Influenza-associated excess mortality in southern Brazil, 1980–2008

2012 ◽  
Vol 141 (8) ◽  
pp. 1731-1740 ◽  
Author(s):  
F. T. M. FREITAS ◽  
L. R. O. SOUZA ◽  
E. AZZIZ-BAUMGARTNER ◽  
P. Y. CHENG ◽  
H. ZHOU ◽  
...  
Keyword(s):  
Regression Models ◽  
Excess Mortality ◽  
Influenza Season ◽  
Age Groups ◽  
Influenza Viruses ◽  
Influenza Surveillance ◽  
Mortality Data ◽  
Southern Brazil ◽  
Northern Brazil ◽  
Vaccination Campaigns

SUMMARYIn order to estimate influenza-associated excess mortality in southern Brazil, we applied Serfling regression models to monthly mortality data from 1980 to 2008 for pneumonia/influenza- and respiratory/circulatory-coded deaths for all ages and for those aged ⩾60 years. According to viral data, 73·5% of influenza viruses were detected between April and August in southern Brazil. There was no clear influenza season for northern Brazil. In southern Brazil, influenza-associated excess mortality was 1·4/100 000 for all ages and 9·2/100 000 person-years for persons aged ⩾60 years using underlying pneumonia/influenza-coded deaths and 10·0/100 000 for all ages and 86·6/100 000 person-years for persons aged ⩾60 years using underlying respiratory/circulatory-coded deaths. Influenza-associated excess mortality rates for southern Brazil are similar to those published for other countries. Our data support the need for continued influenza surveillance to guide vaccination campaigns to age groups most affected by this virus in Brazil.

scholarly journals Seasonal influenza during the COVID-19 pandemic in Bangladesh

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255646
Author(s):  
Zubair Akhtar ◽  
Fahmida Chowdhury ◽  
Mahmudur Rahman ◽  
Probir Kumar Ghosh ◽  
Md. Kaousar Ahmmed ◽  
...  
Keyword(s):  
Seasonal Influenza ◽  
Web Applications ◽  
Influenza Season ◽  
Tertiary Care ◽  
Age Groups ◽  
Influenza Viruses ◽  
Control Measures ◽  
Mitigation Measures ◽  
Influenza Surveillance ◽  
Severe Acute Respiratory Infection

Introduction During the 2019 novel coronavirus infectious disease (COVID-19) pandemic in 2020, limited data from several countries suggested reduced seasonal influenza viruses’ circulation. This was due to community mitigation measures implemented to control the pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We used sentinel surveillance data to identify changes in the 2020 influenza season compared with previous seasons in Bangladesh. Methods We used hospital-based influenza surveillance (HBIS) data of Bangladesh that are generated year-round and are population-representative severe acute respiratory infection (SARI) data for all age groups from seven public and two private tertiary care level hospitals data from 2016 to 2019. We applied the moving epidemic method (MEM) by using R language (v4.0.3), and MEM web applications (v2.14) on influenza-positive rates of SARI cases collected weekly to estimate an average seasonal influenza curve and establish epidemic thresholds. Results The 2016–2019 average season started on epi week 18 (95% CI: 15–25) and lasted 12.5 weeks (95% CI: 12–14 weeks) until week 30.5. The 2020 influenza season started on epi week 36 and ended at epi week 41, lasting for only five weeks. Therefore, influenza epidemic started 18 weeks later, was 7.5 weeks shorter, and was less intense than the average epidemic of the four previous years. The 2020 influenza season started on the same week when COVID-19 control measures were halted, and 13 weeks after the measures were relaxed. Conclusion Our findings suggest that seasonal influenza circulation in Bangladesh was delayed and less intense in 2020 than in previous years. Community mitigation measures may have contributed to this reduction of seasonal influenza transmission. These findings contribute to a limited but growing body of evidence that influenza seasons were altered globally in 2020.

