Age and COVID-19 mortality: A comparison of Gompertz doubling time across countries and causes of death

Evolving patterns of COVID-19 mortality in US counties: A longitudinal study of healthcare, socioeconomic, and vaccination associations

The COVID-19 pandemic emphasized the need for pandemic preparedness strategies to mitigate its impacts, particularly in the United States, which experienced multiple waves with varying policies, population response, and vaccination effects. This study explores the relationships between county-level factors and COVID-19 mortality outcomes in the U.S. from 2020 to 2023, focusing on disparities in healthcare access, vaccination coverage, and socioeconomic characteristics. We conduct multi-variable rolling regression analyses to reveal associations between various factors and COVID-19 mortality outcomes, defined as Case Fatality Rate (CFR) and Overall Mortality to Hospitalization Rate (OMHR), at the U.S. county level. Each analysis examines the association between mortality outcomes and one of the three hierarchical levels of the Social Vulnerability Index (SVI), along with other factors such as access to hospital beds, vaccination coverage, and demographic characteristics. Our results reveal persistent and dynamic correlations between various factors and COVID-19 mortality measures. Access to hospital beds and higher vaccination coverage showed persistent protective effects, while higher Social Vulnerability Index was associated with worse outcomes persistently. Socioeconomic status and vulnerable household characteristics within the SVI consistently associated with elevated mortality. Poverty, lower education, unemployment, housing cost burden, single-parent households, and disability population showed significant associations with Case Fatality Rates during different stages of the pandemic. Vulnerable age groups demonstrated varying associations with mortality measures, with worse outcomes predominantly during the Original strain. Rural-Urban Continuum Code exhibited predominantly positive associations with CFR and OMHR, while it starts with a positive OMHR association during the Original strain. This study reveals longitudinal persistent and dynamic factors associated with two mortality rate measures throughout the pandemic, disproportionately affecting marginalized communities. The findings emphasize the urgency of implementing targeted policies and interventions to address disparities in the fight against future pandemics and the pursuit of improved public health outcomes.

Adult child educational attainment and older parents' psychosocial outcomes during the COVID-19 pandemic

BACKGROUND

Older adults' psychosocial outcomes during the COVID-19 pandemic have been inequitable by socio-economic status (SES). However, studies have focused solely on own SES, ignoring emerging evidence of the relationship between adult child SES and late-life health. We evaluated whether adult child educational attainment - a core marker of SES - is associated with older parents' psychosocial outcomes during the pandemic.

METHODS

We used data from the Survey of Health, Aging, and Retirement in Europe (SHARE) 2004-2018 and the SHARE Corona Surveys (SCS) 2020 and 2021. We included 40,392 respondents ≥ 65 years who had pre-pandemic information on adult child educational attainment and self-reported psychosocial outcomes during the pandemic, including self-assessments of worsened psychosocial outcomes compared to the pre-pandemic period. We used generalized estimating equations with a Poisson distribution and a log link, adjusted for respondent and family-level characteristics, including respondents' own educational attainment.

RESULTS

Older adults whose adult children averaged levels of educational attainment at or above (vs. below) their country-specific mean had a lower prevalence of feeling nervous (Prevalence Ratio [PR]: 0.94, 95% Confidence Interval [CI]: 0.90, 0.97), sad or depressed (PR: 0.94, 95% CI: 0.91, 0.98), and having sleep problems (PR: 0.94, 95% CI: 0.90, 0.97) during the pandemic. Additionally, higher adult child educational attainment was associated with a lower risk of perceiving worsened feelings of nervousness (PR: 0.95, 95% CI: 0.90, 1.01) and worsened sleep problems (PR: 0.91, 95% CI: 0.82, 1.01) as compared to the pre-pandemic. In stratified models, protective associations were observed only in countries experiencing "high" levels of COVID-19 intensity at the time of the survey. All of these results are derived from adjusted models.

CONCLUSIONS

Adult child SES may have "upward" spillover effects on the psychosocial wellbeing of older parents during periods of societal duress like the pandemic.

Global challenges in aging: insights from comparative biology and one health

The well-being of wildlife populations, ecosystem health, and human health are interlinked, and preserving wildlife is crucial for sustaining healthy ecosystems. Wildlife numbers, and in particular avian populations, have steeply declined over the past century, associated with anthropogenic factors originating from industry, urbanization, changing land use, habitat loss, pollution, emerging diseases, and climate change. All these factors combine to exert increasing stress and impair health for both humans and wildlife, with diminished metabolic, immune, and reproductive function, deteriorating overall health, and reduced longevity. The "toxic aging coin" suggests that these stressors may have dual impacts on aging-they can accelerate the aging process, and older individuals may struggle to cope with pollutants compared to younger ones. These responses are reflected in the health and productivity of individuals, and at a larger scale, the health and ability of populations to withstand disturbances. To understand the potential risk to health over the lifespan, it is important to articulate some of these global challenges and consider both their impacts on aging populations and on the aging process. In this review, we use the toxic aging coin and One Health conceptual frameworks to examine the interconnected health of humans, wildlife, and ecosystems. This exploration aims to develop proactive approaches for optimizing wildlife and human health.

The predictive power of data: machine learning analysis for Covid-19 mortality based on personal, clinical, preclinical, and laboratory variables in a case-control study

BACKGROUND AND PURPOSE

The COVID-19 pandemic has presented unprecedented public health challenges worldwide. Understanding the factors contributing to COVID-19 mortality is critical for effective management and intervention strategies. This study aims to unlock the predictive power of data collected from personal, clinical, preclinical, and laboratory variables through machine learning (ML) analyses.

METHODS

A retrospective study was conducted in 2022 in a large hospital in Abadan, Iran. Data were collected and categorized into demographic, clinical, comorbid, treatment, initial vital signs, symptoms, and laboratory test groups. The collected data were subjected to ML analysis to identify predictive factors associated with COVID-19 mortality. Five algorithms were used to analyze the data set and derive the latent predictive power of the variables by the shapely additive explanation values.

