1. FORECASTING THE IMPACT OF HIV/AIDS NATIONALLY AND IN SUB-POPULATIONS
Rob Dorrington
Centre for Actuarial Research (CARe)
University of Cape Town

2. Modelling process

3. Ways of classifying models
Doyle et al
Feeney & Zaba
Garnett
Extrapolation
Simple epidemiological
Population average vs individual-based
Back projection
Complex epidemiological
Deterministic vs stochastic
Macro-simulation
Compartmental vs distributional
Micro-simulation
Discrete vs continuous time
Analytical vs numerical

4. Purpose
to contrast two forms of models, the model developed by the Actuarial Society of South Africa (ASSA) and the Spectrum and EPP combination model (used by UNAIDS and WHO)
behavioural vs parametric curve fitting
examples presented all refer to South Africa, but the arguments are generally applicable
to introduce the ASSA models (

5. Spectrum + EPP
most widely used model for projecting the impact of HIV/AIDS on a national population
involves three steps
DemProj (non-HIV/AIDS demographic projection)
EPP (fitting 4-parameter curve to anc data)
AIMS (assumptions to translate changes in prevalence into new numbers infected by sex and age, and hence deaths)

6. ASSA suite of models origins in the Doyle-Metropolitan model
ASSA500, 600, 2000 (lite and full, provincial and urban-rural)
ASSA2000 calibrated on antenatal data + total deaths by age and year
behavioural cohort component projection model incorporating the impact of the heterosexual spread of the epidemic

8. Fit to antenatal data

9. Antenatal prevalence by age over time

10. Ratio of prevalence of women attending antenatal clinics to that of the population (not using private health)

11. Male population prevalence vs HSRC

12. Female population prevalence vs HSRC

13. Calibration to estimates of the number of deaths by age - Males

14. Calibration to estimates of the number of deaths by age - Females

15. Advantages/disadvantages
Spectrum + EPP is ‘minimally complex’ – still many input parameters
ASSA complex – more input parameters
However, Spectrum + EPP assumptions high-level assumptions and model is sensitive to certain key input parameters
Difficult to develop Spectrum + EPP to cope with behavioural change and interventions

16. Sub-populations Reasons Improving projections
to improve projections
to facilitate planning and management
Improving projections
sexual mixing of risk groups
migration
Spectrum + EPP not designed to handle either

17. Prevalence by risk group over time

18. Prevalence by age for difference risk groups

19. Prevalence by population group over time

20. Effect of migration
In terms of impact on projection of whole population – only important if the number of migrants and difference in prevalence significant
This usually pertains either early in the epidemic or for small geographic sub-populations
Urban-rural epidemics
Example of the Western Cape province in South Africa

21. Prevalence by province

22. Prevalence by health district in Western Cape province, South Africa

23. Comparisons of projections of total SA population, taking race and province into account

24. Other issues ‘Select’ populations Uncertainty Interventions and change
changes in sexual behaviour
Interventions (VCT, MTCTP, STD treatment, ART)
Vaccines

25. Conclusions Two reasons for projecting at sub-population level
Macro-micro hybrids complex requiring much input information
However:
more realistic (which provides greater understanding)
focus on input data provides useful data for managing and monitoring
necessary if one wants to cater for the impact future developments, particularly interventions

26. Acknowledgements Leigh Johnson (CARe, UCT)
Debbie Bradshaw (Burden of Disease Unit, MRC) and Ian Timæus (Centre for Population Studies, LSHTM)
ASSA AIDS Committee, David Schneider, (Actuarial Solutions, Botswana)