Flaws and uncertainties in pandemic global excess death calculations

Was the Allocation of COVID-19 Vaccines Globally Fair and Equitable?

Determining whether an allocation is equitable requires ethical and empirical analyses, specifically answers to 3 questions: (1) What is the ethical standard for fair allocation? (2) What is the quantitative equity metric for this standard? and (3) What do the empirical data demonstrate? Two ethical standards for assessing the fair allocation of scarce medical resources have been delineated: the COVID-19 Vaccines Global Access initiative's population-based standard and the COVID-19 health-burden standard. The equity metric for the population-based standard is vaccine per person in each country, and for the health-burden standard it is vaccine per excess deaths per country. When using excess deaths data from the World Health Organization and vaccine data from the United Nations Children's Fund, the health-burden standard showed that middle-income countries with high excess deaths (e.g., Indonesia, Peru, Mexico, Egypt, South Africa) were treated inequitably: not provided enough vaccines given their high excess deaths. The right standard to assess fair and equitable allocation is a health-burden assessment. According to this standard, contrary to assumptions and initial claims, low-income countries were not treated inequitably in COVID-19 vaccine allocation, and middle-income countries with high excess deaths were treated inequitably. (Am J Public Health. Published online ahead of print May 15, 2025:e1-e10. https://doi.org/10.2105/AJPH.2025.308077).

Post-pandemic excess mortality of COVID-19 in Hong Kong: a retrospective study

Background

As the COVID-19 pandemic shifted into the post-pandemic period in early 2023, following the COVID-19 normalization with relaxation of stringent control measures and high vaccination coverage in Hong Kong, its long-term impact on mortality remains challenging with necessary needs of data-driven insights. This study examined the pattern of post-pandemic excess mortality in Hong Kong.

Methods

We analyzed weekly inpatient death data from public hospitals from January 1, 2013, to June 1, 2024, using a mixed model with over-dispersed Poisson regression. Expected mortality was estimated as the difference between observed mortality and baseline derived from pre-pandemic data. Age-stratified analyses of overall and cause-specific mortality were conducted across the pre-Omicron pandemic, Omicron, and post-pandemic periods.

Findings

In the post-pandemic period, the excess mortality declined but remained six-fold higher (37.66 [95% CI: 32.72-42.60] per 100,000) than pre-Omicron level, maintaining significance after adjusting for age (32.79 [95% CI: 28.13-37.46] per 100,000). The older population experienced sustained excess mortality, with crude estimates of 100.51 and 586.74 per 100,000 among those aged 65-79 years and ≥80 years, respectively, primarily due to respiratory diseases. Younger population showed near-zero overall excess mortality, whereas increased excess mortality among them occurred in heart disease, cerebrovascular disease, and injuries.

Interpretation

Our findings highlight the lasting mortality impact of pandemic among vulnerable populations, specifically the older population, possibly due to the post-COVID conditions and circulating COVID-19, suggesting the need for targeted interventions for this group.

Funding

Health and Medical Research Fund.

Assessing COVID-19 Mortality in Serbia's Capital: Model-Based Analysis of Excess Deaths

Background

Concerns have been raised about discrepancies in COVID-19 mortality data, particularly between preliminary and final datasets of vital statistics in Serbia. In the original preliminary dataset, released daily during the ongoing pandemic, there was an underestimation of deaths in contrast to those reported in the subsequently released yearly dataset of vital statistics.

Objective

This study aimed to assess the accuracy of the final mortality dataset and justify its use in further analyses. In addition, we quantified the relative impact of COVID-19 on the death rate in the Serbian capital's population. In the process, we aimed to explore whether any evidence of cause-of-death misattribution existed in the final published datasets.

Methods

Data were sourced from the electronic databases of the Statistical Office of the Republic of Serbia. The dataset included yearly recorded deaths and the causes of death of all citizens currently living in the territory of Belgrade, the capital of the Republic of Serbia, from 2015 to 2021. Standardization and modeling techniques were utilized to quantify the direct impact of COVID-19 and to estimate excess deaths. To account for year-to-year trends, we used a mixed-effects hierarchical Poisson generalized linear regression model to predict mortality for 2020 and 2021. The model was fitted to the mortality data observed from 2015 to 2019 and used to generate mortality predictions for 2020 and 2021. Actual death rates were then compared to the obtained predictions and used to generate excess mortality estimates.

