"The Graves Cannot Be Dug Fast Enough": Excess Deaths Among US Amish and Mennonites During the 1918 Flu Pandemic

Estimating the lethal impact of a pandemic on a religious community with significant barriers to outsiders can be exceedingly difficult. Nevertheless, Stein and colleagues (2021) developed an innovative means of arriving at such an estimate for the lethal impact of COVID-19 on the Amish community in 2020 by counting user-generated death reports in the widely circulated Amish periodical The Budget. By comparing monthly averages of reported deaths before and during the COVID-19 pandemic, Stein and colleagues were able to arrive at a rough estimate of "excess deaths" during the first year of the pandemic. Our research extends the same research method, applying it to the years during and immediately preceding the global influenza pandemic of 1918. Results show similarly robust findings, including three notable "waves" of excess deaths among Amish and conservative Mennonites in the USA in 1918, 1919, and 1920. Such results point to the promise of utilizing religious periodicals like The Budget as a relatively untapped trove of user-generated data on public health outcomes among religious minorities more than a century in the past.

Do vaccines increase or decrease susceptibility to diseases other than those they protect against?

Contrary to the long-held belief that the effects of vaccines are specific for the disease they were created; compelling evidence has demonstrated that vaccines can exert positive or deleterious non-specific effects (NSEs). In this review, we compiled research reports from the last 40 years, which were found based on the PubMed search for the epidemiological and immunological studies on the non-specific effects (NSEs) of the most common human vaccines. Analysis of information showed that live vaccines induce positive NSEs, whereas non-live vaccines induce several negative NSEs, including increased female mortality associated with enhanced susceptibility to other infectious diseases, especially in developing countries. These negative NSEs are determined by the vaccination sequence, the antigen concentration in vaccines, the type of vaccine used (live vs. non-live), and also by repeated vaccination. We do not recommend stopping using non-live vaccines, as they have demonstrated to protect against their target disease, so the suggestion is that their detrimental NSEs can be minimized simply by changing the current vaccination sequence. High IgG4 antibody levels generated in response to repeated inoculation with mRNA COVID-19 vaccines could be associated with a higher mortality rate from unrelated diseases and infections by suppressing the immune system. Since most COVID-19 vaccinated countries are reporting high percentages of excess mortality not directly attributable to deaths from such disease, the NSEs of mRNA vaccines on overall mortality should be studied in depth.

A comprehensive analysis of all-cause and cause-specific excess deaths in 30 countries during 2020

The impact of COVID-19 on mortality from specific causes of death remains poorly understood. This study analysed cause-of-death data provided by the World Health Organization from 2011 to 2019 to estimate excess deaths in 2020 in 30 countries. Over-dispersed Poisson regression models were used to estimate the number of deaths that would have been expected if the pandemic had not occurred, separately for men and women. The models included year and age categories to account for temporal trends and changes in size and age structure of the populations. Excess deaths were calculated by subtracting observed deaths from expected ones. Our analysis revealed significant excess deaths from ischemic heart diseases (IHD) (in 10 countries), cerebrovascular diseases (CVD) (in 10 countries), and diabetes (in 19 countries). The majority of countries experienced excess mortality greater than 10%, including Mexico (+ 38·8% for IHD, + 34·9% for diabetes), Guatemala (+ 30·0% for IHD, + 10·2% for CVD, + 39·7% for diabetes), Cuba (+ 18·8% for diabetes), Brazil (+ 12·9% for diabetes), the USA (+ 15·1% for diabetes), Slovenia (+ 33·8% for diabetes), Poland (+ 30·2% for IHD, + 19·5% for CVD, + 26 1% for diabetes), Estonia (+ 26·9% for CVD, + 34·7% for diabetes), Bulgaria (+ 22·8% for IHD, + 11·4% for diabetes), Spain (+ 19·7% for diabetes), Italy (+ 18·0% for diabetes), Lithuania (+ 17·6% for diabetes), Finland (+ 13·2% for diabetes) and Georgia (+ 10·7% for IHD, + 19·0% for diabetes). In 2020, 22 out of 30 countries had a significant increase in total mortality. Some of this excess was attributed to COVID-19, but a substantial increase was also observed in deaths attributed to cardiovascular diseases and diabetes.

Impacts of the COVID-19 pandemic on life expectancy at birth in Asia

OBJECTIVE

To investigate the impact of the COVID-19 pandemic on life expectancy at birth (e0) for 51 Asian countries and territories from January 1, 2020 to December 31, 2021.

METHOD

Based on age-sex-specific mortality used for estimating the changes in e0 for years 2019, 2020, and 2021 from the 2022 revision of the World Population Prospects, we employed Arriaga's discrete method to decompose changes in e0 into both absolute and relative contributions of changes in age-specific death rate, and further obtained the age-sex-specific contribution to changes in e0 by country/territory and period (i.e., 2019-2020 and 2020-2021) for Asia.

FINDINGS

The COVID-19 pandemic reduced 1.66 years in e0 of the Asian population from 2019 to 2021, slightly lower than the world average of 1.74 years. South Asia had a high loss of 3.01 years, whereas Eastern Asia had almost no changes. Oman, Lebanon, India, Armenia, Azerbaijan, Indonesia, and the Philippines experienced a high loss of above 2.5 years in e0. Despite significant national and territorial variations, the decline of e0 in Asia was mostly from the age group of 60-79 years, followed by age groups of 80 + and 45-59 years; and age groups of children contributed little (i.e., 0-4 and 5-14 years old). Males suffered more losses than females in this pandemic. Asian nations saw less loss in e0 in the second year of the pandemic, i.e., 2020-2021, than in the first year, i.e., 2019-2020, but this recovery trend was not observed in Southern Asia and South-Eastern Asia. Countries from Central Asia and Western Asia, such as Kazakhstan, Armenia, Azerbaijan, Lebanon, and Oman, had extraordinarily more losses in e0 in the first year at ages around 70.

