Excess mortality related to seasonal influenza and extreme temperatures in Denmark, 1994-2010

Month-to-month all-cause mortality forecasting: a method allowing for changes in seasonal patterns

Forecasting of seasonal mortality patterns can provide useful information for planning health-care demand and capacity. Timely mortality forecasts are needed during severe winter spikes and/or pandemic waves to guide policy-making and public health decisions. In this article, we propose a flexible method for forecasting all-cause mortality in real time considering short-term changes in seasonal patterns within an epidemiologic year. All-cause mortality data have the advantage of being available with less delay than cause-specific mortality data. In this study, we use all-cause monthly death counts obtained from the national statistical offices of Denmark, France, Spain, and Sweden from epidemic seasons 2012-2013 through 2021-2022 to demonstrate the performance of the proposed approach. The method forecasts deaths 1 month ahead, based on their expected ratio to the next month. Prediction intervals are obtained via bootstrapping. The forecasts accurately predict the winter mortality peaks before the COVID-19 pandemic. Although the method predicts mortality less accurately during the first wave of the COVID-19 pandemic, it captures the aspects of later waves better than other traditional methods. The method is attractive for health researchers and governmental offices for aiding public health responses because it uses minimal input data, makes simple and intuitive assumptions, and provides accurate forecasts both during seasonal influenza epidemics and during novel virus pandemics.

Coronary artery culprit lesions progression and ambient temperature exposure - personalised analysis

Introduction

Global warming is claimed to be an important cardiovascular disease risk factor. The air pollution and ambient temperatures are believed to have a significant influence on increased morbidity and premature deaths.

Aim

To point out possible causative factors for coronary angiography progression in patients presenting with chronic coronary syndrome.

Material and methods

There were 66 patients (41 [62%] men and 25 [38%] women) with a median age of 71.5 (62-76) years, who underwent repeated coronary angiographies due to chronic coronary syndrome within a median time interval of 145 (96-296) days. In 18 (27%) patients coronary artery lesion progression was observed despite optimal pharmacotherapy. The demographical, clinical, and personalised epidemiological factors including air pollution particles and ambient temperature exposure were taken into account in the analysis.

Results

In the multivariate logistic regression model with backward stepwise elimination method, tropical nights (p = 0.047) and mean daily temperatures (p = 0.043) were revealed as predictors of coronary lesion progression > 30%. The analysis of seasonal temperature changes showed significant differences related to minimal winter temperatures between both groups (p = 0.018).

Conclusions

Coronary artery lesion progression can be related to either high values of daily temperatures or to low ambient temperature. The dichotomous characteristics of temperature exposure to atherosclerosis progression suggest a detrimental role of environmental extremities on human health.

Revisiting Masselot et al. (2023): assessing the share of excess mortality linked to cold and hot weather in Europe

While a considerable focus has been placed on excess fatalities during hot weather, a reanalysis of European data reveals that excess mortality attributed to cold weather is significantly more pronounced, surpassing that linked to hot weather by an order of magnitude. These ratios are noteworthy: 56.32 for the United Kingdom, 43.56 for Northern Europe, 8.49 for Western Europe, 12.41 for Eastern Europe, 5.50 for Southern Europe, and an overall ratio of 10.09 for Europe as a whole. These ratios of cold to hot excess deaths indicate a significant disparity in the number of excess deaths caused by cold weather compared to those caused by hot weather. This significant difference underscores the greater health risks and vulnerabilities associated with cold weather.

Global, regional and national estimates of influenza-attributable ischemic heart disease mortality

BACKGROUND

Influenza virus infection is associated with incident ischemic heart disease (IHD) events. Here, we estimate the global, regional, and national IHD mortality burden attributable to influenza.

METHODS

We used vital registration data from deaths in adults ≥50 years (13.2 million IHD deaths as underlying cause) to assess the relationship between influenza activity and IHD mortality in a non-linear meta-regression framework from 2010 to 2019. This derived relationship was then used to estimate the global influenza attributable IHD mortality. We estimated the population attributable fraction (PAF) of influenza for IHD deaths based on the relative risk associated with a given level of weekly influenza test positivity rate and multiplied PAFs by IHD mortality from the Global Burden of Disease study.

FINDINGS

Influenza activity was associated with increased risk of IHD mortality across all countries analyzed. The mean PAF of influenza for IHD mortality was 3.9% (95% uncertainty interval [UI] 2.5-5.3%), ranging from <1% to 10%, depending on country and year. Globally, 299,858 IHD deaths (95% UI 191,216-406,809) in adults ≥50 years could be attributed to influenza, with the highest rates per 100,000 population in the Central Europe, Eastern Europe and Central Asia Region (32.3; 95% UI 20.6-43.8), and in the North Africa and Middle East Region (26.7; 95% UI 17-36.2).

INTERPRETATION

Influenza may contribute substantially to the burden of IHD. Our results suggest that if there were no influenza, an average of 4% of IHD deaths globally would not occur.

FUNDING

Collaborative study funded by Sanofi Vaccines.

Reconstruction of the Temporal Correlation Network of All-Cause Mortality Fluctuation across Italian Regions: The Importance of Temperature and Among-Nodes Flux

All-cause mortality is a very coarse grain, albeit very reliable, index to check the health implications of lifestyle determinants, systemic threats and socio-demographic factors. In this work, we adopt a statistical-mechanics approach to the analysis of temporal fluctuations of all-cause mortality, focusing on the correlation structure of this index across different regions of Italy. The correlation network among the 20 Italian regions was reconstructed using temperature oscillations and traveller flux (as a function of distance and region's attractiveness, based on GDP), allowing for a separation between infective and non-infective death causes. The proposed approach allows monitoring of emerging systemic threats in terms of anomalies of correlation network structure.

Estimates of excess mortality for the five Nordic countries during the COVID-19 pandemic 2020-2021

BACKGROUND

Excess mortality during the COVID-19 pandemic is of major scientific and political interest.

METHODS

We critically reviewed different estimates of all-cause excess mortality for the five Nordic countries (Denmark, Finland, Iceland, Norway and Sweden), which have been much studied during the COVID-19 pandemic, using the latest register data to discuss uncertainties and implications.

RESULTS

We show using back-calculation of expected deaths from Nordic all-cause deaths that the Institute for Health Metrics and Evaluation model is a clear outlier in the compared estimates and likely substantially overestimates excess mortality of Finland and Denmark, and probably Sweden. Our review suggests a range of total Nordic excess deaths of perhaps 15 000-20 000, but results are sensitive to assumptions in the models as shown.

CONCLUSIONS

We document substantial heterogeneity and uncertainty in estimates of excess mortality. All estimates should be taken with caution in their interpretation as they miss detailed account of demographics, such as changes in the age group populations over the study period.

What should be the baseline when calculating excess mortality? New approaches suggest that we have underestimated the impact of the COVID-19 pandemic and previous winter peaks

Excess mortality has been used to measure the impact of COVID-19 over time and across countries. But what baseline should be chosen? We propose two novel approaches: an alternative retrospective baseline derived from the lowest weekly death rates achieved in previous years and a within-year baseline based on the average of the 13 lowest weekly death rates within the same year. These baselines express normative levels of the lowest feasible target death rates. The excess death rates calculated from these baselines are not distorted by past mortality peaks and do not treat non-pandemic winter mortality excesses as inevitable.

We obtained weekly series for 35 industrialized countries from the Human Mortality Database for 2000–2020. Observed, baseline and excess mortalities were measured by age-standardized death rates. We assessed weekly and annual excess death rates driven by the COVID-19 pandemic in 2020 and those related to seasonal respiratory infections in earlier years. There was a distinct geographic pattern with high excess death rates in Eastern Europe followed by parts of the UK, and countries of Southern and Western Europe. Some Asia-Pacific and Scandinavian countries experienced lower excess mortality. In 2020 and earlier years, the alternative retrospective and the within-year excess mortality figures were higher than estimates based on conventional metrics. While the latter were typically negative or close to zero in years without extraordinary epidemics, the alternative estimates were substantial. Cumulation of this "usual" excess over 2–3 years results in human losses comparable to those caused by COVID-19.

