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PLoS Med. The Severity of Pandemic H1N1 Influenza in the United States, from April to July 2009: A Bayesian Analysis
The Severity of Pandemic H1N1 Influenza in the United States, from April to July 2009: A Bayesian Analysis (PLoS Medicine, abstract, edited)
The Severity of Pandemic H1N1 Influenza in the United States, from April to July 2009: A Bayesian Analysis
Marc Lipsitch and colleagues use complementary data from two US cities, Milwaukee and New York City, to assess the severity of pandemic (H1N1) 2009 influenza in the United States.
Formal Correction: This article has been formally corrected to address the following errors.
Anne M. Presanis 1, Daniela De Angelis 1,2, The New York City Swine Flu Investigation Team 3, ?, Angela Hagy 4, Carrie Reed 5, Steven Riley 6, Ben S. Cooper 2, Lyn Finelli 5, Paul Biedrzycki 4, Marc Lipsitch 7*
1 Medical Research Council Biostatistics Unit, Cambridge, United Kingdom,
2 Statistics, Modelling and Bioinformatics Department, Health Protection Agency Centre for Infections, London, United Kingdom
4 Department of Health, City of Milwaukee, Milwaukee, Wisconsin, United States of America,
5 Influenza Division, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America,
6 Department of Community Medicine and School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China,
7 Center for Communicable Disease Dynamics, Departments of Epidemiology and Immunology & Infectious Diseases, Harvard School of Public Health, Boston, Massachusetts, United States of America
Abstract
Background
Accurate measures of the severity of pandemic (H1N1) 2009 influenza (pH1N1) are needed to assess the likely impact of an anticipated resurgence in the autumn in the Northern Hemisphere. Severity has been difficult to measure because jurisdictions with large numbers of deaths and other severe outcomes have had too many cases to assess the total number with confidence. Also, detection of severe cases may be more likely, resulting in overestimation of the severity of an average case. We sought to estimate the probabilities that symptomatic infection would lead to hospitalization, ICU admission, and death by combining data from multiple sources.
Methods and Findings
We used complementary data from two US cities: Milwaukee attempted to identify cases of medically attended infection whether or not they required hospitalization, while New York City focused on the identification of hospitalizations, intensive care admission or mechanical ventilation (hereafter, ICU), and deaths. New York data were used to estimate numerators for ICU and death, and two sources of data?medically attended cases in Milwaukee or self-reported influenza-like illness (ILI) in New York?were used to estimate ratios of symptomatic cases to hospitalizations. Combining these data with estimates of the fraction detected for each level of severity, we estimated the proportion of symptomatic patients who died (symptomatic case-fatality ratio, sCFR), required ICU (sCIR), and required hospitalization (sCHR), overall and by age category. Evidence, prior information, and associated uncertainty were analyzed in a Bayesian evidence synthesis framework. Using medically attended cases and estimates of the proportion of symptomatic cases medically attended, we estimated an sCFR of 0.048% (95% credible interval [CI] 0.026%?0.096%), sCIR of 0.239% (0.134%?0.458%), and sCHR of 1.44% (0.83%?2.64%). Using self-reported ILI, we obtained estimates approximately 7?9? lower. sCFR and sCIR appear to be highest in persons aged 18 y and older, and lowest in children aged 5?17 y. sCHR appears to be lowest in persons aged 5?17; our data were too sparse to allow us to determine the group in which it was the highest.
Conclusions
These estimates suggest that an autumn?winter pandemic wave of pH1N1 with comparable severity per case could lead to a number of deaths in the range from considerably below that associated with seasonal influenza to slightly higher, but with the greatest impact in children aged 0?4 and adults 18?64. These estimates of impact depend on assumptions about total incidence of infection and would be larger if incidence of symptomatic infection were higher or shifted toward adults, if viral virulence increased, or if suboptimal treatment resulted from stress on the health care system; numbers would decrease if the total proportion of the population symptomatically infected were lower than assumed.