Results

The total number of excess deaths, calculated from model estimates, was 3175 deaths (99% CI 1715-4094) for 2020 and 8321 deaths (99% CI 6975-9197) for 2021. The ratio of estimated excess deaths to reported COVID-19 deaths was 1.07. The estimated increase in mortality during 2020 and 2021 was 12.93% (99% CI 15.74%-17.33%) and 39.32% (99% CI 35.91%-39.32%) from the expected values, respectively. Those aged 0-19 years experienced an average decrease in mortality of 22.43% and 23.71% during 2020 and 2021, respectively. For those aged up to 39 years, there was a slight increase in mortality (4.72%) during 2020. However, in 2021, even those aged 20-39 years had an estimated increase in mortality of 32.95%. For people aged 60-79 years, there was an estimated increase in mortality of 16.95% and 38.50% in 2020 and 2021, respectively. For those aged >80 years, the increase was estimated at 11.50% and 34.14% in 2020 and 2021, respectively. The model-predicted deaths matched the non-COVID-19 deaths recorded in the territory of Belgrade. This concordance between the predicted and recorded non-COVID-19 deaths provides evidence that the cause-of-death misattribution did not occur in the territory of Belgrade.

Conclusions

The finalized mortality dataset for Belgrade can be safely used in COVID-19 impact analysis. Belgrade experienced a significant increase in mortality during 2020 and 2021, with most of the excess mortality attributable to SARS-CoV-2. Concerns about increased mortality from causes other than COVID-19 in Belgrade seem misplaced as their impact appears negligible.

Mortality in Frankfurt am Main, Germany, 2020-2023: higher excess mortality during an influenza wave in 2022 than during all COVID-19 waves altogether

Introduction

Mortality during the SARS-CoV-2 pandemic was studied in many countries. The results were strongly influenced by the chosen calculation method, the adjustment to the ageing of the population and the reference periods used. Smaller-scale studies sometimes showed considerable differences within countries, but it is unclear whether the differences within a country were due to the fact that the studies were small (sampling error) or whether they were true differences. In an earlier small-scale study in Frankfurt, we examined mortality during the first two years of the pandemic. Our aim was to continue this analysis until the end of 2023, for the first time taking into account other factors influencing mortality such as influenza and heat.

Method

We obtained population data for Frankfurt am Main for 2016-2023 from the Municipal Office of Statistics, City of Frankfurt/Main, mortality data from 2016 to 2023 from the Hessian State Office for Health and Care, data on SARS-CoV-2 and influenza notifications from the homepage of the Robert Koch-Institute and weather data from the homepage of the German Meteorological Office. For calculating standardized mortality ratios (SMR= observed number of deaths divided by the expected number of deaths), we multiplied the mean mortality rate for 5 age groups from 2016-2019 with the total numer of residents in those age groups in the further years or periods, and finally added the numbers of expected deaths per age group.

Results

The update of the assessment of mortality adjusted for age and population trend in the years 2020-2023 in Frankfurt am Main shows an excess mortality (SMR 1.029; 95% CI 1.004-1.054, +185 excess deaths) in 2022, followed by a negative excess mortality in 2023 (SMR 0.972; 95% CI 0.948-0.996). In the years 2020 and 2021 however, no increase in excess mortality had been found (2020: SMR 0.976; 95% CI 0.951-1.001; 2021: 0.998; 95% CI 0,973-1.023). In the second wave of the SARS CoV-2 pandemic with the Wuhan type (fall 2020), a significantly increased mortality was found (SMR 1.106; 95% CI 1.066-1.147, +274 deaths), as well as during the first four waves overall (Wuhan, Alpha and Delta type) (SMR 1.023; 95 CI 1.001-1.045), whereas no increased mortality occurred during the further waves with the Omikron variant in 2022 (SMR 0.988; 95% CI 0.963-1.014). The increased mortality in 2022 was associated with an influenza wave in the last 6 weeks of the year, which had led to a strong increase in mortality (SMR 1.250; 95% CI 1.170-1.330).