RESULTS

Results highlight key factors associated with COVID-19 mortality, including age, comorbidities (hypertension, diabetes), specific treatments (antibiotics, remdesivir, favipiravir, vitamin zinc), and clinical indicators (heart rate, respiratory rate, temperature). Notably, specific symptoms (productive cough, dyspnea, delirium) and laboratory values (D-dimer, ESR) also play a critical role in predicting outcomes. This study highlights the importance of feature selection and the impact of data quantity and quality on model performance.

CONCLUSION

This study highlights the potential of ML analysis to improve the accuracy of COVID-19 mortality prediction and emphasizes the need for a comprehensive approach that considers multiple feature categories. It highlights the critical role of data quality and quantity in improving model performance and contributes to our understanding of the multifaceted factors that influence COVID-19 outcomes.

The epidemiology and treatment outcomes of COVID-19 patients admitted to an intensive care unit in an Iranian hospital in Neyshabur city

Background and Aims

The COVID-19 pandemic and the infection of numerous individuals from diverse societies have emerged as major global challenges. Given the limited resources in intensive care units, effective bed management and resource allocation require a deep understanding of the disease. This study aimed to assess the epidemiology and treatment outcomes of COVID-19 patients admitted to an intensive care unit in an Iranian hospital in Neyshabur city.

Methods

This cross-sectional study was conducted on COVID-19 patients hospitalized in intensive care units in Razavi Khorasan, Iran in 2021. Census sampling was used to include all intensive care units. Of the initial 480 cases, 54 cases were excluded based on the exclusion criteria, leaving 426 cases for the study. Data were collected with the help of a data collection form that was designed by the researcher and its content validity and reliability were measured with Cronbach's alpha coefficient (α = 89%.). Data were analyzed with SPSS version 20 software. Descriptive and inferential statistics were used to analyze the data. Mean, standard deviation, and interquartile range indicators were used for descriptive statistics, and absolute frequency and relative frequency (percentage) were used to show numbers and ratios.

Results

The mean (SD) age of the patients was 66.33 (15.05) years, and 49.3% were female. The results showed that arterial blood oxygen saturation, respiratory rate, and Alzheimer's disease were significant variables for predicting mortality. Furthermore, arterial blood oxygen saturation, respiratory rate, and the need for transfusion of blood products were significant variables in predicting hospitalization and the risk of acute respiratory distress syndrome (ARDS).

Conclusion

This study demonstrated that arterial blood oxygen saturation, respiratory rate, and Alzheimer's disease are crucial variables for predicting death. Furthermore, arterial blood oxygen saturation and respiratory rate are significant factors in predicting the risk of ARDS.

Life Satisfaction Among Diverse Participants

INTRODUCTION

Mental health (MH) conditions are among the most common chronic health conditions in the United States. Previous studies suggested decreased in life satisfaction post-COVID-19 in other populations outside the United States. This study explored the correlations between life satisfaction and MH among diverse community post-COVID-19 pandemic.

METHODS

A cross-sectional, non-experimental, retrospective study. The primary outcome was the Satisfaction with Life Scale total score (SWLS_TS) and the characteristics of the cases from self-reported information. Statistical software SPSS was used for descriptive and inferential analyses.

RESULTS

About 218 cases were included for analysis. Many of the cases were Asian Americans (n = 185, 84.1%). The multiple linear regression model significantly predicted 5.2% of the variance in SWLS_TS. Three out of 6 predictors significantly contributed to the model (age β = .172, t = 2.42, P = .017, ethnicity β = .148, t = 2.07, P = .039, and no history of MH β = .248, t = 3.31, P = .001).

CONCLUSION

A diverse population was examined post-COVID-19 pandemic, and the findings suggest a positive correlation with age, ethnicity, and no history of MH with SWLS_TS.

Racial difference in mortality among COVID-19 hospitalizations in California

In the US, racial disparities in hospital outcomes are well documented. We explored whether race was associated with all-cause in-hospital mortality and intensive care unit (ICU) admission among COVID-19 patients in California. This was a retrospective analysis of California State Inpatient Database during 2020. Hospitalizations ≥ 18 years of age for COVID-19 were included. Cox proportional hazards with mixed effects were used for associations between race and in-hospital mortality. Logistic regression was used for the association between race and ICU admission. Among 87,934 COVID-19 hospitalizations, majority were Hispanics (56.5%), followed by White (27.3%), Asian, Pacific Islander, Native American (9.9%), and Black (6.3%). Cox regression showed higher mortality risk among Hispanics, compared to Whites (hazard ratio, 0.91; 95% CI 0.87-0.96), Blacks (hazard ratio, 0.87; 95% CI 0.79-0.94), and Asian, Pacific Islander, Native American (hazard ratio, 0.89; 95% CI 0.83-0.95). Logistic regression showed that the odds of ICU admission were significantly higher among Hispanics, compared to Whites (OR, 1.70; 95% CI 1.67-1.74), Blacks (OR, 1.70; 95% CI 1.64-1.78), and Asian, Pacific Islander, Native American (OR, 1.82; 95% CI 1.76-1.89). We found significant disparities in mortality among COVID-19 hospitalizations in California. Hispanics were the worst affected with the highest mortality and ICU admission rates.

