respiratory disease burden in the americas azziz...estimating influenza and other respiratory...
TRANSCRIPT
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Estimating influenza and other
respiratory disease burden in the
Americas
Eduardo Azziz-Baumgartner, Sara Mirza, Po-Yung, Chen,
Wilfrido A Clara, Lucinda Johnson, Rakhee Palekar, Danielle
A. Iuliano, Jorge Jara, Daniel Bausch, Yeny O Tinoco, Ann
Moen, Joseph Bresee, Marc-Alain Widdowson,
Admission CT of SARI case-patient with
influenza
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• PAHO/WHO collaborations to advance:
• Surveillance improvements and integration
• Compliance with IHR
• Pandemic preparedness and response
• Partnering with sites to explore critical questions
• Seasonality of influenza and other viruses
• Disease and economic burden estimates
• Seasonal influenza vaccine effectiveness
• Oseltamivir effectiveness
Successful as a result of seven years of
collaboration
Influenza Division
International
Activities: Annual
Report 2012
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Surveillance improvements: the example
of Central America
Number of specimens
processed and reported to
FluNet for 7 Central
American countries
2004–2012
Johnson L, Clara WA, Gambhir M, Chacón R, Marin C, Jara JH et al Changes in
Influenza Pandemic Preparedness in Central America: Results from Evaluations
conducted in 8 Countries between 2008 and 2012, Atlanta, GA 2013
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Preparedness by capability improvements in eight Central American countries
* Difference in average score between 2008
and 2012 is statistically
significant at p ≤ 0.05
Johnson L, Clara WA, Gambhir M, Chacón R, Marin C, Jara JH et al Changes in
Influenza Pandemic Preparedness in Central America: Results from Evaluations
conducted in 8 Countries between 2008 and 2012, Atlanta, GA 2013
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Influenza epidemic period in the tropics
Average
proportion of
respiratory
samples testing
positive for
influenza each
month since the
start of viral
respiratory
surveillance
The red line indicates the average influenza proportion
positivity for each country that when exceeded suggests the
months of typical influenza epidemic activity
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Influenza and other respiratory viruses
disease and economic burden: why we care
Public health officials triage which preventable
public challenges cause the greatest burden :
(e.g. relative burden of dengue vs. influenza)
Information about burden helps officials better
explore the potential value of interventions:
Targeted seasonal influenza vaccine programs
Empiric oseltamivir treatment during the season
Hand washing and respiratory hygiene campaigns
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Multiple methods to estimate the
incidence of respiratory viruses Population based projects and cohorts
Linear models using hospital discharge and
decedent code data
Multiplier models using:
Surveillance sentinel site data and health
utilization surveys
National/subnational administrative data
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Population based surveillance: the
