Robust and Timely Estimation of Excess Mortality: Statistical Analysis of Cremation Data During the COVID-19 Pandemic (Preprint)

BACKGROUND Early estimates of excess mortality are crucial for understanding the impact of COVID-19. However, there is a lag of several months in the reporting of vital statistics mortality data for many jurisdictions. In Ontario, a Canadian province, certification by a coroner is required before cremation can occur, creating timely mortality data that encompasses the majority of deaths within the province. OBJECTIVE Our objectives were to (1) validate the ability of cremation data in permitting real-time estimation of excess all-cause mortality, interim of vital statistics data, and (2) describe the patterns of excess mortality. METHODS Cremation records from January 2020 until April 2021 were compared to the historical records from 2017-2019, grouped according to week, age, sex, and COVID-19 status. Cremation data were compared to Ontario’s provisional vital statistics mortality data released by Statistics Canada. The 2020 and 2021 records were then compared to previous years to determine whether there was excess mortality and if so, which age groups had the greatest number of excess deaths during the COVID Pandemic, and whether deaths attributed to COVID-19 account for the entirety of the excess mortality. RESULTS Between 2017-2019, cremations were performed for 67.4% (95% CI: 67.3–67.5%) of deaths; the proportion of cremated deaths remained stable throughout 2020, establishing that the COVID-19 pandemic did not significantly alter cremation practices, even within age and sex categories. During the first wave (from April to June 2020), cremation records detected a 16.9% increase (95% CI: 14.6–19.3%) in mortality. The accuracy of this excess mortality estimation was later confirmed by vital statistics data. CONCLUSIONS The stability in the percent of Ontarians cremated and the completion of cremation data several months before vital statistics data, enables accurate estimation of all-causes mortality in near real-time with cremation data. These findings demonstrate the utility of cremation data to provide timely mortality information during public health emergencies.

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Real-time estimation of the influenza-associated excess mortality in Hong Kong

Epidemiology and Infection ◽

10.1017/s0950268819001067 ◽

2019 ◽

Vol 147 ◽

Cited By ~ 1

Author(s):

Jessica Y. Wong ◽

Edward Goldstein ◽

Vicky J. Fang ◽

Benjamin J. Cowling ◽

Peng Wu

Keyword(s):

Hong Kong ◽

Real Time ◽

Excess Mortality ◽

Time Estimation ◽

Surveillance Data ◽

Control Measures ◽

Influenza Surveillance ◽

Mortality Data ◽

Real Time Estimation ◽

The Impact

Abstract Statistical models are commonly employed in the estimation of influenza-associated excess mortality that, due to various reasons, is often underestimated by laboratory-confirmed influenza deaths reported by healthcare facilities. However, methodology for timely and reliable estimation of that impact remains limited because of the delay in mortality data reporting. We explored real-time estimation of influenza-associated excess mortality by types/subtypes in each year between 2012 and 2018 in Hong Kong using linear regression models fitted to historical mortality and influenza surveillance data. We could predict that during the winter of 2017/2018, there were ~634 (95% confidence interval (CI): (190, 1033)) influenza-associated excess all-cause deaths in Hong Kong in population ⩾18 years, compared to 259 reported laboratory-confirmed deaths. We estimated that influenza was associated with substantial excess deaths in older adults, suggesting the implementation of control measures, such as administration of antivirals and vaccination, in that age group. The approach that we developed appears to provide robust real-time estimates of the impact of influenza circulation and complement surveillance data on laboratory-confirmed deaths. These results improve our understanding of the impact of influenza epidemics and provide a practical approach for a timely estimation of the mortality burden of influenza circulation during an ongoing epidemic.