Assessing risk of major adverse cardiac event among COVID-19 patients using HEART score

Coronavirus disease 2019 (COVID-19) is known to be associated with cardiovascular complications, but whether the current validated HEART score for chest pain is still applicable for these patients is unknown. This study aims to identify the impact and association of COVID-19 co-infection in patients presenting with chest pain and a calculated HEART score to the emergency departments (ED) with 30-day of major adverse cardiac event (MACE). This is a multicenter, retrospective observational study that included adult (age ≥ 18 years) patients visiting 13 different EDs with chest pain and evaluated using a HEART score. The primary outcome was the percentage of 30-day MACE, which included acute myocardial infarction, emergency percutaneous coronary intervention (PCI), coronary artery bypass graft (CABG), or death among patients who presented with chest pain and had COVID-19 co-infection. The sensitivity and specificity of the HEART score among COVID-19 co-infection for MACE were assessed by the receiver operating curve (ROC). We analyzed records of 46,210 eligible patients, in which 327 (0.7%) patients were identified as infected with COVID-19. Patients with COVID-19 had higher mean total HEART score of 3.3 (1.7), compared to patients who did not have COVID-19 (3.1, SD 1.8, P = 0.048). The rate of MACE was similar between both groups. There were only 2 (0.6%) COVID-19 patients who had MACE, compared to 504 (1.1%) patients in control group. Total HEART score was associated with an area under the ROC (AUROC) of 0.99, while the control group's was 0.78. History was associated with high AUROC in both COVID-19 (0.74) and control groups (0.76). Older age in COVID-19 had higher AUROC (0.89) than control patients (0.63). Among patients presenting to the ED with chest pain and having COVID-19 infection, HEART score had predictive capability for MACE, similar to patients without COVID-19 infection. Further studies with more COVID-19 patients are still necessary to confirm our observation.

COVID-19-Related Age Profiles for SARS-CoV-2 Variants in England and Wales and States of the USA (2020 to 2022): Impact on All-Cause Mortality

Since 2020, COVID-19 has caused serious mortality around the world. Given the ambiguity in establishing COVID-19 as the direct cause of death, we first investigate the effects of age and sex on all-cause mortality during 2020 and 2021 in England and Wales. Since infectious agents have their own unique age profile for death, we use a 9-year time series and several different methods to adjust single-year-of-age deaths in England and Wales during 2019 (the pre-COVID-19 base year) to a pathogen-neutral single-year-of-age baseline. This adjusted base year is then used to confirm the widely reported higher deaths in males for most ages above 43 in both 2020 and 2021. During 2020 (+COVID-19 but no vaccination), both male and female population-adjusted deaths significantly increased above age 35. A significant reduction in all-cause mortality among both males and females aged 75+ could be demonstrated in 2021 during the widespread COVID-19 vaccination period; however, deaths below age 75 progressively increased. This finding arises from a mix of vaccination coverage and year-of-age profiles of deaths for the different SARS-CoV-2 variants. In addition, specific effects of age around puberty were demonstrated, where females had higher deaths than males. There is evidence that year-of-birth cohorts may also be involved, indicating that immune priming to specific pathogen outbreaks in the past may have led to lower deaths for some birth cohorts. To specifically identify the age profile for the COVID-19 variants from 2020 to 2023, we employ the proportion of total deaths at each age that are potentially due to or 'with' COVID-19. The original Wuhan strain and the Alpha variant show somewhat limited divergence in the age profile, with the Alpha variant shifting to a moderately higher proportion of deaths below age 84. The Delta variant specifically targeted individuals below age 65. The Omicron variants showed a significantly lower proportion of overall mortality, with a markedly higher relative proportion of deaths above age 65, steeply increasing with age to a maximum around 100 years of age. A similar age profile for the variants can be seen in the age-banded deaths in US states, although they are slightly obscured by using age bands rather than single years of age. However, the US data shows that higher male deaths are greatly dependent on age and the COVID variant. Deaths assessed to be 'due to' COVID-19 (as opposed to 'involving' COVID-19) in England and Wales were especially overestimated in 2021 relative to the change in all-cause mortality. This arose as a by-product of an increase in COVID-19 testing capacity in late 2020. Potential structure-function mechanisms for the age-specificity of SARS-CoV-2 variants are discussed, along with potential roles for small noncoding RNAs (miRNAs). Using data from England, it is possible to show that the unvaccinated do indeed have a unique age profile for death from each variant and that vaccination alters the shape of the age profile in a manner dependent on age, sex, and the variant. The question is posed as to whether vaccines based on different variants carry a specific age profile.

Racial Capitalism and Neighborhood Health Disparities: the COVID-19 in California Counties

PURPOSE

This article explores the association between racial capitalism and neighborhood-level health disparities, with a particular focus on COVID-19 in California.

METHODS

This article investigates COVID-19 incidence in 58 California counties. To account for racial capitalism, the study looks at the per capita incomes ratios (1) between whites and Blacks and (2) between whites and Hispanics. Other county-level neighborhood characteristics were controlled.

RESULTS

Findings from spatial autoregressive models indicate that increases in white-Black and white-Hispanic income disadvantages lead to an increase in COVID-19 incidence in 58 California counties. Findings also reveal that the disadvantage that results from the white-Black income ratio in COVID-19 spread decreases in counties that report high levels of income inequality between whites and Hispanics.

DISCUSSION

Findings indicate that a greater income disadvantage for racial minorities is connected to a more COVID-19 incidence. With regard to racial demographics in California, the interaction effect between measures for racial income disadvantages is discussed.

Impact of COVID-19 on Spanish mortality rates in 2020 by age and sex

BACKGROUND

Previous studies have used different biometric indicators to measure the effect of Covid-19 on population mortality such as the number of deaths or the decrease of life expectancy showing a dependence of mortality on age and sex. According to them, the impact of the pandemic was greater on women than in men and that the older the population, the greater the number of deaths caused by Covid-19.

METHODS

We apply graduation techniques and non-parametric methods to estimate mortality rates allowing us to obtain an age-by-age picture of changes in mortality rates from 2018-2019 to 2020.

RESULTS

Graduation techniques have detected a significant U-shaped reduction in infant mortality rates although with an anomalous peak in girls aged 10-12. Likewise, we have observed a notable increase in mortality rates of the female population between 28 and 40 years of age. The increase of mortality rates after the age of 70 years was similar for both men and women with a slight decline after the age of 80.

CONCLUSIONS

The use of graduation techniques and the focus on age-by-age changes in mortality rates showed a complex behaviour in some tranches of the mortality curve that might otherwise have gone unnoticed.

