Analyzing HIV Prevalence Trends from Antenatal Clinic (ANC) Sentinel Surveillance Data and Serosurveillance Data from Hi

1. Analyzing HIV Prevalence Trends from Antenatal Clinic (ANC) Sentinel Surveillance Data and Serosurveillance Data from High Risk Groups* Ray Shiraishi† Global AIDS Program Centers for Disease Control and Prevention The findings and conclusions in this presentation are those of the author and do not necessarily represent the views of the Centers for Disease Control and Prevention *Adapted from a presentation titled Analyzing HIV prevalence from antenatal sentinel surveillance data by Theresa Diaz, MD †BCA/CITS-II

2. Overview • Background • Trends based on ANC sentinel surveillance • Trends based on sampling of high risk groups • Issues

3. Background • Data and methods to estimate the prevalence of HIV within countries has improved in the past few years • For ANC sentinel surveillance many countries have added additional sentinel sites to their surveillance systems • For HIV serosurveillance among high risk groups many countries have changed sampling methods over time • These changes have tended to result in improved estimates of HIV prevalence but make it difficult to analyze HIV prevalence trends

4. Data Collection for Sentinel Surveillance in Antenatal Clinics • Select sentinel antenatal clinics (random or convenience sample) • Select consecutive sample of pregnant women (first visit) in selected clinics • Collect data over 4-12 consecutive weeks • Done annually or every 2 years

5. Is Prevalence Increasing, Decreasing or Stable? • The degree of certainty depends on the population of interest: • The women sampled • Women attending the clinics sampled • Women attending ANC clinics in the country • The national population in the country More certainty Less certainty

6. Is Prevalence Increasing, Decreasing or Stable? • Appropriate analysis accounts for design of the survey, i.e., sampling strategy • Confidence • Number of clinics sampled (all, most, few) • Number of women sampled per clinic • Similarity of prevalence values from clinic to clinic • Bias • Sampling design (random or convenience sample) • Representativeness of pregnant women sampled to population of interest

7. Statistical Tests for Trends • Appropriate analysis accounts for design of the survey, i.e., sampling strategy • Example of unsuitable strategy for testing trends over time • Pooling or aggregating data across *ALL* ANC sites does not reflect sampling design or variation in HIV prevalence by site

8. Analyses for Change in Prevalence • Plot data by site and year to visualize trends in HIV prevalence • Limit initial analysis to consistent sites • compare with an analysis using data from all available sites • Use most recent rounds of surveillance data to assess current situation • perhaps the last three

9. Analyses for Change in Prevalence Site-level aggregate prevalence data • Random effects linear regression • specify random effects (intercept and slope) • then test whether the trend is up or down Individual level data • Random effects logistic regression • specify random effects (intercept and slope) • then test whether the trend is up or down

10. Limitations A well done analysis of trends will not correct for: • Selection bias • Changes in quality of laboratory testing • Changes in data collection • Changes in characteristics of ANC clients

11. Summary: ANC surveillance trends • Consider using data from last three surveillance rounds • Appropriate analysis accounts for design of the survey • Limit initial analysis to consistent sites • Plot data by site to look for visual trends

12. Analyzing HIV trends in high risk groups • Differing sampling methods used in different years (example) • IDUs in one city sampled using TLS in 2004 • IDUs in same city sampled using RDS in 2006 • To analyze trends must have consistent sampling method in same population in same place therefore analysis of trends not possible if sampling methods change

13. Analyzing HIV prevalence trends when same sampling methods used • Several countries will now have more than one year of data using RDS to sample a specific population in the same city • It is possible to analyze trends for these populations

14. Proposed Method to Test for Changes In Prevalence over Time with RDS • Use data from most recent two surveys • Assure that populations being measured are identical • Use RDSAT to calculate prevalence estimates and associated standard errors • Assume the estimates for the two time periods are statistically independent (note that this is a fairly strong but necessary assumption.) • Calculate Z test for two proportions

15. Issues • Need to test out suggested HIV trend analysis over time using RDS on real data • Need to assure that sampling is consistent over time • Need to develop consensus as to whether or when any national trends can be reported from high risk groups samples

16. Summary: HIV prevalence trends in high risk groups • If sampling methods differ do not assess trends • Consider applying suggested methodology to assess trends over time if RDS was used in same population in same cities over time

17. Analyzing HIV Prevalence Trends from Antenatal Clinic (ANC) Sentinel Surveillance Data and Serosurveillance Data from High Risk Groups* Ray Shiraishi† Global AIDS Program Centers for Disease Control and Prevention The findings and conclusions in this presentation are those of the author and do not necessarily represent the views of the Centers for Disease Control and Prevention *Adapted from a presentation titled Analyzing HIV prevalence from antenatal sentinel surveillance data by Theresa Diaz, MD †BCA/CITS-II