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Excess mortality in Guadeloupe and Martinique, islands of the French West Indies, during the chikungunya epidemic of 2014

Epidemiology and Infection ◽

10.1017/s0950268818002315 ◽

2018 ◽

Vol 146 (16) ◽

pp. 2059-2065 ◽

Cited By ~ 10

Author(s):

A. R. R. Freitas ◽

P. M. Alarcón-Elbal ◽

M. R. Donalisio

Keyword(s):

Strong Correlation ◽

Excess Mortality ◽

Pearson Correlation ◽

Age Groups ◽

Epidemiological Data ◽

Mortality Data ◽

Surveillance Systems ◽

Mortality And Morbidity ◽

The Impact ◽

Excess Deaths

AbstractIn some chikungunya epidemics, deaths are not completely captured by traditional surveillance systems, which record case and death reports. We evaluated excess deaths associated with the 2014 chikungunya virus (CHIKV) epidemic in Guadeloupe and Martinique, Antilles. Population (784 097 inhabitants) and mortality data, estimated by sex and age, were accessed from the Institut National de la Statistique et des Études Économiques in France. Epidemiological data, cases, hospitalisations and deaths on CHIKV were obtained from the official epidemiological reports of the Cellule de Institut de Veille Sanitaire in France. Excess deaths were calculated as the difference between the expected and observed deaths for all age groups for each month in 2014 and 2015, considering the upper limit of 99% confidence interval. The Pearson correlation coefficient showed a strong correlation between monthly excess deaths and reported cases of chikungunya (R= 0.81,p< 0.005) and with a 1-month lag (R= 0.87,p< 0.001); and a strong correlation was also observed between monthly rates of hospitalisation for CHIKV and excess deaths with a delay of 1 month (R= 0.87,p< 0.0005). The peak of the epidemic occurred in the month with the highest mortality, returning to normal soon after the end of the CHIKV epidemic. There were excess deaths in almost all age groups, and excess mortality rate was higher among the elderly but was similar between male and female individuals. The overall mortality estimated in the current study (639 deaths) was about four times greater than that obtained through death declarations (160 deaths). Although the aetiological diagnosis of all deaths associated with CHIKV infection is not always possible, already well-known statistical tools can contribute to the evaluation of the impact of CHIKV on mortality and morbidity in the different age groups.

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Excess mortality in Guadeloupe and Martinique, islands of the French West Indies, during the chikungunya epidemic of 2014

10.1101/228445 ◽

2017 ◽

Author(s):

A. R. R. Freitas ◽

P. M. Alarcon-Elbal ◽

M. R. Donalisio

Keyword(s):

Excess Mortality ◽

Case Reports ◽

Age Groups ◽

Epidemiological Data ◽

Mortality Data ◽

Population Total ◽

Mortality And Morbidity ◽

Pearson Coefficient ◽

The Difference ◽

The Impact

AbstractIn some chikugunya epidemics, deaths are not fully captured by the traditional surveillance system, based on case reports and death reports. This is a time series study to evaluate the excess of mortality associated with epidemic of chikungunya virus (CHIKV) in Guadeloupe and Martinique, Antilles, 2014. The population (total 784,097 inhabitants) and mortality data estimated by sex and age were accessed at the Institut National de la Statistique et des Etudes Economiques - France. Age adjusted mortality rates were calculated also in Reunion, Indian Ocean for comparison. Epidemiological data on CHIKV (cases, hospitalizations, and deaths) were obtained in the official epidemiological reports of the Cellule de Institut de Veille Sanitaire - France. The excess of deaths for each month in 2014 and 2015 was the difference between the expected and observed deaths for all age groups, considering the 99% confidence interval threshold. Pearson coefficient of correlation between monthly excess of deaths and reported cases of chikungunya show a strong correlation (R = 0.81, p <0.005), also with a 1-month lag (R = 0.87, p <0.001), and between monthly rates of hospitalization for CHIKV and the excess of deaths with a delay of 1 month (R = 0.87, p <0.0005).The peak of the epidemic occurred in the month with the highest mortality, returning to normal soon after the end of the CHIKV epidemic. The overall mortality estimated by this method (639 deaths) was about 4 times greater than that obtained through death declarations (160 deaths). Excess mortality increased with age. Although etiological diagnosis of all deaths associated with CHIKV infection is not possible, already well-known statistical tools can contribute to an evaluation of the impact of this virus on the mortality and morbidity in the different age groups.

