This document provides information on assessing the public health impact of vaccines against invasive meningococcal disease. It discusses key input parameters for models, including disease burden, vaccine strain coverage, and vaccine impact on carriage. Regarding disease burden, the document outlines fluctuating MenB incidence rates in England and Wales from 1984-2013. It also discusses the Meningococcal Antigen Typing System (MATS) for determining strain coverage of Bexsero, predicting 78% coverage of European MenB isolates. The document reviews evidence on the impact of outer membrane vesicle vaccines on nasopharyngeal carriage from multiple studies. It summarizes a recent Bexsero carriage study showing -18.2% efficacy in reducing disease

1. THE EVIDENCE BASE FOR ASSESSING THE PUBLIC
HEALTH IMPACT OF VACCINES AGAINST INVASIVE
MENINGOCOCCAL DISEASES
UK/BEX/13-0047f
Date of prep: Oct 2013

2. Disclosure statement
• Prof Richard Moxon is a member of the Scientific
Advisory Boards of Novartis Vaccines and
Diagnostics (NVD) and GlycoVaxyn from whom he
receives financial compensation for his time; he
holds no intellectual property and has no financial
holdings or potential gains relating to 4CMenB.

3. Milestones in meningococcal vaccines
•
•
•
•
•
•
•
1970s
1992
1999
2003
2010
2013
2013
Polysaccharide vaccines
First conjugates for MenA and MenC
MenC licensed in UK
MenACYW licensed in USA (aged >11y)
MenA licensed in Africa
MenACYW licensed (aged >2 months)
EMA licenses 4CMenB (Bexsero ® )
Bexsero prescribing information can be found on the last slide

4. July 2013: JCVI watershed
recommendation*
• «.......on the basis of the available
evidence .... immunization using
Bexsero is highly unlikely to be costeffective ......
• ...and could not be recommended»
* JCVI interim position statement on use of Bexsero
meningococcal B vaccine in the UK

5. Models of cost effectiveness
• Models simplify: they are supposed to ….
• Some factors are omitted
• Can these omissions introduce bias?
- familiar examples: climate change, markets

6. In defence of models:
“Better to be roughly right than precisely wrong – or
not to make any estimate at all ”
John Maynard Keynes
In opposition to models:
“ Not appropriate tools for decision making as their
use assumes a level of knowledge and precision that is
illusory ”
Robert Pindyck

7. Aims of breakfast session
• To provide information and stimulate discussion on the
evidence base used to inform decision making on the
potential public health impact of 4CMenB (Bexsero)
• Measuring disease burden and estimating vaccine impact
Dr Jamie Findlow
Deputy Head of Vaccine Evaluation Unit, Public Health England,
Manchester
• Challenges to quantifying the severity of meningococcal
disease
Dr Simon Nadel
Consultant in Paediatric Intensive Care, St Mary’s Hospital, London
• Panel discussion

8. Measuring disease burden and
estimating vaccine impact
Jamie Findlow
jamie.findlow@phe.gov.uk
Vaccine Evaluation Unit, Public Health England, Manchester, UK.

9. Disclosure statement

Jamie Findlow undertakes research, advisory and
educational activities on behalf of Novartis as well as other
vaccine manufacturers. All income and payments
associated with these activities are made to his employers,
Public Health England or independent charity.
10
Measuring disease burden and estimating vaccine impact

10. Presentation overview



11
Background information
 Model input parameters
Evidence base for determining Disease burden
 Vaccine strain coverage
 Vaccine impact on carriage (herd protection)
Summary and conclusions
Measuring disease burden and estimating vaccine impact

11. Presentation overview



12
Background information
 Model input parameters
Evidence base for determining Disease burden
 Vaccine strain coverage
 Vaccine impact on carriage (herd protection)
Summary and conclusions
Measuring disease burden and estimating vaccine impact

