Additional file 2 – Standardised data set and data sources used to inform it
Standardised data set
Standardised scenarios consisting of input data sets for two hypothetical countries (a low
income country and a middle income country) to be used by each of the models being
evaluated.
Middle income
Low income
country
country
900,000
1,400,000
76
52
Notes
Demographics
Birth cohort
Life expectancy at birth:
mean (years)
Piecewise
Age distribution
Sex ratio
Benign hysterectomy rate
Rectangular
exponential
1:1
1:1
Assume 0
Assume 0
Typical WHO
South-East
Typical WHO
Only needed for
Asian region
African region
transmission dynamic
country
country
<1 year
0
0.07
Mortality below 76
1-29 years
0
0.007
years in the middle
Sexual mixing patterns
models
Mortality rate
30-75 years
0
0.03
income country is 0
(rectangular age
76+ years
1
1
distribution).
15-24 years
0.0111
0.0773
Ignore HPV prevalence
25-34 years
0.0128
0.0645
if model does not use it
35-44 years
0.0089
0.0510
as an input parameter,
45-54 years
0.0079
0.0390
but use AT LEAST one
55-64 years
0.0069
0.0330
of HPV prevalence and
>64 years
0.0057
0.0330
cervical cancer
HPV 16 prevalence
incidence
HPV 18 prevalence
15-24 years
0.0015
0.0670
25-34 years
0.0017
0.0559
35-44 years
0.0012
0.0442
45-54 years
0.0010
0.0338
55-64 years
0.0009
0.0286
>64 years
0.0008
0.0286
15-44 years
0.00013
0.00025
Ignore cervical cancer
45-54 years
0.00052
0.00135
incidence if model does
55-64 years
0.00063
0.00166
not use it as an input
HPV 16/18-related
cervical cancer incidence
parameter, but use AT
>64 years
0.00050
0.00155
LEAST one of HPV
prevalence and cervical
cancer incidence
Screening
Pap / visual
Assume that the AFRO
inspection
country has no
(modelMethod
dependent)
organised screening
Not applicable
opportunistic screening
Model
Sensitivity
dependent
and minimal
Not applicable
Model
Specificity
Sensitivity (invasive cancer)
dependent
Not applicable
1
Not applicable
1
Not applicable
Specificity (invasive
cancer)
Assume same
Model
individuals screened at
dependenta
0%
Target age groups
30 to 45
Not applicable
Frequency
5 yearly
Not applicable
20%
41%
Coverage
every interval
Vaccination
Coverage (3 doses at year 0)
Use year 10 coverage at
year 0 if model does
Coverage (3 doses at year
not accommodate step-
10)
80%
87%
Vaccine efficacy vs vaccine
100%
100%
up
type infection
Duration of protection
Lifelong
Lifelong
15
10
Up to 26
None
0-2-6 months
0-2-6 months
$4,000
$1,400
Vaccine (dose)
$20
$20
Administration (dose)
$0
0
Pap: CIN1 true positive
$70
Not applicable
Pap: CIN2/3 true positive
$138
Not applicable
Age group
Age group (catch-up)
Delivery
Costs
GDP per capita (exchange
rate parity)
Same as cost of a single
Pap: False positive
$3
Not applicable
Pap: Negative
$2
Not applicable
VIA: CIN1 true positive
$69
Not applicable
VIA: CIN2/3 true positive
$137
Not applicable
Pap smear
Same as cost of a single
VIA: False positive
$3
Not applicable
VIA: Negative
$1
Not applicable
VIA test
Models using cancer
staging can retain their
Cancer treatment (per
episode, over lifetime)
structure as long as the
$1,815
$385
mean cost per episode
is as given here
Utility weights
No HPV related disease
Having had a hysterectomy
0
0
0.18
0.18
perfect health. Assume
no utility decrements
Diagnosed cancer (Stages IIII)
As decrement from
0.08
0.08
for screening and
treatment of pre-
Diagnosed cancer (Stage
IV)
0.75
0.75
cancerous lesions
Diagnosed cancer (terminal)
0.81
0.81
(unless it involves
Post-cancer survival
0.11
0.17
hysterectomy)
Discount rate costs
3%
3%
Discount rate benefits
3%
3%
Health care
Health care
provider
provider
Direct only
Direct only
As long as
As long as
possible
possible
0%, 4%
0%, 4%
protection
20 years
20 years
Vaccine cost
x0.5, x2
x0.5, x2
Cost of screening and pre-
x0.5, x2
x0.5, x2
Economic assumptions
Perspective
Costs
Time horizon
Sensitivity analyses
Discount rate
Mean duration of vaccine
cancer treatment
Cost of cancer treatment
x0.5, x2
x0.5, x2
from perfect health)
x0.5, x2
x0.5, x2
HPV prevalence
x0.5, x2
x0.5, x2
Utility weights (decrement
a
Actual coverage levels for middle income country: GSK model 40% VIA, Merck model
20% Pap 20% VIA, Harvard 40% VIA, South Africa 50% Pap, Thai 40% Pap, WHO
CHOICE 20% Pap 20% VIA.