COVID-19 and All-Cause Mortality by Race, Ethnicity, and Age Across Five Periods of the Pandemic in the United States

Racial/ethnic and age disparities in COVID-19 and all-cause mortality during 2020 are well documented, but less is known about their evolution over time. We examine changes in age-specific mortality across five pandemic periods in the United States from March 2020 to December 2022 among four racial/ethnic groups (non-Hispanic White, non-Hispanic Black, Hispanic, and non-Hispanic Asian) for ages 35+. We fit Gompertz models to all-cause and COVID-19 death rates by 5-year age groups and construct age-specific racial/ethnic mortality ratios across an Initial peak (Mar-Aug 2020), Winter peak (Nov 2020-Feb 2021), Delta peak (Aug-Oct 2021), Omicron peak (Nov 2021-Feb 2022), and Endemic period (Mar-Dec 2022). We then compare to all-cause patterns observed in 2019. The steep age gradients in COVID-19 mortality in the Initial and Winter peak shifted during the Delta peak, with substantial increases in mortality at working ages, before gradually returning to an older age pattern in the subsequent periods. We find a disproportionate COVID-19 mortality burden on racial and ethnic minority populations early in the pandemic, which led to an increase in all-cause mortality disparities and a temporary elimination of the Hispanic mortality advantage at certain age groups. Mortality disparities narrowed over time, with racial/ethnic all-cause inequalities during the Endemic period generally returning to pre-pandemic levels. Black and Hispanic populations, however, faced a younger age gradient in all-cause mortality in the Endemic period relative to 2019, with younger Hispanic and Black adults in a slightly disadvantageous position and older Black adults in a slightly advantageous position, relative to before the pandemic.

Correlates of COVID-19 Preventative Behaviors before and after Vaccination Availability

As the COVID-19 pandemic progressed, various preventative behaviors and eventually vaccinations became available to decrease the spread of the virus. The current study examined a variety of variables (i.e., age, COVID-19-related economic hardship, interpersonal concern, personality, fear of COVID-19, normative beliefs, political beliefs, and vaccine hesitancy) to better understand predictors of preventative behaviors and vaccination status at different points throughout the pandemic. Online questionnaires, administered through Qualtrics, were used to collect data using two convenience samples. One was a small sample (N = 44) of non-student participants before the vaccine was readily available. The other sample (N = 274) included college student participants and occurred after the vaccine had been available to all participants. Results suggest that several variables (i.e., fear of COVID-19, normative beliefs, interpersonal concern, and openness) were consistent predictors of public health behaviors at both points in time and across differently aged samples. Other variables (i.e., agreeableness, extraversion, conscientiousness, and economic hardship) were less consistent with their relationships with public health behaviors. Implications related to both research and public health are discussed.

Using generalized structured additive regression models to determine factors associated with and clusters for COVID-19 hospital deaths in South Africa

BACKGROUND

The first case of COVID-19 in South Africa was reported in March 2020 and the country has since recorded over 3.6 million laboratory-confirmed cases and 100 000 deaths as of March 2022. Transmission and infection of SARS-CoV-2 virus and deaths in general due to COVID-19 have been shown to be spatially associated but spatial patterns in in-hospital deaths have not fully been investigated in South Africa. This study uses national COVID-19 hospitalization data to investigate the spatial effects on hospital deaths after adjusting for known mortality risk factors.

METHODS

COVID-19 hospitalization data and deaths were obtained from the National Institute for Communicable Diseases (NICD). Generalized structured additive logistic regression model was used to assess spatial effects on COVID-19 in-hospital deaths adjusting for demographic and clinical covariates. Continuous covariates were modelled by assuming second-order random walk priors, while spatial autocorrelation was specified with Markov random field prior and fixed effects with vague priors respectively. The inference was fully Bayesian.

RESULTS

The risk of COVID-19 in-hospital mortality increased with patient age, with admission to intensive care unit (ICU) (aOR = 4.16; 95% Credible Interval: 4.05-4.27), being on oxygen (aOR = 1.49; 95% Credible Interval: 1.46-1.51) and on invasive mechanical ventilation (aOR = 3.74; 95% Credible Interval: 3.61-3.87). Being admitted in a public hospital (aOR = 3.16; 95% Credible Interval: 3.10-3.21) was also significantly associated with mortality. Risk of in-hospital deaths increased in months following a surge in infections and dropped after months of successive low infections highlighting crest and troughs lagging the epidemic curve. After controlling for these factors, districts such as Vhembe, Capricorn and Mopani in Limpopo province, and Buffalo City, O.R. Tambo, Joe Gqabi and Chris Hani in Eastern Cape province remained with significantly higher odds of COVID-19 hospital deaths suggesting possible health systems challenges in those districts.

CONCLUSION

The results show substantial COVID-19 in-hospital mortality variation across the 52 districts. Our analysis provides information that can be important for strengthening health policies and the public health system for the benefit of the whole South African population. Understanding differences in in-hospital COVID-19 mortality across space could guide interventions to achieve better health outcomes in affected districts.

US Racial-Ethnic Mortality Gap Adjusted for Population Structure

BACKGROUND

US racial-ethnic mortality disparities are well documented and central to debates on social inequalities in health. Standard measures, such as life expectancy or years of life lost, are based on synthetic populations and do not account for the real underlying populations experiencing the inequalities.

METHODS

We analyze US mortality disparities comparing Asian Americans, Blacks, Hispanics, and Native Americans/Alaska Natives to Whites using 2019 CDC and NCHS data, using a novel approach that estimates the mortality gap, adjusted for population structure by accounting for real-population exposures. This measure is tailored for analyses where age structures are fundamental, not merely a confounder. We highlight the magnitude of inequalities by comparing the population structure-adjusted mortality gap against standard metrics' estimates of loss of life due to leading causes.

RESULTS

Based on the population structure-adjusted mortality gap, Black and Native American mortality disadvantage exceedsmortality from circulatory diseases. The disadvantage is 72% among Blacks (men: 47%, women: 98%) and 65% among Native Americans (men: 45%, women: 92%), larger than life expectancy measured disadvantage. In contrast, estimated advantages for Asian Americans are over three times (men: 176%, women: 283%) and, for Hispanics, two times (men: 123%; women: 190%) larger than those based on life expectancy.

