The mortality burden of the 2009 A/H1N1 pandemic remains unclear in many countries due to delays in reporting of death statistics. We estimate the age- and cause-specific excess mortality impact of the pandemic in France, relative to that of other countries and past epidemic and pandemic seasons.
We applied Serfling and Poisson excess mortality approaches to model weekly age- and cause-specific mortality rates from June 1969 through May 2010 in France. Indicators of influenza activity, time trends, and seasonal terms were included in the models. We also reviewed the literature for country-specific estimates of 2009 pandemic excess mortality rates to characterize geographical differences in the burden of this pandemic.
The 2009 A/H1N1 pandemic was associated with 1.0 (95% Confidence Intervals (CI) 0.2–1.9) excess respiratory deaths per 100,000 population in France, compared to rates per 100,000 of 44 (95% CI 43–45) for the A/H3N2 pandemic and 2.9 (95% CI 2.3–3.7) for average inter-pandemic seasons. The 2009 A/H1N1 pandemic had a 10.6-fold higher impact than inter-pandemic seasons in people aged 5–24 years and 3.8-fold lower impact among people over 65 years.
The 2009 pandemic in France had low mortality impact in most age groups, relative to past influenza seasons, except in school-age children and young adults. The historical A/H3N2 pandemic was associated with much larger mortality impact than the 2009 pandemic, across all age groups and outcomes. Our 2009 pandemic excess mortality estimates for France fall within the range of previous estimates for high-income regions. Based on the analysis of several mortality outcomes and comparison with laboratory-confirmed 2009/H1N1 deaths, we conclude that cardio-respiratory and all-cause mortality lack precision to accurately measure the impact of this pandemic in high-income settings and that use of more specific mortality outcomes is important to obtain reliable age-specific estimates.
Citation: Lemaitre M, Carrat F, Rey G, Miller M, Simonsen L, Viboud C (2012) Mortality Burden of the 2009 A/H1N1 Influenza Pandemic in France: Comparison to Seasonal Influenza and the A/H3N2 Pandemic. PLoS ONE 7(9): e45051. https://doi.org/10.1371/journal.pone.0045051
Editor: Benjamin J. Cowling, University of Hong Kong, Hong Kong
Received: May 11, 2012; Accepted: August 15, 2012; Published: September 20, 2012
This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
Funding: This work was supported by a grant from Fondation de la Recherche Médicale (to M. Lemaitre) and by the in-house Influenza Research Program of the Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, which is funded by the International Influenza Unit, Office of Global Affairs, Department of Health and Human Services. L. S. acknowledges support from the RAPIDD program of the Science and Technology Directorate, Department of Homeland Security. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: M. Lemaitre, L. Simonsen, M. Miller and G. Rey declare they have no potential conflicts of interest. F. Carrat received paid honoraria for a consultancy on influenza epidemiology from Laboratoires Boiron, France. C. Viboud is a PLoS ONE Editorial Board member. This does not alter the authors’ adherence to all the PLoS ONE policies on sharing data and materials.
A novel influenza A/H1N1 virus was first isolated in April 2009 in North America and spread rapidly worldwide, leading the World Health Organization (WHO) to declare the first influenza pandemic of the 21st century in June 2009 . Almost three years later, the mortality impact of the 2009 A/H1N1 pandemic remains poorly quantified in many countries, and comparisons with the burden of previous epidemic and pandemic seasons are scarce –. Early reports have suggested important geographical variation in pandemic disease burden globally; however methodological differences hamper fair comparisons between countries and seasons. Published mortality estimates for the 2009 pandemic suggest a relatively low impact in Europe , –, Asia , and the US , and a moderate impact in Mexico . Overall, all countries have consistently reported an increase of pandemic-related deaths in younger age groups, in contrast to inter-pandemic seasons in which 90% of deaths occur in seniors , –, –.
Methodological issues in the estimation of influenza disease burden make comparisons between countries and seasons difficult. Laboratory confirmation of influenza-related deaths is not conducted routinely. Although influenza testing was greatly strengthened during the pandemic, laboratory-confirmed pandemic deaths remain a gross underestimation of the overall influenza disease burden . Deaths associated with influenza often occur following bacterial super-infection or aggravation of chronic diseases, after primary viral infection has been cleared. Consequently, death is often attributed to the underlying chronic condition rather than influenza . It is now well accepted that the best approach to estimate influenza-related mortality is to apply statistical time series seasonal regression models to different causes of death –.
Although estimates of pandemic mortality burden exist for Europe, they are mostly limited to all-cause mortality, a non-specific outcome, or to deaths coded specifically as influenza, which underestimates disease burden , , . Moreover, no study has compared the impact of the 2009 pandemic with that of past pandemics using a similar methodology. The objectives of our study were to estimate the age- and cause-specific excess mortality rates of the first wave of the A/H1N1 pandemic in France, and compare pandemic estimates with those for seasonal epidemics and the A/H3N2 pandemic. We also reviewed published estimates of 2009 pandemic excess mortality rates in other countries for comparison purposes.
Mortality and population data.