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Advancements in the National Vital Statistics System to Meet the Real-Time Data Needs of a Pandemic

American Journal of Public Health ◽

10.2105/ajph.2021.306519 ◽

2021 ◽

Vol 111 (12) ◽

pp. 2133-2140

Author(s):

Farida B. Ahmad ◽

Robert N. Anderson ◽

Karen Knight ◽

Lauren M. Rossen ◽

Paul D. Sutton

Keyword(s):

Real Time ◽

Death Certificate ◽

Vital Statistics ◽

Mortality Data ◽

Annual Data ◽

Health Statistics ◽

Time Data ◽

Real Time Data ◽

Data Files ◽

The Impact

The National Center for Health Statistics’ (NCHS’s) National Vital Statistics System (NVSS) collects, processes, codes, and reviews death certificate data and disseminates the data in annual data files and reports. With the global rise of COVID-19 in early 2020, the NCHS mobilized to rapidly respond to the growing need for reliable, accurate, and complete real-time data on COVID-19 deaths. Within weeks of the first reported US cases, NCHS developed certification guidance, adjusted internal data processing systems, and stood up a surveillance system to release daily updates of COVID-19 deaths to track the impact of the COVID-19 pandemic on US mortality. This report describes the processes that NCHS took to produce timely mortality data in response to the COVID-19 pandemic. (Am J Public Health. 2021;111(12):2133–2140. https://doi.org/10.2105/AJPH.2021.306519 )

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Influenza-attributable deaths, Canada 1990–1999

Epidemiology and Infection ◽

10.1017/s0950268807007923 ◽

2007 ◽

Vol 135 (7) ◽

pp. 1109-1116 ◽

Cited By ~ 73

Author(s):

D. L. SCHANZER ◽

T. W. S. TAM ◽

J. M. LANGLEY ◽

B. T. WINCHESTER

Keyword(s):

Excess Mortality ◽

Influenza Season ◽

Vital Statistics ◽

Attributable Mortality ◽

Mortality Data ◽

Poisson Regression Model ◽

Severe Influenza ◽

All Cause Mortality ◽

Specific Mortality ◽

The Impact

SUMMARYThe number of deaths attributable to influenza is believed to be considerably higher than the number certified by vital statistics registration as due to influenza. Weekly mortality data for Canada from the 1989/1990 to the 1998/1999 influenza seasons were analysed by cause of death, age group, and place of death to estimate the impact of influenza on mortality. A Poisson regression model was found to accurately predict all-cause, as well as cause-specific mortality, as a function of influenza-certified deaths, after controlling for seasonality, and trend. Influenza-attributable deaths were calculated as predicted less baseline-predicted deaths. In summary, throughout the 1990s there were on average just under 4000 deaths attributable to influenza annually (for an influenza-attributable mortality rate of 13/100 000 persons), varying from no detectable excess mortality for the 1990/1991 influenza season, to 6000–8000 influenza-attributable deaths for the more severe influenza seasons of 1997/1998 and 1998/1999. On average, 8% (95% CI 7–10) of influenza-attributable deaths were certified as influenza, although this percentage varied from 4% to 12% from year to year. Only 15% of the influenza-attributable deaths were certified as pneumonia, and for all respiratory causes, 40%. Deaths were distributed over most causes. The weekly pattern of influenza-certified deaths was a good predictor of excess all-cause mortality.

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The Impact of Heat Waves on Mortality in Nanjing Area from 2004 to 2010

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.726-731.931 ◽

2013 ◽

Vol 726-731 ◽

pp. 931-935

Author(s):

Yuan Shu Jing ◽

Di Zhang ◽

Min Fei Yan ◽

Jian Guo Tan

Keyword(s):

