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Modelling prevention through changes in testing and treatment for HIV in Australia PowerPoint Presentation
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Modelling prevention through changes in testing and treatment for HIV in Australia

Modelling prevention through changes in testing and treatment for HIV in Australia

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Modelling prevention through changes in testing and treatment for HIV in Australia

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  1. Modelling prevention through changes in testing and treatment for HIV in Australia David Wilson and James Jansson

  2. Mathematical model • Utilised National HIV Registry • All cases of HIV • Date of diagnosis • CD4 at diagnosis (+ time since last negative test) • Route of exposure • Demographics (age, sex, jurisdiction) • Individual simulation model • back-projected distribution of expected time from infection to diagnosis • Forward projected incidence and range of estimated level of undiagnosed infections

  3. Model matched diagnoses, by jurisdiction, sex, transmission mode (1998-2010)

  4. Mathematical model • Included • Disease stage, sex and age-based mortality (based on general population mortality from the ABS and mortality risk rates in PLHIV) • Movement between jurisdictions • Estimated average transmission rate from each population group • MSM, heterosexual male/female, MSM IDU, heterosexual male IDU, heterosexual female IDU • For each jurisdiction

  5. ASSUMPTIONS • Model fitted to data, estimating transmission rates, assuming • diagnosed people not on ART have a 20% lower transmission risk compared to undiagnosed people • 70% of diagnosed people are on ART • People on ART are 96% (73-99.4%) less infectious than diagnosed people not on ART • Do not allow for changes in average population-level transmission rates over time (as population ages and average sexual behaviour changes) • All other conditions remain unchanged

  6. Current levels in Australia • Treatment is relatively high: 70% • Testing is relatively high (? - self-report only) • Possibly less relative potential for prevention in Australia compared to other settings

  7. Average transmission rate

  8. Testing • Rather than rely on self-reported testing rates, use CD4 at time of diagnosis as a marker of early/late presentation

  9. Distribution of CD4 at diagnosis

  10. Distribution of CD4 at diagnosis

  11. Distribution of CD4 count in HIV-uninfected people

  12. CD4

  13. CD4

  14. Review and meta-analysis: annual decline

  15. Distribution of time from infection to diagnosis

  16. Average time from infection to diagnosis • 4.58 (IQR: 2.1-9.2) years • Consistent: MSM, heterosexuals, female IDU • MSM-IDU lower 4.0 (1.8-7.8) years • Hetero-male-IDU: 3.4 (1.3-12.9) years • Estimated 23.4% undiagnosed

  17. Relative reduction in incidence in year 2015

  18. Relative reduction in incidence in year 2015

  19. Relative reduction in incidence in year 2015

  20. Relative reduction in incidence in year 2015

  21. Relative reduction in incidence in year 2022