The Fourth Horseman: thoughts on influenza, pandemics and medicine

1. The Fourth Horseman:thoughts on influenza, pandemics and medicine Jon Temte, MD/PhD 12 November 2009 Professor of Family Medicine University of Wisconsin School of Medicine and Public Health Vice Chair, CDC Advisory Committee on Immunization Practices

2. I looked and there before me was a pale horse! Its rider was named Death… …to kill by sword, famine and plague, and by the wild beasts of the earth. The Fourth Horsemen Revelations 6:3-4

3. Katharine Anne Porter (1890-1980) Personal experience with “Spanish” flu (H1N1) in 1918 “Her mind tottered and slithered again, broke from its foundation and spun like a cast wheel in a ditch... She sank easily through deeps and deeps of darkness until she lay like a stone at the farthest bottom of life…” Pale Horse, Pale Rider, 1939

4. The Fourth Horseman The night air is cool and damp. My initial irritation at being awake at 2:15 AM is tempered by my familiarity with the patient I am driving in to admit to the hospital; well-known, pleasant, but defeated by life, and now hemorrhaging. As usual, while making my early morning on-call drive, I catch the BBC World Service on my public radio affiliate. The story deals with the World Health Organization’s efforts to discover the origins of the latest emergence: influenza A(H1N1) (S-OIV). (May 1, 2009)

5. I know that the tranquility of a Wisconsin spring, and of my life are in the process of shattering. I have been in cross training for the last 15 years… a family doctor with an interest in community patterns of infectious disease. I have been a watcher of influenza, studying its recurrent seasonal spread across my state. My skills have been in surveillance and communication. I’ve participated over the years with local, state and national pandemic planning and disaster response. I’ve climbed the ladders of professional society leadership and appointments to national advisory panels. Influenza is an old familiar friend. I know all this cold. And I am not ready.

6. The cases will mount. The calls and emails will grow beyond hope. There will be too much coordination in too many directions. Life goes on. I admit my patient, drive home and settle into a brief and fitful sleep.

7. an overview • basic epidemiology • swine influenza • lessons learned • basic rules of influenza • response

8. Basic Influenza Epidemiology

9. Necessary Conditionsfor Epidemics • exposure to pathogens • susceptible populations • appropriate environment • enhanced person-to-person contact

10. Exposure to pathogen (spark) • immigrant effect • remote communities • global nature of influenza • hemispheric oscillation • seasonality • Zoonoses (“antigenic gift”) • prevalence in numerous species • occasional interspecific transmission

11. Susceptible populations (fuel) • role of antigenic drift • minor year-to-year change in antigens • role of antigenic shift • major change in antigens • hemaglutinin and neuraminidase • immunization • based upon strains likely to circulate • high risk population prophylaxis

12. Neuraminidase Hemaglutinin Source: Dr. Timothy Paustian www.bact.wisc.edu/ microtextbook

13. Environment (condition) • climatic • temperature • humidity • monsoon • anthropogenic • long-term trends

14. Seasonal Correlates of Influenza A in Wisconsin ( Wisconsin Influenza Isolates: 7/1/80 through 6/30/05 )

15. Enhanced contact (catalyst) • cultural / economic • school contacts • employment contacts • the commute • physical • confined spaces (e.g., air travel) • social • holidays

16. Vectors of Respiratory Viruses Mom Dad • Childhood illness • school-aged children • age 5 through 18 • high attack rates • up to 40% • variable symptoms • transmission • parents, grandparents • siblings “we’re vectors” Monday, December 13, 2004 Virus culture on December 20, 2006 positive for Influenza A H3N2 California

17. Influenza in the Community

18. The role of school… Seasonality - the year starting in September… Wisconsin: 9/2007 to 8/2008 UW – Family Medicine Clinical Data Warehouse

19. 1957 Pandemic – Kansas Cityattack rate and mortality Information taken from: Serfling et al., Am J Epidemiol 1967;86:433-41 Chin et al., J Public Health Rep 1960; 75:149-58

20. Basic Epidemic Math

21. Nt = Noert Nt = the number of cases at time “t” No = the number of cases at time 0 e = the natural exponential t = the number of generations

22. A word about “r” • r is the intrinsic rate of growth • can be considered in terms of how many new cases are caused by each existing case • equal to the natural logarithm of the number of new cases per existing case r = ln(N1/N0) If 2 cases caused by current case: r = 0.693 If 3 cases caused by current case: r = 1.099

23. Example: effect of “r”

24. This would lead to exponential growth ...

25. ... except that “r” rapidly declines after a period of relative stability Susceptible individuals become less prevalent In general population

26. because r declines, a classic epidemic curve emerges

27. This is reflected in the average epidemic curve for Wisconsin Peak Transition First Cases Last Cases

28. Modeling Annual Influenza A Epidemics in Wisconsin r = 0.693; pre-season immunity = 30%

29. Effect of pre-season immunityon shape and timing of epidemic curve Time

30. Effect of pre-season immunityon total percent of population infected

31. As the two friends wandered through the snow on their way home, Piglet grinned to himself, thinking how lucky he was to have a best friend like Pooh Swine Flu and Pooh Pooh thought to himself: “If the pig sneezes, he’s … dead.” … Image: CDC/ C. S. Goldsmith and A. Balish

32. Novel Influenza A (H1N1) • swine-originated influenza virus • Genes from • NA pigs • EA pigs • Humans • Turkeys • Chickens • Rapidly evolving situation • April 21 – case report • -2 cases in California Children

33. Doubling Time Global: 1.8 days US: 2.1 days

35. Current Status Report • Global (November 1, 2009) • 482,300 cases • 6,071 deaths (case fatality rate = 1.26%) • U.S. (November 6, 2009) • 17,838 hospitalizations • 672 deaths • Wisconsin (November 6, 2009) • 10,685 confirmed or probable cases • 20 deaths (case fatality rate = 0.094%) • My best guess for case fatality rate = 0.002 to 0.010%

38. Cumulative Casesof novel H1N1 August 30 - November 6Wisconsin, by county

39. Cumulative Ratesof novel H1N1 August 30 - November 6Wisconsin, by county

40. Outpatient influenza-like illness visits

41. Pneumonia and influenza death percent

43. Pediatric Deaths High Risk Condition Any 67% Neurodevelopmental 61% Multiple ND 36% Chronic Pulmonary 28% ND with CPD 25% Congenital Heart 8% Metabolic/Endocrine 6% Immunosuppression 6%

44. H1N1 and pregnancy • Pregnancy is a significant risk factor • Women of childbearing age have increased exposure • Case rate = 1/100,000 pregnancies • four-fold increased rate of hospitalization • Case series: 11 admissions out of 34 cases (32.4%) • 0.32/100,000 in pregnancy • 0.076/100,000 for general population • 6 deaths (antivirals started 8-15 days post-onset) • 5 in 3rd trimester (C-section); 1 in first trimester

45. Signs and Symptoms

46. Basic Demographics • Sex Ratio • female = male • Median Age • All cases = 12 years • Hospitalized cases = 20 years • Deaths = 37 years

48. 1957 Pandemic – Kansas Cityattack rate and mortality Information taken from: Serfling et al., Am J Epidemiol 1967;86:433-41 Chin et al., J Public Health Rep 1960; 75:149-58

49. Hospitalization and Fatality Rates and Case-Fatality Proportion Among Reported Hospitalized Cases California, April 23 Through August 11, 2009 Louie, J. K. et al. JAMA 2009;302:1896-1902.

50. Age Distribution of Hospitalizations