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Infectious Diseases

Case Fatality Risk of Influenza A (H1N1pdm09): A Systematic Review

Wong, Jessica Y.; Kelly, Heath; Ip, Dennis K. M.; Wu, Joseph T.; Leung, Gabriel M.; Cowling, Benjamin J.

Author Information
doi: 10.1097/EDE.0b013e3182a67448

In April 2009, the World Health Organization declared a formal “public health emergency of international concern,” marking the start of an international public health response to the first influenza pandemic of the 21st century. One of the immediate priorities was to quantify the transmissibility of the new pandemic influenza A (H1N1pdm09) virus (denoted H1N1pdm09 hereafter) and the seriousness of infection with this virus because these two epidemiologic measures in combination determine the severity of the pandemic in the absence of control measures.1,2 Whereas a number of transmissibility estimates, based on the reproduction number R, were published with broad agreement from the early stages of the pandemic3; there was far greater difficulty in estimating the seriousness of infections. In the report of the World Health Organization’s Review Committee on the functioning of the 2005 International Health Regulations in relation to H1N1pdm09, Fineberg et al4 identified “the absence of a consistent, measurable and understandable depiction of severity of the pandemic” as one of the major shortcomings of the international public health response.

One measure of the seriousness of infection is the case fatality risk (CFR).5 We prefer “risk” to describe this probability, namely the conditional probability of mortality among classified “cases.” Strictly speaking, the case fatality risk is neither a rate (because there is no unit of time in the denominator) nor a ratio, which applies principally to a relationship between two measures of the same kind (eg, an odds ratio). There are complications in defining and estimating the case fatality risk, associated with both the numerator (deaths) and the denominator (persons classified as cases).1,6 We reviewed published estimates of the case fatality risk of H1N1pdm09 to identify technical challenges in its estimation and to offer recommendations for estimating this measure in the future.

METHODS

Search Strategy

This systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.7 Studies reporting estimates of the case fatality risk of H1N1pdm09 were retrieved from Medline (PubMed) and Embase on 26 April 2013. We searched for articles using the following free search terms in “All fields”:

#1: “fatalit*” OR “case-fatality*” OR “severity” OR “mortality” OR “death” OR “lethal*” OR “virulence”

#2: “influenza” OR “flu” OR “H1N1*” OR “pH1N1*” OR “pdmH1N1*” OR “nH1N1*”

#3: #1 AND #2

The search was limited to studies published after 1 April 2009 (subsequent to the emergence of H1N1pdm09) through 26 April 2013. Additional relevant studies identified by the authors were manually retrieved from the database.

Study Selection

The titles of all papers identified by the search strategy were independently screened by two authors (J.Y.W. and B.J.C.). Abstracts of potentially relevant papers and the full text of manuscripts were reviewed for eligibility. Articles in all languages were selected for assessment if at least one statistical estimate of the case fatality risk for H1N1pdm09 was presented and described in the article. Eligible articles reported one or more population-based estimates of the case fatality risk. We excluded studies that reported only estimates in hospitalized patients or in population subgroups such as pregnant women or those at higher risk of severe illness if infected (eg, persons with chronic health conditions).

Definition of Case Fatality Risk

We defined the case fatality risk as the conditional probability of death associated with H1N1pdm09 for cases that met a specified case definition. The case fatality risk for a population is estimated as the number of H1N1pdm09-associated deaths divided by the number of H1N1pdm09 cases in that population. The numerator could be counts or estimates of the number of deaths among laboratory-confirmed cases, the number of deaths among symptomatic cases, or indirect estimates of the total number of deaths associated with H1N1pdm09. The denominator could be counts or estimates of the number of laboratory-confirmed H1N1pdm09 cases, the number of symptomatic H1N1pdm09 cases, or the number of infections. We classified the denominator of the CFR as the estimated number of infections only when it was estimated based on serologic surveillance or included explicit adjustment for asymptomatic infections.

