Modelling The Demographic Impact Of HIV/AIDS In South .
Demographic Research a free, expedited, online journalof peer-reviewed research and commentaryin the population sciences published by theMax Planck Institute for Demographic ResearchKonrad-Zuse Str. 1, D-18057 Rostock · GERMANYwww.demographic-research.orgDEMOGRAPHIC RESEARCHVOLUME 14, ARTICLE 22, PAGES 541-574PUBLISHED 30 JUNE l14/22/DOI: 10.4054/DemRes.2006.14.22Research ArticleModelling the demographic impactof HIV/AIDS in South Africa and thelikely impact of interventionsLeigh F. JohnsonRob E. Dorrington 2006 Max-Planck-Gesellschaft.
Table of odelling of sexual behaviourModelling of HIV survivalModelling of sexual transmissionModelling vertical transmission and paediatric HIV survivalModelling of prevention and treatment 5564Discussion5625Acknowledgements565References566
Demographic Research: Volume 14, Article 22research articleModelling the demographic impact of HIV/AIDSin South Africa and the likely impact of interventionsLeigh F. Johnson 1Rob E. Dorrington 2AbstractThis paper describes an approach to incorporating the impact of HIV/AIDS and theeffects of HIV/AIDS prevention and treatment programmes into a cohort componentprojection model of the South African population. The modelled HIV-positivepopulation is divided into clinical and treatment stages, and it is demonstrated that theage profile and morbidity profile of the HIV-positive population is changingsignificantly over time. HIV/AIDS is projected to have a substantial demographicimpact in South Africa. Prevention programmes – social marketing, voluntarycounselling and testing, prevention of mother-to-child transmission and improvedtreatment for sexually transmitted diseases – are unlikely to reduce AIDS mortalitysignificantly in the short term. However, more immediate reductions in mortality can beachieved when antiretroviral treatment is introduced.12Centre for Actuarial Research, Leslie Commerce Building, University of Cape Town, Private Bag, Rondebosch 7701, South Africa. Tel: 27 21650 5761, Fax: 27 21 689 7580. E-mail: ljohnson@commerce.uct.ac.za.Centre for Actuarial Research, University of Cape Town.http://www.demographic-research.org541
Johnson & Dorrington: Modelling the demographic impact of HIV/AIDS in South Africa1. IntroductionSouth Africa is one of the few African countries with nationally representative HIVprevalence data and good vital registration data (Department of Health 2004; Bradshawet al. 2004). However, these data cannot provide planners with a direct measure of thedemographic impact of HIV/AIDS or an indication of the likely future evolution of theepidemic. For this, mathematical models, calibrated to these data, are necessary. Thesemodels, if appropriately constructed, can also be used to assess the likely effect ofdifferent prevention and treatment programmes, as well as likely needs for treatmentand orphan care, and are therefore an important tool in policy formulation.A large number of mathematical models have been developed to simulate theimpact of HIV/AIDS and the likely effect of prevention and treatment programmes.These models can be classified into two broad groups: individual-based stochasticsimulation models, which randomly generate events such as infection and death foreach individual in the population; and deterministic models, which typically divide thepopulation into cohorts of individuals, and compute average numbers of events in eachcohort on the assumption that all individuals in a cohort share the same characteristics.Of the stochastic models that have been developed, most have focussed onsimulating the effects of HIV prevention rather than treatment (Korenromp et al. 2000;Van der Ploeg et al. 1998; Bracher, Santow and Cotts Watkins 2004; Robinson et al.1995). Because of the heavy computational requirements associated with individualbased simulation, populations simulated are typically limited in size to 10 000 to 20 000individuals. This results in a significant amount of stochastic variation, which makes itdifficult to calibrate the model to HIV prevalence and mortality data (Korenromp et al.2000). In addition, these models require an extensive range of assumptions as input.Deterministic models tend to be used for larger populations and in situations wheredata for setting assumptions are limited. Many of these models have been used toillustrate the differences between the effects of HIV prevention and treatmentprogrammes (Salomon et al. 2005; Stover et al. 2002; Nagelkerke et al. 2002).However, many of these models are based on very broad age divisions of thepopulation, which makes them inappropriate for projecting the population over longperiods. In some cases, the results of these simple deterministic models have beenincorporated into cohort component projection models such as DemProj (Stover 2004),in an attempt to estimate the demographic impact of HIV/AIDS more accurately.However, Heuveline (2003) identifies a number of problems with this approach,including failure to allow for changes in the age profile of HIV cases over the course ofthe epidemic, difficulties in incorporating the effect of HIV on fertility, and difficultiesin establishing parameters for a cohort component projection model in a hypothetical‘no AIDS’ scenario.542http://www.demographic-research.org