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 What will the next influenza season bring about: seasonal influenza or the new A(H1N1)v? An analysis of German influenza surveillance data

2009 ◽  
Vol 14 (32) ◽  
Author(s):  
H Uphoff ◽  
S Geis ◽  
A Grüber ◽  
A M Hauri
Keyword(s):  
Influenza A ◽  
Seasonal Influenza ◽  
Influenza Season ◽  
Influenza Viruses ◽  
Moderate Intensity ◽  
Influenza Surveillance ◽  
Influenza B ◽  
Low Intensity ◽  
Influenza A H1n1 ◽  
Using Data

For the next influenza season (winter 2009-10) the relative contributions to virus circulation and influenza-associated morbidity of the seasonal influenza viruses A(H3N2), A(H1N1) and B, and the new influenza A(H1N1)v are still unknown. We estimated the chances of seasonal influenza to circulate during the upcoming season using data of the German influenza sentinel scheme from 1992 to 2009. We calculated type and subtype-specific indices for past exposure and the corresponding morbidity indices for each season. For the upcoming season 2009-10 our model suggests that it is unlikely that influenza A(H3N2) will circulate with more than a low intensity, seasonal A(H1N1) with more than a low to moderate intensity, and influenza B with more than a low to median intensity. The probability of a competitive circulation of seasonal influenza A with the new A(H1N1)v is low, increasing the chance for the latter to dominate the next influenza season in Germany.

scholarly journals Utility of Nontraditional Data Sources for Early Detection of Influenza

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Shari Barlow ◽  
Jonathan Temte ◽  
Yenlik Zheteyeva ◽  
Ashley Fowlkes ◽  
Carrie Reed ◽  
...  
Keyword(s):  
Age Groups ◽  
School Closure ◽  
Influenza Viruses ◽  
Influenza Surveillance ◽  
Surveillance Systems ◽  
School Absenteeism ◽  
School Closures ◽  
School Based ◽  
Influenza Transmission ◽  
Influenza Outbreaks

ObjectiveThis session will provide an overview of the current systemsfor influenza surveillance; review the role of schools in influenzatransmission; discuss relationships between school closures, schoolabsenteeism, and influenza transmission; and explore the usefulnessof school absenteeism and unplanned school closure monitoring forearly detection of influenza in schools and broader communities.IntroductionInfluenza surveillance is conducted through a complex networkof laboratory and epidemiologic systems essential for estimatingpopulation burden of disease, selecting influenza vaccine viruses,and detecting novel influenza viruses with pandemic potential (1).Influenza surveillance faces numerous challenges, such as constantlychanging influenza viruses, substantial variability in the number ofaffected people and the severity of disease, nonspecific symptoms,and need for laboratory testing to confirm diagnosis. Exploringadditional components that provide morbidity information mayenhance current influenza surveillance.School-aged children have the highest influenza incidence ratesamong all age groups. Due to the close interaction of children inschools and subsequent introduction of influenza into households,it is recognized that schools can serve as amplification points ofinfluenza transmission in communities. For this reason, pandemicpreparedness recommendations include possible pre-emptive schoolclosures, before transmission is widespread within a school system orbroader community, to slow influenza transmission until appropriatevaccines become available. During seasonal influenza epidemics,school closures are usually reactive, implemented in response tohigh absenteeism of students and staff after the disease is alreadywidespread in the community. Reactive closures are often too late toreduce influenza transmission and are ineffective.To enhance timely influenza detection, a variety of nontraditionaldata sources have been explored. School absenteeism was suggestedby several research groups to improve school-based influenzasurveillance. A study conducted in Japan demonstrated that influenza-associated absenteeism can predict influenza outbreaks with highsensitivity and specificity (2). Another study found the use of all-causes absenteeism to be too nonspecific for utility in influenzasurveillance (3). Creation of school-based early warning systemsfor pandemic influenza remains an interest, and further studies areneeded. The panel will discuss how school-based surveillance cancomplement existing influenza surveillance systems.

Mortality predictions for Ireland, 2001–2015: cancers of the breast, ovary, and cervix and corpus uteri

2006 ◽  
Vol 16 (Suppl 1) ◽  
pp. 1-10
Author(s):  
P. O'LORCAIN ◽  
H. Comber
Keyword(s):  
Regression Models ◽  
Death Certificate ◽  
Uterine Cancer ◽  
Age Groups ◽  
Mortality Rates ◽  
Mortality Data ◽  
Female Population ◽  
Corpus Uteri ◽  
Uterine Cancers ◽  
Log Linear

