THE DEMOGRAPHIC AND EPIDEMIOLOGICAL IMPACT OF HIV/AIDS
TREATMENT AND PREVENTION PROGRAMMES: AN EVALUATION
BASED ON THE ASSA2000 MODEL
By LF Johnson and RE Dorrington
Presented at the 2002 Demographic Association of Southern Africa Conference
Do not quote without authors’ consent.
ABSTRACT
The purpose of this paper is to present a model for assessing the demographic and
epidemiological impacts of a range of HIV/AIDS treatment and prevention
programmes in South Africa. The model on which this analysis is based is the ‘lite’
version of the ASSA2000 AIDS and Demographic model. A number of adaptations
were made to the model prior to the development of intervention-specific
components. Following this, four prevention and treatment programmes were
modelled: improvement to treatment for sexually transmitted diseases, voluntary
counselling and testing, mother-to-child transmission prevention and antiretroviral
treatment. Results suggest that all four programmes can significantly reduce the level
of new HIV infections. However, prevention programmes alone will not address the
short-term consequences of the epidemic in terms of mortality, morbidity and
orphanhood.
KEYWORDS
HIV; AIDS; intervention; prevention; treatment; model
CONTACT DETAILS
Leigh Johnson, Centre for Actuarial Research, Department of Actuarial Science,
Leslie Commerce Building, University of Cape Town, Private Bag, Rondebosch
7701. Tel: (021) 650 5761. Fax: (021) 689 7580. E-mail:
ljohnson@commerce.uct.ac.za.
1. INTRODUCTION
1.1 In the early years of South Africa’s AIDS epidemic, AIDS models were used
primarily as a means of alerting policy-makers to the threats facing the country.
Projections were commonly made on the assumption that there would be no change in
sexual behaviour and no significant health interventions in response to the epidemic.
Little emphasis was placed on the use of AIDS models in the development of
solutions to the problems presented by the AIDS epidemic.
1.2 Of the HIV/AIDS intervention models that have been developed to date, many
suffer from a variety of limitations. Many are capable only of static, short-term
analyses. Others do not allow for investigation into the interactions that may be
expected between intervention programmes. Almost all are based on hypothetical
parameter changes, for which there is little empirical evidence. Our objective has
therefore been to develop a model that overcomes these limitations, and provides a
holistic perspective on the treatment and prevention of AIDS.
1.3 The purpose of this paper is to present a model for assessing the demographic and
epidemiological impacts of a range of HIV/AIDS treatment and prevention
programmes in South Africa. Thus far, four prevention and treatment programmes
have been modelled: improvement to treatment for sexually transmitted diseases
(STDs), voluntary counselling and testing (VCT), mother-to-child transmission
prevention (MTCTP) and antiretroviral treatment (ART).
1.4 Although the results are provisional, it is anticipated that they will be useful in
formulating health policy. This paper summarizes the findings of a more detailed
report (Johnson and Dorrington, 2002). The interested reader is referred to this report
for more detailed discussion of the modelling issues and the key components of
intervention programmes.
2. PREREQUISITES FOR AN INTERVENTIONS MODEL
The model on which this study is based is the ASSA2000 AIDS and Demographic
model. The modelling of interventions necessitated four major adaptations to this
model prior to the development of the intervention-specific components. Before
describing these four adaptations, we provide a background description of the
ASSA2000 model.
2.1 BACKGROUND TO THE ASSA2000 MODEL
2.1.1 The ASSA2000 AIDS and Demographic model is a spreadsheet model that is
used to project the future demographic impact of a heterosexual HIV/AIDS epidemic
on a population, and to calculate various HIV/AIDS statistics. The fundamental
assumption of the ASSA2000 model is that the adult population can be divided into
four ‘risk groups’ that represent different levels of sexual activity and different risks
of HIV transmission. In descending order of level of sex activity, these four groups
are referred to as PRO, STD, RSK and NOT.
2.1.2 The parameters in the model are set in such a way as to enable the model to
reproduce observed levels of mortality and HIV prevalence. The major source of
information on HIV prevalence is the annual antenatal clinic surveys conducted by the
Department of Health. Figure 1 shows the match between the levels of prevalence
estimated in the surveys of pregnant women attending public antenatal clinics, and the
levels of prevalence estimated by the adapted ASSA2000 model for pregnant women
attending public sector clinics. Although the current model estimates of prevalence
are close to the current levels of prevalence being observed in the antenatal surveys,
there has been a deliberate attempt to produce estimates of prevalence that are lower
than those observed in the antenatal surveys in the earlier years of the epidemic, to
allow for a bias towards urban areas in the early antenatal clinic survey results (Webb,
1994).
30%
25%
20%
E s t im a t e d
15%
O b s e rve d
10%
5%
2000
1998
1996
1994
1992
1990
0%
Figure 1: Estimated and observed levels of prevalence among women attending public
antenatal clinics
2.1.3 Attempts have also been made to ensure correspondence between the level of
mortality reported in South Africa (Dorrington et al, 2001) and that estimated by the
adapted ASSA2000 model. Figures 2(a) and (b) show that the model produces an
acceptably close fit to the levels of mortality estimated by Dorrington et al.
25,000
20,000
20,000
16,000
15,000
12,000
10,000
8,000
5,000
4,000
0
R e c o rd e d
deaths
E s t im a t e d
deaths
0
5
15
25
35
45
55
65
75 85+
5
20
(a ) Ma le d e a th s
35
50
65
80
(b ) F e m a le d e a th s
Figure 2: Male and female mortality in 1999/2000
2.1.4 The model developments described below are based on the ‘lite’ version1 of the
ASSA2000 model, which ignores racial and inter-regional differences in HIV
prevalence. The adapted model is not yet publicly available. However, some of the
adaptations presented in this paper will be incorporated in ASSA2001, the next
version of the model due for release.
2.2 DIVISION OF THE HIV POSITIVE POPULATION BETWEEN THE PRIVATE
AND PUBLIC HEALTH SECTORS
2.2.1 The costs of the interventions considered in this document will ultimately be
divided between state and private providers of healthcare. In order to determine the
extent to which these costs are borne by each, it is necessary to estimate the relative
1
This version and other versions of the ASSA2000 model can be downloaded free of charge from the
Actuarial Society of South Africa website: www.assa.org.za/aidsmodel.asp.
sizes of the insured (medical scheme) and uninsured (non-medical scheme)
populations, and the relative levels of HIV prevalence in the two groups. The term
‘non-medical scheme population’ is used to refer to individuals who are not
beneficiaries of medical schemes.
2.2.2 Rama and McLeod (2001) estimate that in 1991 roughly 17% of South Africa’s
population were covered by medical schemes. This proportion fell to roughly 16% in
1999. In the ASSA2000 model it is assumed, for the sake of simplicity, that the initial
(1985) proportion of the adult population in the medical scheme population is 17%,
that 13% of all births2 are to women in medical schemes, and that 13% of individuals
under the age of 14 are covered by medical schemes. This reflects the lower levels of
fertility among women in medical schemes relative to women of lower socioeconomic status. It is further assumed that on reaching age 14, 17% of individuals will
either already be beneficiaries of medical schemes, or will join medical schemes in
the near future.
2.2.3 As part of a separate exercise, the HIV prevalence in the medical scheme
population was estimated using estimates of the demographic profile of the medical
scheme population (based on data from the 1998 October Household Survey, the 1996
Census, and the September 2001 Labour Force Survey), and unpublished estimates of
HIV prevalence by age, gender, race and skill level (currently being worked on by
CARE). The method and results of this exercise are discussed in more detail in
Appendix A.
2.2.4 The model parameters for the medical scheme population have been set to
reproduce roughly the levels of HIV prevalence estimated in Appendix A. As shown
in Appendix A, a range of future prevalence levels are possible in the medical scheme
population, depending on legislative and socio-economic conditions. The objective
has therefore been to set the parameters to obtain a prevalence curve lying roughly
mid-way between the prevalence estimates for scenarios A and C, the two most
extreme scenarios considered.
2.2.5 HIV prevalence levels in the non-medical scheme population are based on the
prevalence of women attending public antenatal clinics (most of whom, it is assumed,
are not medical scheme beneficiaries). Parameters for the non-medical scheme
population are set so as to reproduce these levels of HIV prevalence for pregnant
women.
2.3 DIVISION OF THE HIV POSITIVE POPULATION ACCORDING TO STAGE
OF DISEASE
2.3.1 The modelling of the proportions of the HIV positive population in the various
stages of HIV disease is significant for a number of reasons. Most importantly, it
provides information on the burden of disease in the population, and the extent to
which this burden of disease is reduced is a critical indicator of success for any
2
There is little data on fertility rates for different socio-economic groups, though estimates of fertility
by race are included in the ‘full’ version of the ASSA2000 model. These race-specific fertility rates
were weighted by the proportions of the medical scheme population in the different race groups, to
obtain average fertility rates for the medical scheme and non-medical scheme populations. This yielded
an estimate of 13% of all births being to women in medical schemes.
