VV-ECMO patients with COVID-19 infection can inform the evaluation of ECMO candidates as well as prognostication for those patients on prolonged VV-ECMO. Patients and Methods: This was a retrospective cohort study that included all patients who received either VV- or venoarteriovenous (VAV)-ECMO at one of four ECMO Centers of Excellence in the state of Minnesota between March 1, 2020 and November 1, 2020. The primary outcome was 60-day survival. Secondary outcomes were hospital complications, infectious complications, and complications from ECMO. Results: There were 46 patients who met criteria during this study period and 30 survived to 60-day follow-up (65.2%). Prior to cannulation, older patient age (55.5 in non-survivors vs. 49.1 years in survivors; p = 0.03), lower P/F ratio (62.1 vs. 76.2; p = 0.04), and higher sequential organ failure assessment (SOFA) score (8.1 vs. 6.6; p = 0.02) were identified as risk factors for mortality. After ECMO cannulation, increased mortality was associated with increased number of antibiotic days (25.9 vs. 14.5; p = 0.04), increased number of transfusions (23.9 vs. 9.9; p = 0.03), elevated white blood cell (WBC) count at post-ECMO days one through three, elevated D-dimer at post-ECMO day 21-27, and decreased platelet count from post-ECMO days 14 and onward using univariable analysis. Conclusions: Multiple markers of infection including leukocytosis, thrombocytopenia, and increased antibiotic days are associated with increased mortality in patients placed on VV-ECMO for COVID-19 infection and subsequent ARDS. Knowledge of these factors may assist with determining appropriate candidates for this limited resource as well as direct goals of care in prolonged ECMO courses.

Voting in a global pandemic: Assessing dueling influences of Covid-19 on turnout

OBJECTIVE

We investigate the impact of a global health crisis on political behavior. Specifically, we assess the impact of Covid-19 incidence rates, and the impact of temporal and spatial proximity to the crisis, on voter turnout in the 2020 Brazilian municipal elections.

METHODS

We use Ordinary Least Squares and Spatial Durbin Error models to evaluate sub-national variation in municipal-level Covid-19 incidence and voter turnout. We include controls for political, economic, health, and state context.

RESULTS

Ceteris paribus, increasing deaths in the month leading up to the election from 0.01 to 1 per 1000 people is associated with a 5 percentage point decrease in turnout; higher cases and deaths earlier in the pandemic are generally associated with higher turnout. Covid-19 incidence rates in nearby municipalities affect local turnout in the same directions.

CONCLUSION

Higher Covid-19 incidence near the time of the election decreases voter turnout, while incidence farther from the election increases voter turnout.

Association of AKI-D with Urinary Findings and Baseline eGFR in Hospitalized COVID-19 Patients

Background

AKI is common in patients hospitalized with coronavirus disease 2019 (COVID-19). Risk factors for AKI requiring dialysis (AKI-D) are not fully understood. We aimed to identify risk factors associated with AKI-D and AKI not requiring dialysis (AKI-ND).

Methods

We reviewed electronic health records of 3186 patients aged ≥18 years old who were hospitalized with COVID-19 across six hospitals. Patient characteristics, urinalysis findings, and inflammatory markers were analyzed for association with in-hospital AKI status (AKI-D, AKI-ND, or no AKI), and we subsequently evaluated mortality.

Results

After adjustment for multiple covariates, higher baseline eGFR was associated with 30% lower odds of AKI-D and 11% lower odds of AKI-ND (for AKI-D, OR, 0.70; 95% CI, 0.64 to 0.77; for AKI-ND, OR, 0.89; 95% CI, 0.85 to 0.92). Patients with obesity and those who were Latino had increased odds of AKI-D, whereas patients with congestive heart failure or diabetes with complications had increased odds of AKI-ND. Females had lower odds of in-hospital AKI (for AKI-D, OR, 0.28; 95% CI, 0.17 to 0.46; for AKI-ND, OR, 0.83; 95% CI, 0.70 to 0.99). After adjustment for covariates and baseline eGFR, 1-4+ protein on initial urinalysis was associated with a nine-fold increase in odds of AKI-D (OR, 9.00; 95% CI, 2.16 to 37.38) and more than two-fold higher odds of AKI-ND (OR, 2.28; 95% CI, 1.66 to 3.13). Findings of 1-3+ blood and trace glucose on initial urinalysis were also associated with increased odds of both AKI-D and AKI-ND. AKI-D and AKI-ND were associated with in-hospital death (for AKI-D, OR, 2.64; 95% CI, 1.13 to 6.17; for AKI-ND, OR, 2.44; 95% CI, 1.77 to 3.35).

