respiratory disease burden in the Americas Azziz...Estimating influenza and other respiratory disease burden in the Americas Eduardo Azziz-Baumgartner, Sara Mirza, Po-Yung, Chen, Wilfrido

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

• 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

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

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

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

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

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

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

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

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

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

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

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

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 =


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


health utilization survey: the El Salvador example

Pros Leverages routine influenza

surveillance data

Readily provides numerator data from

health seekers




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


Health utilization surveys:

Logistically complex

Labor intensive

Sentinel data prone to magnify

imprecisions when projected

nationally

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

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

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

Pros

Leverages administrative data and

viral influenza surveillance data

Inexpensive and easy to do

(i.e. no field work)




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


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

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

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

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

Documents

respiratory disease burden in the Americas Azziz...Estimating influenza and other respiratory disease burden in the Americas Eduardo Azziz-Baumgartner, Sara Mirza, Po-Yung, Chen, Wilfrido