1. Developing and testing models for the investigation of barriers to effective HIV / AIDS prevention in the USA Dr Anatole S Menon-Johansson Drs Jean McGuire & Harvey Makadon Harvard School of Public Health Harkness / Health Foundation Fellow 2006 Orlando June, 2007

2. Outline Measuring AIDS prevention performance Describe how AIDS prevention differs between men & women Highlight correlations with AIDS prevention Predicting AIDS prevention performance Policy implications

3. HIV > AIDS > Death Viral transmission Acute retroviral syndrome Recovery + seroconversion Chronic HIV infection Symptomatic HIV infection / AIDS Death 2-3 weeks 2-4 weeks ~ 8 years ~ 1.3 years HIV+ AIDS HIV Rx PREVENTIONPREVENTION 1o1o 2/3 o

4. HIV vs AIDS data HIV data available from 28 states in 1995 and 36 in 2004 ? HIV data reliability –HIV ≤ AIDS diagnoses for some states Estimated 25% HIV+ persons do not know their ‘serostatus’ AIDS = notifiable disease since late 1980’s and disease definition set in 1993

5. Changes in AIDS cases over time Data: Center for Disease Control and Prevention (CDC) 1995-2004

6. AIDS prevention by gender Data: CDC 1995-2004

7. AIDS prevention in men (1995-2004) COLOURCOLOUR Average AIDS change / year (%) - 15-25% - 10-15% - 5-10% - 0-5% + 0-5% + 5%

8. AIDS prevention in women (1995-2004) COLOURCOLOUR Average AIDS change / year (%) - 15-25% - 10-15% - 5-10% - 0-5% + 0-5% + 5%

9. Relationships with known prevention strategies How does AIDS prevention relate to: –Demographics –Economics –Prevention strategies Primary Secondary / Tertiary Linear regression was used to compare AIDS prevention with the above variables

10. Demographics Data: US Census Bureau 2004

11. Income / Poverty & AIDS prevention Data: US Census Bureau 2004

12. Primary prevention Data: Kaiser Family Foundation, Office of Applied Studies, CDC

13. Women’s Health Data: American Cancer Society 2004, CDC 2004, NARAL Pro-choice America 2006

14. The uninsured and state to state disparity in provision KFF / NASTAD ADAP reports –25% of HIV+ people on Rx use ADAP –ADAP formulary varies by state –Waiting lists are used for cost control –Variation in eligibility criterion Kaiser Daily HIV / AIDS reports –August 29 th, 2003 –“Three people with HIV / AIDS die while on West Virginia ADAP waiting list”

15. Health care provision Data: US Census Bureau 2004

16. Secondary / Tertiary prevention Data: KFF / NASTAD ADAP reports 1997-2004, AMA 2004, US Census Bureau 2004

17. Impact of sodomy laws Data: CDC 1995-2003, US Supreme Court

18. Impact of Syringe Exchange Program (SEP) authorization laws Data: Beasley School of Law, Temple University

19. Summary State AIDS prevention can be accurately evaluated using this model Less effective state AIDS prevention is associated with : –Women (Reduced reproductive health) –Poverty (Black) –Poor STD control –History sodomy laws –No SEP authorization laws

20. Prediction AIDS prevention success Key variables: –Poverty –Gonorrhoea rate –Not having a SEP authorization law Properties of predictive model: –Sensitivity 83% –Specificity 79%

21. Policy implications AIDS prevention could be improved by: Standardization of SEP authorization laws Improving sexual and reproductive health Poverty alleviation

22. Acknowledgements Commonwealth Fund Health Foundation Ellison-Cliffe Travelling Fellowship Senta Foulkes Travelling Fellowship Avni Patel (KFF) Drs Sullivan and Campsmith (CDC) Professors and students at HSPH