The A/H3N2 pandemic virus had its major impact in the 1969–70 winter in France, similarly to other countries in Europe . Hence to study the impact of the A/H3N2 pandemic, the 2009 A/H1N1 pandemic, and seasonal epidemics, we obtained weekly age- and cause-specific mortality counts from June 1969 through May 2010 from national death certificates collected by Inserm CépiDc . We categorized mortality based on the underlying cause of death, using the International Classification of Disease codes for influenza, pneumonia, respiratory diseases, cardiovascular diseases, and all causes (Text S1, Table S1). We used conversion factors published by CépiDc to account for transition between classifications in France .
We stratified deaths by 5 different age groups (0–4, 5–24, 25–44, 45–64, ≥65 yrs) and integrated yearly age-specific population data to calculate standardized death rates , using the June 2009 French population structure as reference.
Viral surveillance data.
Weekly percent of respiratory specimens testing positive for influenza were obtained from the FluNet database for France from January 1997 to June 2010, separately for each subtype (seasonal A/H1N1, B, A/H3N2 and pandemic A/H1N1; Figure 1) . No viral surveillance data exist before 1997 in France. Influenza virus surveillance in France relies on a network of general practitioners ,  and participating hospitals . We considered an influenza subtype to be dominant when it accounted for at least 75% of all isolates subtyped in the respiratory season (July-June). When no single subtype accounted for more than 75% of all isolates, we summed the contribution of the two most prevalent subtypes.
The green line represents the incidence of influenza like illnesses (ILI) per 100,000, as monitored by the French Sentinel Network. Vertical bars represent influenza virus activity, specifically the weekly percent positive influenza virus among all respiratory specimens tested, separately for each subtype (red for 2009 pandemic influenza A/H1N1, orange for seasonal A/H1N1-B, grey for seasonal A/H3N2).
Influenza-like illness (ILI) has been shown to be a good proxy for influenza incidence in France and elsewhere , . Weekly ILI incidence was obtained from the French Sentinel system, a nationwide network of general practitioners which has been reporting electronically the weekly number of medical visits for ILI and other infections since 1984 , . One percent of all French practitioners voluntarily participate to disease surveillance through this system. The ILI case definition consisted of a combination of fever >39°C, myalgia, and respiratory symptoms. The French Sentinel Network ILI time series have been used in a number of previous epidemiological and modeling influenza studies aimed at detecting outbreaks, quantifying disease burden, and characterizing the spatio-temporal transmission dynamics of epidemics , –.
Excess Mortality Rates Estimation
The preferred approach to estimate influenza excess mortality is to explicitly link mortality from various causes to indicators of influenza activity, such as ILI or viral surveillance data , . We used Poisson regression models with a log link  and considered both indicators of influenza activity , . Models were fit to data for 1997–2010, a period when both ILI and viral surveillance data were available. A detailed description of the model fitting procedure is provided in Text S1 and summarized below.
First, we selected the indicator of influenza activity that provided the best statistical fit to the mortality data. We considered 3 potential indicators: weekly ILI incidence , weekly influenza virus percent positive , or their combination , . [We tested various lags and moving averages of influenza indicators to account for delays between disease onset and death (Text S1). We selected the best influenza indicator and associated lag based on the Akaike Information Criterion (AIC) of respiratory death models. We focus on all-age respiratory death outcomes for this selection procedure because respiratory mortality has intermediate sensitivity and specificity to capture influenza mortality burden. We also checked that the selected influenza activity indicator maximized the correlation between observed and predicted values of respiratory deaths.
Second, we applied Poisson models using the influenza indicator selected by the above procedure to age- and cause-specific mortality time series. A stepwise selection method was used to identify the significant time trends and seasonal terms in the age- and cause-specific models (See Text S1 for full model equation).
Baseline mortality was calculated from the Poisson model as the expected values when influenza activity indicators are set to zero. Excess death rates due to influenza were estimated as the difference between predicted mortality from the full model and baseline mortality. The seasonal number of excess deaths due to influenza was summed for each respiratory season and age-standardized seasonal excess mortality rates were calculated. Confidence intervals were based on the variance of parameter estimates (See Text S1).
As a sensitivity analysis, we compared excess mortality estimates derived from the Poisson approach to those derived from a traditional Serfling model that does not include indicators of influenza activity, for the same time period, 1997–2010 (details of Serfling model are provided in Text S1 and , , ).
Given the unavailability of ILI and viral surveillance data in the years surrounding the historical A/H3N2 pandemic, we also used a Serfling approach to estimate the mortality impact of this pandemic.
Influenza Virus Activity, 1997–2010
The A/H3N2 subtype was dominant in 7 of the 12 inter-pandemic seasons studied in France, 1997–2010, A/H1N1 and B subtypes were dominant or co-dominant in 4, and there was mixed A/H3N2-B circulation in one season. The A/H1N1 pandemic virus was first isolated on May 1st, 2009 based on the national influenza surveillance system . Increased community transmission of pandemic A/H1N1 started in September 2009, virus activity peaked in the week of November 22, 2009, and A/H1N1 virus remained dominant until February 21, 2010 (Figure 1). A sharp rise in ILI activity coincided with the pandemic period and peaked in the last week of November 2009. Hereafter, we consider the 2009 A/H1N1 pandemic season in France to run from May 1, 2009 to May 1, 2010.
