Literature DB >> 20610325

Case fatality ratio of pandemic influenza.

Hiroshi Nishiura.   

Abstract

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Year:  2010        PMID: 20610325      PMCID: PMC7129997          DOI: 10.1016/S1473-3099(10)70120-1

Source DB:  PubMed          Journal:  Lancet Infect Dis        ISSN: 1473-3099            Impact factor:   25.071


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As pandemic influenza H1N1 spread around the world in 2009, disease severity was one of the main areas of interest. The case fatality ratio (CFR) is a representative measurement of severity of a disease that directly captures virulence (ie, the conditional risk of death for patients with a disease or infection), whereas mortality (ie, the risk of death in a population) depends not only on the disease severity but also the risk of infection in a population. Because there have been several conflicting estimates of the CFR for pandemic H1N1,1, 2, 3, 4, 5, 6 I offer an interpretation of these reports and identify key areas that need to be clarified. In the early stages of the 2009 influenza pandemic, two approaches were taken when calculating the CFR. One approach focused on estimation of CFR during the course of the pandemic.1, 2, 3 During outbreaks of severe acute respiratory syndrome from 2002 to 2003, use of a crude ratio of the cumulative number of deaths to the number of cases at a given point in time underestimated the CFR. To avoid similar underestimations, accounting for the time delay from onset of disease to death, the confirmed CFR of pandemic H1N1, which took confirmed cases as the denominator, was estimated to be about 0·5%.1, 2, 3, 6 However, because the estimate depends on the proportion of symptomatic patients who have confirmatory diagnosis, it is not useful for prediction of the overall mortality. In other words, the difficulty in case ascertainment remains a limitation of confirmed CFR. Motivated by this limitation, the second approach calculated the CFR by taking symptomatic cases as the denominator, although in this study the denominator was not clearly defined. Despite several technical problems, this approach emphasised the importance of accounting for unconfirmed cases to yield an appropriate order of the CFR estimate (figure 1 ).
Figure 1

Case fatality ratios and the natural history of infection

The confirmed case fatality ratio (CFR) uses the confirmed cases as the denominator, whereas symptomatic CFR uses symptomatic cases. To predict mortality, symptomatic CFR (or ideally the CFR taking all infected individuals as the denominator) is required.

Case fatality ratios and the natural history of infection The confirmed case fatality ratio (CFR) uses the confirmed cases as the denominator, whereas symptomatic CFR uses symptomatic cases. To predict mortality, symptomatic CFR (or ideally the CFR taking all infected individuals as the denominator) is required. Subsequent to these earlier efforts, Presanis and colleagues have offered a way to predict the mortality in a population, by explicitly taking symptomatic cases as the denominator and thus calculating the symptomatic CFR. The symptomatic CFR among all medically attended cases was estimated to be 0·048%, one-tenth of the CFR estimate from confirmed cases. In other words, only one of ten symptomatic cases seems to have been confirmed. By use of self-reported influenza-like illness as the denominator, the estimate was even smaller. Presanis and colleagues and a later study in the UK have adeptly shown that 2009 pandemic H1N1 influenza can be subjectively perceived as mild. Caution is needed when interpreting age-specific estimates (figure 2 ). The confirmed CFR in Mexico and the USA increases with age, most probably because underlying medical conditions that can increase the risk of influenza death are most common in elderly people.10, 11 The similar age-specific pattern is also seen in symptomatic CFR. The differing CFR estimates between age-groups hamper precise estimation for entire populations during the early stages of the pandemic. Furthermore, although there might be two countries with very different CFR estimates, comparisons will be futile unless the composition of the cases (ie, age-groups and risk-groups of fatal and non-fatal cases) is known.
Figure 2

Age-specific confirmed case fatality ratio in Mexico and USA

In Mexico there were 72 529 cases and 1227 deaths (as of May 10, 2010) and in the USA there were 37 030 cases and 276 deaths (as of July 24, 2009).9, 10 Shaded area represents the 95% CI.