HIV/AIDS intervention. In addition, the modelling of disease stage allows for
improved accuracy, as levels of infectivity and sexual activity are known to vary by
stage of disease and duration of infection.
2.3.2 In the original version of the ASSA2000 model, survival was modelled in
aggregate using a Weibull distribution (Dorrington, 2000). In the model developed
here, adults are assumed to progress through four stages of HIV infection before
dying from AIDS. These four stages correspond to those defined in the WHO Clinical
Staging System3, and the time spent in each stage is modelled using a Weibull
distribution. The terms spent in each stage were determined from a review of studies
conducted locally and internationally. This review, as well as the procedure used to
set the median and shape parameters in each stage, is described in Appendix B. Table
1 shows the median and shape parameters assumed for each stage.
WHO
Age at infection
Clinical
14 - 24
25 - 34
> 34
Stage Median Shape Median Shape Median Shape
1
2.84
1.87
2.63
1.81
2.42
1.76
2
1.78
1.22
1.65
1.21
1.52
1.19
3
3.98
2.67
3.70
2.56
3.42
2.45
4
1.37
1.00
1.28
1.00
1.19
1.00
Table 1: Median (years) and shape parameter assumptions for adults
2.3.3 Clinical staging systems used for children differ from those used for adults, and
there is little data available on the length of time children spend in each stage. For
these reasons, the survival of children has been modelled using a simple two-stage
survival function: pre-AIDS and AIDS. As for adults, the length of time spent in each
stage is modelled using a Weibull distribution. The median parameters were set in
such a way as to preserve roughly the medians of the original ASSA2000 survivor
functions for children. The estimates of Taha et al (2000) and Spira et al (1999) were
used to estimate the median term to death following progression to AIDS. Table 2
shows the median and shape parameters assumed for each stage.
Perinatally infected Infected by breastmilk
Median
Shape
Median
Shape
Pre-AIDS
0.83
1.0
5.00
2.8
AIDS
0.75
1.0
0.75
1.0
Stage
Table 2: Median (years) and shape parameter assumptions for children
2.3.4 For both adults and children, the staging models described above are used to
calculate, for each year following infection, the mortality rates and the proportions of
survivors in each stage of disease. The staging models have been extended to create
two further stages: receiving antiretroviral treatment and having failed antiretroviral
treatment. This extension is described in more detail in section 6.2.
3
The first two phases are largely asymptomatic, with only minor manifestations of HIV disease. The
third stage is characterized by AIDS-related complexes, and the fourth stage is what is conventionally
known as AIDS (Maartens, 1999).
2.4 IMPROVEMENTS TO THE MODEL OF HIV TRANSMISSION
2.4.1 A limitation of the current model of transmission used in ASSA2000 is that it
does not take into account differences in the levels of infectivity and sexual activity
over the course of HIV infection. Antiretroviral treatment is usually initiated in later
stages of disease, when HIV positive individuals are at their most infectious, and it
has the effect of reducing substantially the risk of transmitting the virus. The exact
extent of this benefit can only be determined if probabilities of transmission are
calculated separately for each stage of disease.
2.4.2 The transmission probability formula used in the original ASSA2000 model is
shown below (Dorrington, 2000). The probability of an individual in risk group i, of
age x, becoming infected in a given year is
 4
59
n s ( x)
1    wij  p j ( y ).h( y | x). 1  rij 1  f i ( x).e  ij
 j 1 y 15


4
59


 1   wij  p j ( y ).h( y | x) 
 j 1 y 15

mi s( x)
where
wij is the proportion of the individual’s partners that are in risk group j
p j ( y ) is the proportion of the individual’s y-year old partners, in risk group j, that are
HIV positive
h( y | x) is the proportion of the individual’s partners that are aged y
rij is the probability that the individual will be infected if they engage in a single act
of unprotected sex with an individual in risk group j
f i (x) is the probability that the individual uses a condom
e is the effectiveness of condoms in preventing HIV transmission
n ij is the number of sexual contacts the individual is likely to have per partner in risk
group j
s (x ) is an index of the level of sex activity at age x
mi is the number of sexual partners the individual has per year
(All parameters, with the exception of e and f i (x) , are gender-specific).
2.4.3 To allow for the effect of disease stage on the probability of transmission, the
same formula is used, but the first term in the first set of brackets is changed slightly.
The first term becomes
4
59
6
j 1
y 15
t 1

 wij  h( y | x) ptj ( y). 1  Ttij ( y)
where

nij s ( y ) Dt
t is the stage of disease (stages 1 to 4 correspond to the four stages of the WHO
Clinical Staging System, stage 5 comprises individuals on antiretroviral treatment,
and stage 6 comprises individuals who have failed antiretroviral treatment)
p tj ( y ) is the proportion of the individual’s y-year old partners, in risk group j, that are
HIV positive and in stage t of disease
Dt is the factor by which the amount of sex is reduced in stage t of disease
Ttij ( y ) is the probability that an HIV positive y-year old, in stage t of disease and in
risk group j, transmits the virus to a partner in risk group i, during a single act of
sexual intercourse
2.4.4 The factor Ttij ( y ) can be expanded as follows:



Ttij ( y )  rij . I t 1  1  1  f j ( y ) Rt  e

where
I t is the factor by which the risk of transmission (per act of unprotected sex) is
increased in stage t of disease
Rt is the factor by which the proportion of sex acts that are unprotected is reduced in
stage t of disease
2.4.5 The Dt , I t and Rt parameters have been set using data from a number of
sources, as shown below. The approach to setting the parameters for stages 5 and 6 is
described in sections 4.2 and 6.2.
Assumption
D1 = D2 = D3 = 1, D4 = 0.5
I 1 = 1, I 2  I 3  0.7 , I 4 = 5
R1 = R2 = R3 = R4 = 1
Source
Based on Gray et al (1998), Gray et al (2001)
De Vincenzi et al (1994)
-
2.5 PHASING-IN OF INTERVENTIONS
2.5.1 Table 3 shows the rates at which interventions are assumed to be phased in4.
Prior to 2001, phase-in rates are assumed to be 0 for all interventions, and the rate of
phase-in is assumed to remain at 90% in all years after 2006. The rates of phase-in are
the same for all the interventions considered, with the exception of mother-to-child
transmission prevention, which has already been partially implemented.
Intervention
STD treatment
Voluntary counselling and testing
Mother-to-child transmission prevention
Antiretroviral treatment
2001
0%
0%
10%
0%
2002
20%
20%
30%
20%
2003
40%
40%
50%
40%
2004
60%
60%
70%
60%
2005
80%
80%
85%
80%
2006
90%
90%
90%
90%
Table 3: Rates of phase-in
4
The rate of phase-in for a given programme can be thought of as the proportion of potential
programme participants who have access to the programme, and choose to participate in it.
2.5.2 The calendar year intervals are defined in the ASSA2000 model to run from
mid-year to mid-year. This means that the phase-in rate for 2002 applies between
mid-2002 and mid-2003. The phase-in rates shown in Table 3 thus correspond,
roughly, to the rates of phase-in that would be expected at the end of the years in
question.
2.5.3 The model has been structured in such a way that the same rates of phase-in
must be assumed for the medical scheme and non-medical scheme populations. This
is a significant limitation of the interventions model, as many of the interventions
considered have already been introduced in the medical scheme environment.
However, given that the rate of medical scheme membership is low among HIV
positive individuals, this simplifying assumption is unlikely to distort significantly the
results of the intervention model for the country as a whole.
2.5.4 In addition to the changes described in preceding sections, a number of changes
have been made to the parameters in the ASSA2000 model, in order to ensure
consistency with the antenatal prevalence and reported death data, and to take into
account more recent research. These changes are described in Appendix C.
3. TREATMENT FOR SEXUALLY TRANSMITTED DISEASES
The risk of HIV transmission is greatly increased in the presence of a sexually
transmitted disease (STD). Effective treatment of STDs is therefore vital in halting the
spread of the HIV/AIDS epidemic.
3.1 INTERVENTION MODELLED
3.1.1 In the intervention scenario presented below, treatment for STDs in South
Africa is assumed to be improved in three ways. Firstly, syndromic management
guidelines for STD treatment are assumed to be adopted by private practitioners.
Secondly, it is assumed that cases of herpes simplex virus-2 (HSV-2) are treated with
acyclovir by both public STD clinics and private practitioners. Thirdly, it is assumed
that drug shortages at public STD clinics are eliminated.
3.1.2 Although modification of health seeking behaviour and improvement in access
to STD treatment are also crucial to the success of an STD treatment programme,
these have not been included in the intervention modelled because of the difficulties
associated with determining appropriate assumptions.
3.2 MODEL STRUCTURE AND PARAMETERS
3.2.1 Ulcerative and non-ulcerative5 STD symptoms differ in terms of their incidence,
their duration and the extent to which they enhance HIV transmission. The approach
taken has therefore been to model separately the two types of STD, as shown in
Figure 3.