Heat Wave ◽

Excess Mortality ◽

Heat Waves ◽

Residual Disease ◽

Meteorological Data ◽

Age Groups ◽

Maximum Temperature ◽

Mortality Data ◽

Average Maximum ◽

The Impact

This paper analyzed the excess mortality change in nine districts of Nanjing city, based on mortality data and meteorological data from 2004 to 2010. Taken a typical heat waves process in summer of 2006 as an example, it was discussed of the effect of the heat process on different gender, different age groups , and various disease death toll and excess mortality changes. The excess mortality was associated with the average maximum temperature and average minimum temperature during the heat waves. Excess mortality occurred in the middle of June heat wave when excess mortality was much higher than in other time periods. In late June, early July to early August, the excess mortality is relatively small. The average daily deaths are increasing with increasing age for male and female, and every age death numbers is higher than that with no heat waves during the heat wave period. In addition to the respiratory system diseases, diseases of the genitourinary system, other diseases, residual disease in the heat waves has increased, and diseases of the nervous system and the endocrine system diseases of excess mortality rate reached a staggering 342.93% and 119.63%, accounting for almost half of the total heat excess mortality. The heat waves effect is very obvious. The conclusion is of great significance for prevention of high temperature heat harm.

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The decomposition of life expectancy for age and cause of death in Tuscany, Italy

European Journal of Public Health ◽

10.1093/eurpub/ckz185.638 ◽

2019 ◽

Vol 29 (Supplement_4) ◽

Author(s):

N Nante ◽

L Kundisova ◽

F Gori ◽

A Martini ◽

F Battisti ◽

...

Keyword(s):

Life Expectancy ◽

Causes Of Death ◽

National Level ◽

Age Groups ◽

Resistant Bacteria ◽

Mortality Data ◽

Life Expectancy At Birth ◽

Icd 10 ◽

Reduction Of Mortality ◽

The Impact

Abstract Introduction Changing of life expectancy at birth (LE) over time reflects variations of mortality rates of a certain population. Italy is amongst the countries with the highest LE, Tuscany ranks fifth at the national level. The aim of the present work was to evaluate the impact of various causes of death in different age groups on the change in LE in the Tuscany region (Italy) during period 1987-2015. Material and methods Mortality data relative to residents that died during the period between 1987/1989 and 2013/2015 were provided by the Tuscan Regional Mortality Registry. The causes of death taken into consideration were cardiovascular (CVS), respiratory (RESP) and infective (INF) diseases and cancer (TUM). The decomposition of LE gain was realized with software Epidat, using the Pollard’s method. Results The overall LE gain during the period between two three-years periods was 6.7 years for males, with a major gain between 65-89, and 4.5 years for females, mainly improved between 75-89, <1 year for both sexes. The major gain (2.6 years) was attributable to the reduction of mortality for CVS, followed by TUM (1.76 in males and 0.83 in females) and RESP (0.4 in males; 0.1 in females). The major loss of years of LE was attributable to INF (-0.15 in females; -0.07 in males) and lung cancer in females (-0.13), for which the opposite result was observed for males (gain of 0.62 years of LE). Conclusions During the study period (1987-2015) the gain in LE was major for males. To the reduction of mortality for CVS have contributed to the tempestuous treatment of acute CVS events and secondary CVS prevention. For TUM the result is attributable to the adherence of population to oncologic screening programmes. The excess of mortality for INF that lead to the loss of LE can be attributed to the passage from ICD-9 to ICD-10 in 2003 (higher sensibility of ICD-10) and to the diffusion of multi-drug resistant bacteria, which lead to elevated mortality in these years. Key messages The gain in LE during the period the 1987-2015 was higher in males. The major contribution to gain in LE was due to a reduction of mortality for CVS diseases.

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The Impact of Video Speed on the Estimation of Time Duration in Sport

Journal of Sport and Exercise Psychology ◽

10.1123/jsep.2020-0248 ◽

2021 ◽

pp. 1-11

Author(s):

Lisa-Marie Schütz ◽

Geoffrey Schweizer ◽

Henning Plessner

Keyword(s):

Real Time ◽

Time Estimation ◽

Slow Motion ◽

Sport Performance ◽

Time Duration ◽

Video Presentation ◽

Sporting Activities ◽

The Impact ◽

Estimation Of Time

The authors investigated the impact of video speed on judging the duration of sport performance. In three experiments, they investigated whether the speed of video presentation (slow motion vs. real time) has an influence on the accuracy of time estimation of sporting activities (n1 = 103; n2 = 100; n3 = 106). In all three studies, the time estimation was more accurate in real time than in slow motion, in which time was overestimated. In two studies, the authors initially investigated whether actions in slow motion are perceived to last longer because the distance they cycled or ran is perceived to be longer (n4 = 92; n5 = 106). The results support the hypothesis that the duration of sporting activities is estimated more accurately when they are presented in real time than in slow motion. Sporting officials’ judgments that require accurate time estimation may thus be biased when based on slow-motion displays.

