Detection of excess influenza severity: associating respiratory hospitalization and mortality data with reports of influenza-like illness by primary care physicians

Inference of age-dependent case-fatality ratios for seasonal influenza virus subtypes A(H3N2) and A(H1N1)pdm09 and B lineages using data from the Netherlands

Background

Despite the known relatively high disease burden of influenza, data are lacking regarding a critical epidemiological indicator, the case-fatality ratio. Our objective was to infer age-group and influenza (sub)type specific values by combining modelled estimates of symptomatic incidence and influenza-attributable mortality.

Methods

The setting was the Netherlands, 2011/2012 through 2019/2020 seasons. Sentinel surveillance data from general practitioners and laboratory testing were synthesised to supply age-group specific estimates of incidence of symptomatic infection, and ecological additive modelling was used to estimate influenza-attributable deaths. These were combined in an Bayesian inferential framework to estimate case-fatality ratios for influenza A(H3N2), A(H1N1)pdm09 and influenza B, per 5-year age-group.

Results

Case-fatality estimates were highest for influenza A(H3N2) followed by influenza B and then A(H1N1)pdm09 and were highest for the 85+ years age-group, at 4.76% (95% credible interval [CrI]: 4.52-5.01%) for A(H3N2), followed by influenza B at 4.08% (95% CrI: 3.77-4.39%) and A(H1N1)pdm09 at 2.51% (95% CrI: 2.09-2.94%). For 55-59 through 85+ years, the case-fatality risk was estimated to double with every 3.7 years of age.

Conclusions

These estimated case-fatality ratios, per influenza sub(type) and per age-group, constitute valuable information for public health decision-making, for assessing the retrospective and prospective value of preventative interventions such as vaccination and for health economic evaluations.

Association of weather, air pollutants, and seasonal influenza with chronic obstructive pulmonary disease hospitalization risks

The influences of weather and air pollutants on chronic obstructive pulmonary disease (COPD) have been well-studied. However, the heterogeneous effects of different influenza viral infections, air pollution and weather on COPD admissions and re-admissions have not been thoroughly examined. In this study, we aimed to elucidate the relationships between meteorological variables, air pollutants, seasonal influenza, and hospital admissions and re-admissions due to COPD in Hong Kong, a non-industrial influenza epicenter. A total number of 507703 hospital admissions (i.e., index admissions) and 301728 re-admission episodes (i.e., episodes within 30 days after the previous discharge) for COPD over 14 years (1998-2011) were obtained from all public hospitals. The aggregated weekly numbers were matched with meteorological records and outdoor air pollutant concentrations. Type-specific and all-type influenza-like illness positive (ILI+) rates were used as proxies for influenza activity. Generalized additive models were used in conjunction with distributed-lag non-linear models to estimate the associations of interest. According to the results, high concentrations of fine particulate matter, oxidant gases, and cold weather were strong independent risk factors of COPD outcomes. The cumulative adjusted relative risks exhibited a monotone increasing trend except for ILI+ B, and the numbers were statistically significant over the entire observed range of ILI+ total and ILI+ A/H3N2 when the reference rate was zero. COPD hospitalization risk from influenza infection was higher in the elderly than that in the general population. In conclusion, our results suggest that health administrators should impose clean air policies, such as strengthening emissions control on petrol vehicles, to reduce pollution from oxidant gases and particulates. An extension of the influenza vaccination program for patients with COPD may need to be encouraged: for example, vaccination may be included in hospital discharge planning, particularly before the winter epidemic.

Cost-Effectiveness of Quadrivalent Versus Trivalent Influenza Vaccination in the Dutch National Influenza Prevention Program

OBJECTIVES

As of 2019, quadrivalent influenza vaccine (QIV) has replaced trivalent influenza vaccine (TIV) in the national immunization program in The Netherlands. Target groups are individuals of 60+ years of age and those with chronic diseases. The objective was to estimate the incremental break-even price of QIV over TIV at a threshold of €20 000 per quality-adjusted life-year (QALY).

METHODS

An age-structured compartmental dynamic model was adapted for The Netherlands to assess health outcomes and associated costs of vaccinating all individuals at higher risk for influenza with QIV instead of TIV over the seasons 2010 to 2018. Influenza incidence rates were derived from a global database. Other parameters (probabilities, QALYs and costs) were extracted from the literature and applied according to Dutch guidelines. A threshold of €20 000 per QALY was applied to estimate the incremental break-even prices of QIV versus TIV. Sensitivity analyses were performed to test the robustness of the model outcomes.