CONCLUSIONS

Mortality inequalities based on standard metrics' synthetic populations can differ markedly from estimates of the population structure-adjusted mortality gap. We demonstrate that standard metrics underestimate racial-ethnic disparities through disregarding actual population age structures. Exposure-corrected measures of inequality may better inform health policies around allocation of scarce resources.

Life satisfaction in UK emerging adults during the COVID-19 pandemic

Current research indicates that young adults are at a higher risk of deteriorating wellbeing during the COVID-19 pandemic compared to older adults. Drawing upon the Understanding Society COVID-19 survey, this study examined the trajectory of life satisfaction in UK emerging adults from May 2020 to September 2021 with social, health, financial, and demographic factors as covariates. The analytic sample included 880 participants (612 females, 268 males) between the ages of 18-29. Growth curve modelling was used to estimate the trajectory of life satisfaction and examine whether the covariates account for variation in the mean level and/or slopes. The trajectory of life satisfaction declined slightly between May 2020 and January 2021 and then increased to September 2021, aligning with the tightening and easing of UK COVID-19 policies. Greater perceived current financial difficulties, pre-existing mental health and physical health conditions, and higher self-reported loneliness were associated with lower life satisfaction. Being female and living with a romantic partner, more face-to-face social interactions, and higher household income were associated with more life satisfaction. Gender interacted with pre-existing mental health conditions. Women with no pre-existing mental health conditions reported the highest level of life satisfaction, while women with pre-existing mental health conditions reported the lowest level, compared to men who reported a similar level of life satisfaction regardless of their mental health. The findings from the present study contribute toward the current understanding of changes in life satisfaction throughout the pandemic among emerging adults. Implications for intervention are discussed.

Clinical predictors of Covid-19 mortality in a tertiary hospital in Lagos, Nigeria: A retrospective cohort study

Background

The predictors of mortality among patients presenting with severe to critical disease in Nigeria are presently unknown.

Aim

The aim of this study was to identify the predictors of mortality among patients with COVID-19 presenting for admission in a tertiary referral hospital in Lagos, Nigeria.

Patients and Methods

The study was a retrospective study. Patients' sociodemographics, clinical characteristics, comorbidities, complications, treatment outcomes, and hospital duration were documented. Pearson's Chi-square, Fischer's Exact test, or Student's t-test were used to assess the relationship between the variables and mortality. To compare the survival experience across medical comorbidities, Kaplan Meir plots and life tables were used. Univariable and multivariable Cox-proportional hazard analyses were conducted.

Results

A total of 734 patients were recruited. Participants' age ranged from five months to 92 years, with a mean ± SD of 47.4 ± 17.2 years, and a male preponderance (58.5% vs. 41.5%). The mortality rate was 9.07 per thousand person-days. About 73.9% (n = 51/69) of the deceased had one or more co-morbidities, compared to 41.6% (252/606) of those discharged. Patients who were older than 50 years, with diabetes mellitus, hypertension, chronic renal illness, and cancer had a statistically significant relationship with mortality.

Conclusion

These findings call for a more comprehensive approach to the control of non-communicable diseases, the allocation of sufficient resources for ICU care during outbreaks, an improvement in the quality of health care available to Nigerians, and further research into the relationship between obesity and COVID-19 in Nigerians.

Along party Lines: Examining the gubernatorial party difference in COVID-19 mortality rates in U.S. Counties

Drawing upon the literatures on risk factors for COVID-19 and the roles of political party and political partisanship in COVID-19 policies and outcomes, this study quantifies the extent to which differences in Republican- and Democrat-governed counties' observable characteristics explain the Republican - Democrat gap in COVID-19 mortality rate in the United States. We analyze the county COVID-19 mortality rate between February 1 and December 31, 2020 and employ the Blinder-Oaxaca decomposition method. We estimate the extent to which differences in county characteristics - demographic, socioeconomic, employment, health status, healthcare access, area geography, and Republican vote share, explain the difference in COVID-19 mortality rates in counties governed by Republican vs Democrat governors. Among 3,114 counties, Republican-governed counties had significantly higher COVID-19 mortality than did Democrat-governed counties (127 ± 86 vs 97 ± 80 per 100,000 population, p < 0.001). Results are sensitive to which weights are used: of the total gap of 30.3 deaths per 100,000 population, 12.8 to 20.5 deaths, or 42.2-67.7 %, are explained by differences in observable characteristics of Republican- and Democratic-governed counties. Difference in support for President Trump between Republican- and Democrat-governed counties explains 25 % of the additional deaths in Republican counties. Policies aimed at improving population health and lowering racial disparity in COVID-19 outcomes may also be correlated with reducing the partisan gap in COVID-19 mortality.

Are COVID-19 age-mortality curves for 2020 flatter in developing countries? Evidence from a cross-sectional observational study of population-level official death counts and excess deaths estimates

OBJECTIVES

Previous studies have found a pattern of flatter COVID-19 age-mortality curves among low-income and middle-income countries (LMICs) using only official COVID-19 death counts. This study examines this question by comparing the age gradient of COVID-19 mortality in a broad set of countries using both official COVID-19 death counts and excess mortality estimates for 2020.

DESIGN

This observational study uses official COVID-19 death counts for 76 countries and excess death estimates for 42 countries. A standardised population analysis was conducted to assess the extent to which variation across countries in the age distribution of COVID-19 deaths was driven by variation in the population age distribution.

SETTING AND PRIMARY OUTCOMES

Officially reported COVID-19 deaths and excess deaths for 2020 for all countries where such data were available in the COVerAGE database and the short-term mortality fluctuations harmonised data series, respectively.

RESULTS

A higher share of pandemic-related deaths in 2020 occurred at younger ages in middle-income countries compared with high-income countries. People under age 65 years constituted on average (1) 10% of official deaths and 11 % of excess deaths in high-income countries, (2) 34% of official deaths and 33% of excess deaths in upper-middle-income countries, and (3) 54% of official deaths in LMICs. These contrasting profiles are due only in part to differences in population age structure.