example of the Peru cohorts
Enrolled ~10,000 persons in ~2000 households
living in 4 diverse communities during 2009–2012
Contacted 2–3/week to identify ILI
Throat and nasal swabs collected from ill
Tested for respiratory viruses through rRT-PCR
Identified subsequent hospitalization or death
Influenza rates = Influenza positive participants
Person time
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Preliminary influenza-like illness and laboratory-confirmed
influenza incidences rates (per 1,000 person years) by year
and study sites, Peru Influenza Cohorts, 2009–2011
Influenza—like Illness
2009-2011 373 ( 355 —392 ) 298 ( 282 — 316 ) 415 ( 396 —436 ) 311 ( 293 —329 ) 351 ( 342 —360 )
Influenza (A+B)
2009 217 ( 188 —251 ) 31 ( 20 — 48 ) 239 ( 206 —277 ) 111 ( 90 —138 ) 154 ( 141 —169 )
2010 139 ( 122 —159 ) 114 ( 98 — 132 ) 162 ( 144 —183 ) 123 ( 107 —142 ) 135 ( 126 —144 )
2011 26 ( 19 —35 ) 65 ( 54 — 79 ) 26 ( 19 —35 ) 58 ( 47 —72 ) 43 ( 38 —49 )
2009-2011 112 ( 102 —122 ) 79 ( 71 — 89 ) 120 ( 110 —131 ) 96 ( 86 —106 ) 102 ( 97 —107 )
Seek health care
2009 119 ( 98 —145 ) 10 ( 5 — 22 ) 147 ( 121 —177 ) 46 ( 33 —64 ) 83 ( 74 —94 )
% 55 33 61 42 54
2010 70 ( 58 —84 ) 39 ( 30 — 50 ) 80 ( 67 —95 ) 39 ( 30 —50 ) 58 ( 52 —64 )
% 50 34 49 32 43
2011 10 ( 6 —17 ) 22 ( 16 — 31 ) 13 ( 8 —19 ) 23 ( 16 —32 ) 17 ( 14 —20 )
% 39 33 49 39 39
2009-2011 57 ( 50 —65 ) 27 ( 22 — 33 ) 65 ( 57 —73 ) 34 ( 29 —41 ) 46 ( 43 —50 )
% 51 34 54 36 45
Influenza A(H1N1)pdm09
2009 215 ( 186 —249 ) 30 ( 19 — 46 ) 223 ( 191 —260 ) 104 ( 84 —130 ) 148 ( 135 —162 )
2010 23 ( 16 —31 ) 40 ( 31 — 51 ) 15 ( 10 —23 ) 8 ( 5 —14 ) 22 ( 18 —26 )
2011 4 ( 2 —8 ) 1 ( 0 — 5 ) 7 ( 4 —12 ) 12 ( 7 —19 ) 6 ( 4 —8 )
2009-2011 55 ( 49 —63 ) 22 ( 18 — 28 ) 50 ( 43 —57 ) 29 ( 24 —35 ) 40 ( 37 —43 )
Influenza A(H3N2)
2009 2 ( 1 —9 ) 1 ( 0 — 11 ) 12 ( 6 —23 ) 3 ( 1 —11 ) 5 ( 3 —8 )
2010 58 ( 48 —71 ) 27 ( 20 — 36 ) 96 ( 82 —112 ) 80 ( 67 —96 ) 65 ( 59 —72 )
2011 22 ( 15 —30 ) 64 ( 53 — 78 ) 10 ( 6 —17 ) 46 ( 36 —58 ) 35 ( 30 —40 )
2009-2011 32 ( 27 — 38 ) 38 ( 32 — 44 ) 45 ( 39 — 52 ) 51 ( 44 — 59 ) 41 ( 38 — 45 )
Influenza B
2009 0 — 0 — 3 ( 1 —11 ) 0 — 1 ( 0 —3 )
2010 58 ( 48 —71 ) 47 ( 38 — 59 ) 51 ( 41 —63 ) 35 ( 27 —46 ) 48 ( 43 —54 )
2011 1 ( 0 —4 ) 0 — 9 ( 5 —15 ) 1 ( 0 —5 ) 3 ( 2 —4 )
2009-2011 24 ( 20 — 29 ) 19 ( 16 — 24 ) 25 ( 20 — 30 ) 15 ( 11 — 19 ) 21 ( 19 — 23 )
Hospitalized
2009-2011 0.5 ( 0.1 —2.0 ) 1.1 ( 0.4 — 2.8 ) 0.2 ( 0.0 —1.8 ) 1.6 ( 0.7 —3.6 ) 0.8 ( 0.5—1.4 )
4 sites (95% CI)IncidenceStudy sites
Lima (95% CI) Tumbes (95% CI) Madre de Dios (95% CI)Cuzco (95% CI)
Tinoco YO, Rázuri H, Kasper MR, Romero C, Fernandez ML, Breña P, et al. Influenza in four
ecologically distinct regions in Peru: Active household-based community cohort study. I.
Epidemiology and regional variation in incidence of laboratory-confirmed influenza, 2009-2011,
NAMRU 6, Lima, Peru 2012
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Population based surveillance: the
example of Peru cohorts
Pros Provides high quality data
Best suited to estimate incidence
of infections and mild illness
Useful to estimate incidence of
influenza, RSV, human
metapneumovirus, etc.
