The hidden HIV epidemic: what do mathematical models tell us? The case of France

1. The hidden HIV epidemic: what do mathematical models tell us? The case of France Virginie Supervie,Jacques Ndawinz & Dominique Costagliola U943 Inserm & Pierre and Marie Curie University, Paris, France

2. Background • Many HIV positive individuals are unaware of their infection. • Undiagnosed HIV infection has serious implications for both the individual and public health. • Persons unaware of their HIV infection cannot benefit from timely treatment. • Persons living with undiagnosed HIV infections may transmit HIV to others. • Information on persons living with undiagnosed HIV infection is essential for guiding screening policy and resource allocation planning.

3. Methods to estimate the size of the hidden HIV epidemic • Direct method (based on prevalence surveys) • Back-calculation method (based on reported numbers of HIV/AIDS diagnoses) • Method based on simultaneous HIV/AIDS diagnosis and CD4 cell count at diagnosis Working Group on Estimation of HIV Prevalence in Europe (2011) HIV in hiding: methods and data requirements for the estimation of the number of people living with undiagnosed HIV. AIDS 25, 1017-1023.

4. Original “back-calculation” approach, before availability of treatment What can this tell us about how many people were infected and when they were infected ? Observed number of AIDS cases diagnosed Calendar year Source: Phillips A (2009) Estimation of the number of people with undiagnosed HIV infection in a country. HIV in Europe Conference.

5. Original “back-calculation” approach, before availability of treatment Estimated number of people infected (incidence curve) Observed number of AIDS cases diagnosed Calendar year From the incidence curve it is possible to work out the size of the hidden epidemic, by subtracting the number of deaths, the number of HIV-infected individuals in care and those diagnosed but not yet in care. Source: adapted from Phillips A (2009) Estimation of the number of people with undiagnosed HIV infection in a country. HIV in Europe Conference.

6. Estimated HIV incidence in France by transmission category (using extended back-calculation model) Ndawinz JD, Costagliola D, Supervie V. (2011) New method for estimating HIV incidence and time from infection to diagnosis using HIV surveillance data: results for France. AIDS 25:1905-13

7. Estimated distribution of time interval between infection and diagnosis by transmission category (using extended back-calculation model) Ndawinz JD, Costagliola D, Supervie V. (2011) New method for estimating HIV incidence and time from infection to diagnosis using HIV surveillance data: results for France. AIDS 25:1905-13

8. Estimated distribution of time interval between infection and diagnosis by transmission category (using extended back-calculation model) • The probability of not being diagnosed 10 years after the infection occurred is very small among each transmission group (<5%); therefore, most people infected before 2000 were aware of their HIV status at the end of 2010.

9. Estimated HIV incidence in France by transmission category (using extended back-calculation model) Extrapolating our estimated curves of HIV incidence over the whole 2000-2010 period, and using our estimated distribution of times from infection to diagnosis, we obtained the size of the hidden epidemic in France in 2010.

10. Estimated undiagnosed HIV prevalence and rates in France in 2010 (using extended back-calculation model)

11. Estimated undiagnosed HIV prevalence and rates in France in 2010 (using extended back-calculation model)

12. Estimated undiagnosed HIV prevalence and rates in France in 2010 (using extended back-calculation model) Essential to design and interpret HIV screening survey/program

13. Estimated undiagnosed HIV prevalence and rates in France in 2010 (using extended back-calculation model) Essential to design and interpret HIV screening survey/program • Non-targeted HIV screening survey in emergency departments in France: • 12754 persons screened; • All newly diagnosed HIV patients belonged to a traditional high-risk group: • 7 MSM (out of 268 tested); • 11 non French-national heterosexuals (out of 2658 tested); • 0 out of 8430 French heterosexuals tested had undiagnosed HIV infection; • Were these results unexpected?

14. Estimated undiagnosed HIV prevalence and rates in France in 2010 (using extended back-calculation model) HIV screening survey* Undiagnosed prevalence rate per 10000 7/268  (106-531)  (0-4) 0/8430 11/2658  (21-74) The results of the survey were not unexpected and confirm that we have to test a lot of French heterosexuals to find the ones living with undiagnosed HIV infection. D’Almeida KW et al. (2012) Modest public health impact of nontargeted human immunodeficiency virus screening in 29 emergency departments. Arch Intern Med, 172:12-20.

15. Estimated undiagnosed HIV prevalence and rates in France in 2010 (using extended back-calculation model) What could be a cost-effective HIV screening strategy? A screening strategy is cost-effective as long as at least 0.1%* of results are HIV-positive. Universal screening of the whole French population would not be cost-effective because the undiagnosed prevalence rate is 0.07% (95% CI: 0.05%-0.09%) in the general population. *Yazdanpanah Y et al. (2010) Routine HIV Screening in France: Clinical Impact and Cost-Effectiveness. PLoS ONE 5(10): e13132.

16. Estimated undiagnosed HIV prevalence and rates in France in 2010 (using extended back-calculation model) What could be a cost-effective HIV screening strategy? A screening strategy is cost-effective as long as at least 0.1% of results are HIV-positive. Pr. Y. Yazdanpanah suggested universal testing among men. Is that strategy cost-effective? According to our estimates, undiagnosed prevalence rate is 0.10% (95% CI: 0.07%-0.13%) among men.

