Excess mortality in England and Wales during the first wave of the COVID-19 pandemic

AbstractBackgroundDeaths during the COVID-19 pandemic result directly from infection and exacerbation of other diseases and indirectly from deferment of care for other conditions, and are socially and geographically patterned. We quantified excess mortality in regions of England and Wales during the pandemic, for all causes and for non-COVID-19 associated deaths.MethodsWeekly mortality data for 1 Jan 2010 to 1 May 2020 for England and Wales were obtained from the Office of National Statistics. Mean-dispersion negative binomial regressions were used to model death counts based on pre-pandemic trends and exponentiated linear predictions were subtracted from: i) all-cause deaths; and ii) all-cause deaths minus COVID-19 related deaths for the pandemic period (07-13 March to 25 April to 8 May).FindingsBetween 7 March and 8 May 2020, there were 47,243 (95%CI: 46,671 to 47,815) excess deaths in England and Wales, of which 9,948 (95%CI: 9,376 to 10,520) were not associated with COVID-19. Overall excess mortality rates varied from 49 per 100,000 (95%CI: 49 to 50) in the South West to 102 per 100,000 (95%CI: 102 to 103) in London. Non-COVID-19 associated excess mortality rates ranged from −1 per 100,000 (95%CI: −1 to 0) in Wales (i.e. mortality rates were no higher than expected) to 26 per 100,000 (95%CI: 25 to 26) in the West Midlands.InterpretationThe COVID-19 pandemic has had markedly different impacts on the regions of England and Wales, both for deaths directly attributable to COVID-19 infection and for deaths resulting from the national public health response.FundingNoneSummary boxWhat is already known on the subjectThe number of deaths due to COVID-19 have been quantified by the Office of National StatisticsThese have also been reported across age groups and regionsWhat this study addsWe report the number of excess deaths, using weekly mortality data from 1/1/2010We also quantify the number of excess deaths, excluding COVID-19 associated deaths, which can be attributed to COVID-19 directly (but not coded as such) or indirectly (due to other urgent but unmet health need)Highest excess mortality, excluding COVID-19 deaths, was observed in the West Midlands, followed by London and the North WestAlthough males had larger excess mortality rates than females across all age groups, female excess mortality rates excluding COVID-19 were higher in the 85+ age group, indicating a large undocumented impact of the virus on older females (direct and/or indirect)The three provided appendices will be updated weekly on the BMJ-JECH website, to provide up-to-date information of excess mortality by region, sex and age group

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Excess mortality in England and Wales during the first wave of the COVID-19 pandemic

Journal of Epidemiology & Community Health ◽

10.1136/jech-2020-214764 ◽

2020 ◽

pp. jech-2020-214764 ◽

Cited By ~ 3

Author(s):

Evangelos Kontopantelis ◽

Mamas A Mamas ◽

John Deanfield ◽

Miqdad Asaria ◽

Tim Doran

Keyword(s):

Excess Mortality ◽

Negative Binomial ◽

Mortality Rates ◽

Mortality Data ◽

Public Health Response ◽

England And Wales ◽

South West ◽

Health Response ◽

West Midlands ◽

National Public Health

BackgroundDeaths during the COVID-19 pandemic result directly from infection and exacerbation of other diseases and indirectly from deferment of care for other conditions, and are socially and geographically patterned. We quantified excess mortality in regions of England and Wales during the pandemic, for all causes and for non-COVID-19-associated deaths.MethodsWeekly mortality data for 1 January 2010 to 1 May 2020 for England and Wales were obtained from the Office of National Statistics. Mean-dispersion negative binomial regressions were used to model death counts based on pre-pandemic trends and exponentiated linear predictions were subtracted from: (i) all-cause deaths and (ii) all-cause deaths minus COVID-19 related deaths for the pandemic period (week starting 7 March, to week ending 8 May).FindingsBetween 7 March and 8 May 2020, there were 47 243 (95% CI: 46 671 to 47 815) excess deaths in England and Wales, of which 9948 (95% CI: 9376 to 10 520) were not associated with COVID-19. Overall excess mortality rates varied from 49 per 100 000 (95% CI: 49 to 50) in the South West to 102 per 100 000 (95% CI: 102 to 103) in London. Non-COVID-19 associated excess mortality rates ranged from −1 per 100 000 (95% CI: −1 to 0) in Wales (ie, mortality rates were no higher than expected) to 26 per 100 000 (95% CI: 25 to 26) in the West Midlands.InterpretationThe COVID-19 pandemic has had markedly different impacts on the regions of England and Wales, both for deaths directly attributable to COVID-19 infection and for deaths resulting from the national public health response.

