Estimating excess mortality in Canada during the COVID-19 pandemic: Statistical methods adapted for rapid response in an evolving crisis

Statistical Journal of the IAOS ◽

10.3233/sji-210871 ◽

2021 ◽

pp. 1-8

Author(s):

Elham Sirag ◽

Gautier Gissler

Keyword(s):

Statistical Methods ◽

Excess Mortality ◽

Rapid Response ◽

Expected Number ◽

Baseline Mortality ◽

Statistics Canada ◽

Mortality Estimates

This paper presents the approach adopted at Statistics Canada to produce timely and accurate estimates of excess mortality during the ongoing COVID-19 pandemic. It focuses primarily on the two models involved in the estimation of excess mortality: the model used to estimate the expected number of deaths in the absence of the pandemic (baseline mortality), and the model used to adjust provisional death counts for undercoverage. We describe both, including how the models were adapted to fit our specific criteria as well as the various limitations they both possess. We conclude by presenting selected results from Statistics Canada’s official release of excess mortality estimates from February 8th, 2021.

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Local mortality estimates during the COVID-19 pandemic in Italy

Journal of Population Economics ◽

10.1007/s00148-021-00857-y ◽

2021 ◽

Author(s):

Augusto Cerqua ◽

Roberta Di Stefano ◽

Marco Letta ◽

Sara Miccoli

Keyword(s):

Machine Learning ◽

Excess Mortality ◽

Control Method ◽

Local Level ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Mortality Data ◽

Official Method ◽

Learning Techniques ◽

Mortality Estimates

AbstractEstimates of the real death toll of the COVID-19 pandemic have proven to be problematic in many countries, Italy being no exception. Mortality estimates at the local level are even more uncertain as they require stringent conditions, such as granularity and accuracy of the data at hand, which are rarely met. The “official” approach adopted by public institutions to estimate the “excess mortality” during the pandemic draws on a comparison between observed all-cause mortality data for 2020 and averages of mortality figures in the past years for the same period. In this paper, we apply the recently developed machine learning control method to build a more realistic counterfactual scenario of mortality in the absence of COVID-19. We demonstrate that supervised machine learning techniques outperform the official method by substantially improving the prediction accuracy of the local mortality in “ordinary” years, especially in small- and medium-sized municipalities. We then apply the best-performing algorithms to derive estimates of local excess mortality for the period between February and September 2020. Such estimates allow us to provide insights about the demographic evolution of the first wave of the pandemic throughout the country. To help improve diagnostic and monitoring efforts, our dataset is freely available to the research community.

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Influenza Related Excess Mortality Estimates Among All Cause Deaths in Mongolia, 2004–2007

International Journal of Infectious Diseases ◽

10.1016/j.ijid.2008.05.225 ◽

2008 ◽

Vol 12 ◽

pp. e90 ◽

Cited By ~ 1

Author(s):

B. Alyeksandr ◽

T. Kamigaki ◽

H Oshitani ◽

N. Pagbajabun

Keyword(s):

Excess Mortality ◽

Mortality Estimates

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Assessing excess mortality in Vienna and Austria after the first year of the COVID-19 pandemic

10.1553/populationyearbook2022.dat.1 ◽

2022 ◽

Author(s):

Ramon Bauer ◽

Markus Speringer ◽

Peter Frühwirt ◽

Roman Seidl ◽

Franz Trautinger

Keyword(s):

Excess Mortality ◽

Heat Waves ◽

National Level ◽

Age Groups ◽

Prediction Intervals ◽

First Year ◽

Expected Number ◽

Mortality Trends ◽

The Public ◽

Definition Of

In Austria, the first confirmed COVID-19 death occurred in early March 2020. Since then, the question as to whether and, if so, to what extent the COVID-19 pandemic has increased overall mortality has been raised in the public and academic discourse. In an effort to answer this question, Statistics Vienna (City of Vienna, Department for Economic Affairs, Labour and Statistics) has evaluated the weekly mortality trends in Vienna, and compared them to the trends in other Austrian provinces. For our analysis, we draw on data from Statistics Austria and the Austrian Agency for Health and Food Safety (AGES), which are published along with data on the actual and the expected weekly numbers of deaths via the Vienna Mortality Monitoring website. Based on the definition of excess mortality as the actual number of reported deaths from all causes minus the expected number of deaths, we calculate the weekly prediction intervals of the expected number of deaths for two age groups (0 to 64 years and 65 years and older). The temporal scope of the analysis covers not only the current COVID-19 pandemic, but also previous flu seasons and summer heat waves. The results show the actual weekly numbers of deaths and the corresponding prediction intervals for Vienna and the other Austrian provinces since 2007. Our analysis underlines the importance of comparing time series of COVID-19-related excess deaths at the sub-national level in order to highlight within-country heterogeneities.

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The impact of the 2003 heat wave on daily mortality in England and Wales and the use of rapid weekly mortality estimates

Eurosurveillance ◽

10.2807/esm.10.07.00558-en ◽

2005 ◽

Vol 10 (7) ◽

pp. 15-16 ◽

Cited By ~ 65

Author(s):

H Johnson ◽

S Kovats ◽

G McGregor ◽

J Stedman ◽

M Gibbs ◽

...