Data Extraction

All data were extracted onto a standardized form. The primary data were the estimates of the case fatality risk, the estimates or counts of the number of H1N1pdm09-associated deaths (numerator), and the estimates or counts of the number of H1N1pdm09 cases (denominator). Whenever available, we extracted case fatality risks stratified by age group. Although age groupings differed across studies, we defined children as those up to age 19, adults as those 20–64, and elderly as those 65 years or older.

Statistical Analysis

Statistical heterogeneity was assessed by the I2 statistic, with higher values signifying a greater degree of variation.8 Due to very substantial heterogeneity, we did not make pooled estimates of the case fatality risk. All analyses were conducted with R version 2.15.1 (R Foundation for Statistical Computing, Vienna, Austria) and the metafor package.9

RESULTS

Of the 12,197 papers initially identified, we examined 120 full-length articles, of which 70 were subsequently excluded (eTable, http://links.lww.com/EDE/A708) (Figure 1).6,10–58 The 50 articles we included, reporting a total of 77 case fatality risk estimates, are summarized in the Table. One-third (16/50) of the studies were published within the first 9 months of the pandemic (Figure 2). The lag time between the end of the particular study period and the publication date increased over time (median = 236 days) (eFigure 2, http://links.lww.com/EDE/A708). In total, our analysis was based on reports from 32 countries or regions, specifically Abu Dhabi, Argentina, Australia, Canada, Chile, Colombia, European Union, French Guiana, Germany, Greece, Guadeloupe, Hong Kong, India, Japan, Korea, Martinique, Mauritius, Mexico, New Zealand, Nepal, Netherlands, New Caledonia, Peru, Pacific Island countries, Reunion Island, Singapore, Spain, St. Martin, Taiwan, Thailand, United Kingdom, and the United States (including metropolitan Atlanta). In addition, three publications estimated the case fatality risk worldwide,54 for 45 specified countries,43 and for developed countries.57 Illustrating one of the potential sources of heterogeneity, the number of cases or infections adopted as denominators ranged from 172 to 61,000,000 (eFigure 1, http://links.lww.com/EDE/A708).

TABLE
TABLE:
Summary of Case Fatality Risk Studies of Influenza A (H1N1-2009) Included in the Systematic Review
FIGURE 1
FIGURE 1:
Flow diagram of study selection.
FIGURE 2
FIGURE 2:
Study dates (solid horizontal lines), eventual publication dates of the studies included in the review (points) compared with the histogram of confirmed H1N1pdm09 deaths reported to the World Health Organization (WHO) (underlying histogram). See Table for details of each study.

Overall, the case fatality risk estimates based on laboratory-confirmed cases were higher than the other estimates and published earlier (Figure 3). Estimates based on laboratory-confirmed cases ranged from 0 to 13,500 deaths per 100,000 cases, with very substantial heterogeneity (I2 = 99.97%). Most (25/29) of the risk estimates in this category fell within the range of 100 to 5000 deaths per 100,000 cases.

FIGURE 3
FIGURE 3:
Estimated risk of death by eventual publication dates of the studies included in the review (points with 95% confidence interval) compared with the histogram of confirmed H1N1pdm09 deaths reported to the World Health Organization (WHO) (underlying histogram).

There was also substantial heterogeneity among the 37 risk estimates based on symptomatic cases, ranging from 0 to 1200 deaths per 100,000 [I2 = 99.98%]. Most of the estimates in this category fell in the range of 5 to 50 deaths per 100,000 cases. In age-stratified analyses, risk estimates rose monotonically with age, from approximately one death per 100,000 symptomatic cases in children to approximately 1000 deaths per 100,000 symptomatic cases in the elderly, although with substantial variation in the estimates within each age group (Figure 4).

FIGURE 4
FIGURE 4:
Age-specific estimated risk of death.

