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HIV prevalence and incidence in Zimbabwe.
What on earth is going on?
John Hargrove, Brian Williams
MMED Workshop
June 2017 Muizenberg, South Africa
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The first indication that something strange was happening with the HIV epidemic in Harare came from the ZVITAMBO
study – carried out between 1997 and 2000.Over this time >14,000 women and their new-born babies were
recruited into the study and followed up for up to 2 yeras
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The study produced a huge amount of information on HIV prevalence, incidence and mortality – which helped us to
understand what was go
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4
•
.In the ZVITAMBO Trial, women and their babies were recruited over a 27-mo period between Oct 1997 and Jan 2000.
• HIV prevalence initially increases with age – peaking at a horrendous level of 50% for women aged about 30. Then declines sharply..
•
Why the decline?
Age
13 16 19 22 25 28 31 34 37 40 43 46 49
Prev
alen
ce (
perc
ent)
0
5
10
15
20
25
30
35
40
45
50
55
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• Now pool on age and see whether there is any relationship between HIV prevalence and time.
• Is there any trend in the prevalence with date of recruitment??
Mean prevalence vs month of recruitment
Month of recruitment
0 4 8 12 16 20 24 28
Prev
alenc
e at
recr
uitme
nt
22
24
26
28
30
32
34
36
38
40
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•
.For the ZVITAMBO Trial, HIV prevalence increasedsignificantly during 1998, thereafter it declined significantly.
• Prevalence
Nov 1997 29%.
Dec 1998 34%.
Jan 2000 31%.
Mean prevalence vs month of recruitment
Month and year of recruitment
Prev
alen
ce a
t re
crui
tmen
t
22
24
26
28
30
32
34
36
38
40
N J M M J S N J M M J S N J1997 1998 1999 2000
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When the ZVITAMBO data are amalgamated with other data from Harare ANC sites, prevalence appears to have peaked at the end of 1998 and seems to have been declining ever since.
Can we model these changes?
0
5
10
15
20
25
30
35
1980 1984 1988 1992 1996 2000 2004 2008 2012 2016
HIV
pre
vale
nce
(%)
Year
Harare ANCsHIV prevalence
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Things look quite different in South Africa ....
Why the difference?
We will try to answer that question later ...
0
5
10
15
20
25
30
35
1980 1984 1988 1992 1996 2000 2004 2008 2012 2016
HIV
prev
alen
ce (%
)
Year
Harare ANCsHIV prevalence
000•
Mortality in Harare.•
With the end of the war in Zimbabwe in 1980 there was a large influx of foreign aid, jobs were created, and health and education services were improved.
•Mortality in Harare declined – until the effects of the HIV-AIDS epidemic made themselves felt.
Mortality in Harare Zimbabwe
-2
0
2
4
6
8
10
12
14
1980 1985 1990 1995 2000 2005
Year
Mor
talit
y pe
r 10
00
000
b = birth rate
N = S + I
l = rate at which new infections occur
d = mortality
µS IIb N lSI/N dISµ
Thebasicmodel
000
0
20
40
60
80
100
1970 1990 2010 2030
Prev
alen
ce (%
)
012345678910
Inci
denc
e/M
orta
lity
(%/y
r)
ANC women in Uganda
R0 =3.3
0
0
-1= 70%RR
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b = birth rateN = S + Il = infection ratedI = Weibull mortality
µS IIbN lSI/N dIS
0.0
0.2
0.4
0.6
0.8
1.0
0 10 20 30Time (years)
P(su
rviv
ing)
Normal (Weibull 2)
Exponential(Weibull 1)
000
0.0
0.2
0.4
0.6
0.8
1.0
1980 1990 2000 2010 2020Year
0.00
0.05
0.10
0.15
0.20Prevalence
Incidence/mortality
000
b = birth rateN = populationl = le–aP
dI = Weibull mort.
~
~
µS IIbN lSI /N dI
S
0.0
0.2
0.4
0.6
0.8
1.0
0 10 20 30Prevalence (%)
Rel
ativ
e tra
nsm
issi
on .
µ
–aPe
Heterogeneity in sexual behaviour
000
0.0
0.1
0.2
0.3
0.4
1980 1990 2000 2010 2020Year
0.00
0.02
0.04
0.06Prevalence
Incidence/mortality
000
0.0
0.2
0.4
0.6
0.8
1.0
1985 1990 1995 2000Year
Rel
ativ
e tra
nsm
issi
on .
~
µS IIbN lSI /N dI
S~
b = birth rateN = populationl = lC(t)dI = mortality
~~
C(t)
Including control
000
0.0
0.1
0.2
0.3
0.4
1980 1990 2000 2010 2020Year
0.00
0.02
0.04
0.06Prevalence
Incidence/mortality
000
The number of condoms distributed in Zimbabwe has risen steadily since 1994 – as has the proportion purchased rather than donated.
Condoms distributed in Zimbabwe1990-2004
Year1990 1992 1994 1996 1998 2000 2002 2004
Cond
oms
dist
ribu
ted
(mill
ions
)
0
10
20
30
40
50
60
70
80
90
Public sectorSocial marketing
000
000
000
000~
µS IIbN lSI /N dI
S*
b = birth rateN = populationl = ledI = mortality
~
* –aM0.0
0.2
0.4
0.6
0.8
1.0
0 2 4Annual mortality (%)
Rel
ativ
e tra
nsm
issi
on . –aMe
Mortality leads to behaviour change
000
0.0
0.1
0.2
0.3
0.4
1980 1990 2000 2010 2020Year
0.00
0.02
0.04
0.06Prevalence
Incidence/mortality
000
• When we plot HIV prevalence vs date for women of different ages we see some interesting patterns.
• What can we tell from the changes in prevalence among teenage mothers?