Excess Mortality Model Selection
The best model for respiratory deaths included a 2-week lagged moving average (t-2, t-1 and t) of ILI incidence. Based on AIC, this model was better than models using the proportion of virus positive or ILI multiplied by the proportion of virus positive.
A good fit was achieved for most age groups and causes of death (Pearson correlation>0.56). Model fit was poorer for younger age groups (especially the 0–4 yrs; Figure 2, Text S1 and Figure S1). Model-predicted excess mortality peaks due to P&I, respiratory and cardio-respiratory were synchronous with ILI peaks (correlation between peak weeks: r>0.86, p<.0001; average lag between peaks 2.9 weeks).
Observed death rates (black line) and predicted death rates (blue line) by a Poisson model integrating seasonal terms, time trends and ILI data. Baseline mortality rates predicted by the Poisson model in the absence of influenza activity are indicated by a dashed blue line. Death rates were age-standardized using the 2009 French population as reference.
As regards the A/H1N1 pandemic season, excess P&I and respiratory mortality rates peaked in November 2009 in all age groups, within 1 week of the peak in ILI and viral activity. Excess respiratory mortality increased substantially over baseline in age groups 5–64 years during the period of peak pandemic activity. This coincided with a small but significant increase in respiratory mortality in people over 65 years (Figure 3).
The purple line represents the incidence of influenza like illnesses (ILI) per 100,000. Red line represents excess mortality estimates for Pneumonia and influenza, green line is for respiratory mortality, and blue for cardio-respiratory outcomes. Excess deaths are predicted by a Poisson regression model integrating influenza activity data, time trends and seasonal terms.
Peaks in excess cardio-respiratory mortality coinciding with pandemic activity were observed in age groups 5–44 yrs. In contrast, other age groups did not experience significant excess cardio-respiratory mortality (Figure 3, Table 1). Given the relatively low impact of the pandemic and the young age distribution of deaths, we concentrate below on respiratory outcomes, and provide estimates for other outcomes as relevant.
2009–10 Pandemic Excess Mortality Estimates
We estimate that 613 excess respiratory deaths (95% Confidence Interval (CI): 125; 1,188) were attributable to the A/H1N1 pandemic period in France, May 2009-May 2010, corresponding to excess death rates of 0.98 (95% CI: 0.20; 1.9) per 100,000 population (table 1). The 2009–10 pandemic excess mortality rate is 2.8 fold lower than those of inter-pandemic influenza seasons. The proportion of respiratory deaths attributable to influenza was 5% for the 2009–10 A/H1N1 pandemic season, 2 times lower than for seasonal epidemics. A more specific outcome such as P&I excess mortality suggests a similarly low relative impact of the pandemic (Table 1). Less specific outcomes such as cardio-respiratory and all-cause mortality provided estimates even lower than those of respiratory outcomes, and most of these estimates were non-significant. Sensitivity analysis using a Serfling approach estimated that 514 (95% CI 55; 973)_excess respiratory deaths were attributable to the A/H1N1 pandemic, which is 16% lower than the estimate derived from the Poisson approach (table 1).
The age group 5–24 yrs was the most severely affected by the 2009–10 A/H1N1 pandemic, relative to the impact of seasonal epidemics (figures 2 and 3). In this age group, A/H1N1 pandemic excess respiratory and cardio-respiratory death rates were 10.6 to 14- fold higher than those of seasonal epidemics and 2 to 4-fold higher than those of the most severe A/H3N2 epidemic in 1999–2000 (table 1, table S2). In age group 25–44 yrs, the A/H1N1 pandemic excess respiratory mortality rate was 1.9 -fold higher than for typical influenza seasons. In age groups 45–64 yrs, pandemic excess mortality rates were similar to or lower than those of typical A/H3N2 seasons, depending on the outcome.
Those over the age of 65 yrs were least affected by the A/H1N1 pandemic, relative to the impact of past seasons (Figure 3, Table 1), with an excess respiratory death rate 3.8 fold lower than for typical influenza seasons. The A/H1N1 pandemic estimate for cardio-respiratory outcome was non-significant in this age group. Respiratory estimates among children under 5 yrs were not significant for the pandemic season, but lacked significance in previous inter-pandemic seasons as well (table 1).
All cause excess mortality estimates were negative and non-significant in all age groups, except for the 5–24 yrs old. A more detailed age curve of influenza-related respiratory mortality based on 10-yr age groups suggests that individuals under 55 yrs experienced higher mortality rates in the A/H1N1 pandemic than in typical influenza seasons, while older individuals experienced mortality sparing (Figure 4).
Excess mortality rates are estimated by a Serfling seasonal regression model, P&I: Pneumonia and influenza.
In people aged 5–24 years old, the proportion of respiratory deaths attributable to A/H1N1 pandemic influenza was unusually high at 55%, even relative to the most severe H3N2 season in 1999–2000 (24%, P<0.03). Similarly, 28% of cardio-respiratory mortality were attributable to the pandemic in this age group, compared to only 7% in the most severe H3N2 season (p<0.03). Use of a more specific outcome, such as P&I, revealed similar age patterns, with 2.2-fold higher mortality impact during the pandemic among those aged 5–44 yrs, relative to seasonal epidemics, and almost 2-fold lower impact among seniors over 65 yrs (table 1).