CONCLUSION

The COVID-19 pandemic had significantly affected e0 of Asian populations, and most contribution to the reduction of e0 came from the three older age groups, 60-79 years, 80 + years, and 45-59 years, with great variations across countries/territories. Our findings could have important implications for development of more resilient public health systems in Asian societies with better policy interventions for vulnerable demographic groups.

Assessment of Suspected COVID-19 Deaths in Nonhealthcare Settings in Côte d'Ivoire, March 11 to July 31, 2020

According to hospital records, 5 months after reporting its first case of COVID-19, Côte d'Ivoire reported only 102 deaths. We conducted a community mortality survey in the 13 districts where 95% of COVID-19 cases were reported to assess COVID-19 mortality in nonhealthcare settings. To identify suspected COVID-19 deaths in communities, we used data from social and administrative institutions, such as police and fire departments, funeral homes, and places of worship, whose functions include providing services related to deaths. Our survey identified 54 (17.6%) suspected COVID-19 deaths, which is more than half of the official reported number. Our study showed that in areas with low access to healthcare and poorly functioning death notification and registration systems, community-based data sources could be used to identify suspected COVID-19 deaths outside of the health sector. They can provide early warning data on events, such as an unusual number of community deaths or diseases.

The contribution of population age-sex structure to the excess mortality estimates of 2020-2021 in Denmark, Finland, Iceland, Norway, and Sweden

The Nordic countries offer an ideal case study of the COVID-19 pandemic due to their comparability, high data quality, and variable mitigations. We investigated the age- and sex-specific mortality patterns during 2020-2021 for the five Nordic countries and analysed the total age- and sex-adjusted excess deaths, ratios of actual to expected death rates, and age-standardized excess death estimates. We assessed excess deaths using several time periods and sensitivity tests, and 42 sex and age groups. Declining pre-pandemic age-specific death rates reflected improving health demographics. These affect the expected death estimates and should be accounted for in excess mortality models. Denmark had the highest death rates both before and during the pandemic, whereas in 2020 Sweden had the largest mortality increase. The age-standardized mortality of Denmark, Iceland and Norway was lowest in 2020. 2021 was one of the lowest mortality years for all Nordic countries. The total excess deaths in 2020-2021 were dominated by 70-89-year-olds, were not identified in children, and were more pronounced among men than women. Sweden had more excess deaths in 2020 than in 2021, whereas Finland, Norway and Denmark had the opposite. Our study provides new details on Nordic sex- and age-specific mortality during the first two years of the pandemic and shows that several metrics are important to enable a full understanding and comparison of the pandemic mortality.

Temporal variation of excess deaths from diabetes during the COVID-19 pandemic in the United States

BACKGROUND

Although the COVID-19 pandemic has persisted for more than two years with the evident excess mortality from diabetes, few studies have investigated its temporal patterns. This study aims to estimate the excess deaths from diabetes in the United States (US) during the COVID-19 pandemic and evaluate the excess deaths by spatiotemporal pattern, age groups, sex, and race/ethnicity.

METHODS

Diabetes as one of multiple causes of death or an underlying cause of death were both considered into analyses. The Poisson log-linear regression model was used to estimate weekly expected counts of deaths during the pandemic with adjustments for long-term trend and seasonality. Excess deaths were measured by the difference between observed and expected death counts, including weekly average excess deaths, excess death rate, and excess risk. We calculated the excess estimates by pandemic wave, US state, and demographic characteristic.

RESULTS

From March 2020 to March 2022, deaths that diabetes as one of multiple causes of death and an underlying cause of death were about 47.6 % and 18.4 % higher than the expected. The excess deaths of diabetes had evident temporal patterns with two large percentage increases observed during March 2020, to June 2020, and June 2021 to November 2021. The regional heterogeneity and underlying age and racial/ethnic disparities of the excess deaths were also clearly observed.

CONCLUSIONS

This study highlighted the increased risks of diabetes mortality, heterogeneous spatiotemporal patterns, and associated demographic disparities during the pandemic. Practical actions are warranted to monitor disease progression, and lessen health disparities in patients with diabetes during the COVID-19 pandemic.

Excess mortality from COVID 19 in Costa Rica: a registry based study using Poisson regression

Background

Official death toll related to COVID-19 has been considerably underestimated in reports from some Latin American countries. This study aimed to analyze the mortality associated with the COVID-19 pandemic in Costa Rica between March 2020 and December 2021.

Methods

A registry based study based on 2017-2021 data from the National Institute of Statistics and Census was designed (N = 128,106). Excess deaths were defined by the WHO as "the difference in the total number of deaths in a crisis compared to those expected under normal conditions"; and were estimated using a Poisson regression, and mortality and years of potential life lost (YPLL) rates were calculated.

Findings

The COVID-19 pandemic represented 15% of the deaths in Costa Rica between March 2020 and December 2021. The mortality rate related to COVID-19 was 83 per 100,000 person-years. Between March and July 2020 (low-incidence period), observed number of deaths was 9%-lower than expected, whereas it was 15% and 24% higher than expected between July 2020 and March 2021 (high incidence period - no vaccination), and between March 2021 and December 2021 (high incidence period - progressive vaccination) respectively. Between July 2020 and December 2021, excess deaths observed and COVID-19 deaths reported were comparable (7461 and 7620 respectively). Nevertheless, there were more deaths than expected for conditions that predispose to COVID-19 deaths. YPLL and mortality rates increased with age, but significant excess deaths were observed in all age-groups older than 30-39 years. No large differences were noted by districts' socioeconomic characteristics although excess death rate was lower in rural compared to urban areas.

