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Understanding the Report on HIV/AIDS in Ontario

Understanding the Report on HIV/AIDS in Ontario. Greta Bauer, PhD, MPH Epidemiology & Biostatistics The University of Western Ontario. John Maxwell Director of Policy and Communications AIDS Committee of Toronto. Report on HIV/AIDS in Ontario. Annual report produced since 1998

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Understanding the Report on HIV/AIDS in Ontario

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  1. Understanding the Report on HIV/AIDS in Ontario Greta Bauer, PhD, MPH Epidemiology & Biostatistics The University of Western Ontario John Maxwell Director of Policy and Communications AIDS Committee of Toronto

  2. Report on HIV/AIDS in Ontario • Annual report produced since 1998 • Funded by the AIDS Bureau • Produced by the Ontario HIV Epidemiologic Monitoring Unit at the University of Toronto • Authors (2008): Robert Remis, Carol Swantee, Lorraine Schiedel, Juan Liu • Tracks the HIV epidemic in Ontario • Available at: www.phs.utoronto.ca/ohemu

  3. Jargon Alert

  4. HIV/AIDS Epidemiology • Not perfect • Not meaningless

  5. What is needed to have perfect versions of current statistics? • Know HIV status of everyone • Identify new infections when acquired • Be able to determine exactly how each transmission occurred • Have accurate AIDS diagnoses for everyone • Know perfectly whether HIV/AIDS was a cause for each death • Know exact population sizes for exposure categories (e.g. MSM, IDU, people from endemic countries, high-risk heterosexual…)

  6. STATISTICS = DATA + ASSUMPTIONS

  7. Where do data used in the Ontario Report come from?

  8. Understanding Exposure Categories • Exposure categories (vs. transmissions) • Men who have sex with men (MSM) • MSM-IDU • Injection drug use (IDU) • Mother-to-child transmission (MTC) • Blood product recipient (pre Nov 1985) • Transfusion recipient (pre Nov 1985) • Origin/residence in HIV endemic countries • High-risk heterosexual • Low-risk heterosexual • No identified risk (NIR)

  9. Some assumptions… Continued…

  10. Proportion of New HIV Diagnoses, Including Unknown Exposure Group

  11. Proportion of New HIV Diagnoses, Where Exposure Group Known

  12. Number and Proportion of New HIV Diagnoses

  13. Raw vs. Modelled Statistics • Modelling • Adjust for duplicate HIV tests • Estimate proportion undiagnosed • Assume those with unknown exposure group distributed in accordance with known exposure groups • Estimate distribution by sex and geographic area • Adjust for different rates of testing • Estimate HIV infection numbers • Estimate AIDS cases, adjusting for reporting delays • Adjust estimates of HIV-related mortality for under ascertainment • Estimate population sizes

  14. Some Statistics in the Report • HIV Diagnoses (case counts) • Proportion of HIV Diagnoses by Exposure Category • HIV Prevalence • HIV Cumulative Incidence • HIV Incidence / Incidence Density • AIDS Diagnoses (case counts) • Proportions of AIDS Cases by Exposure Category • AIDS Cumulative Incidence • HIV-related Mortality

  15. PREVALENCE vs INCIDENCE • Prevalence – How common is it for people to be living with HIV? • Incidence – At what rate do new infections occur amongst those at risk?

  16. HIV PREVALENCE # People Living with HIV = x 100 # in Population presented as a percentage

  17. Modelled MSM number and HIV prevalence by health region, Ontario, 2006 R. Remis, 2008

  18. What influences changes in HIV prevalence statistics over time? • Changes in number of new cases of HIV • Changes in duration of illness • Longer survival = higher prevalence of HIV • Changes in HIV testing • Overall changes in rates of testing • Different rates of testing between groups (adjusted statistically) • Policies (immigration testing, prenatal testing) • Improvements in HIV testing • Changes in estimates of (sub)population size

  19. What influences changes in proportion of prevalent cases over time? • Changes in prevalence for the group you’re interested in • Changes in prevalence for all other groups

  20. PREVALENCE vs INCIDENCE • Prevalence – How common is it for people to be living with HIV? • Incidence – At what rate do new infections occur amongst those at risk?

  21. HIV CUMULATIVE INCIDENCE # cumulative HIV diagnoses = 1996 population (midpoint)

  22. HIV INCIDENCE DENSITY # new HIV diagnoses = x 100 Person-years at risk presented per 100 person-years

  23. Example: 1 per 50 person-years                                                            

  24. Modelled MSM number, HIV prevalence andincidence by health region, Ontario, 2006 R. Remis, 2008

  25. What influences changes in HIV incidence statistics over time? • Changes in number of new cases of HIV • Changes in estimates of prevalence • Changes in HIV testing • Overall changes in rates • Different rates of testing between groups (adjusted statistically) • Policies (immigration testing, prenatal testing) • Improvements in HIV testing • Changes in estimates of (sub)population size

  26. AIDS Diagnoses and Cumulative Incidence • HIV vs. AIDS

  27. What influences changes in AIDS statistics over time? • Changes in treatment and duration of illness • Healthier survival = lower prevalence of AIDS • Different rates of AIDS diagnosis between groups • Changes in definition of AIDS • Changes in estimates of (sub)population size

  28. HIV-RELATED MORTALITY # HIV-related deaths = x 100,000 Population presented per 100,000 population

  29. What influences changes in HIV-related mortality statistics over time? • Changes in incidence of HIV • Changes in treatment and duration of illness • Healthier survival = lower mortality • Changes in HIV testing – number of people who are known to have HIV • Overall changes in rates • Different rates of testing between groups • Policies (immigration testing, prenatal testing) • Changes in ICD codes

  30. Major changes to be aware of • 1985 – Testing of the blood supply • 1993 – Change in AIDS definition • Increased classification of women with AIDS • 1996 – Introduction of protease inhibitors • Decreased AIDS cases, increased survival (and prevalence) • 2000 – Change in ICD definitions from ICD-9 to ICD-10 • 10% increase in HIV-related deaths • 2002 – Required testing of all immigrants • Ontario tests for visa purposes: 1294 in 2001, 28,712 in 2006 • 54% increase in testing for the HIV-endemic exposure classification • Prenatal HIV testing • 41% in 1999, 89% in 2006 • 62% increase in testing and 41% increase in new diagnoses in the low-risk heterosexual exposure classification from 2001 to 2006

  31. Thank you! Contact: gbauer@uwo.ca

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