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

2011 ◽  
Vol 139 (9) ◽  
pp. 1431-1439 ◽  
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.

scholarly journals High prevalence of antibodies to the 2009 pandemic influenza A(H1N1) virus in the Norwegian population following a major epidemic and a large vaccination campaign in autumn 2009

2010 ◽  
Vol 15 (31) ◽  
Author(s):  
K Waalen ◽  
A Kilander ◽  
S G Dudman ◽  
G H Krogh ◽  
T Aune ◽  
...  
Keyword(s):  
Pandemic Influenza ◽  
Influenza A ◽  
H1n1 Virus ◽  
Vaccination Campaign ◽  
Age Group ◽  
Norwegian Population ◽  
Influenza A H1n1 ◽  
High Prevalence ◽  
Substantial Rise ◽  
Epidemic Wave

The prevalence of antibodies reactive to the 2009 pandemic influenza A(H1N1) was determined in sera collected before the start of the pandemic, during the early phase, and after the main epidemic wave and nationwide vaccination campaign in Norway. A substantial rise in prevalence of antibodies at protective titres, from 3.2% to 44.9%, was observed between August 2009 and January 2010. The highest prevalence, 65.3%, was seen in the age group of 10-19 year-olds.

scholarly journals Evaluation of Strategies to Fight COVID-19: The French Paradigm

2021 ◽  
Vol 10 (13) ◽  
pp. 2942
Author(s):  
Audrey Giraud-Gatineau ◽  
Philippe Gautret ◽  
Philippe Colson ◽  
Hervé Chaudet ◽  
Didier Raoult
Keyword(s):  
Surveillance System ◽  
Excess Mortality ◽  
Age Distribution ◽  
European Countries ◽  
Biological Assessment ◽  
Epidemiological Surveillance ◽  
Mortality Data ◽  
Age Group ◽  
All Cause Mortality ◽  
Epidemiological Surveillance System

(1) Background: We collected COVID-19 mortality data and the age distribution of the deceased in France and other European countries, as well as specifically in the cities of Paris and Marseille, and compared them. (2) Methods: Data on mortality related to COVID-19 and the associated age distribution were collected from government institutions in various European countries. In France, data were obtained from INSEE and Santé Publique France. All-cause mortality was also examined in order to study potential excess mortality using EuroMOMO. The Marseille data came from the epidemiological surveillance system. (3) Results: France is one of the European countries most impacted by COVID-19. Its proportion of deaths in people under 60 years of age is higher (6.5%) than that of Italy (4.6%) or Spain (4.7%). Excess mortality (5% more deaths) was also observed. Ile-de-France and the Grand-Est are the two French regions with the highest mortality. The proportion of deaths in the under-60 age group was considerable in Ile-de-France (9.9% vs. 4.5% in the Southern region). There are significantly higher numbers of patients hospitalized, in intensive care and deceased in Paris than in Marseille. (4) Conclusions: No patient management, i.e., from screening to diagnosis, including biological assessment and clinical examination, likely explains the high mortality associated with COVID-19.

scholarly journals Estimating the impact of the 2009 influenza A(H1N1) pandemic on mortality in the elderly in Navarre, Spain

2010 ◽  
Vol 15 (5) ◽  
Author(s):  
J Castilla ◽  
J Etxeberria ◽  
E Ardanaz ◽  
Y Floristán ◽  
R López Escudero ◽  
...  
Keyword(s):  
Pandemic Influenza ◽  
Influenza A ◽  
Excess Mortality ◽  
Seasonal Influenza ◽  
The Elderly ◽  
Significant Excess ◽  
Influenza A H1n1 ◽  
The Mean ◽  
The Impact ◽  
Second Wave

We analysed mortality among people aged 65 years or older in Navarre, Spain in 2009 and compared it with the mean for the same period of time in the previous three years. In the pandemic weeks 24 to 52 2009 we observed 4.9% more deaths than expected (p=0.0268). Excess mortality occurred during the circulation of seasonal influenza (8.0%, p=0.0367) and the first wave of pandemic influenza (9.9%, p=0.0079). In the second wave of pandemic influenza there was a non-significant excess of deaths (5.2%, p=0.1166). Surveillance of laboratory-confirmed severe influenza cases detected only one death in this age group.

scholarly journals Pandemic influenza A/H1N1 vaccination coverage, adverse reactions, and reasons for vaccine refusal among medical students in Brazil

2012 ◽  
Vol 54 (2) ◽  
pp. 77-82 ◽  
Author(s):  
Eduardo Pernambuco de Souza ◽  
Marcelo de Souza Teixeira
Keyword(s):  
Medical Students ◽  
Vaccination Coverage ◽  
Pandemic Influenza ◽  
Influenza A ◽  
Adverse Reactions ◽  
Vaccine Safety ◽  
Age Group ◽  
H1n1 Vaccine ◽  
Vaccine Refusal ◽  
Influenza A H1n1