In 14% (11/77) of the case fatality risk estimates, the denominator was based on an estimated number of infections. Among those 11 studies, 8 used denominators based on population serological studies, whereas the others used modeling or multiplier approaches. Those risks tended to remain stable over time, although no such estimates were published within the first year of the pandemic (Figure 3). Estimates ranged from 1 to 10 deaths per 100,000 infections, with substantial heterogeneity (I2 = 94.46%). The majority of studies adopted laboratory-confirmed deaths as the numerator (Table), whereas one study estimated that the number of “excess” deaths attributable to H1N1pdm09 was higher than the number of deaths of laboratory-confirmed cases, leading to a generally higher case fatality risk based on the excess-deaths numerator compared with the confirmed-deaths numerator.6 The increase in age-specific risk estimates by age based on infection were similar to those based on symptomatic cases (data not shown).

The highest case fatality risk estimates were observed in Argentina,30,42 Mexico,20,42 and Colombia.18 All of these studies reported case fatality risks among laboratory-confirmed cases. Apart from differences in case definition, it is not clear why estimates during the early stage of the pandemic were so high in Mexico (ranging from 100 to 5580 deaths per 100,000).20,26,28,29,42,55

DISCUSSION

There is very substantial heterogeneity in published estimates of case fatality risk for H1N1pdm09, ranging from <1 to >10,000 per 100,000 infections (Figure 3). Large differences were associated with the choice of case definition (denominator). Because influenza virus infections are typically mild and self-limiting, and a substantial proportion of infections are subclinical and do not require medical attention, it is challenging to enumerate all symptomatic cases or infections.2,45 In 2009, some of the earliest available information on fatality risk was provided by estimates based primarily on confirmed cases. However, because most H1N1pdm09 infections were not laboratory-confirmed, the estimates based on confirmed cases were up to 500 times higher than those based on symptomatic cases or infections (Figure 3). The consequent uncertainty about the case fatality risk—and hence about the severity of H1N1pdm09—was problematic for risk assessment and risk communication during the period when many decisions about control and mitigation measures were being made.

Whereas our review has focused on the case fatality risk, there are other measures of seriousness of infection that can be useful in assessing the risk associated with pandemic and seasonal influenza viruses. For example, the outbreak of H1N1pdm09 in a New York school in April–May 2009 was informative because none of the more than 800 students and staff with influenza-like illness had severe illness,59 implying a low risk of hospitalization among infected cases. At the other end of the spectrum of disease, a description of the risk of mortality among hospitalized patients could be affected by flexible thresholds for admission depending on demand and capacity. A comprehensive seriousness profile could include age-specific estimates of the risks of illness, hospitalization, and mortality on a per-infection basis. In addition, although transmissibility and seriousness profiles can allow prediction of the number of serious illnesses, the availability of healthcare resources would also affect the likely impact of an epidemic or pandemic.60

Arguably one of the ideal estimates of seriousness of infection is the infection fatality risk,6 that is, the case fatality risk based on infections as denominator. This risk estimate can be compared across populations without concerns over differences in symptom perceptions and reporting, healthcare seeking behaviors, or laboratory-testing capacity.61 Estimates of the infection fatality risk can also be used in combination with estimates of transmissibility to directly inform predictions of population impact.62 The use of the term “infection fatality” differentiates this risk estimate from the case fatality risk since the asymptomatic, undetected and undiagnosed infections included in the denominator would not appear as “cases” under typical case definitions. However, few estimates of the infection fatality risk were available for H1N1pdm09, and none was available early in the pandemic (Figure 3). Serologic studies,63 or estimates of the cumulative incidence of symptomatic infections in a population adjusted for the proportion of infections that are asymptomatic,52,64 can be used to estimate the denominator for the infection fatality risk. However, one has to define how to estimate infection rates from serologic data, and there is not yet consensus on the best approach.

Given that estimates of the infection fatality risk are unlikely to be available early enough for decision making in a pandemic, a more feasible solution may be to measure the case fatality risk among symptomatic cases. One obvious limitation is that differences in the definition of a symptomatic case are likely to affect comparability and limit generalizability. In our review, the case fatality risk estimates for symptomatic cases covered a wide range (eFigure 1, http://links.lww.com/EDE/A708). In practice, estimation of fatality risk among symptomatic cases could be based on outpatient surveillance data in combination with laboratory data, and with adjustment for the proportion of symptomatic cases not presenting for medical attention. These may vary by age, sex, location, and other factors.13,17,24,44,45,49,52 Such estimates based on symptomatic cases may provide timely but imprecise estimates of seriousness for risk assessment.