Please see later in the article for the Editors' Summary
Citation: Presanis AM, De Angelis D, The New York City Swine Flu Investigation Team3, ?, Hagy A, Reed C, et al. (2009) The Severity of Pandemic H1N1 Influenza in the United States, from April to July 2009: A Bayesian Analysis. PLoS Med 6(12): e1000207.
doi:10.1371/journal.pmed.1000207
Academic Editor: Lone Simonsen, George Washington University, United States of America
Received: September 17, 2009; Accepted: November 19, 2009; Published: December 8, 2009
This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration which stipulates that, once placed in the public domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose.
Funding: AMP and DDA were funded by the UK Medical Research Council (grants G0600675 and U.1052.00.007). DDA was funded also by the UK Health Protection Agency. ML and SR were supported by Cooperative Agreements 1U54GM088558 and 5U01GM076497 of the Models of Infectious Disease Agent Study program of the US National Institutes of Health (US NIH). SR also received funding from grant 3R01TW008246-01S1 from the US NIH from the RAPIDD program of the Fogarty International Center of the US NIH and the Science and Technology Directorate of the US 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: ML has received consulting fees from the Avian/Pandemic Flu Registry (Outcome Sciences), sponsored in part by Roche.
Abbreviations: BRFSS, Behavioral Risk Factor Surveillance Survey; CDC, US Centers for Disease Control and Prevention; CFR, case-fatality ratio; CHR, case-hospitalization ratio; CIR, case-intensive care ratio; CI, credible interval; DOHMH, [New York] Department of Health and Mental Hygiene; ICU, intensive care unit; ILI, influenza-like illness; pH1N1, pandemic (H1N1) 2009 [virus/influenza]; RDD, random-digit dialing; RT-PCR, reverse transcriptase polymerase chain reaction; sCFR, symptomatic case-fatality ratio; sCHR, symptomatic case-hospitalization ratio; sCIR, symptomatic case-ICU admission ratio
* E-mail: mlipsitc@hsph.harvard.edu
? Membership of The New York City Swine Flu Investigation Team is provided in the Acknowledgments.
[Source Full Free PDF Document: LINK. EDITED.]
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<cite cite="http://www.plosmedicine.org/article/info%3Adoi%2F10.1371%2Fjournal.pmed.1000207?utm_so urce=feedburner&utm_medium=feed&utm_campaign=Feed% 3A+plosmedicine%2FNewArticles+%28PLoS+Medicine%3A+ New+Articles%29">PLoS Medicine: The Severity of Pandemic H1N1 Influenza in the United States, from April to July 2009: A Bayesian Analysis</cite>
Marc Lipsitch and colleagues use complementary data from two US cities, Milwaukee and New York City, to assess the severity of pandemic (H1N1) 2009 influenza in the United States.
Formal Correction: This article has been formally corrected to address the following errors.
Anne M. Presanis 1, Daniela De Angelis 1,2, The New York City Swine Flu Investigation Team 3, ?, Angela Hagy 4, Carrie Reed 5, Steven Riley 6, Ben S. Cooper 2, Lyn Finelli 5, Paul Biedrzycki 4, Marc Lipsitch 7*
1 Medical Research Council Biostatistics Unit, Cambridge, United Kingdom,
2 Statistics, Modelling and Bioinformatics Department, Health Protection Agency Centre for Infections, London, United Kingdom
4 Department of Health, City of Milwaukee, Milwaukee, Wisconsin, United States of America,
5 Influenza Division, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America,
6 Department of Community Medicine and School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China,
7 Center for Communicable Disease Dynamics, Departments of Epidemiology and Immunology & Infectious Diseases, Harvard School of Public Health, Boston, Massachusetts, United States of America
Abstract
Background
Accurate measures of the severity of pandemic (H1N1) 2009 influenza (pH1N1) are needed to assess the likely impact of an anticipated resurgence in the autumn in the Northern Hemisphere. Severity has been difficult to measure because jurisdictions with large numbers of deaths and other severe outcomes have had too many cases to assess the total number with confidence. Also, detection of severe cases may be more likely, resulting in overestimation of the severity of an average case. We sought to estimate the probabilities that symptomatic infection would lead to hospitalization, ICU admission, and death by combining data from multiple sources.