Challenging the view that non-pandemic seasonal winter mortality is inevitable would focus attention on reducing premature mortality in many countries. As SARS-CoV-2 is unlikely to be the last respiratory pathogen with the potential to cause a pandemic, such measures would also strengthen global resilience in the face of similar threats in the future.

•

Conventional estimates of excess mortality underestimate potentially avoidable losses.

•

We propose metrics based on best weeks in the same and in earlier years.

•

Our alternative metrics estimate higher annual excess mortality in 2020 and 2005-19.

•

Mortality peaks in non-pandemic years should be regarded as potentially avoidable.

On assessing excess mortality in Germany during the COVID-19 pandemic

Coronavirus disease 2019 (COVID-19) is associated with a very high number of casualties in the general population. Assessing the exact magnitude of this number is a non-trivial problem, as relying only on officially reported COVID-19 associated fatalities runs the risk of incurring in several kinds of biases. One of the ways to approach the issue is to compare overall mortality during the pandemic with expected mortality computed using the observed mortality figures of previous years. In this paper, we build on existing methodology and propose two ways to compute expected as well as excess mortality, namely at the weekly and at the yearly level. Particular focus is put on the role of age, which plays a central part in both COVID-19-associated and overall mortality. We illustrate our methods by making use of age-stratified mortality data from the years 2016 to 2020 in Germany to compute age group-specific excess mortality during the COVID-19 pandemic in 2020.

Estimating the burden of influenza-related and associated hospitalizations and deaths in France: An eight-season data study, 2010-2018

Background

In France, each year, influenza viruses are responsible for seasonal epidemics leading to 2–6 million cases. Influenza can cause severe disease that may lead to hospitalization or death. As severe disease may be due to the virus itself or to disease complications, estimating the burden of severe influenza is complex. The present study aimed at estimating the epidemiological and economic burden of severe influenza in France during eight consecutive influenza seasons (2010–2018).

Methods

Influenza‐related hospitalization and mortality data and patient characteristics were taken from the French hospital information database, PMSI. An ecological approach using cyclic regression models integrating the incidence of influenza syndrome from the Sentinelles network supplemented the PMSI data analysis in estimating excess hospitalization and mortality (CépiDc—2010–2015) and medical costs.

Results

Each season, the average number of influenza‐related hospitalizations was 18,979 (range: 8627–44,024), with an average length of stay of 8 days. The average number of respiratory hospitalizations indirectly related with influenza (i.e., influenza associated) was 31,490 (95% confidence interval [CI]: 24,542–39,012), with an average cost of €141 million (range: 54–217); 70% of these hospitalizations and 77% of their costs concerned individuals ≥65 years of age (65+). More than 90% of excess mortality was in 65+ subjects.

Conclusions

The combination of two complementary approaches allowed estimation of both influenza‐related and associated hospitalizations and deaths and their burden in France, showing the substantial impact of complications. The present study highlighted the major public health burden of influenza and its severe complications, especially in 65+ subjects.

Mortality and Excess Mortality: Improving FluMOMO

FluMOMO is a universal formula to forecast mortality in 27 European countries and was developed on EuroMOMO context, http://www.euromomo.eu. The model has a trigonometric baseline and considers any upwards deviation from that to come from flu or extreme temperatures. To measure it, the model considers two variables: influenza activity and extreme temperatures. With the former, the model gives the number of deaths because of flu and with the latter the number of deaths because of extreme temperatures. In this article, we show that FluMOMO lacks important variables to be an accurate measure of all-cause mortality and flu mortality. Indeed, we found, as expected, that population ageing and exposure to the risk of death cannot be excluded from the linear predictor. We model weekly deaths as an autoregressive process (lag of one together with a lead of one week). This step allowed us to avoid FluMOMO trigonometric baseline and have a fit to weekly deaths through demographic variables. Our model uses data from Portugal between 2009 and 2020, on ISO-week basis. We use negative binomial-generalized linear models to estimate the weekly number of deaths as an alternative to traditional overdispersion Poisson. As explanatory variables were found to be statistically significant, we registered the number of deaths from the previous week, the influenza activity index, the population average age, the heat waves, the flu season, the number of deaths with COVID-19, and the population exposed to the risk of dying. Considering as excess mortality the number of deaths above the best estimate of deaths from our model, we conclude that excess mortality in 2020 (net of COVID-19 deaths, heat wave of July, and ageing) is low or inexistent. The model also allows us to have the number of deaths arising from flu and we conclude that FluMOMO is overestimating deaths from flu by 78%. Averages from the probability of dying are obtained as well as the probability of dying from flu. The latter is shown to be decreasing over time, probably due to the increase of flu vaccination. Higher mortality detected with the start of COVID-19, in March-April 2020, was probably due to COVID-19 deaths not recognized as COVID-19 deaths.

The modification effect of temperature on the relationship between air pollutants and daily incidence of influenza in Ningbo, China

Background

Although exposure to air pollution has been linked to many health issues, few studies have quantified the modification effect of temperature on the relationship between air pollutants and daily incidence of influenza in Ningbo, China.

Methods

The data of daily incidence of influenza and the relevant meteorological data and air pollution data in Ningbo from 2014 to 2017 were retrieved. Low, medium and high temperature layers were stratified by the daily mean temperature with 25th and 75th percentiles. The potential modification effect of temperature on the relationship between air pollutants and daily incidence of influenza in Ningbo was investigated through analyzing the effects of air pollutants stratified by temperature stratum using distributed lag non-linear model (DLNM). Stratified analysis by sex and age were also conducted.

Results

Overall, a 10 μg/m³ increment of O₃, PM_2.5, PM₁₀ and NO₂ could increase the incidence risk of influenza with the cumulative relative risk of 1.028 (95% CI 1.007, 1.050), 1.061 (95% CI 1.004, 1.122), 1.043 (95% CI 1.003, 1.085), and 1.118 (95% CI 1.028, 1.216), respectively. Male and aged 7–17 years were more sensitive to air pollutants. Through the temperature stratification analysis, we found that temperature could modify the impacts of air pollution on daily incidence of influenza with high temperature exacerbating the impact of air pollutants. At high temperature layer, male and the groups aged 0–6 years and 18–64 years were more sensitive to air pollution.

Conclusion

Temperature modified the relationship between air pollution and daily incidence of influenza and high temperature would exacerbate the effects of air pollutants in Ningbo.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12931-021-01744-6.

Short-term forecasts of expected deaths

We introduce a method for making short-term mortality forecasts of a few months, illustrating it by estimating how many deaths might have happened if some major shock had not occurred. We apply the method to assess excess mortality from March to June 2020 in Denmark and Sweden as a result of the first wave of the coronavirus pandemic; associated policy interventions; and behavioral, healthcare, social, and economic changes. We chose to compare Denmark and Sweden because reliable data were available and because the two countries are similar but chose different responses to COVID-19: Denmark imposed a rather severe lockdown; Sweden did not. We make forecasts by age and sex to predict expected deaths if COVID-19 had not struck. Subtracting these forecasts from observed deaths gives the excess death count. Excess deaths were lower in Denmark than Sweden during the first wave of the pandemic. The later/earlier ratio we propose for shortcasting is easy to understand, requires less data than more elaborate approaches, and may be useful in many countries in making both predictions about the future and the past to study the impact on mortality of coronavirus and other epidemics. In the application to Denmark and Sweden, prediction intervals are narrower and bias is less than when forecasts are based on averages of the last 5 y, as is often done. More generally, later/earlier ratios may prove useful in short-term forecasting of illnesses and births as well as economic and other activity that varies seasonally or periodically.