In addition to differences in case fatality risk estimates due to the differences in case definition (denominator), the definition of the numerator is also an important issue. Almost all of the studies in our review based the numerator on deaths among patients with laboratory-confirmed influenza infection. In contrast, most estimates of the population impact of seasonal influenza epidemics have been based on estimation of the number of excess deaths associated with influenza (ie, estimated deaths), with the greatest annual impact in the elderly—despite influenza virus infections rarely being confirmed in this age group.65,66 The use of excess deaths rather than laboratory-confirmed deaths in the numerator of the infection fatality risk would theoretically be justified because the denominator includes all infections and not only those with a positive laboratory result. For a similar reason, deaths of patients with laboratory-confirmed infection might be a more appropriate numerator for the case fatality risk based on symptomatic case denominators.

In addition to the differences in risk estimates due to differences in numerators and denominators, our review also identified substantial variability by age, ranging from approximately one death per 100,000 symptomatic cases in children to 1000 deaths per 100,000 symptomatic cases in the elderly (Figure 4). This variability complicates the interpretation of the overall risk and the comparison of risk among countries because overall risk would depend on the age structure of the population and the age distribution of infections.67,68 Age standardization is an accepted method for comparing incidence rates among the populations of various countries,69 but no similar approach has yet been discussed or recommended for comparison of case fatality risks.

Further limitations of our review are that most of the included estimates corresponded to the initial epidemics of H1N1pdm09 in 2009 (Figure 2). Fewer data are available on possible changes in the case fatality risk after that period, although there have been reports of up to four epidemic waves in some countries.20,70 We excluded studies that focused on specific risk groups (eg, those with underlying diseases) to improve consistency among studies. There was also substantial heterogeneity among estimates of the case fatality risk, depending particularly on case definition (eFigure 1, http://links.lww.com/EDE/A708). We could not determine whether these differences were artefactual or real, perhaps related to differences in the prevalence of underlying risk factors for severe illness in various populations.71,72 Further work could attempt to identify additional factors that explain this heterogeneity, for example, by exploring very different estimates of risk apparently based on the same case definition (eg, from Argentina,30,42 Mexico,20,26,28 and the United States).29,41,42

Results of this review point to possible improvements in future studies of the case fatality risk. First, there is a problem in using confirmed cases as the denominator of CFR for influenza, given that most infections are mild and do not present for medical attention. Because it is not feasible to diagnose all suspected cases with laboratory testing except at the very beginning of a pandemic,2 it is unrealistic that risk estimates based on confirmed cases can be consistently calculated and remain directly comparable over time, age groups, and location. We suggest avoiding entirely the use of case fatality risk based on confirmed cases. The case fatality risk based on symptomatic cases would provide a more reliable early assessment of seriousness for seasonal influenza or the next influenza pandemic. Second, estimation of seriousness in real-time is complicated by delays in reporting and analysis. Estimation of the case fatality risk based on confirmed deaths and symptomatic cases may be possible if relevant models can be prepared in advance and quickly fitted to available data during the pandemic. We have previously discussed real-time estimation of the cumulative incidence of infection based on serologic data.73 This would form the denominator of the infection fatality risk, but, as noted previously, this is unlikely to be available early in the pandemic.

In preparation for the next influenza pandemic, it is essential to reach a consensus on how to define and measure the seriousness of infection (an important indicator of the severity of the pandemic), and whether the analysis can be based entirely on estimates of age-specific risk of death among cases. The consistent estimates of the infection fatality risk at around 1 to 10 deaths per 100,000 infections identified in our review (Figure 3) may represent the seriousness of H1N1pdm09 in developed countries where data were available. Similar estimates for seasonal influenza viruses, however, are not available for comparison, and neither are estimates from less developed countries in which the seriousness profile would likely be higher.74

An alternative assessment of seriousness of infection may be to compare an absolute value of case fatality risk against an arbitrarily defined threshold value.1 In either case, the experience from 2009 indicates that explicit and consistent definitions are required to permit comparisons. As highlighted by the Fineberg report,4 updated pandemic plans must clarify how severity will be estimated and interpreted. Improvements in measuring seriousness of infection, perhaps within a multidimensional metric not limited to the case fatality risk, could be a valuable objective of the new World Health Organization Pandemic Influenza Preparedness Framework. Our review also identified substantial delays between study completion dates and publication dates. A mechanism for early sharing of data that does not interfere with subsequent publication would be welcome.