17. CD4 count distributions among undiagnosed HIV-infected individuals in 2010 in France CD4 count • Among people living with undiagnosed HIV infection: • 59% were eligible for ART; • 39% were late presenters; • 19% had advanced HIV disease;

18. CD4 count distributions among undiagnosed HIV-infected individuals in 2010 in France CD4 count Lower % of individuals with CD4 counts > 500 and higher % of individuals with CD4 counts < 200 among heterosexual men than among other transmission categories.

19. Estimated number and percentage of HIV-infected persons engaged in selected stages of the continuum of HIV care in France in 2010 150,200 100% 121,400 111,300 81% 96,800 74% 84,200 64% 56% 81% 92% 87% 87% Data from health insurance scheme (CNAMTS) and French Hospital Database on HIV ANRS-CO4

20. Estimated number and percentage of HIV-infected persons engaged in selected stages of the continuum of HIV care in France and in US* *Cohen SM et al. (2011) Vital sign: HIV prevention through care and treatment – United States. MMRW, 60:1618-23.

21. Estimated number and percentage of HIV-infected persons engaged in selected stages of the continuum of HIV care in France and in US* 81% 92% 87% 87% 81% 51% 89% 77% *Cohen SM et al. (2011) Vital sign: HIV prevention through care and treatment – United States. MMRW, 60:1618-23.

22. Estimated number and percentage of HIV-infected MSM engaged in selected stages of the continuum of HIV care in France in 2010 51,100 100% 42,100 39,000 34,000 82% 76% 30,300 67% 59% 82% 93% 87% 89% Although 59% of HIV-infected MSM were virally suppressed incidence of HIV infection did not decrease among MSM

23. Conclusion • 28,800 undiagnosed HIV-infected individuals. Similar to the 32,000* recently estimated by InVS. This represents about 20% of HIV-infected people. • About 40,000 HIV-infected individuals are not in HIV care. • About 60% of undiagnosed HIV-infected individuals are eligible for antiretroviral treatment. • Although 56% of HIV-infected persons are virally suppressed, HIV incidence is not decreasing. • Increasing HIV testing opportunities is thus essential. *Cazein F et al. (2012) Prevalence and Characteristics of Individuals With Undiagnosed HIV Infection in France: Evidence From a Survey on Hepatitis B and C Seroprevalence. JAIDS, 60:e114-e116.

24. Acknoledgements • Andrew Phillips • Sara Lodi

25. Awareness of HIV infection and transmission of HIV (Adapted from Marks G. et al. AIDS (2006) with French estimated data) 19% unaware of HIV infection Account for: 43%* of new HIV infections 81% aware of HIV infection 57%* of new HIV infections People living with HIV/AIDS: 150,200 New infections each year : 7,500 * Assuming no reduction in the prevalence of non protected sex acts with individuals at risk of HIV infection among diagnosed HIV-positive individuals

26. Awareness of HIV infection and transmission of HIV (Adapted from Marks G. et al. AIDS (2006) with French estimated data) 19% unaware of HIV infection Account for: 64%* of new HIV infections 81% aware of HIV infection 36%* of new HIV infections People living with HIV/AIDS: 150,200 New infections each year : 7,500 * Assuming 57% reduction in the prevalence of non protected sex acts with individuals at risk of HIV infection among diagnosed HIV-positive individuals

27. CD4 count distributions among undiagnosed HIV-infected individuals • To estimate the distribution of CD4 counts among undiagnosed HIV-infected individuals we combined: • data on CD4 cell count decline* • year of infection of undiagnosed HIV-infected individuals • We assigned to each HIV-infected individuals still undiagnosed in 2010 a value of CD4 cell count based on the time elapsed between 2010 and the year of infection of this individual *Lodi S. et al. (2011) Time From Human Immunodeficiency Virus Seroconversion to Reaching CD4 Cell Count thresholds <200, <350, and <500 Cells/mm3: Assessment of Need Following Changes in Treatment Guidelines. CID, 53:817-825.

28. Curve linking infection to AIDS, without treatment Expected number of new AIDS cases per year after 1000 people infected - illustration 2 3 10 25 40 65 80 90 100 100 100 90 80 70 55 30 25 15 10 5 5 Number of new AIDS cases per year Curve known from seroconverter cohorts 0 5 10 15 20 Years from infection Source: Philips A. (2009) Estimation of the number of people with undiagnosed HIV infection in a country. HIV in Europe Conference.

29. Curve linking infection to AIDS, without treatment Expected number of new AIDS cases per year after 1000 people infected - illustration 2 3 10 25 40 65 80 90 100 100 100 90 80 70 55 30 25 15 10 5 5 Number of new AIDS cases per year Curve known from seroconverter cohorts 0 5 10 15 20 Years from infection BC uses reported number of AIDS cases and information on the incubation period to “work backwards” and infer the numbers of persons infected in previous years that would reproduce the observed AIDS surveillance data. Source: Philips A. (2009) Estimation of the number of people with undiagnosed HIV infection in a country. HIV in Europe Conference.

30. Extended back-calculation approach Question changes… How many people must be infected, and when must they have been infected, in order to produce the numbers of new AIDS we have observed ? How many people must be infected, and when must they have been infected, and what must the probability of getting diagnosed have been, in order to produce the numbers of new HIV diagnoses we have observed ? from: infection AIDS to: infection HIV diagnosis Source: Philips A. (2009) Estimation of the number of people with undiagnosed HIV infection in a country. HIV in Europe Conference.