Keyword(s):

Heat Wave ◽

Excess Mortality ◽

Heat Waves ◽

Mortality Data ◽

Daily Mortality ◽

England And Wales ◽

National Statistics ◽

The United Kingdom ◽

The Impact ◽

Mortality Estimates

This paper describes a retrospective analysis of the impact of the 2003 heat wave on mortality in England and Wales, and compares this with rapid estimates based on the Office for National Statistics routine weekly deaths reporting system. Daily mortality data for 4 to 13 August 2003, when temperatures were much hotter than normally seen in England, were compared with averages for the same period in years 1998 to 2002. The August 2003 heat wave was associated with a large short-term increase in mortality, particularly in London. Ozone and particulate matter concentrations were also elevated during the heat wave. Overall, there were 2139 (16%) excess deaths in England and Wales. Worst affected were people over the age of 75 years. The impact was greatest in the London region where deaths in those over the age of 75 increased by 59%. Estimated excess mortality was greater than for other recent heat waves in the United Kingdom. The estimated number of deaths registered each week is reported by the Office for National Statistics. The first clear indication of a substantial increase in deaths was published on 21 August 2003. This provided a quick first estimate of the number of deaths attributable to the heat wave and reflected the pattern of daily deaths in relation to the hottest days, but underestimated the excess when compared with the later analysis.

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Cancer Survival and Excess Mortality Estimates among Adolescents and Young Adults in Western Australia, 1982–2004: A Population-Based Study

PLoS ONE ◽

10.1371/journal.pone.0055630 ◽

2013 ◽

Vol 8 (2) ◽

pp. e55630 ◽

Cited By ~ 13

Author(s):

Fatima A. Haggar ◽

Gavin Pereira ◽

David D. Preen ◽

C. D’Arcy J Holman ◽

Kristjana Einarsdottir

Keyword(s):

Young Adults ◽

Western Australia ◽

Excess Mortality ◽

Cancer Survival ◽

Population Based ◽

Adolescents And Young Adults ◽

Population Based Study ◽

Mortality Estimates

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Subnational mortality estimates for India in 2019: a baseline for evaluating excess deaths due to the COVID-19 pandemic

BMJ Global Health ◽

10.1136/bmjgh-2021-007399 ◽

2021 ◽

Vol 6 (11) ◽

pp. e007399

Author(s):

Chalapati Rao ◽

Amrit Jose John ◽

Ajit Kumar Yadav ◽

Mansha Siraj

Keyword(s):

Excess Mortality ◽

National Level ◽

Age Groups ◽

Uttar Pradesh ◽

The Elderly ◽

Registration System ◽

Infant Deaths ◽

Civil Registration ◽

The Impact ◽

Mortality Estimates

BackgroundEstimates of excess mortality are required to assess and compare the impact of the COVID-19 pandemic across populations. For India, reliable baseline prepandemic mortality patterns at national and subnational level are necessary for such assessments. However, available data from the Civil Registration System (CRS) is affected by incompleteness of death recording that varies by sex, age and location.MethodsUnder-reporting of CRS 2019 deaths was assessed for three age groups (< 5 years, 15–59 years and ≥60 years) at subnational level, through comparison with age-specific death rates from alternate sources. Age-specific corrections for under-reporting were applied to derive adjusted death counts by sex for each location. These were used to compute life expectancy (LE) at birth by sex in 2019, which were compared with subnational LEs from the Global Burden of Disease (GBD) 2019 Study.ResultsA total of 9.92 million deaths (95% UI 9.70 to 10.02) were estimated across India in 2019, about 2.28 million more than CRS reports. Adjustments to under-five and elderly mortality accounted for 30% and 56% of additional deaths, respectively. Adjustments in Bihar, Jharkhand, Madhya Pradesh, Maharashtra, Rajasthan and Uttar Pradesh accounted for 75% of all additional deaths. Adjusted LEs were below corresponding GBD estimates by ≥2 years for males at national level and in 20 states, and by ≥1 year for females in 12 states.ConclusionsThese results represent the first-ever subnational mortality estimates for India derived from CRS reported deaths, and serve as a baseline for assessing excess mortality from the COVID-19 pandemic. Adjusted life expectancies indicate higher mortality patterns in India than previously perceived. Under-reporting of infant deaths and those among women and the elderly is evident in many locations. Further CRS strengthening is required to improve the empirical basis for local mortality measurement across the country.

