Southern Hemisphere Influenza and Vaccine Effectiveness Research and

Surveillance (SHIVERS)

Comprehensive investigation of influenza epidemiology, aetiology, immunology and

vaccine effectiveness

US CDC 5 year funded projectStarted 2012

9 objectives

1. Understand severe respiratory diseases caused by influenza & other pathogens2. Assess influenza vaccine effectiveness3. Investigate interaction between influenza & other pathogens4. Understand causes of respiratory mortality5. Understand non-severe respiratory diseases caused by influenza & other

pathogens6. Estimate influenza infection by conducting serosurvey7. Identify & quantify risk factors (age, ethnicity, SES etc) for getting influenza 8. Assess immune response among individuals with varying disease spectrum9. Estimate healthcare, societal economic burden caused by influenza and

vaccine cost-effectiveness

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 illnessSue 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 EffectivenessNikki Turner/Heath Kelly/Nevil Pierse/Ange Bissielo/Michael Baker/Don Bandaranayake/Sue Huang – Objectives 3 & 7 Interactions between pathogens; risk factors for fluMichael Baker: – Objective 4 causes of respiratory mortalityColin McArthur/Sally Roberts: – Objective 5 Primary Care SurveillanceSue Huang/Nikki Turner– Objective 6 infection riskSue Huang/Don Bandaranayake: – Objective 8 immune responsesRichard Webby, Paul Thomas– Objective 9 economicsDes O’Dea:

Study site - Auckland

ADHB and CMDHB Population: 837,696

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

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.2 2014

Aims - Hospital-based surveillance (SARI)

1. 5-year surveillance for SARI cases 2. Non-SARI cases: contribution of influenza3. Incidence, prevalence, demographics, clinical outcomes:

SARI, influenza4. Vaccine effectiveness5. Etiology of SARI cases caused by influenza and other

pathogens6. Validity of hospital discharge data6. Risk factors (pregnancy, high BMI etc):

SARI Case ascertainment

SHIVERS SARI and influenza cases, 2013

0

20

40

60

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120

140

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52 2 4 6 8 10

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May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr

SA

RI

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se

s

Week number 2013/2014

SARI cases - all others

SARI cases - influenza positive

2012/2013 SARI cases

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

SHIVERS Influenza cases by type, 2013

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18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52

Pro

po

rtio

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osi

tive

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mb

er o

f vi

ruse

s

Week

A (Not subtyped)A(H3)A(H1N1)pdm09B (Lineage not determined)B (Yamagata lineage)B (Victoria)Proportion positive for influenza

SARI related influenza hospitalisations by age groups

0 to 1 1 to 4 5 to 19 20 to 34 35 to 49 50 to 64 65 to 79 80 and over

influenza in-cidence

121.9884 48.80429 7.361602 9.72937 11.51175 31.17335 72.27981 69.26921

10

30

50

70

90

110

130SA

RI in

fluen

za in

cide

nce

(cas

es p

er 1

00 0

00)

SARI related Influenza incidence by ethnic groups

Maori Pacific Asian Others

Influenza incidence 26.78038 50.59705 10.54264 17.30869

5

15

25

35

45

55

SARI

influ

enza

inci

denc

e (c

ases

per

100

000

)

SARI related Influenza incidence by socioeconomic status

Known and unknown etiologies for SARI cases

Non-influenza Respiratory Viruses Number (%)

No. of specimens tested 870

No. of positive specimens 388

Rhinovirus 168 (44)

Respiratory Syncytial Virus 162 (42)

Parainfluenza 55 (14) - Parainfluenza 3 - 34 % of all PIV

- Parainfluenza 2 - 18 % of all PIV

- Parainfluenza 1 - 3 % of all PIV

Human metapneumovirus 46 (12)

Single virus detection (% of positive) 303 (78)

Multiple virus detection (% of positives) 85 (22)

SHIVERS SARI - other non-influenza respiratory viruses, 2013

0

10

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18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52

Pro

port

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posi

tive

Num

ber

of v

irus

es

Week

RSV

parainfluenza 1

parainfluenza 2

parainfluenza 3

rhinovirus

adenovirus

hMPV

Proportion positive for non-influenza pathogen

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

Advanced form in MedTech

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%

SHIVERS ILI and influenza cases, 2013

SHIVERS ILI and influenza 29 April – 3 November 2013

Non-influenza viruses isolated from ILI samplesNon-influenza Respiratory Viruses Number (%)

No. of specimens tested 1686

No. of positive specimens 552

Rhinovirus 221 (40%)

Respiratory Syncytial Virus 154 (28%)

Parainfluenza 97 (17.5%)

- Parainfluenza 2 43 (8 %)

- Parainfluenza 3 43 (8%)

- Parainfluenza 1 11 (2%)

Human metapneumovirus 56 (10%)

Single virus detection (% of positive) 495 (89.7%)

Multiple virus detection (% of positives) 57 (10.3%)

Influenza disease burden by age, ILI vs SARI

Influenza incidence by ethnic groups, ILI vs SARI

Influenza incidence by SES groups, ILI vs SARI

Influenza disease burden, 2013

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

Flowchart of all selected, recruited and tested ILI and SARI cases for VE analysis