Discussion

During the SARS-CoV-2 pandemic, significant excess mortality occurred in Frankfurt am Main only in the second wave at the end of 2020 before vaccination was introduced; in all other waves, no significant excess mortality was recorded. Overall, there was a non-significant negative excess mortality in Frankfurt am Main in 2020 and 2021 and a significant negative excess mortality in 2023. In 2022, however, a significant excess mortality was observed, which could not be attributed to SARS-CoV-2 but to a short, intense wave of influenza in the last 6 weeks at the end of that year, which had also led to a significant increase in mortality throughout Germany. This influenza wave was associated with an excess mortality rate in Frankfurt am Main, which was higher than in any wave of the SARS-CoV-2 pandemic in Frankfurt am Main. The number of excess deaths during that influenza waves was larger than the excess deaths during all SARS-CoV-2 waves altogether. This remarkable fact should be taken into account when dealing with the evaluation of the pandemic, a process which is increasingly beeing called for in many ways in Germany but is still pending.

Excess mortality during the first 2 years of the COVID-19 pandemic (2020-2021) in the Netherlands: Overall and across demographic subgroups

Objectives

The overall impact of the COVID-19 pandemic on mortality can be estimated by the assessment of excess deaths from all causes because the reported number of deaths due to COVID-19 do not accurately reflect the true death toll. We assessed excess mortality in 2020 and 2021 in the Netherlands.

Methods

All analyses were performed on data from comprehensive nationwide registers provided by Statistics Netherlands (Centraal Bureau voor de Statistiek), including demographic characteristics and mortality. All-cause mortality incidence rates were calculated per calendar month and compared against COVID-19 infections and preventive strategies. The all-cause mortality incidence rate ratios (IRRs) with 95% confidence intervals (95% CIs) were estimated per calendar year using Poisson regression (overall and for subgroups).

Results

Compared with predicted mortality based on 2019 rates, the overall excess mortality was 8.9% (IRR 1.089, 95% CI 1.081-1.097) in 2020 and 8.5% (IRR 1.085, 95% CI 1.077-1.092) in 2021. Relative excess mortality was higher for men, people with low household income, first-generation immigrants, and individuals living in extremely urbanized areas. In 2020, excess mortality was highest in age groups above 75 years (over 10%.); in 2021, it was clearly present even in the 20-39 years age group (6.6%).

Conclusions

Our results quantify excess mortality during the first 2 years of the COVID-19 pandemic in the Netherlands. We show that the extent of excess mortality varies considerably across demographic groups, which may help in identifying target groups for preventive strategies during future health crises.

Productivity Losses due to Health Problems Arising from COVID-19 Pandemic: A Systematic Review of Population-Level Studies Worldwide

AIM

To systematically review the evidence on productivity losses due to health problems arising from the COVID-19 pandemic based on evidence from population-level studies.

METHODS

Following PRISMA statement, we conducted a systematic review using Medline, Embase, Scopus, Web of Science, EconLit, WHO COVID-19 Research and EuropePMC databases and a grey literature search. We included population-level studies using secondary data and qualitatively assessed eligible studies. For a quantitative cross-study comparison, we calculated losses in 2020 international dollars and as a share of gross domestic product. PROSPERO registration number: CRD42023478059.

RESULTS

Thirty-eight studies were eligible for review, most of which reported losses in high-income countries and the European region. COVID-19 was a focus of 33 studies while 3 studies investigated losses from both long COVID and excess mortality. The Human Capital Approach dominated (30 studies) and no study used the Friction Cost Approach. Most studies (84%) reported on premature mortality losses and a quarter provided estimates of losses due to absenteeism. Of the 33 studies eligible for quantitative comparison, we found that the productivity losses ranged from 0 to 2.1% of gross domestic product; the greatest losses were in the high-income countries and for those aged 40-59 years; and losses among men contributed to around 3/4 of the total burden.

CONCLUSION

The available evidence on the topic is limited, particularly considering the methodological approaches used. Thus, more research is needed to reach a more comprehensive understanding of economy-level productivity losses resulting from the recent COVID-19 pandemic.

Excess mortality and years of life lost from 2020 to 2023 in France: a cohort study of the overall impact of the COVID-19 pandemic on mortality

Introduction

Excess mortality has been frequently used worldwide for summarising the COVID-19 pandemic-related burden. Estimates for France for the years 2020-2022 vary substantially from one report to another, and the year 2023 is poorly documented. The present study assessed the level of excess mortality that occurred in France between 2020 and 2023 together with the corresponding years of life lost (YLL), in order to provide a reliable, detailed and comprehensive description of the overall impact of the pandemic.