Allows investigators to assess
secondary household transmission
Useful to identify modifiable risk
factors (e.g. second hand smoke)
Excellent platform for randomized
control trials
Cons Expensive
Labor intensive
Non-sustainable
Require IRB
May underestimate
hospitalizations/deaths
Politically sensitive
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Linear models: the Brazil example Quantify the weekly/monthly number of
respiratory and circulatory
death/hospitalizations
Assess the proportion of respiratory
samples positive for influenza and
other viruses
Use the non-epidemic number of
deaths/hospitalizations and a linear
model to predict the number expected
during influenza epidemic
weeks/months
Quantify the excess respiratory and
circulatory cases during the influenza
season from the predicted (the number
we may attribute to influenza)
20000
30000
40000
50000
12 24 36 48
Consecutive month
Observed respiratory and cardiac hospitalizations
Modelled respiratory and cardiac hospitalizations
Upper 95% confidence interval of modeled hospitalizations
Excess
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Linear models: the Brazil example
F. T. M. FREITAS, L. R. O. SOUZA, E. AZZIZBAUMGARTNER, P. Y. CHENG, H. ZHOU, M. A.
WIDDOWSON, D. K. SHAY, W. K. OLIVEIRA and W. N. ARAUJO Influenza associated excess
mortality in southern Brazil, 1980–2008. Epidemiology and Infection, Available on CJO 2012
doi:10.1017/S0950268812002221
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Linear models: the Brazil example
Pros
Does not involve field work
Uses commonly available
administrative data
Simple and inexpensive
May be used to explore rate
ratios among demographic
groups targeted for vaccines
Yield similar influenza rates to
more intensive data collection
Cons
Requires:
Weekly or monthly data
Populations > 5 million
Time-series > 3–5 years
Defined epidemic periods
Stable data collection
Difficult to disaggregate
excess attributable to RSV
Not useful to assess
ambulatory influenza illness
necessary for economic
burden
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Multiplier model using influenza sentinel sites and
health utilization survey: the El Salvador example
Add number of severe acute respiratory infections
identified through the PAHO/CDC influenza
surveillance at sentinel hospitals per month (Ck)
Estimate the proportion of case-patients testing
positive for influenza per month (p/t)
Conduct a health utilization survey to assess
proportion in catchment with ILI subsequently
hospitalized at sentinel vs. other sites (u)
Divide by catchment census population (P) and adjust
for SARI sampling proportion (S)
yy
kk
Pus
cdecember
januarykt
p
ISARI/severe pneumonia, y =
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Multiplier model using influenza sentinel sites and
health utilization survey: the El Salvador example Estimation components 2008 2009 2010 Total
Case-patients identified at the sentinel site, No.* 495 1367 692 2554
Case-patients who were tested for respiratory viruses, No. 153 217
238
608
Case-patients who tested (+) for Flu, No.(%) [95% IC]† 11 (7) [3–11] 21 (10) [6–14] 5 (2) [0.2–4] 37 (6) [4–8]
Influenza-associated case-patients [imputed data], No. 33 165 14 212
aCensus population aged < 5 years of catchment area, No.
36,858
37,016
37,205 111,079
bCensus population that utilizes the sentinel site when ill, (%) 63 63 63 63
cDays when surveillance was carried out, No./365 (%) 343/365 (94) 340/365 (93) 339/365 (93) 1022/1095 (93)
Adjusted census population, person-years 21,827 21,688 21,798 65,081
Incidence rate, cases per 1,000 person-years (95% CI)†† 1.5 (1–2) 7.6 (6.5–8.9) 0.65 (0.3–1) 3.2 (2.8–3.7)
Clara A, Armero J, Rodriguez D, Lozano C, Bonilla L, Minaya P et al. Influenza-associated severe
pneumonia rates in children younger than 5 years in El Salvador, 2008–2010. WHO Bulletin, In
press. Available at http://www.who.int/bulletin/online_first/11-098202.pdf
http://www.who.int/bulletin/online_first/11-098202.pdfhttp://www.who.int/bulletin/online_first/11-098202.pdfhttp://www.who.int/bulletin/online_first/11-098202.pdf
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Multiplier model using influenza sentinel sites and
health utilization survey: the El Salvador example
Pros Leverages routine influenza
surveillance data
Readily provides numerator data from
health seekers
Useful to estimate incidence of
influenza, RSV, human
metapneumovirus, etc.
