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Modelling HIV/AIDS in Southern Africa

Centre for Actuarial Research (CARe) A Research Unit of the University of Cape Town. Modelling HIV/AIDS in Southern Africa. History of the ASSA AIDS and Demographic model. Doyle-Metropolitan model (c1990) ASSA500 (c1995) ASSA600 (c1998)

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Modelling HIV/AIDS in Southern Africa

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  1. Centre for Actuarial Research (CARe) A Research Unit of the University of Cape Town Modelling HIV/AIDS in Southern Africa

  2. History of the ASSA AIDS and Demographic model • Doyle-Metropolitan model (c1990) • ASSA500 (c1995) • ASSA600 (c1998) • ASSA2000 suite (2001): lite, full, provincial (beta 2002) • ASSA2002 lite and full (2004) • ASSA2003 suite (2005): lite, full, provincial • Other models(www.assa.org.za/aidsmodel.asp) • Orphans, select populations, other countries

  3. Adjust for bias(public anc vs all women) Antenatal data (by age) Calibration Adult death data • Detailed output including: • No. infected • No. new infections • No. AIDS deaths, etc Demographic parameters(base population, fertility, non-AIDS mortality and migration) Cohort component projection model Epi and behavioural parameters (e.g. % in risk groups, amount of sex, probability of transmission, probability a condom used, etc) Epidemiological, behavioural, intervention model Interventions (IEC, VCT, STI, PMTCT, ART) Methodology: ASSA model

  4. Features of the ASSA lite model • Heterosexual behavioural cohort component projection model (individual ages/years) • Population divided by risk by: • Age (young, adult, old) • ‘Behaviour’ (PRO, STD, RSK, NOT) • ‘Previous socio-economic disadvantage’ (racial groups) • Geographic region (province) • Sex activity • Risk group of partner, probability of transmission, number of new partners p.a., number of contacts per partner, condom usage, • No sex between racial groups or provinces

  5. Modelling prevention and treatment • Five interventions: • Social marketing, information and education campaigns (IEC) • Improved treatment for sexually transmitted diseases (STDs) • Voluntary counselling and testing (VCT) • Prevention of mother-to-child transmission (PMTCT) • Antiretroviral treatment (ART)

  6. The fitting process - calibration • Set as many of the parameters/assumptions from independent estimates (% STD, probability of transmission, condom usage, age of (male) partners, the median term to survival of adults and children, impact of HIV on fertility and bias in ANC data, all non-HIV demographic assumptions) • Set some other assumptions (which are not particularly important) by reasonable guesses (e.g. relative fertility, and risk groups of migrants) • The remaining assumptions are set in order to produce known data of the prevalence or impact of the epidemic such as the antenatal prevalence and the mortality figures - calibration(e.g. size of the RSK group, the mixing of risk groups, sex activity by age, no. of partners, number of contacts per partner)

  7. Calibration targets • Prevalence levels • Antenatal – overall prevalence • Antenatal – prevalence by age over time • Ratio of antenatal to national by age • HSRC prevalence by sex and age • Deaths • Population or vital registration – overall by sex, age and over time • Cause of Death – proportion AIDS in adults by sex and age • Cause of Death – proportion AIDS in children by age • Cause of Death – ratio of male to female by age over time

  8. Calibration targets(cont’d) • Census • Numbers by sex and age nationally and provincially • Mortality rates by age and sex • Orphanhood • CEB/CS • Deaths in household • Other • Numbers on treatment (private and public)

  9. Antenatal prevalence: South Africa Confidence intervals prior to 1998 were incorrectly calculated – should be wider

  10. Number of deaths - men

  11. Number of deaths - women

  12. Uncertainty • Demography (Base population, Fertility, Mortality & Migration) • Epidemiological assumptions (% in risk groups, mixing of the risk groups, probabilities of transmission, infectivity and infectiousness by stage, etc) • Interventions (in particular treatment) • Roll-out • Effectiveness • Behaviour • Future developments (e.g. vaccine)

  13. Selected results

  14. 40.0% 35.0% 30.0% 25.0% ASSA2003 20.0% HSRC05 15.0% 10.0% 5.0% 0.0% 60+ 2-14 30-34 35-39 40-44 45-49 50-54 55-59 15-19 20-24 25-29 Comparison with HSRC05: South Africa (Prevalence: males and females)

  15. Prevalence: adults 20-64: South Africa

  16. Numbers infected by province: South Africa

  17. Numbers on HAART by province: South Africa

  18. Prevalence 15-49 by sub-district: Botswana

  19. Numbers infected by stage by year: Botswana

  20. Numbers of deaths by year: Botswana

  21. Future developments • Circumcision • Vaccine • Age-specific interventions • Pregnancy and transmission? • Risk group migration? • Better demographic estimation • Uncertainty • Education? • Household impact? • Fitting to other (SADC) countries

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