ACKNOWLEDGMENTS

We thank Vicky Fang and Lincoln Lau for technical support. We also thank Eric Lau and Peng Wu for helpful discussions.

REFERENCES

1. Van Kerkhove MD, Asikainen T, Becker NG, et al. Studies needed to address public health challenges of the 2009 H1N1 influenza pandemic: insights from modeling. PLoS Med. 2010;7:e1000275
2. Lipsitch M, Hayden FG, Cowling BJ, Leung GM. How to maintain surveillance for novel influenza A H1N1 when there are too many cases to count. Lancet. 2009;374:1209–1211
3. Boëlle PY, Ansart S, Cori A, Valleron AJ. Transmission parameters of the A/H1N1 (2009) influenza virus pandemic: a review. Influenza Other Respi Viruses. 2011;5:306–316
4. World Health Organization. . >Implementation of the International Health Regulations (2005): Report of the Review Committee on the Functioning of the International Health Regulations (2005) in relation to Pandemic (H1N1) 2009. Geneva: World Health Organization; 2011. Available at: http://apps.who.int/gb/ebwha/pdf_files/WHA64/A64_10-en.pdf Accessed 14 February 2013
5. Nishiura H. Case fatality ratio of pandemic influenza. Lancet Infect Dis. 2010;10:443–444
6. Wong JY, Wu P, Nishiura H, et al. The infection fatality risk of pandemic influenza A(H1N1) in Hong Kong in 2009. Am J Epidemiol. 2013;177:834–840
7. Moher D, Liberati A, Tetzlaff J, Altman DGPRISMA Group. . Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. J Clin Epidemiol. 2009;62:1006–1012
8. Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med. 2002;21:1539–1558
9. Metafor. . Meta-analysis package for R [computer program]. 2011 Version 1.6-0
10. Abdalla E, HabteMariam T, Nganwa D, et al. Epidemiology of influenza A 2009 H1N1 virus pandemic in the U.S. J Health Care Poor Underserved. 2011;22(suppl 4):39–60
11. Adhikari BR, Shakya G, Upadhyay BP, et al. Outbreak of pandemic influenza A/H1N1 2009 in Nepal. Virol J. 2011;8:133
12. Ahmed F, Al Hosani F, Al Mannaie A, Harrison O. Early outcomes of pandemic influenza (H1N1) 2009 surveillance in Abu Dhabi Emirate, May-August 2009. East Mediterr Health J. 2012;18:31–36
13. Baker MG, Wilson N, Huang QS, et al. Pandemic influenza A(H1N1)v in New Zealand: the experience from April to August 2009. Euro Surveill. 2009;14:19319
14. Balaganesakumar SR, Murhekar MV, Swamy KK, et al. Risk factors associated with death among influenza A (H1N1) patients, Tamil Nadu, India, 2010. J Postgrad Med. 2013;59:9–14
15. Bandaranayake D, Huang QS, Bissielo A, et al. Risk factors and immunity in a nationally representative population following the 2009 influenza A(H1N1) pandemic. PLoS One. 2010;5:e13211
16. Bandaranayake D, Jacobs M, Baker M, et al. The second wave of 2009 pandemic influenza A(H1N1) in New Zealand, January-October 2010. Euro Surveill. 2011;16:19788
17. Brooks-Pollock E, Tilston N, Edmunds WJ, et al. Using an online survey of healthcare-seeking behaviour to estimate the magnitude and severity of the 2009 H1N1v influenza epidemic in England. BMC Infect Dis. 2011;11:68
18. Castro-Jiménez MA, Castillo-Pabón JO, Rey-Benito GJ, et al.Virology Group; Communicable Diseases Surveillance Group. Epidemiologic analysis of the laboratory-confirmed cases of influenza A(H1N1)v in Colombia. Euro Surveill. 2009;14:19284
19. Chen CJ, Lee PI, Chang SC, et al. Seroprevalence and severity of 2009 pandemic influenza A H1N1 in Taiwan. PLoS One. 2011;6:e24440
