Prevention Economic Impact Model (PEIM): Model Design and Pilot Test on South Dakota Substance Abuse Prevention Programs

Background: Fiscal restraints and fund-raising needs create demand for data on costs of substance abuse prevention services and the dollar savings they generate. Federal reporting requirements for substance abuse prevention programs and effective program registries provide much of the data required to construct a Prevention Economic Impact Model (PEIM) that meets these needs. PEIM is a technology transfer and diffusion tool. It adapts techniques originally developed for national analyses, using them to provide estimates at state, community, and program levels.

Design and Methods: PEIM has 5 modules. (1) Intervention Selection lets the user select interventions for PEIM to evaluate. It currently is loaded with 59 interventions, with more to be loaded soon. The user can input cost and effectiveness estimates for user-specified interventions. (2) Activity graphically displays the volume of services delivered spatially, overall and by delivery agent (e.g., by a specific community coalition). This module displays data loaded by the user, primarily mandatory-report data under the federal Substance Abuse Prevention Block Grant. (3) Prevention Cost graphically displays the costs of service delivery spatially. This module applies local price adjusters to national estimates, but gives the user the option of substituting local data. (4) Problem Cost provides state cost estimates (at a 3% discount rate) for underage drinking, smoking, illicit drug use, and alcohol misuse. If youth substance use data are available by county, the system also provides county estimates. This module uses state/local substance use data to estimate local problem costs from national costs. (5) Economic Impact displays return on investment by intervention and by delivery agent. This model relies on effect size estimates by intervention drawn from meta-analyses. It applies that effect size to the costs of substance abuse locally to compute the benefits of the intervention, then uses the intervention costs to compute benefit-cost ratios from user-selected perspectives, or at user option, a cost per quality-adjusted life year saved. PEIM was programmed for online use using Tableau visual analytic technology, a Big Data tool. As a pilot, PEIM was loaded with South Dakota data.

Results: Underage drinking cost an estimated $645 million in South Dakota in 2013, primarily for violence and traffic crashes. In FY 2014-15, the state delivered roughly 300,000 units of prevention service to 10,000 people at a cost of more than $3 million. Benefit-cost ratios for programs funded by the state ranged from 0 to 13.

Conclusion: PEIM is a valuable tool that can encourage shifting to more effective prevention programs. Big Data technology supports a flexible, comprehensible, and visually appealing model.