1. HIV Surveillance and data availability MTT Winter School, Durban August 2004 Dr Anthony Kinghorn

3. Controversy and HIV/AIDS Antenatal Survey reported that 24% of pregnant women are HIV positive The HSRC study reported that 11.4% of people are HIV positive. Who is right?

4. Prevalence of HIV Infection in the Under 20 Year Age Group of Antenatal Clinic Attendees in SA Source: DOH. National HIV Surveys of Women Attending Public ANC Clinics in SA

5. HIV INFECTION LEVELS - 15 - 19 year olds Source: National Surveys of Women Attending Antenatal Clinics

6. Outline of Presentation –Why measure? –What can we measure? HIV Prevalence HIV Incidence AIDS Prevalence and incidence Mortality –Using models to understand the epidemic and it’s impacts –Second Generation surveillance

7. Why measure? –Identify trends in infections and impact –Identify levels of infection and impact –Predict future trends and levels of impact Advocacy and planning Evaluate interventions for staff and learners

8. The Prevalence (Rate) Definition: The proportion of a population at risk affected by a disease at a specific point in time Prevalence = No. of people with the disease or condition at a specific time No. of people at risk in the population at the specified time population at the specified time

9. Population at risk for Cancer of the Cervix All MenAll Women 0-25 years 26-69 years 70+ years

10. Factors influencing the prevalence: Increased by: Longer duration of disease Prolonging life but no cure Increase in incidence In-migration of cases Out-migration of healthy Improved diagnosis/ reporting Decreased by: Shorter duration of disease High death rate Decrease in incidence Out-migration of cases In-migration of healthy Increased cure rate

11. The Incidence Rate This is the rate at which new events occur in a population = No. of new cases of a disease in a specified time Total number of people at risk Total number of people at risk

12. HIV Prevalence –The main source of HIV Prevalence data is National Surveys of Pregnant Women at Antenatal Clinics –Other sources include: Hospital admissions TB patients STD clinic attendees Blood donors Pre-insurance testing Workplace and population surveys –What are the limitations of these sources? –What are they useful for?

13. Antenatal HIV Seroprevalence Survey Source: DOH. National HIV Surveys of Women Attending Public ANC Clinics in SA

14. Limitations of the Antenatal Data Usually designed to track trends not national levels Rising ANC prevalence usually reflects rise in general population May overestimate HIV female and male adult prevalence Reflects sexually active women, reproductive years, not using condoms Over estimates prevalence in teens and high age groups But may also underestimate HIV Excludes women on contraceptives HIV positive women have a decreased fertility Some studies suggest that ANC HIV prevalence is a reasonable proxy for community adult rate Other sampling biases Rural populations often under-sampled? Other? So we need to use models to estimate levels of HIV infection in the population and sub-populations

15. Trends in HIV infection levels in pregnant women Source: Rwanda HIV Sentinel Sero Surveys and adjustments from population surveys

16. Limitations of the Antenatal Data Increasing difficulties of interpreting ANC data in mature epidemics –Deaths off-setting new infections –Prolonged life due to ARVs –Plateaux due to saturation or behaviour change? –Etc

17. Community Prevalence Studies  Community studies more representative of all settings, ages, both sexes  Can link with behavioural surveillance/KAPB/ other data  Big differences from ANC prevalence in the young and old – due to sample bias  Can refine assumptions about community infections used in interpreting ANC data  Results can be surprising or easy to misinterpret eg. HSRC/NMF study in South Africa  HIV prevalence of 11.4% in all > 2 years old  32% prevalence in women aged 25-29

18. HIV prevalence in Zambia DHS vs Antenatal * DHS Total = men and women)

19. ANC vs ZDHS (cont) ZDHS: 15.6% prevalence all adults ANC 2002: 19% prevalence adult ♀ (15- 44yrs) ZDHS: 18% prevalence adult ♀ (15-49yrs) –Similar estimates indicate epidemic still severe –Overall, ANC estimates fairly robust?

20. KDHS versus ANC (2003) Adult prevalence –DHS 2003 (women & men): 6.7% –ANC 2003: 9.4% → previous over-estimation? However, for women 15-49: –ANC 2003: prevalence estimated 9.4% –DHS 2003: prevalence estimated 8.7%

21. Age profile of HIV infection levels – Men vs Women (Zambia DHS 2001) Source: Zambia DHS 2001, Preliminary Report

22. Age profile of HIV infection levels – Men vs Women (Kenya DHS 2003) Source: Kenya DHS 2003, Preliminary Report

23. Community Prevalence Studies Limitations  Sample sizes  Especially for sub-groups  Biases  Non-Response  Other  Expense and complexity  Time to establish new time series and trend data  Frequency  Probably only repeat every 3-5 years if initial results in line with ANC and expectations

24. Biological surveillance - workplace sero-prevalence surveys Blood or saliva tests for HIV; (STD rates) Unlinked anonymous surveys –VCT usually inadequate for workforce levels Advantages –Accurate refection of risk, including for employee sub-categories –Plausible –Inform projections (still required) –Track changes and monitor success

25. Biological surveillance cont. Challenges Clear objectives and use of data, including strategy to communicate results Limited accuracy if low participation Employee buy-in –Credible confidentiality, non-discrimination, programme and response options needed Ethics –VCT availability and promotion –Informed consent, anonymity –Ethics committee approval Technical and analytical issues –eg. sampling; response rates; stats analysis; lab Cost Limited trend data

26. HIV prevalence in a service sector workforce (South Africa) Which data source gave most information and value for money?