RESULTS

Retrospectively, vaccination with QIV instead of TIV could have prevented on average 9500 symptomatic influenza cases, 2130 outpatient visits, 84 hospitalizations, and 38 deaths per year over the seasons 2010 to 2018. This translates into 385 QALYs and 398 life-years potentially gained. On average, totals of €431 527 direct and €2 388 810 indirect costs could have been saved each year.

CONCLUSION

Using QIV over TIV during the influenza seasons 2010 to 2018 would have been cost-effective at an incremental price of maximally €3.81 (95% confidence interval, €3.26-4.31). Sensitivity analysis showed consistent findings on the incremental break-even price in the same range.

Cost-Effectiveness of Pediatric Influenza Vaccination in The Netherlands

OBJECTIVE

This study evaluates the cost-effectiveness of extending the Dutch influenza vaccination program for elderly and medical high-risk groups to include pediatric influenza vaccination, taking indirect protection into account.

METHODS

An age-structured dynamic transmission model was used that was calibrated to influenza-associated GP visits over 4 seasons (2010-2011 to 2013-2014). The clinical and economic impact of different pediatric vaccination strategies were compared over 20 years, varying the targeted age range, the vaccine type for children or elderly and high-risk groups. Outcome measures include averted symptomatic infections and deaths, societal costs and quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratios. Costs and QALYs were discounted at 4% and 1.5% annually.

RESULTS

At an assumed coverage of 50%, adding pediatric vaccination for 2- to 17-year-olds with quadrivalent live-attenuated vaccine to the current vaccination program for elderly and medical high-groups with quadrivalent inactivated vaccine was estimated to avert, on average, 401 820 symptomatic cases and 72 deaths per year. Approximately half of averted symptomatic cases and 99% of averted deaths were prevented in other age groups than 2- to 17-year-olds due to herd immunity. The cumulative discounted 20-year economic impact was 35 068 QALYs gained and €1687 million saved, that is, the intervention was cost-saving. This vaccination strategy had the highest probability of being the most cost-effective strategy considered, dominating pediatric strategies targeting 2- to 6-year-olds or 2- to 12-year-olds or strategies with trivalent inactivated vaccine.

CONCLUSION

Modeling indicates that introducing pediatric influenza vaccination in The Netherlands is cost-saving, reducing the influenza-related disease burden substantially.

Using influenza surveillance networks to estimate state-specific prevalence of SARS-CoV-2 in the United States

Detection of SARS-CoV-2 infections to date has relied heavily on RT-PCR testing. However, limited test availability, high false-negative rates, and the existence of asymptomatic or sub-clinical infections have resulted in an under-counting of the true prevalence of SARS-CoV-2. Here, we show how influenza-like illness (ILI) outpatient surveillance data can be used to estimate the prevalence of SARS-CoV-2. We found a surge of non-influenza ILI above the seasonal average in March 2020 and showed that this surge correlated with COVID-19 case counts across states. If 1/3 of patients infected with SARS-CoV-2 in the US sought care, this ILI surge would have corresponded to more than 8.7 million new SARS-CoV-2 infections across the US during the three-week period from March 8 to March 28, 2020. Combining excess ILI counts with the date of onset of community transmission in the US, we also show that the early epidemic in the US was unlikely to have been doubling slower than every 4 days. Together these results suggest a conceptual model for the COVID-19 epidemic in the US characterized by rapid spread across the US with over 80% infected patients remaining undetected. We emphasize the importance of testing these findings with seroprevalence data and discuss the broader potential to use syndromic surveillance for early detection and understanding of emerging infectious diseases.