Short-term forecasts of expected deaths

We introduce a method for making short-term mortality forecasts of a few months, illustrating it by estimating how many deaths might have happened if some major shock had not occurred. We apply the method to assess excess mortality from March to June 2020 in Denmark and Sweden as a result of the first wave of the coronavirus pandemic; associated policy interventions; and behavioral, healthcare, social, and economic changes. We chose to compare Denmark and Sweden because reliable data were available and because the two countries are similar but chose different responses to COVID-19: Denmark imposed a rather severe lockdown; Sweden did not. We make forecasts by age and sex to predict expected deaths if COVID-19 had not struck. Subtracting these forecasts from observed deaths gives the excess death count. Excess deaths were lower in Denmark than Sweden during the first wave of the pandemic. The later/earlier ratio we propose for shortcasting is easy to understand, requires less data than more elaborate approaches, and may be useful in many countries in making both predictions about the future and the past to study the impact on mortality of coronavirus and other epidemics. In the application to Denmark and Sweden, prediction intervals are narrower and bias is less than when forecasts are based on averages of the last 5 y, as is often done. More generally, later/earlier ratios may prove useful in short-term forecasting of illnesses and births as well as economic and other activity that varies seasonally or periodically.

Relationship Between Influenza, Temperature, and Type 1 Myocardial Infarction: An Ecological Time-Series Study

Background Previous studies investigating the relationship of influenza with acute myocardial infarction (AMI) have not distinguished between AMI types 1 and 2. Influenza and cold temperature can explain the increased incidence of AMI during winter but, because they are closely related in temperate regions, their relative contribution is unknown. Methods and Results The temporal relationship between incidence rates of AMI with demonstrated culprit plaque (type 1 AMI) from the regional primary angioplasty network and influenza, adjusted for ambient temperature, was studied in Madrid region (Spain) during 5 influenza seasons (from June 2013 to June 2018). A time-series analysis with quasi-Poisson regression models and distributed lag-nonlinear models was used. The incidence rate of type 1 AMI according to influenza vaccination status was also explored. A total of 8240 cases of confirmed type 1 AMI were recorded. The overall risk ratio (RR) of type 1 AMI during epidemic periods, adjusted for year, month, and temperature, was 1.23 (95% CI, 1.03-1.47). An increase of weekly influenza rate of 50 cases per 100 000 inhabitants resulted in an RR for type 1 AMI of 1.16 (95% CI, 1.09-1.23) during the same week, disappearing 1 week after. When adjusted for influenza, a decrease of 1ºC in the minimum temperature resulted in an increase of 2.5% type 1 AMI. Influenza vaccination was associated with a decreased risk of type 1 AMI in subjects aged 60 to 64 years (RR, 0.58; 95% CI, 0.47-0.71) and ≥65 years (RR, 0.53; 95% CI, 0.49-0.57). Conclusions Influenza and cold temperature were both independently associated with an increased risk of type 1 AMI, whereas vaccination was associated with a reduced risk among older patients.

An open-sourced, web-based application to analyze weekly excess mortality based on the Short-term Mortality Fluctuations data series

The COVID-19 pandemic stimulated the interest of scientists, decision makers and the general public in short-term mortality fluctuations caused by epidemics and other natural or man-made disasters. To address this interest and provide a basis for further research, in May 2020, the Short-term Mortality Fluctuations data series was launched as a new section of the Human Mortality Database. At present, this unique data resource provides weekly mortality death counts and rates by age and sex for 38 countries and regions. The main objective of this paper is to detail the web-based application for visualizing and analyzing the excess mortality based on the Short-term Mortality Fluctuation data series. The application yields a visual representation of the database that enhances the understanding of the underlying data. Besides, it enables the users to explore data on weekly mortality and excess mortality across years and countries. The contribution of this paper is twofold. First, to describe a visualization tool that aims to facilitate research on short-term mortality fluctuations. Second, to provide a comprehensive open-source software solution for demographic data to encourage data holders to promote their datasets in a visual framework.

Influence of Meteorological Factors on the COVID-19 Transmission with Season and Geographic Location

The purpose of this study is to investigate whether the relationship between meteorological factors (i.e., daily maximum temperature, minimum temperature, average temperature, temperature range, relative humidity, average wind speed and total precipitation) and COVID-19 transmission is affected by season and geographical location during the period of community-based pandemic prevention and control. COVID-19 infected case records and meteorological data in four cities (Wuhan, Beijing, Urumqi and Dalian) in China were collected. Then, the best-fitting model of COVID-19 infected cases was selected from four statistic models (Gaussian, logistic, lognormal distribution and allometric models), and the relationship between meteorological factors and COVID-19 infected cases was analyzed using multiple stepwise regression and Pearson correlation. The results showed that the lognormal distribution model was well adapted to describing the change of COVID-19 infected cases compared with other models (R2 > 0.78; p-values < 0.001). Under the condition of implementing community-based pandemic prevention and control, relationship between COVID-19 infected cases and meteorological factors differed among the four cities. Temperature and relative humidity were mainly the driving factors on COVID-19 transmission, but their relations obviously varied with season and geographical location. In summer, the increase in relative humidity and the decrease in maximum temperature facilitate COVID-19 transmission in arid inland cities, while at this point the decrease in relative humidity is good for the spread of COVID-19 in coastal cities. For the humid cities, the reduction of relative humidity and the lowest temperature in the winter promote COVID-19 transmission.

Winter Is Coming: A Southern Hemisphere Perspective of the Environmental Drivers of SARS-CoV-2 and the Potential Seasonality of COVID-19

SARS-CoV-2 virus infections in humans were first reported in December 2019, the boreal winter. The resulting COVID-19 pandemic was declared by the WHO in March 2020. By July 2020, COVID-19 was present in 213 countries and territories, with over 12 million confirmed cases and over half a million attributed deaths. Knowledge of other viral respiratory diseases suggests that the transmission of SARS-CoV-2 could be modulated by seasonally varying environmental factors such as temperature and humidity. Many studies on the environmental sensitivity of COVID-19 are appearing online, and some have been published in peer-reviewed journals. Initially, these studies raised the hypothesis that climatic conditions would subdue the viral transmission rate in places entering the boreal summer, and that southern hemisphere countries would experience enhanced disease spread. For the latter, the COVID-19 peak would coincide with the peak of the influenza season, increasing misdiagnosis and placing an additional burden on health systems. In this review, we assess the evidence that environmental drivers are a significant factor in the trajectory of the COVID-19 pandemic, globally and regionally. We critically assessed 42 peer-reviewed and 80 preprint publications that met qualifying criteria. Since the disease has been prevalent for only half a year in the northern, and one-quarter of a year in the southern hemisphere, datasets capturing a full seasonal cycle in one locality are not yet available. Analyses based on space-for-time substitutions, i.e., using data from climatically distinct locations as a surrogate for seasonal progression, have been inconclusive. The reported studies present a strong northern bias. Socio-economic conditions peculiar to the 'Global South' have been omitted as confounding variables, thereby weakening evidence of environmental signals. We explore why research to date has failed to show convincing evidence for environmental modulation of COVID-19, and discuss directions for future research. We conclude that the evidence thus far suggests a weak modulation effect, currently overwhelmed by the scale and rate of the spread of COVID-19. Seasonally modulated transmission, if it exists, will be more evident in 2021 and subsequent years.

Excess all-cause mortality during the COVID-19 pandemic in Europe - preliminary pooled estimates from the EuroMOMO network, March to April 2020

A remarkable excess mortality has coincided with the COVID-19 pandemic in Europe. We present preliminary pooled estimates of all-cause mortality for 24 European countries/federal states participating in the European monitoring of excess mortality for public health action (EuroMOMO) network, for the period March–April 2020. Excess mortality particularly affected  ≥ 65 year olds (91% of all excess deaths), but also 45–64 (8%) and 15–44 year olds (1%). No excess mortality was observed in 0–14 year olds.

Effect of meteorological factors on influenza-like illness from 2012 to 2015 in Huludao, a northeastern city in China

Background

This study aims to describe the epidemiological patterns of influenza-like illness (ILI) in Huludao, China and seek scientific evidence on the link of ILI activity with weather factors.

