Lessons from the 2009 influenza pandemic
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Lessons from the 2009 Influenza Pandemic Marc Lipsitch How to measure severity? Per case case-fatality ratio etc. useful for evaluating treatment: who should get antivirals, hospital beds, etc.? Per capita (= per case severity X risk of infection)

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How to measure severity l.jpg
How to measure severity?

Per case

  • case-fatality ratio etc.

  • useful for evaluating treatment: who should get antivirals, hospital beds, etc.?

Per capita

(= per case severity

X risk of infection)

  • death, hospitalization rate per population

  • useful for prioritizing prevention: who should get vaccine, prophylaxis etc.

It depends on the question!


Part 1 l.jpg
Part 1

Overall severity per case


Case fatality ratio range of estimates l.jpg
Case-Fatality Ratio: Range of Estimates

Mexico May 4

509 confirmed

19 deaths (4%)

US May 4

268+786 confirmed + probable

1 death (0.1%)

Censoring bias (missing deaths)

Detection of mild cases (missing cases)

Garske et al. BMJ 14 Jul

CFR 0.2-1.2%

Focus on censoring bias


Censoring bias l.jpg
Censoring bias

Extent of bias increases with rate of epidemic growth, and with interval from onset to death


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Censoring bias: a big problem in SARS

Deaths / Cases

Deaths /

(Cases with known outcome)

Statistically valid method:C Donnelly et al. Lancet 2003

Boston Globe, May 6, 2003


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Assessing the Severity of the 2009 Pandemic in US Cities

Anne Presanis1

Daniela DeAngelis2,1

The New York City Swine Flu Investigation Team3

Angela Hagy4

Carrie Reed5

Steven Riley5

Ben S. Cooper2

Lyn Finelli5

Paul Biedryzcki4

Marc Lipsitch6

  • MRC Biostatistics Unit, Cambridge

  • HPA, London

  • NYC Dept of Health & Mental Hygiene

  • City of Milwaukee Dept of Health

  • Hong Kong Univ

  • Harvard School of Public Health

    PLoS Medicine 2009


Severity pyramid l.jpg
Severity pyramid

Dead

NYC

Hospitalized

Milwaukee

sCFR

Medically attended

CDC surveys

http://knol.google.com/k/the-severity-of-pandemic-h1n1-influenza-in-the-united-states-april-july-2009?collectionId=28qm4w0q65e4w.1&position=16#

Symptomatic

Serologically infected


Alternative approach l.jpg
Alternative approach

Dead

NYC

NYC

Hospitalized

sCFR

Medically attended

NYC phone survey

Symptomatic

Serologically infected


Age specific severity estimates l.jpg
Age-specific severity estimates

Self-reported

ILI denominator

(NYC data only)

Self-reported

frequency of

seeking care

(NYC/Milw./

CDC data)


Severity pyramid11 l.jpg
Severity pyramid

Dead

∂∂

NYC

Hospitalized

∂∂

Milwaukee

sCFR

Medically attended

∂∂

CDC surveys

Symptomatic

Serologically infected


Severity pyramid12 l.jpg
Severity pyramid

Dead

Decision to test

Test sensitivity(ies)

Reporting

Reported dead

Reported hospitalized

Hospitalized

Reported med attended

Medically attended

Care-seeking behavior

Symptomatic

Serologically infected


Problem of synthesizing evidence from various sources with associated uncertainty l.jpg
Problem of synthesizing evidence from various sources with associated uncertainty

Familiar from HIV

Methods in A Goubar et al., J R Stat Soc A 2007

Bayesian hierarchical model incorporates prior distributions and data to provide evidence synthesis: point estimates and uncertainty


Input data cities l.jpg
Input data: cities associated uncertainty


Bayesian inference l.jpg
Bayesian Inference associated uncertainty


Inputs other l.jpg
Inputs: other associated uncertainty

  • PCR 95-100% sensitive (assumption)

  • Medically attended cases tested, positive, and reported: 20-35% (CDC Epi-Aids)

  • Hospitalized cases tested, positive and reported: 20-40% (assumption)

  • Testing for NYC hospitalized cases: all in ICU, only if rapid antigen + for non-ICU (20-71% sensitivity of rapid Ag)

  • 40-58% of symptomatic cases sought care (CDC survey data)


Initial estimates l.jpg
Initial Estimates associated uncertainty

*Assumes that detection gets no worse as severity increases


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Alternate perspective associated uncertainty

New York City

Phone survey: 12% ILI during the period of high pH1N1 activity

Same survey: ~50% of symptomatic cases reported seeking care

Prior study in NYC: approx 5% of ILI cases sought care

Also found >12% ILI incidence in one month outside of flu season

  • 18:1 symptomatic to medically attended ratio

    • Despite 30% self-reported care-seeking behavior, MJ Baker pers. Comm

  • 10x lower estimate of CFR (1/20,000)

Metzger K et al. MMWR 2004


Alternative approach19 l.jpg
Alternative approach associated uncertainty

Dead

NYC

NYC

Hospitalized

sCFR

Medically attended

NYC phone survey

Symptomatic

Serologically infected


Bayesian inference approach 2 l.jpg
Bayesian Inference: Approach 2 associated uncertainty


Age specific severity estimates21 l.jpg
Age-specific severity estimates associated uncertainty

Self-reported

ILI denominator

(NYC data only)

Self-reported

frequency of

seeking care

(NYC/Milw./

CDC data)


Severity further considerations l.jpg
Severity: further considerations associated uncertainty

  • Age strongly affects severity (mildest in 5-17, most affected age group)

  • Change in age could increase severity with no change in virus

  • Change in virus, or prevalence of coinfections, could change severity


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Conclusions associated uncertainty

  • Per case severity relatively modest, with age-specific variation

  • This was not clear to most observers until late summer/early autumn

  • Data quality and completeness a constant issue despite remarkable efforts by public health officials

  • Use of data from multiple sources can be connected in a statistically rigorous way to assemble severity measures

  • Biases vary in different epidemics: here, the population dynamical biases (censoring) were outweighed by the biases of underascertainment of mild cases

  • Demonstrates value of conceptual, general approaches to optimizing interventions, as opposed to detailed ones: data aren’t good enough to support precise optimizations


Part 2 l.jpg
Part 2 associated uncertainty

Perspectives on relative severity per capita


Hospitalization highest risk in kids l.jpg
Hospitalization: Highest risk in kids associated uncertainty

Source: CDC Director’s Brief 23Oct2009


Death rates highest in adults 50 l.jpg
Death rates: highest in adults 50+ associated uncertainty

Source: CDC Director’s Brief 23Oct2009


Most adults with severe outcomes were in defined high risk groups l.jpg
Most adults with severe outcomes were in defined high-risk groups

http://www.cdc.gov/vaccines/recs/acip/downloads/mtg-slides-feb10/05-2-flu-vac.pdf


Pandemics take a relative toll on the young l.jpg
Pandemics take a (relative) toll on the young groups

Simonsen et al., J Infect Dis 1998


But in absolute terms mortality rate it was worse for adults l.jpg
But in absolute terms (mortality groupsrate) it was worse for adults


Conclusions part 2 l.jpg
Conclusions (part 2) groups

  • Simple measurements of per capita risk can inform prioritization

  • Hospitalization risk fell with age, while death risk rose

  • In absolute terms, pH1N1 was worse for adults than children

  • Compared to seasonal flu, pH1N1 was worse for children and better for adults