Years of Life Lost Due to Influenza-Attributable Mortality in Older Adults in the Netherlands: A Competing-Risks Approach

We estimated the influenza mortality burden in adults aged 60 years or older in the Netherlands in terms of years of life lost, taking into account competing mortality risks. Weekly laboratory surveillance data for influenza and other respiratory pathogens and weekly extreme temperature served as covariates in Poisson regression models fitted to weekly mortality data, specific to age group, for the period 1999-2000 through 2012-2013. Burden for age groups 60-64 years through 85-89 years was computed as years of life lost before age 90 (YLL90), using restricted mean lifetime survival analysis and accounting for competing risks. Influenza-attributable mortality burden was greatest for persons aged 80-84 years, at 914 YLL90 per 100,000 persons (95% uncertainty interval: 867, 963), followed by persons aged 85-89 years (787 YLL90/100,000; 95% uncertainty interval: 741, 834). Ignoring competing mortality risks in the computation of influenza-attributable YLL90 would lead to substantial overestimation of burden, from 3.5% for persons aged 60-64 years to 82% for those aged 80-89 years at death. Failure to account for competing mortality risks has implications for the accuracy of disease-burden estimates, especially among persons aged 80 years or older. Because the mortality burden borne by the elderly is notably high, prevention initiatives may benefit from being redesigned to more effectively prevent infection in the oldest age groups.

An Evidence Synthesis Approach to Estimating the Proportion of Influenza Among Influenza-like Illness Patients

BACKGROUND

Estimation of the national-level incidence of seasonal influenza is notoriously challenging. Surveillance of influenza-like illness is carried out in many countries using a variety of data sources, and several methods have been developed to estimate influenza incidence. Our aim was to obtain maximally informed estimates of the proportion of influenza-like illness that is true influenza using all available data.

METHODS

We combined data on weekly general practice sentinel surveillance consultation rates for influenza-like illness, virologic testing of sampled patients with influenza-like illness, and positive laboratory tests for influenza and other pathogens, applying Bayesian evidence synthesis to estimate the positive predictive value (PPV) of influenza-like illness as a test for influenza virus infection. We estimated the weekly number of influenza-like illness consultations attributable to influenza for nine influenza seasons, and for four age groups.

RESULTS

The estimated PPV for influenza in influenza-like illness patients was highest in the weeks surrounding seasonal peaks in influenza-like illness rates, dropping to near zero in between-peak periods. Overall, 14.1% (95% credible interval [CrI]: 13.5%, 14.8%) of influenza-like illness consultations were attributed to influenza infection; the estimated PPV was 50% (95% CrI: 48%, 53%) for the peak weeks and 5.8% during the summer periods.

CONCLUSIONS

The model quantifies the correspondence between influenza-like illness consultations and influenza at a weekly granularity. Even during peak periods, a substantial proportion of influenza-like illness-61%-was not attributed to influenza. The much lower proportion of influenza outside the peak periods reflects the greater circulation of other respiratory pathogens relative to influenza.

Excess pneumonia and influenza hospitalizations associated with influenza epidemics in Portugal from season 1998/1999 to 2014/2015

BACKGROUND

The aim of this study was to estimate excess pneumonia and influenza (P&I) hospitalizations during influenza epidemics and measure their correlation with influenza vaccine coverage in the 65 and more years old, according to the type/subtype of influenza virus.

METHODS

The study period comprised week 40/1998-40/2015. Age-specific weekly P&I hospitalizations (ICD-9: 480-487) as main diagnosis were extracted from the National Hospital Discharge database. Age-specific baseline hospitalization rates were estimated by autoregressive integrated moving average (ARIMA) model without time periods with excess hospitalizations. Excess hospitalizations were calculated by subtracting expected hospitalization rates from the observed during influenza epidemic periods. Correlation between excess P&I hospitalizations and influenza vaccine coverage in the elderly was measured with Pearson correlation coefficient.

RESULTS

The average excess P&I hospitalizations/season was 19.4/105 (range 0-46.1/105 ), and higher excess was observed in young children with <2 years (79.8/105 ) and ≥65 years (68.3/105 ). In epidemics with A(H3) dominant, the highest excess hospitalizations were observed among 65 and over. Seasons which influenza B or A(H1)pdm09 dominance the highest excess was observed in children with <2 years. High negative correlation was estimated between excess hospitalizations associated with A(H3) circulation and vaccine coverage in the elderly (r = -.653; 95% CI: -0.950 to -0.137).

CONCLUSION

Over 80% of the influenza epidemics were associated with excess hospitalizations. However, excess P&I hospitalizations pattern differed from age group and circulating virus. This ecologic approach also identified a reduction in excess P&I associated with A(H3) circulation with increasing vaccine coverage in the elderly.