5
Symptoms of non-ulcerative STDs include discharge, painful urination and pelvic inflammatory
disease (PID).
Genital ulcers
(with/without
other STDs)
Symptomfree
Unsuccessful
treatment
Successful
treatment
Discharge or
PID, with no
genital ulcers
Unsuccessful
treatment
Figure 3: A multi-state model of STD infection and treatment
3.2.2 A number of assumptions are made in developing this model.
(a) It is assumed that asymtomatic STDs do not enhance the transmission of HIV.
The above model is thus concerned only with STD symptoms.
(b) It is assumed that, with the exception of herpes, the incidence of STD
symptoms is directly proportional to the prevalence of the associated
symptoms. For herpes, the incidence of symptoms is assumed to be
proportional to the prevalence of the herpes virus, and the incidence of herpes
infection is, in turn, assumed to be proportional to the prevalence of herpes
symptoms (not herpes infection).
(c) For the sake of simplicity, it is assumed that all STDs are confined to the STD
and PRO risk groups in the ASSA2000 model. If individuals in the RSK group
do become infected with an STD, it is assumed that they receive prompt
treatment for it, and that it does not, therefore, increase their susceptibility to
HIV infection.
(d) For the sake of simplicity, the rate of STD incidence is assumed not to vary
according to age. The reality is that STD prevalence tends to be highest at
young ages, particularly among women (DOH, 2002).
(e) It is assumed, for the purpose of modelling STD incidence and HIV
transmission probabilities, that the risks of STD infection in an individual and
their partner are uncorrelated. This simplifies the model mathematically and
avoids the need to model the risk of partner re-infection. Although this is an
extreme assumption, individuals in the STD and PRO risk groups are assumed
to be very sexually active, and would in many cases have multiple partners.
The assumption may therefore serve as an approximation to reality.
(f) Adherence to STD treatment is not modelled explicitly. There is also no
explicit allowance for resistance to antibiotics, although this is known to be a
significant problem in the treatment of chancroid and gonorrhoea (PhamKanter et al, 1996).
3.2.3 The parameters assumed and the sources on which they are based are shown in
Table 4 below. These are the assumptions used in the baseline ‘no intervention’
scenario. The changes that are made to these assumptions in the intervention scenario
are described in section 3.1 above.
Parameter
Value
Source
Rate at which genital ulcers heal
25% /week D. Coetzee (personal
Rate at which non-ulcerative symptoms heal 0% /week communication)
Mean time to seeking treatment (men)
15 days
Wilkinson et al (1997),
Mean time to seeking treatment (women)
27 days
Robinson et al (1997)
% of STD patients seeking treatment at
Based on D. Coetzee
Private practitioners/workplace clinics
50%
(pers. communication),
Public STD clinics
40%
Wilkinson et al (1997),
Traditional healers
10%
Williams et al (2000)
Effectiveness of single drug treatment, based
Based on DOH (1998)
50%
on clinical diagnosis, for non-viral STD
Effectiveness of syndromic treatment for
Korenromp et al (2000)
95%
non-viral STD
Effectiveness of intermediate levels of
75%
treatment for non-viral STD
Effectiveness of acyclovir in alleviating
95%
herpes ulcers
Effectiveness of traditional healers
5%
Proportions of private practitioners treating
Varies with Dartnall et al (1997)
STDs syndromically, with 1 drug, or at
syndrome
an intermediate level
and sex
Proportion of public STD clinics treating
Based on Schneider et
100%
STDs syndromically
al (2001)
Proportion of STDs not treated at public
D. Coetzee (personal
20%
STD clinics due to drug shortages
communication)
Proportion of public STD clinics treating
D. Coetzee (personal
0%
herpes with acyclovir
communication)
Proportion of private practitioners treating
50%
herpes with acyclovir
Proportion of ulcers attributable to herpes
36%
Chen et al (2000)
Reduction in frequency of unprotected sex
Based on O’Farrell et
while experiencing STD symptoms
50%
al (1992), Williams et
al (2000)
Table 4: STD natural history and treatment assumptions
3.2.4 Rates of incidence for STD symptoms were set so that the model reproduced
levels of male STD prevalence observed in the South African Demographic and
Health Survey (DOH, 1999), and levels of female STD prevalence obtained by
adjusting the male rates to reflect male:female prevalence ratios observed in other
African countries (Gerbase et al, 1998).
3.2.5 Estimates of HIV transmission probabilities in the presence and absence of
STDs are made by Rehle et al (1998). In the absence of other STDs, the male-to-
female HIV transmission probability, per sexual contact, is assumed to be 0.002, and
the female-to-male rate is assumed to be 0.001. In the presence of STD symptoms
other than ulcers, these probabilities are assumed to increase to 0.02 and 0.01
respectively, and in the presence of ulcers, both probabilities are assumed to increase
to 0.06. It is also assumed that the risk of HIV transmission is not dependent on which
of the partners has the STD.
3.3 RESULTS
3.3.1 Utilization rates
Figure 4 shows the number of visits to public STD clinics in each year, with and
without the improvement in STD treatment. In the absence of any change in
treatment, a gradual decline in the number of STD cases would be expected, as those
who frequently experience STDs are likely to be co-infected with HIV, and are
therefore likely to experience high AIDS mortality. With the introduction of the
proposed changes in STD treatment, the prevalence of STDs is reduced dramatically,
and this results in a further reduction in STD clinic attendance. By 2015, the number
of STD clinic attendees is 2.0 million, less than half of the level that would be
expected if no improvement in STD treatment were introduced.
8,000,000
7,000,000
6,000,000
5,000,000
No
4,000,000
in t e rve n t io n
3,000,000
W it h
in t e rve n t io n
2,000,000
1,000,000
2015
2013
2011
2009
2007
2005
2003
2001
1999
1997
1995
0
Figure 4: Number of visits to public STD clinics
3.3.2 STD prevalence rates
Figure 5 shows the reductions in STD prevalence for females, with and without the
improvement in STD treatment (similar reductions are observed for males). STD
prevalence is defined for this purpose as the percentage of people experiencing a
particular symptom in the last three months. As explained in the previous paragraph, a
decline in STD prevalence would be expected even in the absence of any change in
STD treatment protocols. Improvements in the quality of treatment result in even
greater reductions in STD prevalence. The reduction in the prevalence of ulcerative
STDs is more significant than the reduction in the prevalence of discharge symptoms,
as the provision of acyclovir reduces the incidence of herpes (an ulcerative STD), but
has no effect on other STDs. The proportionately greater reduction in the prevalence
of ulcers is encouraging, as ulcerative symptoms are believed to increase the risk of
HIV transmission by substantially more than discharge symptoms.
12%
10%
U lc e r p re va le n c e , n o
in t e rve n t io n
8%
D is c h a rg e p re va le n c e ,
n o in t e rve n t io n
6%
U lc e r p re va le n c e , w it h
in t e rve n t io n
4%
D is c h a rg e p re va le n c e ,
w it h in t e rve n t io n
2%
2015
2013
2011
2009
2007
2005
2003
2001
1999
1997
1995
0%
Figure 5: Percentage of women experiencing STD symptoms in the last three months
3.3.3 New HIV infections
3.3.3.1 Figure 6 shows the number of HIV infections averted in each year as a result
of the improvements in STD treatment. The number of new HIV infections is reduced
by 81,000 by 2009, and then drops slightly in subsequent years. A drop would be
expected, as – in the absence of interventions – STDs account for a lower proportion
of new HIV infections as the epidemic matures (Robinson et al, 1997). By 2015, it is
expected that roughly 880,000 HIV infections would have been averted as a result of
the improvement in STD treatment.
90,000
80,000
70,000
60,000
50,000
40,000
30,000
20,000
10,000
2014
2012
2010
2008
2006
2004
2002
2000
0
Figure 6: Number of HIV infections averted
3.3.3.2 This analysis ignores the further reductions in numbers of new HIV infections
that may be possible if programmes are introduced to encourage prompt treatment for
STDs and to improve access to treatment in rural areas.
4. VOLUNTARY COUNSELLING AND TESTING
A variety of studies have shown voluntary counselling and testing (VCT) to be highly
effective in modifying sexual behaviour patterns (Merson et al, 2000). Although this
intervention is often closely integrated with mother-to-child transmission prevention
and antiretroviral treatment programmes, it is also often provided as an independent
service.
4.1 INTERVENTION MODELLED
Although a VCT programme is likely to be more effective if targeted at high-risk
individuals, the modelled intervention allows anyone to access the service. All preand post-test counselling is assumed to be provided to individuals, and not to couples
or groups. Condom promotion is assumed to be included in the counselling service.
4.2 MODEL STRUCTURE AND PARAMETERS
4.2.1 Figure 7 is a graphical representation of the model used to determine the
numbers of individuals exposed and unexposed to the VCT programme.