Interpretation

Reporting of deaths was only slightly underestimated. In the pre-vaccination period, mortality rate and YPLL rates increased with age, being highest in people aged 60 years or older and justifying the decision to initially prioritize vaccination of older individuals.

Funding

The study was supported by the University of Costa Rica and the Agencia Costarricense de Investigaciones Biomédicas - Fundación Inciensa.

Years of life lost during the Covid-19 pandemic in Sweden considering variation in life expectancy by level of geriatric care

The Covid-19 pandemic has not affected the population evenly. This must be acknowledged when it comes to understanding the Covid-19 death toll and answering the question of how many life years have been lost. We use level of geriatric care to account for variation in remaining life expectancy among individuals that died during 2020. Based on a linkage of administrative registers, we estimate remaining life expectancy stratified by age, sex, and care status using an incidence-based multistate model and analyze the number of years of life lost (YLL) during 2020 in Sweden. Our results show that remaining life expectancy between individuals with and without care differs substantially. More than half of all Covid-19 deaths had a remaining life expectancy lower than 4 years. Yet, in a 1-year perspective, Covid-19 did not seem to replace other causes of death. Not considering the differences in remaining life expectancy in the affected populations overestimated YLL by 40% for women and 30% for men, or around 2 years per death. While the unadjusted YLL from Covid-19 amounted to an average of 7.5 years for women and 8.6 years for men, the corresponding YLL adjusted for care status were 5.4 and 6.6, respectively. The total number of YLL to Covid-19 in 2020 is comparable to YLL from ischemic heart disease in 2019 and 2020. Our results urge the use of subgroup specific mortality when counting the burden of Covid-19. YLL are considerably reduced when the varying susceptibility for death is considered, but even if most lifespans were cut in the last years of life, the YLL are still substantial.

Excess deaths in the United States during the first year of COVID-19

Accurately determining the number of excess deaths caused by the COVID-19 pandemic is hard. The most important challenge is determining the counterfactual count of baseline deaths that would have occurred in its absence. Flexible estimation methods were used here to provide this baseline number and plausibility of the resulting estimates was evaluated by examining how changes between baseline and actual prior year deaths compared to historical year-over-year changes during the previous decade. Similar comparisons were used to examine the reasonableness of excess death estimates obtained in prior research. Total, group-specific and cause-specific excess deaths in the U.S. from March 2020 through February 2021 were calculated using publicly available data covering all deaths from March 2009 through December 2020 and provisional data for January 2021 and February 2021. The estimates indicate that there were 649,411 (95% CI: 600,133 to 698,689) excess deaths in the U.S. from 3/20-2/21, a 23% (95% CI: 21%-25%) increase over baseline, with 82.9% (95% CI: 77.0% - 89.7%) of these attributed directly to COVID-19. There were substantial differences across population groups and causes in the ratio of actual-to-baseline deaths, and in the contribution of COVID-19 to excess mortality. Prior research has probably often underestimated baseline mortality and so overstated both excess deaths and the percentage of them attributed to non-COVID-19 causes.

Particulate matter and COVID-19 excess deaths: Decomposing long-term exposure and short-term effects

We investigate the time-varying effect of particulate matter (PM) on COVID-19 deaths in Italian municipalities. We find that the lagged moving averages of PM_2.5 and PM₁₀ are significantly related to higher excess deceases during the first wave of the disease, after controlling, among other factors, for time-varying mobility, regional and municipality fixed effects, the nonlinear contagion trend, and lockdown effects. Our findings are confirmed after accounting for potential endogeneity, heterogeneous pandemic dynamics, and spatial correlation through pooled and fixed-effect instrumental variable estimates using municipal and provincial data. In addition, we decompose the overall PM effect and find that both pre-COVID long-term exposure and short-term variation during the pandemic matter. In terms of magnitude, we observe that a 1 μg/m³ increase in PM_2.5 can lead to up to 20% more deaths in Italian municipalities, which is equivalent to a 5.9% increase in mortality rate.

Effects of country success on COVID-19 cumulative cases and excess deaths in 169 countries

COVID-19 has caused over 260 million confirmed cases and over 5 million deaths globally. The results of statistical and multiple criteria analyses on the success of 169 countries and on COVID-19 cumulative cases and excess deaths show that the prosperity of a country relates directly to the consequences due to the pandemic. The topic of this article is the Country Success and COVID-19 (CSC) Map of the World. As a country's success grows, this map shows how cumulative cases of COVID-19 increase; at the same time, excess deaths decrease. The indicators in the system of criteria regarding country success and sustainability are interrelated. Conditional country successes remain quite similar, despite changes to the numbers of countries and their indicators. Likewise, the seven clusters of countries under consideration group together independently of which system of indicators had been applied for their analysis. The 2020 Inglehart-Welzel Cultural Map of the World, which is grounded on surveys, and the CSC Map, which is grounded on statistical indicators, have axes that correlate with one another significantly. The CSC Map Model explains over 63% of the dispersions pertinent to COVID-19 cumulative cases, over 52% of COVID-19 excess deaths, and over 95% of country success variables. The layout of the clusters on the CSC Map changes little over time. Upon performance of the correlation analysis, it was established that strong and statistically significant relationships exist between 169 countries success and sustainability linked with their current air quality score (r = 0.602, p < 0.01) and the environmental performance index (EPI) score (r = 0.931, p < 0.01). The results obtained show that when a country's EPI score and current air quality improve by 1%, excess deaths decrease, respectively, by 2.33 and 1.55%. Global integrated analysis on country successes, COVID-19 cumulative cases, and excess deaths comprise this study.

