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DIMACS Workshop
Tübingen, October 2008
National Institute
for Public Health
and the Environment
Comparison of network models for STI transmission
and intervention: how useful are they for public health?
Mirjam Kretzschmar
Centre for Infectious Disease Control, RIVM, and
Julius Center for Health Sciences & Primary Care
University Medical Centre Utrecht, The Netherlands
Acknowledgements
Katy Turner, Imperial College London, UK
Pelham Barton, Birmingham University, UK
John Edmunds, London School of Hygiene and Tropical Medicine, UK
Nicola Low, University of Bern, Switzerland
National Institute
for Public Health
and the Environment
Aim of this study
Compare three models for the transmission dynamics of
chlamydia infection that have been used to assess the
effectiveness of different screening strategies.
All three models are built on the same principles and ideas.
National Institute
for Public Health
and the Environment
Background of the models
•
•
•
RIVM model: to evaluate effectiveness of opportunistic
chlamydia screening program in the Netherlands
(Kretzschmar et al 2001)
ClaSS model: to evaluate proactive, register-based
chlamydia screening using home sampling in the UK (Low
et al 2007)
HPA model: to evaluate opportunistic national chlamydia
screening programme in England (Turner et al 2006)
Population
A
Age
Partners
Sex
Disease status
- Aging, death
- Formation, dissolution of partnerships
- Transmission of infection
Outcomes:
Individuals
B
D
C
Events:
Epidemiological measures:
- Incidence
- Prevalence
Measures of network structure:
- connected components
- paths between individuals
- centrality
Sexual partnerships
•
•
Only heterosexual partnerships
singles
Steady and casual partnerships
separation
- duration
- frequency of sex acts
•
Highly active core group
Steady
pairs
formation
Casual
pairs
Only core group
- 5% of age class 15-35, gradual movement out of core group
with increasing age
- at most one steady partnership, simultaneous casual
partnerships
Common features of all models
•
•
•
•
•
Individual based stochastic simulation models
Models describe partnership formation and dissolution,
sexual networks
Sexual behaviour parameters validated using sexual
behaviour surveys
Transmission and course of infection was described in a
similar way
Baseline prevalence comparable ( ~ 3%)
Screening and partner notification
•
Standardized hypothetical opportunistic screening and PN
protocols
- Age group screened, 16-24
- Women only or men and women
- Screening uptake 35% every year
- Treatment for 45% of partners
•
Compare prevalence after 10 years of screening
Change in chlamydia prevalence in women (16-44) after 10
years of screening
4
pre-screening prevalence
post-screening women only
Prevalence (%)
.
post-screening women and men
3
2
1
0
RIVM model
ClaSS model
HPA model
Where are differences between models?
R0 the basic reproduction number
R0  cD
Duration infectious period
Transmission probability
Treatment
Condom use
Partner notification
Screening
Contact rate
number sex partners
Transmission probabilities
RIVM
ClaSS
HPA
Male–Female
0.11
0.154
0.0375
Female–Male
0.11
0.122
0.0375
Frequency of
sex acts
1/day casual
0.25/day steady
0.5/day
1/day for 30 days
then 0.25/day
Duration of infection
RIVM
ClaSS
HPA
200
200
180
180
Duration asymptomatic infection (mean in days)
Men
Women
200
370
Duration symptomatic infection (active treatment-seeking, mean in days)
Men
Women
33
40
30
40
30
30
0.5
0.3
0.64
0.25
0.0
0.045
45%
45%
20%
Proportion seeking treatment (symptomatic only)
Men
Women
Effective partner notification (%)
Partnership formation
Concurrency
Gaps between
partnerships
Data set
RIVM
ClaSS
HPA
5% 15-35 yr olds
can have 2+
concurrent
partners
9% men, 5% women
15-30 yrs can have
2+ concurrent
partners
5% of people
desire 2 partners
until they are 35
Yes
No
Yes
NL survey 1989
ClaSS survey 2001
NATSAL 2000
Effective contact rate
v
c  m
m
m is mean and v variance in number of partners in last year.
Then for populations stratified by numbers of contacts:
R0  cD
Anderson & May 1991
.
Summary measures sexual activity
10
number partners last year
9
8
7
mean RIVM
6
c RIVM
mean ClaSS
5
c ClaSS
4
mean HPA
3
c HPA
2
1
0
16-19
20-24
25-30
16-19
20-24
25-30
Females
Males
Age group
Effective contact rate is very high for youngest age group in HPA model.
Effective contact rate is higher for females than for males in RIVM and ClaSS models.
Chlamydia prevalence by age
8%
prevalence
.
6%
RIVM Model
4%
ClaSS model
HPA Model
2%
0%
16-19
20-24
25-30
16-19
Males
20-24
Female
Age group
25-30
Incidence of transmission events
transmissions per 100000
.
50000
40000
30000
RIVM Model
ClaSS model
HPA Model
20000
10000
0
16-19
20-24
25-30
16-19
Males
20-24
Female
Age group
25-30
Proportion ever treated for Chlamydia
(no screening)
35%
.
30%
RIVM (M)
Proportion treated
25%
RIVM (F)
ClaSS (M)
20%
ClaSS (F)
HPA (M)
15%
HPA (F)
NATSAL (M)
10%
NATSAL (F)
5%
0%
15
17
19
21
Age (years)
23
25
Comparison
•
•
Prescreening prevalence similar in all 3 models but differences in
underlying dynamics
HPA model, UK
- Low transmission, low treatment levels, high contact rates, low PN
rate
- Screening can have large impact, because treatment before
screening was low (fitted to observed data).
•
ClaSS model, UK
- High transmission, high pre-screening treatment and partner
notification
- Screening has little added value.
•
RIVM model, Netherlands
- High transmission, medium pre-screening treatment, high partner
notification
- Medium impact of screening
•
Large discrepancy in prevalence of young males between models –
impact of male screening?
Conclusions
•
•
•
•
•
We do not know which, if any, model accurately predicts
transmission or impact of intervention
Between model variations in sexual contact rate, percontact transmission probability, and proportion treated
before screening all influence differences in output.
Further comparison work to examine effects of these
differences needed
Better empirical data needed for models
External validation needed
Questions for discussion
• How do we best validate individual based models?
• When do we know that model results can be
trusted?
• Meta-modelling studies?
• Are individual based models of this complexity
useful for public health decision making?
References
Kretzschmar M, Turner KME, Barton PM, Edmunds WJ, Low N. Modeling the
population impact of Chlamydia screening programs: comparative study.
Submitted.
Turner KM et al. Developing a realistic sexual network model of chlamydia
transmission in Britain. Theor.Biol Med Model. 2006;3:3.
Kretzschmar M et al. Comparative model-based analysis of screening programs
for Chlamydia trachomatis infections. Am.J.Epidemiol. 2001;153:90-101.
Low N et al. Epidemiological, social, diagnostic and economic evaluation of
population screening for genital chlamydial infection. Health Technol.Assess.
2007;11:1-184.
Low N, Heijne JCM, Kretzschmar M. Use of mathematical modeling to inform
chlamydia screening policy decisions. J Infect Dis; in press.
National Institute
for Public Health
and the Environment