Model-driven decision support: A community-based meta-implementation strategy to predict population impact

Kimberly Johnson; Wouter Vermeer; Holly Hills; Lia Chin-Purcell; Joshua T Barnett; Timothy Burns; Marianne J Dean; C Hendricks Brown

doi:10.1016/j.annepidem.2024.05.002

Model-driven decision support: A community-based meta-implementation strategy to predict population impact

Ann Epidemiol. 2024 May 14:95:12-18. doi: 10.1016/j.annepidem.2024.05.002. Online ahead of print.

Authors

Kimberly Johnson¹, Wouter Vermeer², Holly Hills³, Lia Chin-Purcell⁴, Joshua T Barnett⁵, Timothy Burns⁵, Marianne J Dean⁶, C Hendricks Brown⁷

Affiliations

¹ 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. Electronic address: kjohnson33@usf.edu.
² 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.
³ 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.
⁴ Center for Dissemination and Implementation At Stanford (C-DIAS), Stanford University, 1070 Arastradero Road, Palo Alto, CA 94304, USA.
⁵ Department of Human Services, Pinellas County Government, 440 Court Street, 2nd Floor, Clearwater, FL 33756, USA.
⁶ Pinellas County Opioid Task Force, Pinellas County, FL, USA.
⁷ Department of Psychiatry and Behavioral Sciences, Northwestern University, Chicago, IL, USA; Department of Preventive Medicine, Northwestern University, Chicago, IL, USA; Department of Medical Social Sciences, Northwestern University, Chicago, IL, USA.

PMID: 38754571
DOI: 10.1016/j.annepidem.2024.05.002

Abstract

Purpose: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Keywords: Agent-based modeling; Community; Drug overdose; HIV; Model-driven decision support.