12. What are the input parameters into
a model?
Epidemiological parameters
Vaccination parameters
 Disease burden/incidence
 Case fatality rate
 Years of life lost
 Vaccination coverage
 Vaccine efficacy
 Vaccine strain coverage
 Impact (reduction) on carriage
 Adverse reactions
 Vaccine cost
 Delivery costs
Treatment costs
 Ambulance & hospital costs
 Specialist/intensive care costs
 Follow up care costs
Public Health response
 Response to each case
Long-term effects of disease
 Sequelae
 QALY
13
Aim: present the scientific data for deriving
an input value for each of these parameters
Measuring disease burden and estimating vaccine impact

13. Presentation overview



14
Background information
 Model input parameters
Evidence base for determining Disease burden
 Vaccine strain coverage
 Vaccine impact on carriage (herd protection)
Summary and conclusions
Measuring disease burden and estimating vaccine impact

14. Changing epidemiology- Laboratory confirmed
meningococcal disease cases in England & Wales1
MenW
Number of cases
140
120
58 cases
100
80
60
2000/01 “Hajj” outbreak
21 cases
Natural fluctuation
40
20
0
MenY
Number of cases
100
87 cases
80
60
40
17 cases
20
0
15
Measuring disease burden and estimating vaccine impact
1
Public Health England Meningococcal Reference Unit, Unpublished data

15. Laboratory confirmed cases of meningococcal disease
from England & Wales, 1984/85 to 2012/131
3000
2500
NG/ND
Z/29E
W
Y
X
C
B
A
„Latest‟ MenB burden
622 cases
2004/05 to 2005/06
Ave 1174 MenB cases per year
Highestconfirmation
PCR MenB burden
1624 MenB cases
introduced in 1996
1997/98 to 2005/06
Ave 1305 MenB cases per year
No. of cases
2000
Lowest MenB burden
344 cases
1984/85 to 2012/13
Ave 974 MenB cases per year
Last two years (2011/12 to 2012/13)
Ave 633 MenB cases per year
1500
1000
500
0
16
Measuring disease burden and estimating vaccine impact
1
Public Health England Meningococcal Reference Unit, Unpublished data

16. Other considerations when determining
disease burden
 Which data source should be used?
 PHE Meningococcal Reference Unit (MRU).
 PHE infectious disease surveillance reports (LabBase).
 Hospital Episode Statistics (HES).
 Office National Statistics (ONS) death registrations.
 Disease burden varies across age groups (and varies by capsular group).
 Meningococcal epidemiology is unpredictable Naturally fluctuates with peaks and troughs.
 Outbreaks may occur.
17
Measuring disease burden and estimating vaccine impact

17. Presentation overview



18
Background information
 Model input parameters
Evidence base for determining Disease burden
 Vaccine strain coverage
 Vaccine impact on carriage (herd protection)
Summary and conclusions
Measuring disease burden and estimating vaccine impact

18. Meningococcal Antigen Typing
System (MATS)
Are any of the Bexsero components in the
test strain:
(i)
Expressed to a sufficient degree?
and
(ii)
Similar enough to the antigens in the
vaccine such that the antibodies
generated by Bexsero will kill the
bacteria?
MATS ELISA determines the minimum amount of recognisable antigen needed to
result in bacterial killing for each of fHbp, Nad A and NHBA (PorA characterised by
sero/genotyping).
For a strain to be „covered‟, at least one antigen must be greater than the positive
bactericidal threshold (PBT) or possess homologous PorA.
19
Measuring disease burden and estimating vaccine impact

19. MATS predicted coverage of European
MenB isolates from 2007/08
100%
73%
(57-87)
85%
(69-93)
82%
(69-92)
87%
(70-93)
85%
(76-98)
78% Overall coverage
(63-90)
(95% CI)
90%
80%
70%
4Ag>PBT*
60%
3Ag>PBT*
2Ag>PBT*
50%
1Ag>PBT*
40%
*> MATS PBT for
fHBP, NadA and
NHBA/homologous
PorA.
30%
20%
10%
0%
England and
Wales
20
France
Germany
Italy
Measuring disease burden and estimating vaccine impact
Norway
Combined
Vogel U et al., Lancet Infect Dis 2013;13:416-25.