Methods

Surveillance data of ILI cases between January 2012 and December 2015 was collected in Huludao Central Hospital, meteorological data was obtained from the China Meteorological Data Service Center. Generalized additive model (GAM) was used to seek the relationship between the number of ILI cases and the meteorological factors. Multiple Smoothing parameter estimation was made on the basis of Poisson distribution, where the number of weekly ILI cases was treated as response, and the smoothness of weather was treated as covariates. Lag time was determined by the smallest Akaike information criterion (AIC). Smoothing coefficients were estimated for the prediction of the number of ILI cases.

Results

A total of 29, 622 ILI cases were observed during the study period, with children ILI cases constituted 86.77%. The association between ILI activity and meteorological factors varied across different lag periods. The lag time for average air temperature, maximum air temperature, minimum air temperature, vapor pressure and relative humidity were 2, 2, 1, 1 and 0 weeks, respectively. Average air temperature, maximum air temperature, minimum air temperature, vapor pressure and relative humidity could explain 16.5%, 9.5%, 18.0%, 15.9% and 7.7% of the deviance, respectively. Among the temperature indexes, the minimum temperature played the most important role. The number of ILI cases peaked when minimum temperature was around -13 °C in winter and 18 °C in summer. The number of cases peaked when the relative humidity was equal to 43% and then began to decrease with the increase of relative humidity. When the humidity exceeded 76%, the number of ILI cases began to rise.

Conclusions

The present study first analyzed the relationship between meteorological factors and ILI cases with special consideration of the length of lag period in Huludao, China. Low air temperature and low relative humidity (cold and dry weather condition) played a considerable role in the epidemic pattern of ILI cases. The trend of ILI activity could be possibly predicted by the variation of meteorological factors.

Significant spike in excess mortality in England in winter 2014/15 - influenza the likely culprit

Significant increases in excess all-cause mortality, particularly in the elderly, were observed during the winter of 2014/15 in England. With influenza A(H3N2) the dominant circulating influenza A subtype, this paper determines the contribution of influenza to this excess controlling for weather. A standardised multivariable Poisson regression model was employed with weekly all-cause deaths the dependent variable for the period 2008-2015. Adjusting for extreme temperature, a total of 26 542 (95% CI 25 301-27 804) deaths in 65+ and 1942 (95% CI 1834-2052) in 15-64-year-olds were associated with influenza from week 40, 2014 to week 20, 2015. This is compatible with the circulation of influenza A(H3N2). It is the largest estimated number of influenza-related deaths in England since prior to 2008/09. The findings highlight the potential health impact of influenza and the important role of the annual influenza vaccination programme that is required to protect the population including the elderly, who are vulnerable to a severe outcome.

Influenza-associated mortality determined from all-cause mortality, Denmark 2010/11-2016/17: The FluMOMO model

Background

In temperate zones, all‐cause mortality exhibits a marked seasonality, and influenza represents a major cause of winter excess mortality. We present a statistical model, FluMOMO, which estimate influenza‐associated mortality from all‐cause mortality data and apply it to Danish data from 2010/11 to 2016/17.

Methods

We applied a multivariable time series model with all‐cause mortality as outcome, influenza activity and extreme temperatures as explanatory variables while adjusting for time trend and seasonality. Three indicators of weekly influenza activity (IA) were explored: percentage of consultations for influenza‐like illness (ILI) at primary health care, national percentage of influenza‐positive samples, and the product of ILI percentage and percentage of influenza‐positive specimens in a given week, that is, the Goldstein index.

Results

Independent of the choice of parameter to represent influenza activity, the estimated influenza‐associated mortality showed similar patterns with the Goldstein index being the most conservative. Over the 7 winter seasons, the median influenza‐associated mortality per 100 000 population was 17.6 (range: 0.0‐36.8), 14.1 (0.3‐31.6) and 8.3 (0.0‐25.0) for the 3 indicators, respectively, for all ages.

Conclusion

The FluMOMO model fitted the Danish data well and has the potential to estimate all‐cause influenza‐associated mortality in near real time and could be used as a standardised method in other countries. We recommend using the Goldstein index as the influenza activity indicator in the FluMOMO model. Further work is needed to improve the interpretation of the estimated effects.

Estimates of global seasonal influenza-associated respiratory mortality: a modelling study

BACKGROUND

Estimates of influenza-associated mortality are important for national and international decision making on public health priorities. Previous estimates of 250 000-500 000 annual influenza deaths are outdated. We updated the estimated number of global annual influenza-associated respiratory deaths using country-specific influenza-associated excess respiratory mortality estimates from 1999-2015.

METHODS

We estimated country-specific influenza-associated respiratory excess mortality rates (EMR) for 33 countries using time series log-linear regression models with vital death records and influenza surveillance data. To extrapolate estimates to countries without data, we divided countries into three analytic divisions for three age groups (<65 years, 65-74 years, and ≥75 years) using WHO Global Health Estimate (GHE) respiratory infection mortality rates. We calculated mortality rate ratios (MRR) to account for differences in risk of influenza death across countries by comparing GHE respiratory infection mortality rates from countries without EMR estimates with those with estimates. To calculate death estimates for individual countries within each age-specific analytic division, we multiplied randomly selected mean annual EMRs by the country's MRR and population. Global 95% credible interval (CrI) estimates were obtained from the posterior distribution of the sum of country-specific estimates to represent the range of possible influenza-associated deaths in a season or year. We calculated influenza-associated deaths for children younger than 5 years for 92 countries with high rates of mortality due to respiratory infection using the same methods.

FINDINGS

EMR-contributing countries represented 57% of the global population. The estimated mean annual influenza-associated respiratory EMR ranged from 0·1 to 6·4 per 100 000 individuals for people younger than 65 years, 2·9 to 44·0 per 100 000 individuals for people aged between 65 and 74 years, and 17·9 to 223·5 per 100 000 for people older than 75 years. We estimated that 291 243-645 832 seasonal influenza-associated respiratory deaths (4·0-8·8 per 100 000 individuals) occur annually. The highest mortality rates were estimated in sub-Saharan Africa (2·8-16·5 per 100 000 individuals), southeast Asia (3·5-9·2 per 100 000 individuals), and among people aged 75 years or older (51·3-99·4 per 100 000 individuals). For 92 countries, we estimated that among children younger than 5 years, 9243-105 690 influenza-associated respiratory deaths occur annually.

INTERPRETATION

These global influenza-associated respiratory mortality estimates are higher than previously reported, suggesting that previous estimates might have underestimated disease burden. The contribution of non-respiratory causes of death to global influenza-associated mortality should be investigated.

FUNDING

None.

Heterogeneity in Estimates of the Impact of Influenza on Population Mortality: A Systematic Review

Influenza viruses are associated with a substantial global burden of morbidity and mortality every year. Estimates of influenza-associated mortality often vary between studies due to differences in study settings, methods, and measurement of outcomes. We reviewed 103 published articles assessing population-based influenza-associated mortality through searches of PubMed and Embase, and we identified considerable variation in the statistical methods used across studies. Studies using regression models with an influenza activity proxy applied 4 approaches to estimate influenza-associated mortality. The estimates increased with age and ranged widely, from -0.3-1.3 and 0.6-8.3 respiratory deaths per 100,000 population for children and adults, respectively, to 4-119 respiratory deaths per 100,000 population for older adults. Meta-regression analysis identified that study design features were associated with the observed variation in estimates. The estimates increased with broader cause-of-death classification and were higher for older adults than for children. The multiplier methods tended to produce lower estimates, while Serfling-type models were associated with higher estimates than other methods. No "average" estimate of excess mortality could reliably be made due to the substantial variability of the estimates, partially attributable to methodological differences in the studies. Standardization of methodology in estimation of influenza-associated mortality would permit improved comparisons in the future.

Mortality burden from seasonal influenza and 2009 H1N1 pandemic influenza in Beijing, China, 2007-2013

BACKGROUND

Data about influenza mortality burden in northern China are limited. This study estimated mortality burden in Beijing associated with seasonal influenza from 2007 to 2013 and the 2009 H1N1 pandemic.