Increased incidence of subarachnoid hemorrhage during cold temperatures and influenza epidemics

OBJECTIVE

This study investigated whether the increased incidence of aneurysmal subarachnoid hemorrhage (SAH) in winter is related to temperature or increased incidence of influenza. Such relationships may elucidate the pathogenesis of intracranial aneurysm rupture.

METHODS

A nationwide sample of 18,714 patients with SAH was linked with weekly temperature and influenza-like illness consultation data. Poisson regression analyses were used to calculate incidence density ratios (IDRs) with corresponding 95% CIs for the association of SAH incidence with temperature and influenza epidemics; IDRs were adjusted for study year (aIDR). In addition, SAH incidence data from 30 European population-based studies were linked with daily temperature data from the European Climate Assessment.

RESULTS

The aIDR for SAH during influenza epidemics was 1.061 (95% CI 1.022–1.101) in the univariable and 1.030 (95% CI 0.989–1.074) in the multivariable analysis. This association declined gradually during the weeks after epidemics. Per 1°C temperature drop, the aIDR was 1.005 (95% CI 1.003–1.008) in the univariable and 1.004 (95% CI 1.002–1.007) in the multivariable analysis. In the European population-based studies, the IDR was 1.143 (95% CI 1.129–1.157) per 1°C temperature drop.

CONCLUSIONS

The incidence of SAH is increased during cold temperatures and epidemic influenza. Future studies with individual patient data are needed to investigate causality between temperature or influenza and SAH.

The impact of national vaccination policy changes on influenza incidence in the Netherlands

Background

We assessed the impact of two major modifications of the Dutch National Influenza Prevention Programme – the introduction in 1997 of free‐of‐charge vaccination to persons aged ≥65 years and to high‐risk groups (previously only advised, and not free of charge), and the lowering of the eligible age to 60 years in 2008 – on the estimated incidence of influenza infection leading to influenza‐like illness (ILI).

Methods

Additive negative‐binomial segmented regression models were fitted to ILI data from GP sentinel surveillance in two‐eight‐season intervals (1993/4 to 2000/1, 2004/5 to 2011/12, comparing pre‐ and post‐policy‐change periods within each interval), with laboratory virological reporting of samples positive for influenza or other ILI‐causing pathogens as covariates.

Results

For the 2008 policy change, there was a significant step decrease in influenza contribution considering all ages (=−111 per 100 positives; 95% CI: −162, −65·0), <60 years and 60–64 years age groups (B = −92·1 per 100; 95% CI: −134, −55·5; B = −5·2; 95% CI: −10·3, −1·2, respectively). There was no evidence for a decrease associated with the 1997 policy change targeting the ≥65 years age group.

Conclusions

In the Netherlands, a 56% reduction in influenza contribution was associated with the 2008 policy targeting 60–64 year‐olds, but there was no effect of the earlier policy targeting ≥65‐year‐olds, for whom vaccination coverage was already rising before the policy change.

Detecting signals of seasonal influenza severity through age dynamics

BACKGROUND

Measures of population-level influenza severity are important for public health planning, but estimates are often based on case-fatality and case-hospitalization risks, which require multiple data sources, are prone to surveillance biases, and are typically unavailable in the early stages of an outbreak. To address the limitations of traditional indicators, we propose a novel severity index based on influenza age dynamics estimated from routine physician diagnosis data that can be used retrospectively and for early warning.

METHODS

We developed a quantitative 'ground truth' severity benchmark that synthesizes multiple traditional severity indicators from publicly available influenza surveillance data in the United States. Observing that the age distribution of cases may signal severity early in an epidemic, we constructed novel retrospective and early warning severity indexes based on the relative risk of influenza-like illness (ILI) among working-age adults to that among school-aged children using weekly outpatient medical claims. We compared our relative risk-based indexes to the composite benchmark and estimated seasonal severity for flu seasons from 2001-02 to 2008-09 at the national and state levels.

RESULTS

The severity classifications made by the benchmark were not uniquely captured by any single contributing metric, including pneumonia and influenza mortality; the influenza epidemics of 2003-04 and 2007-08 were correctly identified as the most severe of the study period. The retrospective index was well correlated with the severity benchmark and correctly identified the two most severe seasons. The early warning index performance varied, but it projected 2007-08 as relatively severe 10 weeks prior to the epidemic peak. Influenza severity varied significantly among states within seasons, and four states were identified as possible early warning sentinels for national severity.

