who influenza meeting paget flu pandemic
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The Global Pandemic
Mortality Burden project
(GLaMOR)
GLaMOR Core Team:
NIVEL, Utrecht, The Netherlands: Madelon Kroneman, John Paget, FrancoisSchellevis, Peter Spreeuwenberg
George Washington Universi ty, Washington DC, USA: Lone SimonsenSage Analytica, Bethesda, MD, USA: Roger Lustig, Robert Taylor
Royal College of GPs, Birmingham, UK: Douglas Fleming
WHO: Maria Van Kerkhove and Anthony Mounts
NIVEL Contract with WHO-Geneva 2011
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GlaMOR Objective
To estimate the global mortality burden ofthe 2009 influenza pandemic
– Based on statistical attributions derived frommultiple regression models applied tomortality data from 2009 and prior years
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Background
WHO’s global seasonal influenza mortalityestimate: 250,000-500,000 deaths per annum
There were 18,209 lab-confirmed pandemic
deaths reported to WHO (25 June 2011) – severely underestimated burden
CDC is also working on a global 2009 estimate(using a probabilistic modeling approach)
GLaMOR started July 2011 – report to WHO August 2012
A WHO Pandemic Advisory Committee is
supporting the work of the GLaMOR project
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The GLaMOR Strategy Use multiple regression modeling to estimate pandemicmortality in ~20 collaborating countries
Weekly mortality and virology data
Use extrapolation methods to project those single-countryresults to rest of the world
Based on GDP, pop structure, co-morbidities, access to care, more
Strategy inspired by Murray et al (Lancet 06)Estimated the global 1918 pandemic mortality from annual mortality data
~20 single-
countrypandemic
mortality
estimates
Stage 1
Global
pandemicmortality
estimates
Raw
mortality
andvirology
time
series
Stage 2
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Weekly Time Series for 2005-2009 or longer
Mortality Data – 6 age groups
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GlaMOR Collaborating Countries
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Countriescollaborating withGlaMOR Stage 1
19% of world population
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GlaMOR Collaborating Countries: WHO Euro area
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France, Germany, Spain, the Netherlands, Poland, Romania, Slovenia, United Kingdom, Israel,Denmark (all-cause only)
Thank you for this great collaboration
67% EU/EEA pop
38% WHO Region pop
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Different types of mortality data All-cause deaths
– More likely to be available – More sensitive (captures more deaths) – Less specific (more related to influenza)
Respiratory & Cardiovascular deaths
– all “I” and “J codes – Intermediate sensitivity and specificity – commonly used to estimates influenza burden
Respiratory deaths – all “J” codes – Less sensitive (captures fewer deaths) – More specific (more related to influenza)
Pneumonia and Influenza deaths6/13/2012 8
Higherestimate
Lower
precisio
Lowerestimate
Higherprecision
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Mild impact country: Mortality Data Time SeriesCountry X: All 4 causes,
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High impact country: Mortality Data Time SeriesCountry Y: All 4 causes plus diabetes,
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Mild impact country - Stage 1 Model Results Age
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High impact country - Stage 1 Model Results Age=
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Mild Impact Pandemic Mortalityfor
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Mild Impact country 2 Pandemic Mortality for
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Importance of having good virological data
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Virology Data – Ideally nationally representative, fully subtyped, and RSV – We used
Influenza virology by type, sub-type (no RSV)
FluNET-WHO data has been a fantastic resource – we have been able to concentrate our efforts on the mortalitydata and data analysis
For Europe, we noticed some data gaps in FluNet – In those cases we asked countries to provide their national data
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Stage 2: Four distinct strategies
1. Survey sampling (simple projection)
2. Matching (theory-based relations)
3. Multiple Imputation (statistical relations)4. Murray et al. method (Lancet, 2006)
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Example of Stage 2 Modeled Global Burden pattern
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Placeholderdarker color = higher 2009 pandemic mortality rate
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Summary: Stage 2
Choice of extrapolation method – Drop survey method: too simplistic – “Matching” and “imputation” produce different
patterns even when using the same factors