We conducted several sensitivity analyses to test to robustness of our estimates to the choice of the link in Poisson models (identity, log), the definition of the pandemic period, and the choice of the modeling approach (Poisson vs Serfling). Estimates changes by less than 3% when using an identity link instead of a log link. Estimates were within 2% of those in the main analysis when using a stricter definition of the pandemic period limited to weeks when A/H1N1pdm was predominant, Sep 2009 to Feb 2010. Finally, sensitivity analyses using a Serfling approach confirmed the age patterns reported in the main analysis, with excess respiratory estimates within 0–23% of those derived from the Poisson model (Table 1 and S2).
Comparison with the Historical A/H3N2 Pandemic
Based on the Serfling approach, we estimate that 27,600 and 42,600 excess respiratory and cardio-respiratory deaths respectively were attributable to the major A/H3N2 pandemic season in France in 1969–70 (rates of 44 and 68 deaths per 100,000 population, table 1). The proportions of deaths attributable to the A/H3N2 pandemic were 65% and 27% respectively for respiratory and cardio-respiratory mortality outcomes.
To provide a fair comparison of respiratory and cardio-respiratory mortality burden between the A/H3N2 and the 2009–10 A/H1N1 pandemics, we considered estimates derived from the same Serfling approach. The A/H3N2 pandemic was associated with substantially higher excess mortality impact than the A/H1N1 pandemic across all age groups and outcomes (Figure 4 and Table 1). Excess mortality rates were between 42 and 60-fold higher for the A/H3N2 pandemic than for the A/H1N1 pandemic depending on the outcome.
A mortality age shift was noted in both the A/H3N2 and A/H1N1 pandemics. While the proportions of excess respiratory deaths occurring in people under 65 years were 23% and 35% during the A/H3N2 and A/H1N1 pandemics, respectively, this age group accounted for only 7% of excess respiratory deaths during seasonal epidemics (p<0.0001). Analysis of cardio-respiratory outcomes suggests a similar age shift in both pandemic seasons (22% and 23% of excess deaths in people under 65 yrs in the A/H3N2 and A/H1N1 pandemics, respectively, vs. 6% during seasonal epidemics).
This is the first study to provide a systematic comparison of the age-specific mortality burden of the 2009–10 A/H1N1 pandemic with the historical A/H3N2 pandemic, based on detailed comparison of mortality outcomes and modeling approaches. Our burden estimates are based on a rigorous model selection approach considering several indicators of influenza activity. We estimate that the first season of circulation of the novel A/H1N1 pandemic virus in France (May 2009-May 2010) was associated with 0.98 excess respiratory deaths (95% CI: 0.20; 1.9) per 100,000. Relative to seasonal influenza, individuals aged 5–24 yrs were the most severely affected by the pandemic and experienced excess respiratory death rates 10.6 fold higher than those of seasonal epidemics. In contrast, the pandemic mortality burden in seniors over 65 years old was 3.8 fold lower than that of typical inter-pandemic seasons. The 2009–10 A/H1N1 pandemic was substantially milder than the A/H3N2 pandemic in France, consistently across all age groups and mortality indicators.
The French authorities identified 264 laboratory-confirmed pandemic A/H1N1 influenza deaths between July 1st, 2009 and February 28, 2010  and 349 deaths listing an influenza code on the death certificate . Our relatively conservative estimate of 613 pandemic excess respiratory deaths suggests that nearly 1 in 2 influenza-related deaths was laboratory confirmed, in a period where testing was considerably strengthened. For comparison purpose, a Mexican study suggested that only 1 in 7 influenza-related deaths were captured in laboratory-confirmed surveillance . In France, the proportion of deaths occurring among seniors over 65 yrs was 26% based on laboratory-confirmed deaths , 44% based on deaths specifically coded as influenza  and 68% based on excess mortality models. The age distribution difference could be explained by a lesser propensity to confirm and diagnose influenza-related deaths among seniors than among younger individuals, as was reported for Mexico . Alternatively, we could have overestimated excess respiratory death rates in seniors. However, a rise in P&I and respiratory mortality in this age group coincided with a period of intense pandemic activity in November-December 2009, lending validity to our estimates. In addition, we obtained a similar age distribution of influenza-related pandemic deaths by the Serfling approach (65% of excess respiratory deaths among seniors).
Overall, the mortality burden of the 2009–10 A/H1N1 pandemic in France was particularly mild, relative to the impact of seasonal influenza, except in the 5–44 yrs age group. Older populations did not experience significant excess cardio-respiratory and all cause mortality coinciding with the period of intense pandemic A/H1N1 activity. Most remarkably, the only age group to experience significant all-cause excess mortality rates was the 5–24 yrs, while all-cause estimates were negative in all other age groups, consistent with mortality patterns in other European countries , . We therefore consider that P&I and respiratory deaths are the most reliable outcomes to model the age-specific mortality burden of the A/H1N1 pandemic in Europe and note that previously published all-cause mortality estimates from high-income countries in North America, Europe, and Asia may lack precision –, , .
