Making HIV Prevalence and AIDS Estimates

1. Making HIV Prevalence and AIDS Estimates UNAIDS/WHO Working Group on Global HIV/AIDS and STI Surveillance

2. Content • Introduction: data needs and epidemic curve • Steps in making HIV estimates: low level and generalised epidemics • Calculating HIV Prevalence • Curve Fitting • Generating other variables

3. HIV/AIDS: Data Needs • What are the levels and trends in HIV infection? • Who is getting infected? • Who is more at risk for or vulnerable to HIV infection? • Impact assessment (need for care, planning) • Is the response effective?

4. Relationship between incidence, prevalence, and mortality HIV EPIDEMIC STAGES 35 Rt > 1 Rt < 1 Rt = 1 Prevalence Incidence Death 30 25 Percent 20 15 10 5 0 5 10 15 20 25 30 0 Time (Years) HIV INCIDENCE HIV HIV HIV PREVALENCE HIV HIV MORTALITY Epidemic Growth Phase Endemic Steady State Transition Phase Source: FHI Evaluation Handbook 2001

5. UNAIDS/WHO Classification of epidemic states • LOW LEVEL: • HIV prevalence has not consistently exceeded five percent in any defined sub-population • CONCENTRATED • HIV prevalence consistently over five percent in at least one defined sub-population but below one percent in pregnant women in urban areas. • GENERALISED • HIV prevalence consistently over one percent in pregnant women nation-wide

6. Three Steps in Making Estimates • Calculating HIV Prevalence • Curve Fitting • Generating other variables (e.g., mortality, incidence)

7. Calculating HIV prevalence: Concentrated Epidemics • Estimates are made by adding together: • the number of individuals assumed to be infected in each identifiable sub-population at risk. • a minimum estimate of HIV infection in the general population

8. Calculating HIV prevalence:concentrated and low-level epidemics • Identify risk groups • Estimate size of groups • Estimate HIV prevalence in risk groups • Estimate HIV prevalence in the general population • Sum of all groups

9. Estimating Prevalence in a Concentrated Epidemic

10. Calculating HIV prevalence: Generalized Epidemics • Prevalence estimates are based primarily on surveillance data collected from women attending antenatal clinics. • Two groups of clinics based on their location • major urban areas • outside major urban areas • median prevalence rates are calculated separately for the two groups

11. Calculating Prevalence 1. Determine median of urban and outside major urban sites 2. Adjust medians based on representativeness of sites 3. Apply adjusted rates to female urban and outside urban populations (15-49) 4. Use M/F ratio to determine number of men infected 5. Combine males and females to get adult rate

12. Critical Issues in Estimating Prevalence • Representativeness of ANC sites • Effects of HIV infection on fertility • Male-to-female ratio • Urban to rural prevalence differential

13. Representativeness of ANC:Other Factors • Age-related fertility reduction in HIV positive women • Changes in risk behaviors (condom use, contraception) • “Active aging” (risk beyond reproductive ages) • Selection and participation bias (users fees, availability/access to ANC services)

14. Representativeness of ANC for total Population: Male/Female Differentials Population-based HIV prevalence in men and women - Lusaka, Zambia, 1995

15. HIV prevalence in adults (community studies) and pregnant women (ANC)

16. Three Steps in Making Estimates • Calculating HIV Prevalence • Curve Fitting • Generating other variables (e.g., mortality, incidence)

17. Curve Fitting • EPP model is used with the available surveillance data to estimate the time trend of adult prevalence of HIV-1 at the national level. • Use Rural and Urban models • 4 parameters used: • to: the start year of epidemic • r: transmission rate • f0: initial fraction of population exposed • phi: the trend after the epidemic’s peak

18. HIV Prevalence for Pregnant Women Major Urban and Outside Major Urban Areas Zambia 50.0 40.0 30.0 20.0 10.0 0.0 1985 1987.5 1990 1992.5 1995 1997.5 2000 Year URBAN OUTSIDE MED-URBAN MED-OUTSIDE UNAIDS/WHO adult prevalence curve

19. Curve Fitting

20. Curve Fitting

21. Three Steps in Making Estimates • Calculating HIV Prevalence • Curve Fitting • Generating other variables (e.g., mortality, incidence, orphans)

22. Progression Rates • Infection to death for adults • Median 9 years in countries with poor health care • Age of infection can be important, so in SS Africa, women = 9.4, men = 8.6 median survival • Infection to death for children • Two sets of progressors, fast and slow • Median survival 3 years, but then long survival until age 12

23. Generating Other Variables:information needed • Population size (15-49) • Progression rates from infection to death • Age specific fertility rates • Fertility reduction for HIV by age • Male-to-female ratio • Mother-to-child transmission rate

24. Estimating Vertical Transmission • Male to female ratio • Fertility rate • Fertility reduction for HIV+ • Vertical transmission rate • (25% 10%) • Impact of ARV prophylaxis on MTCT

25. Progression Rate for Adults

26. Progression rates for children

27. Generating Other Variables • EPP is used to generate additional variables using Spectrum software: 6 indicators are provided: • HIV population • Adult prevalence • New HIV infections • Adult HIV incidence • New AIDS cases • Annual AIDS deaths