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Stephanie L. Sansom, PhD, MPP, MPH Division of HIV/AIDS Prevention NCHHSTP, CDC

Modeling to Identify Optimal Allocation of HIV Prevention Resources in a C ity Health Department. Stephanie L. Sansom, PhD, MPP, MPH Division of HIV/AIDS Prevention NCHHSTP, CDC. The Value of Modeling . Modeling of resource allocation helps state and local health departments

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Stephanie L. Sansom, PhD, MPP, MPH Division of HIV/AIDS Prevention NCHHSTP, CDC

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  1. Modeling to IdentifyOptimal Allocation of HIV Prevention Resources in a City Health Department Stephanie L. Sansom, PhD, MPP, MPH Division of HIV/AIDS Prevention NCHHSTP, CDC

  2. The Value of Modeling • Modeling of resource allocation helps state and local health departments • Divide scarce prevention dollars among programs and populations • Achieve the most impact at least cost

  3. CDC-Philadelphia Collaboration2011-2012 • Model optimal HIV prevention portfolio to address the city’s HIV epidemic • Develop a method that other local jurisdictions might use

  4. Methods Epidemic • Projects new HIV cases for 1 to 5 years Optimization • Best allocation of HIV prevention budget • Among populations and programs • To prevent most HIV cases • Model requirements • HIV prevention budget: $12 million • Size and HIV characteristics of risk populations • % of risk population reachable • Prevention intervention characteristics • Cost • Efficacy • Duration

  5. Methods • Calculate reduced likelihood of HIV infection following prevention intervention • Number of and type of HIV exposures • Unprotected sex and needle sharing • HIV prevalence among partners • HIV transmission probability per exposure • Efficacy of intervention in preventing HIV • Calculate cost of intervention per infection averted • Cost of providing intervention divided by reduced likelihood of infection

  6. Methods • CDC criteria: Inclusion of interventions that are • Required in high-prevalence cities • Targeting populations with greatest number of new infections • Supported by scientific evidence on infection rate reduction

  7. Methods: Interventions Testing in clinical and non-clinical settings Linkage to care Retention in care Adherence to treatment Partner services Behavioral interventions

  8. Philadelphia HIV Community Profile * New HIV diagnoses in Philadelphia in 2009 & PLWH, People living with HIV, undiagnosed and diagnosed in Philadelphia in 2009 # Estimated number of people at high risk of HIV infection in each risk category.

  9. Cost per Infection Averted

  10. Optimal Allocation under $12 M budget

  11. Optimal Allocation under $12 M budget

  12. Optimal Allocation under $25 M budget

  13. Optimal Allocation under $50 M budget

  14. Infections Prevented under Different Budgets

  15. Limitations of Modeling • Models often rely on uncertain data and assumptions • Sensitivity analyses are critical • Validate projected outcomes against empirical data • Models may not incorporate important elements • Equity • Political or practical barriers to implementation • Synergies among prevention interventions

  16. Modeling can Synthesize data from many sources Allow for inclusion of local data Summarize complex issues in transparent way Serve as a methodology for comparing interventions Illuminate planning and programmatic decisions Philadelphia has used model results to inform funding decisions CDC continues to refine models to help support planning of local HIV prevention Advantages of Modeling

  17. Thank you! For more information: Stephanie L. Sansom sos9@cdc.gov Findings and conclusions in this presentation are those of the author and do not necessarily represent the views of the CDC.

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