Our data confirm that the main waves of the A/H3N2 and A/H1N1 pandemics in France were characterized by mortality age shifts, with a significant increase in the proportion of excess respiratory deaths among people under 65 years during pandemics, relative to inter-pandemic seasons. Our results and those of others reinforce one of the signature features of influenza pandemics – a younger than usual age distribution of influenza-related deaths , –. In pandemic situations, a more appropriate estimation of disease burden may be the years of life lost approach, which integrates the age distribution of deaths with excess mortality estimates . We applied the years of life lost approach to French data, revealing a 2–2.6 fold higher impact for the 2009 pandemic than for seasonal epidemics in people under 45 yrs, corroborating findings from other settings (table 2, , ).
There are several limitations to our study. First, we developed a Poisson regression model linking mortality with influenza activity data, which is a more specific approach to estimate influenza-related mortality than the traditional Serfling method . However, this approach may be less widely applicable than the Serfling method in that it requires several years of reliable weekly viral activity data collected with systematic surveillance criteria, which is not always the case in pandemic situations . Also, we obtained negative 2009–10 pandemic estimates for less specific outcomes in individuals over 45 yrs by the Poisson approach, while the Serfling method gave positive estimates, in particular for cardio-respiratory deaths. Another caveat relates to the use of ILI as a proxy of influenza activity in Poisson models. Sensitivity analyses identified ILI as the best covariate to model respiratory deaths, over any indicator relying on influenza virus surveillance data. The poorer statistical fit of models incorporating the influenza virus surveillance data, even after standardization, smoothing, and allowing for lags, could be related to age biases in influenza testing or lack of information on circulation of other respiratory viruses. Specifically, if the majority of virus specimens were collected from children, weekly viral surveillance activity could be asynchronous with weekly senior mortality rates and decrease model fit. Our models driven by ILI, which were selected based on statistical grounds, may not be the most biologically relevant in that they may include the contribution of various pathogens and overestimate the contribution of influenza. However, ILI data do not introduce an age bias if all age groups are represented and the probability of influenza infection given ILI is similar across age groups . Most importantly, models driven by viral activity data suggest a similar pandemic mortality burden as models driven by ILI, with an excess mortality rate estimated at 0.93 per 100,000.
A third limitation relates to the fact that we did not include weekly indicators of respiratory syncytial virus (RSV) activity and other pathogens proxies in our models. Lack of RSV information could explain why our mortality estimates for seasonal influenza lack significance in children under 4 years, an age group where RSV predominates . RSV-coded deaths were concentrated in January-March 2010 in France, suggesting that RSV activity did not overlap with the period of intense pandemic A/H1N1 virus activity in late fall. It is possible however that RSV circulation was displaced by pandemic activity , potentially affecting our estimate of baseline mortality in younger age groups. Similarly, the contribution of other seasonal factors such Streptococcus Pneumoniae and cold spells would be more important in winter, several weeks and months after pandemic activity had peaked . Overall, many countries lack age-specific information on various respiratory pathogen activity; such data will be useful to improve excess mortality models in the future .
A final caveat of our study is the lack of adjustment on time trends in health care and treatment, which matter for comparison of mortality rates associated with recent and historical pandemics. During the historical A/H3N2 pandemic, pediatric intensive care units did not exist in some countries and some of the children who survived in 2009 would have probably succumbed during the earlier pandemic. Treatment of secondary bacterial infections  and complications has greatly improved since the 1960 s, together with widespread use of antivirals in the community and hospital settings, and occasional use of extracorporeal membrane oxygenation. Although our analyses did not adjust for these factors, the order of magnitude difference between mortality estimates for the two pandemics suggests factors that go beyond healthcare and treatment. The substantial severity of the A/H3N2 pandemic in France, relative to the 2009 A/H1N1 pandemic, could be explained by differences in virus virulence or prior immunity. The burden of the 2009 pandemic was likely mitigated by substantial cross protective immunity with previously circulating A/H1N1 viruses –, ,  and/or cross-subtypic immunity from previous exposure to A/H3N2. By contrast, immunity from vaccination was minimal in the first wave of the 2009 pandemic in most countries due to delays in vaccine production and delivery. Pandemic vaccination was initiated on November 2nd 2009 in France, just 3 weeks before peak viral activity, and only 7.9% of the population was vaccinated by February 2010 .
