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Incidence Estimates

Incidence Estimates. Nanette Benbow, Past-Chair HIV Workgroup Council of State and Territorial Epidemiologists (CSTE) 2009 NASTAD Annual Meeting May 3, 2009. Outline. History of Incidence Estimation Description of Incidence Surveillance Incidence Estimation

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Incidence Estimates

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  1. Incidence Estimates Nanette Benbow, Past-Chair HIV Workgroup Council of State and Territorial Epidemiologists (CSTE) 2009 NASTAD Annual Meeting May 3, 2009

  2. Outline • History of Incidence Estimation • Description of Incidence Surveillance • Incidence Estimation • Uses of the Incidence Estimate

  3. History of Incidence Estimation

  4. HIV Incidence Methodology Timeline

  5. HIV Incidence Methodology Timeline

  6. CDC Incidence Estimate 56,300 55,400 40,000 Old Estimate (avg. of studies) New Estimate (back-calculation) New estimate (incidence surveillance)

  7. Description of Incidence Surveillance

  8. Case-based Surveillance Data • Information from HIV/AIDS case reports • All diagnosed cases • Personal characteristics • Status at diagnosis • Information from other sources • Laboratory results (BED, CD4) • Questionnaires (HIV test, ART) • Individually linked

  9. How does the HIV incidence reporting system relate to routine HIV reporting? • HIV incidence reporting is an extension of the population-based HIV reporting system • It uses the existing reporting infrastructure to collect the information necessary to estimate HIV incidence from all newly diagnosed HIV cases that are reported • Adds additional information to complement standard HIV reporting

  10. Questions?

  11. Incidence Estimation • Explanation of Probabilities • The three probabilities • Calculating the Estimate • Stratification, 200x40x10 Rule • Multiple Imputation

  12. Calculating the Estimate—Important Variables and How they Interact • Using simple survey sampling methodology, the following is needed to estimate the size of a specific population based on a random subset sample: • Observable Sample (R) • Probability of being in the sample (P)

  13. Sampling Frame Sample Selected R N HIV/AIDS Diagnosed R Estimation of HIV Incidence Sampling Frame = N All persons who became infected with HIV in the selected period of time including those not diagnosed. N Sample Selected = R All persons who were diagnosed in the selected period of time and classified as BED “recent” represent the sample selected.

  14. Estimating P – The probability that a new infection is classifiedas a BED recent • Does everyone in the sample have the same probability of being selected? • No, it changes depending on the following: • Infected person was tested within 1 year after infection • Person diagnosed with HIV had a BED test result* • BED result for a person tested within 1 year after infection was “recent” • *Because some of the people sampled do not have a BED test result, a BED result is “filled” in using a statistical technique called “multiple imputation”

  15. Estimating P (cont.) • P1= Probability of being tested within 1 year after infection (changes depending on whether or not a person tests frequently or not) • P2= Probability that a person diagnosed with HIV had a BED test result • P3= Probability of having a BED test “recent” if the test is within one year after infection P= P1* P2* P3 “P” is calculated for each demographic/risk subgroup (Strata) to adjust for difference in testing patterns between these different groups

  16. 67 Strata • Hispanic • Male • 13-29 • 30-39 • 40-49 • >=50 • MSM • IDU • MSM/IDU • Heterosex. • Total • Female • 13-29 • 30-39 • 40-49 • >=50 • IDU • Heterosex. • Total • Total • White, non-Hispanic • Male • 13-29 • 30-39 • 40-49 • >=50 • MSM • IDU • MSM/IDU • Heterosex. • Total • Female • 13-29 • 30-39 • 40-49 • >=50 • IDU • Heterosex. • Total • Total • Black, non-Hispanic • Male • 13-29 • 30-39 • 40-49 • >=50 • MSM • IDU • MSM/IDU • Heterosex. • Total • Female • 13-29 • 30-39 • 40-49 • >=50 • IDU • Heterosex. • Total • Total • Other, non-Hispanic • Male • 13-29 • 30-39 • 40-49 • >=50 • MSM • IDU • MSM/IDU • Heterosex. • Total • Female • 13-29 • 30-39 • 40-49 • >=50 • IDU • Heterosex. • Total • Total Male Female Total

  17. The total incidence in the population is the sum of incidences of all strata: Final Incidence Estimate Within each group, incidence is estimated by the number of BED-recent specimens divided by the probability of being classified as recent: N = R/P

  18. Questions?

  19. Uses of the Incidence Estimate Information

  20. Use of Incidence Estimate (1) • Using this method, CDC estimates that 56,300 adolescents and adults were newly infected with HIV in 2006 in the US (95% confidence interval [CI], 48,200-64,500) • Because the Incidence number is a statistical estimate (commonly referred to as a “point estimate”) you need to also consider the confidence interval and interpret the estimate as follows: • If you were to take 100 samples to estimate the number of new HIV infections in 2006, 95% of the samples will produce an HIV incidence estimate between - [48,000 - 64,500] • The accuracy of point estimates is highly dependant on the number of observations used to make the estimate. Small sample sizes produce wider confidence intervals (CI).

