Data-driven decision-making tools to improve public resource allocation for care and prevention of HIV/AIDS

Public health agencies face difficult decisions when allocating scarce resources to control the spread of HIV/AIDS. Decisions are often made with few local empirical data. We demonstrated the use of the robust decision making approach in Los Angeles County, an approach that is data driven and allows decision makers to compare the performance of various intervention strategies across thousands of simulated future scenarios. We found that the prevailing strategy of emphasizing behavioral risk reduction interventions was unlikely to achieve the policy goals of the national HIV/AIDS strategy. Of the alternative strategies we examined, those that invested most heavily in interventions to initiate antiretroviral treatment and support treatment adherence were the most likely to achieve policy objectives. By employing similar methods, other public health agencies can identify robust strategies and invest in interventions more likely to achieve HIV/AIDS policy goals.