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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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Age, time and cohort factors in mortality from cancer of the cervix

Journal of Hygiene ◽

10.1017/s0022172400022725 ◽

1973 ◽

Vol 71 (2) ◽

pp. 253-259 ◽

Cited By ~ 29

Author(s):

J. C. Barrett

Keyword(s):

Age Groups ◽

Mortality Rates ◽

Age Group ◽

Mortality Pattern ◽

England And Wales ◽

Cancer Of The Cervix

SUMMARYData for mortality from cancer of the cervix in England and Wales by 5-year age groups and four quinquennia (1951–70) are analysed. The logarithms of the mortality rates are regressed on age group, epoch of death and epoch of birth. The factors obtained are considered in relation to particular features of the mortality pattern, such as the reversal of trend in certain age groups.

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Excess mortality monitoring in England and Wales during the influenza A(H1N1) 2009 pandemic

Epidemiology and Infection ◽

10.1017/s0950268811000410 ◽

2011 ◽

Vol 139 (9) ◽

pp. 1431-1439 ◽

Cited By ~ 10

Author(s):

P. HARDELID ◽

N. ANDREWS ◽

R. PEBODY

Keyword(s):

Pandemic Influenza ◽

Influenza A ◽

Excess Mortality ◽

Cold Weather ◽

Mortality Data ◽

Age Group ◽

England And Wales ◽

Health Events ◽

Influenza A H1n1 ◽

All Cause Mortality

SUMMARYWe present the results from a novel surveillance system for detecting excess all-cause mortality by age group in England and Wales developed during the pandemic influenza A(H1N1) 2009 period from April 2009 to March 2010. A Poisson regression model was fitted to age-specific mortality data from 1999 to 2008 and used to predict the expected number of weekly deaths in the absence of extreme health events. The system included adjustment for reporting delays. During the pandemic, excess all-cause mortality was seen in the 5–14 years age group, where mortality was flagged as being in excess for 1 week after the second peak in pandemic influenza activity; and in age groups >45 years during a period of very cold weather. This new system has utility for rapidly estimating excess mortality for other acute public health events such as extreme heat or cold weather.

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Excess Mortality During the COVID-19 Pandemic in Guatemala

American Journal of Public Health ◽

10.2105/ajph.2021.306452 ◽

2021 ◽

pp. e1-e8

Author(s):

Kevin Martinez-Folgar ◽

Diego Alburez-Gutierrez ◽

Alejandra Paniagua-Avila ◽

Manuel Ramirez-Zea ◽

Usama Bilal

Keyword(s):

Excess Mortality ◽

Generalized Additive Model ◽

Additive Model ◽

Mortality Rates ◽

Place Of Death ◽

Mortality Data ◽

Registration System ◽

Health Crisis ◽

Vital Registration System ◽

Excess Deaths

Objectives. To describe excess mortality during the COVID-19 pandemic in Guatemala during 2020 by week, age, sex, and place of death. Methods. We used mortality data from 2015 to 2020, gathered through the vital registration system of Guatemala. We calculated weekly mortality rates, overall and stratified by age, sex, and place of death. We fitted a generalized additive model to calculate excess deaths, adjusting for seasonality and secular trends and compared excess deaths to the official COVID-19 mortality count. Results. We found an initial decline of 26% in mortality rates during the first weeks of the pandemic in 2020, compared with 2015 to 2019. These declines were sustained through October 2020 for the population younger than 20 years and for deaths in public spaces and returned to normal from July onward in the population aged 20 to 39 years. We found a peak of 73% excess mortality in mid-July, especially in the population aged 40 years or older. We estimated a total of 8036 excess deaths (95% confidence interval = 7935, 8137) in 2020, 46% higher than the official COVID-19 mortality count. Conclusions. The extent of this health crisis is underestimated when COVID-19 confirmed death counts are used. (Am J Public Health. Published online ahead of print September 23, 2021: e1–e8. https://doi.org/10.2105/AJPH.2021.306452 )

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Estimating the burden of the COVID-19 pandemic on mortality, life expectancy and lifespan inequality in England and Wales: a population-level analysis

Journal of Epidemiology & Community Health ◽

10.1136/jech-2020-215505 ◽

2021 ◽

pp. jech-2020-215505

Author(s):

Jose Manuel Aburto ◽

Ridhi Kashyap ◽

Jonas Schöley ◽

Colin Angus ◽

John Ermisch ◽

...