  21. Use of Incidence Estimate (2) • When comparing two point estimates with their respective CIs, you can say that the two numbers are statistically significantly different if the two CIs overlap (i.e. have values in common). If they do not overlap, you need to perform a statistical test to determine if the numbers are significantly different • Example – 2006 US Incidence Estimate: • Males 41,400 95% CI [35,100 – 46,600] • Females 15,000 95% CI [12,600 – 17,300] • CIs do not overlap, thus, the incidence rate for men is significantly higher than for females • White 19,600 95% CI [16,400 – 22,800] • Black 24,900 95% CI [21,100 – 28,700] • CIs overlap, thus, you cannot conlcude that there is statistically significant difference between the incidence for Whites and Blacks

  22. Uses of data obtained from the Incidence Estimation Process:Data from North Point State 2006 HIV Diagnoses D = 1,446 2006 HIV Incidence N = 1,368 • Estimated number of people newly infected in 2006 but not diagnosed in 2006: • not tested (hence, unaware of their status) • tested anonymously • N – C = 729 Estimated number of people infected in previous years but not diagnosed until 2006 D–C = 807 Estimated number of people newly infected with HIV in 2006 who are diagnosed that year C=Recently Infected Cases = 639

  23. Interpretation (1) • Out of every 100 people newly infected with HIV in 2006, what percentage are unaware of their status in that year? • 53% • Out of every 100 people newly infected with HIV in 2006, what percentage are diagnosed in that year? • 47% • Out of every 100 HIV diagnoses in 2006, what percentage are recent infections? • 56% How can you use these quantities to guide, target or evaluate prevention efforts?

  24. Uses of data obtained from the Incidence Estimation Process:Data from North Point State (2) 2006 HIV Incidence N = 223 Whites 2006 HIV Diagnoses D = 312 • Estimated number of people newly infected in 2006 but not diagnosed in 2006: • not tested (hence, unaware of their status) • tested anonymously • N – C = 106 Estimated number of people newly infected with HIV in 2006 who are diagnosed that year Blacks Estimated number of people infected in previous years but not diagnosed until 2006 D–C =312 2006 HIV Incidence N = 844 2006 HIV Diagnoses D = 312 • Estimated number of people newly infected in 2006 but not diagnosed in 2006: • not tested (hence, unaware of their status) • tested anonymously • N – C = 465 Estimated number of people newly infected with HIV in 2006 who are diagnosed that year Estimated number of people infected in previous years but not diagnosed until 2006 D–C = 358 C=Recently Infected Cases = 117 What can you say about the differences/similarities in new infections in these two populations? C=Recently Infected Cases = 379

  25. Interpretation (2) • Out of every 100 people estimated to be newly infected with HIV in 2006, what percentage are unaware of their status in that year? • Whites: 48% Blacks: 55% • Out of every 100 people estimated to be newly infected with HIV in 2006, what percentage are diagnosed in that year? • Whites: 52% Blacks: 45% • Out of every 100 HIV diagnoses in 2006, what percentage are estimated to be recent infections? • Whites: 38% Blacks: 51%

  26. Uses of data obtained from the Incidence Estimation Process:Data from North Point State (3) Transmission Rates • For every 100 people living with HIV, the number of HIV infections transmitted to HIV-seronegative partners in a year • 23,500 people living with HIV/AIDS • 1,368/23,500 x 100 = 5.8 = 6 • For every 100 persons living with HIV in North State, there are six HIV transmission per year • And…94% of person living with HIV did not transmit the virus Having an incidence estimate allows us to calculate transmission rates which can be used as a way to measure the speed of spread of HIV infection .

  27. Summary • This is a new surveillance system. Because it is so new, we probably should not assume that the data are 100% accurate until the surveillance system is mature (this usually takes about 4 years) • By combining diagnoses, prevalence and incidence data we are able to get a more accurate and timely picture of the epidemic and its changes over time that will helpful to guide and evaluate prevention efforts • More time is still needed to assess how these data can be used locally over time

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