HIV –
untested
HIV +
untested
AIDS sick
untested
AIDS
death
HIV –
tested
HIV +
tested
AIDS sick
tested
Receiving
ART
Figure 7: A model for determining rates of exposure to the VCT programme
4.2.2 In the absence of other interventions, the number of individuals receiving VCT
in a given year, when the VCT programme is fully implemented, is assumed to be 4%
of untested HIV positive individuals, 3% of untested HIV negative individuals who
are at risk of infection, and 1.5% of untested individuals who are not at risk of HIV
infection. Although these rates are arbitrary in absolute terms, their relative levels
have been chosen so that the rate of HIV prevalence among individuals receiving
VCT is consistent with the levels observed by Smith (2000) among VCT recipients. If
a mother-to-child transmission prevention programme is implemented, it is assumed
that a certain percentage of pregnant women will also receive the VCT service. In
addition, any individuals starting antiretroviral treatment, who have not previously
received VCT, are assumed to receive VCT in the year that they start antiretroviral
treatment.
4.2.3 Although it is assumed that those individuals receiving VCT modify their sexual
behaviour, this modification is not assumed to be sustained indefinitely. A 20%
reduction in the improvement in ‘safe sex behaviour’ is assumed for each year
following the VCT session. This is therefore an allowance for the ‘wearing off’ of the
beneficial effect of the VCT programme as individuals gradually forget what they
have learnt through it, and revert to their former sexual practices. This attrition is
represented in Figure 7 by a movement from the ‘HIV negative tested’ state back into
the ‘untested’ state. No attrition is allowed for in individuals who test HIV positive,
however, as these individuals are likely to initiate more permanent changes in sexual
behaviour on learning their HIV status.
4.2.4 Using the above assumptions, it is possible to calculate, at any point in time, the
proportion of HIV negative individuals who have been tested as part of the
programme, and proportions of HIV positive individuals in each stage of disease who
have been tested.
4.2.5 Combining the results from two studies (VCTESG (2000), de Vincenzi et al
(1994)), the following assumptions are made about the effect of VCT:
HIV
HIV +
negative (pre-AIDS)
Status when VCT is received
Reduction in proportion of sex acts that are unprotected
Reduction in amount of sex
24%
9%
36%
19%
HIV +
(AIDS)
53%
31%
4.2.6 Using the above assumptions regarding changes in sexual behaviour for tested
individuals, and the calculated proportions of people who have been tested, it is
possible to make adjustments to the Rt and Dt factors described in section 2.4.
4.3 RESULTS
4.3.1 Utilization rates
Figure 8 shows the number of individuals receiving VCT in each year, split between
medical scheme members and non-members. The numbers peak in 2006, at 510,000
in the non-medical scheme population and at 100,000 in the medical scheme
population. Thereafter the numbers drop substantially, as there are fewer untested
people to receive counselling and testing.
700,000
600,000
500,000
N o n -m e d ic a l
400,000
s c hem e
300,000
M e d ic a l s c h e m e
200,000
100,000
2014
2012
2010
2008
2006
2004
2002
2000
0
Figure 8: Numbers of individuals receiving VCT
4.3.2 Reductions in unsafe sex
Voluntary counselling and testing result in substantial reductions in the level of unsafe
sex, as Figure 9 shows. In modelling HIV transmission, it is the rate of unsafe sex
between discordant couples (i.e. couples in which one partner is HIV positive and the
other HIV negative) that is of primary importance. It is expected that the VCT
intervention would lead to a roughly 16% reduction in the amount of unsafe sex in
discordant couples, by 2015. This would be the product of a roughly 5% reduction in
the frequency of sex, and a roughly 11% reduction in the proportion of sex contacts
that are unprotected.
18%
16%
R e d u c t io n in a m o u n t
14%
of s ex
12%
R e d u c t io n in % o f s e x
10%
c o n t a c t s t h a t a re
8%
u n p ro t e c t e d
6%
R e d u c t io n in a m o u n t
o f u n p ro t e c t e d s e x
4%
2%
2014
2012
2010
2008
2006
2004
2002
2000
0%
Figure 9: Reductions in unsafe sex in discordant couples
4.3.3 New HIV infections
Figure 10 shows the reduction in the number of new HIV infections that would result
from a VCT programme. The number of infections averted peaks at 35,000 per year in
2010, and then drops to 30,000 in 2015. The VCT intervention does not reduce the
risk of transmission per unprotected sex act, and as the epidemic matures this risk
increases because of the greater proportion of HIV positive people in late stages of
disease, when transmission probabilities are highest. The benefit from the reduction in
unsafe sex is therefore mitigated in later years, as transmission probabilities increase,
and this causes the decline observed after 2010. The benefits from the VCT
intervention are nevertheless substantial, with the cumulative number of infections
averted reaching 360,000 by 2015.
40,000
35,000
30,000
25,000
20,000
15,000
10,000
5,000
2014
2012
2010
2008
2006
2004
2002
2000
0
Figure 10: Number of HIV infections averted
4.3.4 These projections are sensitive to changes in the assumptions regarding the
percentage of people receiving VCT. It follows that a key success factor for a VCT
programme is its ability to encourage people who are at risk of HIV infection to
receive the service. Participation is more likely to occur if those who test positive are
offered treatment, and antiretroviral treatment programmes may therefore achieve
substantial reductions in high-risk behaviour if coupled with VCT.
5. MOTHER-TO-CHILD TRANSMISSION PREVENTION
In the absence of any intervention, between 30 and 40% of all children born to HIV
positive women are infected via their mothers. In the majority of cases, transmission
occurs perinatally, and in most other cases, transmission occurs through
breastfeeding. The provision of short-course antiretroviral treatment to pregnant HIV
positive women, and to their children soon after birth, can reduce substantially the
risk of perinatal transmission. The risk of transmission through breastfeeding can also
be reduced through the promotion of formula feeding.
5.1 INTERVENTION MODELLED
The mother-to-child transmission prevention (MTCTP) intervention modelled is
based on the HIVNET 012 regimen, which involves the administration of nevirapine
to pregnant women at the onset of labour, and to their infants soon after birth. The
intervention modelled also includes the provision of formula milk to HIV positive
pregnant mothers for 6 months after birth. All counselling is assumed to be conducted
on an individual basis.
5.2 MODEL STRUCTURE AND PARAMETERS
5.2.1 The parameters assumed and the sources on which they are based are shown in
Table 5 below.
Parameter
Value
Proportion of pregnant women agreeing
80%
to receive counselling and testing
Proportion of tested HIV+ pregnant women
50%
choosing to receive formula milk
Proportion of tested HIV+ pregnant women
100%
agreeing to receive nevirapine
Effectiveness of nevirapine in reducing
47%
perinatal transmission
Effectiveness of formula milk in reducing
100%
transmission by breast-milk
Proportion of births that are live births
98.25%
Source
Based on
Abdullah et al (2001)
Guay et al (1999)
T. Moultrie (personal
communication)
Table 5: Assumptions on MTCTP uptake and effectiveness
5.2.2 The behavioural impacts of the counselling and testing component of the
mother-to-child transmission prevention programme are assumed to be the same as
those described in section 4.2 for individuals attending independent VCT services.
5.3 RESULTS
5.3.1 Utilization rates
Figure 11 shows the number of pregnant women agreeing to receive VCT as part of
the MTCTP programme. The rate of phase-in for the MTCTP programme reaches
90% in 2006, at which stage the number of pregnant women receiving VCT is at its
highest, at 690,000 women in the non-medical scheme population and 110,000
women in the medical scheme population. The decline in the number of women
receiving VCT after 2006 is attributable to falling fertility rates.
9 0 0 ,0 0 0
8 0 0 ,0 0 0
7 0 0 ,0 0 0
6 0 0 ,0 0 0
No n - m e d ic a l
5 0 0 ,0 0 0
scheme
4 0 0 ,0 0 0
M e d ic a l
3 0 0 ,0 0 0
scheme
2 0 0 ,0 0 0
1 0 0 ,0 0 0
2014
2012
2010
2008
2006
2004
2002
2000
0
Figure 11: Number of pregnant women receiving VCT
5.3.2 Mortality rates in children
Figures 12(a) and 12(b) show the infant and child mortality rates6 respectively,
expressed as deaths per 1,000 lives, for scenarios with and without mother-to-child
transmission prevention. In the first year of life, non-AIDS mortality is more
significant than AIDS mortality, and the MTCTP intervention therefore has only a
limited effect on the infant mortality rate. By contrast, the child mortality rate is more
strongly affected by AIDS mortality, as non-AIDS mortality rates reduce substantially
after the first year of life. The child mortality rate thus shows a greater reduction than
the infant mortality rate in response to the MTCTP intervention.
6
The infant mortality rate is the probability that an infant dies within its first year of life, and the child
mortality rate is the probability that an infant dies before its fifth birthday.