Important between-country variation in the mortality burden of historical pandemics has been reported , . Our study reveals the particularly high excess mortality impact of the A/H3N2 pandemic in France, relative to more recent epidemic or pandemic seasons. Previous work has highlighted the severity of the A/H3N2 pandemic in Western Europe, as compared with the Americas , . To gauge geographical variation in 2009 pandemic mortality burden, we reviewed published national studies providing age-specific mortality estimates for the 2009–10 A/H1N1 season (Table 3). Despite between-country variations in absolute estimates, most countries reported lower all-age excess mortality burden for the 2009 pandemic than for recent seasonal epidemics. School-age children and young adults experienced atypical mortality increase in most countries, including the UK, US and Mexico , , , , , consistent with our data. Similar results were found by pooling all-cause mortality data from eight European countries or regions . In contrast to other European studies, the Netherlands reported unusually high pandemic burden in children under 4 yrs . All countries reported lower than expected mortality burden among seniors, with a ratio of pandemic to epidemic excess mortality rates ranging from 0.16–0.34 across 6 countries on 3 continents. We note however that of the 7 studies providing excess mortality estimates based on vital statistics , , , , , 5 studies focus on all-cause mortality. This is unfortunate because our study suggests that all-cause mortality may not provide the most precise estimates of 2009 pandemic burden, especially in Europe where the pandemic was particularly mild. Moreover, not all studies provide 95% confidence limits on the estimates, and methodologies differ between studies. Finally, little information on the mortality burden of past and recent pandemics is available from low-income countries of Asia and Africa, which is a key area for future research.
Our study adds to the growing body of evidence on the burden of the 2009 A/H1N1 pandemic and suggests a relatively low excess mortality impact in France compared to seasonal influenza, except in school-age children and young adults. Our results suggest that it is important to analyze cause-specific mortality outcomes, such as respiratory deaths, to accurately capture the burden of influenza in mild seasons, such as the 2009 pandemic. All-cause mortality models provided negative estimates for the pandemic in France, which is clearly imprecise as at least 264 laboratory-confirmed deaths are directly attributable to pandemic influenza A/H1N1. Estimates from many more settings, including low and middle income countries, are needed before the full spectrum of geographical variation in pandemic mortality burden can be established and the socio-economic and population determinants of disease burden are fully elucidated.
Weekly pneumonia and influenza death rates by age group, France, July 1997-June 2010. Observed death rates (black line) and predicted death rates (blue line) by a Poisson model integrating seasonal terms, time trends and influenza activity data. Baseline mortality rates predicted by the Poisson model in the absence of influenza activity are indicated by a dashed blue line. Death rates were standardized to the population of 2009.
Codes used to identify mortality due to pneumonia and influenza, respiratory causes, respiratory and cardiac, and all causes in France, 1968–2010, based on the 8, 9 and 10th revisions of the International Classification of Diseases (ICD). Underlying causes of death were considered. No important coding change occurred at the transition between ICD-8 and ICD-9 in France. To account for the transition from ICD-9 to ICD-10, we used conversion factors published by CépiDc in France .
Excess mortality rates associated with the severe 1999–2000 seasonal A/H3N2 epidemic by age group and death outcome. Estimates are based on the Serfling model.
We thank the general practitioners of //the French Sentinelles network//for providing weekly data on influenza-like illness, Clément TurbulinThierry Blanchon and Thomas Hanslik for facilitating contacts with the French Sentinels System, Rosemary Delabre for critical review of the manuscript. This research was conducted in the context of the Multinational Influenza Seasonal Mortality Study (MISMS), an ongoing international collaborative effort to understand influenza epidemiological and evolutionary patterns, led by the Fogarty International Center, National Institutes of Health (http://www.origem.info/misms/index.php).
Conceived and designed the experiments: ML FC CV LS. Performed the experiments: ML CV FC. Analyzed the data: ML GR CV. Contributed reagents/materials/analysis tools: GR ML. Wrote the paper: ML FC GR MM LS CV.
- 1. World Health Organisation (2009) Available: http://www.who.int/csr/don/2010_02_12/en/index.html.Accessed 2012 Jul 12.
- 2. Castilla J, Etxeberria J, Ardanaz E, Floristan Y, Lopez Escudero R, et al. (2010) Estimating the impact of the 2009 influenza A(H1N1) pandemic on mortality in the elderly in Navarre, Spain. Euro Surveill, 15(5).
- 3. Muscatello DJ, Cretikos MA, Macintyre CR (2010) All-cause mortality during first wave of pandemic (H1N1) 2009, New South Wales, Australia, 2009. Emerg Infect Dis 16(9): 1396–1402.
- 4. Viboud C, Miller M, Olson D, Osterholm M, Simonsen L (2010) Preliminary Estimates of Mortality and Years of Life Lost Associated with the 2009 A/H1N1 Pandemic in the US and Comparison with Past Influenza Seasons. PLoS Curr:RRN1153.
- 5. Yang L, Chan KP, Cowling BJ, Chiu SS, Chan KH, et al. (2011) Excess mortality associated with the 2009 pandemic of influenza A(H1N1) in Hong Kong. Epidemiol Infect: 1–9.
- 6. Charu V, Chowell G, Palacio Mejia LS, Echevarria-Zuno S, Borja-Aburto VH, et al. (2011) Mortality burden of the A/H1N1 pandemic in Mexico: a comparison of deaths and years of life lost to seasonal influenza. Clin Infect Dis 53(10): 985–993.
- 7. Wielders CC, van Lier EA, van ‘t Klooster TM, van Gageldonk-Lafeber AB, van den Wijngaard CC, et al. (2012) The burden of 2009 pandemic influenza A(H1N1) in the Netherlands. Eur J Public Health 22(1): 150–157.
