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

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Presentation on theme: "Southern Hemisphere Influenza and Vaccine Effectiveness Research and Surveillance (SHIVERS)"— 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 1.Understand severe respiratory diseases caused by influenza & other pathogens 2.Assess influenza vaccine effectiveness 3.Investigate interaction between influenza & other pathogens 4.Understand causes of respiratory mortality 5.Understand non-severe respiratory diseases caused by influenza & other pathogens 6.Estimate influenza infection by conducting serosurvey 7.Identify & quantify risk factors (age, ethnicity, SES etc) for getting influenza 8.Assess immune response among individuals with varying disease spectrum 9.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/Nevil Pierse/Ange Bissielo/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 Starship Childrens 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

8 Aims - Hospital-based surveillance (SARI) 1.5-year surveillance for SARI cases 2.Non-SARI cases: contribution of influenza 3.Incidence, prevalence, demographics, clinical outcomes: SARI, influenza 4.Vaccine effectiveness 5.Etiology of SARI cases caused by influenza and other pathogens 6.Validity of hospital discharge data 6. 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 Non-influenza Respiratory VirusesNumber (%) No. of specimens tested870 No. of positive specimens388 Rhinovirus168 (44) Respiratory Syncytial Virus162 (42) Parainfluenza55 (14) - Parainfluenza 3-34 % of all PIV - Parainfluenza 2-18 % of all PIV - Parainfluenza 1-3 % of all PIV Human metapneumovirus46 (12) Single virus detection (% of positive)303 (78) Multiple virus detection (% of positives)85 (22)

18 SHIVERS SARI - other non-influenza respiratory viruses, 2013

19 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

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21 Advanced form in MedTech

22 181,603 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%

23 SHIVERS ILI and influenza cases, 2013

24 SHIVERS ILI and influenza 29 April – 3 November 2013

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26 Non-influenza viruses isolated from ILI samples Non-influenza Respiratory VirusesNumber (%) No. of specimens tested1686 No. of positive specimens552 Rhinovirus221 (40%) Respiratory Syncytial Virus154 (28%) Parainfluenza97 (17.5%) - Parainfluenza 243 (8 %) - Parainfluenza 343 (8%) - Parainfluenza 111 (2%) Human metapneumovirus56 (10%) Single virus detection (% of positive)495 (89.7%) Multiple virus detection (% of positives)57 (10.3%)

27 Influenza disease burden by age, ILI vs SARI

28 Influenza incidence by ethnic groups, ILI vs SARI

29 Influenza incidence by SES groups, ILI vs SARI

30 Influenza disease burden, 2013

31 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

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33 Estimated vaccine effectiveness (VE), overall by age group and by influenza type and sub-type: crude and propensity adjusted models Hospitalised with Severe Acute Respiratory Illness General Practice visit for Influenza-like illness Crude Model* Propensity Adjusted Model* Crude Model* Propensity Adjusted Model* VE % (95%CI) Overall 32 (7,50) 52 (32, 66) 56 (37,70) 56 (34,70) Influenza type or sub- type A(H1N1) 25 (-132,76) 48 (-74,85) 50 (-68,85) 49 (-90,86) A(H3N2) 11 (-33,40) 34 (-2,57) 56 (27,74) 61 (32,77) All A 15 (-21,40) 39 (10,58) 55 (29,71) 58 (32,74) All B 65 (36,81) 76 (54,87) 60 (32,77) 54 (19,75) Age Group (years) 6m to (-22,93) 78 (2,95) 56 (6,79) 18 to (43,-79) 61 (34,77) 59 (32,75) 55 (24,73) (-25,66) 34 (-28,66) 74 (12,92) 76 (15,93) * 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 Euro surveillance: bulletin Européen sur les maladies transmissibles= European communicable disease bulletin, 19(34).

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

35 Conclusions: 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, ), then 80+ (69/ ) – 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

36 … Average flu season Dominated by A(H1N1), occasional A(H3N2) 12% B

37 ….2014 Dominated by A(H1N1) Few A(H3N2) 12% B Ref: ESR 2014

38 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

39 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 Influenza-positiveInfluenza-negativeVaccine Effectiveness Unadjusted Adjusted 1 Influenza type/ age group Number Vaccinated Total% Number Vaccinated Total%VE %95% CIVE %95% CI SARI Overall (years) mo N/A 2 N/A A(H1N1)pdm ILI Overall mo N/A 2 N/A N/A 2 N/A A(H1N1)pdm N/A 2 N/A Manuscript in preparation Turner et al 2014

40 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’

41 Vaccine Effectiveness: Outstanding challenges

42 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

43 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

44 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) – Adjuvanted for elderly, higher risk

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

46 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, Nevil Pierse, David Murdoch Primarycare Advisory Group from PHOs (Procare, East Tamaki, Auckland) and ARPHS: John Cameron, Bruce Adlam, Gary Reynolds, Rosemary Gordon, Sam Wong, Leane Els, 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: 1U01IP


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