Recruited sample SARI = 1716 ILI: 1891

Incomplete records: No vaccination status

SARI = 71 ILI = 0

No date of birth SARI = 4

Lab sample not tested SARI = 111 ILI = 82

Complete records SARI = 1530 ILI = 1809

Influenza negative SARI = 818 (79%) ILI = 1013 (68%) Vaccinated SARI = 372 (45%) ILI = 177 (17%)

Meets SARI definition = 2120 Meets ILI definition = 1891

Unique persons SARI = 1042 ILI = 1495

Influenza positive SARI = 224 (21%) ILI = 482 (32%)

Vaccinated SARI = 82 (36%) ILI = 44 (9%)

SARI no consent = 404

SARI cases = 1232 ILI cases = 1663

Exclusions : < 6months of age

SARI = 153 ILI = 5

< 9 yrs one dose SARI = 3 ILI = 34

<14 days since vaccination SARI = 33 ILI = 15 >7 days since onset symptoms SARI = 109

ILI = 92

Not in flu season SARI = 167 ILI = 110 Unused repeat admissions

SARI = 23 ILI = 58

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) VE% (95% CI) VE % (95% CI) 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 A15 (-21,40) 39 (10,58) 55 (29,71) 58 (32,74)

All B65 (36,81) 76 (54,87) 60 (32,77) 54 (19,75)

Age Group(years)

6m to 1772 (-22,93) 78 (2,95) 56 (6,79) 56 (6,79)

18 to 6466 (43,-79) 61 (34,77) 59 (32,75) 55 (24,73)

65 +35 (-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 2013. Euro surveillance: bulletin Européen sur les maladies transmissibles= European communicable disease bulletin, 19(34).

NISG 2014, Refs Section 4.9

Elderly aged 65 years and over

(C oc hra ne rev iew 2010)

Elderly aged 65 years and

over (R earranged ana lys is of

C ochra ne stud ie s)

E f fectiveness in preventing

in fluenza, in fluenza- like-illne ss,

hosp ita lisations, complications

and m orta lity

Effec tive ness aga ins t non-fa ta l

and fa ta l complications

In fluenza-like illness

L aborato ry confirmed in flue nza

Inconc lus ive due to poor

qua lity of studies19

28% (26%-30%)20

39% (35%-43%)20

49% (33% - 62%)20

In fan ts under 6-months whose mothers rece ived in flue nz a vacc ine

during pregnancy

E f ficacy aga ins t laboratory confirmed in flue nza

41% - 48%15,16

H ea lthy c h ild ren under 2 years of age Ef ficacy aga ins t laboratory confirmed in flue nza

Insu ffic ien t da ta13,17

Effec tive ness aga ins t labora tory

confirmed in flue nza

66% (9% - 88%)18

H ea lthy c h ild ren aged 6-35 months Effec tive ness aga ins t labora tory confirmed in flue nza

66% (29% - 84%)18

H ea lthy c h ild ren under 16 years

of age TIV vacc ine e f ficacy in

prevention of laborato ry confirmed in flue nza in

R a ndom ised C on tro lle d Tr ia ls

59% (41% - 71%)17

H ea lthy adults (18-65 years) E ffec tive ness aga ins t in flue nza- like-illness

30% (17% - 41%)14

Ef ficacy aga inst

in fluenza symptoms

73% (54% - 84%)14

Population Type of outcome Level of protection (95% CIs)

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

…..2014

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

….2014• Dominated by A(H1N1)• Few A(H3N2)• 12% B

Ref: ESR 2014

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

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-positive Influenza-negative Vaccine Effectiveness Unadjusted Adjusted1

Influenza type/ age group

Number Vaccinated

Total % Number Vaccinated

Total % VE % 95% CI VE % 95% CI

SARI Overall(years)

35 148 24 118 371 32 34 -3 - 57 54 19 - 746mo -17 4 42 10 15 193 8 N/A2 N/A N/A N/A

18-49 9 58 16 13 52 25 45 -42 - 79 46 -42 - 8050-64 10 29 34 29 51 57 60 -3 - 84 74 23 - 9165+ 12 19 63 61 75 81 61 -18 - 87 58 -36 - 87

A(H1N1)pdm09

22 119 18 118 371 32 51 19 - 71 65 33 - 81

ILIOverall 37 384 10 116 535 22 61 43 - 74 67 48 - 796mo-17 2 143 1 26 226 12 N/A2 N/A N/A N/A18-49 12 168 7 32 195 16 61 21 - 81 66 30 - 8450-64 12 60 20 26 75 35 53 -4 - 79 57 -1 - 8265+ 11 13 85 32 39 82 N/A2 N/A N/A N/A

A(H1N1)pdm09

14 220 6 116 535 22 75 56 - 86 73 50 - 8565+ 1 2 50 32 39 82 N/A2 N/A N/A N/A

Manuscript in preparation Turner et al 2014

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’

Vaccine Effectiveness: Outstanding challenges

• 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

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

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

Thank you

The second SHIVERS science meeting, 7-8 November, 2012

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: 1U01IP000480-01