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Excess Mortality Estimation During the COVID-19 Pandemic: Preliminary Data from Portugal

Acta Médica Portuguesa ◽

10.20344/amp.13928 ◽

2020 ◽

Vol 33 (6) ◽

pp. 376 ◽

Cited By ~ 14

Author(s):

Paulo Jorge Nogueira ◽

Miguel De Araújo Nobre ◽

Paulo Jorge Nicola ◽

Cristina Furtado ◽

António Vaz Carneiro

Keyword(s):

Preliminary Data ◽

Excess Mortality ◽

Age Groups ◽

Access To Healthcare ◽

Older Age ◽

Mortality Estimation ◽

Public Data

Introduction: Portugal is experiencing the effects of the COVID-19 pandemic since March 2020. All-causes mortality in Portugal increased during March and April 2020 compared to previous years, but this increase is not explained by COVID-19 reported deaths. The aim of this study was to analyze and consider other criteria for estimating excessive all-causes mortality during the early COVID-19pandemic period.Material and Methods: Public data was used to estimate excess mortality by age and region between March 1 and April 22, proposing baselines adjusted for the lockdown period.Results: Despite the inherent uncertainty, it is safe to assume an observed excess mortality of 2400 to 4000 deaths. Excess mortality was associated with older age groups (over age 65).Discussion: The data suggests a ternary explanation for early excess mortality: COVID-19, non-identified COVID-19 and decrease in access to healthcare. The estimates have implications in terms of communication of non-pharmaceutical actions, for research, and to healthcare professionals.Conclusion: The excess mortality occurred between March 1 and April 22 was 3.5- to 5-fold higher than what can be explained by the official COVID-19 deaths.

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Tracking excess mortality across countries during the COVID-19 pandemic with the World Mortality Dataset

eLife ◽

10.7554/elife.69336 ◽

2021 ◽

Vol 10 ◽

Author(s):

Ariel Karlinsky ◽

Dmitry Kobak

Keyword(s):

New Zealand ◽

Excess Mortality ◽

Mortality Data ◽

Objective Indicator ◽

Death Toll ◽

The World ◽

All Cause Mortality ◽

Expected Mortality ◽

The Impact ◽

Annual Mortality

Comparing the impact of the COVID-19 pandemic between countries or across time is difficult because the reported numbers of cases and deaths can be strongly affected by testing capacity and reporting policy. Excess mortality, defined as the increase in all-cause mortality relative to the expected mortality, is widely considered as a more objective indicator of the COVID-19 death toll. However, there has been no global, frequently-updated repository of the all-cause mortality data across countries. To fill this gap, we have collected weekly, monthly, or quarterly all-cause mortality data from 94 countries and territories, openly available as the regularly-updated World Mortality Dataset. We used this dataset to compute the excess mortality in each country during the COVID-19 pandemic. We found that in several worst-affected countries (Peru, Ecuador, Bolivia, Mexico) the excess mortality was above 50% of the expected annual mortality. At the same time, in several other countries (Australia, New Zealand) mortality during the pandemic was below the usual level, presumably due to social distancing measures decreasing the non-COVID infectious mortality. Furthermore, we found that while many countries have been reporting the COVID-19 deaths very accurately, some countries have been substantially underreporting their COVID-19 deaths (e.g. Nicaragua, Russia, Uzbekistan), sometimes by two orders of magnitude (Tajikistan). Our results highlight the importance of open and rapid all-cause mortality reporting for pandemic monitoring.

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