Challenges of Influenza Control W. Paul Glezen, M.D. Baylor College of Medicine Houston

Newly Recognized Respiratory Agents 1. SARS coronavirus – SARS - CoV 2. Human metapneumonvirus - hmpv 3. Avain influenzaviruses a) A (H5N1) b) A (H7N7) 4. Hendra – Nipah viruses

Hemagglutinin Subtypes of Influenza A Virus Adapted with permission from Levine AJ. Viruses. 1992;165.

Type of NucleoproteinHemagglutinin Neuraminidase Virus Type Geographic Origin Strain Number Year of Isolation Virus Subtype A/USSR/90/77 (H1N1) Influenza Virus Nomenclature

Antigenic Variants of Influenza A (H3N2) and Changing Hemagglutinin Amino Acid Positions Year Variant A/Hong Kong/ A/England/ A/Port Chalmers/ A/Victoria/ A/Texas/ A/Bangkok/ A/Philippines/ A/Stockholm/ A/Sichuan/ A/Shanghai/ A/Beijing/ A/Beijing/ A/Shangdong/ A/Johannesburg/ A/Wuhan/ A/Sydney/ A/Panama/99 Smith etal J Infect Dis 2002;185:980-5.

ESTIMATED ANNUAL AGE-SPECIFIC INFLUENZA DEATHS FOR THROUGH SEASONS* Age InfluenzaInfluenza Influenza GroupA(H1N1)A(H3N2)BTotal < , , , ,8667,159 43,979 Totals2,83740,0178,34951,203 *Thompson, WW, et al, JAMA 2003; 289:179-86

Season A(H1N1)A(H3N2)BTotal ,9886,03317,54925, ,51845, , ,19019,89219,03040, , , ,7677,12941, ,72723,6057,50945, ,93712,60968, , , ,3679,69865,358 Mean (SD)2,836 (4,909)40,017 (20,656)8,349 (7,105)51,203 (15,081) Estimated Annual Influenza-Associated Deaths for Through Seasons Using the Influenza Model No. of Influenza Deaths All-Cause Deaths Thompson WW et al. JAMA. 2003;289: Abbreviations: NA, not applicable. *Pneumonia and influenza estimates are based on the through seasons.

AGE-SPECIFIC ANNUAL AVERAGE RATES FOR INFLUENZA-ASSOCIATED HOSPITALIZATIONS, Age NumberRate/10,000 < 5 21, , , , , , , >85 57, Totals294,

ANNUAL INFLUENZA-ASSOCIATED HOSPITALIZATIONS, U.S., Year Predominant NumberRate/10,000 Virus B 221, A(H3N2) 326, B+A(H3N2) 304, A(H3N2) 322, A(H3N2)+B 288, A(H1N1)+(H3N2) 296, A(H3N2)+B 490, A(H3N2) 530, A(H3N2)+B 503, A(H3N2) 544, A(H1N1)+B 316,

Estimated number of persons, influenza vaccine target groups, United States, July 1, 2002 GroupPopulation (millions) Increased Risk82.8 Aged > 65 y35.6 Chronic illness39.7 Pregnant women2 Other children aged 6-23 mo5.5 Other (healthy) target groups102.6 Health care personnel aged < 65 y7 Household contacts of persons at increased risk 75.5 Other persons aged y20.1 Total, target groups185.4 Total, persons aged > 6 mo O’Mara etal, Infect Med 2003 (Nov)

Problems With Targeting High Risk Patients  High risk patients are not easily accessible for vaccination  Many high risk patients are debilitated or immunocompromized and fail to respond optimally to vaccine

Update: Influenza Activity, US. January , Centers for Disease Control and Prevention. MMRW January 30, 2004 / 53(03);63-65

<6 months old11% 6-23 months old30% 2-5 years old22% >5 years old37% ACIP high-risk condition27% Other underlying medical condition31% Previously healthy40% Unknown2% Influenza Mortality in U.S. Children 2003/ Children <18 years reportedly died of Influenza-related causes* *70 percent of these children had not been vaccinated. Bhat N. ACIP, June 23, 2004.

Influenza Virus Infection and Illness Rates Houston Family Study, <  35 Age (years) Rate Per 100 Persons

Excess illness episodes28* Secondary illness episodes (family members)22* Days of work missed by parents20* Average school days missed/child2.25* Impact of Influenza on School Children and Their Families Influenza-associated outcomesRate/100* *Prospective cohort study of 313 children (K-8) in 216 families followed during 1 influenza season. Neuzil KM, et al. Arch Pediatr Adolesc Med., 2002;156:

Excess Hospitalizations per 10,000 Children/Year Patient Age Average Excess Hospitalizations per 10,000 Children/Year* *Values are weighted averages of annual excess hospitalizations for a population of 10,000 persons within the specified age group. Neuzil KM et al. N Engl J Med. 2000;342:

Respiratory Virus Infections Associated with Hospitalizations for Acute Respiratory Conditions, Houston, Influenza Viruses Parainfluenza Viruses <  65 Age (years) Number Positive per 100

