ArticleAnnals of epidemiology2024
Model-driven decision support: A community-based meta-implementation strategy to predict population impact.
Article in Annals of epidemiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- What scientific inferences can be made with randomized implementation rollout trials.Implementation science : IS · 2025Article
- Integrating Decision Science and Implementation Science to Inform Policy Decision Making.MDM policy & practiceReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
purposeStandard 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.
methodsWe 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.
resultsUsing 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.
conclusionsMDDS 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.