Data-Driven Decision Making in the Prevention of Substance-Related Harm: Results from the Strategic Prevention Framework State Incentive Grant Program

Abstract

The Strategic Prevention Framework State Incentive Grant Program (SPF SIG) is a national public-health initiative in the United States to prevent and reduce substance-related harm. The model promotes data-driven decision making (DDDM), with an emphasis on using epidemiological data to help select prevention priorities and to allocate prevention resources. This article examines how well the first two cohorts of SPF SIG states (N = 26) implemented DDDM, and also explores what factors facilitated and hindered the process. Data were collected by reviewing and coding states' strategic plans, supplemented by interviews with state project directors, evaluators, and epidemiological workgroup chairs. Fidelity to the process was scored as high, medium, or low, based on transparency and support from relevant evidence. On selecting prevention priorities, 81% of states received high or medium scores on all priorities selected. On allocating prevention resources, 85% received a high or medium score. Facilitators included collaboration among stakeholders, training and technical assistance, and efforts of epidemiological workgroups and evaluators. However, states that lacked established data infrastructures for prevention were at a decided disadvantage in implementing the model. Future implications for SPF SIG states and ongoing challenges to DDDM in general are discussed.

Keywords

Data-driven decision making prevention policy efficiency and effectiveness substance use and consequences

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