20. Chowell G, Echevarria-Zuno S, Viboud C, et al. Characterizing the epidemiology of the 2009 influenza A/H1N1 pandemic in Mexico. PLoS Med. 2011;8:e1000436
21. Cutter JL, Ang LW, Lai FY, Subramony H, Ma S, James L. Outbreak of pandemic influenza A (H1N1-2009) in Singapore, May to September 2009. Ann Acad Med Singapore. 2010;39:273–210
22. Dawood FS, Hope KG, Durrheim DN, et al. Estimating the disease burden of pandemic (H1N1) 2009 virus infection in Hunter New England, Northern New South Wales, Australia, 2009. PLoS One. 2010;5:e9880
23. de Silva UC, Warachit J, Waicharoen S, Chittaganpitch M. A preliminary analysis of the epidemiology of influenza A(H1N1)v virus infection in Thailand from early outbreak data, June-July 2009. Euro Surveill. 2009;14:19292
24. Donaldson LJ, Rutter PD, Ellis BM, et al. Mortality from pandemic A/H1N1 2009 influenza in England: public health surveillance study. BMJ. 2009;339:b5213
25. Doshi SS, Stauffer KE, Parker Fiebelkorn A, et al. The burden and severity of illness due to 2009 pandemic influenza A (H1N1) in a large US city during the late summer and early fall of 2009. Am J Epidemiol. 2012;176:519–526
26. Echevarría-Zuno S, Mejía-Aranguré JM, Mar-Obeso AJ, et al. Infection and death from influenza A H1N1 virus in Mexico: a retrospective analysis. Lancet. 2009;374:2072–2079
27. Flahault A. First estimation of direct H1N1pdm virulence: From reported non consolidated data from Mauritius and New Caledonia. PLoS Curr. 2009;1:RRN1010
28. Fraser C, Donnelly CA, Cauchemez S, et al.WHO Rapid Pandemic Assessment Collaboration. Pandemic potential of a strain of influenza A (H1N1): early findings. Science. 2009;324:1557–1561
29. Garske T, Legrand J, Donnelly CA, et al. Assessing the severity of the novel influenza A/H1N1 pandemic. BMJ. 2009;339:b2840
30. Glinsky GV. Real-time case fatality analysis points to emerging evidence of increasing severity of pandemic (H1N1) 2009. Cell Cycle. 2009;8:3057–3062
31. Godoy P, Pumarola T, Martinez A, et al. Surveillance of the pandemic influenza (H1N1) 2009 in Catalonia: results and implications. Rev Esp Salud Publica. 2011;85:37–45
32. Gomez J, Munayco C, Arrasco J, et al. Pandemic influenza in a southern hemisphere setting: the experience in Peru from May to September, 2009. Euro Surveill. 2009;14:19371
33. Gu Y, Shimada T, Yasui Y, et al. National surveillance of influenza-associated encephalopathy in Japan over six years, before and during the 2009–2010 influenza pandemic. PLoS One. 2013;8:e54786
34. Hadler JL, Konty K, McVeigh KH, et al. Case fatality rates based on population estimates of influenza-like illness due to novel H1N1 influenza: New York City, May-June 2009. PLoS One. 2010;5:e11677
35. Kamigaki T, Oshitani H. Epidemiological characteristics and low case fatality rate of pandemic (H1N1) 2009 in Japan. PLoS Curr. 2009;1:RRN1139
36. Kim HS, Kim JH, Shin SY, et al. Fatal cases of 2009 pandemic influenza A (H1N1) in Korea. J Korean Med Sci. 2011;26:22–27
37. Kool JL, Pavlin BI, Musto J, et al. Influenza surveillance in the Pacific Island countries and territories during the 2009 pandemic: an observational study. BMC Infect Dis. 2013;13:6
38. Larrieu S, Rosine J, Ledrans M, et al. Epidemic of influenza A(H1N1) 2009 in the French overseas territories of the Americas: epidemiological surveillance set up and main results, April 2009-January 2010. Bull Soc Pathol Exot. 2011;104:119–124