20. Considerations for interpreting
MATS data
 MATS PBT derived using pooled sera from 12-13 month toddlers
post booster.1
 Infants antibody responses are less cross-reactive.
 Older children's and adolescents antibody responses may be
more cross-reactive.
 MATS may underestimate NadA expression due NadR repression
during the in-vitro assay growth conditions.1,3
 MATS concept is “conservative” and does not account for Any antibody synergy.1-3
 Any responses against minor OMV components.1-3
1Donnelly
21
Measuring disease burden and estimating vaccine impact
J et al., Proc Natl Acad Sci USA 2010;107:19490-5.
G et al., Vaccine 2013: in press
3Vogel U et al., Lancet Infect Dis 2013;13:416-25.
2Frosi

21. MATS Prediction
(535 MenB strains from
2007/07)1
MATS Prediction
(40 MenB strains
subset)
73%
(95% CI 57-87)
70%
(95% CI 55-85)
Percentage of 40 strain subset killed
in hSBA assay
100
Percentage coverage
80
60
40
88%
(95% CI 72-95)
88%
(95% CI 72-95)
Toddler sera
Adolescent sera
20
0
MATS prediction (full data
set)
22
MATS prediction (40 sub
set)
Measuring disease burden and estimating vaccine impact
1Vogel
U et al., Lancet Infect Dis 2013;13:416-25.

22. Capsular group distribution of laboratory
confirmed meningococcal disease, England
and Wales, 2012/131
Q1- Could protection be
afforded against non-MenB
strains?
Age breakdown of MenW cases in
England and Wales 2006/07 to
Other
C
2012/131 4%
W
1%
200
7%
Y
10%
Q2- Should any „additional‟
protection be considered?
Number of cases
150
31% of cases in
<20 years of age
B
78%
100
69% of cases in
>20 years of age
 One study suggested that
27/57 (48%) of MenC and
14/20 (70%) of MenW
strains could be killed in the
SBA assay by pooled postBexsero vaccination sera
from toddlers.2
50
0
<1
1-4
5-9
10-14 15-19 20-24
Age group
25-44
45-64
>=65
1
23
Measuring disease burden and estimating vaccine impact
Public Health England Meningococcal Reference Unit, Unpublished data
et al., Poster 273, International Pathogenic Neisseria Conference
2012, Wurzburg, Germany, 9-14 September 2012.
2Claus

23. Presentation overview



24
Background information
 Model input parameters
Evidence base for determining Disease burden
 Vaccine strain coverage
 Vaccine impact on carriage (herd protection)
Summary and conclusions
Measuring disease burden and estimating vaccine impact

24. Why is carriage and herd protection
important?
 Glycoconjugate vaccines reduce the acquisition of nasopharyngeal carriage of
Haemophilus influenzae type b1, Streptococcus pneumoniae2, MenC3 and
MenA4.
 Imparts herd protection, and impacts on immunisation strategy.
2
71%
reduction
81%
reduction
1
8
6
67% reduction in rate in
unvaccinated cohort 2001/02
4
2
0
0
1999
25
Direct and Herd protection5
Attack rate per 100,000
Percentage of MenC isolates
3
Reduction in MenC carriage3
(immunised 15-19 year olds)
2000
Unvaccinated
1998/99
2001
Measuring disease burden and estimating vaccine impact
1Takala
Unvaccinated
2001/2002
Vaccinated
2001/2002
AK et al., J Infect Dis 1991;164:982-6. 2Dagan R et al., J Infect Dis.
1996;174:1271-8. 3Maiden MC et al., J Infect Dis. 2008;197:737-743. 4Kristiansen
PA et al., Clin infect Dis 2013;56:354-63. 5Ramsay ME et al., BMJ; 326:365-6.