Method

This open cohort study of the whole French population analysed the 8 451 372 death occurrences reported for the years 2010-2023. A Poisson regression model was trained with years 2010-2019 for determining the age-specific and sex-specific evolution trends of mortality before the pandemic period. These trends were then used for estimating the excess mortality during the pandemic period (years 2020-2023). The life expectancies of the persons in excess mortality were used for estimating the corresponding YLL.

Results

From 2020 to 2023, the number of excess deaths (mean (95% CI) (percentage of change versus expected mortality)) was, respectively, 49 541 (48 467; 50 616) (+8.0%), 42 667 (41 410; 43 909) (+6.9%), 53 129 (51 696; 54 551) (+8.5%), and 17 355 (15 760; 18 917) (+2.8%). Corresponding YLL were 512 753 (496 029; 529 633), 583 580 (564 137; 602 747), 663 588 (641 863; 685 723), and 312 133 (288 051; 335 929). Individuals younger than 60 years old accounted for 17% of the YLL in 2020, 26% in 2021, 32% in 2022 and 50% in 2023. Men were more affected than women by both excess mortality and YLL.

Conclusion

This study highlights the long-lasting impact of the pandemic on mortality in France, with four consecutive years of excess mortality and a growing impact on people under 60, particularly men, suggesting lasting and profound disruption to the healthcare system.

Relationship between COVID-19 cases and monthly mortality from all causes, cancer, cardiovascular diseases and diabetes in 16 countries, 2020-21

BACKGROUND

During the COVID-19 pandemic, mortality from some chronic diseases increased. In this study, we evaluated monthly excess mortality from all causes, cancer, cardiovascular diseases (CVD) and diabetes during the months of 2020 and 2021, examining its relationship with COVID-19 cases.

METHODS

Monthly cause-specific mortality data were downloaded from public repositories of national statistics offices or directly requested from them, and population data were obtained from the United Nations archives. Excess deaths were estimated as the difference between observed and expected deaths. Monthly expected deaths for 2020 and 2021 were calculated using a quasi-Poisson regression model trained on 2010-19 data (or a shorter timespan if the full decade of data was not available). To quantify the correlation between COVID-19 cases and monthly excess mortality, we used the Spearman's correlation coefficient (rs).

RESULTS

The study included 16 countries that provided monthly national data on causes of death (Argentina, Austria, Brazil, Switzerland, Chile, the Czech Republic, Germany, Georgia, Hungary, Italy, Lithuania, Latvia, Mexico, Serbia, Slovakia and the USA). A positive correlation was found between COVID-19 cases and monthly excess mortality from all causes in all countries (rs ranging from 0.61 to 0.91), from CVD in 11 countries (rs ranging from 0.45 to 0.85) and for diabetes in 13 countries (rs ranging from 0.42 to 0.79). Excess mortality above 5% was estimated from all causes in 14 countries for both 2020 and 2021, from CVD in seven countries for 2020 and in nine countries for 2021, and from diabetes in 11 countries for 2020 and in 12 countries for 2021. No excess above 5% was estimated for cancer mortality in any of the countries considered.

CONCLUSIONS

Excess mortality from CVD and diabetes persisted in several countries throughout 2021. These increases coincide with COVID-19 peaks, supporting a short-term impact of the COVID-19 pandemic on mortality from these causes.

Differential risk of healthcare workers versus the general population during outbreak, war and pandemic crises

Healthcare workers may have different risk for severe outcomes compared with the general population during diverse crises. This paper introduces the concept of healthcare worker versus population hazard (HPH), the risk of an outcome of interest in active healthcare workers compared with the general population they serve. HPH can be expressed with relative risk (HPH(r)) and absolute risk difference (HPH(a)) metrics. Illustrative examples are drawn from infectious outbreaks, war, and the COVID-19 pandemic on death outcomes. HPH can be extreme for lethal outbreaks (HPH(r) = 30 to 143, HPH(a) = 8 to 91 per 1000 for Ebola deaths in 3 Western African countries in 2013-5), and modestly high in relative terms and very high in absolute terms for protracted, major armed conflicts (HPH(r) = 1.38 and HPH(a) = 10.2 for Syria during 2011-2024). Conversely, healthcare workers had 8-12-fold lower risk than the population they served for pandemic excess deaths (physicians in USA) or COVID-19 deaths (physicians in Ontario, healthcare workers in Finland), while healthcare workers in Indonesia did not have this advantage for COVID-19 deaths versus the general population. HPH is susceptible to data inaccuracies in numbers of at-risk populations and of outcomes of interest. Importantly, inferences about healthcare worker risk can be misleading, if deaths of retired healthcare workers contaminate the risk calculations- as in the case of misleading early perceptions of exaggerated COVID-19 risk for healthcare professionals. HPH can offer useful insights for risk assessment to healthcare professionals, the general public, and policy makers and may be useful to monitor for planning and interventions during crises.

