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Article: The severity of pandemic H1N1 influenza in the United States, from April to July 2009: A Bayesian analysis

TitleThe severity of pandemic H1N1 influenza in the United States, from April to July 2009: A Bayesian analysis
Authors
Presanis, AMDe Angelis, DHagy, AReed, CRiley, SCooper, BSFinelli, LBiedrzycki, PLipsitch, MAckelsberg, JAdamski, AAdman, GAgborTabi, EAston, CAtamian, JBackman, PBalter, SBarbot, OBeatrice, STBeaudry, GBegier, EBell, GBerg, DBerger, MBetz, JBlank, SBornschlegel, KBregman, BBurke, MButts, BCai, LCajigal, ACampbell, MCamurati, LChamany, SCimini, DCone, JCook, HCook, DCorey, CCostarella, RCoyle, CCrouch, BDa Costa, CADaniels, ADarkins, BDeGrasse, ADeGrechie, SDel Cid, ODeocharan, BDiaz, LDiCaprio, KDiGrande, LDuquaine, DDurrah, JEavey, JEdghill, ZEdwin, BEgger, JEisenhower, DEvans, MFarley, SFeliciano, RFernandez, MFilsAime, CFine, AFireteanu, AMFitzgerald, KFrance, AMFrieden, TFriedman, SFu, JFung, LGirdharrie, LGlaser, MGoranson, CGriffing, FGupta, LHamilton, CHanson, HHarper, SHartmanO'Connell, IHasnain, QHedge, SHeller, MHendrickson, DHerskovitz, AHinterland, KHolmes, RHom, JHon, JHopke, THsieh, JHughes, SImmerwahr, SIncalicchio, AMJasek, JJimenez, JJohns, MJones, LJordan, HKambili, CKang, JKapell, DKarpati, AKerker, BKonty, KKornblum, JKrigsman, GLaraque, FLayton, MLee, ELee, LLee, SLim, SMarx, MMcGibbon, EMahoney, KMarin, GMatte, TMcAnanama, RMcKay, RMcKay, CMcVeigh, KMedina, EMedina, WMichelangelo, DMilhofer, JMilyavskaya, IMisener, MMizrahi, JMoskin, LMotherwell, MMyers, CNair, HPNguyen, TNilsen, DNival, JNorton, JOleszko, WOlson, CPaladini, MPalumbo, LPapadopoulos, PParton, HPaternostro, JPaynter, LPerkins, KPerlman, SPersaud, HPeters, CPfeiffer, MPlatt, RPool, LPunsalang, ARasul, ZRawlins, VReddy, VRinchiuso, ARodriguez, TRosal, RRyan, MSanderson, MScaccia, ASeligson, ALSeupersad, JSevereDildy, JSiddiqi, ASiemetzki, USingh, TSlavinski, SSlopen, MSnuggs, TStarr, DStayton, CStoute, ATerlonge, JTernier, AThorpe, LTravers, CTsoi, BTurner, KTzou, JVines, SWaddell, ENWalker, DWarner, CWeisfuse, IWeiss, DWilliamsAkita, AWilson, EWilson, EWong, MWu, CYang, DYounis, MYusuff, SZimmerman, CZucker, J
Issue Date2009
PublisherPublic Library of Science. The Journal's web site is located at http://medicine.plosjournals.org/perlserv/?request=index-html&issn=1549-1676
Citation
Plos Medicine, 2009, v. 6 n. 12 How to Cite?
AbstractBackground: 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 7-96lower. 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.
Persistent Identifierhttp://hdl.handle.net/10722/86446
ISSN
2023 Impact Factor: 10.5
2023 SCImago Journal Rankings: 4.198
PubMed Central ID
ISI Accession Number ID
Funding AgencyGrant Number
UK Medical Research CouncilG0600675
U.1052.00.007
UK Health Protection Agency
Cooperative Agreements1U54GM088558
5U01GM076497
US National Institutes of Health (US NIH)
US NIH3R01TW008246-01S1
US Department of Homeland Security
Funding Information:

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.

References

 

