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

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 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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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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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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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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Excess mortality associated with the 2009 A(H1N1)v influenza pandemic in Antananarivo, Madagascar

Epidemiology and Infection ◽

10.1017/s0950268812001215 ◽

2012 ◽

Vol 141 (4) ◽

pp. 745-750 ◽

Cited By ~ 6

Author(s):

S. RAJATONIRINA ◽

B. RAKOTOSOLOFO ◽

F. RAKOTOMANANA ◽

L. RANDRIANASOLO ◽

M. RATSITOHARINA ◽

...

Keyword(s):

Poisson Regression ◽

Excess Mortality ◽

H1n1 Virus ◽

Influenza Pandemic ◽

Mortality Data ◽

Age Group ◽

Influenza Epidemic ◽

Poisson Regression Analysis ◽

Mortality Burden ◽

The Impact

SUMMARYIt is difficult to assess the mortality burden of influenza epidemics in tropical countries. Until recently, the burden of influenza was believed to be negligible in Africa. We assessed the impact of the 2009 influenza epidemic on mortality in Madagascar by conducting Poisson regression analysis on mortality data from the deaths registry, after the first wave of the 2009 A(H1N1) virus pandemic. There were 20% more human deaths than expected in Antananarivo, Madagascar in November 2009, with excess mortality in the ⩾50 years age group (relative risk 1·41). Furthermore, the number of deaths from pulmonary disease was significantly higher than the number of deaths from other causes during this pandemic period. These results suggest that the A(H1N1) 2009 virus pandemic may have been accompanied by an increase in mortality.

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

10.1101/2020.05.26.20113357 ◽

2020 ◽

Cited By ~ 4

Author(s):

Evangelos Kontopantelis ◽

Mamas A Mamas ◽

John Deanfield ◽

Miqdad Asaria ◽

Tim Doran

Keyword(s):

Excess Mortality ◽

Age Groups ◽

Mortality Rates ◽

Mortality Data ◽

List Type ◽

Age Group ◽

The West ◽

England And Wales ◽

West Midlands ◽

Excess Deaths

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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229 Sleep-wake behavior, meal timings and digital media duration of Indians during COVID-19 Lockdown

SLEEP ◽

10.1093/sleep/zsab072.228 ◽

2021 ◽

Vol 44 (Supplement_2) ◽

pp. A91-A92

Author(s):

Babita Pande ◽

Meenakshi Sinha ◽

Ramanjan Sinha

Keyword(s):

Digital Media ◽

Sleep Duration ◽

Sleep Onset ◽

Therapeutic Management ◽

Daily Routine ◽

Age Group ◽

Younger Age ◽

Gender And Age ◽

Delayed Sleep ◽

The Impact

Abstract Introduction Lockdown and stay home order has been imposed on people in many countries including India to prevent the community transmission of COVID-19 pandemic. However this social restriction led to disturbed daily routine and lifestyle behaviour that is needed to be attended for proper therapeutic management of overall health during such crisis. The impact of lockdown on the most apparent behavioral changes viz. sleep-wake behaviour, major meal timings, and digital screen duration of Indians were investigated. In addition the effects of gender and age were explored. Methods After seeking permission from Ethical Institution, an online questionnaire based survey was circulated within India in the first week of May, 2020 for which total 1511 male and female (age ≥18 years) subjects participated. The sleep-wake behavior observed were sleep-wake timings, sleep duration, mid sleep time (MST) as function of lockdown, and social (lockdown) jetlag (SJL = MST before lockdown-MST during lockdown). Results The sleep onset-wakeup and meal times were significantly delayed during lockdown, which was more pronounced in younger age group. The sleep duration increased, specifically in young individuals during lockdown. Females showed more delayed sleep onset-waking times and first meal timing with longer sleep duration during lockdown. Increased digital media duration was observed in all age groups, primarily in males. The younger age group and specifically female reported higher SJL and delayed MST. A positive association was obtained between sleep duration & first meal time, and SJL & major meal timings/screen duration, and a significant negative relationship of sleep duration and SJL with age. Conclusion The study shows delayed sleep-wake schedule, meal timings and increased digital media duration among Indians during COVID-19 lockdown compared to before lockdown. Also, gender and age emerged as important mediating factors for this alteration. The pandemic has given opportunity to sleep more and compensate for the sleep. In spite of that, the higher social jetlag in young age group and female showed the compromised sleep and maladaption with societal timing. These findings have applied implications in sleep health during longer social isolation conditions and for proper therapeutic management. Support (if any) No