Excess Deaths in Korea During the COVID-19 Pandemic: 2020-2022

OBJECTIVES

Excess deaths, an indicator that compares total mortality rates before and during a pandemic, offer a comprehensive view of the pandemic's impact. However, discrepancies may arise from variations in estimating expected deaths. This study aims to compare excess deaths in Korea during the coronavirus disease 2019 pandemic using 3 methods and to analyze patterns using the most appropriate method.

METHODS

Expected deaths from 2020 to 2022 were estimated using mortality data from 2015-2019 as reference years. This estimation employed 3 approaches: (1) simple average, (2) age-adjusted average, and (3) age-adjusted linear regression. Excess deaths by age, gender, and cause of death were also presented.

RESULTS

The number of excess deaths varied depending on the estimation method used, reaching its highest point with the simple average and its lowest with the age-adjusted average. Age-adjusted linear regression, which accounts for both the aging population and declining mortality rates, was considered most appropriate. Using this model, excess deaths were estimated at 0.3% for 2020, 4.0% for 2021, and 20.7% for 2022. Excess deaths surged among individuals in their 20s throughout the pandemic, largely attributed to a rise in self-harm and suicide. Additionally, the results indicated sharp increases in deaths associated with "endocrine, nutritional, and metabolic diseases" and "symptoms, signs, and abnormal clinical and laboratory findings, not elsewhere classified."

CONCLUSIONS

Substantial variations in excess deaths were evident based on estimation method, with a notable increase in 2022. The heightened excess deaths among young adults and specific causes underscore key considerations for future pandemic responses.

Analyses of academician cohorts generate biased pandemic excess death estimates

OBJECTIVE

Death data from cohorts of academicians have been used to estimate pandemic excess deaths. We aimed to evaluate the validity of this approach.

STUDY DESIGN AND SETTING

Data were analyzed from living and deceased member lists from Mainland China, UK and Greece academies; and Nobel laureates (and US subset thereof). Samples of early elected academicians were probed for unrecorded deaths; datasets overtly missing deaths were excluded from further analyses. Actuarial risks were compared against the general population in the same country in respective age strata. Relative incidence risk increases in death in active pandemic periods were compared to population-wide pandemic excess death estimates for the same country.

RESULTS

Royal Society and Academy of Athens datasets overtly missed deaths. Prepandemic death rates were 4- to 12-fold lower in the Chinese Academy of Engineering (CAE) vs respective age strata of the Mainland China population. A +158% relative increase in death risk was seen in CAE data during the first 12-month of wide viral spread. Both increases (+34% in British Academy) and decreases (-27% in US Nobel laureates) in death rates occurred in pandemic (2020-22) vs prepandemic (2017-19) years; point estimates were far from known excess deaths in the respective countries (+6% and +14%, respectively). Published excess death estimates for urban-dwelling Mainland China selectively analyzed CAE that had double the pandemic death rates than another Chinese academy (Chinese Academy of Sciences).

CONCLUSION

Missingness, lack of representativeness, large uncertainty, and selective analysis reporting make data from academy rosters unreliable for estimating general population excess deaths.

Construction and validation of a covariate-based model for district-level estimation of excess deaths due to COVID-19 in India

Background

Different statistical approaches for estimating excess deaths due to coronavirus disease 2019 (COVID-19) pandemic have led to varying estimates. In this study, we developed and validated a covariate-based model (CBM) with imputation for prediction of district-level excess deaths in India.