Age-specific confirmed case fatality ratio in Mexico and USA In Mexico there were 72 529 cases and 1227 deaths (as of May 10, 2010) and in the USA there were 37 030 cases and 276 deaths (as of July 24, 2009).9, 10 Shaded area represents the 95% CI. The best way to describe the severity of pandemic influenza to non-experts is to compare its virulence with that of other influenza epidemics. However, because different methods have been used to predict the mortality impact associated with pandemic H1N1 and non-pandemic influenza, the strict comparison of virulence has been difficult. Estimations of mortality for non-pandemic influenza have been made mainly with Serfling cyclical regression, which accounts for deaths that are both directly and indirectly associated with influenza. A recent study in the USA suggested that there were up to 44 100 excess deaths in May to December, 2009, implying that the mortality effect of the influenza pandemic surpassed that of non-pandemic influenza seasons. However, because this estimate of excess mortality reflects both transmission potential and virulence, a comparative assessment of virulence alone has yet to be established. In addition to the above-mentioned issues surrounding the estimation of CFR, pharmaceutical interventions such as antiretroviral treatment or immunisation programmes also bias the estimated risk of death. Thus, to provide an unbiased CFR for the 2009 influenza pandemic that can accurately represent the overall virulence and permit comparisons within and between populations, we are faced with a challenge to adjust for such potential treatment effects that may require substantial epidemiological and statistical efforts.
  12 in total

1.  The emerging influenza pandemic: estimating the case fatality ratio.

Authors:  N Wilson; M G Baker
Journal:  Euro Surveill       Date:  2009-07-02

2.  Assessing the severity of the novel influenza A/H1N1 pandemic.

Authors:  Tini Garske; Judith Legrand; Christl A Donnelly; Helen Ward; Simon Cauchemez; Christophe Fraser; Neil M Ferguson; Azra C Ghani
Journal:  BMJ       Date:  2009-07-14

3.  Methods for estimating the case fatality ratio for a novel, emerging infectious disease.

Authors:  A C Ghani; C A Donnelly; D R Cox; J T Griffin; C Fraser; T H Lam; L M Ho; W S Chan; R M Anderson; A J Hedley; G M Leung
Journal:  Am J Epidemiol       Date:  2005-08-02       Impact factor: 4.897

4.  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.

Authors:  Cecile Viboud; Mark Miller; Don Olson; Michael Osterholm; Lone Simonsen
Journal:  PLoS Curr       Date:  2010-03-20

5.  The severity of pandemic H1N1 influenza in the United States, from April to July 2009: a Bayesian analysis.

Authors:  Anne M Presanis; Daniela De Angelis; Angela Hagy; Carrie Reed; Steven Riley; Ben S Cooper; Lyn Finelli; Paul Biedrzycki; Marc Lipsitch
Journal:  PLoS Med       Date:  2009-12-08       Impact factor: 11.069

6.  Managing and reducing uncertainty in an emerging influenza pandemic.

Authors:  Marc Lipsitch; Steven Riley; Simon Cauchemez; Azra C Ghani; Neil M Ferguson
Journal:  N Engl J Med       Date:  2009-05-27       Impact factor: 91.245

7.  An epidemiological analysis of severe cases of the influenza A (H1N1) 2009 virus infection in Japan.

Authors:  Koji Wada; Hiroshi Nishiura; Akihiko Kawana
Journal:  Influenza Other Respir Viruses       Date:  2010-07       Impact factor: 4.380

8.  Mortality from pandemic A/H1N1 2009 influenza in England: public health surveillance study.

Authors:  Liam J Donaldson; Paul D Rutter; Benjamin M Ellis; Felix E C Greaves; Oliver T Mytton; Richard G Pebody; Iain E Yardley
Journal:  BMJ       Date:  2009-12-10