Linear and log-linear Poisson regression models of Irish breast, ovarian, and cervical and corpus uterine cancer mortality data for the years 1953–2000 were used to predict European age standardized mortality rates (EASMRs) per 100,000 person years and numbers of deaths for the period 2001–2015. Rates for the whole population and for those under 65 are expected to fall from their current levels for breast and corpus uterine cancers but not for ovarian and cervical uterine cancers. EASMRs for postmenopausal women aged between 55 and 69 years are predicted to fall for breast, ovarian, and cervical and corpus uterine cancers. The continuing expansion of the Irish female population is the primary reason why the numbers of deaths arising from breast, ovarian, and cervical uterine cancer are predicted to increase in all of the above age groups. It is not exactly clear why the numbers of corpus uterine cancer deaths are expected to continue to decline, but it may be a matter of improvement in overall death-certificate coding or their diagnoses as cervical cancer deaths.

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

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.

scholarly journals When the COVID-19 Pandemic Surges during Influenza Season: Lessons Learnt from the Sentinel Laboratory-Based Surveillance of Influenza-Like Illness in Lombardy during the 2019–2020 Season

Viruses ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 695
Author(s):  
Cristina Galli ◽  
Laura Pellegrinelli ◽  
Laura Bubba ◽  
Valeria Primache ◽  
Giovanni Anselmi ◽  
...  
Keyword(s):  
Real Time ◽  
Influenza Season ◽  
Age Groups ◽  
Northern Italy ◽  
Influenza Viruses ◽  
School Age Children ◽  
Reference Laboratory ◽  
School Age ◽  
Surveillance Period

This paper outlines the role of Lombardy’s regional influenza reference laboratory (Northern Italy) in the surveillance of influenza-like illnesses (ILIs) in monitoring SARS-CoV-2 circulation by analyzing 631 consecutive nasopharyngeal swabs (NPSs) collected from ILI outpatients by sentinel physicians during the 2019–2020 season. The samples were tested by specific real-time RT-PCRs targeting SARS-CoV-2, influenza viruses, and RSVs. Results: Of these NPSs, 31% tested positive for influenza viruses, 10% for SARS-CoV-2, and 7% for RSV. No coinfections were detected. Influenza viruses and RSVs circulated throughout the surveillance period until the end of February (week 9-2020), when they suddenly ceased to circulate seven weeks earlier than during the previous five influenza seasons. After the first detection of SARS-CoV-2 in our ILI outpatients at the beginning of March (week 10-2020), SARS-CoV-2 remained the only virus identified throughout the surveillance period. Patients ≥ 65 years had a 3.2-fold greater risk of being infected with SARS-CoV-2, while school-age children (5–14 years) and children < 5 years proved to be the age groups most at risk of contracting influenza viruses and RSV, respectively. Our experience demonstrates that laboratory-based ILI surveillance networks are essential for identifying SARS-CoV-2 cases that would otherwise remain undetected, in order to stop their spread within our communities.

scholarly journals Age-specific differences in influenza virus type and subtype distribution in the 2012/2013 season in 12 European countries

2015 ◽  
Vol 143 (14) ◽  
pp. 2950-2958 ◽  
Author(s):  
J. BEAUTÉ ◽  
P. ZUCS ◽  
N. KORSUN ◽  
K. BRAGSTAD ◽  
V. ENOUF ◽  
...  
Keyword(s):  
Syndromic Surveillance ◽  
Influenza Season ◽  
Age Groups ◽  
Primary Care Providers ◽  
Surveillance Data ◽  
Influenza Surveillance ◽  
Influenza B Virus ◽  
Influenza B ◽  
Age Group ◽  
Care Providers

SUMMARYThe epidemiology of seasonal influenza is influenced by age. During the influenza season, the European Influenza Surveillance Network (EISN) reports weekly virological and syndromic surveillance data [mostly influenza-like illness (ILI)] based on national networks of sentinel primary-care providers. Aggregated numbers by age group are available for ILI, but not linked to the virological data. At the end of the influenza season 2012/2013, all EISN laboratories were invited to submit a subset of their virological data for this season, including information on age. The analysis by age group suggests that the overall distribution of circulating (sub)types may mask substantial differences between age groups. Thus, in cases aged 5–14 years, 75% tested positive for influenza B virus whereas all other age groups had an even distribution of influenza A and B viruses. This means that the intepretation of syndromic surveillance data without age group-specific virological data may be misleading. Surveillance at the European level would benefit from the reporting of age-specific influenza data.

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

2009 ◽  
Vol 14 (18) ◽  
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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