27. HIV Incidence –Very few sources of data on HIV Incidence –Usually from large HIV prevention studies –Main measure of vaccine effectiveness

28. AIDS Prevalence / Incidence Very difficult to measure without notification Only tells us about HIV infections from 5-10 years ago Critical to use a consistent and recognized classification system!

29. Other Sources of Data Death Registration –Can be a very useful way to track AIDS trends, as age related mortality from AIDS is unique EMIS; HR and payroll databases; pension funds Measuring incidence of opportunistic diseases, especially TB, is very important for health service planning

30. DEATHS by age band 1998 DHS vs Projected (Botswana) (For previous 24 months)

31. Behavioural surveillance - KAPB Standardised questionnaires generate indicators of Knowledge, Attitudes, Practices, Behaviour eg. –Basic knowledge –Risk e.g. number of non-regular partners; condom use –Views on HIV/AIDS programme Can link to blood or saliva tests Objectives –Identify target knowledge gaps, behaviour, groups –Identify sources of e.g. information, services –Assess manager and supervisor preparedness –Track levels and trends: baseline and follow-up –Advocacy

32. KAPB cont. Challenges –Usually outsourced for expertise and neutrality Employee and union buy-in Sample size or biases, incl. % responding; truthfulness Survey administration skills Ethics –Cost –Interpreting, using and communicating results –Simple or complex questionnaires/ surveys? –Interfering programmes and influences on KAPB? –May miss unexpected issues and suggestions

33. PERCENTAGE OF CHILDREN IN AGE GROUPS WHO WILL BE ORPHANED BY AIDS Source: Kinghorn et al (2001). The impact of HIV/AIDS on Education in Namibia

34. What is a Model? –A model is a hypothesis or theory that tries to explain the real world It gives a framework for design of tools to give answers to questions about the 'model world' –A model is only as good as: Its underlying assumptions Quality of input data Some use of modeling is probably inescapable to make sense of any empirical data

35. Models - Examples –ASSA 2000/ Doyle/ Metropolitan Mix of macro- and micro-model features Includes risk groups and geographic differences –AIM –US Bureau of Census –Epimodel –Other

36. Projection methodology Antenatal data – levels and trends in infection General population projections: age, gender, geographic region Cross mapping of e.g. educators by age, gender, location, origin Scenarios; validation/calibration using prevalence, mortality data Analysis and action Extrapolation to all women and men Modifiers Mortality data (HIV prevalence data) (Risk behaviour data)

37. ASSA 2000 Output *Source: Prof R Dorrington, ASSA

38. Projections - challenges 1. Limitations of all models 2. Demographic data limitations Population and personnel Migration Fertility 3. Epidemiological data limitations, particularly Extrapolation from ANC to general population Survival time Fertility impacts - multiple determinants Epidemic curves for urban/rural, local areas, sub- groups

39. Projections - challenges(2) 4. Other enrollment or attrition influences Policy, other factors – often dominate AIDS 5. Key techniques Validation – quality of data? Sensitivity testing Intervention modeling - Behaviour change; ARVS Qualitative data 6. Experienced modelers 7. Shorter term and more aggregated projections probably more accurate Severity of limitations depends on the planning question to be answered

40. Second Generation Surveillance Continue with ante-natal surveys Behavioural Surveillance Focus on young people High-risk sub-groups Morbidity and mortality surveillance BUT – every country is different – needs it’s own research agenda

41. HIV prevalence in a large company workforce (South Africa) Category % HIV+ (95% CI) Sexual behaviours Sex with non-regular partner (last 3 months) 16.8 (14.5 – 19) No non-regular partner (last 3 months) 6.9 (6.1 – 7.7) Condom use Used condom with last non regular partner 14.4 (12 – 16.8) No condom with last non-regular partner 11.8 (10 – 13.5) No non-regular partners4.5 (3.7 – 5.3) Source: Colvin M Gouws E Kleinschmidt I Dlamini M. The prevalence of HIV in a South African working population. AIDS 2000 Conference poster, Durban 2000

42. Summary Data maybe limited, and the models may be inaccurate, but the main messages in terms of levels and trends are usually clear But the epidemic is complex and needs customised responses “What is occurring is a collection of epidemics in different stages of increase, stability, and decline” (Sentinel Surveillance of HIV/Syphilis in Zambia, 2003) Averages hide variation – much worse or less affected communities Don’t contribute to confusion through lack of understanding of HIV/AIDS statistics OR enthusiasm for technical debate