Demographic Research: Volume 14, Article 22The ASSA2002 AIDS and Demographic model is a combined cohort componentprojection and HIV/AIDS model, developed by the Actuarial Society of South Africa toestimate the impact of HIV/AIDS in South Africa. The basic structure of the earlierASSA2000 version of the model has been described previously (Dorrington 2000). Theobjective of this paper is to describe how the basic cohort component projection modelhas been extended to model the demographic impact of HIV/AIDS, to describe howHIV prevention and treatment programmes are modelled, and to demonstrate thesignificance of these prevention and treatment programmes in demographic terms.2. MethodThe ASSA2002 model was developed from an earlier version of the model, ASSA2000,which did not allow for the effects of prevention and treatment programmes. It isassumed that the epidemic is started by the ‘importing’ of a number of infectedindividuals in 1985, a few years prior to the first reports of AIDS cases in theheterosexual population. The epidemic is modelled as being spread by heterosexualintercourse and mother-to-child transmission. Age-specific assumptions about thepopulation profile in 1985, fertility, non-AIDS mortality and international migration arebased on analyses of the 1970, 1996 and 2001 censuses, vital registration data from1985 onwards and the 1998 Demographic and Health Survey (DHS), and are notdescribed here.The model has been programmed both in Excel/VBA and in Visual C , and theExcel/VBA version is freely available online (Actuarial Society of South Africa 2004)3.A user guide for the model (Dorrington, Johnson and Budlender 2004) is also availableonline. A separate model, which makes use of output from the AIDS and Demographicmodel, has been developed to estimate numbers of orphans from the ASSA2002outputs, and is described elsewhere (Johnson and Dorrington 2001).2.1 Modelling of sexual behaviourIndividuals are assumed to be at risk of acquiring HIV through heterosexual contactbetween the ages of 14 and 59. Within this age band, individuals are split into four ‘riskgroups’: a ‘PRO’ group, which represents sex workers and their frequent clients; a‘STD’ group, which represents individuals who are regularly infected with sexuallytransmitted diseases (STDs) although not frequently engaging in commercial sex; a3The version of the ASSA2002 model used in this paper is the ASSA2002 ‘lite’ model, number 040701.http://www.demographic-research.org543
Johnson & Dorrington: Modelling the demographic impact of HIV/AIDS in South Africa‘RSK’ group, consisting of individuals who are at risk of HIV infection although notregularly infected with other STDs; and a ‘NOT’ group, comprising individuals who arenot at risk of infection (either because they are not sexually active or because they are inlong-term mutually monogamous relationships). The assumed relative sizes of theserisk groups and their sexual behaviour characteristics are shown in Table 1.The relative sizes of the risk groups are assumed to be the same for males andfemales, as are the relative frequencies of condom usage in each risk group. Malepreferences regarding the risk group of their female partners, as well as average annualnumbers of female partners, are determined to be consistent with the assumptions forfemales. Average fertility rates are assumed to apply to women in the RSK group, whilewomen in the PRO and STD groups have fertility rates that are lower than the averageby 60% and 30% respectively. This allows for the effects of higher contraceptive usageand STD incidence in the PRO and STD groups.Rates of condom use are assumed to be higher in the PRO and STD groups, ascondoms tend to be used more frequently in short-term casual relationships (Van derRyst et al. 2001; Williams et al. 2000; Department of Health 1999). The condom usageadjustment factors in Table 1 represent the factor by which the average age-specificTable 1:Characteristics of risk groups% of 25-59 population inrisk group in 1985Average annual numberof new partners% of new partners inPRO groupSTD groupRSK groupAverage # coital acts perpartnership if partner is inPRO groupSTD groupRSK groupCondom usageadjustment factorFertility adjustment factorFemale risk groupPRO STD RSKNOTMale risk groupPRO 51-----*†*23%13*†* Determined to be consistent with female assumptions. Determined to be such that the fertility adjustment factors, when weightedby the proportion of the sexually experienced population in each risk group, averaged to 1.544http://www.demographic-research.org