May be used to explore rate ratios
among demographic groups targeted
for vaccines
Yields similar influenza rates to more
intensive data collection methods
Cons Requires sufficient:
Weekly case-patients
Respiratory sampling by
age strata
Stable data collection
Health utilization surveys:
Logistically complex
Labor intensive
Sentinel data prone to magnify
imprecisions when projected
nationally
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Multiplier model using administrative data and viral
surveillance: the Costa Rica example
Identify national hospital discharge diagnostic data
Quantify the number of respiratory discharges per
week and age group
Identify routine influenza viral surveillance data
Estimate the proportion of patients testing positive for
influenza per week and their 95% confidence interval
Use census population as denominator
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Influenza-associated hospitalizations for respiratory
illnesses in Costa Rica during 2005–2011
Year Age group Hospitalizations for
respiratory illness
Proportion of
influenza positive
samples (%)
Influenza-associated
hospitalizations for
respiratory illness a
Person-years
b
Influenza -associated
hospitalization for
respiratory illness
rate per 100,000
person-years a
2006c
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Respiratory syncytial virus-associated hospitalizations for
respiratory illnesses in Costa Rica during 2005–2011
Guzman G, Clara AW, Garcia A, Quesada F, Palekar R, Schneider E, Peret T et al. Influenza
testing through immunofluorescence and polymerase chain reaction. Influenza and respiratory
syncytial virus-associated hospitalizations and deaths in Costa Rica, during 2005–2011. San Jose
Costa Rica, 2013.
Year Age group Hospitalizations for
respiratory illness
Proportion of
respiratory
syncytial virus
positive samples
(%)
Respiratory syncytial
virus-associated
hospitalizations for
respiratory illness a
Person-years
b
Respiratory syncytial
virus-associated
hospitalization for
respiratory illness
rate per 100,000
person-years a
2005
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Pros
Leverages administrative data and
viral influenza surveillance data
Inexpensive and easy to do
(i.e. no field work)
Useful to estimate incidence of
influenza, RSV, human
metapneumovirus, etc.
May be used to explore rate ratios
among groups targeted for vaccines
Yields similar influenza rates to more
intensive data collection methods
Cons Requires sufficient:
Weekly viral data
Stable data collection
Respiratory cases are not
necessarily those sampled:
Prone to bias because of the
potential differences in influenza proportion positivity among diagnosed
and sampled:
Age and sex distribution
Severity of illness
Health seeking patterns
Multiplier model using administrative data and viral
surveillance: the Costa Rica example
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AMRO countries that have or intend to publish influenza rates Published
Mortality :
Argentina
Brazil
Mexico
Hospitalization
Argentina
El Salvador
Mexico
Outpatient illness
Argentina
Guatemala
Ongoing Mortality –all AMRO
Hospitalization
Belize
Brazil
Costa Rica
Guatemala
Honduras
Panama
Paraguay
Peru
Outpatient illness
Costa Rica
Ecuador
Guatemala
Nicaragua
Peru
Po-Yung Cheng
modeling of
influenza mortality
burden
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Conclusion and recommendations
Multiple methods to leverage existing data to estimate
influenza and other virus burden
Consider using multiple methods
WHO burden guidelines will soon be released
Fully operationalize and audit PAHO ILI and SARI
surveillance guidelines to better:
Comply with IHR and rapidly identify novel strains
Inform vaccine composition
Estimate:
Respiratory influenza RSV, human metapneumovirus burden
Estimate direct and indirect cost of in and outpatient care
Vaccine coverage, effectiveness and value
Model potential value of empiric seasonal antiviral treatment
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Gracias Alexander Klimov
Alba Maria Ropero
Ana María Cabrera
Ana Balanzat
Ann Moen
Anna Brinkley
Anthony Mounts
Clarisa Baez
Carolyn Bridges
Dan Bausch
Felipe Freitas
Elena Sarrouf
Elena Pedroni
Horacio Echenique
Jackie Katz
Joel Montgomery
Jorge Jara
Joseph Bresee
Mauricio Cerpas
Meg McCarron
Marc-Alain Widdowson
Nancy Cox
Natalia Blanco
Nivaldo Linares Perez
Otavio Oliva
Osvaldo Uez
Percy Minaya
Rafael Chacón
Rakhee Palekar
Ricardo Cortez-Alcala
Romina Cuezzo
Rogelio Cali
Steve Lindstrom
Thais dos Santos
Tomas Rodriguez
Wanderson Oliveira
Wilfrido Clara
México Dirección General de Epidemiología