Conclusions

Active urine sediments, even after adjustment for baseline kidney function, and reduced baseline eGFR are significantly associated with increased odds of AKI-D and AKI-ND. In-hospital AKI was associated with in-hospital death. These findings may help prognosticate patients hospitalized with COVID-19.

Identifying Communities at Risk for COVID-19-Related Burden Across 500 US Cities and Within New York City: Unsupervised Learning of the Coprevalence of Health Indicators

BACKGROUND

Although it is well-known that older individuals with certain comorbidities are at the highest risk for complications related to COVID-19 including hospitalization and death, we lack tools to identify communities at the highest risk with fine-grained spatial resolution. Information collected at a county level obscures local risk and complex interactions between clinical comorbidities, the built environment, population factors, and other social determinants of health.

OBJECTIVE

This study aims to develop a COVID-19 community risk score that summarizes complex disease prevalence together with age and sex, and compares the score to different social determinants of health indicators and built environment measures derived from satellite images using deep learning.

METHODS

We developed a robust COVID-19 community risk score (COVID-19 risk score) that summarizes the complex disease co-occurrences (using data for 2019) for individual census tracts with unsupervised learning, selected on the basis of their association with risk for COVID-19 complications such as death. We mapped the COVID-19 risk score to corresponding zip codes in New York City and associated the score with COVID-19-related death. We further modeled the variance of the COVID-19 risk score using satellite imagery and social determinants of health.

RESULTS

Using 2019 chronic disease data, the COVID-19 risk score described 85% of the variation in the co-occurrence of 15 diseases and health behaviors that are risk factors for COVID-19 complications among ~28,000 census tract neighborhoods (median population size of tracts 4091). The COVID-19 risk score was associated with a 40% greater risk for COVID-19-related death across New York City (April and September 2020) for a 1 SD change in the score (risk ratio for 1 SD change in COVID-19 risk score 1.4; P<.001) at the zip code level. Satellite imagery coupled with social determinants of health explain nearly 90% of the variance in the COVID-19 risk score in the United States in census tracts (r²=0.87).

CONCLUSIONS

The COVID-19 risk score localizes risk at the census tract level and was able to predict COVID-19-related mortality in New York City. The built environment explained significant variations in the score, suggesting risk models could be enhanced with satellite imagery.

Identifying Communities at Risk for COVID-19-Related Burden Across 500 US Cities and Within New York City: Unsupervised Learning of the Coprevalence of Health Indicators

BACKGROUND

Although it is well-known that older individuals with certain comorbidities are at the highest risk for complications related to COVID-19 including hospitalization and death, we lack tools to identify communities at the highest risk with fine-grained spatial resolution. Information collected at a county level obscures local risk and complex interactions between clinical comorbidities, the built environment, population factors, and other social determinants of health.

OBJECTIVE

This study aims to develop a COVID-19 community risk score that summarizes complex disease prevalence together with age and sex, and compares the score to different social determinants of health indicators and built environment measures derived from satellite images using deep learning.

METHODS

We developed a robust COVID-19 community risk score (COVID-19 risk score) that summarizes the complex disease co-occurrences (using data for 2019) for individual census tracts with unsupervised learning, selected on the basis of their association with risk for COVID-19 complications such as death. We mapped the COVID-19 risk score to corresponding zip codes in New York City and associated the score with COVID-19-related death. We further modeled the variance of the COVID-19 risk score using satellite imagery and social determinants of health.

RESULTS

Using 2019 chronic disease data, the COVID-19 risk score described 85% of the variation in the co-occurrence of 15 diseases and health behaviors that are risk factors for COVID-19 complications among ~28,000 census tract neighborhoods (median population size of tracts 4091). The COVID-19 risk score was associated with a 40% greater risk for COVID-19-related death across New York City (April and September 2020) for a 1 SD change in the score (risk ratio for 1 SD change in COVID-19 risk score 1.4; P<.001) at the zip code level. Satellite imagery coupled with social determinants of health explain nearly 90% of the variance in the COVID-19 risk score in the United States in census tracts (r2=0.87).