Methods

We used data extracted from deaths registered under the Civil Registration System for 2015-19 for 684 of 713 districts in India to estimate expected deaths for 2020 through a negative binomial regression model (NBRM) and to calculate excess observed deaths. Specifically, we used 15 covariates across four domains (state, health system, population, COVID-19) in a zero inflated NBRM to identify covariates significantly (P < 0.05) associated with excess deaths estimate in 460 districts. We then validated this CBM in 140 districts by comparing predicted and estimated excess. For 84 districts with missing covariates, we validated the imputation with CBM by comparing estimated with predicted excess deaths. We imputed covariate data to predict excess deaths for 29 districts which did not have data on deaths.

Results

The share of elderly and urban population, the under-five mortality rate, prevalence of diabetes, and bed availability were significantly associated with estimated excess deaths and were used for CBM. The mean of the CBM-predicted excess deaths per district (x̄ = 989, standard deviation (SD) = 1588) was not significantly different from the estimated one (x̄ = 1448, SD = 3062) (P = 0.25). The estimated excess deaths (n = 67 540; 95% confidence interval (CI) = 35 431, 99 648) were similar to the predicted excess death (n = 64 570; 95% CI = 54 140, 75 000) by CBM with imputation. The total national estimate of excess deaths for all 713 districts was 794 989 (95% CI = 664 895, 925 082).

Conclusions

A CBM with imputation can be used to predict excess deaths in an appropriate context.

Variability in excess deaths across countries with different vulnerability during 2020-2023

Excess deaths provide total impact estimates of major crises, such as the COVID-19 pandemic. We evaluated excess death trajectories across countries with accurate death registration and population age structure data and assessed relationships with vulnerability indicators. Using the Human Mortality Database on 34 countries, excess deaths were calculated for 2020-2023 (to week 29, 2023) using 2017-2019 as reference, with adjustment for 5 age strata. Countries were divided into less and more vulnerable; the latter had per capita nominal GDP < $30,000, Gini > 0.35 for income inequality and/or at least ≥2.5% of their population living in poverty. Excess deaths (as proportion of expected deaths, p%) were inversely correlated with per capita GDP (r = -0.60), correlated with proportion living in poverty (r = 0.66), and modestly correlated with income inequality (r = 0.45). Incidence rate ratio for deaths was 1.062 (95% CI, 1.038-1.087) in more versus less vulnerable countries. Excess deaths started deviating in the two groups after the first wave. Between-country heterogeneity diminished gradually within each group. Less vulnerable countries had mean p% = -0.8% and 0.4% in 0-64 and >65-y-old strata. More vulnerable countries had mean p% = 7.0% and 7.2%, respectively. Lower death rates were seen in children of age 0-14 y during 2020-2023 versus prepandemic years. While the pandemic hit some countries earlier than others, country vulnerability dominated eventually the cumulative impact. Half the analyzed countries witnessed no substantial excess deaths versus prepandemic levels, while the others suffered major death tolls.

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.

Variability in excess deaths across countries with different vulnerability during 2020-2023

Excess deaths provide total impact estimates of major crises, such as the COVID-19 pandemic. We evaluated excess death's trajectories during 2020-2023 across countries with accurate death registration and population age structure data; and assessed relationships with economic indicators of vulnerability. Using the Human Mortality Database on 34 countries, excess deaths were calculated for 2020-2023 (to week 29, 2023) using 2017-2019 as reference, with weekly expected death calculations and adjustment for 5 age strata. Countries were divided into less and more vulnerable; the latter had per capita nominal GDP<$30,000, Gini>0.35 for income inequality and/or at least 2.5% of their population living in poverty. Excess deaths (as proportion of expected deaths, p%) were inversely correlated with per capita GDP (r=-0.60), correlated with proportion living in poverty (r=0.66) and modestly correlated with income inequality (r=0.45). Incidence rate ratio for deaths was 1.06 (95% confidence interval, 1.04-1.08) in the more versus less vulnerable countries. Excess deaths started deviating in the two groups after the first wave. Between-country heterogeneity diminished over time within each of the two groups. Less vulnerable countries had mean p%=-0.8% and 0.4% in 0-64 and >65 year-old strata while more vulnerable countries had mean p%=7.0% and 7.2%, respectively. Usually lower death rates were seen in children 0-14 years old during 2020-2023 versus pre-pandemic years. While the pandemic hit some countries earlier than others, country vulnerability dominated eventually the cumulative impact. Half of the analyzed countries witnessed no substantial excess deaths versus pre-pandemic levels, while the other half suffered major death tolls.