County-level estimates of excess mortality associated with COVID-19 in the United States

The COVID-19 pandemic in the U.S. has been largely monitored using death certificates containing reference to COVID-19. However, prior analyses reveal that a significant percentage of excess deaths associated with the pandemic were not directly assigned to COVID-19. In this study, we estimated a generalized linear model of expected mortality based on historical trends in deaths by county of residence between 2011 and 2019. We used the results of the model to generate estimates of excess mortality and excess deaths not assigned to COVID-19 in 2020 for 1470 county sets in the U.S. representing 3138 counties. Across the country, we estimated that 438,386 excess deaths occurred in 2020, among which 87.5% were assigned to COVID-19. Some regions (Mideast, Great Lakes, New England, and Far West) reported the most excess deaths in large central metros, whereas other regions (Southwest, Southeast, Plains, and Rocky Mountains) reported the highest excess mortality in nonmetro areas. The proportion assigned to COVID-19 was lowest in large central metro areas (79.3%). Regionally, the proportion of excess deaths assigned to COVID-19 was lowest in the Southeast (81.6%), Southwest (82.6%), Far West (83.7%), and Rocky Mountains (86.7%). Across the regions, the number of excess deaths exceeded the number of directly assigned COVID-19 deaths in most counties. The exception to this pattern occurred in New England, which reported more directly assigned COVID-19 deaths than excess deaths in metro and nonmetro areas. Many county sets had substantial numbers of excess deaths that were not accounted for in direct COVID-19 death counts. Estimates of excess mortality at the local level can inform the allocation of resources to areas most impacted by the pandemic and contribute to positive behavior feedback loops, such as increases in mask-wearing and vaccine uptake.

Excess deaths reveal the true spatial, temporal and demographic impact of COVID-19 on mortality in Ecuador

BACKGROUND

In early 2020, Ecuador reported one of the highest surges of per capita deaths across the globe.

METHODS

We collected a comprehensive dataset containing individual death records between 2015 and 2020, from the Ecuadorian National Institute of Statistics and Census and the Ecuadorian Ministry of Government. We computed the number of excess deaths across time, geographical locations and demographic groups using Poisson regression methods.

RESULTS

Between 1 January and 23 September 2020, the number of excess deaths in Ecuador was 36 402 [95% confidence interval (CI): 35 762-36 827] or 208 per 100 000 people, which is 171% of the expected deaths in that period in a typical year. Only 20% of the excess deaths are attributable to confirmed COVID-19 deaths. Strikingly, in provinces that were most affected by COVID-19 such as Guayas and Santa Elena, the all-cause deaths are more than double the expected number of deaths that would have occurred in a normal year. The extent of excess deaths in men is higher than in women, and the number of excess deaths increases with age. Indigenous populations had the highest level of excess deaths among all ethnic groups.

CONCLUSIONS

Overall, the exceptionally high level of excess deaths in Ecuador highlights the enormous burden and heterogeneous impact of COVID-19 on mortality, especially in older age groups and Indigenous populations in Ecuador, which was not fully revealed by COVID-19 death counts. Together with the limited testing in Ecuador, our results suggest that the majority of the excess deaths were likely to be undocumented COVID-19 deaths.

County-level estimates of excess mortality associated with COVID-19 in the United States

The COVID-19 pandemic in the U.S. has been largely monitored using death certificates containing reference to COVID-19. However, prior analyses reveal that a significant percentage of excess deaths associated with the pandemic were not directly assigned to COVID-19. In this study, we estimated a generalized linear model of expected mortality based on historical trends in deaths by county of residence between 2011 and 2019. We used the results of the model to generate estimates of excess mortality and excess deaths not assigned to COVID-19 in 2020 for 1470 county sets in the U.S. representing 3138 counties. Across the country, we estimated that 438,386 excess deaths occurred in 2020, among which 87.5% were assigned to COVID-19. Some regions (Mideast, Great Lakes, New England, and Far West) reported the most excess deaths in large central metros, whereas other regions (Southwest, Southeast, Plains, and Rocky Mountains) reported the highest excess mortality in nonmetro areas. The proportion assigned to COVID-19 was lowest in large central metro areas (79.3%). Regionally, the proportion of excess deaths assigned to COVID-19 was lowest in the Southeast (81.6%), Southwest (82.6%), Far West (83.7%), and Rocky Mountains (86.7%). Across the regions, the number of excess deaths exceeded the number of directly assigned COVID-19 deaths in most counties. The exception to this pattern occurred in New England, which reported more directly assigned COVID-19 deaths than excess deaths in metro and nonmetro areas. Many county sets had substantial numbers of excess deaths that were not accounted for in direct COVID-19 death counts. Estimates of excess mortality at the local level can inform the allocation of resources to areas most impacted by the pandemic and contribute to positive behavior feedback loops, such as increases in mask-wearing and vaccine uptake.

•

438,386 excess deaths occurred in 2020, among which 87.5% were assigned to COVID-19.

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There was substantial heterogeneity in excess death rates across counties.

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The mortality impact of the Covid-19 pandemic was effectively hidden in many counties.

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The percent of excess deaths assigned to COVID-19 was lowest in the South and West.

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New England uniquely reported more direct COVID-19 deaths than excess deaths.