CONCLUSIONS

These findings are driven by some combination of variation in age patterns of infection rates and infection fatality rates. They indicate that COVID-19 is not just a danger to older people in developing countries, where a large share of victims are people of working age, who are caregivers and breadwinners for their families.

Characterising public transport shifting to active and private modes in South American capitals during the COVID-19 pandemic

During the year 2020, the COVID-19 pandemic affected mobility around the world, significantly reducing the number of trips by public transport. In this paper, we study its impact in five South American capitals (i.e., Bogotá, Buenos Aires, Lima, Quito and Santiago). A decline in public transport patronage could be very bad news for these cities in the long term, particularly if users change to less sustainable modes, such as cars or motorbikes. Notwithstanding, it could be even beneficial if users selected more sustainable modes, such as active transport (e.g., bicycles and walking). To better understand this phenomenon in the short term, we conducted surveys in these five cities looking for the main explanation for changes from public transport to active and private modes in terms of user perceptions, activity patterns and sociodemographic information. To forecast people's mode shifts in each city, we integrated both objective and subjective information collected in this study using a SEM-MIMIC model. We found five latent variables (i.e., COVID-19 impact, Entities response, Health risk, Life related activities comfort and Subjective well-being), two COVID-19 related attributes (i.e., new cases and deaths), two trip attributes (i.e., cost savings and time), and six socio-demographic attributes (i.e., age, civil status, household characteristics, income level, occupation and gender) influencing the shift from public transport to other modes. Furthermore, both the number of cases and the number of deaths caused by COVID-19 increased the probability of moving from public transport to other modes but, in general, we found a smaller probability of moving to active modes than to private modes. The paper proposes a novel way for understanding geographical and contextual similarities in the pandemic scenario for these metropolises from a transportation perspective.

The Importance of Behavioral and Native Factors on COVID-19 Infection and Severity: Insights from a Preliminary Cross-Sectional Study

The COVID-19 pandemic has had a major impact on a global scale. Understanding the innate and lifestyle-related factors influencing the rate and severity of COVID-19 is important for making evidence-based recommendations. This cross-sectional study aims at establishing a potential relationship between human characteristics and vulnerability/resistance to SARS-CoV-2. We hypothesize that the impact of the virus is not the same due to cultural and ethnic differences. A cross-sectional study was performed using an online questionnaire. The methodology included the development of a multi-language survey, expert evaluation, and data analysis. Data were collected using a 13-item pre-tested questionnaire based on a literature review between 9 December 2020 and 21 July 2021. Data were statistically analyzed using logistic regression. For a total of 1125 respondents, 332 (29.5%) were COVID-19 positive; among them, 130 (11.5%) required home-based treatment, and 14 (1.2%) intensive care. The significant and most influential factors on infection included age, physical activity, and health status (p < 0.05), i.e., better physical activity and better health status significantly reduced the possibility of infection, while older age significantly increased it. The severity of infection was negatively associated with the acceptance (adherence and respect) of preventive measures and positively associated with tobacco (p < 0.05), i.e., smoking regularly significantly increases the severity of COVID-19 infection. This suggests the importance of behavioral factors compared to innate ones. Apparently, individual behavior is mainly responsible for the spread of the virus. Therefore, adopting a healthy lifestyle and scrupulously observing preventive measures, including vaccination, would greatly limit the probability of infection and prevent the development of severe COVID-19.

The intersection of age, sex, race and socioeconomic status in COVID-19 hospital admissions and deaths in South Africa

Older age, male sex, and non-white race have been reported to be risk factors for COVID-19 mortality. Few studies have explored how these intersecting factors contribute to COVID-19 outcomes. This study aimed to compare demographic characteristics and trends in SARS-CoV-2 admissions and the health care they received. Hospital admission data were collected through DATCOV, an active national COVID-19 surveillance programme. Descriptive analysis was used to compare admissions and deaths by age, sex, race, and health sector as a proxy for socio-economic status. COVID-19 mortality and healthcare utilisation were compared by race using random effect multivariable logistic regression models. On multivariable analysis, black African patients (adjusted OR [aOR] 1.3, 95% confidence interval [CI] 1.2, 1.3), coloured patients (aOR 1.2, 95% CI 1.1, 1.3), and patients of Indian descent (aOR 1.2, 95% CI 1.2, 1.3) had increased risk of in-hospital COVID-19 mortality compared to white patients; and admission in the public health sector (aOR 1.5, 95% CI 1.5, 1.6) was associated with increased risk of mortality compared to those in the private sector. There were higher percentages of COVID-19 hospitalised individuals treated in ICU, ventilated, and treated with supplemental oxygen in the private compared to the public sector. There were increased odds of non-white patients being treated in ICU or ventilated in the private sector, but decreased odds of black African patients being treated in ICU (aOR 0.5; 95% CI 0.4, 0.5) or ventilated (aOR 0.5; 95% CI 0.4, 0.6) compared to white patients in the public sector. These findings demonstrate the importance of collecting and analysing data on race and socio-economic status to ensure that disease control measures address the most vulnerable populations affected by COVID-19.

Variation in the COVID-19 infection-fatality ratio by age, time, and geography during the pre-vaccine era: a systematic analysis

BACKGROUND

The infection-fatality ratio (IFR) is a metric that quantifies the likelihood of an individual dying once infected with a pathogen. Understanding the determinants of IFR variation for COVID-19, the disease caused by the SARS-CoV-2 virus, has direct implications for mitigation efforts with respect to clinical practice, non-pharmaceutical interventions, and the prioritisation of risk groups for targeted vaccine delivery. The IFR is also a crucial parameter in COVID-19 dynamic transmission models, providing a way to convert a population's mortality rate into an estimate of infections.