CONCLUSIONS

The COVID-19 risk score localizes risk at the census tract level and was able to predict COVID-19-related mortality in New York City. The built environment explained significant variations in the score, suggesting risk models could be enhanced with satellite imagery.

Machine learning approach to dynamic risk modeling of mortality in COVID-19: a UK Biobank study

The COVID-19 pandemic has created an urgent need for robust, scalable monitoring tools supporting stratification of high-risk patients. This research aims to develop and validate prediction models, using the UK Biobank, to estimate COVID-19 mortality risk in confirmed cases. From the 11,245 participants testing positive for COVID-19, we develop a data-driven random forest classification model with excellent performance (AUC: 0.91), using baseline characteristics, pre-existing conditions, symptoms, and vital signs, such that the score could dynamically assess mortality risk with disease deterioration. We also identify several significant novel predictors of COVID-19 mortality with equivalent or greater predictive value than established high-risk comorbidities, such as detailed anthropometrics and prior acute kidney failure, urinary tract infection, and pneumonias. The model design and feature selection enables utility in outpatient settings. Possible applications include supporting individual-level risk profiling and monitoring disease progression across patients with COVID-19 at-scale, especially in hospital-at-home settings.

COVID-19 Pandemic in India: Through the Lens of Modeling

We reflect on and review India's COVID-19 pandemic response through the lens of modeling and data. The lessons learned from the Indian context may be beneficial for other countries.

COVID-19: Heterogeneous Excess Mortality and "Burden of Disease" in Germany and Italy and Their States and Regions, January-June 2020

Total mortality and "burden of disease" in Germany and Italy and their states and regions were explored during the first COVID-19 wave by using publicly available data for 16 German states and 20 Italian regions from January 2016 to June 2020. Based on expectations from 2016 to 2019, simplified Standardized Mortality Ratios (SMRs) for deaths occurring in the first half of 2020 and the effect of changed excess mortality in terms of "burden of disease" were assessed. Moreover, whether two German states and 19 Italian cities appropriately represent the countries within the European monitoring of excess mortality for public health action (EuroMOMO) network was explored. Significantly elevated SMRs were observed (Germany: week 14-18, Italy: week 11-18) with SMR peaks in week 15 in Germany (1.15, 95%-CI: 1.09-1.21) and in week 13 in Italy (1.79, 95%-CI: 1.75-1.83). Overall, SMRs were 1.00 (95%-CI: 0.97-1.04) in Germany and 1.06 (95%-CI: 1.03-1.10) in Italy. Significant SMR heterogeneity was found within both countries. Age and sex were strong modifiers. Loss of life expectancy was 0.34 days (1.66 days in men) for Germany and 5.3 days (6.3 days in men) for Italy [with upper limits of 3 and 6 weeks among elderly populations (≥65 years) after maximum potential bias adjustments]. Restricted data used within EuroMOMO neither represents mortality in the countries as a whole nor in their states and regions adequately. Mortality analyses with high spatial and temporal resolution are needed to monitor the COVID-19 pandemic's course.

Informing patients that they are at high risk for serious complications of viral infection increases vaccination rates

For many vaccine-preventable diseases like influenza, vaccination rates are lower than optimal to achieve community protection. Those at high risk for infection and serious complications are especially advised to be vaccinated to protect themselves. Using influenza as a model, we studied one method of increasing vaccine uptake: informing high-risk patients, identified by a machine learning model, about their risk status. Patients (N=39,717) were evenly randomized to (1) a control condition (exposure only to standard direct mail or patient portal vaccine promotion efforts) or to be told via direct mail, patient portal, and/or SMS that they were (2) at high risk for influenza and its complications if not vaccinated; (3) at high risk according to a review of their medical records; or (4) at high risk according to a computer algorithm analysis of their medical records. Patients in the three treatment conditions were 5.7% more likely to get vaccinated during the 112 days post-intervention (p < .001), and did so 1.4 days earlier (p < .001), on average, than those in the control group. There were no significant differences among risk messages, suggesting that patients are neither especially averse to nor uniquely appreciative of learning their records had been reviewed or that computer algorithms were involved. Similar approaches should be considered for COVID-19 vaccination campaigns.