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South African Journal of Science

versión On-line ISSN 1996-7489

S. Afr. j. sci. vol.103 no.3-4 Pretoria mar./abr. 2007

 

RESEARCH ARTICLES

 

An investigation into the extent of uncertainty surrounding estimates of the impact of HIV/AIDS in South Africa

 

 

Leigh F. JohnsonI; Rob E. DorringtonI; Alan P. MatthewsII

ICentre for Actuarial Research, University of Cape Town, Private Bag, Rondebosch 7701, South Africa
IISchool of Physics, University of KwaZulu-Natal, Private Bag X54001, Durban 4000

 

 


ABSTRACT

HIV/AIDS statistics have been the source of much controversy in South Africa, but often the extent of uncertainty around these estimates is ignored. There is need for an assessment of the range of uncertainty around often-quoted HIV/AIDS statistics. This analysis determines ranges of uncertainty around the inputs and outputs of the ASSA2002 AIDS and Demographic model of the South African HIV/AIDS epidemic, using a generalized likelihood uncertainty estimation approach. A sample of 500 parameter combinations was drawn by weighting randomly generated parameter combinations by likelihood functions defined on the basis of four South African HIV/AIDS data sets. The estimated number of HIV infections in mid-2005 was 5.1 million (95% prediction interval: 4.2-6.0 million), equivalent to an HIV prevalence rate of 11.1% (9.1-13.1%). Between mid-2004 and mid-2005, the estimated number of new HIV infections was 490 000 (370 000-590 000) and the estimated number of AIDS deaths was 320 000 (270 000-380 000). The posterior mean HIV survival time was estimated to be 11.5 years (95% credibility interval: 10.0-12.9 years), longer than estimated for elsewhere in the developing world. This analysis confirms that South Africa is experiencing a severe HIV/AIDS epidemic, and suggests that HIV/AIDS epidemiology in the country probably differs from that elsewhere in Africa.


 

 

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REFERENCES

1. Statistics South Africa (2005). Mid-year population estimates, South Africa, 2005. Online: http://www.statssa.gov.za        [ Links ]

2. Department of Health (2005). National HIV and syphilis antenatal sero-prevalence survey in South Africa 2004. Online: http://www.doh.gov.za/docs/reports/2004/hiv-syphilis.pdf        [ Links ]

3. BlowerS. M. andDowlatabadiH. (1994). Sensitivityanduncertaintyanalysisof complex models of disease transmission: an HIV model, as an example. Int. Stat. Rev. 62(2), 229-243.         [ Links ]

4. Blower S.M., Gershengorn H.B. and Grant R.M. (2000). A tale of two futures: HIV and antiretroviral therapy in San Francisco. Science 287, 650-654.         [ Links ]

5. Davenport M.P., Ribeiro R.M., Chao D.L. and Perelson A.S. (2004). Predicting the impact of a nonsterilizing vaccine against human immunodeficiency virus. J. Virol. 78(20), 11340-11351.         [ Links ]

6. Law M.G., Prestage G., Grulich A., Van de Ven Ρ and Kippax S. (2001). Modelling the effects of combination antiretroviral treatments on HIV incidence. AIDS 15, 1287-1294.

7. Boily M., Bastos F.I., Desai K. and Másse B. (2004). Changes in the transmission dynamicsofthe HIVepidemicafterthe wide-scale use ofantiretroviral therapy could explain increases in sexually transmitted infections. Sex. Transm. Dis. 31(2), 100-113.         [ Links ]

8. Grassly N.C., Morgan M., WalkerN., Garnett G., Stanecki K.A., StoverJ., Brown T and Ghys P.D. (2004). Uncertaintyin estimates of HIV/AIDS: the estimation andapplicationofplausibilitybounds. Sex. Transm. Infect. 80 (Suppl. 1),i31-38.         [ Links ]

9. Salomon J.A. and Murray C.J. (2001). Modelling HIV/AIDS epidemics in sub-Saharan Africa using seroprevalence data from antenatal clinics. Bull. WHO 79(7), 596-607.         [ Links ]

10. Greenland S. (2000). Principles of multilevel modelling. Int. J. Epidemiol. 29(1), 158-167.         [ Links ]

11. Gilks W.R., De Angelis D. and Day N.E. (1999). Bayesian conditional- independence modeling of the AIDS epidemic in England and Wales. Physica D 133(1-4), 145-151.         [ Links ]

