Department of Mental Health Law and Policy, College of Community and Behavioral Sciences, University of South Florida, 13301 Bruce B Downs Blvd, Tampa, FL 33612, USA.
Center for Prevention Implementation Methodology for Drug Abuse and HIV (Ce-PIM), Feinberg School of Medicine, Northwestern University, Chicago, IL, USA; Department of Psychiatry and Behavioral Sciences, Northwestern University, Chicago, IL, USA; Center for Connected Learning and Computer-Based Modeling (CCL), School of Education and Social Policy, Northwestern University, Evanston, IL, USA; Northwestern Institute for Complex Systems (NICO), Northwestern University, Evanston, IL, USA.
Ann Epidemiol. 2024 Jul;95:12-18. doi: 10.1016/j.annepidem.2024.05.002. Epub 2024 May 14.
Standard tools for public health decision making such as data dashboards, trial repositories, and intervention briefs may be necessary but insufficient for guiding community leaders in optimizing local public health strategy. Predictive modeling decision support tools may be the missing link that allows community level decision makers to confidently direct funding and other resources to interventions and implementation strategies that will improve upon the status quo.
We describe a community-based model-driven decision support (MDDS) approach that requires community engagement, local data, and predictive modeling tools (agent-based modeling in our case studies) to improve decision-making on implementing strategies to address complex public health problems such as overdose deaths. We refer to our approach as a meta-implementation strategy as it provides guidance to a community on what intervention combinations and their required implementation strategies are needed to achieve desired outcomes. We use standard implementation measures including the Stages of Implementation Completion to assess adoption of this meta-implementation approach.
Using two case studies, we illustrate how MDDS can be used to support decision making related to HIV prevention and reductions in overdose deaths at the city and county level. Even when community acceptance seems high, data acquisition and diffuse responsibility for implementing specific strategies recommended by modeling are barriers to adoption.
MDDS has the capacity to improve community decision makers use of scientific knowledge by providing projections of the impact of intervention strategies under various scenarios. Further research is necessary to assess its effectiveness and the best strategies to implement it.
数据仪表盘、试验存储库和干预简报等公共卫生决策的标准工具可能是必要的，但对于指导社区领导优化当地公共卫生策略来说可能还不够。预测建模决策支持工具可能是缺失的一环，它使社区层面的决策者能够有信心地将资金和其他资源引导到干预措施和实施策略上，从而改善现状。
我们描述了一种基于社区的模型驱动决策支持 (MDDS) 方法，该方法需要社区参与、本地数据和预测建模工具（在我们的案例研究中是基于代理的建模），以改善实施策略以解决复杂公共卫生问题（如过量死亡）的决策。我们将我们的方法称为元实施策略，因为它为社区提供了关于需要组合哪些干预措施及其所需的实施策略才能实现预期结果的指导。我们使用标准的实施措施，包括实施完成阶段，来评估对这种元实施方法的采用情况。
使用两个案例研究，我们说明了 MDDS 如何用于支持与城市和县级 HIV 预防和减少过量死亡相关的决策。即使社区接受度似乎很高，但数据获取和实施建模推荐的具体策略的责任分散是采用的障碍。
MDDS 通过提供各种情况下干预策略影响的预测，有能力改善社区决策者对科学知识的利用。需要进一步研究来评估其有效性和最佳实施策略。