Data sources used to inform the standardised data sets
The data sets given to model developers were hypothetical and not meant to inform decision
making for any actual country. However, they were to some extent based on data from real
countries. The hypothetical low-income country was composed from data on several low
income countries in the WHO African region (Malawi, Mozambique, Tanzania, Uganda and
Zambia), while the hypothetical middle-income country composed from data on several
lower middle income countries in the WHO South-East Asia and Western Pacific regions
(Indonesia, the Philippines, Thailand and Vietnam). Details of the sources for key parameters
are provided below.
Parameter
Birth cohort (millions)
Low income country
Middle income country
Value
Sources
Value
Sources
0.9
Thailand: 0.87
1.4
Tanzania: 1.38
[1]
Mean female life
76
Indonesia: 69
[1]
52
Malawi: 54
expectancy at birth (years)
Philippines:
Tanzania: 49
74
Uganda: 53
Thailand: 74
Zambia: 49
Vietnam: 75
[1]
[1]
HPV prevalence
16: 0.6%-
16: 3.3%-
Mozambique
1.3%
7.7%
[3]
18: 0.2%-
18:2.9%-
0.2%
6.7%
Cervical cancer incidence
0.00013 –
(due to HPV 16/18)
0.00063
Thailand [2]
Thailand [4]
0.00025 –
Tanzania [5]
0.0017
The age-dependent mortality rate for the hypothetical low and middle income countries
compared to that for countries on which the data were based is shown below. Data were
obtained from the 2008 figures in the World Health Organization’s Global Health
Observatory Database [1].
0.070
0.060
0.050
Mortality
Model
0.040
Malawi
Tanzania
0.030
Uganda
0.020
Zambia
0.010
0.000
0
20
40
60
Age
0.07
0.06
0.05
Mortality
Model
0.04
Philippines
Indonesia
0.03
Thailand
0.02
Vietnam
0.01
0
0
20
40
60
Age
Parameters for screening in the hypothetical middle income country were obtained from a
health technology assessment of cervical cancer control options in Thailand [6].
Reference List
[1] World Health Organization. Global Health Observatory Database (2008 data).
http://apps who int/ghodata/?vid=720# 2011 February 22 [cited 2011 Feb 22];
[2] Sukvirach S, Smith JS, Tunsakul S, Munoz N, Kesararat V, Opasatian O, et al.
Population-based human papillomavirus prevalence in Lampang and Songkla,
Thailand. J Infect Dis 2003 Apr 15;187(8):1246-56.
[3] Castellsague X, Menendez C, Loscertales MP, Kornegay JR, dos SF, Gomez-Olive
FX, et al. Human papillomavirus genotypes in rural Mozambique. Lancet 2001 Oct
27;358(9291):1429-30.
[4] World Health Organization. WHO/ICO Information Centre on Human Papilloma
Virus (HPV) and Cervical Cancer. World Health Organization 2010 September
12Available from: URL: http://www.who.int/hpvcentre/en/
[5] Goldie SJ, O'Shea M, Campos NG, Diaz M, Sweet S, Kim SY. Health and economic
outcomes of HPV 16,18 vaccination in 72 GAVI-eligible countries. Vaccine 2008 Jul
29;26(32):4080-93.
[6] Tangcharoensathien V, Limwattananon S, Chaugwon R, Praditsittikorn N,
Teerawattananon Y, Tantavess S. Research for Development of an Optimal Policy
Strategy for Prevention and Control of Cervical Cancer in Thailand. Research report
submitted to the World Bank. Nonthaburi, Thailand: International Health Policy
Program, Thailand (IHPP) and Health Intervention and Technology Assessment
Program (HITAP), Ministry of Public Health, 2008.