scholarly journals Excess mortality during COVID-19 in five European countries and a critique of mortality data analysis

2020 ◽  
Author(s):  
J. Félix-Cardoso ◽  
H. Vasconcelos ◽  
P. Pereira Rodrigues ◽  
R. Cruz-Correia
Keyword(s):  
Excess Mortality ◽  
Surveillance Data ◽  
European Countries ◽  
Mortality Data ◽  
Surveillance Systems ◽  
Policy Responses ◽  
Seasonal Time Series ◽  
And Gender ◽  
The Impact ◽  
Mortality Surveillance

AbstractINTRODUCTIONThe COVID-19 pandemic is an ongoing event disrupting lives, health systems, and economies worldwide. Clear data about the pandemic’s impact is lacking, namely regarding mortality. This work aims to study the impact of COVID-19 through the analysis of all-cause mortality data made available by different European countries, and to critique their mortality surveillance data.METHODSEuropean countries that had publicly available data about the number of deaths per day/week were selected (England and Wales, France, Italy, Netherlands and Portugal). Two different methods were selected to estimate the excess mortality due to COVID19: (DEV) deviation from the expected value from homologue periods, and (RSTS) remainder after seasonal time series decomposition. We estimate total, age- and gender-specific excess mortality. Furthermore, we compare different policy responses to COVID-19.RESULTSExcess mortality was found in all 5 countries, ranging from 10.6% in Portugal (DEV) to 98.5% in Italy (DEV). Furthermore, excess mortality is higher than COVID-attributed deaths in all 5 countries.DISCUSSIONThe impact of COVID-19 on mortality appears to be larger than officially attributed deaths, in varying degrees in different countries. Comparisons between countries would be useful, but large disparities in mortality surveillance data could not be overcome. Unreliable data, and even a lack of cause-specific mortality data undermine the understanding of the impact of policy choices on both direct and indirect deaths during COVID-19. European countries should invest more on mortality surveillance systems to improve the publicly available data.

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

2018 ◽  
Vol 146 (16) ◽  
pp. 2059-2065 ◽  
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.

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

2019 ◽  
Vol 147 ◽  
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.

scholarly journals Excess Mortality in the United States During the First Peak of the COVID-19 Pandemic

2020 ◽  
Author(s):  
R. Rivera ◽  
J. E. Rosenbaum ◽  
W. Quispe
Keyword(s):  
United States ◽  
New York ◽  
New Jersey ◽  
Excess Mortality ◽  
Case Fatality ◽  
The United States ◽  
Mortality Data ◽  
Surveillance Systems ◽  
Pandemic Response ◽  
Mortality Surveillance

1AbstractDeaths are frequently under-estimated during emergencies, times when accurate mortality estimates are crucial for pandemic response and public adherence to non-pharmaceutical interventions. This study estimates excess all-cause, pneumonia, and influenza mortality during the COVID-19 health emergency using the June 12, 2020 release of weekly mortality data from the United States (U.S.) Mortality Surveillance Survey (MSS) from September 27, 2015 to May 9, 2020, using semiparametric and conventional time-series models in 9 states with high reported COVID-19 deaths and apparently complete mortality data: California, Colorado, Florida, Illinois, Massachusetts, Michigan, New Jersey, New York, and Washington. The May 9 endpoint was chosen due to apparently increased reporting lags in provisional mortality counts. We estimated greater excess mortality than official COVID-19 mortality in the U.S. (excess mortality 95% confidence interval (CI) (80862, 107284) vs. 78834 COVID-19 deaths) and 6 states: California (excess mortality 95% CI (2891, 5873) vs. 2849 COVID-19 deaths); Illinois (95% CI (4412, 5871) vs. 3525 COVID-19 deaths); Massachusetts (95% CI (5061, 6317) vs. 5050 COVID-19 deaths); New Jersey (95% CI (12497, 15307) vs. 10465 COVID-19 deaths); and New York (95% CI (30469, 37722) vs. 26584 COVID-19 deaths). Conventional model results were consistent with semiparametric results but less precise.Official COVID-19 mortality substantially understates actual mortality, suggesting greater case-fatality rates. Mortality reporting lags appeared to worsen during the pandemic, when timeliness in surveillance systems was most crucial for improving pandemic response.

scholarly journals Excess mortality in the United States during the first three months of the COVID-19 pandemic

2020 ◽  
Vol 148 ◽  
Author(s):  
R. Rivera ◽  
J. E. Rosenbaum ◽  
W. Quispe
Keyword(s):  
United States ◽  
New York ◽  
New Jersey ◽  
Excess Mortality ◽  
The United States ◽  
Mortality Data ◽  
Surveillance Systems ◽  
Pandemic Response ◽  
Mortality Surveillance ◽  
Mortality Estimates