METHODS

We estimated influenza-associated excess mortality by fitting a negative binomial model using weekly mortality data as the outcome of interest with the percent of influenza-positive samples by type/subtype as predictor variables.

RESULTS

From 2007 to 2013, an average of 2375 (CI 1002-8688) deaths was attributed to influenza per season, accounting for 3% of all deaths. Overall, 81% of the deaths attributed to influenza occurred in adults aged ≥65 years, and the influenza-associated mortality rate in this age group was higher than the rate among those aged <65 years (113.6 [CI 49.5-397.4] versus 4.4 [CI 1.7-18.6] per 100 000, P < .05). The mortality rate associated with the 2009 H1N1 pandemic in 2009/2010 was comparable to that of seasonal influenza during the seasonal years (19.9 [CI 10.4-33.1] vs 17.2 [CI 7.2-67.5] per 100 000). People aged <65 years represented a greater proportion of all deaths during the influenza A(H1N1)pdm09 pandemic period than during the seasonal epidemics (27.0% vs 17.7%, P < .05).

CONCLUSIONS

Influenza is an important contributor to mortality in Beijing, especially among those aged ≥65 years. These results support current policies to give priority to older adults for seasonal influenza vaccination and help to define the populations at highest risk for death that could be targeted for pandemic influenza vaccination.

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

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.

Modelled seasonal influenza mortality shows marked differences in risk by age, sex, ethnicity and socioeconomic position in New Zealand

OBJECTIVES

Influenza is responsible for a large number of deaths which can only be estimated using modelling methods. Such methods have rarely been applied to describe the major socio-demographic characteristics of this disease burden.

METHODS

We used quasi Poisson regression models with weekly counts of deaths and isolates of influenza A, B and respiratory syncytial virus for the period 1994 to 2008.

RESULTS

The estimated average mortality rate was 13.5 per 100,000 people which was 1.8% of all deaths in New Zealand. Influenza mortality differed markedly by age, sex, ethnicity and socioeconomic position. Relatively vulnerable groups were males aged 65-79 years (Rate ratio (RR) = 1.9, 95% CI: 1.9, 1.9 compared with females), Māori (RR = 3.6, 95% CI: 3.6, 3.7 compared with European/Others aged 65-79 years), Pacific (RR = 2.4, 95% CI: 2.4, 2.4 compared with European/Others aged 65-79 years) and those living in the most deprived areas (RR = 1.8, 95% CI: 1.3, 2.4) for New Zealand Deprivation (NZDep) 9&10 (the most deprived) compared with NZDep 1&2 (the least deprived).

CONCLUSIONS

These results support targeting influenza vaccination and other interventions to the most vulnerable groups, in particular Māori and Pacific people and men aged 65-79 years and those living in the most deprived areas.

Analysis of Risk Factors for Severe Acute Respiratory Infection and Pneumonia and among Adult Patients with Acute Respiratory Illness during 2011-2014 Influenza Seasons in Korea

Background

The World Health Organization recommends the surveillance of influenza-like illness (ILI) and severe acute respiratory infection (SARI) to respond effectively to both seasonal influenza epidemics and pandemics. In Korea, the “Hospital-based Influenza Morbidity and Mortality (HIMM)” surveillance system has been operated to monitor ILI and SARI occurrences.

Materials and Methods

A multi-center prospective observational study was conducted. Adult patients with acute respiratory infection (ARI) were enrolled during the 2011-12, 2012-2013, and 2013-2014 influenza seasons at the 10 university hospitals using the HIMM surveillance system. With respect to SARI and pneumonia development, risk profiles were analyzed in patients with ARI in Korea.

Results

A total of 5,459 cases were eligible for this analysis. Among 5,459 cases with ARI, 2,887 cases (52.9%) were identified that they had influenza infection. Among enrolled cases, 750 cases belonged to the SARI group, while 4,709 cases belonged to the non-SARI group. With respect to pneumonia development, 317 cases were accompanied by pneumonia, and 5,142 cases were not. Multivariate analyses revealed that the following factors were associated with an increased risk of SARI: Old age (≥65 years) (odds ratio [OR] 2.69, 95% confidence interval [CI] 2.2-3.32), chronic heart disease (CHD) (OR 2.24, 95% CI 1.68-2.98), cerebrovascular disease (CVD) (OR 1.49, 95% CI 1.05-2.10), chronic obstructive pulmonary disease (COPD) (OR 2.34, 95% CI 1.48-3.69), asthma (OR 2.33, 95% CI 1.62-3.36), chronic kidney disease (CKD) (OR 2.62, 95% CI 1.73-3.99), chronic liver disease (OR 1.71, 95% CI 1.04-2.81), and autoimmune diseases (OR 2.53, 1.57-4.08). Multivariate analyses revealed that the following factors were independent risk factors for pneumonia development: Old age (≥65 years) (OR 5.71, 95% CI 4.10-7.94), CHD (OR 1.54, 95% CI 1.07-2.22), COPD (OR 2.34, 95% CI 1.48-3.69), asthma (OR 2.33, 95% CI 1.62-3.36), CKD (OR 2.62, 95% CI 1.73-3.99), immunocompromised conditions (OR 3.12, 95% CI 1.47-6.62), and autoimmune diseases (OR 3.35, 95% CI 1.79-6.27). The risk of SARI and pneumonia was increased by the number of concurrent chronic medical conditions.

Conclusion

The risk of SARI and pneumonia development among adult patient with ARI was significantly increased by the presence or number of concurrent chronic medical conditions in Korea.

A four year seasonal survey of the relationship between outdoor climate and epidemiology of viral respiratory tract infections in a temperate climate

BACKGROUND

The relation between weather conditions, viral transmission and seasonal activity of respiratory viruses is not fully understood.

OBJECTIVES

To investigate the impact of outdoor weather in a temperate climate setting on the seasonal epidemiology of viruses causing respiratory tract infections, particularly influenza A (IFA).

STUDY DESIGN

In total, 20,062 clinical nasopharyngeal swab samples referred for detection of respiratory pathogens using a multiplex PCR panel, between October 2010 and July 2013, were included. Results of PCR detection were compared with local meteorological data for the same period.

RESULTS

Low temperature and vapor pressure (VP) were associated with weekly incidence of IFA, respiratory syncytial virus, metapneumovirus, bocavirus and adenovirus but no association with relative humidity was found. The incidence of human rhinovirus and enterovirus was independent of temperature. During seasonal IFA outbreaks, the weekly drop of average temperature (compared with the week before) was strongly associated with the IFA incidence recorded the following week.

CONCLUSION

A sudden drop in outdoor temperature might activate the annual influenza epidemic in a temperate climate by facilitating aerosol spread in dry air. These conditions also seem to affect the incidence of other respiratory pathogens but not human rhino- or enterovirus, suggesting that routes of infection other than aerosol may be relevant for these agents.

Influenza-Associated Excess Mortality in South Korea

INTRODUCTION

It is important to determine the health impact of influenza in order to calibrate public health measures. The objective of this study was to estimate excess mortality associated with influenza in Korea in 2003-2013.

METHODS

The authors constructed multiple linear regression models in 2014 with weekly mortality rates stratified by age, region, and cause of death against weekly surveillance data on influenza virus collected in 2003-2013. Excess mortality rates were estimated using the difference between predicted mortality rates from the fitted model versus predicted mortality rates with the influenza covariate for each strain set to 0.

RESULTS

During the study period, influenza was associated with an average of 2,900 excess deaths per year. The impact of influenza on mortality was significantly higher in older people; the overall all-cause excess annual mortality rate per 100,000 people was 5.97 (95% CI=4.89, 7.19), whereas it was 46.98 (95% CI=36.40, 55.82) for adults aged ≥65 years. It also greatly varied from year to year, ranging from 2.04 in 2009-2010 to 18.76 in 2011-2012.