– Initial runs indicate Murray et al. method givesvery high estimates (as GDP based)
Final GLaMOR estimates will be based onRespiratory deaths as a confident
minimum burden estimateConfidence on parameter estimates better All-cause and Cardio-Respiratory estimates higher thanRespiratory in hardest hit countries, but for countrieswith low burden, respiratory [and Cardio-resp] is higher
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New initiatives for the Glamor Euro network
WHO Euro project: country specif ic 2009 pandemicmortality estimates
– GLaMOR will only publish Regional estimates – Publication of country-specific mortality estimates for the
WHO Euro Region – We would like to include some more countries (?Russia?) – We would like to produce more detailed estimates (e.g. more
age groups) – We would be able to produce more accurate estimates as the
Stage II procedure will be based on WHO Euro countries (53
countries). This would exclude global outliers (e.g. Americas)
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New global initiatives
Global mortality impact for 2010 pandemic
– Repeat analysis for 2010 and obtain more complete pictureof global pandemic impact (2009 and 2010)
Global mortality impact of seasonal influenza – Estimate mortality impact of seasonal influenza (based on
data for previous ten seasons) – Validation of WHO’s global seasonal mortality estimate
of 250,000-500,000 deaths per annum
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A k l d t C t C ll b t
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Acknowledgments -- Country Collaborators
AustraliaDavid Muscatello, Raina MacEntyre,Dominic.Dwyer
ArgentinaHoracio Echenique, Vilma Savy, LuisCarlino
BrazilWladimir Alonso, Cynthia Schuck,Fernanda Edna Araujo Moura, TerezinhaMaria de Paiva, Marcia Lopes de Carvalho
CanadaDena Schanzer
ChileRodrigo Fuentes, Andrea Olea,VivianaSotomayor, Fatima Marinho de Souza
Denmark
Jens Nielsen, Kåre MølbakGermany
Udo Buchholz, Brunhilde Schweiger
FranceMagali Lemaitre, Fabrice Carrat, BrunoLina, Sylvie van der Werf
Hong KongBen Cowling, Gabriel Leung
Israel
Michal Bromberg, Zalman Kaufman, TamarShohat, Rita Dichtiar, Yair Amikam
JapanNorio Sugaya, Shuichiro Hayashi, MegumiMatsuda
MexicoHugo Lopez-Gatell-Ramirez, CeliaMercedes Alpuche Aranda, Daniel ErnestoNoyola Cherpitel
Netherlands Adam Meijer, Kees Van den Wijngaard,Marianne van der Sande, Liselotte Van
Asten, Mirjam Kretzschmar
New ZealandMichael BakerRomania
Laurentiu Zolotusca, Adriana.Pistol, FlorinPopovici, Odette Nicolae, Rodica Popescu
A k l d t C t C ll b t
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Acknowledgments -- Country Collaborators
PolandBogdan Wojtyniak and Daniel Rabczenko
Singapore
Li Wei Ang, Mark Chen, Jeffery Cutter, VernonLee, Raymond Lin
SloveniaMaja Socan and Katarina Prosenc
Spain Amparo Larrauri Cámara, Salvador de Mateo and
colleaguesSouth KoreaUn-Yeong Go, Yeonkyeng Lee
South AfricaCheryl Cohen
United Kingdom
Richard Peabody, Helen GreenUSA
David Shay, Joseph Breese, Cecile Viboud
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Extras
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Lab-confirmed H1N1 pandemic deaths reported to WHO
Pandemic (H1N1) 2009 - update 106 (25 June 2011): http://www.who.int/csr/don/2010_06_25/en/index.html
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Pros and Cons of
GLaMOR approach
Pros
– Based on actual mortality data
– Comparable between countries (same model, outcomes)
– Comparable to historic pandemic burden estimates
– Comparable to seasonal burden estimates
Cons
– Need up-to-date weekly mortality data
– Need up-to-date weekly virology data
Especially for tropics, sub-tropics, and for 2010-11 due to H1N1 co-circulation with A/H3 and B
RSV lab data (or proxy) needed to control for RSV mortality burden
– Must project an ~25 country convenience sample to 170 countriesaround the world.
Model Form
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Model FormMultiple regression (additive model)
Outcomet=β1t + β2t
2+ β3t3 +β4-5sin(2Πt/w)+ β6-7cos(2Πt/w)
+ β7FluAt + β8FluBt + (β10RSVt)
FluA dummy variables,
For each season This βxFluAt for 2009yields the pandemicinfluenza attribution
Pandemic Excess Mortality=
=Σ βxFluAt for pandemic period, Apr1 to Dec31 2009
Dropped RSVTerm due to lack ofVirology data
sin, cos terms for ½and 1 year cycles
Virology data= numbers (not %)
Deaths/100,000 popt = running week number
Dropped log linkSAS Proc Reg
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Stage 1 Model Fit
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Stage 1 Model Fitand contribution of influenza terms in model (lift)Lift Table, expanded age groups, all outcomes
Model also fi ts well for all age groups, except for diabetes outcomes,
with a strong lift” for respiratory outcomes in age groups 5-6413/06/2012 29
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Exampleof model
output
Agegrp:Under 65 years
Outcome:Respiratory
Model
Adj R2=.85
Pandemic Flu Termsignificant (p