METHODS

We estimated age-specific and all-age IFR by matching seroprevalence surveys to total COVID-19 mortality rates in a population. The term total COVID-19 mortality refers to an estimate of the total number of deaths directly attributable to COVID-19. After applying exclusion criteria to 5131 seroprevalence surveys, the IFR analyses were informed by 2073 all-age surveys and 718 age-specific surveys (3012 age-specific observations). When seroprevalence was reported by age group, we split total COVID-19 mortality into corresponding age groups using a Bayesian hierarchical model to characterise the non-linear age pattern of reported deaths for a given location. To remove the impact of vaccines on the estimated IFR age pattern, we excluded age-specific observations of seroprevalence and deaths that occurred after vaccines were introduced in a location. We estimated age-specific IFR with a non-linear meta-regression and used the resulting age pattern to standardise all-age IFR observations to the global age distribution. All IFR observations were adjusted for baseline and waning antibody-test sensitivity. We then modelled age-standardised IFR as a function of time, geography, and an ensemble of 100 of the top-performing covariate sets. The covariates included seven clinical predictors (eg, age-standardised obesity prevalence) and two measures of health system performance. Final estimates for 190 countries and territories, as well as subnational locations in 11 countries and territories, were obtained by predicting age-standardised IFR conditional on covariates and reversing the age standardisation.

FINDINGS

We report IFR estimates for April 15, 2020, to January 1, 2021, the period before the introduction of vaccines and widespread evolution of variants. We found substantial heterogeneity in the IFR by age, location, and time. Age-specific IFR estimates form a J shape, with the lowest IFR occurring at age 7 years (0·0023%, 95% uncertainty interval [UI] 0·0015-0·0039) and increasing exponentially through ages 30 years (0·0573%, 0·0418-0·0870), 60 years (1·0035%, 0·7002-1·5727), and 90 years (20·3292%, 14·6888-28·9754). The countries with the highest IFR on July 15, 2020, were Portugal (2·085%, 0·946-4·395), Monaco (1·778%, 1·265-2·915), Japan (1·750%, 1·302-2·690), Spain (1·710%, 0·991-2·718), and Greece (1·637%, 1·155-2·678). All-age IFR varied by a factor of more than 30 among 190 countries and territories. After age standardisation, the countries with the highest IFR on July 15, 2020, were Peru (0·911%, 0·636-1·538), Portugal (0·850%, 0·386-1·793), Oman (0·762%, 0·381-1·399), Spain (0·751%, 0·435-1·193), and Mexico (0·717%, 0·426-1·404). Subnational locations with high IFRs also included hotspots in the UK and southern and eastern states of the USA. Sub-Saharan African countries and Asian countries generally had the lowest all-age and age-standardised IFRs. Population age structure accounted for 74% of logit-scale variation in IFRs estimated for 39 in-sample countries on July 15, 2020. A post-hoc analysis showed that high rates of transmission in the care home population might account for higher IFRs in some locations. Among all countries and territories, we found that the median IFR decreased from 0·466% (interquartile range 0·223-0·840) to 0·314% (0·143-0·551) between April 15, 2020, and Jan 1, 2021.

INTERPRETATION

Estimating the IFR for global populations helps to identify relative vulnerabilities to COVID-19. Information about how IFR varies by age, time, and location informs clinical practice and non-pharmaceutical interventions like physical distancing measures, and underpins vaccine risk stratification. IFR and mortality risk form a J shape with respect to age, which previous research, such as that by Glynn and Moss in 2020, has identified to be a common pattern among infectious diseases. Understanding the experience of a population with COVID-19 mortality requires consideration for local factors; IFRs varied by a factor of more than 30 among 190 countries and territories in this analysis. In particular, the presence of elevated age-standardised IFRs in countries with well resourced health-care systems indicates that factors beyond health-care capacity are important. Potential extenuating circumstances include outbreaks among care home residents, variable burdens of severe cases, and the population prevalence of comorbid conditions that increase the severity of COVID-19 disease. During the pre-vaccine period, the estimated 33% decrease in median IFR over 8 months suggests that treatment for COVID-19 has improved over time. Estimating IFR for the pre-vaccine era provides an important baseline for describing the progression of COVID-19 mortality patterns.

FUNDING

Bill & Melinda Gates Foundation, J Stanton, T Gillespie, and J and E Nordstrom.

The demography of COVID-19 deaths database, a gateway to well-documented international data

National authorities publish COVID-19 death counts, which are extensively re-circulated and compared; but data are generally poorly sourced and documented. Academics and stakeholders need tools to assess data quality and to track data-related discrepancies for comparability over time or across countries. “The Demography of COVID-19 Deaths” database aims at bridging this gap. It provides COVID-19 death counts along with associated documentation, which includes the exact data sources and points out issues of quality and coverage of the data. The database — launched in April 2020 and continuously updated — contains daily cumulative death counts attributable to COVID-19 broken down by sex and age, place and date of occurrence of the death. Data and metadata undergo quality control checks prior to online release. As of mid-December 2021, it covers 21 countries in Europe and beyond. It is open access at a bilingual (English and French) website with content intended for expert users and non-specialists (https://dc-covid.site.ined.fr/en/; figshare: 10.6084/m9.figshare.c.5807027). Data and metadata are available for each country separately and pooled over all countries.

Measurement(s)	mortality
Technology Type(s)	digital curation
Factor Type(s)	Death counts
Sample Characteristic - Organism	Homo sapiens • Covid-19 deaths
Sample Characteristic - Environment	Country
Sample Characteristic - Location	Austria, Belgium, Canada, England and Wales, Denmark, France, Germany, Italy, Japan, the Netherlands, Norway, Portugal, the Republic of Korea, the Republic of Moldova, Romania, Scotland, Spain, Sweden, Switzerland, Ukraine, and the United States of America