12. Tan WY. and Ye Z.Z. (2000). Estimation of HIV infection and incubation via state space models. Math. Biosci. 167(1), 31-50.         [ Links ]

13. Alkema L., Raftery A.E. and Clark S.J. (2007). Probabilistic projections ofHIV prevalence using Bayesian melding. Centre for Studies in Demography and Ecology, University of Washington. Working paper no. 07-01. Online: http://csde.washington.edu/downloads/07-01.pdf        [ Links ]

14. Johnson L.F. and Dorrington R.E. (2006). Modellingthe demographicimpactof HIV/AIDS in South Africa and the likely impact of interventions. Demographic Res. 14, 541-74.         [ Links ]

15. Actuarial Society of South Africa (2004). ASSA2002 AIDS and Demographic Model. Online: http://www.assa.org.za/default.asp?id=1000000050        [ Links ]

16. Johnson L.F., Dorrington R.E. and Matthews A. (2006). An uncertainty analysis and sensitivity analysis of the ASSA2002 AIDS and Demographic model. Centre for Actuarial Research, University of Cape Town. Working paper. Online: http://www.commerce.uct.ac.za/Research_Units/CARE/        [ Links ]

17. Statistics South Africa (2005). Mortality and causes of death in South Africa, 1997-2003: Findings from death notification. Online: http://www.statssa.gov.za        [ Links ]

18. Connolly C., Shisana O., Colvin M. and Stoker D. (2004). Epidemiology ofHIV in South Africa - results of a national, community-based survey. S. Afr. Med. J. 94(9), 776-781.         [ Links ]

19. Shisana O., Rehle T., Simbayi L.C., Parker W, Zuma K., Bhana A., Connolly C., Jooste S. and Pillay V. (2005). South African National HIV Prevalence, HIV Incidence, Behaviours and Communication Survey, 2005. HSRC Press, Cape Town. Online: http://www.hsrcpress.ac.za        [ Links ]

20. Pettifor A.E., Rees H.V., Kleinschmidt I., Steffenson A.E., Macphail C., Hlongwa-Madikizela L., Vermaak K. and Padian N.S. (2005). Young people's sexual health in South Africa: HIV prevalence and sexual behaviors from a nationally representative household survey. AIDS 19(14), 1525-1534.         [ Links ]

21. Beven K. and Binley A. (1992). The future of distributed models - model calibration and uncertainty prediction. Hydrol. Process. 6(3), 279-298.         [ Links ]

22. Smith A.F.M. and Gelfand A.E. (1992). Bayesian statistics without tears - a sampling resampling perspective. Am. Stat. 46(2), 84-88.         [ Links ]

23. Boerma J.T., Ghys P.D. and Walker N. (2003). Estimates of HIV-1 prevalence from national population-based surveys as a new gold standard. Lancet 362(9399), 1929-1931.         [ Links ]

24. Timaus I.M. and Jasseh M. (2004). Adult mortality in sub-Saharan Africa: evidence from Demographic and Health Surveys. Demography 41(4), 757-772.         [ Links ]

25. Porter K. and Zaba B. (2004). The empirical evidence for the impact of HIV on adult mortality in the developing world: data from serological studies. AIDS 18 (Suppl. 2), S9-S17.         [ Links ]

26. Glynn J.R., Sonnenberg P., Nelson G., Bester A., Shearer S. and Murray J. (2005).The effect of HIV on adult mortality: evidence from a large cohort of SouthAfrican gold-miners withknowndates ofseroconversionand 10years of follow-up. International Union for the Scientific Study of Population Conference, Tours, France, 18-23 July 2005.         [ Links ]

27. Maartens G., WoodR.,O'Keefe E. and Byrne C. (1997). Independentepidemics of heterosexual and homosexual infection in South Africa - survival differences. Q. J. Med. 90, 449-454.         [ Links ]

28. Peeters M., Toure-Kane C. and Nkengasong J.N. (2003). Genetic diversity of HIV in Africa: impact on diagnosis, treatment, vaccine development and trials. AIDS 17, 2547-2560.         [ Links ]