70
140
60
120
50
100
40
80
in t e rve n t io n
30
60
W it h
20
40
10
20
0
0
( a ) In fa n t m o r ta lity
2015
2013
2011
2009
2007
2005
2003
2001
1999
1997
1995
in t e rve n t io n
2015
2013
2011
2009
2007
2005
2003
2001
1999
1997
1995
No
( b ) C h ild m o r ta lity
Figure 12: Infant and child mortality rates
5.3.3 New HIV infections
5.3.3.1 Figure 13 shows the annual reduction in the number of new HIV infections
that can be expected if an MTCTP programme is introduced. The number peaks at
54,000 per annum in 2009. Of the infections averted by 2015, 44% are adult
infections, as a result of the voluntary counselling and testing component of the
MTCTP programme. This is a significant benefit of the MTCTP programme, which is
often overlooked in cost-effectiveness studies (Geffen, 2001).
60,000
50,000
40,000
C h ild
30,000
A d u lt
20,000
10,000
2014
2012
2010
2008
2006
2004
2002
2000
0
Figure 13: Number of HIV infections averted
5.3.3.2 The reduction in the number of adult infections averted after 2009 occurs for
the same reasons as the reduction observed in section 4.3. The number of child
infections averted peaks at 29,000 per annum in 2006, and thereafter reduces in line
with declining fertility trends. The cumulative number of child and adult infections
averted reaches 630,000 by 2015.
6. ANTIRETROVIRAL TREATMENT
The widespread availability of antiretroviral drugs in developed countries has led to a
dramatic reduction in AIDS-related morbidity and mortality. The benefits from the
provision of antiretroviral treatment (ART) encompass both improvements in burden
of disease measures and improvements in socio-economic outcomes.
6.1 INTERVENTION MODELLED
In the intervention scenario modelled, it is assumed that triple therapy is made freely
available to all adults who are HIV positive and who have experienced AIDS-defining
symptoms, or whose CD4+ counts are below 200/l. For children, it is assumed that
triple therapy is initiated if the child is experiencing AIDS-defining symptoms,
according to the CDC Clinical Staging System for Children, or if their CD4+
percentage is below 15%.
6.2 MODEL STRUCTURE AND PARAMETERS
6.2.1 The multi-state model used in this study to determine the overall rates of
mortality and morbidity, and the number of individuals receiving treatment, is
represented in Figure 14 below.
Stages 1-3:
Incubation
period
Stage 4:
AIDS
sick
Stage 5:
Receiving
ART
Stage 6:
Off
ART
Stage 7:
AIDS
death
Figure 14: A multi-state model of disease progression under HIV infection
6.2.2 Stages 1 to 3, the incubation period, correspond to the first three stages in the
WHO Clinical Staging System referred to in section 2.3. Initiation of antiretroviral
treatment is modelled by transferring a proportion of the individuals moving into the
‘AIDS sick’ phase into the ‘Receiving ART’ phase.
6.2.3 The parameters used to determine the rates of transition between the stages in
Figure 14, and the levels of morbidity and infectivity following antiretroviral
treatment, are shown in Table 6 below.
Parameter
Value
Adult probability of death
In first 6 months on ART
0.082
Per annum thereafter
0.058
Probability of adult discontinuing ART
In first 6 months on ART
0.091
Per annum thereafter
0.058
Source
Based on assumptions
of Kasper (2001),
consistent with
Palella et al (1998),
Hogg et al (2001),
Murphy et al (2001)
Jordan et al (2002)
Child probability of death
In first 6 months on ART
Per annum thereafter
Probability of child discontinuing ART
In first 6 months on ART
Per annum thereafter
Mortality rate once off treatment
Reduction in AIDS morbidity on ART
for adults and children
Morbidity rate once off treatment
Reduction in viral load while on ART
Reduction in infectivity per log
reduction in viral load
Viral load once off treatment
0.096
0.114
Based on assumptions
of Kasper (2001),
consistent with de
Martino et al (2000),
Gortmaker et al (2001)
0.137
0.058
As for untreated AIDS
R. Wood (personal
75%
communication),
Palella et al (1998)
As for untreated AIDS
1.76 logs
Jordan et al (2002)
Quinn et al (2000)
67%
As for untreated AIDS
Table 6: Assumptions on mortality, morbidity and infectivity after initiation of ART
6.2.4 It has been further assumed that all individuals starting antiretroviral treatment
first undergo voluntary counselling and testing, and hence modify their sexual
behaviour in line with the assumptions discussed in section 4.2.
6.2.5 Triple therapy has been available since 1996, and much is still not known
regarding the long-term effects of antiretroviral treatment. Drug-resistant strains of
HIV are becoming increasingly common in developed countries in which
antiretroviral treatment has been available for some time (Houston, 2001). It has also
been suggested that the availability of ART may change attitudes towards the risk of
HIV infection, and result in higher levels of unsafe sex. Although neither of these
possible effects have been allowed for in the model, it may be necessary to make
allowance for them if more substantial evidence of these effects becomes available in
future.
6.3 RESULTS
6.3.1 Utilization rates
Figure 15 shows the number of individuals receiving antiretroviral treatment, in the
medical scheme and non-medical scheme populations. The number of people on
treatment levels off at 2.7 million in the non-medical scheme population, and at
260,000 in the medical scheme population, around 2015. The provision of
antiretroviral treatment to 2.7 million people would pose a significant challenge to the
public health sector, and would come at a considerable expense. However, these
projections are based on the assumption that the ultimate proportion of new AIDS
cases receiving antiretroviral treatment is 90%. This may be unrealistic, as fear of loss
of confidentiality and discrimination are powerful forces, which often lead to HIV
positive people choosing not to receive the benefits for which they are eligible.
3,500,000
3,000,000
2,500,000
2,000,000
N o n -m e d ic a l
1,500,000
s c hem e
1,000,000
M e d ic a l
s c hem e
500,000
2014
2012
2010
2008
2006
2004
2002
2000
0
Figure 15: Number of people receiving antiretroviral treatment
6.3.2 Impacts on AIDS mortality and morbidity
6.3.2.1 Figure 16 shows the number of AIDS cases that would be expected with and
without the combined ART and VCT intervention. In the absence of any intervention,
the number of AIDS cases would be expected to peak at 1.24 million per year in 2010.
Antiretroviral treatment reduces the incidence of AIDS-defining conditions, but
because of the increasing number of people on antiretroviral treatment, the overall
number of AIDS cases increases to reach a later peak of 920,000 cases per year in
2015. The reduction in the number of AIDS cases peaks at roughly 510,000 per
annum in 2008.
1,400,000
1,200,000
1,000,000
800,000
No A R T
VCT + ART
600,000
400,000
200,000
2015
2013
2011
2009
2007
2005
2003
2001
1999
1997
1995
0
Figure 16: AIDS cases
6.3.2.2 The pattern of AIDS mortality follows the pattern of AIDS morbidity closely,
as Figure 17 shows. If no interventions are introduced, 710,000 deaths per annum are
expected by 2009. Antiretroviral treatment, by extending the lives of HIV positive
people, results in a later peak in the number of AIDS deaths – at 470,000 per annum
in 2015. The cumulative reduction in AIDS deaths reaches 2.8 million by 2015.
800,000
700,000
600,000
500,000
No A R T
400,000
VCT + ART
300,000
200,000
100,000
2015
2013
2011
2009
2007
2005
2003
2001
1999
1997
1995
0
Figure 17: AIDS deaths
6.3.3 Impacts on aggregate mortality indicators
6.3.3.1 Figure 18 shows male and female life expectancies in each future year, with
and without the combined VCT and ART intervention. Trends in life expectancy
mirror the trends in AIDS mortality shown in Figure 17. Without any intervention,
life expectancies are expected to drop by 17 years for males and 24 years for females,
between 1995 and 2010. With the combined VCT and ART intervention, the ultimate
drop in life expectancy (by 2014) is 11 years for males and 16 years for females.
70
65
60
M a le , n o in t e rve n t io n
55
M a le , V C T + A R T
F e m a le , n o in t e rve n t io n
50
F e m a le , V C T + A R T
45
40
2015
2013
2011
2009
2007
2005
2003
2001
1999
1997
1995
35
Figure 18: Life expectancies for males and females
6.3.3.2 Figures 19(a) and 19(b) show the infant and child mortality rates resulting
from the combined VCT and ART intervention. The reductions in these mortality
rates are more substantial than the reductions observed in section 5.3 for a mother-tochild transmission prevention programme.
2013
2015
2009
2011
2005
1995
( a ) In fa n t m o r ta lity
2007
0
2003
0
V CT + A RT
2001
20
in t e rve n t io n
1997
10
No
1999
40
2015
20
2013
60
2009
30
2011
80
2005
40
2007
100
2003
50
1999
120
2001
60
1995
140
1997
70
(b ) C h ild m o rta lity
Figure 19: Infant and child mortality rates
6.3.4 Impacts on numbers of orphans
By extending the lives of HIV positive parents, an antiretroviral treatment programme
can reduce substantially the number of orphaned children. Figure 20 shows the
number of children under the age of 18 who have lost their mothers. Without any
intervention, this number is expected to peak at 2.9 million in 2015. With the
combined VCT and ART intervention, it is expected to peak at 2.1 million in 2015.