Disparities in Excess Mortality Between Indigenous and Non-Indigenous Brazilians in 2020: Measuring the Effects of the COVID-19 Pandemic

This study aimed to estimate the number of excess deaths among Indigenous Peoples associated with the COVID-19 pandemic in 2020 and to assess the disparities in excess mortality between Indigenous and non-Indigenous Brazilians. A time series analysis of weekly mortality data including all deaths from January 2015 to December 2020 was conducted. The number of expected deaths for 2020 was estimated using an over-dispersed Poisson model that accounts for demographic changes, temporal trends, and seasonal effects in mortality. Weekly excess deaths were calculated as the difference between the number of observed deaths and the expected deaths. Regional differences in Indigenous mortality were investigated. A significant increase in Indigenous mortality was observed from April 1 to December 31, 2020. An estimated 1149 (95% CI 1018-1281) excess deaths was found among Indigenous Brazilians in 2020, representing a 34.8% increase from the expected deaths for this population. The overall increase in non-Indigenous mortality was 18.1%. The Indigenous population living in the Brazilian Amazon area was the earliest-affected Indigenous group, with one of the highest proportional increases in mortality. Disparities in excess mortality revealed a disproportionate burden of COVID-19 among Indigenous Brazilians compared to their non-Indigenous counterparts. Findings highlight the importance of implementing an effective emergency plan that addresses the increased vulnerability of Indigenous Peoples to COVID-19.

Racial disparities for COVID19 mortality in Georgia: Spatial analysis by age based on excess deaths

Introduction

This study uses multiple measures of excess deaths to analyze racial disparities in COVID-19 mortality across Georgia.

Methods

The Georgia Department of Public Health provided monthly mortality data for 2010–2020 stratified by race/ethnicity, age, county, and recorded cause of death. We first calculate crude mortality rates by health district during the time period for all groups for March through June for our historical period to identify significant time-series outliers in 2020 distinguishable from general trend variations. We then calculate the mean and standard deviation of mortality rates by age and racial subgroup to create historic confidence intervals that contextualize rates in 2020. Lastly, we use risk ratios to identify disparities in mortality between Black and White mortality rates both in the 2010–2019 period and in 2020.

Results

Time-series analysis identified three health districts with significant increases in mortality in 2020, located in metro Atlanta and Southwest Georgia. Mortality rates decreased sharply in 2020 for children in both racial categories in all sections of the state, but rose in a majority of districts for both categories in adult and older populations. Risk ratios also increased significantly in 2020 for children and older populations, showing rising disparities in mortality during the pandemic even as crude mortality rates declined for children classified as Black.

Conclusions

Increased mortality during the COVID-19 outbreak disproportionately affected African-Americans, possibly due, in part, to pre-existing disparities prior to the pandemic linked to social determinants of health. The pandemic deepened these disparities, perhaps due to unequal resources to effectively shelter-in-place or access medical care. Future research may identify local factors underlying geographically heterogenous differences in mortality rates to inform future policy interventions.

Disparities in excess deaths from the COVID-19 pandemic among migrant workers in Kuwait

BACKGROUND

The actual human cost of the pandemic cannot be viewed through the COVID-19 mortality rates alone, especially when the pandemic is widening the existing health disparities among different subpopulations within the same society. In Kuwait, migrant workers were already disproportionately impacted by COVID-19 and its unintended consequences. The totality of that effect on mortality is yet to be fully understood.

OBJECTIVE

To estimate excess deaths in the pandemic year of 2020 among the Kuwaiti and non-Kuwaiti migrant populations.

METHODS

We analyzed publicly available retrospective data in Kuwait on total annual mortality historically (from 2005 to 2019) and in 2020. We fitted a quasi-poisson generalized linear model adjusted for yearly trend and nationality to estimate the expected deaths in 2020 in the absence of the pandemic. We calculated excess deaths as the difference between observed and expected mortality for the year of the pandemic in both Kuwaitis and non-Kuwaitis.

RESULTS

In the absence of the pandemic, we expected the total mortality in Kuwait to be 6629 (95% CI: 6472 to 6789) deaths. However, the observed total mortality in 2020 was 9975 deaths; about 3346 (3186 to 3503) more deaths above the expected historical trend. Deaths among migrant workers would have been approximately 71.9% (67.8 to 76.0) lower in the absence of the pandemic. On the other hand, deaths among Kuwaitis would have been 32.4% (29.3 to 35.6) lower if the country had not been hit by the pandemic.

CONCLUSION

The burden of mortality brought on by the COVID-19 pandemic is substantially higher than what the official tally might suggest. Systematically disadvantaged migrant workers shouldered a larger burden of deaths in the pandemic year. Public health interventions must consider structural and societal determinants that give rise to the health disparities seen among migrant workers.

Rapid mortality surveillance using a national population register to monitor excess deaths during SARS-CoV-2 pandemic in South Africa

This paper describes how an up-to-date national population register recording deaths by age and sex, whether deaths were due to natural or unnatural causes, and the offices at which the deaths were recorded can be used to monitor excess death during the SARS-CoV-2 pandemic, both nationally, and sub-nationally, in a country with a vital registration system that is neither up to date nor complete. Apart from suggesting an approach for estimating completeness of reporting at a sub-national level, the application produces estimates of the number of deaths in excess of those expected in the absence of the SARS-CoV-2 epidemic that are highly correlated with the confirmed number of COVID-19 deaths over time, but at a level 2.5 to 3 times higher than the official numbers of COVID-19 deaths. Apportioning the observed excess deaths more precisely to COVID, COVID-related and collateral deaths, and non-COVID deaths averted by interventions with reduced mobility and gatherings, etc., requires access to real-time cause-of-death information. It is suggested that the transition from ICD-10 to ICD-11 should be used as an opportunity to change from a paper-based system to electronic capture of the medical cause-of-death information.