Demographic Research: Volume 14, Article 22rates of condom usage are multiplied to obtain age-specific condom usage rates in eachrisk group. The average age-specific rates for 1998 were set to be the same as thoserecorded in the 1998 DHS, and are shown in Table 2. Frequency of condom use appearsto have increased substantially in recent years (Human Sciences Research Council2002; Reproductive Health Research Unit 2004), and it is assumed that this is the resultof social marketing programmes. In the absence of these social marketing programmes,it is assumed that condom usage would have remained constant at half the levelobserved in 1998, in all years.Table 2:Age-specific behavioural assumptionsAge groupAverage rate of condom 14.4%7.6%6.6%3.0%3.0%3.0%3.0%3.0%% 00%Age of male 6.8656.05†45.24†34.43†* Allowing for the effect of social marketing programmes. Extrapolated from estimates at younger agesIndividuals become sexually active between the ages of 13 and 25. At age 14, it isassumed that 10% of individuals are sexually experienced, and 12% of the remainderare assumed to become sexually experienced in the next year. The annual probability ofbecoming sexually experienced is assumed to increase linearly with respect to age, untilall individuals are sexually experienced at age 25. These assumptions were set toproduce rates of sexual experience consistent with those observed in surveys(Reproductive Health Research Unit 2004; Department of Health 1999), and are shownin Table 2. The same sexual debut assumptions are used for males and females, assurveys do not suggest that there is a significant difference in sexual experiencebetween males and females at young ages (Williams et al. 2000; Reproductive HealthResearch Unit 2004). Individuals remain in the NOT group until they become sexuallyexperienced, after which they either remain in the NOT group or get moved into theother risk groups. The model assumes that people do not change risk group after sexualdebut.http://www.demographic-research.org545
Johnson & Dorrington: Modelling the demographic impact of HIV/AIDS in South AfricaAlthough individuals cannot move between risk groups after sexual debut,allowance is made for rates of partnership formation and coital frequencies to vary withage. For females, a sexual activity index is constructed using the formulaSx () (u 13) exp( 0.005(u 13) )46( x 13) exp 0.005(x 13)592(1)2u 14where S x is the multiple by which the average number of partners and the averagenumber of coital acts per partnership increases at age x. The shape parameter (0.005)and position parameter (13) were set at levels that ensured patterns of HIV prevalenceby age were as far as possible consistent with those observed in surveys (Department ofHealth 2004). The mean age of male partners and the variance of male partner ageswere estimated from the 1998 DHS, for each five-year age band, and are shown inTable 2. For a woman aged x, the distribution of male partner ages, f ( y x ) , isassumed to be gamma, with mean and variance determined from the values in Table 2.Thus,f (y x) y y 1λα (u 13)α 1 exp( λ (u 13))du .Γ(α )(2)For a man aged y, the sexual activity index is calculated using the formula59S *y 46 f ( y x ) S x2x 1459 Su 142u,(3)and the proportion of female partners aged x is calculated as59f * (x y ) f ( y x )S x2S546*2y S2u S*2uu 1459u 14.(4)http://www.demographic-research.org
Demographic Research: Volume 14, Article 222.2 Modelling of HIV survivalIn the absence of treatment and other interventions, adult HIV survival is modelledusing a four-stage model of HIV disease progression, with the four stagescorresponding to the four stages of the WHO Clinical Staging System (WHOInternational Collaborating Group for the study of the WHO Staging System 1993). Theeffect of highly active antiretroviral treatment (HAART) is modelled by adding twostages to the basic four-stage model of HIV survival: stage 5 represents individualscurrently on HAART, and stage 6 represents individuals who have discontinuedHAART. Individuals are assumed to start treatment at the time of their first AIDSdefining illness, i.e. on progressing from HIV stage 3 to HIV stage 4. For each of stages1 to 4, a record is kept of the proportion of individuals who have received voluntarycounselling and testing (VCT) and know their HIV status. All individuals who are onHAART or who were previously on HAART are assumed to know their HIV status.This model of disease progression is represented in Figure 1.The total time from HIV infection to death in adults has been found to depend onthe age at HIV infection (Collaborative Group on AIDS Incubation and HIV Survival2000). AIDS mortality rates and proportions of individuals in each of the six diseasestages are therefore calculated at each integer duration of HIV infection for threeinfection ages: 19, 29 and 