Abstract Deaths are frequently under-estimated during emergencies, times when accurate mortality estimates are crucial for emergency response. This study estimates excess all-cause, pneumonia and influenza mortality during the coronavirus disease 2019 (COVID-19) pandemic using the 11 September 2020 release of weekly mortality data from the United States (U.S.) Mortality Surveillance System (MSS) from 27 September 2015 to 9 May 2020, using semiparametric and conventional time-series models in 13 states with high reported COVID-19 deaths and apparently complete mortality data: California, Colorado, Connecticut, Florida, Illinois, Indiana, Louisiana, Massachusetts, Michigan, New Jersey, New York, Pennsylvania and Washington. We estimated greater excess mortality than official COVID-19 mortality in the U.S. (excess mortality 95% confidence interval (CI) 100 013–127 501 vs. 78 834 COVID-19 deaths) and 9 states: California (excess mortality 95% CI 3338–6344) vs. 2849 COVID-19 deaths); Connecticut (excess mortality 95% CI 3095–3952) vs. 2932 COVID-19 deaths); Illinois (95% CI 4646–6111) vs. 3525 COVID-19 deaths); Louisiana (excess mortality 95% CI 2341–3183 vs. 2267 COVID-19 deaths); Massachusetts (95% CI 5562–7201 vs. 5050 COVID-19 deaths); New Jersey (95% CI 13 170–16 058 vs. 10 465 COVID-19 deaths); New York (95% CI 32 538–39 960 vs. 26 584 COVID-19 deaths); and Pennsylvania (95% CI 5125–6560 vs. 3793 COVID-19 deaths). Conventional model results were consistent with semiparametric results but less precise. Significant excess pneumonia deaths were also found for all locations and we estimated hundreds of excess influenza deaths in New York. We find that official COVID-19 mortality substantially understates actual mortality, excess deaths cannot be explained entirely by official COVID-19 death counts. Mortality reporting lags appeared to worsen during the pandemic, when timeliness in surveillance systems was most crucial for improving pandemic response.

scholarly journals The new automated daily mortality surveillance system in Portugal

2010 ◽  
Vol 15 (13) ◽  
Author(s):  
P J Nogueira ◽  
A Machado ◽  
E Rodrigues ◽  
B Nunes ◽  
L Sousa ◽  
...  
Keyword(s):  
Information Acquisition ◽  
Surveillance System ◽  
Warning System ◽  
Automated System ◽  
Mortality Data ◽  
Fast Method ◽  
Daily Mortality ◽  
Public Health Action ◽  
The Impact ◽  
Mortality Surveillance

The experience reported in an earlier Eurosurveillance issue on a fast method to evaluate the impact of the 2003 heatwave on mortality in Portugal, generated a daily mortality surveillance system (VDM) that has been operating ever since jointly with the Portuguese Heat Health Watch Warning System. This work describes the VDM system and how it evolved to become an automated system operating year-round, and shows briefly its potential using mortality data from January 2006 to June 2009 collected by the system itself. The new system has important advantages such as: rapid information acquisition, completeness (the entire population is included), lightness (very little information is exchanged, date of death, age, sex, place of death registration). It allows rapid detection of impacts (within five days) and allows a quick preliminary quantification of impacts that usually took several years to be done. These characteristics make this system a powerful tool for public health action. The VDM system also represents an example of inter-institutional cooperation, bringing together organisations from two different ministries, Health and Justice, aiming at improving knowledge about the mortality in the population.

Feasibility and validity of using death surveillance data and SmartVA for fact and cause of death in clinical trials in rural China: a substudy of the China salt substitute and stroke study (SSaSS)

2020 ◽  
pp. jech-2020-214063
Author(s):  
Liping Huang ◽  
Jie Yu ◽  
Bruce Neal ◽  
Yishu Liu ◽  
Xuejun Yin ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Causes Of Death ◽  
Rural China ◽  
Surveillance Data ◽  
Primary Data ◽  
Mortality Data ◽  
Face To Face ◽  
Stroke Study ◽  
Salt Substitute ◽  
Mortality Surveillance

BackgroundIn rural China, mortality surveillance data may be an alternative to primary data collection in clinical trials; SmartVA (verbal autopsy) is also a potential alternative for endpoint adjudication. The feasibility and validity of both need to be assessed.MethodsWe used mortality data from the first 24 months of the China Salt Substitute and Stroke Study (SSaSS) trial and assessed the agreement between (1) mortality surveillance data and face-to-face visits for fact of death; (2) mortality surveillance data and SSaSS adjudication for causes of death; (3) SmartVA and SSaSS adjudication for causes of death; (4) cause-specific mortality fraction of different methods. Face-to-face visits and SSaSS adjudication were taken as reference methods. The agreement was measured by sensitivity, specificity and positive predictive value (PPV) across different 10th Revision of International Statistical Classification of Diseases chapters.ResultsOne thousand three hundred and sixty-five deaths were included. Mortality surveillance data had 82% sensitivity for fact of death and 81% sensitivity for causes of death, with substantial variances across different disease types and reasonable quality for circulatory death (91% sensitivity and 94% PPV). The sensitivity of SmartVA for causes of death was 61%, with reasonable quality for deaths of external causes of morbidity (90% sensitivity). The leading causes of death from different sources were the same with some variances in the fractions.ConclusionUsing mortality surveillance data for fact of death in clinical trials need to account for under-reporting. A face-to-face visit to all participants at the completion of trials may be warranted. Neither mortality surveillance data nor SmartVA provided valid data source for endpoint events.

