Modelling the demographic impact of HIV/AIDS Joubert Ferreira (President ASSA) Wim Els (Executive Director) David Schneider (Convenor AIDS Committee) Rob Dorrington (member AIDS Committee)

Overview  The ASSA AIDS Committee and the suite of models  Features of the model and calibration  The fit to the provinces  Models vs surveys  Comparison of ASSA2001 prototype with the HSRC results by sex and age

The ASSA suite of models

ASSA AIDS Committee  Set up in 1987  Objective: To assist the actuarial profession and society in assessing and addressing the impact of the AIDS epidemic in South Africa  Membership: Over 20 members split roughly 50/50 between Cape and Gauteng, with one person (the present convenor, David Schneider) working in Botswana

ASSA AIDS Committee  Some of the current projects:  ASSA2001  Professional guidance notes  Economic impact of HIV/AIDS  CPD, including AIDS impact consulting  Data, including life assurance, pathology lab, and blood transfusion data  PR  Urban-Rural model  Impact on medical schemes

History of the ASSA model  Doyle-Metropolitan model (c1990)  ASSA500 (c1995)  ASSA600 (c1998)  The ASSA2000 suite (2001) -ASSA2000 lite -ASSA2000 full -Aggregate of application to the provinces (2002)

Additional models  Other models: -urban-rural (not released) -multi-state select population model -interventions model (not released)  Add-ons (not released) -orphans (maternal, paternal and dual) -numbers by stages of infection

Features of the model  A heterosexual behavioural cohort component projection model  Population divided by risk by :  Age (young, adult, old)  ‘behaviour’(PRO, STD, RSK, NOT)  ‘previous social disadvantage’ (population group)  Geographic (province)  Sex activity  Source of partner, probability of transmission, number of new partners p.a., number of contacts per partner, condom usage, no sex between population groups and no sex between provinces

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, 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 replicate known data of the prevalence or impact of the epidemic such as the antenatal prevalence and the mortality figures - calibration ( e.g. the mixing of risk groups, sex activity, no. of partners, age of partners )

Calibration targets  Prevalence levels -*Antenatal – overall prevalence by province and population group over time -*Antenatal – prevalence by age over time -Ratio of antenatal to national by age -HSRC prevalence by sex and age  Deaths - *Population register – 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

Calibration targets (not yet available)  Census - Numbers by sex and age -Mortality rates by age and sex (and province?) -orphanhood -CEB/CS -deaths in household

Calibration: antenatal vs model - African

Calibration: antenatal vs model - Coloured

Calibration: antenatal vs model - Indian

Calibration: antenatal vs model - White

National calibration: antenatal vs model

Projected vs actual curve of deaths - males

Projected vs actual curve of deaths - females

Eastern Cape

Free State

Gauteng

KwaZulu-Natal

Limpopo

Mpumalanga

Northern Cape

North West

Western Cape

Models vs surveys  ASSA involved in modelling, not surveying  Modelling involves creating a tool that tries to simulate reality in a way that is consistent with empirical data  Modelling does not produce empirical data, but rather an interpretation of, and extrapolation from, empirical data  Conclusions to be drawn from models are limited to the extent that modelling involves a great many simplifications and assumptions  However, to the extent that models attempt to tie together data from many sources, with some sort of consistency, they can give useful indications of errors (random or otherwise) in surveys

HSRC survey - limitations  Invaluable piece of research – particularly if prepared to share with other researchers  Potential for bias  high non-response  By design excludes some high-risk populations (prisons, military and hospitals), by default others (e.g. truck drivers, and those not part of permanent homes, criminals, etc)  Use of retired nurses to ask about sexual behaviour  Wide confidence intervals  Unwillingness to share (even questionnaires)

Prevalence by province (all women 15-49)

Male population prevalence vs HSRC

Female population prevalence vs HSRC