CONCLUSIONS

Differences in age patterns of ILI may be used to characterize seasonal influenza severity in the United States in real-time and in a spatially resolved way. Future research on antigenic changes among circulating viruses, pre-existing immunity, and changing contact patterns may better elucidate the mechanisms underlying these indexes. Researchers and practitioners should consider the use of composite or ILI-based severity metrics in addition to traditional severity measures to inform epidemiological understanding and situational awareness in future seasonal outbreaks.

Do intensive care data on respiratory infections reflect influenza epidemics?

OBJECTIVES

Severe influenza can lead to Intensive Care Unit (ICU) admission. We explored whether ICU data reflect influenza like illness (ILI) activity in the general population, and whether ICU respiratory infections can predict influenza epidemics.

METHODS

We calculated the time lag and correlation between ILI incidence (from ILI sentinel surveillance, based on general practitioners (GP) consultations) and percentages of ICU admissions with a respiratory infection (from the Dutch National Intensive Care Registry) over the years 2003-2011. In addition, ICU data of the first three years was used to build three regression models to predict the start and end of influenza epidemics in the years thereafter, one to three weeks ahead. The predicted start and end of influenza epidemics were compared with observed start and end of such epidemics according to the incidence of ILI.

RESULTS

Peaks in respiratory ICU admissions lasted longer than peaks in ILI incidence rates. Increases in ICU admissions occurred on average two days earlier compared to ILI. Predicting influenza epidemics one, two, or three weeks ahead yielded positive predictive values ranging from 0.52 to 0.78, and sensitivities from 0.34 to 0.51.

CONCLUSIONS

ICU data was associated with ILI activity, with increases in ICU data often occurring earlier and for a longer time period. However, in the Netherlands, predicting influenza epidemics in the general population using ICU data was imprecise, with low positive predictive values and sensitivities.

Reassessing Google Flu Trends data for detection of seasonal and pandemic influenza: a comparative epidemiological study at three geographic scales

The goal of influenza-like illness (ILI) surveillance is to determine the timing, location and magnitude of outbreaks by monitoring the frequency and progression of clinical case incidence. Advances in computational and information technology have allowed for automated collection of higher volumes of electronic data and more timely analyses than previously possible. Novel surveillance systems, including those based on internet search query data like Google Flu Trends (GFT), are being used as surrogates for clinically-based reporting of influenza-like-illness (ILI). We investigated the reliability of GFT during the last decade (2003 to 2013), and compared weekly public health surveillance with search query data to characterize the timing and intensity of seasonal and pandemic influenza at the national (United States), regional (Mid-Atlantic) and local (New York City) levels. We identified substantial flaws in the original and updated GFT models at all three geographic scales, including completely missing the first wave of the 2009 influenza A/H1N1 pandemic, and greatly overestimating the intensity of the A/H3N2 epidemic during the 2012/2013 season. These results were obtained for both the original (2008) and the updated (2009) GFT algorithms. The performance of both models was problematic, perhaps because of changes in internet search behavior and differences in the seasonality, geographical heterogeneity and age-distribution of the epidemics between the periods of GFT model-fitting and prospective use. We conclude that GFT data may not provide reliable surveillance for seasonal or pandemic influenza and should be interpreted with caution until the algorithm can be improved and evaluated. Current internet search query data are no substitute for timely local clinical and laboratory surveillance, or national surveillance based on local data collection. New generation surveillance systems such as GFT should incorporate the use of near-real time electronic health data and computational methods for continued model-fitting and ongoing evaluation and improvement.

A comparison of the clinical and epidemiological characteristics of adult patients with laboratory-confirmed influenza A or B during the 2011-2012 influenza season in Korea: a multi-center study

BACKGROUND

During the 2011/2012 winter influenza season in the Republic of Korea, influenza A (H3N2) was the predominant virus in the first peak period of influenza activity during the second half of January 2012. On the other hand, influenza B was the predominant virus in the second peak period of influenza activity during the second half of March 2012. The objectives of this study were to compare the clinical and epidemiological characteristics of patients with laboratory-confirmed influenza A or influenza B.