25. Impact of outer membrane vesicle
vaccines on carriage
Bjune G et
al., 19921
Subject age
range
Number of
subjects
(vaccinated/contr
ols)
Vaccine
Reduction of
carriage
Prevention of
acquisition
Holmes JD et
al., 20083
Delbos V et
al., 20134
Norway
Country
Rosenqvist E et
al., 19942
Norway
New Zealand
France
Do 13-21 vaccines have an impact on
OMV
13-14
17-24
carriage?
Observations from
multiple phase II
529/265
57/152
Conflicting results/inconclusive.
trials
3-7

321/761
MenBvac
MenBvac
MeNZB
 Small numbers of subjects in each study.
MenBvac
 Low carriage ratesNo
have
No
No
Yes
Yes
(100% for
hindered evaluations. (85% for all
vaccine/outbreak
meningococci)
strain)
Yes
(59% for all
meningococci)
No
ND
ND: Not determined
1Bjune
26
Measuring disease burden and estimating vaccine impact
G et al., Lancet 1992;340:315. 2Rosenqvist et al., In VIII International
Pathogenic Neisseria conference, 1994, Cuernavaca, Mexico, 4-9 October 1992.
3Holmes JD et al., Epidemiol Infect 2008;136:790-9. 4Delbos V et al., Vaccine
2013;31:4416-20.

26. Bexsero carriage study1,2
Trial Design
Group
Visit 1
Day 1
Visit 2
Month 1
Visit 3
Month 2
Visit 4
Month 4
Visit 5
Month 6
Visit 6
Month 12
Enrolled
Bexsero
Swab
Bexsero
Swab
Bexsero
Swab
Swab
Swab
Swab
Menveo
974
Control
Swab
JE vaccine
Swab
JE vaccine
Swab
Swab
Swab
Swab
Menveo
983
MenACWY
Swab
MenACWY
Swab
Placebo
Swab
Swab
Swab
Swab
-
984
JE- Japanese Encephalitis vaccine
Primary analysis- Carriage of disease associated sequence types (ST) of N.
meningitidis capsular group B, 1 month post-2nd dose of Bexsero.
1Read
27
Measuring disease burden and estimating vaccine impact
R et al., In The 31st meeting of the European Society for paediatric
infectious diseases. 2013, Milan, Italy, 28th May-1st June 2013. 2Borrow et al., In
The 13th European Meningococcal Disease Society, 2013, Bad
Loipersdorf, Austria, 17-19th September 2013.

27. Bexsero carriage study- Primary
analysis1,2
Primary analysis- Carriage of disease associated sequence types (ST) of N.
meningitidis capsular group B, 1 month post-2nd dose of
Bexsero.*
Study limitations
Bexsero
Group
Control
Group
 High baseline carriage rates (~33%).
Efficacy %
(95% CI)
Number
87
75
 Access to students prior to period of
-18.2
(-73.7 to
 Assessment of individual impact, not of 19.4)
herd protection. 916
N
928
Visit 3 high transmission not possible.
%
9.50
8.08
(Month 2)
* Analyses adjusted for baseline carriage, treatment group, centre and significant risk factors as identified within the multivariate model.
1Read
28
Measuring disease burden and estimating vaccine impact
R et al., In The 31st meeting of the European Society for paediatric
infectious diseases. 2013, Milan, Italy, 28th May-1st June 2013. 2Borrow et al., In
The 13th European Meningococcal Disease Society, 2013, Bad Loipersdorf,
Austria, 17-19th September 2013.

28. Bexsero carriage study- Further
analyses1,2
Further analysis- Efficacy % (95% CI) of Bexsero group compared to control group
undertaken for visits 4 to 6 (months 4 to 12).*
Group
Capsular group
B, C, W & Y
Any N.
meningitidis
All
Risk factor
subgroups
with high
transmission
/acquisition
Capsular
group B
(all STs)
15.6
(-11.0 to 35.9)
26.6
(10.5 to 39.9)
18.2
(3.4 to 30.8)
Early enrollers (<30 days
after start of semester)
17.0
(-28.9 to 46.5)
32.0
(8.2 to 49.6)
33.7
(13.9 to 49.0)
Smokers
38.1
(-9.1 to 64.9)
44.8
(14.0 to 64.5)
32.2
(2.5 to 52.9)
<21 years of age at
enrolment
23.9
(-4.0 to 44.4)
28.0
(9.9 to 42.4)
22.5
(6.3 to 35.9)
* Analyses adjusted for baseline carriage, treatment group, centre and
significant risk factors as identified within the multivariate model.
29
Measuring disease burden and estimating vaccine impact
1Read
R et al., In The 31st meeting of the European Society for paediatric
infectious diseases. 2013, Milan, Italy, 28th May-1st June 2013. 2Borrow et al., In
The 13th European Meningococcal Disease Society, 2013, Bad Loipersdorf,
Austria, 17-19th September 2013.