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Excess Mortality During the COVID-19 Pandemic in Philadelphia

American Journal of Public Health ◽

10.2105/ajph.2021.306285 ◽

2021 ◽

pp. e1-e6

Author(s):

Megan Todd ◽

Meagan Pharis ◽

Sam P. Gulino ◽

Jessica M. Robbins ◽

Cheryl Bettigole

Keyword(s):

Public Health ◽

Excess Mortality ◽

Poisson Model ◽

People Of Color ◽

Mortality Data ◽

Registration System ◽

Vital Registration System ◽

All Cause Mortality ◽

Race Ethnicity ◽

Excess Deaths

Objectives. To estimate excess all-cause mortality in Philadelphia, Pennsylvania, during the COVID-19 pandemic and understand the distribution of excess mortality in the population. Methods. With a Poisson model trained on recent historical data from the Pennsylvania vital registration system, we estimated expected weekly mortality in 2020. We compared these estimates with observed mortality to estimate excess mortality. We further examined the distribution of excess mortality by age, sex, and race/ethnicity. Results. There were an estimated 3550 excess deaths between March 22, 2020, and January 2, 2021, a 32% increase above expectations. Only 77% of excess deaths (n=2725) were attributed to COVID-19 on the death certiﬁcate. Excess mortality was disproportionately high among older adults and people of color. Sex differences varied by race/ethnicity. Conclusions. Excess deaths during the pandemic were not fully explained by COVID-19 mortality; ofﬁcial counts signiﬁcantly undercount the true death toll. Far from being a great equalizer, the COVID-19 pandemic has exacerbated preexisting disparities in mortality by race/ethnicity. Public Health Implications. Mortality data must be disaggregated by age, sex, and race/ethnicity to accurately understand disparities among groups. (Am J Public Health. Published online ahead of print June 10, 2021: e1–e6. https://doi.org/10.2105/AJPH.2021.306285 )

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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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Influenza Vaccine Match with Circulating Strains and Indicators of Influenza Strain Impact - Canada 1980 to 1992

Canadian Journal of Infectious Diseases ◽

10.1155/1998/374524 ◽

1998 ◽

Vol 9 (3) ◽

pp. 143-148 ◽

Cited By ~ 1

Author(s):

Edward Ellis ◽

John M Weber ◽

Wilf Cuff ◽

Susan G Mackenzie

Keyword(s):

Influenza Vaccine ◽

Ecological Study ◽

Influenza Season ◽

National Laboratory ◽

Mortality Rates ◽

Mortality Data ◽

Influenza Strain ◽

Previous Season ◽

Vaccine Antigens ◽

Excess Deaths

OBJECTIVES: To determine the similarity between influenza vaccine antigens and viruses associated with laboratory-confirmed infections by virus type/subtype, strain and influenza season; to correlate pneumonia and influenza hospitalization and mortality rates with the number of laboratory-confirmed influenza infections in an influenza season; and to develop predictive indicators of the likely incidence of current strains in the following season.DESIGN: Ecological study using national laboratory, pneumonia and influenza hospitalization and mortality data.SETTING: Canada, influenza seasons from 1980 to 1992.POPULATION STUDIED: Individuals with laboratory-confirmed influenza infections, pneumonia and influenza hospitalizations or deaths.INTERVENTION: Influenza immunization.MAIN RESULTS: Similarity of circulating strains and vaccine antigens was 99% for A(H1N1), 65% for A(H3N2) and 65% for B strains. During outbreaks, pneumonia and influenza hospitalization, and mortality rates increased 19% or less and 21% or less for A(H1N1), respectively; 28% or less and 51% or less for A(H3N2), and 19% or less and 16% or less for B strains. There were usually fewer than 25 laboratory-confirmed A(H1N1) infections with a particular strain in a season if there had been more than 25 infections with similar strains the previous season. For A(H3N2), the figure was 100, and for B it was 150.CONCLUSIONS: Matches were excellent for A(H1N1) and good for A(H3N2) plus B strains. Hospitalization and mortality rates increased substantially during outbreaks, eg, estimated 1609 excess deaths during a widespread A(H3N2) outbreak. This study identifies relationships that provide some ability to predict the incidence of a particular influenza strain in a coming season based on the incidence of strains similar to it in the previous season.

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