METHODOLOGY/PRINCIPAL FINDINGS

We analyzed data from 2,129 adult patients with influenza-like illnesses who visited the emergency rooms of seven university hospitals in Korea from October 2011 to May 2012. Of 850 patients with laboratory-confirmed influenza, 656 (77.2%) had influenza A (H3N2), and 194 (22.8%) influenza B. Age, and the frequencies of cardiovascular disorders, diabetes, hypertension were significantly higher in patients with influenza A (H3N2) (P<0.05). The frequencies of leukopenia or thrombocytopenia in patients with influenza B at initial presentation were statistically higher than those in patients with influenza A (H3N2) (P<0.05). The rate of hospitalization, and length of hospital stay were statistically higher in patients with influenza A (H3N2) (P<0.05), and of the 79 hospitalized patients, the frequency of diabetes, hypertension, cases having at least one of the comorbid conditions, and the proportion of elderly were significantly higher in patients with influenza A (H3N2) (P<0.05).

CONCLUSIONS

The proportion of males to females and elderly population were significantly higher for influenza A (H3N2) patients group compared with influenza B group. Hypertension, diabetes, chronic lung diseases, cardiovascular disorders, and neuromuscular diseases were independently associated with hospitalization due to influenza. Physicians should assess and treat the underlying comorbid conditions as well as influenza viral infections for the appropriate management of patients with influenza.

Surveillance strategy for early detection of unusual infectious disease events

New pathogens continue to emerge, and the increased connectedness of populations across the globe through international travel and trade favors rapid dispersal of any new disease. The ability to respond to such events has increased but the question is what ‘preparedness’ means at the level of the clinician. Clinicians deal with patients with unexplained illness on a daily basis, and even with syndromes highly indicative of infectious diseases, the cause of illness is often not detected, unless extensive and costly diagnostic work-ups are done. This review discusses innovations in diagnostics and surveillance aimed at early detection of unusual disease. Risk based approaches are promising, but optimal preparedness planning requires multidisciplinary partnerships across domains, and a global translational research agenda to develop tools, systems, and evidence for interventions.

New methodology for estimating the burden of infectious diseases in Europe

Mirjam Kretzschmar and colleagues describe the BCoDE project, which uses a pathogen-based incidence approach to better estimate the infectious disease burden in Europe.

Mortality caused by influenza and respiratory syncytial virus by age group in England and Wales 1999-2010

BACKGROUND

The mortality burden caused by influenza cannot be quantified directly from death certificates because of under-recording; therefore, the estimated number of influenza deaths has to be obtained through statistical modelling.

OBJECTIVE

To estimate the number of deaths caused by influenza and respiratory syncytial virus (RSV) in England and Wales between 1999 and 2010 using a multivariable regression model.

METHODS

Generalised linear models were used to estimate weekly deaths by age group (<15, 15-44, 45-74 and 75+ years) as a function of positive influenza and RSV isolates. Adjustment was made for temperature variation (using weekly means of daily Central England temperature time series), underlying seasonal variation and temporal trends. The parameters from the model were used to predict the number of deaths caused by influenza and RSV across winter seasons.

RESULTS

Between 7000 and 25 000 deaths across all ages were associated with influenza in the winter periods 1999-2009. The mortality burden was the highest among the over 75 age group, among whom 2·5-8·1% of deaths were caused by influenza. The lowest number of influenza deaths was estimated for the winter 2009/2010 when pandemic influenza A/H1N1 (2009) was the predominant circulating strain. RSV accounted for 5000-7500 deaths each winter season.

CONCLUSIONS

The model presented provides a robust and reasonable approach to estimating the number of deaths caused by influenza and RSV by age group at the end of each winter.

Comparing pandemic to seasonal influenza mortality: moderate impact overall but high mortality in young children

BACKGROUND

We assessed the severity of the 2009 influenza pandemic by comparing pandemic mortality to seasonal influenza mortality. However, reported pandemic deaths were laboratory-confirmed - and thus an underestimation - whereas seasonal influenza mortality is often more inclusively estimated. For a valid comparison, our study used the same statistical methodology and data types to estimate pandemic and seasonal influenza mortality.

