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Emine Yaylali: Resource allocation models for HIV prevention

Resource allocation models for HIV prevention

Emine Yaylali, Ph.D.
Istanbul Technical University


We will briefly explain resource allocation models, how these models could be used in improving HIV prevention and present some examples from literature. Then, we will describe HIV-RAMP, a resource allocation model to optimize health departments’ Centers for Disease Control and Prevention (CDC)–funded HIV prevention budgets to prevent the most new cases of HIV and to evaluate the model’s implementation in four health departments
Design, Settings, and Participants: 
We developed a linear programming model combined with a Bernoulli process model that allocated a fixed budget among HIV prevention interventions and risk subpopulations to maximize the number of new infections prevented. The model, which required epidemiologic, behavioral, budgetary, and programmatic data, was implemented in health departments in Philadelphia, Chicago, Alabama, and Nebraska. 
Main Outcome Measures: 
The optimal allocation of funds, the site-specific cost per case of HIV prevented rankings by intervention, and the expected number of HIV cases prevented.  
The model suggested allocating funds to HIV testing and continuum-of-care interventions in all 4 health departments. Behavioral interventions did not receive funding in the optimal allocation for any health department. The most cost-effective intervention for all sites was HIV testing for men who have sex with men (MSM) in non-clinical settings, and the least cost-effective interventions were behavioral interventions for HIV-negative persons. The pilot sites required 3-4 months of technical assistance to develop data inputs and generate and interpret the results. Although the sites found the model easy to use in providing quantitative evidence on allocating HIV prevention resources, they criticized the exclusion of structural interventions and the use of model to allocate only CDC funds.   
Resource allocation models have potential to improve the allocation of limited HIV prevention sources and can be used as a decision-making guide for state and local health departments. Using such models may require substantial staff time and technical assistance. Model results suggesting allocating funds towards testing and continuum-of-care interventions and risk populations at highest risk of HIV transmission may lead to better health outcomes. These model results emphasize the allocation of CDC funds toward testing and continuum--of-care interventions and populations at highest risk of HIV transmission.