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

www.assa.org.za/aidsmodel.asp

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

Diagram 1: A schematic diagram of the ASSA600 Aids Model

Adu

lt (1

4 -

59)

Old

(60+

)

HIV- Young HIV+ Young

NOT RSK STD PRO

Increasing sexual mobility

Increasing risk of HIV infection

HIV- Old HIV+ Old

Dea

ths

Normal Deaths AIDS Deaths

Imported HIV

Migrants (0-59)

Migrants (Aged 60+)

HIV- Births HIV+ Births

You

ng (0

-

13)

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

0%

5%

10%

15%

20%

25%

30%

35%

1985 1987 1989 1991 1993 1995 1997 1999 2001 2003

ANC(model)

ANCsurvey

ANCsurvey(adjusted)

Calibration: antenatal vs model - Coloured

0%

5%

10%

15%

20%

25%

30%

35%

1985 1987 1989 1991 1993 1995 1997 1999 2001 2003

ANC(model)

ANCsurvey

Calibration: antenatal vs model - Indian

0%

5%

10%

15%

20%

25%

30%

35%

1985 1987 1989 1991 1993 1995 1997 1999 2001 2003

ANC(model)

ANCsurvey

ANCTarget

Calibration: antenatal vs model - White

0%

5%

10%

15%

20%

25%

30%

35%

1985 1987 1989 1991 1993 1995 1997 1999 2001 2003

ANC(model)

ANCsurvey

ANCTarget

National calibration:antenatal vs model

0%

5%

10%

15%

20%

25%

30%

35%

1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005

ANC(model)

ANC survey

ASSA2001

ANC survey(adjusted)

Projected vs actual curve of deaths - males

0

5000

10000

15000

20000

25000

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85+

Proj1999

1996

1997

1998

1999

Projected vs actual curve of deaths - females

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85+

Proj1999

1996

1997

1998

1999

Eastern Cape

Eastern Cape

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

19

90

19

92

19

94

19

96

19

98

20

00

20

02

20

04

20

06

20

08

20

10

Pe

rce

nta

ge

Model

anc prevalence

adjusted for bias

Free State

Free State

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

19

90

19

92

19

94

19

96

19

98

20

00

20

02

20

04

20

06

20

08

20

10

Pe

rce

nta

ge Model

anc prevalence

adjusted for bias

Gauteng

Gauteng

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

Pe

rce

nta

ge Model

anc prevalence

adjusted for bias

KwaZulu-Natal

KwaZulu-Natal

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

45.0%

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

Pe

rce

nta

ge Model

anc prevalence

adjusted for bias

Limpopo

Limpopo

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

19

90

19

92

19

94

19

96

19

98

20

00

20

02

20

04

20

06

20

08

20

10

Pe

rce

nta

ge

Model

anc prevalence

adjusted for bias

Mpumalanga

Mpumalanga

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

Pe

rce

nta

ge Model

anc prevalence

adjusted for bias

Northern Cape

Northern Cape

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

19

90

19

92

19

94

19

96

19

98

20

00

20

02

20

04

20

06

20

08

20

10

Pe

rce

nta

ge

Model

anc prevalence

adjusted for bias

North West

North West

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

Pe

rce

nta

ge Model

anc prevalence

adjusted for bias

Western Cape

Western Cape

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

19

90

19

92

19

94

19

96

19

98

20

00

20

02

20

04

20

06

20

08

20

10

Pe

rce

nta

ge

Model

anc prevalence

adjusted for bias

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)

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

WC EC NC FS KZ NW GT MP LP SA

ASSA2000

HSRC

Male population prevalence vs HSRC

0%

5%

10%

15%

20%

25%

30%

35%

2-14

15-1

9

20-2

4

25-2

9

30-3

4

35-3

9

40-4

4

45-4

9

50-5

4

55+

ASSA2001

HSRC

Female population prevalence vs HSRC

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%2-

14

15-1

9

20-2

4

25-2

9

30-3

4

35-3

9

40-4

4

45-4

9

50-5

4

55+

ASSA2001

HSRC