39. McVernon J, Laurie K, Nolan T, et al. Seroprevalence of 2009 pandemic influenza A(H1N1) virus in Australian blood donors, October - December 2009. Euro Surveill. 2010;15:19678
40. Mishra AC, Chadha MS, Choudhary ML, et al. Pandemic influenza (H1N1) 2009 is associated with severe disease in India. PLoS One. 2010;5:e10540
41. Nishiura H, Klinkenberg D, Roberts M, Heesterbeek JA. Early epidemiological assessment of the virulence of emerging infectious diseases: a case study of an influenza pandemic. PLoS One. 2009;4:e6852
42. Nishiura H. The relationship between the cumulative numbers of cases and deaths reveals the confirmed case fatality ratio of a novel influenza A (H1N1) virus. Jpn J Infect Dis. 2010;63:154–156
43. Nishiura H. The virulence of pandemic influenza A (H1N1) 2009: an epidemiological perspective on the case-fatality ratio. Expert Rev Respir Med. 2010;4:329–338
44. Pebody RG, McLean E, Zhao H, et al. Pandemic Influenza A (H1N1) 2009 and mortality in the United Kingdom: risk factors for death, April 2009 to March 2010. Euro Surveill. 2010;15:19571
45. Presanis AM, De Angelis D, Hagy A, et al.New York City Swine Flu Investigation Team. The severity of pandemic H1N1 influenza in the United States, from April to July 2009: a Bayesian analysis. PLoS Med. 2009;6:e1000207
46. Presanis AM, Pebody RG, Paterson BJ, et al. Changes in severity of 2009 pandemic A/H1N1 influenza in England: a Bayesian evidence synthesis. BMJ. 2011;343:d5408
47. Renault P, Thouillot F, Do C, et al. Epidemic of influenza A(H1N1) 2009 in Reunion Island: epidemiological data. Bull Soc Pathol Exot. 2011;104:108–113
48. Riley S, Kwok KO, Wu KM, et al. Epidemiological characteristics of 2009 (H1N1) pandemic influenza based on paired sera from a longitudinal community cohort study. PLoS Med. 2011;8:e1000442
49. Sachedina N, Donaldson LJ. Paediatric mortality related to pandemic influenza A H1N1 infection in England: an observational population-based study. Lancet. 2010;376:1846–1852
50. Simon Mendez L, de Mateo Ontanon S, Larrauri Camara A, et al. Transmissibility and severity of the pandemic influenza A (H1N1) 2009 virus in Spain. Gac Sanit. 2011;25:296–302
51. Steens A, Waaijenborg S, Teunis PF, et al. Age-dependent patterns of infection and severity explaining the low impact of 2009 influenza A (H1N1): evidence from serial serologic surveys in the Netherlands. Am J Epidemiol. 2011;174:1307–1315
52. Sypsa V, Bonovas S, Tsiodras S, et al. Estimating the disease burden of 2009 pandemic influenza A(H1N1) from surveillance and household surveys in Greece. PLoS One. 2011;6:e20593
53. Tuite AR, Greer AL, Whelan M, et al. Estimated epidemiologic parameters and morbidity associated with pandemic H1N1 influenza. CMAJ. 2010;182:131–136
54. Vaillant L, La Ruche G, Tarantola A, Barboza PEpidemic Intelligence Team at InVS. . Epidemiology of fatal cases associated with pandemic H1N1 influenza 2009. Euro Surveill. 2009;14:19309
55. . Human infection with new influenza A (H1N1) virus: clinical observations from Mexico and other affected countries, May 2009. Wkly Epidemiol Rec. 2009;84:185–189
56. Wilking H, Buda S, von der Lippe E, et al. Mortality of 2009 pandemic influenza A(H1N1) in Germany. Euro Surveill. 2010;15:19741
