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HIV Surveillance and data availability

This presentation explores the controversy surrounding HIV/AIDS prevalence in South Africa and discusses the limitations of different data sources. It emphasizes the need to use models to estimate HIV infection levels in the population.

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HIV Surveillance and data availability

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

  2. 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?

  3. 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

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

  5. 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

  6. 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

  7. 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

  8. 0-25 years 26-69 years All Men All Women 70+ years Population at risk for Cancer of the Cervix

  9. 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 Factors influencing the prevalence:

  10. 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

  11. 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?

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

  13. 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

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

  15. 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

  16. 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

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

  18. 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?

  19. 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%

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

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

  22. 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

  23. 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

  24. 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

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

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

  27. 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!

  28. 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

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

  30. 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

  31. 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

  32. 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

  33. 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

  34. 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

  35. 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)

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

  37. 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

  38. 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

  39. 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

  40. HIV prevalence in a large company workforce (South Africa) 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

  41. 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

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