Developing and testing models for the investigation of barriers

to effective HIV / AIDS prevention in the USA

Dr Anatole S Menon-JohanssonDrs Jean McGuire & Harvey Makadon

Harvard School of Public Health

Harkness / Health Foundation Fellow 2006Orlando June, 2007

Outline• Measuring AIDS prevention performance

• Describe how AIDS prevention differs between men & women

• Highlight correlations with AIDS prevention

• Predicting AIDS prevention performance

• Policy implications

HIV > AIDS > DeathViral transmission

Acute retroviral syndrome

Recovery + seroconversion

Chronic HIV infection

Symptomatic HIV infection / AIDS

Death

2-3 weeks

2-3 weeks

2-4 weeks

~ 8 years

~ 1.3 years

HIV+

AIDS

HIV Rx

PREVENTION

1o

2/3o

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

Changes in AIDS cases over time

Data: Center for Disease Control and Prevention (CDC) 1995-2004

AIDS prevention by gender

Data: CDC 1995-2004

AIDS prevention in men (1995-2004)

COLOUR

Average AIDS

change / year (%)

- 15-25%

- 10-15%

- 5-10%

- 0-5%

+ 0-5%

+ 5%

AIDS prevention in women (1995-2004)

COLOUR

Average AIDS

change / year (%)

- 15-25%

- 10-15%

- 5-10%

- 0-5%

+ 0-5%

+ 5%

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

Demographics

Data: US Census Bureau 2004

Income / Poverty & AIDS prevention

Data: US Census Bureau 2004

Primary prevention

Data: Kaiser Family Foundation, Office of Applied Studies, CDC

Women’s Health

Data: American Cancer Society 2004, CDC 2004, NARAL Pro-choice America 2006

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 29th, 2003– “Three people with HIV / AIDS die while on West

Virginia ADAP waiting list”

Health care provision

Data: US Census Bureau 2004

Secondary / Tertiary prevention

Data: KFF / NASTAD ADAP reports 1997-2004, AMA 2004, US Census Bureau 2004

Impact of sodomy laws

Data: CDC 1995-2003, US Supreme Court

Impact of Syringe Exchange Program (SEP) authorization laws

Data: Beasley School of Law, Temple University

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

Prediction AIDS prevention success

• Key variables:– Poverty– Gonorrhoea rate– Not having a SEP authorization law

• Properties of predictive model:– Sensitivity 83%– Specificity 79%

Policy implications

AIDS prevention could be improved by:

• Standardization of SEP authorization laws

• Improving sexual and reproductive health

• Poverty alleviation

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