57. Wilson N, Baker MG. The emerging influenza pandemic: estimating the case fatality ratio. Euro Surveill. 2009;14:19255
58. Wu JT, Ma ES, Lee CK, et al. The infection attack rate and severity of 2009 pandemic H1N1 influenza in Hong Kong. Clin Infect Dis. 2010;51:1184–1191
59. Lessler J, Reich NG, Cummings DA, Nair HP, Jordan HT, Thompson NNew York City Department of Health and Mental Hygiene Swine Influenza Investigation Team. . Outbreak of 2009 pandemic influenza A (H1N1) at a New York City school. N Engl J Med. 2009;361:2628–2636
60. Rudge JW, Hanvoravongchai P, Krumkamp R, et al.AsiaFluCap Project Consortium. Health system resource gaps and associated mortality from pandemic influenza across six Asian territories. PLoS One. 2012;7:e31800
61. SteelFisher GK, Blendon RJ, Ward JR, et al. Public response to the 2009 influenza A H1N1 pandemic: a polling study in five countries. Lancet Infect Dis. 2012;12:845–850
62. Baguelin M, Hoek AJ, Jit M, Flasche S, White PJ, Edmunds WJ. Vaccination against pandemic influenza A/H1N1v in England: a real-time economic evaluation. Vaccine. 2010;28:2370–2384
63. Kelly H, Peck HA, Laurie KL, et al. The age-specific cumulative incidence of infection with pandemic influenza H1N1 2009 was similar in various countries prior to vaccination. PLoS One. 2011;6:e21828
64. Lau LL, Nishiura H, Kelly H, et al. Household transmission of 2009 pandemic influenza A (H1N1): a systematic review and meta-analysis. Epidemiology. 2012;23:531–542
65. Simonsen L, Reichert TA, Viboud C, Blackwelder WC, Taylor RJ, Miller MA. Impact of influenza vaccination on seasonal mortality in the US elderly population. Arch Intern Med. 2005;165:265–272
66. Thompson WW, Moore MR, Weintraub E, et al. Estimating influenza-associated deaths in the United States. Am J Public Health. 2009;99(suppl 2):S225–S230
67. Melegaro A, Jit M, Gay N, Zagheni E, Edmunds WJ. What types of contacts are important for the spread of infections?: using contact survey data to explore European mixing patterns. Epidemics. 2011;3:143–151
68. Hollingsworth TD, Klinkenberg D, Heesterbeek H, Anderson RM. Mitigation strategies for pandemic influenza A: balancing conflicting policy objectives. PLoS Comput Biol. 2011;7:e1001076
69. Ahmad OB, Boschi-Pinto C, Lopez AD Age Standardization of Rates: A New WHO Standard. GPE Discussion Paper No. 31. 2001 Geneva World Health Organisation
70. Chowell G, Echevarria-Zuno S, Viboud C, et al. Recrudescent wave of pandemic A/H1N1 influenza in Mexico, winter 2011–2012: Age shift and severity. PLoS Curr. 2012;4:RRN1306
71. Van Kerkhove MD, Vandemaele KA, Shinde V, et al. Risk factors for severe outcomes following 2009 influenza A (H1N1) infection: a global pooled analysis. PLoS Med. 2011;8:e1001053
72. Zhang YH, Zhao Y, Li N, et al. Interferon-induced transmembrane protein-3 genetic variant rs12252-C is associated with severe influenza in Chinese individuals. Nat Commun. 2013;4:1418
73. Wu JT, Ho A, Ma ES, et al. Estimating infection attack rates and severity in real time during an influenza pandemic: analysis of serial cross-sectional serologic surveillance data. PLoS Med. 2011;8:e1001103
74. Dawood FS, Iuliano AD, Reed C, et al. Estimated global mortality associated with the first 12 months of 2009 pandemic influenza A H1N1 virus circulation: a modelling study. Lancet Infect Dis. 2012;12:687–695

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