CONCLUSIONS

The impact of influenza on mortality in Korea is substantial, particularly among the elderly and the rural population. More-comprehensive studies may be needed to estimate the full impact of influenza.

Influenza vaccination in high-risk groups: a revision of existing guidelines and rationale for an evidence-based preventive strategy

Influenza, an infectious respiratory disease, is one of the main causes of excess winter deaths (EWDs) in Europe. Annual flu epidemics are associated with high morbidity and mortality rates, especially among the elderly, those with underlying health conditions and pregnant women. Health Care Workers (HCWs) are also considered at high risk of both contracting influenza and spreading the virus to vulnerable patients. During the 2014/2015 season, the excess winter mortality rates observed in countries of the northern hemisphere (EuroMOMO network) and in Italy (+13%) were strongly related to the intensity of influenza circulation. Influenza vaccination is the most important public health intervention to prevent seasonal influenza transmission and infection. However, to date, influenza vaccination coverage reported in Europe (including high-risk groups) is still largely unsatisfactory. This study analyzes some international and European guidelines on influenza vaccination and the rationale that underlies evidence- based public health intervention for the prevention of influenza among the principal high-risk groups: a) the elderly (subjects aged 65 years or older); b) subjects with underlying health conditions; c) pregnant women; d) healthcare workers. Only by achievement recommended influenza vaccination coverage among high-risk groups in all European countries can we reduce the burden of disease.

Geographical variation in relative risks associated with cold waves in Spain: The need for a cold wave prevention plan

In general, there are few studies that analyse the impact of low temperatures on mortality and fewer still that use cold-wave-definition thresholds based on epidemiological and non-climatological criteria. Such a threshold definition, which took into account population features such as socio-economic and demographic characteristics, made it possible for a specific threshold temperature to be obtained for each of Spain's 52 provincial capitals in this study. Using generalised linear models with the Poisson regression link, and controlling for trend, autocorrelations and seasonalities of the series, and influenza epidemics, we obtained the impact of low temperatures on mortality in each provincial capital by calculating the relative risks (RRs) and attributable risks (ARs) for natural as well as circulatory and respiratory causes. The study showed higher minimum temperature thresholds in coastal areas, and an overall impact of cold on mortality in Spain due to natural causes RR=1.13 (95% CI: 1.11-1.16), circulatory causes RR=1.18 (95% CI: 1.15-1.22) and respiratory causes RR=1.24 (95% CI: 1.20-1.29) slightly greater than that obtained to date for heat. From a public health standpoint, there is a need for specific cold wave prevention plans at a regional level which would enable mortality attributable to low temperatures to be reduced. These plans have shown themselves to be effective in decreasing heat-related mortality, and we feel that they are essential for reducing cold-related effects on morbidity and mortality.

Deaths averted by influenza vaccination in the U.S. during the seasons 2005/06 through 2013/14

BACKGROUND

Excess mortality due to seasonal influenza is substantial, yet quantitative estimates of the benefit of annual vaccination programs on influenza-associated mortality are lacking.

METHODS

We estimated the numbers of deaths averted by vaccination in four age groups (0.5 to 4, 5 to 19, 20 to 64 and ≥65 yrs.) for the nine influenza seasons from 2005/6 through 2013/14. These estimates were obtained using a Monte Carlo approach applied to weekly U.S. age group-specific estimates of influenza-associated excess mortality, monthly vaccination coverage estimates and summary seasonal influenza vaccine effectiveness estimates to obtain estimates of the number of deaths averted by vaccination. The estimates are conservative as they do not include indirect vaccination effects.

RESULTS

From August, 2005 through June, 2014, we estimated that 40,127 (95% confidence interval [CI] 25,694 to 59,210) deaths were averted by influenza vaccination. We found that of all studied seasons the most deaths were averted by influenza vaccination during the 2012/13 season (9398; 95% CI 2,386 to 19,897) and the fewest during the 2009/10 pandemic (222; 95% CI 79 to 347). Of all influenza-associated deaths averted, 88.9% (95% CI 83 to 92.5%) were in people ≥65 yrs. old.

CONCLUSIONS

The estimated number of deaths averted by the US annual influenza vaccination program is considerable, especially among elderly adults and even when vaccine effectiveness is modest, such as in the 2012/13 season. As indirect effects ("herd immunity") of vaccination are ignored, these estimates represent lower bound estimates and are thus conservative given valid excess mortality estimates.

Excess mortality among the elderly in European countries, December 2014 to February 2015

Since December 2014 and up to February 2015, the weekly number of excess deaths from all-causes among individuals?≥?65 years of age in 14 European countries have been significantly higher than in the four previous winter seasons. The rise in unspecified excess mortality coincides with increased proportion of influenza detection in the European influenza surveillance schemes with a main predominance of influenza A(H3N2) viruses seen throughout Europe in the current season, though cold snaps and other respiratory infections may also have had an effect.

[Excess mortality associated with influenza in Spain in winter 2012]

OBJECTIVE

An excess of mortality was detected in Spain in February and March 2012 by the Spanish daily mortality surveillance system and the «European monitoring of excess mortality for public health action» program. The objective of this article was to determine whether this excess could be attributed to influenza in this period.

METHODS

Excess mortality from all causes from 2006 to 2012 were studied using time series in the Spanish daily mortality surveillance system, and Poisson regression in the European mortality surveillance system, as well as the FluMOMO model, which estimates the mortality attributable to influenza. Excess mortality due to influenza and pneumonia attributable to influenza were studied by a modification of the Serfling model. To detect the periods of excess, we compared observed and expected mortality.

RESULTS

In February and March 2012, both the Spanish daily mortality surveillance system and the European mortality surveillance system detected a mortality excess of 8,110 and 10,872 deaths (mortality ratio (MR): 1.22 (95% CI:1.21-1.23) and 1.32 (95% CI: 1.29-1.31), respectively). In the 2011-12 season, the FluMOMO model identified the maximum percentage (97%) of deaths attributable to influenza in people older than 64 years with respect to the mortality total associated with influenza (13,822 deaths). The rate of excess mortality due to influenza and pneumonia and respiratory causes in people older than 64 years, obtained by the Serfling model, also reached a peak in the 2011-2012 season: 18.07 and 77.20, deaths per 100,000 inhabitants, respectively.

CONCLUSION

A significant increase in mortality in elderly people in Spain was detected by the Spanish daily mortality surveillance system and by the European mortality surveillance system in the winter of 2012, coinciding with a late influenza season, with a predominance of the A(H3N2) virus, and a cold wave in Spain. This study suggests that influenza could have been one of the main factors contributing to the mortality excess observed in the winter of 2012 in Spain.

Prevalence of influenza A/H3N2 virus in northern Iran from 2011 to 2013

BACKGROUND

Influenza A virus is the most virulent human pathogen and causes the most serious problem. Having epidemiological knowledge about this disease is important. The aim of this study was to determine the prevalence of influenza A/H3N2 virus infection in northern Iran from 2011 to 2013 using the real-time polymerase chain reaction (RT-PCR).

METHODS

In this cross-sectional study 57 samples were collected from patients with influenza-like illness (T≥ 38 °C and cough or sore throat. Influenza-RNA was extracted from the samples using PureLink(TM) Viral RNA/DNA Kit. RT-PCR was one using SuperScript III Platinum, Quantitive Real Time PCR system from invitrogen with a specific type of primers and probs. All samples were examined in the Influenza laboratory of Mazandaran University of Medical Sciences.

RESULTS

The mean age of patients was 38.2±14.5 year, 278 (48.69%) were males and 293 (51.31%) females. A total number of 201 patients (35.2%) were diagnosed as influenza A1 H3 N2 infection.

CONCLUSION

The results show that the prevalence of A/H3N2 in the North of Iran is considerable and needs more attention for preventive measures.