29. Ball S.C., Abraha A., Collins K.R., Marozsan A.J., Baird H., Quinones-Mateu M.E., Penn-Nicholson A., Murray M., Richard N., Lobritz M., Zimmerman PA., Kawamura T., Blauvelt A. and Arts E.J. (2003). Comparing the ex vivo fitness of CCR5-tropic human immunodeficiency virus type 1 isolates of subtypes B and C. J. Virol. 77(2), 1021-1038.         [ Links ]

30. Quinones-Mateu M.E. (2005). Is HIV-1 evolving to a less virulent (pathogenic) virus? AIDS 19(15), 1689-1690.         [ Links ]

31. BadriM.,BekkerL.G.,OrrellC.,PittJ.,CilliersF. andWoodR. (2004).Initiating highlyactive antiretroviraltherapy in sub-Saharan Africa: an assessmentofthe revised World Health Organization scaling-up guidelines. AIDS 18(8), 1159-1168.         [ Links ]

32. Heuveline P. (2003). HIV and population dynamics: a general model and maximum-likelihood standards for East Africa. Demography 40(2), 217-245.         [ Links ]

33. Zaba B. and Gregson S. (1998). Measuring the impact of HIV on fertility in Africa. AIDS 12 (Suppl. 1), S41-S50.         [ Links ]

34. Lewis J.J.C., Ronsmans C., Ezeh A. and Gregson S. (2004). The population impact of HIV on fertility in sub-Saharan Africa. AIDS 18 (Suppl. 2), S35-S43.         [ Links ]

35. Gregson S., Terceira N., Kakowa M., Mason P., Anderson R., Chandiwana S. and CaraëlM. (2002). SludyofbiasinantenatalcHnicHIV-1 surveillance datain a high contraceptive prevalence population in sub-Saharan Africa. AIDS 16, 643-652.         [ Links ]

36. Moultrie T.A. and Timaus I.M. (2003). The South African fertility decline: evidence from two censuses and a Demographic and Health Survey. Pop. Stud. 57(3), 265-283.         [ Links ]

37. UNAIDS (2006). 2006 Report on the global AIDS epidemic. Online: http://www.unaids.org/en^HIV_data/2006GlobalReport/default.asp        [ Links ]

38. Morgan M., WalkerN., Gouws E.,Stanecki K.A. and StoverJ. (2006). Improved plausibility bounds about the 2005 HIV and AIDS estimates. Sex. Transm. Infect. 82 (suppl. 3), iii71-7.         [ Links ]

39. PooleD. andRafteryA.E. (2000). Inference fordeterministicsimulationmodels: the Bayesian melding approach. J. Am. Stat. Assoc. 95(452), 1244-1255.         [ Links ]

40. Furber A.S., Maheswaran R., Newell J N. and Carroll C. (2007). Is smoking tobacco an independent risk factor for HIV infection and progression to AIDS? A systemic review. Sex. Transm. Infect. 83(1), 41-46.         [ Links ]

41. Simbayi L.C., Kalichman S.C., Jooste S., Mathiti V., Cain D. and Cherry C. (2004). Alcohol use and sexual risks forHIV infection among men and women receiving sexually transmitted infection clinic services in Cape Town, South Africa. J. Stud. Alcohol. 65(4), 434-442.         [ Links ]

42. Shisana O., Zungu-Dirwayi N., Toefy Y., Simbayi L.C., Malik S. and Zuma K. (2004). Marital status and risk of HIV infection in South Africa. S. Afr. Med. J. 94(7), 537-543.         [ Links ]

43. Evian C., Fox M., MacLeod W., Slotow S.J. and Rosen S. (2004). Prevalence of HIVinworkforcesinsouthernAfrica,2000-2001. S. Afr. Med. J. 94(2),125-130.         [ Links ]

44. Hallman K. (2004). Socioeconomic disadvantage and unsafe sexual behaviors among young women and men in South Africa. Population Council, Policy Research Division. Working Paper no. 190.         [ Links ]

45. Hoeting J.A., Madigan D., Raftery A.E. and Volinsky C.T. (1999). Bayesian Model Averaging: a tutorial. Stat. Sci. 14(4), 382-417.         [ Links ]

46. Lutz W. and Goldstein J.R. (2004). Introduction: How to deal with uncertainty in population forecasting? Int. Stat. Rev. 72(1), 1-4.         [ Links ]

 

 

* Author for correspondence. E-mail: leigh.johnson@uct.ac.za

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