Keyword(s):

Life Expectancy ◽

Excess Mortality ◽

Prediction Interval ◽

Age Groups ◽

Population Level ◽

England And Wales ◽

Life Expectancy At Birth ◽

Using Data ◽

The Impact ◽

Excess Deaths

BackgroundDeaths directly linked to COVID-19 infection may be misclassified, and the pandemic may have indirectly affected other causes of death. To overcome these measurement challenges, we estimate the impact of the COVID-19 pandemic on mortality, life expectancy and lifespan inequality from week 10 of 2020, when the first COVID-19 death was registered, to week 47 ending 20 November 2020 in England and Wales through an analysis of excess mortality.MethodsWe estimated age and sex-specific excess mortality risk and deaths above a baseline adjusted for seasonality with a systematic comparison of four different models using data from the Office for National Statistics. We additionally provide estimates of life expectancy at birth and lifespan inequality defined as the SD in age at death.ResultsThere have been 57 419 (95% prediction interval: 54 197, 60 752) excess deaths in the first 47 weeks of 2020, 55% of which occurred in men. Excess deaths increased sharply with age and men experienced elevated risks of death in all age groups. Life expectancy at birth dropped 0.9 and 1.2 years for women and men relative to the 2019 levels, respectively. Lifespan inequality also fell over the same period by 5 months for both sexes.ConclusionQuantifying excess deaths and their impact on life expectancy at birth provide a more comprehensive picture of the burden of COVID-19 on mortality. Whether mortality will return to—or even fall below—the baseline level remains to be seen as the pandemic continues to unfold and diverse interventions are put in place.

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Early estimates of the excess mortality associated with the 2008-9 influenza season in Portugal

Eurosurveillance ◽

10.2807/ese.14.18.19194-en ◽

2009 ◽

Vol 14 (18) ◽

Cited By ~ 10

Author(s):

P J Nogueira ◽

B Nunes ◽

A Machado ◽

E Rodrigues ◽

V Gómez ◽

...

Keyword(s):

Excess Mortality ◽

Total Population ◽

Influenza Season ◽

Statistical Modelling ◽

Mortality Data ◽

Age Group ◽

Gender And Age ◽

Influenza Activity ◽

The Impact ◽

Excess Deaths

The aim of this study was to estimate the excess mortality associated with the influenza activity registered in Portugal between week 49 of 2008 and week 5 of 2009. For this purpose available mortality data from the Portuguese Daily Mortality Monitoring (VDM) System were used. Several estimates of excess deaths associated with the recent recorded influenza activity were determined through statistical modelling (cyclic regression) for the total population and disaggregated by gender and age group. The results show that the impact of the 2008-9 influenza season was 1,961 excess deaths, with approximately 82% of these occurring in the age group of 75 years and older.

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Excess Deaths associated with the COVID-19 Pandemic in Ukraine in 2020

10.1101/2021.09.28.21264266 ◽

2021 ◽

Author(s):

Neil K. Mehta ◽

Ihor Honchar ◽

Olena Doroshenko ◽

Igor Brovchenko ◽

Khrystyna Pak ◽

...

Keyword(s):

Excess Mortality ◽

Age Groups ◽

European Countries ◽

List Type ◽

Younger Age ◽

Influenza Activity ◽

Expected Mortality ◽

The Impact ◽

Excess Deaths

AbstractCOVID-19 related mortality has been understudied in Ukraine. As part of a World Bank project, we estimated excess mortality in Ukraine during 2020. Data on all deaths registered in government-controlled Ukraine from 2016-2020 (N=2,946,505) were utilized. We predicted deaths in 2020 by five-year age groups, sex, and month and calculated the number of deaths that deviated from expected levels (excess deaths). We compared excess deaths with the number of recorded COVID-19 deaths on death certificates and with published estimates for 30 European countries. We estimated 38,095 excess deaths in 2020 (6% of all deaths). Death rates were above expected levels in February and from June-December and lower in January and March-May. From June-December, we estimated 52,124 excess deaths with a peak in November (16,891 deaths). COVID-19 recorded deaths were approximately one-third of excess deaths in June-December (18,959 vs. 52,124). Higher than expected mortality was detected for all age groups 40-44 years and above and for those ages 0-4, 15-19, and 20-24. Ukraine’s excess mortality was about average compared to 30 other European countries. Excess deaths may be attributed directly to SARS-COV2 infection or indirectly to death causes associated with social and economic upheavals resulting in from the pandemic. Lower than expected mortality during the early part of 2020 is consistent with low influenza activity and reductions in deaths from restricted movement. Further studies are required to examine the causes of death that have contributed to positive excess mortality, particularly among younger aged groups.Key MessagesUkraine has experienced sizeable changes in its recent demography and the impact of the COVID-19 pandemic on the country’s aggregate mortality patterns is understudiedBased on recent death trends, we found that Ukraine experienced lower than expected mortality during the early part of 2020 and consistently higher than expected mortality from June-December with peak levels occurring in NovemberPositive excess mortality was observed for all age groups beginning at ages 40-44 as well as some younger age groups.

