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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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.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..

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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

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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

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b = birth rate

N = S + I

l = rate at which new infections occur

d = mortality

µS IIb N lSI/N dISµ

Thebasicmodel

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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)

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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

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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

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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

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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

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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

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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

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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

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• 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?