Download
southern hemisphere influenza and vaccine effectiveness research and surveillance shivers n.
Skip this Video
Loading SlideShow in 5 Seconds..
Southern Hemisphere Influenza and Vaccine Effectiveness Research and Surveillance (SHIVERS) PowerPoint Presentation
Download Presentation
Southern Hemisphere Influenza and Vaccine Effectiveness Research and Surveillance (SHIVERS)

Southern Hemisphere Influenza and Vaccine Effectiveness Research and Surveillance (SHIVERS)

165 Views Download Presentation
Download Presentation

Southern Hemisphere Influenza and Vaccine Effectiveness Research and Surveillance (SHIVERS)

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

  1. Southern Hemisphere Influenza and Vaccine Effectiveness Research and Surveillance (SHIVERS)

  2. Comprehensive investigation of influenza epidemiology, aetiology, immunology and vaccine effectiveness US CDC 5 year funded project Started 2012

  3. 9 objectives • Understand severe respiratory diseases caused by influenza & other pathogens • Assess influenza vaccine effectiveness • Investigate interaction between influenza & other pathogens • Understand causes of respiratory mortality • Understand non-severe respiratory diseases caused by influenza & other pathogens • Estimate influenza infection by conducting serosurvey • Identify & quantify risk factors (age, ethnicity, SES etc) for getting influenza • Assess immune response among individuals with varying disease spectrum • Estimate healthcare, societal economic burden caused by influenza and vaccine cost-effectiveness

  4. Project Team – multi-centre and multi-disciplinary collaboration • ESR—leading organization • Sue Huang—Principle Investigator (PI) • Graham Mackereth – Project Manager • Ruth Seeds – Project Officer • Science teams: • Objective 1 Severe illness Sue Huang/Sally Roberts/Colin McArthur/Cameron Grant/Debbie Williamson/Adrian Trenholme/Conroy Wong/Susan Taylor/Graham Mackereth/Don Bandaranayake/Diane Gross/Marc-Alain Widdowson: • Objective 2 Vaccine Effectiveness Nikki Turner/Heath Kelly/NevilPierse/AngeBissielo/Michael Baker/Don Bandaranayake/Sue Huang • Objectives 3 & 7 Interactions between pathogens; risk factors for flu Michael Baker: • Objective 4 causes of respiratory mortality Colin McArthur/Sally Roberts: • Objective 5 Primary Care Surveillance Sue Huang/Nikki Turner • Objective 6 infection risk Sue Huang/Don Bandaranayake: • Objective 8 immune responses Richard Webby, Paul Thomas • Objective 9 economics Des O’Dea:

  5. Study site - Auckland ADHB and CMDHB Population: 837,696

  6. Two surveillance systems • Hospital-based surveillance: enhanced, active, longitudinal (5 yrs), population based surveillance for hospital SARI cases, ICU admissions and deaths caused by influenza and other respiratory pathogens in Auckland • Community-based surveillance: enhanced, active, longitudinal (4 yrs), population based surveillance for community ILI cases caused by influenza and other respiratory pathogens in Auckland

  7. SHIVERS - Hospital SARI surveillance • all public hospitals in ADHB & CMDHB: • Auckland City hospital and StarshipChildrens hospital • Middlemore hospital and Kidz First Childrens hospital • SARI case definition: An acute respiratory illness with onset in the last 7 (10) days with a history of fever or measured fever of ≥ 38°C, and cough, requiring hospitalisation • Data captured by case report form • Medical records/lab results • Interview patients • Sample: NPS/NPA Q Sue Huang et al Implementing hospital-based surveillance for severe acute respiratory infections caused by influenza and other respiratory pathogens in New Zealand WPSAR Vol 5, No.2 2014

  8. Aims - Hospital-based surveillance (SARI) • 5-year surveillance for SARI cases • Non-SARI cases: contribution of influenza • Incidence, prevalence, demographics, clinical outcomes: SARI, influenza • Vaccine effectiveness • Etiology of SARI cases caused by influenza and other pathogens • Validity of hospital discharge data • Risk factors (pregnancy, high BMI etc):

  9. SARI Case ascertainment

  10. SHIVERS SARI and influenza cases, 2013

  11. SARI definition • Sensitivity of 84% • Specificity 31% • Positive predictive value of 17% • Negative predictive value of 92%.

  12. SHIVERS Influenza cases by type, 2013

  13. SARI related influenza hospitalisations by age groups

  14. SARI related Influenza incidence by ethnic groups

  15. SARI related Influenza incidence by socioeconomic status

  16. Known and unknown etiologies for SARI cases

  17. SHIVERS SARI - other non-influenza respiratory viruses, 2013

  18. SHIVERS - Community ILI surveillance • 18 practices: 103,752 enrolled patients (~14% ADHB & CMDHB popn) • ADHB (60,068): ~17% ADHB popn • CMDHB (43,684): ~10% of CMDHB popn • ILI case definition: An acute respiratory illness with onset in the last 10 (7) days with a history of fever or measured fever of ≥ 38°C, and cough, requiring GP consultation • Data requirement: • Data from existing PMS • Data from an advanced form (includes specimen request form) • Sample: NPS/throat swab

  19. Advanced form in MedTech

  20. 181,603 GP consultations • 2016 (1.1%) met ILI definition • 1802 (89.4%) had lab test • 448 (24.9%) flu positive ILI case definition • Sensitivity of 92% • Specificity 27% • Positive predictive value of 45% • Negative predictive value of 85%