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Impact of COVID-19 on mortality in Croatia

Stanovnistvo ◽

10.2298/stnv2101001c ◽

2021 ◽

Vol 59 (1) ◽

pp. 1-16

Author(s):

Ivan Cipin ◽

Dario Mustac ◽

Petra Medjimurec

Keyword(s):

Life Expectancy ◽

Poisson Regression ◽

Excess Mortality ◽

Age Groups ◽

Poisson Regression Model ◽

Decomposition Techniques ◽

Expected Number ◽

The Difference ◽

Age And Sex ◽

Related Mortality

The main goal of this paper is to assess the effects of the COVID-19 pandemic on mortality in Croatia. We estimate two effects of the pandemic on mortality: (1) excess mortality during 2020 and (2) the age- and cause-specific components of life expectancy decline in 2020. We calculate excess mortality in 2020 as the difference between the registered number of deaths in 2020 and the expected number of deaths from a Poisson regression model based on weekly death counts and population exposures by age and sex from 2016 to 2019. Using decomposition techniques, we estimate age- and cause-specific components (distinguishing COVID-19-related deaths from deaths from other causes) of life expectancy decline in 2020. Our results show that excess mortality in 2020 almost entirely results from the second, autumn-winter wave of the epidemic in Croatia. Expectedly, we find the highest excess in deaths in older age groups. In Croatia, life expectancy in 2020 fell by almost eight months for men and about seven months for women. This decline is mostly attributable to COVID-19-related mortality in older ages, especially among men.

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Suicide Mortality in Canada after the Death of Robin Williams, in the Context of High-Fidelity to Suicide Reporting Guidelines in the Canadian Media

The Canadian Journal of Psychiatry ◽

10.1177/0706743719854073 ◽

2019 ◽

Vol 64 (11) ◽

pp. 805-812 ◽

Cited By ~ 5

Author(s):

Rob Whitley ◽

David S. Fink ◽

Julian Santaella-Tenorio ◽

Katherine M. Keyes

Keyword(s):

Time Series ◽

Media Coverage ◽

The United States ◽

Reporting Guidelines ◽

Suicide Mortality ◽

High Fidelity ◽

Expected Number ◽

Mainstream Media ◽

High Profile ◽

Statistics Canada

Background: Evidence suggests that suicide mortality increases after high-profile suicide deaths. Indeed, suicide in the United States increased disproportionately after the suicide by suffocation of well-known comedian Robin Williams in August 2014. Such increases are often attributed to irresponsible media coverage of the suicide contributing to “copycat suicides.” However, recent research indicates that the mainstream Canadian media have significantly improved their suicide coverage, with high fidelity to suicide reporting guidelines after Williams’ death. As such, the aim of the present study is to examine suicide mortality in Canada after Robin Williams’ suicide. Methods: We obtained deidentified monthly suicide count data from January 1999 to December 2015 stratified by age, sex, and method of suicide from Statistics Canada. We used time-series analyses to estimate the expected number of suicides in the months following Robin Williams’ death. This was done using a seasonal autoregressive integrated moving averages (SARIMA) method. Expected suicides were then compared with observed suicides. Results: August 2014 was the month with the highest number of suicides from 2010 to 2015. The time-series model indicated a 16% increase in the expected number of suicides during the months from August to December 2014 inclusive. Moreover, males over 30 had the greatest number of excess suicides, and suicides by suffocation (the method used by Robin Williams) were also higher in August and the following months. Interpretation: Suicides increased in Canada after Robin Williams’ death, despite the improved mainstream media coverage witnessed in other studies. Other factors (e.g., social and alternative media) may have contributed to the observed increase in suicide.

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Gender specific excess mortality in Italy during the COVID-19 pandemic accounting for age

European Journal of Epidemiology ◽

10.1007/s10654-021-00717-9 ◽

2021 ◽

Author(s):

Emilio A. L. Gianicolo ◽

Antonello Russo ◽

Britta Büchler ◽

Katherine Taylor ◽

Andreas Stang ◽

...

Keyword(s):

Excess Mortality ◽

Mortality Rates ◽

Published Data ◽

Italian Population ◽

Expected Number ◽

Mortality Experience ◽

Short Time Span ◽

Short Time ◽

Age And Sex ◽

Excess Deaths

AbstractSince the beginning of the COVID-19 pandemic, data have been accumulated to examine excess mortality in the first half of 2020. Mortality in the preceding year or years is used to calculate the expected number of deaths, which is then compared with the actual number of deaths in 2020. We calculated weekly age- and sex-specific mortality rates for 93.1% of the Italian municipalities for the years 2015–2019 and for the first 26 weeks in 2020. We assumed the mortality experience during 2015–2019 as the reference period to calculate standardised mortality ratios. Furthermore, in order to compare the mortality experience of males and females, we calculated sex- and age- specific weekly direct standardised mortality rates and differences between the observed and expected number of deaths. We observed considerable changes in the demographics in the Italian population between the years 2015 and 2020, particularly among people 60 years and older and among males. The population is aging and the proportion of elderly males has increased, which was not reflected adequately in previous estimates of excess mortality. Standardized excess mortality results show that in Italy between the 8th and 26th weeks in 2020, there were 33,035 excess deaths, which is only 643 fewer deaths than the official COVID-19 death toll for this time period. A comparative increase in the mortality rates was observed in March among both sexes, but particularly for males. Comparisons with recently published data show considerably higher excess deaths, but these data were either not covering the complete country or did not account for age and sex. Neglecting the demographic changes in a region, even over a short time span, can result in biased estimates.

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