The anticipated reduction in the number of orphans, by 850,000, is one of the
strongest arguments for making antiretroviral treatment freely available.
3,500,000
3,000,000
2,500,000
2,000,000
No in te r v e n tio n
VCT + ART
1,500,000
1,000,000
500,000
2015
2013
2011
2009
2007
2005
2003
2001
1999
1997
1995
0
Figure 20: Maternal orphans under the age of 18
6.3.5 New HIV infections
6.3.5.1 Figure 21 shows the number of infections that would be averted if a combined
VCT and ART programme were introduced, compared with the number that would be
averted if only a VCT programme were introduced. The number of infections averted
is likely to peak at 146,000 per annum in 2009. Thereafter, the number of infections
averted reduces as more and more people discontinue therapy, and revert to the higher
levels of infectivity that they would have experienced had they not received treatment.
160,000
140,000
120,000
100,000
V CT + A RT
80,000
V C T o n ly
60,000
40,000
20,000
2014
2012
2010
2008
2006
2004
2002
2000
0
Figure 21: Number of HIV infections averted
6.3.5.2 By 2015, the cumulative number of infections averted is 1.4 million. This is
well in excess of the 360,000 cumulative infections that would be averted if only a
VCT programme were introduced. The results of this analysis suggest, therefore, that
treatment can play a critical role in a prevention programme.
7. COMBINING INTERVENTIONS
7.1 In previous sections, the effects of the various interventions have been considered
in isolation of other intervention programmes. The purpose of the following analysis
is to demonstrate the effects of the interventions when they are combined. Three
scenarios have been considered. In the first there are no intervention programmes. In
the second, STD treatment is improved and VCT and MTCTP programmes are
introduced. In the third, all four interventions are introduced. The second scenario can
thus be regarded as a ‘prevention only’ scenario, while the third scenario incorporates
antiretroviral treatment as well. The assumptions used are in all cases the same as
those used in sections 3 to 6.
7.2 AIDS MORTALITY
Figure 22 shows the levels of AIDS mortality under the three scenarios. Prevention
programmes alone do not achieve a substantial decline in AIDS mortality levels,
except in the longer term. By including antiretroviral treatment in the intervention
programme, however, a more substantial reduction in AIDS mortality can be achieved
in the short to medium term. Under the ‘prevention only’ scenario, the number of
AIDS deaths peaks at 680,000 per annum in 2009. Under the ‘treatment and
prevention’ scenario, a lower and later peak is reached in 2013, at 430,000 deaths per
annum.
800,000
700,000
600,000
N o in t e rve n t io n
500,000
S TD + V C T +
400,000
M TC TP
300,000
S TD + V C T +
M TC TP + A R T
200,000
100,000
2015
2013
2011
2009
2007
2005
2003
2001
1999
1997
1995
0
Figure 22: AIDS deaths
7.3 HIV PREVALENCE
Figure 23 shows the levels of HIV prevalence in the South African population under
each scenario. In the absence of any intervention, HIV prevalence would be expected
to peak between 14 and 15% in 2006, and then decline gradually as the rate of AIDS
mortality begins to exceed the rate at which new infections occur. Under the
‘prevention only’ scenario, HIV prevalence levels are reduced significantly over the
long term, and by 2015, levels of HIV prevalence are 2.5% lower than they would be
if no intervention programmes were introduced. As may be expected, the introduction
of antiretroviral treatment increases HIV prevalence rates by extending the lives of
HIV positive people. The HIV prevalence level in the ‘treatment and prevention’
scenario is therefore similar to that in the ‘no intervention’ scenario, but the former
would be expected to be lower than the latter over the longer term.
16%
14%
12%
N o in t e rve n t io n
10%
S TD + V C T +
8%
M TC TP
6%
S TD + V C T +
M TC TP + A R T
4%
2%
2015
2013
2011
2009
2007
2005
2003
2001
1999
1997
1995
0%
Figure 23: HIV prevalence
7.4 NUMBER OF ORPHANS
Figure 24 shows the number of maternal orphans under the age of 18, for each
scenario. Under the ‘prevention only’ scenario, the number of orphans increases
relative to that which would be expected under the ‘no intervention’ scenario, and
then reduces over the longer term. The initial increase can be attributable to the
mother-to-child transmission prevention programme, which improves child survival
rates, and hence increases orphan numbers. Under the ‘treatment and prevention’
scenario, the growth in orphan numbers is curbed, with the number of orphans
peaking at 2.2 million in 2015.
3,500,000
3,000,000
2,500,000
N o in t e rve n t io n
2,000,000
S TD + V C T +
1,500,000
M TC TP
S T + V CT +
1,000,000
M TC TP + A R T
500,000
2015
2013
2011
2009
2007
2005
2003
2001
1999
1997
1995
0
Figure 24: Maternal orphans under the age of 18
7.5 HIV INCIDENCE RATES
Figure 25 shows the number of new HIV infections occurring in each year for each of
the three scenarios. In the ‘prevention only’ scenario, the number of new HIV
infections in each year is ultimately 27% lower than the level that would occur in the
absence of any intervention, and in the ‘treatment and prevention scenario’ the
reduction is 44%. The cumulative numbers of infections averted by 2015, under the
‘prevention only’ and ‘treatment only’ scenarios, are 1.8 million and 2.9 million
respectively.
1,000,000
900,000
800,000
700,000
N o in t e rve n t io n
600,000
500,000
S TD + V C T +
M TC TP
400,000
S TD + V C T +
300,000
M TC TP + A R T
200,000
100,000
2015
2013
2011
2009
2007
2005
2003
2001
1999
1997
1995
0
Figure 25: New HIV infections
7.6 COMPARISON OF INTERVENTIONS
7.6.1 The various interventions and combinations of interventions can be compared in
terms of the cumulative reductions they can be expected to achieve in numbers of new
HIV infections and numbers of AIDS deaths. Figure 26 shows these cumulative
reductions up to 2015.
3,500,000
3,000,000
2,500,000
In fe c t io n s
2,000,000
a ve rt e d
1,500,000
R e d u c t io n in
1,000,000
A ID S d e a t h s
500,000
0
V CT
M TC TP
S TD
V CT +
S TD +
A RT
V CT +
S TD +
V CT +
M TC TP
M TC TP
+ A RT
Figure 26: Cumulative reductions in new HIV infections and AIDS deaths, by 2015
7.6.2 Clearly, all four interventions can contribute significantly to reducing the
number of new HIV infections. However, prevention strategies alone are unlikely to
achieve a short-term reduction in the number of AIDS deaths. In the ‘prevention only’
scenario, the cumulative reduction in AIDS deaths would reach 510,000 by 2015. A
combined ‘treatment and prevention’ programme, on the other hand, would result in a
cumulative reduction in the number of AIDS deaths of 3.1 million by 2015.
8. DISCUSSION
8.1 KEY FINDINGS
8.1.1 Although the results presented in this report are provisional, and further research
is required on a range of issues, a number of important conclusions can be drawn on
the basis of this analysis.
8.1.2 Improvements to the treatment of sexually transmitted diseases (STDs) result in
dramatic reductions in the incidence of HIV and other STDs. The inclusion of
acyclovir in treatment protocols is particularly important in reducing the incidence of
herpes. Reductions in the prevalence of STD symptoms are likely to be greater for
ulcerative STDs than for non-ulcerative STDs. This is encouraging, as ulcerative
STDs are believed to increase the risk of HIV transmission by a much greater factor
than non-ulcerative STDs.
8.1.3 Voluntary counselling and testing (VCT) is likely to achieve substantial
reductions in the amount of unsafe sex, particularly when it is targeted at individuals
who are at a high risk of HIV infection. The effectiveness of the programme in
preventing new HIV infections is, however, very dependent on the number of
individuals volunteering to receive the service.
8.1.4 An MTCTP programme would result in significant reductions in mortality rates
in children, particularly after the first year of life. An MTCTP programme would have
significant benefits for adults as well, and the results suggest that 44% of the
infections averted by 2015 would be adult infections. This benefit is often overlooked
in cost-effectiveness studies of MTCTP programmes.
8.1.5 Antiretroviral treatment (ART) programmes are likely to achieve substantial
reductions in levels of AIDS mortality and AIDS morbidity in the short to medium
term. By extending the lives of HIV positive parents, it is also likely that they will
reduce by 850,000 the number of maternal orphans under the age of 18. Significantly,
antiretroviral treatment also plays an important role in reducing the incidence of HIV
infection, and antiretroviral treatment is therefore a form of prevention in its own
right. A major concern, however, is the vast number of people that would ultimately
be receiving ART if it were made freely available, and the associated financial and
operational strains on the public health system.