The aim of this cross-sectional study was to determine, among medical students at a public university in Rio de Janeiro, Brazil, the acceptance of the pandemic influenza A/H1N1 vaccine during the 2010 mass immunization campaign and the vaccine safety in this group and, among unvaccinated students, the reasons for refusing vaccination. Of a total of 858 students, 678 (79%) participated in the study. Vaccination coverage was 60.4% among students aged 20 to 39 years (an age group targeted for vaccination) and 43.8% among those who did not belong to this age group. The most frequent adverse reactions to the vaccine were pain at the injection site (8.7%) and fever (7.9%). There were no serious adverse reactions. Among students aged 20 to 39 years, the most common reasons for refusing the vaccine were "lack of time" (42.4%), "fear of adverse reactions" (41.9%), and "difficult access to the vaccine" (11.5%). Other reasons for vaccine refusal were "uncertainties about vaccine safety and efficacy" and "vaccination was not needed". To increase the acceptance of the influenza vaccine, a comprehensive immunization program should be offered to these students.

scholarly journals Pooling European all-cause mortality: methodology and findings for the seasons 2008/2009 to 2010/2011

2012 ◽  
Vol 141 (9) ◽  
pp. 1996-2010 ◽  
Author(s):  
J. NIELSEN ◽  
A. MAZICK ◽  
N. ANDREWS ◽  
M. DETSIS ◽  
T. M. FENECH ◽  
...  
Keyword(s):  
Influenza A ◽  
Excess Mortality ◽  
National Level ◽  
Age Groups ◽  
Pooled Analysis ◽  
European Countries ◽  
Younger Age ◽  
Influenza A H1n1 ◽  
All Cause Mortality ◽  
Fertile Women

SUMMARYSeveral European countries have timely all-cause mortality monitoring. However, small changes in mortality may not give rise to signals at the national level. Pooling data across countries may overcome this, particularly if changes in mortality occur simultaneously. Additionally, pooling may increase the power of monitoring populations with small numbers of expected deaths, e.g. younger age groups or fertile women. Finally, pooled analyses may reveal patterns of diseases across Europe. We describe a pooled analysis of all-cause mortality across 16 European countries. Two approaches were explored. In the ‘summarized’ approach, data across countries were summarized and analysed as one overall country. In the ‘stratified’ approach, heterogeneities between countries were taken into account. Pooling using the ‘stratified’ approach was the most appropriate as it reflects variations in mortality. Excess mortality was observed in all winter seasons albeit slightly higher in 2008/09 than 2009/10 and 2010/11. In the 2008/09 season, excess mortality was mainly in elderly adults. In 2009/10, when pandemic influenza A(H1N1) dominated, excess mortality was mainly in children. The 2010/11 season reflected a similar pattern, although increased mortality in children came later. These patterns were less clear in analyses based on data from individual countries. We have demonstrated that with stratified pooling we can combine local mortality monitoring systems and enhance monitoring of mortality across Europe.

scholarly journals Excess all-cause and influenza-attributable mortality in Europe, December 2016 to February 2017

2017 ◽  
Vol 22 (14) ◽  
Author(s):  
Lasse S Vestergaard ◽  
Jens Nielsen ◽  
Tyra G Krause ◽  
Laura Espenhain ◽  
Katrien Tersago ◽  
...  
Keyword(s):  
Influenza Virus ◽  
Influenza A ◽  
Excess Mortality ◽  
Influenza Season ◽  
European Countries ◽  
Cold Weather ◽  
Attributable Mortality ◽  
Influenza Virus A ◽  
All Cause Mortality

Since December 2016, excess all-cause mortality was observed in many European countries, especially among people aged ≥ 65 years. We estimated all-cause and influenza-attributable mortality in 19 European countries/regions. Excess mortality was primarily explained by circulation of influenza virus A(H3N2). Cold weather snaps contributed in some countries. The pattern was similar to the last major influenza A(H3N2) season in 2014/15 in Europe, although starting earlier in line with the early influenza season start.

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

2020 ◽  
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

scholarly journals Expression of Surfactant protein D (SP-D) distinguishes severe pandemic influenza A(H1N1) from COVID-19

2021 ◽  
Author(s):  
José Alberto Choreño-Parra ◽  
Luis Armando Jiménez-Álvarez ◽  
Gustavo Ramírez-Martínez ◽  
Alfredo Cruz-Lagunas ◽  
Mahima Thapa ◽  
...  
Keyword(s):  
Pandemic Influenza ◽  
Influenza A ◽  
Alveolar Epithelium ◽  
Surfactant Protein ◽  
High Serum ◽  
Surfactant Protein D ◽  
Diagnostic Challenge ◽  
Moderate Disease ◽  
Influenza A H1n1 ◽  
Novel Biomarkers

Abstract The differentiation of influenza and COVID-19 could constitute a diagnostic challenge during the ongoing winter due to their clinical similitude. Thus, novel biomarkers that enable distinguishing both diseases are required. Here, we evaluated whether the surfactant protein D (SP-D), a collectin produced at the alveolar epithelium with known immune properties, was useful to differentiate pandemic influenza A(H1N1) from COVID-19 in critically ill patients. Our results revealed high serum SP-D levels in severe pandemic influenza but not COVID-19 patients. This finding was validated in a separate cohort of mechanically ventilated COVID-19 patients who also showed low plasma SP-D levels. However, plasma SP-D levels did not distinguish seasonal influenza from COVID-19 in mild-to-moderate disease. Finally, we found that high serum SP-D levels were associated with mortality and renal failure among severe pandemic influenza cases. Thus, our studies have identified SP-D as a unique biomarker expressed during severe pandemic influenza but not COVID-19.

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