Lessons from the 2009 Influenza Pandemic

1. Lessons from the 2009 Influenza Pandemic Marc Lipsitch

2. 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!

3. Part 1 Overall severity per case

4. 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

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

6. 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

7. 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

8. 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

9. Alternative approach Dead NYC NYC Hospitalized sCFR Medically attended NYC phone survey Symptomatic Serologically infected

10. Age-specific severity estimates Self-reported ILI denominator (NYC data only) Self-reported frequency of seeking care (NYC/Milw./ CDC data)

11. Severity pyramid Dead ∂∂ NYC Hospitalized ∂∂ Milwaukee sCFR Medically attended ∂∂ CDC surveys Symptomatic Serologically infected

12. 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

13. 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

14. Input data: cities

15. Bayesian Inference

16. Inputs: other • 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)

17. Initial Estimates *Assumes that detection gets no worse as severity increases

18. Alternate perspective 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

19. Alternative approach Dead NYC NYC Hospitalized sCFR Medically attended NYC phone survey Symptomatic Serologically infected

20. Bayesian Inference: Approach 2

21. Age-specific severity estimates Self-reported ILI denominator (NYC data only) Self-reported frequency of seeking care (NYC/Milw./ CDC data)

22. Severity: further considerations • 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

23. Conclusions • 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

24. Part 2 Perspectives on relative severity per capita

25. Hospitalization: Highest risk in kids Source: CDC Director’s Brief 23Oct2009

26. Death rates: highest in adults 50+ Source: CDC Director’s Brief 23Oct2009

27. 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

28. Pandemics take a (relative) toll on the young Simonsen et al., J Infect Dis 1998

29. But in absolute terms (mortality rate) it was worse for adults

30. Conclusions (part 2) • 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