8.1.6 When programmes that focus exclusively on HIV prevention are compared with
ART programmes, it is apparent that prevention alone will not address many of the
immediate consequences of the epidemic. Although prevention will, in the longer
term, reduce levels of AIDS mortality, morbidity and orphanhood, it will not mitigate
these effects by a substantial amount within the next decade. Antiretroviral treatment
programmes may result in a more significant improvement in conditions in the short
term, but even with an ART programme in place, the short-term effects of the
epidemic are likely to be severe.
8.2 LIMITATIONS
This analysis is subject to a number of limitations. Most significantly:
(a) The model developed thus far ignores the effects of interventions that have
already been introduced in South Africa, such as the adoption of syndromic
management protocols in public STD clinics.
(b) Although there are many examples of how interventions may be mutually
enhancing, few of these interactions have been allowed for in the model. For
example, no allowance has been made for the fact that VCT may reduce the
incidence of STDs other than HIV, and thus support STD treatment
programmes.
(c) In modelling STD dynamics, a number of simplifying assumptions have been
made, and these require more detailed investigation. The model ignores, for
example, the risk of STD reinfection if the individual’s partner is infected with
the same STD and does not receive treatment.
(d) HIV prevalence estimates in the medical scheme population are subject to
much uncertainty, and there is no direct empirical evidence that can be used to
validate these estimates.
(e) The long-term projections presented ignore the likely development of an AIDS
vaccine within the next decade.
8.3 FURTHER RESEARCH
In addition to addressing the shortcomings referred to in section 8.2, a number of
further model components will need to be added to allow for the effects of other
interventions. Social marketing, AIDS vaccine programmes, condom distribution,
treatment and prophylaxis for opportunistic infections, microbicides and changes in
usage of hormonal contraceptives are all examples of other interventions that may
need to be modelled in the near future.
ACKNOWLEDGEMENTS
The authors would like to thank the following individuals for their input on various
aspects of this research: Nathan Geffen, Zackie Achmat, David Coetzee, Andrew
Boulle, Toby Kaspar, Meghna Majmudar, Heather McLeod, Gregory Douglas, Chris
Raubenheimer, Shamim Aghdasi, Debbie Budlender, Robin Wood, Alex van den
Heever, Neva Makgetla, Tom Moultrie and Debbie Bradshaw. This research was
partly funded by the Treatment Action Campaign.
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APPENDIX A
ESTIMATION OF HIV PREVALENCE LEVELS IN THE SOUTH AFRICAN
MEDICAL SCHEME POPULATION
The purpose of this appendix is to present a method for estimating the HIV prevalence
in the South African medical scheme population. These results are preliminary and
are likely to be updated in the next few months.
A.1 METHOD
A.1.1 To estimate the HIV prevalence in the medical scheme population, it is
necessary to consider the profile of the population in terms of the factors that are
known to determine the risk of HIV infection. Among the most significant of these
factors are age, gender, skill level and race (Johnson and Budlender, 2002). The
profile of the medical scheme population, by age, gender and race, was obtained from
1999 October Household Survey data (S. Aghdasi, personal communication), and
adjusted for known sources of bias (Dorrington, 1999).
A.1.2 Information on the profile of the medical scheme population by skill level is not
available directly. However, the skill profile of the 18 – 65 age group in the general
population is known from the 1996 census, by race and gender. In addition, rates of
medical scheme membership are known for the employed population, by age, race,
gender and skill level, from the September 2001 Labour Force Survey. The latter
survey, however, under-estimates rates of medical scheme membership, and
adjustments were made to these rates in order to ensure consistency with the skill
profile observed in the 1996 census (Johnson and Dorrington, 2002).
A.1.3 Using historical data (Rama and McLeod, 2001), the profile of the medical
scheme population can be determined approximately for years prior to 1999. There is,
however, much uncertainty surrounding the future profile of the medical scheme
population. To illustrate the variation in HIV prevalence that is possible, we have
explored three scenarios. In scenario A there is no change in the medical scheme
profile by age, gender, skill level or race. In scenario B, it is assumed that the skill
profile changes, for example, as a result of attempts to extend medical scheme
membership to lower income groups. It is assumed that medical scheme membership
rates increase by a factor of 1.25 for skilled workers and 1.5 for unskilled workers and
unemployed individuals, while membership rates remain unchanged for the highly
skilled. The change in skill profile is phased in uniformly between 1999 and 2010,
after which the scheme profile is assumed to remain stable.
A.1.4 In scenario C, it is assumed that the race profile changes over the next ten years,
in line with the trend observed between 1991 and 1999. Table A.1 shows the assumed
change in the race profile from 1999 to 2010. As for scenario B, it is assumed that the
change occurs uniformly between 1999 and 2010, and that the medical scheme profile
is stable thereafter.
Proportion of medical scheme
members in each race group
Black
Coloured
Asian
White
40.1%
11.6%
4.5%
43.7%
50.0%
11.0%
4.0%
35.0%
1999
2010
Table A.1: Change in race profile for scenario C
A.1.5 Having established the profile of the medical scheme population by age, gender,
race and skill level, for each scenario, it is necessary to estimate HIV prevalence
levels corresponding to these demographic and socio-economic factors. This has been
done using unpublished estimates by CARE of HIV prevalence in different age,
gender, skill and race groups for 1999. These estimates are based on attempts to
reconcile outputs from the ASSA2000 model with data from a number of workforce
prevalence surveys conducted in various industries.
A.1.6 Using this technique, it is estimated that in 1999 roughly 6.7% of medical
scheme beneficiaries between the ages of 18 and 65 were HIV positive. To extend the
method to other years, it is necessary to calculate the ratio of HIV prevalence in the
medical scheme population to that in the general population, for each age, gender and
race group. Rates of prevalence in the general population are estimated from the
ASSA2000 model. If it is assumed that the ratios apply equally to years other than
1999, the levels of HIV prevalence in other years can then be estimated.
A.2 RESULTS
A.2.1 Figure A.1 shows the levels of HIV prevalence in the medical scheme
population under scenario A. It is estimated that in 2002, 6.1% of all medical scheme
beneficiaries are HIV positive. The prevalence of HIV infection in the medical
scheme population is expected to rise to a peak of roughly 7.5% in 2008.
8%
7%
6%
5%
4%
3%
2%
1%
2015
2013
2011
2009
2007
2005
2003
2001
1999
1997
1995
0%
Figure A.1: HIV prevalence levels in medical schemes (scenario A) and in the general
population
A.2.2 The prevalence levels estimated for scenarios B and C do not differ
substantially from those estimated for scenario A. Figure A.2 shows the prevalence
projections for the three scenarios. In scenario B, prevalence peaks at 8.0% in 2009,
and in scenario C, prevalence peaks at the same time, at 8.5%. In both scenarios,
prevalence is never more than 1% above that expected for scenario A. Although
scenario C represents the effect of a change in race profile, there is implicit within this
a substantial change in the skill profile of the population as well, as black medical
scheme members are – as a result of historical disadvantage – more likely to be
employed in low-skill jobs. The socio-economic profile of the medical scheme
population remains high even when allowance is made for greater inclusion of lower
income groups. This accounts for the closeness of the prevalence levels projected for
the three scenarios.
9%
8%
7%
6%
S c e n a rio A
5%
S c e n a rio C
4%
S c e n a rio B
3%
2%
1%
2015
2013
2011
2009
2007
2005
2003
2001
1999
1997
1995
0%
Figure A.2: Projected prevalence levels for scenarios A, B and C
A.3 SHORTCOMINGS
A.3.1 The estimates of medical scheme prevalence are subject to much uncertainty.
The method used for adjusting the medical scheme membership rates from the Labour
Force Survey is crude. In addition, there are slight inconsistencies between the skill
definitions used to analyse the 1996 Census and 2001 Labour Force Survey data.
A.3.2 It is assumed, in making these projections, that there are no interventions to
reduce the incidence of HIV infection or to treat people with AIDS. This is an
unrealistic assumption, particularly in the medical scheme population, where
substantial amounts have already been spent on treatment and prevention programmes
(Stein et al, 2002).
A.3.3 The most significant shortcoming of this analysis, however, is the absence of
empirical evidence to verify these estimates. The estimates of HIV prevalence rates
by age, gender, race and skill level are drawn from unpublished work by CARE,
which is at this stage still very exploratory. As more data on HIV prevalence in the
private sector become available, it will be possible to arrive at estimates in which we
can express greater confidence.
APPENDIX B
ESTIMATION OF PARAMETERS FOR HIV/AIDS CLINICAL STAGING
SYSTEMS
B.1 The purpose of this appendix is to review studies that have estimated the length of
time spent in the various clinical stages of HIV disease, and to use these estimates to
determine parameter values for the staging model described in section 2.3.
B.2 The two most commonly used clinical staging systems are the WHO Clinical
Staging System, a four-stage system, and the CDC Clinical Staging System, a threestage system. The two staging systems are similar, with stages 1 and 2 of the WHO
system corresponding roughly to stage A of the CDC system, and stages 3 and 4 of
the WHO system corresponding to stages B and C respectively of the CDC system.