Quantifying and communicating the burden of COVID-19

Background

An essential aspect of preventing further COVID-19 outbreaks and to learn for future pandemics is the evaluation of different political strategies, which aim at reducing transmission of and mortality due to COVID-19. One important aspect in this context is the comparison of attributable mortality.

Methods

We give a comprehensive overview of six epidemiological measures that are used to quantify COVID-19 attributable mortality (p-score, standardized mortality ratio, absolute number of excess deaths, per capita rate, z-score and the population attributable fraction).

Results

By defining the six measures based on observed and expected deaths, we explain their relationship. Moreover, three publicly available data examples serve to illustrate the interpretational strengths and weaknesses of the various measures.

Finally, we give recommendation which measures are suitable for an evaluation of public health strategies against COVID-19. The R code to reproduce the results is available as online supplementary material.

Conclusion

The number of excess deaths should be always reported together with the population attributable fraction, the p-score or the standardized mortality ratio instead of a per capita rate. For a complete picture of COVID-19 attributable mortality, quantifying and communicating its relative burden also to a lay audience is of major importance.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-021-01349-z.

Over- and under-estimation of COVID-19 deaths

The ratio of COVID-19-attributable deaths versus “true” COVID-19 deaths depends on the synchronicity of the epidemic wave with population mortality; duration of test positivity, diagnostic time window, and testing practices close to and at death; infection prevalence; the extent of diagnosing without testing documentation; and the ratio of overall (all-cause) population mortality rate and infection fatality rate. A nomogram is offered to assess the potential extent of over- and under-counting in different situations. COVID-19 deaths were apparently under-counted early in the pandemic and continue to be under-counted in several countries, especially in Africa, while over-counting probably currently exists for several other countries, especially those with intensive testing and high sensitization and/or incentives for COVID-19 diagnoses. Death attribution in a syndemic like COVID-19 needs great caution. Finally, excess death estimates are subject to substantial annual variability and include also indirect effects of the pandemic and the effects of measures taken.

Bowling alone or distancing together? The role of social capital in excess death rates from COVID19

Much attention on the spread and impact of the ongoing pandemic has focused on institutional factors such as government capacity along with population-level characteristics such as race, income, and age. This paper draws on a growing body of evidence that bonding, bridging, and linking social capital - the horizontal and vertical ties that bind societies together - impact public health to explain why some U.S. counties have seen higher (or lower) excess deaths during the COVID19 pandemic than others. Drawing on county-level reports from the Centers for Disease Control and Prevention (CDC) since February 2020, we calculated the number of excess deaths per county compared to 2018. Starting with a panel dataset of county observations over time, we used coarsened exact matching to create smaller but more similar sets of communities that differ primarily in social capital. Controlling for several factors, including politics and governance, health care quality, and demographic characteristics, we find that bonding and linking social capital reduce the toll of COVID-19 on communities. Public health officials and community organizations should prioritize building and maintaining strong social ties and trust in government to help combat the pandemic.

Estimating the wave 1 and wave 2 infection fatality rates from SARS-CoV-2 in India

OBJECTIVE

There has been much discussion and debate around the underreporting of COVID-19 infections and deaths in India. In this short report we first estimate the underreporting factor for infections from publicly available data released by the Indian Council of Medical Research on reported number of cases and national seroprevalence surveys. We then use a compartmental epidemiologic model to estimate the undetected number of infections and deaths, yielding estimates of the corresponding underreporting factors. We compare the serosurvey based ad hoc estimate of the infection fatality rate (IFR) with the model-based estimate. Since the first and second waves in India are intrinsically different in nature, we carry out this exercise in two periods: the first wave (April 1, 2020-January 31, 2021) and part of the second wave (February 1, 2021-May 15, 2021). The latest national seroprevalence estimate is from January 2021, and thus only relevant to our wave 1 calculations.

RESULTS

Both wave 1 and wave 2 estimates qualitatively show that there is a large degree of "covert infections" in India, with model-based estimated underreporting factor for infections as 11.11 (95% credible interval (CrI) 10.71-11.47) and for deaths as 3.56 (95% CrI 3.48-3.64) for wave 1. For wave 2, underreporting factor for infections escalate to 26.77 (95% CrI 24.26-28.81) and to 5.77 (95% CrI 5.34-6.15) for deaths. If we rely on only reported deaths, the IFR estimate is 0.13% for wave 1 and 0.03% for part of wave 2. Taking underreporting of deaths into account, the IFR estimate is 0.46% for wave 1 and 0.18% for wave 2 (till May 15). Combining waves 1 and 2, as of May 15, while India reported a total of nearly 25 million cases and 270 thousand deaths, the estimated number of infections and deaths stand at 491 million (36% of the population) and 1.21 million respectively, yielding an estimated (combined) infection fatality rate of 0.25%. There is considerable variation in these estimates across Indian states. Up to date seroprevalence studies and mortality data are needed to validate these model-based estimates.

