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

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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Outcomes of patients with COVID-19 on kidney replacement therapy: a comparison among modalities in England

Clinical Kidney Journal ◽

10.1093/ckj/sfab160 ◽

2021 ◽

Author(s):

Manuela Savino ◽

Shalini Santhakumaran ◽

Katharine M Evans ◽

Retha Steenkamp ◽

Fran Benoy-Deeney ◽

...

Keyword(s):

Kidney Disease ◽

Replacement Therapy ◽

Retrospective Cohort ◽

Excess Mortality ◽

Mortality Data ◽

Kidney Replacement Therapy ◽

Poor Outcomes ◽

The Uk ◽

The Impact ◽

Excess Deaths

Abstract Background Chronic kidney disease (CKD) is a recognised risk factor of poor outcomes from COVID-19. Methods This retrospective cohort study used the UK Renal Registry (UKRR) database of people on kidney replacement therapy (KRT) at the end of 2019 in England and who tested positive for SARS-CoV-2 between 01/03/2020 and 31/08/2020, to analyse incidence and outcomes of COVID-19 among different KRT modalities. Comparisons with 2015-2019 mortality data were used to estimate excess deaths. Results 2,783 individuals on KRT tested positive for SARS-CoV-2. Patients from more deprived areas (most deprived vs least deprived HR 1.20, 95% CI 1.04-1.39) and those with diabetes compared to those without (HR 1.51, 95% CI 1.39-1.64) were more likely to test positive. Approximately 25% of in-centre haemodialysis and transplanted patients died within 28 days of testing positive, compared to 36% of those on home therapies. Mortality was higher in those aged ≥80 years compared to those aged 60-79 years (OR 1.71, 95% CI 1.34-2.19) and much lower in those listed for transplantation compared to those not listed (OR 0.56, 95% CI 0.40-0.80). Overall, excess mortality in 2020 for people on KRT was 36% higher than the 2015-2019 average. Excess deaths peaked in April 2020 at the height of the pandemic and were characterised by wide ethnic and regional disparities. Conclusions The impact of COVID-19 on the English KRT population highlights their extreme vulnerability and emphasises the need to protect and prioritise this group for vaccination. COVID-19 has widened underlying inequalities in people with kidney disease making interventions that address health inequalities a priority.

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

10.1101/2020.04.28.20083147 ◽

2020 ◽

Cited By ~ 6

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.

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Date of introduction and epidemiologic patterns of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Mogadishu, Somalia: estimates from transmission modelling of satellite-based excess mortality data in 2020

Wellcome Open Research ◽

10.12688/wellcomeopenres.17247.1 ◽

2021 ◽

Vol 6 ◽

pp. 255

Author(s):

Mihaly Koltai ◽

Abdihamid Warsame ◽

Farah Bashiir ◽

Terri Freemantle ◽

Chris Reeve ◽

...

Keyword(s):

Severe Acute Respiratory Syndrome ◽

Low Income ◽

Excess Mortality ◽

Model Fitting ◽

Transmission Model ◽

Mortality Data ◽

Surveillance Systems ◽

Mortality Estimation ◽

Correlated Parameters ◽

Excess Deaths

Background: In countries with weak surveillance systems, confirmed coronavirus disease 2019 (COVID-19) deaths are likely to underestimate the pandemic’s death toll. Many countries also have incomplete vital registration systems, hampering excess mortality estimation. Here, we fitted a dynamic transmission model to satellite imagery data of cemeteries in Mogadishu, Somalia during 2020 to estimate the date of introduction and other epidemiologic parameters of the early spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in this low-income, crisis-affected setting. Methods: We performed Markov chain Monte Carlo (MCMC) fitting with an age-structured compartmental COVID-19 model to provide median estimates and credible intervals for the date of introduction, the basic reproduction number (R0) and the effect of non-pharmaceutical interventions (NPIs) up to August 2020. Results: Under the assumption that excess deaths in Mogadishu March-August 2020 were attributable to SARS-CoV-2 infections, we arrived at median estimates of November-December 2019 for the date of introduction and low R0 estimates (1.4-1.7) reflecting the slow and early rise and long plateau of excess deaths. The date of introduction, the amount of external seeding, the infection fatality rate (IFR) and the effectiveness of NPIs are correlated parameters and not separately identifiable in a narrow range from deaths data. Nevertheless, to obtain introduction dates no earlier than November 2019 a higher population-wide IFR (≥0.7%) had to be assumed than obtained by applying age-specific IFRs from high-income countries to Somalia’s age structure. Conclusions: Model fitting of excess mortality data across a range of plausible values of the IFR and the amount of external seeding suggests an early SARS-CoV-2 introduction event may have occurred in Somalia in November-December 2019. Transmissibility in the first epidemic wave was estimated to be lower than in European settings. Alternatively, there was another, unidentified source of sustained excess mortality in Mogadishu from March to August 2020.

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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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Date of introduction and epidemiologic patterns of SARS-CoV-2 in Mogadishu, Somalia: estimates from transmission modelling of 2020 excess mortality data

10.1101/2021.06.15.21258924 ◽

2021 ◽

Author(s):

Mihaly Koltai ◽

Abdihamid Warsame ◽

Farah Bashiir ◽

Terri Freemantle ◽

Chris Williams ◽

...

Keyword(s):

Low Income ◽

Excess Mortality ◽

Control Measures ◽

Transmission Model ◽

Mortality Data ◽

Surveillance Systems ◽

Mortality Estimation ◽

Credible Intervals ◽

Age Structured ◽

Excess Deaths

Introduction In countries with weak surveillance systems confirmed COVID-19 deaths are likely to underestimate the death toll of the pandemic. Many countries also have incomplete vital registration systems, hampering excess mortality estimation. Here, we fitted a dynamic transmission model to satellite imagery data on burial patterns in Mogadishu, Somalia during 2020 to estimate the date of introduction, transmissibility and other epidemiologic characteristics of SARS-CoV-2 in this low-income, crisis-affected setting. Methods We performed Markov chain Monte Carlo (MCMC) fitting with an age-structured compartmental COVID-19 model to provide median estimates and credible intervals for the date of introduction, the basic reproduction number (R0) and the effect of non-pharmaceutical interventions in Mogadishu up to September 2020. Results Under the assumption that excess deaths in Mogadishu February-September 2020 were directly attributable to SARS-CoV-2 infection we arrived at median estimates of October-November 2019 for the date of introduction and low R0 estimates (1.3-1.5) stemming from the early and slow rise of excess deaths. The effect of control measures on transmissibility appeared small. Conclusion Subject to study assumptions, a very early SARS-CoV-2 introduction event may have occurred in Somalia. Estimated transmissibility in the first epidemic wave was lower than observed in European settings.

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

10.2196/preprints.32426 ◽

2021 ◽

Author(s):

Gemma Postill ◽

Regan Murray ◽

Andrew S Wilton ◽

Richard A Wells ◽

Renee Sirbu ◽

...

Keyword(s):

Real Time ◽

Excess Mortality ◽

Age Groups ◽

Time Estimation ◽

Vital Statistics ◽

Accurate Estimation ◽

Mortality Data ◽

Mortality Estimation ◽

Statistics Canada ◽

The Impact

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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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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