9.  Pandemic potential of a strain of influenza A (H1N1): early findings.

Authors:  Christophe Fraser; Christl A Donnelly; Simon Cauchemez; William P Hanage; Maria D Van Kerkhove; T Déirdre Hollingsworth; Jamie Griffin; Rebecca F Baggaley; Helen E Jenkins; Emily J Lyons; Thibaut Jombart; Wes R Hinsley; Nicholas C Grassly; Francois Balloux; Azra C Ghani; Neil M Ferguson; Andrew Rambaut; Oliver G Pybus; Hugo Lopez-Gatell; Celia M Alpuche-Aranda; Ietza Bojorquez Chapela; Ethel Palacios Zavala; Dulce Ma Espejo Guevara; Francesco Checchi; Erika Garcia; Stephane Hugonnet; Cathy Roth
Journal:  Science       Date:  2009-05-11       Impact factor: 47.728

10.  Estimates of US influenza-associated deaths made using four different methods.

Authors:  William W Thompson; Eric Weintraub; Praveen Dhankhar; Po-Yung Cheng; Lynnette Brammer; Martin I Meltzer; Joseph S Bresee; David K Shay
Journal:  Influenza Other Respir Viruses       Date:  2009-01       Impact factor: 4.380

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  26 in total

1.  Epidemiological characteristics of the influenza A(H1N1) 2009 pandemic in the Western Pacific Region.

Authors:  Lisa McCallum; Jeffrey Partridge
Journal:  Western Pac Surveill Response J       Date:  2010-12-10

Review 2.  Pandemic influenza: a never-ending story.

Authors:  Seiji Kageyama
Journal:  Yonago Acta Med       Date:  2011-09-01       Impact factor: 1.641

3.  High incidence of severe influenza among individuals over 50 years of age.

Authors:  Anna J X Zhang; Kelvin K W To; Herman Tse; Kwok-Hung Chan; Kun-Yuan Guo; Can Li; Ivan F N Hung; Jasper F W Chan; Honglin Chen; Sidney Tam; Kwok-Yung Yuen
Journal:  Clin Vaccine Immunol       Date:  2011-09-07

Review 4.  Case fatality risk of influenza A (H1N1pdm09): a systematic review.

Authors:  Jessica Y Wong; Heath Kelly; Dennis K M Ip; Joseph T Wu; Gabriel M Leung; Benjamin J Cowling
Journal:  Epidemiology       Date:  2013-11       Impact factor: 4.822

5.  Infection fatality risk of the pandemic A(H1N1)2009 virus in Hong Kong.

Authors:  Jessica Y Wong; Peng Wu; Hiroshi Nishiura; Edward Goldstein; Eric H Y Lau; Lin Yang; S K Chuang; Thomas Tsang; J S Malik Peiris; Joseph T Wu; Benjamin J Cowling
Journal:  Am J Epidemiol       Date:  2013-03-03       Impact factor: 4.897

6.  The time required to estimate the case fatality ratio of influenza using only the tip of an iceberg: joint estimation of the virulence and the transmission potential.

Authors:  Keisuke Ejima; Ryosuke Omori; Benjamin J Cowling; Kazuyuki Aihara; Hiroshi Nishiura
Journal:  Comput Math Methods Med       Date:  2012-05-10       Impact factor: 2.238

7.  Characterizing the epidemiology of the 2009 influenza A/H1N1 pandemic in Mexico.

Authors:  Gerardo Chowell; Santiago Echevarría-Zuno; Cécile Viboud; Lone Simonsen; James Tamerius; Mark A Miller; Víctor H Borja-Aburto
Journal:  PLoS Med       Date:  2011-05-24       Impact factor: 11.069

8.  The relationship between tuberculosis and influenza death during the influenza (H1N1) pandemic from 1918-19.

Authors:  Welling Oei; Hiroshi Nishiura
Journal:  Comput Math Methods Med       Date:  2012-07-17       Impact factor: 2.238

Review 9.  Effectiveness of antiviral prophylaxis coupled with contact tracing in reducing the transmission of the influenza A (H1N1-2009): a systematic review.

Authors:  Kenji Mizumoto; Hiroshi Nishiura; Taro Yamamoto
Journal:  Theor Biol Med Model       Date:  2013-01-16       Impact factor: 2.432

Review 10.  Modeling rapidly disseminating infectious disease during mass gatherings.

Authors:  Gerardo Chowell; Hiroshi Nishiura; Cécile Viboud
Journal:  BMC Med       Date:  2012-12-07       Impact factor: 8.775

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