An alternative estimation of the death toll of the Covid-19 pandemic in India

The absence of reliable registration of Covid-19 deaths in India has prevented proper assessment and monitoring of the coronavirus pandemic. In addition, India's relatively young age structure tends to conceal the severity of Covid-19 mortality, which is concentrated in older age groups. In this paper, we present four different demographic samples of Indian populations for which we have information on both their demographic structures and death outcomes. We show that we can model the age distribution of Covid-19 mortality in India and use this modeling to estimate Covid-19 mortality in the country. Our findings point to a death toll of approximately 3.2-3.7 million persons by early November 2021. Once India's age structure is factored in, these figures correspond to one of the most severe cases of Covid-19 mortality in the world. India has recorded after February 2021 the second outbreak of coronavirus that has affected the entire country. The accuracy of official statistics of Covid-19 mortality has been questioned, and the real number of Covid-19 deaths is thought to be several times higher than reported. In this paper, we assembled four independent population samples to model and estimate the level of Covid-19 mortality in India. We first used a population sample with the age and sex of Covid-19 victims to develop a Gompertz model of Covid-19 mortality in India. We applied and adjusted this mortality model on two other national population samples after factoring in the demographic characteristics of these samples. We finally derive from these samples the most reasonable estimate of Covid-19 mortality level in India and confirm this result using a fourth population sample. Our findings point to a death toll of about 3.2-3.7 million persons by late May 2021. This is by far the largest number of Covid-19 deaths in the world. Once standardized for age and sex structure, India's Covid-19 mortality rate is above Brazil and the USA. Our analysis shows that existing population samples allow an alternative estimation of deaths due to Covid-19 in India. The results imply that only one out of 7-8 deaths appear to have been recorded as a Covid-19 death in India. The estimates also point to a very high Covid-19 mortality rate, which is even higher after age and sex standardization. The magnitude of the pandemic in India requires immediate attention. In the absence of effective remedies, this calls for a strong response based on a combination of non-pharmaceutical interventions and the scale-up of vaccination to make them accessible to all, with an improved surveillance system to monitor the progression of the pandemic and its spread across India's regions and social groups.

Association between metabolic syndrome and mortality in patients with COVID-19: A nationwide cohort study

OBJECTIVES

We investigated the association between metabolic syndrome (MetS) and mortality among coronavirus disease 2019 (COVID-19) patients in Korea.

METHODS

We analyzed 3876 individuals aged ≥ 20 years who were confirmed with COVID-19 from January 1 to June 4, 2020 based on the Korea National Health Insurance Service (NHIS)-COVID-19 database and had undergone health examination by NHIS between 2015 and 2017. Multivariable Cox proportional hazard regression analyses were performed.

RESULTS

Of total participants, the prevalence of MetS was 21.0% (n = 815). During 58.6 days of mean follow-up, 3.1 % (n = 120) of the participants died. Compared to individuals without MetS, COVID-19 patients with MetS had a significantly increased mortality risk after adjusting for confounders in total participants (hazard ratio [HR]: 1.68, 95 % confidence interval [CI]: 1.14-2.47) and women (HR: 2.41, 95 % CI: 1.17-4.96). A low high-density lipoprotein cholesterol level in total participants (HR: 1.63, 95 % CI: 1.12-2.37) and hyperglycemia in women (HR: 1.97, 95 % CI: 1.01-3.84) was associated with higher mortality risk. The mortality risk increased as the number of MetS components increased among total participants and women (P for trend = 0.009 and 0.016, respectively). In addition, MetS groups had higher mortality risk in aged ≥ 60 years (HR: 1.60, 95 % CI: 1.07-2.39), and never-smokers (2.08, 1.21-3.59).

CONCLUSIONS

The presence of MetS and greater number of its components were associated with increased mortality risks particularly in female patients with COVID-19. Managing MetS may contribute to better outcomes of COVID-19.

Assessing relative COVID-19 mortality during the second wave: a prospective Swiss population-based study

OBJECTIVE

During the first COVID-19 wave in Switzerland, relative mortality was at least eight times higher compared with the uninfected general population. We aimed to assess sex-specific and age-specific relative mortality associated with a SARS-CoV-2 diagnosis during the second wave.

DESIGN

Prospective population-based study.

SETTING

Individuals testing positive for SARS-CoV-2 after the start of the second wave on 1 October 2020 were followed up until death or administrative censoring on 31 December 2020.

PARTICIPANTS

5 179 740 inhabitants of Switzerland in fall 2018 aged 35-95 years (without COVID-19) and 257 288 persons tested positive for SARS-CoV-2 by PCR or antigen testing during the second wave.

PRIMARY AND SECONDARY OUTCOME MEASURES

The planned outcome measure was time to death from any cause, measured from the date of a SARS-CoV-2 diagnosis or 1 October in the general population. Information on confirmed SARS-CoV-2 diagnoses and deaths was matched by calendar time with the all-cause mortality of the general Swiss population of 2018. Proportional hazards models were used to estimate sex-specific and age-specific mortality rates and probabilities of death within 60 days.

RESULTS

The risk of death for individuals tested positive for SARS-CoV-2 in the second wave in Switzerland increased at least sixfold compared with the general population. HRs, reflecting the risk attributable to a SARS-CoV-2 infection, were higher for men (1.40, 95% CI 1.29 to 1.52) and increased for each additional year of age (1.01, 95% CI 1.01 to 1.02). COVID-19 mortality was reduced by at least 20% compared with the first wave in spring 2020.

CONCLUSION

General mortality patterns, increased for men and older persons, were similar in spring and in fall. Absolute and relative COVID-19 mortality was smaller in fall.

TRIAL REGISTRATION

The protocol for this study was registered on 3 December 2020 at https://osf.io/gbd6r.

Constructing personal networks in light of COVID-19 containment measures

The policies for containing the spread of the SARS-CoV2 virus include a number of measures aimed at reducing physical contacts. In this paper, we explore the potential impact of such containment measures on social relations of both young adults and the elderly in Italy. We propose two ego-centered network definitions accounting for physical distance in light of the COVID-19 containment measures: the easy-to-reach network, that represents an accessible source of support that can be activate in case of new lockdown; the accustomed-to-reach network, which includes proximity and habit to meet in person. The approach used for constructing personal (ego-centered) networks on data from the most recent release of Families and Social Subject survey allows us to bring to the foreground people exposed to relational vulnerability. The analysis of the most vulnerable individuals by age, gender, and place of residence reveals that living alone is often associated with a condition of relational vulnerability for both the elderly and for young adults.