  21. SHIVERS ILI and influenza cases, 2013

  22. SHIVERS ILI and influenza 29 April – 3 November 2013

  23. Non-influenza viruses isolated from ILI samples

  24. Influenza disease burden by age, ILI vs SARI

  25. Influenza incidence by ethnic groups, ILI vs SARI

  26. Influenza incidence by SES groups, ILI vs SARI

  27. Influenza disease burden, 2013

  28. Vaccine Effectiveness • Case test-negative design • SARI and ILI • Cases = flu positive by PCR • Controls = flu negative by PCR • Adjusted for timing of influenza season and propensity to be vaccinated = adjOR • Older, chronic diseases more likely to be vaccinated • No difference by ethnicity, gender, income, pregnancy, obesity, self rated health, smoking, assisted living, or timing of admission

  29. Estimated vaccine effectiveness (VE), overall by age group and by influenza type and sub-type: crude and propensity adjusted models *All models were adjusted for the number of weeks from the influenza peak Turner, N. M., Pierse, N., Bissielo, A., Huang, Q. S., Radke, S., Kelly, H. (2014). Effectiveness of seasonal trivalent inactivated influenza vaccine in preventing influenza hospitalisations and primary care visits in Auckland, New Zealand, in 2013. Euro surveillance: bulletin Européensur les maladies transmissibles= European communicable disease bulletin, 19(34).

  30. Population Type of outcome Level of protection (95% CIs) NISG 2014, Refs Section 4.9

  31. Conclusions: 2013 • 2013 season low incidence and late peak • Influenza activity peaked late in week 37 (mid Sept). • A (H3N2) and B most commonly detected • Very high hospitalisation rates in very young (122,100 000), then 80+ (69/100 000) • Pacific hospitalisation rates 4 times higher, Maori 1.5 times higher than other groups • Large differences by deprivation with lower quintile 4 times higher rates than upper quintile • 2013 the first year of SHIVERS ILI surveillance • Approach was acceptable to working general practice • GP visits for influenza different pattern from hospitalisations • higher rates in mid-ages • less lower socioeconomic presentations • Vaccine is ‘moderately’ effective against hospitalisation and general practice influenza

  32. …..2014 • Average flu season • Dominated by A(H1N1), occasional A(H3N2) • 12% B

  33. ….2014 • Dominated by A(H1N1) • Few A(H3N2) • 12% B Ref: ESR 2014

  34. Study participants with influenza-like illness (ILI) and severe acute respiratory infections (SARI) who were influenza positive or negative, by week, New Zealand, 28 April to 31 August 2014

  35. Estimated influenza vaccine effectiveness, by participant age group and by influenza virus type and subtype: crude plus age and time adjusted models, New Zealand, 28 April to 31 August 2014 Manuscript in preparation Turner et al 2014

  36. Gains • SHIVERS data contributed to influenza vaccination policy changes 2013 • <5 yrs with significant respiratory illness • SHIVERS data contributed to finalising WHO SARI case definitions for ‘global influenza surveillance standards’

  37. Vaccine Effectiveness: Outstanding challenges

  38. Further delineation of higher risk groups • VE by different age groups, other risk groups, history of vaccination • Do we have the right schedule? • Do we have the right vaccines? • Mediocre VE • Likely to be lower in some groups • Directed at personal protection • May be less effective in higher risk individuals

  39. Future VE • Better capture of vaccination record • NIR • Consider possible other confounders • ?previous presentations with respiratory illness • Analysis also include by history of previous vaccination • Analysis by numbers of hospitalisations and GP visits prevented

  40. Future for flu vaccines? • Schedule decisions • Personal protection versus community immunity • Ring protection around very vulnerable • Targeted high risk groups • Newer vaccines ? • Quadrivalent (x2A, x2 B) • Live attenuated for children (LAIV) • Adjuvantedfor elderly, higher risk

  41. Thank you The second SHIVERS science meeting, 7-8 November, 2012

  42. Acknowledgement • ESR: Don Bandaranayake, Ruth Seeds, Tim Wood, Ange Bissielo, Sarah Radke, Graham Mackereth, Thomas Metz, Anne McNicholas, Angela Todd, Laboratory staff, IT staff • ADHB: Sally Roberts, Colin McArthur, Debbie Williamson, Research nurses, clinical team staff, laboratory staff, IT staff • CMDHB: Adrian Trenholme, Conroy Wong, Susan Taylor, Lyndsay Le Comte, Research nurses, clinical team staff, laboratory staff, IT staff • University of Auckland: Nikki Turner, Cameron Grant, Gary Reynolds, Barbara McArdle, Tracey Poole, Anne McLean, Debbie Raroa, Carol Taylor • University of Otago: Michael Baker, NevilPierse, David Murdoch • Primarycare Advisory Group from PHOs (Procare, East Tamaki, Auckland) and ARPHS: John Cameron, Bruce Adlam, Gary Reynolds, Rosemary Gordon, Sam Wong, LeaneEls, Marion Howie, Gillian Davies • ILI sentinel practices • WHOCC-St Jude: Richard Webby, Paul Thomas • US-CDC: Marc-Alain Widdowson, Mark Thompson, Jazmin Duque, Diane Gross • Funding from US-CDC: 1U01IP000480-01