Bacterial Disease in Children with Proven Precursor Influenza 1.Severe Pneumococcal Pneumonia in Previously Healthy Children: The Role of Preceding Influenza Infections. O’Brien KL et al. Clin Infect Dis 2000;30: Risk-Factors for Meningococcal Disease in Victoria, Australia, in Robinson P et al. Epidemiol Infect 2001;127: Is Bacterial Tracheitis Changing? A 14-Month Experience in a Pediatric Intensive Care Unit. Bernstein T et al. Clin Infect Dis 1998;27: Glezen WP. Prevention of Acute Otitis Media by Prophylaxis and Treatment of Influenza Virus Infections. Vaccine 2001; 19:S56-S58.

Other Complications of Influenza Acute myositis Neurologic –Reye’s syndrome –Encephalopathy –Febrile convulsions Cardiac –Pericarditis –Myocarditis

Rationale for Alternative Approaches  School children and working adults are the major spreaders of influenza in the community and introducers into the household  School children have the highest annual attack rate for influenza

Rationale for Alternative Approaches  Immunization of school children and working adults to:  decrease absenteeism for school and work  decrease visits for medical care  decrease antibiotic prescriptions

Influenza Vaccinations in Japanese School Children all-cause deaths/100,000 P&I deaths/100,000 (age adj LT/GE 65 & ref US 1970 pop) (A) all cause baseline (B) all cause excess (C) P & I baseline (D) P& I excess A B C D Reichert, TA Seminars Pediatr Infect Dis; 13:104-11

Site of CAIV-T Field Trial Central Texas

Non-randomized, Open Label Field Trial of Trivalent Cold Adapted Influenza Vaccine (CAIV-T) in Central Texas, Indirect Effectiveness (Herd Immunity) – Age-specific rates of medically-attended acute respiratory illness (MAARI) for the intervention site compared to those for the comparison sites. Direct Effectiveness and Adjusted Efficacy – MAARI rates in CAIV-T recipients compared to rates in 9,325 age-eligible non-recipients at the intervention site and adjusted for culture-positive MAARI. Total Effectiveness – MAARI rates in CAIV-T recipients compared to rates in 16,264 age-eligible non-recipients in the comparison sites. Safety: a) Occurrence of serious adverse events (SAEs) for 42 days after vaccination b) Occurrence of rare events associated with natural influenza virus infection c) Comparison of MAARI rates 0-14 days after vaccination to the pre- vaccination.

MAARI Rates in the Intervention and Comparison Sites during Influenza Outbreaks for SWHP Members > 35 years old MAARI rates per 100 person-season Overall effectiveness (1-RR) YearIntervention site: T-B Comparison site: W/B-CS 95% CI ; base line ; Year ; Year ; Year

CAIV-T Direct Effectiveness for all MAARI and Adjusted Efficacy for Culture-Positive MAARI with both Influenza A(H1N1) and B, Temple-Belton, TX, Age Direct (95% CI)Adjusted (95% CI) (years)EffectivenessEfficacy *(0.14,0.25)0.91(-0.34,0.99) (0.15,0.34)0.80(0.26,0.95) (0.01, 0.26)0.70(0.13,0.90) Total0.18(0.11,0.24)0.79(0.51,0.91) SubsetsInfluenza A(H1N1)0.92(0.42,0.99) Influenza B0.66(0.09,0.87) *statistically significant in bold numbers

Safety Summary Years 1, 2, 3 and 4: 18,780 doses of CAIV-T have been administered to 11,096 children in this community-based, open-label trial No CAIV-T vaccine attributable serious adverse event has been observed No CAIV-T vaccine attributable rare or unusual adverse event has been observed Six pregnancies originating proximal to receipt of vaccine were uncomplicated (healthy full-term infants).

CAIV-T FIELD TRIAL Summary 1.Safe-side effects do not increase direct medical costs. 2.Direct Effectiveness a.Protection inversely related to age (VEadj ) b.Persists through two seasons c.Heterovariant d.Single dose is sufficient 3.Indirect Effectiveness (Herd Immunity) – For proportion vaccinated compatible with Longini Model.

Implications for Control of Both Interpandemic and Pandemic Influenza  School children have the highest attack rates for influenza.  School children are the principle spreaders of influenza.  School children are accessible for rapid distribution of influenza vaccine.

Acknowledgements W. Paul Glezen – Control of Epidemic Influenza Grant Co-Investigators:Pedro A. Piedra – PI, Baylor College of Medicine Mangusha Gaglani – PI, Scott & White Clinics Gayla Herschler – Coordinator, S & W Mark Riggs – Biostatistics, S & W Claudia Kozinetz – Analysis and Data Management, BCM Consultants:Ira Longini – Emory University Elizabeth Halloran – Emory University Vaccine:Paul Mendelman, MedImmune Vaccines Colin Hessel, Biostatistics, Safety Analysis, MedImmune Program Officer:Linda Lambert - NIAID