29. Presentation overview



30
Background information
 Model input parameters
Evidence base for determining Disease burden
 Vaccine strain coverage
 Vaccine impact on carriage (herd protection)
Summary and conclusions
Measuring disease burden and estimating vaccine impact

30. Summary and conclusions
Disease burden
 Meningococcal epidemiology is unpredictable and continually fluctuating.
Strain coverage
 MATS is “conservative” with recent data indicating higher coverage than that
predicted by MATS.
 Should protection against non-MenB strains be included in coverage?
Carriage impact
 Although the Bexsero carriage study failed to show any positive impact for the
primary analysis, due to various limitations, further analyses demonstrated an impact.
Other considerations
 The scientific data behind each input parameter is variable and it is difficult to
decide upon which is the appropriate or „correct‟ value.
 Any value derived is a prediction of the future, which may or may not be accurate.
31
Measuring disease burden and estimating vaccine impact

31. Acknowledgements
Vaccine Evaluation Unit,
Public Health England, Manchester
Ray Borrow.
Meningococcal Reference Unit,
Public Health England, Manchester
Ed Kaczmarski, Steve Gray and Tony Carr.
Immunisation, Hepatitis and Blood Safety Department,
Public Health England, London
Mary Ramsay, Shamez Ladhani and Helen Campbell.
32
Measuring disease burden and estimating vaccine impact

32. Challenges to quantifying
the severity of
meningococcal disease.
Novartis Symposium 2013
Dr Simon Nadel

33. Disclosure statement
Dr Simon Nadel undertakes research, advisory and
educational activities on behalf of Novartis, Pfizer and
the National Meningitis charities.

34. Challenges in quantifying disease
• Can we accurately quantify mortality and
morbidity?
• Can wider impacts of meningococcal disease
(social, economic and public health
considerations ) be quantified?

35. Challenges in quantifying disease
• Can we accurately quantify mortality and
morbidity?
• Can wider impacts of meningococcal disease
(social, economic and public health
considerations ) be quantified?

36. Variability in reported
mortality & morbidity
• Key differences in study inclusion criteria and definitions:
–
–
–
–
–
Disease focus (meningitis, septicaemia, IMD, acute life-threatening illness)
Study populations (age, geography, hospitalisation/ ICU admission)
Follow-up period (acute vs long-term)
Categorisation, scoring and weighting of sequelae (impact on QoL)
Physical +/- neuro-psychological impact on individuals
• No consensus on how to weigh the impact of different sequelae
–
–
–
–
Do “major” and “minor” sequelae have different impact on Quality of Life?
Include immediate and long-term effects of sequelae?
Can we effectively quantify Quality of Life loss in children?
Impact on patients +/- carers +/- families +/- healthcare system +/- society?

37. Variability in reported mortality:
CFR
Agegroup (y)
Causative
organism(s)
Data
collection
Data
source
Reference
23% - 2%
0-18
All capsular
groups
1992-1997
St. Mary‟s Hospital PICU
Booy, 2001
5.2%
0-19
Capsular
group B
2006/72010/11
HPA enhanced surveill,
England & Wales
Ladhani, 2013
4%
All ages
Not specified
1997/82005/6
HES data, England
Christensen, 2013
4.9%
All ages
All capsular
groups
1999-2010
English national linked
database
Goldacre, 2013
12.4% - 10.6%
0-19
All capsular
groups
1995, 2000,
2005
Severe mening sepsis
data from 7 US states
Hartmann, 2013
4.4%
0-1
All capsular
groups
1985-7
England & Wales
De Louvois, 1991