Methods and Findings
We used complementary data from two US cities: Milwaukee attempted to identify cases of medically attended infection whether or not they required hospitalization, while New York City focused on the identification of hospitalizations, intensive care admission or mechanical ventilation (hereafter, ICU), and deaths. New York data were used to estimate numerators for ICU and death, and two sources of data?medically attended cases in Milwaukee or self-reported influenza-like illness (ILI) in New York?were used to estimate ratios of symptomatic cases to hospitalizations. Combining these data with estimates of the fraction detected for each level of severity, we estimated the proportion of symptomatic patients who died (symptomatic case-fatality ratio, sCFR), required ICU (sCIR), and required hospitalization (sCHR), overall and by age category. Evidence, prior information, and associated uncertainty were analyzed in a Bayesian evidence synthesis framework. Using medically attended cases and estimates of the proportion of symptomatic cases medically attended, we estimated an sCFR of 0.048% (95% credible interval [CI] 0.026%?0.096%), sCIR of 0.239% (0.134%?0.458%), and sCHR of 1.44% (0.83%?2.64%). Using self-reported ILI, we obtained estimates approximately 7?9? lower. sCFR and sCIR appear to be highest in persons aged 18 y and older, and lowest in children aged 5?17 y. sCHR appears to be lowest in persons aged 5?17; our data were too sparse to allow us to determine the group in which it was the highest.
Conclusions
These estimates suggest that an autumn?winter pandemic wave of pH1N1 with comparable severity per case could lead to a number of deaths in the range from considerably below that associated with seasonal influenza to slightly higher, but with the greatest impact in children aged 0?4 and adults 18?64. These estimates of impact depend on assumptions about total incidence of infection and would be larger if incidence of symptomatic infection were higher or shifted toward adults, if viral virulence increased, or if suboptimal treatment resulted from stress on the health care system; numbers would decrease if the total proportion of the population symptomatically infected were lower than assumed.
Please see later in the article for the Editors' Summary
Citation: Presanis AM, De Angelis D, The New York City Swine Flu Investigation Team3, ?, Hagy A, Reed C, et al. (2009) The Severity of Pandemic H1N1 Influenza in the United States, from April to July 2009: A Bayesian Analysis. PLoS Med 6(12): e1000207.
doi:10.1371/journal.pmed.1000207
Academic Editor: Lone Simonsen, George Washington University, United States of America
Received: September 17, 2009; Accepted: November 19, 2009; Published: December 8, 2009
This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration which stipulates that, once placed in the public domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose.
Funding: AMP and DDA were funded by the UK Medical Research Council (grants G0600675 and U.1052.00.007). DDA was funded also by the UK Health Protection Agency. ML and SR were supported by Cooperative Agreements 1U54GM088558 and 5U01GM076497 of the Models of Infectious Disease Agent Study program of the US National Institutes of Health (US NIH). SR also received funding from grant 3R01TW008246-01S1 from the US NIH from the RAPIDD program of the Fogarty International Center of the US NIH and the Science and Technology Directorate of the US 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: ML has received consulting fees from the Avian/Pandemic Flu Registry (Outcome Sciences), sponsored in part by Roche.
Abbreviations: BRFSS, Behavioral Risk Factor Surveillance Survey; CDC, US Centers for Disease Control and Prevention; CFR, case-fatality ratio; CHR, case-hospitalization ratio; CIR, case-intensive care ratio; CI, credible interval; DOHMH, [New York] Department of Health and Mental Hygiene; ICU, intensive care unit; ILI, influenza-like illness; pH1N1, pandemic (H1N1) 2009 [virus/influenza]; RDD, random-digit dialing; RT-PCR, reverse transcriptase polymerase chain reaction; sCFR, symptomatic case-fatality ratio; sCHR, symptomatic case-hospitalization ratio; sCIR, symptomatic case-ICU admission ratio
* E-mail: mlipsitc@hsph.harvard.edu
? Membership of The New York City Swine Flu Investigation Team is provided in the Acknowledgments.
[Source Full Free PDF Document: LINK. EDITED.]
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