DC FieldValueLanguage
dc.contributor.authorPresanis, AMen_HK
dc.contributor.authorDe Angelis, Den_HK
dc.contributor.authorHagy, Aen_HK
dc.contributor.authorReed, Cen_HK
dc.contributor.authorRiley, Sen_HK
dc.contributor.authorCooper, BSen_HK
dc.contributor.authorFinelli, Len_HK
dc.contributor.authorBiedrzycki, Pen_HK
dc.contributor.authorLipsitch, Men_HK
dc.contributor.authorAckelsberg, Jen_HK
dc.contributor.authorAdamski, Aen_HK
dc.contributor.authorAdman, Gen_HK
dc.contributor.authorAgborTabi, Een_HK
dc.contributor.authorAston, Cen_HK
dc.contributor.authorAtamian, Jen_HK
dc.contributor.authorBackman, Pen_HK
dc.contributor.authorBalter, Sen_HK
dc.contributor.authorBarbot, Oen_HK
dc.contributor.authorBeatrice, STen_HK
dc.contributor.authorBeaudry, Gen_HK
dc.contributor.authorBegier, Een_HK
dc.contributor.authorBell, Gen_HK
dc.contributor.authorBerg, Den_HK
dc.contributor.authorBerger, Men_HK
dc.contributor.authorBetz, Jen_HK
dc.contributor.authorBlank, Sen_HK
dc.contributor.authorBornschlegel, Ken_HK
dc.contributor.authorBregman, Ben_HK
dc.contributor.authorBurke, Men_HK
dc.contributor.authorButts, Ben_HK
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dc.contributor.authorCajigal, Aen_HK
dc.contributor.authorCampbell, Men_HK
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dc.contributor.authorCorey, Cen_HK
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dc.contributor.authorCrouch, Ben_HK
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dc.contributor.authorDeGrasse, Aen_HK
dc.contributor.authorDeGrechie, Sen_HK
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dc.contributor.authorDiaz, Len_HK
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dc.contributor.authorHon, Jen_HK
dc.contributor.authorHopke, Ten_HK
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dc.contributor.authorJohns, Men_HK
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dc.contributor.authorSiddiqi, Aen_HK
dc.contributor.authorSiemetzki, Uen_HK
dc.contributor.authorSingh, Ten_HK
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dc.contributor.authorSlopen, Men_HK
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dc.contributor.authorWong, Men_HK
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dc.date.accessioned2010-09-06T09:17:08Z-
dc.date.available2010-09-06T09:17:08Z-
dc.date.issued2009en_HK
dc.identifier.citationPlos Medicine, 2009, v. 6 n. 12en_HK
dc.identifier.issn1549-1277en_HK
dc.identifier.urihttp://hdl.handle.net/10722/86446-
dc.description.abstractBackground: 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 7-96lower. 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.en_HK
dc.languageengen_HK
dc.publisherPublic Library of Science. The Journal's web site is located at http://medicine.plosjournals.org/perlserv/?request=index-html&issn=1549-1676en_HK
dc.relation.ispartofPLoS Medicineen_HK
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subject.meshBayes Theorem-
dc.subject.meshDisease Outbreaks-
dc.subject.meshHospitalization - statistics and numerical data-
dc.subject.meshInfluenza A Virus, H1N1 Subtype-
dc.subject.meshInfluenza, Human - classification - epidemiology-
dc.titleThe severity of pandemic H1N1 influenza in the United States, from April to July 2009: A Bayesian analysisen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1549-1277&volume=6&issue=12, article no. e1000207&spage=&epage=&date=2009&atitle=The+severity+of+pandemic+H1N1+influenza+in+the+United+States,+from+April+to+July+2009:+a+Bayesian+analysisen_HK
dc.identifier.emailRiley, S:sriley@hkucc.hku.hk, steven.riley@hku.hken_HK
dc.identifier.authorityRiley, S=rp00511en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1371/journal.pmed.1000207en_HK
dc.identifier.pmid19997612-
dc.identifier.pmcidPMC2784967-
dc.identifier.scopuseid_2-s2.0-74049092311en_HK
dc.identifier.hkuros168609en_HK
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dc.identifier.volume6en_HK
dc.identifier.issue12en_HK
dc.identifier.isiWOS:000273060600018-
dc.publisher.placeUnited Statesen_HK
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dc.identifier.scopusauthoridDe Angelis, D=7004604875en_HK
dc.identifier.scopusauthoridHagy, A=35302485000en_HK
dc.identifier.scopusauthoridReed, C=35222238200en_HK
dc.identifier.scopusauthoridRiley, S=7102619416en_HK
dc.identifier.scopusauthoridCooper, BS=7401623515en_HK
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dc.identifier.scopusauthoridHeller, M=7202415610en_HK
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dc.identifier.scopusauthoridHerskovitz, A=6508278028en_HK
dc.identifier.scopusauthoridHinterland, K=35302093700en_HK
dc.identifier.scopusauthoridHolmes, R=35388588300en_HK
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dc.identifier.scopusauthoridHon, J=36950563600en_HK
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dc.identifier.scopusauthoridHughes, S=7402137722en_HK
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dc.identifier.scopusauthoridKapell, D=6602390820en_HK
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dc.identifier.scopusauthoridWilliamsAkita, A=6506046754en_HK
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dc.identifier.scopusauthoridWong, M=7403907922en_HK
dc.identifier.scopusauthoridWu, C=35390304400en_HK
dc.identifier.scopusauthoridYang, D=35390498800en_HK
dc.identifier.scopusauthoridYounis, M=35390434900en_HK
dc.identifier.scopusauthoridYusuff, S=35390460300en_HK
dc.identifier.scopusauthoridZimmerman, C=7103371821en_HK
dc.identifier.scopusauthoridZucker, J=16237791900en_HK
dc.identifier.citeulike6345977-
dc.identifier.issnl1549-1277-

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