modelling the impact of male circumcision on hiv/aids in sub-saharan africa brian williams, sacema
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Modelling the impact of male circumcision on HIV/AIDS in
sub-Saharan Africa
Brian Williams, SACEMA14 November 2007
The purpose of models is not to fit the data but to sharpen the questions
Karlin, S. 11th R.A. Fisher Memorial Lecture, Royal Society, 6 Carlton House Terrace, London. 20 April 1983.
Question 1
Male circumcision reduces female-to-male transmission by
60%; what is the overall population level effect?
Question 2
How many men do we need to circumcise now to avert one
future case of infection?
Question 3
How many men do we need to circumcise to avert one future
case in a women?
Question 4
At what age should we circumcise men or boys?
Question 5
Over what time scale will we see the effects?
Question 6
How does the age-specific prevalence/incidence of infection
vary over time in response to MC?
Question 7
How do sexual mixing patterns affect the impact of MC?
Question 8
Sexual network are almost always scale free. How does this
affect the impact of MC?
Question 9
How many (discounted) dollars and lives do we save in the
future for each dollar spent now?
Question 10
What kind of synergies might we expect from other interventions?
Summary
• Overall impact• Targeting• Impact• Time scale• Effectiveness• Cost benefit
Circumcision reduces incidence in men by
= 60% (32% to 76%)
Reduces overall incidence (both ways) by
* = = 37% (18% to 51%)
Equivalent to a one-shot vaccine with life-long protection and an efficacy of 37%
1- 1-
Auvert, B. et al., Randomized, controlled intervention trial of male circumcision for reduction of HIV infection risk PLOS Medicine, 2005, 2; Bailey, A. et al. Male circumcision for HIV prevention in young men in Kisumu, Kenya: a randomised controlled trial. The Lancet, 2007. 369: 643-656; Gray, R. et al., Male circumcision for HIV prevention in men in Rakai, Uganda: a randomised trial. The Lancet, 2007. 369: 657-666.
**
I 1-(%/yr) 1-
1- 10P
(k/yr) NI I
Change in incidence if all men were circumcised
Greatest benefits to be had where prevalence is high and circumcision rates are low.
51.2
25.6
12.8
6.4
3.2
1.6
0.8
0.4
0.2
0.1
Circumcisions to avert one HIV infection
0 20 40 60 80 100
Pre
vale
nce
of H
IV (%
)
51.2
25.6
12.8
6.4
3.2
1.6
0.8
0.4
0.2
0.1
Prevalence of male circumcision (%)
Life-time infections averted per circumcision done approximately equal to the prevalence
Impact of male circumcision on HIV in South Africa. Reduction
in transmission = 37%. Full coverage by 2015.
Mor
talit
y/yr
Pre
vale
nce
I
ncid
ence
/yr
0.00
0.01
0.02
0.03
1990 2000 2010 2020
0.00
0.10
0.20
1990 2000 2010 2020
0.00
0.01
0.02
1990 2000 2010 2020
Over 20 years this could:
Avert 1.4M incident cases
Reduce prevalence by 1.6M
Save 0.8M lives (or people on ART).
The personal benefit is immediate; the full public health benefit will only be seen
over ten years or more.
Inci
denc
e/ye
ar
Pre
vale
nce
Age (years)
Age (years)
Williams, B.G. et al. Patterns of infection: using age prevalence data to understand the epidemic of HIV in South Africa. South African Journal of Science, 2000. 96: 305-312.Williams, B.G. et al. Estimating HIV incidence rates from age prevalence data in epidemic situations. Statistics in Medicine, 2001. 20: 2003-2016.
Prevalence and incidence (per susceptible person)
of HIV among men in Khutsong, South Africa
0.0
0.2
0.4
10 20 30 40 50 60
0.00
0.05
0.10
0.15
10 20 30 40 50 60
0.0
0.1
0.2
0.3
1990 1995 2000 2005AN
C H
IV p
reva
lenc
e
0.0
0.2
0.4
0.6
0.8
1.0
0 20 40 60
Life time risk of infection if susceptible
Life time risk of infection (total)
Discounted male infections averted per circumcision
Ris
k of
infe
ctio
n/In
fect
ions
ave
rted
Age at circumcision (years)
0.0
0.1
0.2
0.3
0.4
0 20 40 600
10
20
30
Mean time to infection
Discounted male infections averted per circumcision
Infe
ctio
ns a
vert
ed
Age at circumcision (years)
Year
s
We need to think about the age at circumcision and the time over which the impact will be seen
Heterogeneity in sexual activity?
Two kinds of networks
Erdos-Renyi or Poisson networks: All partners are equal (but some are more equal than others).
Scale free or power-law networks: To him that hath shall be given (and to him that hath not shall be taken away even that which he hath).
-3
-2
-1
0
0 1
Carletonville
Sweden20+
1 2 4 10 20
No. sexual partners in the last year (Sweden) month (Carletonville)Gilgen, D., et al., The natural history of HIV/AIDS in a major gold-mining centre in South Africa: results of a biomedical and social survey. South African Journal of Science, 2001. 97: 387-392.Liljeros, F. et al., The web of human sexual contacts. Nature, 2001. 411(6840): 907-8.
Power k = 2.31
0.1
0.01
0.001
Rel
ativ
e fre
quen
cy
Change in prevalence by partners and time (years given inset). Power law network cut-off at 30 partners
0.0
0.1
0.2
0.3
0.4
0 10 20 30
Pre
vale
nce
of H
IV
Number of partners
5
40
20
10
0
0.1
0.2
0.3
0 0.2 0.4 0.6 0.8 1Transmission parameter
Ste
ady
stat
e pr
eval
ence Circumcise all men
Circ
umci
se
5% w
ith 5
+ pa
rtne
rs
Prevalence versus transmission for a power law network. Power = 2.3. Cut-off at 30 red; 5 green; circumcise if more than 5 blue.
Remove 5% of men with 5+ partners
Mean no. of sexual partners in last month
0
2
4
6
8
10 20 30 40 50 60Mea
n nu
mbe
r of p
artn
ers
Age (years)
Men
Women
Gilgen, D., et al., The natural history of HIV/AIDS in a major gold-mining centre in South Africa: results of a biomedical and social survey. South African Journal of Science, 2001. 97: 387-392.
Start with men aged 25 to 35
Summary
MC reduces overall transmission by ~40%.
Greatest benefit where prevalence is high, circumcision is low and populations are large.
Infections averted per circumcision approximately equal to the prevalence.
Personal benefit is immediate; the public health benefit takes much longer.
Circumcise young men, then middle aged men then children.
Find ways to target high risk men.
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