scholarly journals Excess mortality among the elderly in 12 European countries, February and March 2012

2012 ◽  
Vol 17 (14) ◽  
Author(s):  
A Mazick ◽  
B Gergonne ◽  
J Nielsen ◽  
F Wuillaume ◽  
M J Virtanen ◽  
...  
Keyword(s):  
Preliminary Data ◽  
Influenza A ◽  
Excess Mortality ◽  
The Elderly ◽  
European Countries ◽  
H3n2 Virus ◽  
All Cause Mortality ◽  
Cold Snap ◽  
The Impact ◽  
Excess Deaths

In February and March 2012, excess deaths among the elderly have been observed in 12 European countries that carry out weekly monitoring of all-cause mortality. These preliminary data indicate that the impact of influenza in Europe differs from the recent pandemic and post-pandemic seasons. The current excess mortality among the elderly may be related to the return of influenza A(H3N2) virus, potentially with added effects of a cold snap.

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

eLife ◽  
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.

scholarly journals Excess mortality during the COVID-19 outbreak in Italy: a two-stage interrupted time series analysis

2020 ◽  
Author(s):  
Matteo Scortichini ◽  
Rochelle Schneider dos Santos ◽  
Francesca De' Donato ◽  
Manuela De Sario ◽  
Paola Michelozzi ◽  
...  
Keyword(s):  
Time Series ◽  
Excess Mortality ◽  
Temporal Distribution ◽  
Interrupted Time Series ◽  
Baseline Risk ◽  
Mortality Data ◽  
Interrupted Time Series Analysis ◽  
Two Stage ◽  
Smooth Functions ◽  
The Impact

Background: Italy was the first country outside China to experience the impact of the COVID-19 pandemic, which resulted in a significant health burden. This study presents an analysis of the excess mortality across the 107 Italian provinces, stratified by sex, age group, and period of the outbreak. Methods: The analysis was performed using a two-stage interrupted time series design using daily mortality data for the period January 2015 - May 2020. In the first stage, we performed province-level quasi-Poisson regression models, with smooth functions to define a baseline risk while accounting for trends and weather conditions and to flexibly estimate the variation in excess risk during the outbreak. Estimates were pooled in the second stage using a mixed-effects multivariate meta-analysis. Results: In the period 15 February - 15 May 2020, we estimated an excess of 47,490 (95% empirical confidence intervals: 43,984 to 50,362) deaths in Italy, corresponding to an increase of 29.5% (95%eCI: 26.8 to 31.9%) from the expected mortality. The analysis indicates a strong geographical pattern, with the majority of excess deaths occurring in northern regions, where few provinces experienced up to 800% increase during the peak in late March. There were differences by sex, age, and area both in the overall impact and in its temporal distribution. Conclusions: This study offers a detailed picture of excess mortality during the first months of the COVID-19 pandemic in Italy. The strong geographical and temporal patterns can be related to implementation of lockdown policies and multiple direct and indirect pathways in mortality risk.

scholarly journals Impact of the COVID-19 pandemic on life expectancy in Madrid (Spain)

2020 ◽  
Author(s):  
Sergi Trias-Llimós ◽  
Usama Bilal
Keyword(s):  
Life Expectancy ◽  
Surveillance System ◽  
Excess Mortality ◽  
Daily Mortality ◽  
Life Expectancy At Birth ◽  
The World ◽  
Using Data ◽  
The Impact ◽  
Mortality Surveillance ◽  
Annual Mortality

The COVID-19 pandemic is causing substantial increases in mortality across populations, potentially causing stagnation or decline in life expectancy. We explored this idea by examining the impact of excess mortality linked to the COVID-19 crisis on life expectancy in the region of Madrid (Spain). Using data from the Daily Mortality Surveillance System (MoMo), we calculated excess mortality (death counts) for the weeks 10th to 14th in 2020 using data on expected and observed mortality, assuming no further excess mortality during the rest of the year. The expected annual mortality variation was +6%, +21% and +25% among men aged under 65, between 65 and 74 and over 75, respectively, and +5%, +13%, and 18% for women, respectively. This excess mortality during weeks 10th to 14th resulted in a life expectancy at birth decline of 1.6 years among men and 1.1 years among women. These estimates confirm that Madrid and other severely hit regions in the world may face substantial life expectancy declines.

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