The potential impact of climate change and ultraviolet radiation on vaccine-preventable infectious diseases and immunization service delivery system

Climate change and solar ultraviolet radiation may affect vaccine-preventable infectious diseases (VPID), the human immune response process and the immunization service delivery system. We systematically reviewed the scientific literature and identified 37 relevant publications. Our study shows that climate variability and ultraviolet radiation may potentially affect VPID and the immunization delivery system through modulating vector reproduction and vaccination effectiveness, possibly influencing human immune response systems to the vaccination, and disturbing immunization service delivery. Further research is needed to determine these affects on climate-sensitive VPID and on human immune response to common vaccines. Such research will facilitate the development and delivery of optimal vaccination programs for target populations, to meet the goal of disease control and elimination.

Vaccination against the M protein of Streptococcus pyogenes prevents death after influenza virus: S. pyogenes super-infection

Influenza virus infections are associated with a significant number of illnesses and deaths on an annual basis. Many of the deaths are due to complications from secondary bacterial invaders, including Streptococcus pneumoniae, Staphylococcus aureus, Haemophilus influenzae, and Streptococcus pyogenes. The β-hemolytic bacteria S. pyogenes colonizes both skin and respiratory surfaces, and frequently presents clinically as strep throat or impetigo. However, when these bacteria gain access to normally sterile sites, they can cause deadly diseases including sepsis, necrotizing fasciitis, and pneumonia. We previously developed a model of influenza virus:S. pyogenes super-infection, which we used to demonstrate that vaccination against influenza virus can limit deaths associated with a secondary bacterial infection, but this protection was not complete. In the current study, we evaluated the efficacy of a vaccine that targets the M protein of S. pyogenes to determine whether immunity toward the bacteria alone would allow the host to survive an influenza virus:S. pyogenes super-infection. Our data demonstrate that vaccination against the M protein induces IgG antibodies, in particular those of the IgG1 and IgG2a isotypes, and that these antibodies can interact with macrophages. Ultimately, this vaccine-induced immunity eliminated death within our influenza virus:S. pyogenes super-infection model, despite the fact that all M protein-vaccinated mice showed signs of illness following influenza virus inoculation. These findings identify immunity against bacteria as an important component of protection against influenza virus:bacteria super-infection.

Estimating influenza deaths in Canada, 1992-2009

Background

Poisson regression modelling has been widely used to estimate the disease burden attributable to influenza, though not without concerns that some of the excess burden could be due to other causes. This study aims to provide annual estimates of the mortality and hospitalization burden attributable to both seasonal influenza and the 2009 A/H1N1 pandemic influenza for Canada, and to discuss issues related to the reliability of these estimates.

Methods

Weekly time-series for all-cause mortality and regression models were used to estimate the number of deaths in Canada attributable to influenza from September 1992 to December 2009. To assess their robustness, the annual estimates derived from different parameterizations of the regression model for all-cause mortality were compared. In addition, the association between the annual estimates for mortality and hospitalization by age group, underlying cause of death or primary reason for admission and discharge status is discussed.

Results

The crude influenza-attributed mortality rate based on all-cause mortality and averaged over 17 influenza seasons prior to the 2009 A/H1N1 pandemic was 11.3 (95%CI, 10.5 - 12.1) deaths per 100 000 population per year, or an average of 3,500 (95%CI, 3,200 - 3,700) deaths per year attributable to seasonal influenza. The estimated annual rates ranged from undetectable at the ecological level to more than 6000 deaths per year over the three A/Sydney seasons. In comparison, we attributed an estimated 740 deaths (95%CI, 350–1500) to A(H1N1)pdm09. Annual estimates from different model parameterizations were strongly correlated, as were estimates for mortality and morbidity; the higher A(H1N1)pdm09 burden in younger age groups was the most notable exception.

Interpretation

With the exception of some of the Serfling models, differences in the ecological estimates of the disease burden attributable to influenza were small in comparison to the variation in disease burden from one season to another.

Ischaemic heart disease, influenza and influenza vaccination: a prospective case control study

Background

Abundant, indirect epidemiological evidence indicates that influenza contributes to all-cause mortality and cardiovascular hospitalisations with studies showing increases in acute myocardial infarction (AMI) and death during the influenza season.

Objective

To investigate whether influenza is a significant and unrecognised underlying precipitant of AMI.

Design

Case-control study.

Setting

Tertiary referral hospital in Sydney, Australia, during 2008 to 2010.

Patients

Cases were inpatients with AMI and controls were outpatients without AMI at a hospital in Sydney, Australia.

Main outcome measures

Primary outcome was laboratory evidence of influenza. Secondary outcome was baseline self-reported acute respiratory tract infection.

Results

Of 559 participants, 34/275 (12.4%) cases and 19/284 (6.7%) controls had influenza (OR 1.97, 95% CI 1.09 to 3.54); half were vaccinated. None were recognised as having influenza during their clinical encounter. After adjustment, influenza infection was no longer a significant predictor of recent AMI. However, influenza vaccination was significantly protective (OR 0.55, 95% CI 0.35 to 0.85), with a vaccine effectiveness of 45% (95% CI 15% to 65%).

Conclusions

Recent influenza infection was an unrecognised comorbidity in almost 10% of hospital patients. Influenza did not predict AMI, but vaccination was significantly protective but underused. The potential population health impact of influenza vaccination, particularly in the age group 50–64 years, who are at risk for AMI but not targeted for vaccination, should be further explored. Our data should inform vaccination policy and cardiologists should be aware of missed opportunities to vaccinate individuals with ischaemic heart disease against influenza.

Serotype-specific effect of influenza on adult invasive pneumococcal pneumonia

BACKGROUND

 Influenza affects host susceptibility to pneumococcus. We sought to evaluate whether this relationship varies by pneumococcal serotype using a large epidemiological database covering 3 decades.

METHODS

 Weekly rates of invasive pneumococcal pneumonia (IPP) were obtained from the Danish National Laboratory Surveillance System, and influenza-like illness (ILI) data were collected from Danish sentinel surveillance, Statens Serum Institut, 1977-2007. We fit Poisson regression models for each age and comorbidity group, with predictors for seasonality and secular changes, ILI activity, and serotype.

RESULTS

 Among individuals with low levels of comorbidities, influenza had the largest impact on IPP incidence among low-invasiveness serotypes (influenza attributable percent: 17.9%, 95% confidence interval [CI], 13.6-21.9) as compared with high-invasiveness serotypes (6.7%, 95% CI, 3.8%-11.7%). Among those with higher levels of comorbidities, the effect of influenza was smaller, but high-invasiveness serotypes increased more than low-invasiveness serotypes (8.9% [95% CI, 6.6-11.8] vs. 1.3% [95% CI, -1.6-5.4].

CONCLUSIONS

Influenza was associated with the greatest increases in the incidence of disease caused by serotypes with lower invasive potential and among individuals with low levels of comorbid conditions. The importance of influenza for adult IPP varies by serotype and host comorbidity.

Temperature changes between neighboring days and mortality in summer: a distributed lag non-linear time series analysis

BACKGROUND

Many studies have shown that high temperatures or heat waves were associated with mortality and morbidity. However, few studies have examined whether temperature changes between neighboring days have any significant impact on human health.

METHOD

A distributed lag non-linear model was employed to investigate the effect of temperature changes on mortality in summer during 2006-2010 in two subtropical Chinese cities. The temperature change was defined as the difference of the current day's and the previous day's mean temperature.

RESULTS

We found non-linear effects of temperature changes between neighboring days in summer on mortality in both cities. Temperature increase was associated with increased mortality from non-accidental diseases and cardiovascular diseases, while temperature decrease had a protective effect on non-accidental mortality and cardiovascular mortality in both cities. Significant association between temperature changes and respiratory mortality was only found in Guangzhou.

CONCLUSION

This study suggests that temperature changes between neighboring days might be an alternative temperature indicator for studying temperature-mortality relationship.

Mortality attributable to seasonal and pandemic influenza, Australia, 2003 to 2009, using a novel time series smoothing approach

BACKGROUND

Official statistics under-estimate influenza deaths. Time series methods allow the estimation of influenza-attributable mortality. The methods often model background, non-influenza mortality using a cyclic, harmonic regression model based on the Serfling approach. This approach assumes that the seasonal pattern of non-influenza mortality is the same each year, which may not always be accurate.