A new extension of state-space SIR model to account for Underreporting - An application to the COVID-19 transmission in California and Florida

In the absence of sufficient testing capacity for COVID-19, a substantial number of infecteds are expected to remain undetected. Since the undetected cases are not quarantined, they can be expected to transmit the infection at a much higher rate than their quarantined counterparts. That is, in the absence of extensive random testing, the actual prevalence and incidence of the SARS-CoV-2 infection can be significantly higher than that being reported. Thus, it is imperative that the information on the percentage of undetected (or unreported) cases be incorporated in the mechanism for estimating the key epidemiological parameters, like rate of transmission, rate of recovery, reproduction rate, etc., and hence, for forecasting the transmission dynamics of the epidemic. In this paper, we have developed a new dynamic version of the basic susceptible-infected-removed (SIR) compartmental model, called the susceptible-infected (quarantined/ free) - recovered- deceased [SI(Q/F)RD] model, to assimilate the impact of the time-varying proportion of undetected cases on the transmission dynamics of the epidemic. Further, we have presented a Dirichlet-Beta state-space formulation of the SI(Q/F)RD model for the estimation of its parameters using posterior realizations from the Gibbs sampling procedure. As a demonstration, the proposed methodology has been implemented to forecast the COVID-19 transmission in California and Florida. Results suggest significant amount of underreporting of cases in both states. Further, posterior estimates obtained from the state-space SI(Q/F)RD model show that average reproduction numbers associated with the undetected infectives [California: 1.464; Florida: 1.612] are substantially higher than those associated with the quarantined infectives [California: 0.497; Florida: 0.359]. The long-term forecasts of death counts show trends similar to those of the estimates of excess deaths for the comparison period post training data timeline.

Using excess deaths and testing statistics to determine COVID-19 mortalities

Factors such as varied definitions of mortality, uncertainty in disease prevalence, and biased sampling complicate the quantification of fatality during an epidemic. Regardless of the employed fatality measure, the infected population and the number of infection-caused deaths need to be consistently estimated for comparing mortality across regions. We combine historical and current mortality data, a statistical testing model, and an SIR epidemic model, to improve estimation of mortality. We find that the average excess death across the entire US from January 2020 until February 2021 is 9[Formula: see text] higher than the number of reported COVID-19 deaths. In some areas, such as New York City, the number of weekly deaths is about eight times higher than in previous years. Other countries such as Peru, Ecuador, Mexico, and Spain exhibit excess deaths significantly higher than their reported COVID-19 deaths. Conversely, we find statistically insignificant or even negative excess deaths for at least most of 2020 in places such as Germany, Denmark, and Norway.

Counting the dead and making the dead count: configuring data and accountability

This article examines the relation between counting, counts and accountability. It does so by comparing the responses of the British government to deaths associated with Covid-19 in 2020 to its responses to deaths associated with the 2003 invasion of Iraq. Similarities and dissimilarities between the cases regarding what counted as data, what data were taken to count, what data counted for, and how data were counted provide the basis for considering how the bounds of democratic accountability are constituted. Based on these two cases, the article sets out the metaphors of leaks and cascades as ways of characterising the data practices whereby counts, counting and accountability get configured. By situating deaths associated with Covid-19 against previous experience with deaths from war, the article also proposes how claims to truth and ignorance might figure in any future official inquiry into the handling of the pandemic.

Excess mortality for care home residents during the first 23 weeks of the COVID-19 pandemic in England: a national cohort study

BACKGROUND

To estimate excess mortality for care home residents during the COVID-19 pandemic in England, exploring associations with care home characteristics.

METHODS

Daily number of deaths in all residential and nursing homes in England notified to the Care Quality Commission (CQC) from 1 January 2017 to 7 August 2020. Care home-level data linked with CQC care home register to identify home characteristics: client type (over 65s/children and adults), ownership status (for-profit/not-for-profit; branded/independent) and size (small/medium/large). Excess deaths computed as the difference between observed and predicted deaths using local authority fixed-effect Poisson regressions on pre-pandemic data. Fixed-effect logistic regressions were used to model odds of experiencing COVID-19 suspected/confirmed deaths.

RESULTS

Up to 7 August 2020, there were 29,542 (95% CI 25,176 to 33,908) excess deaths in all care homes. Excess deaths represented 6.5% (95% CI 5.5 to 7.4%) of all care home beds, higher in nursing (8.4%) than residential (4.6%) homes. 64.7% (95% CI 56.4 to 76.0%) of the excess deaths were confirmed/suspected COVID-19. Almost all excess deaths were recorded in the quarter (27.4%) of homes with any COVID-19 fatalities. The odds of experiencing COVID-19 attributable deaths were higher in homes providing nursing services (OR 1.8, 95% CI 1.6 to 2.0), to older people and/or with dementia (OR 5.5, 95% CI 4.4 to 6.8), amongst larger (vs. small) homes (OR 13.3, 95% CI 11.5 to 15.4) and belonging to a large provider/brand (OR 1.2, 95% CI 1.1 to 1.3). There was no significant association with for-profit status of providers.

CONCLUSIONS

To limit excess mortality, policy should be targeted at care homes to minimise the risk of ingress of disease and limit subsequent transmission. Our findings provide specific characteristic targets for further research on mechanisms and policy priority.

Trends in deaths from road injuries during the COVID-19 pandemic in Japan, January to September 2020

Background

In Japan, the latest estimates of excess all-cause deaths through January to July 2020 showed that the overall (direct and indirect) mortality burden from the Coronavirus Disease 2019 (COVID-19) in Japan was relatively low compared to Europe and the United States. However, consistency between the reported number of COVID-19 deaths and excess all-cause deaths was limited across prefectures, suggesting the necessity of distinguishing the direct and indirect consequences of COVID-19 by cause-specific analysis. To examine whether deaths from road injuries decreased during the COVID-19 pandemic in Japan, consistent with a possible reduction of road transport activity connected to Japan’s state of emergency declaration, we estimated the exiguous deaths from road injuries in each week from January to September 2020 by 47 prefectures.