Of the five studies identified, three used the WHO Clinical Staging System and two
used the CDC Clinical Staging System. Details of these studies are given in Table B.1
below.
No. of
Dates of
Staging
subjects seroconversion system
Morgan et al (2002a)
142
Uganda
Known
WHO
7
Morgan et al (2002b)
168
Malamba et al (1999)
Uganda
232
125 known
WHO
Davidse (2000)
Cape Town
1282
Mostly unknown WHO
Longini et al (1989)
San Francisco
603
Mostly unknown CDC
Deschamps et al (2000)
Haiti
42
Known
CDC
Study
Location
Table B.1: Clinical staging studies
B.3 In comparing the results of these studies, adjustment needs to be made for
differences in statistical techniques employed. The studies of Morgan et al (2002a and
2002b), Malamba et al (1999) and Deschamps et al (2000) are non-parametric
analyses, which report only median survival to or from each stage. The studies of
Davidse (2000) and Longini et al (1989) both involve the fitting of a four-stage
Markov model. To compare the various studies, it is necessary to apply a four-stage
model of disease progression to each of the non-parametric analyses, and to set the
rates of transition between the stages in such a way as to derive a model that
reproduces the medians reported. The shortcoming of this four-stage Markov model is
that it assumes the time spent in each stage follows an exponential distribution. This is
not a realistic assumption, particularly for the first stage of disease (Morgan et al,
2002a).
B.4 The resulting estimates of the mean waiting times (in years) are represented in
Table B.2 below.
7
The first study (Morgan et al, 2002a) estimated the median time to stages 1 and 2 following
seroconversion, and the second (Morgan et al, 2002b) estimated median survival from seroconversion
to death and from AIDS to death. Although there is some discrepancy between the cohorts used for
these two studies, their results can be combined to give a crude indication of the time spent in each
stage.
Study
Stage 1 Stage 2 Stage 3 Stage 4 Total
Morgan et al (2002a, 2002b)
3.0
1.6
5.5
1.1
11.2
Malamba et al (1999)
2.7
2.8
2.2
1.2
8.8
Davidse (2000)
2.6
1.9
3.0
1.8
9.3
Longini et al (1989)
4.6
5.2
2.0
11.8
Deschamps et al (2000)
3.6
2.3
2.2
8.1
Table B.2: Mean waiting times in WHO clinical stages
B.5 Since there are substantial differences between the studies in terms of the overall
survival from infection to death, it may be more useful to analyse the proportions of
total survival spent in each stage of disease. These proportions are shown in Figure
B.1 (ignoring the split between stages 1 and 2). Figure B.2 shows the split between
stages 1 and 2 for those studies in which the WHO Clinical Staging System was used.
100%
80%
S tage 4
60%
S tage 3
40%
S tages 1 & 2
20%
Deschamps
et al, 2000
Malamba et
al, 1999
Morgan et
al, 2002b
Davidse,
2000
Longini et
al, 1989
0%
Figure B.1: Proportions of total survival in each stage
100%
80%
60%
S tage 2
S tage 1
40%
20%
0%
D a vid s e ,
M o rg a n e t
M a la m b a e t
2000
a l, 2 0 0 2 a
a l, 1 9 9 9
Figure B.2: Proportions of pre-stage 3 survival in stages 1 and 2
B.6 For the five studies represented in Figure B.1 the average percentage of time from
infection to death spent in stage 1 or 2 is 47%, the average percentage of time spent in
stage 3 is 36%, and the average percentage of time spent in stage 4 (AIDS) is 17%.
From the four studies represented in Figure B.2, it is estimated that the average
percentage of time spent in stage 1 prior to entry into stage 3 is 58%, with the
remaining 42% of time spent in stage 2. It is thus estimated that the average
proportions of overall survival spent in stages 1, 2, 3 and 4 are 27%, 20%, 36% and
17% respectively. These estimates correspond fairly closely to those of Davidse
(28%, 20%, 32% and 19%), which suggests that it may be appropriate to apply these
proportions in modelling the South African epidemic.
B.7 In the four-stage system described in section 2.3, the time spent in each stage is
modelled using a Weibull distribution. If the mean survival under HIV infection is
assumed to be 11.35 years for individuals infected from ages 14 to 24, 10.6 years for
those infected from ages 25 to 34, and 9.85 years for those infected over the age of 34,
the mean time in each stage can be calculated using the proportions referred to in the
previous paragraph, and the median term spent in each stage can then be determined
using the formula
Mean .  ln 2  
Median 
 1  1 


where  is the shape parameter for the Weibull distribution.
1
B.8 Studies suggest that survival in stage 4 follows an exponential distribution
(Malamba et al (1999), Maartens (1999)), and the Weibull shape parameter for stage
4 has thus been set to 1. Williams and Gouws (2001) observe that when modelling
HIV survival using a Weibull distribution, there is a linear relationship between the
median and shape parameters. It is assumed that there is a similar linear relationship
between the mean and shape parameter in each stage of disease. Given the constraint
that the shape parameter in stage 4 is 1, the shape parameters in the other HIV stages
can be determined once an appropriate gradient m is chosen for the linear relationship
i  4  m(i  4 )
where  i is the shape parameter for stage i (  4 = 1) and  i is the mean time spent in
stage i. The value of m was set at 0.8, to produce an acceptable fit to the reported
death data.
B.9 The resulting aggregate survivor functions (ignoring non-AIDS mortality) for
individuals infected at different ages are shown in Figure B.3 below. The median
survival, from infection to AIDS-related death, is roughly 11 years for individuals
infected from ages 14 to 24, roughly 10.25 years for those infected from ages 25 to
34, and roughly 9.5 years for those infected over the age of 34.
1.2
Proportion surviving
1
0.8
14 - 24
0.6
25 - 34
35 +
0.4
0.2
20
18
16
14
12
10
8
6
4
2
0
0
C u rt a t e d u ra t io n s in c e in fe c t io n
Figure B.3: Rates of survival from HIV infection at different ages
B.10 The median and shape parameters estimated using this methodology are shown
in Table 1 of section 2.3.
APPENDIX C
PARAMETER CHANGES TO THE ASSA2000 MODEL
C.1 In addition to the changes to the ASSA2000 model described in sections 2.2 to
2.5, a number of changes have been made to the parameters of the ASSA2000 model
in order to ensure that the model produces an acceptable fit to the antenatal prevalence
and reported death data, and to correct earlier problems. These changes are listed
below8:
(a) The number of sexual partners per annum was changed, for females, from 200
to 170 for the PRO group, from 10 to 8.7 for the STD group, and from 1 to 0.9
for the RSK group.
(b) The initial proportion of the 15 – 59 population in the RSK group was changed
from 30% to 28%. The same adjustment was made to the proportion of
immigrants in the RSK group.
(c) The proportion of women in the STD group with partners in the male STD
group was changed from 70% to 75%.
(d) The shape factor for the sex activity curve was changed from 0.0035 to
0.0048. Between 1996 and 2000, the position factor was changed from 12 to
14.5, in order to produce an acceptable fit to the antenatal clinic prevalence
data by age.
(e) In the original version of the ASSA2000 model, the mean and variance of the
age of women’s partners were assumed to be the same for all women under the
age of 22 (the mean was assumed to be 26.12 and the variance 20.99). This
has been changed so that different assumptions are made for each age.
(f) The method used to interpolate AIDS mortality rates between different
infection ages was modified.
(g) The shape of the survivor function for children infected through breastfeeding
was changed substantially, as the shape parameter of 0.8 assumed in the
original ASSA2000 model was regarded as unreasonable. The shape and
median survival assumptions for other ages and modes of infection have also
been changed, as described in section 2.3, but remain similar to the
assumptions used in ASSA2000.
(h) For years prior to 1996, fertility rates in HIV negative women are calculated
on the assumption that observed fertility rates are a weighted average of
fertility rates in HIV positive and HIV negative women. In the original version
of the ASSA2000 model, fertility rates in HIV negative women, prior to 1996,
were assumed to be the same as those observed for all women.
C.2 The most significant parameter changes have been those used to determine the
effect of HIV on fertility. The initial impact factors have all been set to 0, the
reduction factors have been changed from 0.9 to 0.92, and the ‘start ratio’ factors have
been smoothed. Recent studies (Gregson et al (2002), Zaba et al (2000)) suggest that
in African societies with low fertility rates and high rates of contraceptive use, the
difference between HIV prevalence levels in pregnant women and women in the
general population is likely to be small. As South Africa is in an advanced stage of its
fertility transition, relative to other African countries, its HIV prevalence levels, as
8
For an explanation of these parameters, see the ASSA2000 User Manual, which can be downloaded
from www.assa.org.za/aidsmodel.asp.
determined on the basis of antenatal clinic data, may be lower than previously
thought. The changes to the HIV fertility parameters described above represent an
attempt to decrease the differential between antenatal and general female prevalence.
This results in levels of HIV infection slightly lower than those previously estimated
by the ASSA2000 model.