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Covid-19 and excess mortality rates not comparable across countries

10.1101/2021.03.31.21254689 ◽

2021 ◽

Author(s):

Gabrielle E Kelly ◽

Stefano Petti ◽

Norman Noah

Keyword(s):

Excess Mortality ◽

Spatial Clustering ◽

Mortality Rates ◽

Mortality Data ◽

Published Data ◽

Death Rates ◽

Government Response ◽

Health Measures ◽

Specific Mortality ◽

Excess Deaths

Abstract: Evidence that more people in some countries and fewer in others are dying because of the pandemic, than is reflected by reported Covid-19 mortality rates, is derived from mortality data. Worldwide, mortality data is used to estimate the full extent of the effects of the Covid-19 pandemic, both direct and indirect; the possible short fall in the number of cases reported to the WHO; and to suggest explanations for differences between countries. Excess mortality data is largely varying across countries and is not directly proportional to Covid-19 mortality. Using publicly available databases, deaths attributed to Covid-19 in 2020 and all deaths for the years 2015-2020 were tabulated for 36 countries together with economic, health, demographic, and government response stringency index variables. Residual death rates in 2020 were calculated as excess deaths minus death rates due to Covid-19 where excess deaths were observed deaths in 2020 minus the average for 2015-2019. For about half the countries, residual deaths were negative and for half, positive. The absolute rates in some countries were double those in others. In a regression analysis, the stringency index (p=0.026) was positively associated with residual mortality. There was no evidence of spatial clustering of residual mortality. The results show that published data on mortality from Covid-19 cannot be directly comparable across countries, likely due to differences in Covid-19 death reporting. In addition, the unprecedented public health measures implemented to control the pandemic may have produced either increased or reduced excess deaths due to other diseases. Further data on cause-specific mortality is required to determine the extent to which residual mortality represents non-Covid-19 deaths and to explain differences between countries.

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Epidemiological Characteristics of Pancreatic Cancer in China From 1990 to 2019

Cancer Control ◽

10.1177/10732748211051536 ◽

2021 ◽

Vol 28 ◽

pp. 107327482110515

Author(s):

Bo Zhu ◽

Xiaomei Wu ◽

Tianyu Guo ◽

Ning Guan ◽

Yefu Liu

Keyword(s):

Pancreatic Cancer ◽

Healthy Lifestyle ◽

Age Groups ◽

Mortality Rates ◽

Mortality Data ◽

Age Group ◽

Incidence And Mortality ◽

Rate Ratios ◽

Epidemiological Characteristics ◽

Incident Cases

Background Pancreatic cancer is an aggressive cancer and is predicted to become the second leading cause of cancer-related deaths in China. To understand the epidemic trend of pancreatic cancer and formulate targeted preventive measures, it is important to analyze the incidence and mortality of pancreatic cancer. Methods The incidence and mortality data of pancreatic cancer in China were obtained from Global Burden of Disease (GBD) data. We used joinpoint regression analysis to calculate the magnitude and direction of trends, and the age-period-cohort method to analyze the effects of chronological age, time period, and birth cohort. Results The age-standardized rates (ASRs) for both incidence and mortality of pancreatic cancer increased from 1990 to 2019, and were higher in males than females. The incidence and mortality rates have increased year by year in the age group above 25 years. The most common age group was 55–79 years, accounting for approximately 50% of all incident cases. In terms of incidence and mortality rates, the overall net drifts were above 0. The local drifts in all age groups were above 0 in both sexes and males, while the local drifts in the 15–39 age groups were below 0 in females. The longitudinal age curves increased with age, with higher incidence and mortality rates, mainly in older age groups. The period rate ratios increased by year. The cohort rate ratios showed an upward trend before 1970 and fluctuated after 1975. Conclusions The burden of pancreatic cancer is still very high in China, and attention should be paid to the key population that is, males and older people. The results of our study can be used by policy makers to allocate resources efficiently to improve early diagnosis and treatment, improving the awareness of self-protection, and advocating a healthy lifestyle to prevent pancreatic cancer.

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