39. For other ages at infection, AIDS mortality rates andproportions in different stages, at integer durations of infection, are interpolated orextrapolated from those calculated at the three pivot ages. The mean time from HIVinfection to death, in the absence of HAART, is assumed to be 12 years for individualsinfected at age 19, 11 years for individuals infected at age 29 and 9.5 years forindividuals infected at age 39 or older (median survival times are roughly half a yearshorter in each case). These assumptions are set to be consistent with survival rates inthe developed world, as local studies do not suggest that HIV survival in South Africadiffers substantially from that in the developed world (Glynn et al. 2005; Maartens etal. 1997; Badri et al. 2004), and lower HIV survival assumptions tend to produce higherestimates of the number of deaths than have been observed in South Africa.Estimates of the proportion of adult HIV survival time spent in each of WHOstages 1 to 4, in the absence of HAART, are derived from application of simple Markovmodels to survival data collected from a number of settings (Morgan et al. 2002a;Morgan et al. 2002b; Malamba et al. 1999; Deschamps et al. 2000; Longini et al. 1989;Davidse 2000). The estimated proportions, shown in Table 3, are used to estimate themean time spent in each stage of disease, for each of the three pivot ages. The timespent in stage t of disease is assumed to follow a Weibull distribution, parameterized interms of a median ( mt ) and shape parameter ( φ t ), both of which are related to the mean( µ t ) by the following formulae:http://www.demographic-research.org547
Johnson & Dorrington: Modelling the demographic impact of HIV/AIDS in South Africaφt φ4 b(µ t µ 4 )mt (5)µ t (ln 2 )1 / φΓ(1 1 φ t )t(6)As equation (5) shows, the means and shape parameters for the different stages areassumed to be linearly related. Parameter φ 4 is set at 1, and parameter b is set at 0.35,in order to replicate the ‘shape’ of the survivor functions observed in the developedworld (Collaborative Group on AIDS Incubation and HIV Survival 2000).Table 3:Stage-specific parametersEffect of VCTReduction in %Reduction inof sex actsfrequency ofunprotectedsex% of totalsurvival time*spent in stageRelativefrequencyof %36%53%-0.831%53%-0.2531%53%WHO stage 1WHO stage 2WHO stage 3WHO stage 4Stage 5 (onHAART)Stage 6 (offHAART)* Assuming HAART is not available.As shown in Figure 1, individuals on HAART ultimately either discontinuetreatment or die while on treatment. The probabilities of AIDS death anddiscontinuation of HAART in the first six months on HAART (0.0821 and 0.0914respectively) are assumed to be particularly high. Thereafter, annual probabilities ofAIDS death while on HAART and discontinuation of HAART (both 0.0584) areassumed to remain constant. After discontinuing HAART, individuals are assumed toexperience the same AIDS mortality rates as untreated individuals in HIV stage 4.These assumptions result in a decline in AIDS mortality rates consistent with the 70 to80% reductions in AIDS mortality rates observed after starting HAART in variousstudies (Palella et al. 1998; Murphy et al. 2001; Jordan et al. 2002). These and otherstudies (Badri et al. 2004) also suggest a 60 to 85% reduction in the incidence ofopportunistic infections after starting HAART. It is therefore assumed that only 25% ofindividuals in stages 5 and 6 are classified as ‘AIDS sick’.548http://www.demographic-research.org
Demographic Research: Volume 14, Article 22Figure 1:Model of HIV disease progression in adults, incorporating theeffects of HAART and knowledge of HIV statusHIVnegativeStatusunknownHIVstage ge 1StatusunknownHIVstage 1StatusknownHIVstage 2StatusunknownHIVstage 2StatusknownHIVstage 3StatusunknownHIVstage 3StatusknownHIVstage 4StatusunknownHIVstage 4StatusknownHIVstage 5OnHAARTAIDSdeath549
Johnson & Dorrington: Modelling the demographic impact of HIV/AIDS in South AfricaAs individuals enter the later stages of HIV disease, coital frequencies decreasedue to increased morbidity (Ross et al. 2004; Terceira et al. 2003; Hankins, Tran andLapointe 1998; Wilson et al. 2004). The frequency of sex in stage t, expressed as amultiple of the frequency of sex in stages 1 and 2 (the asymptomatic stages), is set tovary according to the severity of symptoms in stage t. The assumed values of thesemultiples are shown in Table 3. Further changes in sexual behaviour are assumed tooccur when HIV-infected individuals learn their HIV status through VCT programmes.Table 3 also shows the
Modelling the demographic impact of HIV/AIDS in South Africa and the likely impact of interventions Leigh F. Johnson 1 Rob E. Dorrington 2 Abstract This paper describes an approach to incorporating the impact of HIV/AIDS and the effects of HIV/AIDS prevention and treatment programmes into a cohort component
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