38. Variability in reported morbidity:
Meningococcal disease and meningitis
Invasive meningococcal serogroup B disease in
children and adolescents (MOSAIC)
Meningitis in infancy in England and Wales:
follow up at age 5 years
244 survivors of group B meningococcal disease
402 survivors of meningococcal meningitis
1% disabling amputations
2.9% severe disability
9% major disabling deficits
6.5% moderate disability
36% at least one deficit
29.8% mild disorder
2% profound bilateral SNHL
60.7% no disability
5% moderate bilateral SNHL
6% any SNHL (control <1%)
4% speech/ communication difficulty
IQ, memory and executive function significantly
worse
Significantly higher risk of mental health disorder
(26% vs 10%)
Viner, 2012
Bedford, 2001

39. Variability in reported morbidity:
Survivors of meningococcal septic shock that required PICU treatment
Long term skin-scarring &
orthopaedic sequelae
Long term overall outcome and
health-related QoL
Long term health status
n=170, 4-16y after discharge
n=120, 3-18y after discharge
n=120, 10y after discharge
34% scarring
61% had 1 of 4 major adverse
outcome variables
35% one or more neurological
impairment
5.8% amputations
21.7% had >1 major adverse
outcome
4% severe mental retardation
4.1% lower limb length
discrepancy
39.2% had 1 major adverse
outcome
3% epilepsy
All had higher severity of illness
scores
7.6% major physical adverse
outcome
2% hearing loss
15.8% mild neurological outcome
6% focal neurology (i.e. paresis)
5.8% problem behaviour
6.7% had IQ<85
Longer LOS and higher severity
score predicted worse outcome
Buysse, 2009
Buysse, 2010
Buysse, 2008

40. Main determinants of outcome
• Severity/pathophysiology
• Management
• Pre-morbid condition
• Genetics
• Family
• Other factors

41. Challenges in quantifying disease
• Can we accurately quantify mortality and
morbidity?
• Can wider impacts of meningococcal disease
(social, economic and public health
considerations ) be quantified?

42. Clinical evidence of longer-term effects
•
“Longer-term psychiatric adjustment of children and parents after
meningococcal disease”
Garralda ME, Gledhill J, Nadel S, Neasham D, O'Connor M, Shears D.
Pediatr Crit Care Med. 2009 Nov;10(6):675-80. doi: 10.1097/PCC.0b013e3181ae785a.
•
Prospective cohort study of 70 children aged 3-16y with MD, admitted
to 3 PICUs and 22 general paediatric wards in London
–Parents and children seen 2-5d after hospital admission, and followed up following
discharge at 3m (postal questionnaire) and 12m (interview)
–Psychiatric risk assessed in children (SDQ), parents (GHQ) and both (IES)

43. Psychological after effects in parents & carers
Outcomes in children and parents, 3 months and 12 months post discharge
Children < 6y
Children >6y
11% had PTSD
3 months
post
discharge
Psychological symptoms linked to: PICU admission, illness severity,
similar symptoms in parents and pre-morbid psychological symptoms
Parents
~50% mothers and
~25% fathers had
PTSD symptoms
MD associated with emotional and hyperactivity symptoms
11% children at risk for PTSD
Psychological symptoms linked to illness-related changes in parenting
12 months
post
discharge
1 child developed PTSD
22% scored above cut-off for
psychiatric disorder
Problems: tantrums, difficult to
manage, sleep problems,
fears and feeding problems
•
50% at least one disorder
16% major depression
10% minor depression
8% adjustment disorder
8% oppositional defiant disorder
2% phobic disorder
2% panic disorder
2% organic psychotic disorder
24% mothers and
15% fathers at high
risk for PTSD
Maternal PTSD
linked to paternal
PTSD
Summary: 50% of children develop mostly new psychopathology following MD
(primarily depressive and anxiety-related disorders). In 25% this persisted at one year
Garralda ME, et al. Pediatr Crit Care Med. 2009;10(6):675-80