- 8. Hardelid P, Andrews N, Pebody R (2011) Excess mortality monitoring in England and Wales during the influenza A(H1N1) 2009 pandemic. Epidemiol Infect 139(9): 1431–1439.
- 9. van den Wijngaard C, van Asten L, Koopmans M, van Pelt W, Nagelkerke N, et al. (2012) Comparing Pandemic to Seasonal Influenza Mortality: Moderate Impact Overall but High Mortality in Young Children. PLoS One, 7(2).
- 10. Shrestha SS, Swerdlow DL, Borse RH, Prabhu VS, Finelli L, et al. (2011) Estimating the burden of 2009 pandemic influenza A (H1N1) in the United States (April 2009-April 2010). Clin Infect Dis 52 Suppl 1: S75–82.
- 11. Lemaitre M, Carrat F (2010) Comparative age distribution of influenza morbidity and mortality during seasonal influenza epidemics and the 2009 H1N1 pandemic. BMC Infect Dis 10: 162.
- 12. Donaldson LJ, Rutter PD, Ellis BM, Greaves FE, Mytton OT, et al. (2009) Mortality from pandemic A/H1N1 2009 influenza in England: public health surveillance study. Bmj 339: b5213.
- 13. Simonsen L (1999) The global impact of influenza on morbidity and mortality. Vaccine 17 Suppl 1: S3–10.
- 14. Serfling RE (1963) Methods for Current Statistical-Analysis of Excess Pneumonia-Influenza Deaths. Public Health Reports 78(6): 494–506.
- 15. Thompson WW, Shay DK, Weintraub E, Brammer L, Cox N, et al. (2003) Mortality associated with influenza and respiratory syncytial virus in the United States. JAMA 289(2): 179–186.
- 16. Thompson WW, Weintraub E, Dhankhar P, Cheng PY, Brammer L, et al. (2009) Estimates of US influenza-associated deaths made using four different methods. Influenza Other Respi Viruses 3(1): 37–49.
- 17. Viboud C, Grais RF, Lafont BA, Miller MA, Simonsen L (2005) Multinational impact of the 1968 Hong Kong influenza pandemic: evidence for a smoldering pandemic. J Infect Dis 192(2): 233–248.
- 18. Centre d’épidémiologie sur les causes médicales de décès Available: http://www.cepidc.vesinet.inserm.fr/.Accessed 2012 Jul 12.
- 19. Pavillon G, Boileau J, Renaud G, Lefèvre H, Jougla E (2004) Bridge coding ICD9–ICD10 and effects on French mortality data. In: WHO FAMILY OF INTERNATIONAL CLASSIFICATIONS NETWORK MEETING. Reykjavik, Iceland.
- 20. Institut National de la Statistique et des Etudes Economiques Available: http://www.insee.fr/fr/ffc/docs_ffc/elp_reg_dep.htm.Accessed 2012 Jul 12.
- 21. FluNet - World Health Organization [http://apps.who.int/globalatlas/dataQuery/default.asp], http://apps.who.int/globalatlas/dataQuery/default.asp.
- 22. Groupe Régionaux d’Observation de la Grippe (2011) Available: http://orsbretagne.typepad.fr/GROG-2011/GROG34.pdf.Report of 27 April 2011.
- 23. Groupe régionaux d’observation de la grippe Available: http://www.grog.org/.Accessed 2012 Jul 12.
- 24. Centre national de référence du virus influenzae Available: http://www.pasteur.fr/ip/easysite/pasteur/fr/sante/centres-nationaux-de-reference-et-centres-collaborateurs-de-l-oms/cnr-et-ccoms/cnr-de-la-grippe-france-nord-ccoms-grippe-et-autres-virus-respiratoires/identite-et-coordonnees.Accessed 2012 Jul 12.
- 25. Goldstein E, Cobey S, Takahashi S, Miller JC, Lipsitch M (2011) Predicting the epidemic sizes of influenza A/H1N1, A/H3N2, and B: a statistical method. PLoS Med 8(7): e1001051.
- 26. Denoeud L, Turbelin C, Ansart S, Valleron AJ, Flahault A, et al. (2007) Predicting pneumonia and influenza mortality from morbidity data. PLoS One 2(5): e464.
- 27. Réseau Sentinelles Available: http://websenti.u707.jussieu.fr/sentiweb/.Accessed 2012 Jul 12.
- 28. Falchi A, Turbelin C, Andreoletti L, Arena C, Blanchon T, et al. (2011) Nationwide surveillance of 18 respiratory viruses in patients with influenza-like illnesses: a pilot feasibility study in the French Sentinel Network. J Med Virol 83(8): 1451–1457.
- 29. Cauchemez S, Valleron AJ, Boelle PY, Flahault A, Ferguson NM (2008) Estimating the impact of school closure on influenza transmission from Sentinel data. Nature 452(7188): 750–754.
- 30. Costagliola D, Flahault A, Galinec D, Garnerin P, Menares J, et al. (1991) A routine tool for detection and assessment of epidemics of influenza-like syndromes in France. Am J Public Health 81(1): 97–99.
- 31. Crepey P, Barthelemy M (2007) Detecting robust patterns in the spread of epidemics: a case study of influenza in the United States and France. Am J Epidemiol 166(11): 1244–1251.