METHODS AND FINDINGS

We used data on all-cause mortality (1999-2010, 100% coverage, 16.5 million Dutch population) and influenza-like-illness (ILI) incidence (0.8% coverage). Data was aggregated by week and age category. Using generalized estimating equation regression models, we attributed mortality to influenza by associating mortality with ILI-incidence, while adjusting for annual shifts in association. We also adjusted for respiratory syncytial virus, hot/cold weather, other seasonal factors and autocorrelation. For the 2009 pandemic season, we estimated 612 (range 266-958) influenza-attributed deaths; for seasonal influenza 1,956 (range 0-3,990). 15,845 years-of-life-lost were estimated for the pandemic; for an average seasonal epidemic 17,908. For 0-4 yrs of age the number of influenza-attributed deaths during the pandemic were higher than in any seasonal epidemic; 77 deaths (range 61-93) compared to 16 deaths (range 0-45). The ≥75 yrs of age showed a far below average number of deaths. Using pneumonia/influenza and respiratory/cardiovascular instead of all-cause deaths consistently resulted in relatively low total pandemic mortality, combined with high impact in the youngest age category.

CONCLUSION

The pandemic had an overall moderate impact on mortality compared to 10 preceding seasonal epidemics, with higher mortality in young children and low mortality in the elderly. This resulted in a total number of pandemic deaths far below the average for seasonal influenza, and a total number of years-of-life-lost somewhat below average. Comparing pandemic and seasonal influenza mortality as in our study will help assessing the worldwide impact of the 2009 pandemic.

Ten lessons for the next influenza pandemic-an English perspective: a personal reflection based on community surveillance data

We review experience in England of the swine flu pandemic between May 2009 and April 2010. The surveillance data from the Royal College of General Practitioners Weekly Returns Service and the linked virological data collected in the integrated program with the Health Protection Agency are used as a reference frame to consider issues emerging during the pandemic. Ten lessons are summarized. (1) Delay between illness onset in the first worldwide cases and virological diagnosis restricted opportunities for containment by regional prophylaxis. (2) Pandemic vaccines are unlikely to be available for effective prevention during the first wave of a pandemic. (3) Open, realistic and continuing communication with the public is important. (4) Surveillance programs should be continued through summer as well as winter. (5) Severity of illness should be incorporated in pandemic definition. (6) The reliability of diagnostic tests as used in routine clinical practice calls for further investigation. (7) Evidence from serological studies is not consistent with evidence based on health care requests made by sick persons and is thus of limited value in cost effectiveness studies. (8) Pregnancy is an important risk factor. (9) New strategies for administering vaccines need to be explored. (10) Acceptance by the public and by health professionals of influenza vaccination as the major plank on which the impact of influenza is controlled has still not been achieved.

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

Background

In temperate zones, all-cause mortality exhibits a marked seasonality, and one of the main causes of winter excess mortality is influenza. There is a tradition of using statistical models based on mortality from respiratory illnesses (Pneumonia and Influenza: PI) or all-cause mortality for estimating the number of deaths related to influenza. Different authors have applied different estimation methodologies. We estimated mortality related to influenza and periods with extreme temperatures in Denmark over the seasons 1994/95 to 2009/10.

Methods

We applied a multivariable time-series model with all-cause mortality as outcome, activity of influenza-like illness (ILI) and excess temperatures as explanatory variables, controlling for trend, season, age, and gender. Two estimates of excess mortality related to influenza were obtained: (1) ILI-attributable mortality modelled directly on ILI-activity, and (2) influenza-associated mortality based on an influenza-index, designed to mimic the influenza transmission.

Results

The median ILI-attributable mortality per 100,000 population was 35 (range 6 to 100) per season which corresponds to findings from comparable countries. Overall, 88% of these deaths occurred among persons ≥ 65 years of age. The median influenza-associated mortality per 100,000 population was 26 (range 0 to 73), slightly higher than estimates based on pneumonia and influenza cause-specific mortality as estimated from other countries. Further, there was a tendency of declining mortality over the years. The influenza A(H3N2) seasons of 1995/96 and 1998/99 stood out with a high mortality, whereas the A(H3N2) 2005/6 season and the 2009 A(H1N1) influenza pandemic had none or only modest impact on mortality. Variations in mortality were also related to extreme temperatures: cold winters periods and hot summers periods were associated with excess mortality.

Conclusion

It is doable to model influenza-related mortality based on data on all-cause mortality and ILI, data that are easily obtainable in many countries and less subject to bias and subjective interpretation than cause-of-death data. Further work is needed to understand the variations in mortality observed across seasons and in particular the impact of vaccination against influenza.