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

Modelling process

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

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 (www.assa.org.za)

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)

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

Fit to antenatal data

Antenatal prevalence by age over time

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

Male population prevalence vs HSRC

Female population prevalence vs HSRC

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

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

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

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

Prevalence by risk group over time

Prevalence by age for difference risk groups

Prevalence by population group over time

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

Prevalence by province

Prevalence by health district in Western Cape province, South Africa

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

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

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

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)