Dynamic systems-modeling as a means to estimate community-based prevention effects

Area of Science:

• Public Health
• Health Services Research
• Computational Modeling

Background:

• Community-based strategies are optimal for prevention programming.
• Research on comprehensive, community-based prevention programming is limited.
• Communities require evidence-based guidance on effective intervention strategies.

Purpose of the Study:

• To explore the utility of computer-based dynamic simulation modeling for community-based substance abuse prevention.
• To address the need for context-specific intervention strategies to reduce alcohol and other drug (AOD) abuse.
• To present a simulation model for testing various prevention options.

Main Methods:

• Development of computer-based dynamic simulation models.
• Replication of historical community patterns of substance availability, use, and misuse.
• Simulation of future patterns under alternative intervention mixes.
• Utilizing models to answer "what if" questions for community planners.

Main Results:

• Dynamic simulation models can replicate historical substance abuse patterns.
• Models allow for the simulation of future trends based on different intervention strategies.
• These models can identify politically acceptable and feasible intervention mixes for maximum AOD problem reduction.

Conclusions:

• Computer-based dynamic simulation modeling shows significant promise for guiding community-based AOD prevention efforts.
• Models provide a mechanism for communities to test and select optimal intervention strategies.
• Further development and application of these models can enhance the effectiveness of prevention programming.