AIM

To estimate Australian seasonal and pandemic influenza-attributable mortality from 2003 to 2009, and to assess a more flexible influenza mortality estimation approach.

METHODS

We used a semi-parametric generalized additive model (GAM) to replace the conventional seasonal harmonic terms with a smoothing spline of time ('spline model') to estimate influenza-attributable respiratory, respiratory and circulatory, and all-cause mortality in persons aged <65 and ≥ 65 years. Influenza A(H1N1)pdm09, seasonal influenza A and B virus laboratory detection time series were used as independent variables. Model fit and estimates were compared with those of a harmonic model.

RESULTS

Compared with the harmonic model, the spline model improved model fit by up to 20%. In <65 year-olds, the estimated respiratory mortality attributable to pandemic influenza A(H1N1)pdm09 was 0.5 (95% confidence interval (CI), 0.3, 0.7) per 100,000; similar to that of the years with the highest seasonal influenza A mortality, 2003 and 2007 (A/H3N2 years). In ≥ 65 year-olds, the highest annual seasonal influenza A mortality estimate was 25.8 (95% CI 22.2, 29.5) per 100,000 in 2003, five-fold higher than the non-statistically significant 2009 pandemic influenza estimate in that age group. Seasonal influenza B mortality estimates were negligible.

CONCLUSIONS

The spline model achieved a better model fit. The study provides additional evidence that seasonal influenza, particularly A/H3N2, remains an important cause of mortality in Australia and that the epidemic of pandemic influenza A (H1N1)pdm09 virus in 2009 did not result in mortality greater than seasonal A/H3N2 influenza mortality, even in younger age groups.

Influenza mortality in the United States, 2009 pandemic: burden, timing and age distribution

BACKGROUND

In April 2009, the most recent pandemic of influenza A began. We present the first estimates of pandemic mortality based on the newly-released final data on deaths in 2009 and 2010 in the United States.

METHODS

We obtained data on influenza and pneumonia deaths from the National Center for Health Statistics (NCHS). Age- and sex-specific death rates, and age-standardized death rates, were calculated. Using negative binomial Serfling-type methods, excess mortality was calculated separately by sex and age groups.

RESULTS

In many age groups, observed pneumonia and influenza cause-specific mortality rates in October and November 2009 broke month-specific records since 1959 when the current series of detailed US mortality data began. Compared to the typical pattern of seasonal flu deaths, the 2009 pandemic age-specific mortality, as well as influenza-attributable (excess) mortality, skewed much younger. We estimate 2,634 excess pneumonia and influenza deaths in 2009-10; the excess death rate in 2009 was 0.79 per 100,000.

CONCLUSIONS

Pandemic influenza mortality skews younger than seasonal influenza. This can be explained by a protective effect due to antigenic cycling. When older cohorts have been previously exposed to a similar antigen, immune memory results in lower death rates at older ages. Age-targeted vaccination of younger people should be considered in future pandemics.

Regional variation in mortality impact of the 2009 A(H1N1) influenza pandemic in China

BACKGROUND

Laboratory-confirmed deaths grossly underestimate influenza mortality burden, so that reliable burden estimates are derived from indirect statistical studies, which are scarce in low- and middle-income settings.

OBJECTIVES

Here, we used statistical excess mortality models to estimate the burden of seasonal and pandemic influenza in China.

METHODS

We modeled data from a nationally representative population-based death registration system, combined with influenza virological surveillance data, to estimate influenza-associated excess mortality for the 2004-2005 through 2009-2010 seasons, by age and region.

RESULTS

The A(H1N1) pandemic was associated with 11·4-12·1 excess respiratory and circulatory (R&C) deaths per 100,000 population in rural sites of northern and southern China during 2009-2010; these rates were 2·2-2·8 times higher than those of urban sites (P<0·01). Influenza B accounted for a larger proportion of deaths than pandemic A(H1N1) in 2009-2010 in some regions. Nationally, we attribute 126,200 (95% CI, 61,000-248,400) excess R&C deaths (rate of 9·4/100,000) and 2,323,000 (1,166,000-4,533,000) years of life lost (YLL) to the first year of A(H1N1)pdm circulation.

CONCLUSIONS

The A(H1N1) pandemic posed a mortality and YLL burden comparable to that of interpandemic influenza in China. Our high burden estimates in rural areas highlight the need to enhance epidemiological surveillance and healthcare services, in underdeveloped and remote areas.

Surveillance of influenza viruses in the post-pandemic era (2010-2012) in Northern Italy

The activity and circulation of influenza viruses in Lombardy - Northern Italy - (a region with nearly 10 out of the 60 million inhabitants of Italy) were investigated during two consecutive seasons (2010-2011 and 2011-2012), as part of the Italian Influenza Surveillance Network. The molecular characteristics of the hemagglutinin (HA) sequence of circulating viruses were analyzed to investigate the emergence of influenza viral variants. In the surveyed area, the influenza activity of these two post-pandemic seasons was similar in terms of both time frame and impact. The timing of the influenza epidemics was similar to the timing seen prior to the emergence of the pandemic A(H1N1) virus in 2009. A(H1N1)pdm09 was the predominant virus circulating during the 2010-2011 post-pandemic season and then--unexpectedly--almost disappeared. The HA sequences of these A(H1N1)pdm09 viruses segregated in a different genetic group with respect to those identified during the 2009 pandemic, although they were still closely related to the vaccine viral strain A/California/07/2009. Influenza A(H3N2) viruses were the predominant viruses circulating during the 2011-2012 season, accounting for nearly 88% of influenza viruses identified. All HA sequences of the A(H3N2) viruses isolated in the 2011-2012 season fell into the A/Victoria/208/2009 genetic clade (although the A/Perth/16/2009 virus was the reference vaccine strain). B viruses presented with a mixed circulation of viral variants during these two seasons: viruses belonging to both B/Victoria and B/Yamagata lineages co-circulated in different proportions, with a notable rise in the proportion of B/Yamagata viruses (B/Wisconsin/1/2010-like) during the 2011-2012 epidemic. In conclusion, the continuous monitoring of the characteristics of circulating viruses is an essential tool for understanding the epidemiological and virological features of influenza viruses, for monitoring their matching with seasonal vaccine strains, and for tuning vaccination strategies.

Genetic drift influenza A(H3N2) virus hemagglutinin (HA) variants originated during the last pandemic turn out to be predominant in the 2011-2012 season in Northern Italy

Influenza A(H3N2) virus is once again the predominant strain after the 2009 pandemic. Its molecular epidemiology and phylogeny were investigated during the 2011-2012 season in Northern Italy. The epidemiological and virological influenza surveillance was carried out within the framework of the Italian Influenza Surveillance Network. The hemagglutinin (HA) gene of the A(H3N2) viruses detected was analyzed by means of a time-scaled phylogenetic approach. In Northern Italy, the 2011-2012 epidemic wave was sustained almost exclusively by influenza A(H3N2) viruses (87.2% of total influenza virus detections). The consultation rates for influenza-like illness (ILI) in the age group ≥65 years were 1.5 to 6-fold higher than those registered during the previous eight epidemics: A(H3N2) was the only virus identified in this group. The phylogenetic analysis of A(H3N2) viruses showed viruses belonging to the A/Victoria/208/2009 genetic clade, characterized by substitutions in HA antigenic sites with respect to the A/Perth/16/2009-like 2011-2012 vaccine strain. About one-third of analyzed sequences fell into group 6 and two thirds into group 3 (subdivided into 3A, 3B, and 3C). The time scale reconstruction of the phylogeny showed several independent introductions of A(H3N2) groups between summer and winter of 2011. However, the common origin of all the circulating A(H3N2) strains dated back to the 2009 pandemic period (November 2009). The time scale phylogenetic approach is of particular importance for the evaluation of the introduction and circulation of new variants in the area. Therefore, it should be implemented within the framework of influenza virological surveillance.