- 32. Estimates of deaths associated with seasonal influenza –- United States, 1976–2007. MMWR Morb Mortal Wkly Rep (2010) 59(33): 1057–1062.
- 33. Goldstein E, Viboud C, Charu V, Lipsitch M (2012) Improving the estimation of influenza-related mortality over a seasonal baseline. Epidemiology. In press.
- 34. Lui KJ, Kendal AP (1987) Impact of influenza epidemics on mortality in the United States from October 1972 to May 1985. Am J Public Health 77(6): 712–716.
- 35. Simonsen L, Clarke MJ, Williamson GD, Stroup DF, Arden NH, et al. (1997) The impact of influenza epidemics on mortality: introducing a severity index. Am J Public Health 87(12): 1944–1950.
- 36. Vicente P, Aouba A, Lévy-Bruhl D, Jougla E, Rey G (2011) Spécificité des caractéristiques de la mortalité liée à la grippe lors de la pandémie de grippe A(H1N1) en 2009–2010 en France. BEH 1: 1–5.
- 37. Vaux S, Brouard C, Fuhrman C, Turbelin C, Cohen J-M, et al. (2010) Dynamique et impact de l’épidémie A(H1N1) 2009 en France métropolitaine, 2009–2010. BEH.
- 38. Nielsen J, Mazick A, Glismann S, Molbak K (2010) Excess mortality related to seasonal influenza and extreme temperatures in Denmark, 1994–2010. BMC Infect Dis 11: 350.
- 39. Miller MA, Viboud C, Balinska M, Simonsen L (2009) The signature features of influenza pandemics–implications for policy. N Engl J Med 360(25): 2595–2598.
- 40. Simonsen L, Clarke MJ, Schonberger LB, Arden NH, Cox NJ, et al. (1998) Pandemic versus epidemic influenza mortality: a pattern of changing age distribution. J Infect Dis 178(1): 53–60.
- 41. Chowell G, Echevarria-Zuno S, Viboud C, Simonsen L, Tamerius J, et al. (2011) Characterizing the epidemiology of the 2009 influenza A/H1N1 pandemic in Mexico. PLoS Med 8(5): e1000436.
- 42. Miller MA, Viboud C, Olson DR, Grais RF, Rabaa MA, et al. (2008) Prioritization of influenza pandemic vaccination to minimize years of life lost. J Infect Dis 198(3): 305–311.
- 43. Carrat F, Tachet A, Rouzioux C, Housset B, Valleron AJ (1999) Evaluation of clinical case definitions of influenza: detailed investigation of patients during the 1995–1996 epidemic in France. Clin Infect Dis 28(2): 283–290.
- 44. Casalegno JS, Ottmann M, Bouscambert-Duchamp M, Valette M, Morfin F, et al. (2010) Impact of the 2009 influenza A(H1N1) pandemic wave on the pattern of hibernal respiratory virus epidemics, France, 2009. Euro Surveill, 15(6).
- 45. Ballester F, Corella D, Perez-Hoyos S, Saez M, Hervas A (1997) Mortality as a function of temperature. A study in Valencia, Spain, 1991–1993. Int J Epidemiol 26(3): 551–561.
- 46. Simonsen L, Viboud C (2012) The art of estimating mortality from winter-seasonal pathogens. J Infect Dis (In press).
- 47. Taubenberger JK, Morens DM (2006) 1918 Influenza: the mother of all pandemics. Emerg Infect Dis 12(1): 15–22.
- 48. Miller E, Hoschler K, Hardelid P, Stanford E, Andrews N, et al. (2010) Incidence of 2009 pandemic influenza A H1N1 infection in England: a cross-sectional serological study. Lancet 375(9720): 1100–1108.
- 49. Lemaitre M, Leruez-Ville M, De Lamballerie XN, Salez N, Garrone P, et al. (2011) Seasonal H1N1 2007 influenza virus infection is associated with elevated pre-exposure antibody titers to the 2009 pandemic influenza A (H1N1) virus. Clin Microbiol Infect 17(5): 732–737.
- 50. Guthmann J, Bone A, Nicolau J, Lévy-Bruhl D (2010) Insufficient influenza A(H1N1)2009 vaccination coverage in the global population and high risk groups during the 2009–2010 pandemic in France, Institut de Veille sanitaire. BEH.
- 51. Murray CJ, Lopez AD, Chin B, Feehan D, Hill KH (2006) Estimation of potential global pandemic influenza mortality on the basis of vital registry data from the 1918–20 pandemic: a quantitative analysis. Lancet 368(9554): 2211–2218.
- 52. Rizzo C, Bella A, Viboud C, Simonsen L, Miller MA, et al. (2007) Trends for influenza-related deaths during pandemic and epidemic seasons, Italy, 1969–2001. Emerg Infect Dis 13(5): 694–699.
- 53. Mazick A, Gergonne B, Wuillaume F, Danis K, Vantarakis A, et al. (2010) Higher all-cause mortality in children during autumn 2009 compared with the three previous years: pooled results from eight European countries. Euro Surveill, 15(5).