44. Clinical evidence of neuropsychological effects
•
“Neuropsychologic function three to six months following admission to
the PICU with meningoencephalitis, sepsis, and other disorders: a
prospective study of school-aged children”
Als LC, Nadel S, Cooper M, Pierce CM, Sahakian BJ, Garralda ME.
Crit Care Med. 2013 Apr;41(4):1094-103. doi: 10.1097/CCM.0b013e318275d032.
•
Prospective observational case-control study of 88 children aged 5-16y
admitted to ICUs between 2007-2010 c.f. 100 healthy controls
–Follow-up 3-6 months after PICU admission
–Data encompassing demographic and critical illness details were obtained, and children
were assessed using tests of intellectual function, memory, and attention
–Questionnaires addressing academic performance were returned by teachers.
–Measurement tools: WASI, WRIT, CMS, Cambridge Neuropsychological Test Automated,
Battery (CANTAB)

45. Neuropsychologic function after PICU admission
Psychiatric risk
PTSD risk
Cognitive function
•
Summary: Meningoencephalitis and sepsis particularly associated with reduced
neuropsychological function
•
Are these effects fully considered in evaluation of new vaccines?
Adapted from Als LC, et al. Crit Care Med. 2013;41(4):1094-103

46. Can social, economic and
public health considerations
be quantified?

47. Counting the cost of Meningitis:
Estimates for the management costs of a severe case of meningitis
Discounted
costs
Undiscounted
costs
£600,000
to
£1,000,000
£1,230,000
to
£2,360,000
£1,300,000
to
£1,700,000
£2,980,000
to
£4,280,000
Costs to NHS
Acute costs
26 days in PICU, 155 days on rehabilitation ward
Outpatient appointments, including:
-Physiotherapists
-Speech and language therapists
-Occupational therapists
Other specialist treatments
Costs to Personal Social Services
Social care assessment, direct pay payments, short break provision, residential
Costs to government
Education, disabled facilities grant, specialised vehicle fund, lost income tax
revenue, transfer payments
Wright C, Wordsworth R, Glennie L. Meningitis Research Foundation: Counting the costs of meningitis. 2011.

48. Costs to society
Siblings of disabled children
are more likely to experience
Families are
four times more
likely
behavioural and
emotional problems1
to be living in poverty
(84% of mothers of disabled children do not
work compared to 39% of mothers of nondisabled children1)
Day to day costs to the family
with a disabled child are
three times more
than with a non-disabled child
(Minimum budget to bring up a disabled child
£7,355 per year compared to £2,100 for a nondisabled child2 )
1. New Philanthropy Capital, 2007
2. Joseph Rowntree Foundation, 1998
Depression
and anxiety
are more common among
family members1

49. Summary
• Estimates of mortality and morbidity (frequency and impact) vary
considerably
• The values selected for inclusion directly affect cost-effectiveness
calculations
• HE models currently include values to represent:
–
–
–
–
–
Case fatality rate
Proportion of survivors with „minor‟ sequalae
Proportion of survivors with „major‟ sequalae
QALY for survivors without sequalae
QALY loss for survivors with sequalae
• Can we be confident that the selected data adequately capture
the clinical burden of meningococcal disease?

50. PANEL DISCUSSION
UK/BEX/13-0047f
Date of prep: Oct 2013

51. Strengths of models
• Validated method for assessing complex outcomes:
multiple components and non-linearity result in
counterintuitive results
• Provides systematic benchmarking of economic
gains or losses to facilitate policy decisions
• Rigorous exploration (e.g. through sensitivity
analyses) of worst case and best case scenarios –
parameter space

52. Weaknesses of models
• Results depend crucially on the confidence -- or lack
of it -- in the data used to construct the models
• Biases are introduced because some essential
factors are not easily quantified
• It is only one of many methodologies used to
assemble an evidence base.
• A blunt tool for assessing public health impact as
compared to economic gains

53. THE EVIDENCE BASE FOR ASSESSING THE PUBLIC
HEALTH IMPACT OF VACCINES AGAINST INVASIVE
MENINGOCOCCAL DISEASES
UK/BEX/13-0047f
Date of prep: Oct 2013