Methods

To estimate the expected weekly number of deaths from road injuries, a quasi-Poisson regression was applied to daily traffic fatalities data obtained from Traffic Accident Research and Data Analysis, Japan. We set two thresholds, point estimate and lower bound of the two-sided 95% prediction interval, for exiguous deaths, and report the range of differences between the observed number of deaths and each of these thresholds as exiguous deaths.

Results

Since January 2020, in a few weeks the observed deaths from road injuries fell below the 95% lower bound, such as April 6–12 (exiguous deaths 5–21, percent deficit 2.82–38.14), May 4–10 (8–23, 21.05–43.01), July 20–26 (12–29, 30.77–51.53), and August 3–9 (3–20, 7.32–34.41). However, those less than the 95% lower bound were also observed in weeks in the previous years.

Conclusions

The number of road traffic fatalities during the COVID-19 pandemic in Japan has decreased slightly, but not significantly, in several weeks compared with the average year. This suggests that the relatively small changes in excess all-cause mortality observed in Japan during the COVID-19 pandemic could not be explained simply by an offsetting reduction in traffic deaths. Considering a variety of other indirect effects, evaluating an independent, unbiased measure of COVID-19-related mortality burden could provide insight into the design of future broad-based infectious disease counter-measures and offer lessons to other countries.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40621-020-00294-7.

Measuring the impact of the COVID-19 outbreak in Ecuador using preliminary estimates of excess mortality, March 17-October 22, 2020

Objectives

Ecuador is among the worst-hit countries in the world by the coronavirus disease 2019 (COVID-19) pandemic. In terms of confirmed deaths per million inhabitants, as of October 22, Ecuador ranks fourth in the Americas and ninth worldwide according to data from the World Health Organization. In this report, we estimate excess deaths due to any cause in Ecuador since the start of the lockdown measures on March 17, 2020 until October 22, 2020.

Methods

Estimates of excess deaths were calculated as the difference between the number of observed deaths from all causes and estimates of expected deaths from all causes. Expected deaths were estimated for the period March 17–October 22, 2020 from forecasts of an ARIMA model of order (3,0,1) with drift which was applied to daily mortality data for the period from January 1, 2014 to March 16, 2020.

Results

The number of all-cause excess deaths in Ecuador was estimated to be 36,922 (95% bootstrap confidence interval: 32,314–42,696) during the study period. The peak in all-cause excess mortality in Ecuador may have occurred on April 4, 2020, with 909 excess deaths.

Conclusions

Our results suggest that the real impact of the pandemic in Ecuador was much worse than that indicated by reports from national institutions. Estimates of excess mortality might provide a better approximation of the true COVID-19 death toll. These estimates might capture not only deaths directly attributable to the COVID-19 pandemic but also deaths from other diseases that resulted from indirect effects of the pandemic.

All-cause excess mortality and COVID-19-related deaths in Iran

Background: Iran reported its first COVID-19 deaths on February 19, 2020 and announced 1284 deaths with a laboratory-confirmed SARS-CoV-2 infection by March 19, 2020 (end of the winter 1398 SH). We estimated all-cause excess mortality, compared to the historical trends, to obtain an indirect estimate of COVID-19-related deaths.

Methods: We assembled time series of the seasonal number of all-cause mortalities from March 21, 2013 (spring of 1392 SH) to March 19, 2020 (winter 1398 SH) for each province of Iran and nationwide with the vital statistics data from the National Organization for Civil Registration (NOCR). We estimated the expected seasonal mortality and excess mortality (the difference between the number of registered and expected deaths). Moreover, we reviewed the provincial number of confirmed cases of COVID-19 to assess their association with excess deaths.

Results: The results of our analysis showed around 7507 (95% CI: 3,350 – 11,664) and 5180 (95% CI: 1,023 – 9,337) all-cause excess mortality in fall and winter, respectively. There were 3778 excess deaths occurred in Qom, Gilan, Mazandaran, and Golestan provinces in the winter, all among the COVID-19 epicenters based on the number of confirmed cases.

Conclusion: We think most of the excess deaths in the winter were related to COVID-19. Also, we think the influenza epidemic might have been the main reason for the excess mortality in the fall and parts of excess deaths in the winter of 1398 SH. Moreover, a review of all available clinical and paraclinical records and through analyses of the surveillance data for severe acute respiratory infections (SARI) can help to obtain a more accurate estimate of COVID-19 mortality.

Influenza associated mortality in Southern China, 2010-2012

OBJECTIVE

Influenza caused substantial morbidity and mortality worldwide. The mortality burden caused by influenza has been under evaluation; however, data assessing this burden have been relatively sparse in tropical or subtropical regions. We estimated influenza-associated mortality in Guangzhou, China and assessed the excess mortality due to different influenza virus subtypes.

METHODS

We estimated influenza-associated excess mortality due to all-cause, pneumonia and influenza, cardiorespiratory disease and other influenza-associated diagnoses from weekly numbers of deaths and influenza surveillance data through negative binomial regression model during 2010-2012.

RESULTS

Estimates derived from the model indicated that influenza resulted in 14.72 (95% confidence interval (CI), 12.12-17.31) deaths per 100,000 population per year from all-cause death among all ages group. Most deaths (84.2%) occurred among people aged ≥65 years. B virus caused 5.84 (95%CI, 4.10-7.58) deaths per 100,000 population for all-cause death, which was higher than A (H3N2) (4.89, 95%CI, 3.19-6.59) or A(H1N1)pdm09 (3.99, 95%CI, 2.32-5.66).

CONCLUSIONS

Influenza is responsible for a substantial mortality especially among people aged ≥65 years and influenza B virus caused the highest influenza-associated mortality. The results highlight the need for seasonal influenza vaccination programs in subtropical areas to decrease excess mortality.