Creating Logic Models to Support Epidemiology and Evaluation

By Iris Smith

 

The essential role of epidemiology in measuring the frequency, severity, and distribution of the factors that contribute to the prevention focus is also supported by the logic model. Well-designed logic models can be helpful to epidemiologists by clearly identifying the assumptions underlying the intervention, the contextual influences, sources of heterogeneity, population sub-groups targeted by the intervention, as well as measurement strategies and data sources. 

 

Logic models have long been recognized as an essential tool for program design and evaluation. They have been described as conceptual frameworks, concept maps, and graphic representations of program theories of change. Ideally, a logic model should be a graphic representation of the link between the identified targets of change, predisposing/ influencing factors, and anticipated short, intermediate, and long-term outcomes. The classic logic model template includes the inputs necessary to execute the intervention, the specific actions or strategies that will be implemented, and the assumed behavioral, organizational, or community outcomes that will occur as a direct result of the intervention.

 

A scoping review of evaluation methods commonly used to assess the effectiveness of community coalitions in public health by Kegler, Halpin, and Butterfoss (2020) found that the creation of logic models that guide the design of the intervention with empirical support for linkages between short, intermediate, and long-term outcomes in the model can help to focus the evaluation on indicators of change and enable them to more easily document the role of community coalitions in achieving outcomes. 1

 

Logic models can also be helpful in making sense of complex multi-level interventions by being explicit about contextual influences including epidemiological, social, legal, and political factors that influence the prevention target and/or the proposed outcomes.  It is equally important to understand how the individual intervention components are expected to yield desired outcomes and how these components and their individual outcomes interact to yield program-level, short-term, and intermediate outcomes.

 

Resources

W.K. Kellogg Foundation (2004).  Using Logic Models to Bring Together Planning, Evaluation, and Action, Logic Model Development Guide. https://wkkf.issuelab.org/resource/logic-model-development-guide.html

 

Meyer ML, Louder CN, Guerda N (2021).  Creating With, Not for People: Theory of Change and Logic Models for Culturally Responsive Community-Based Intervention.  American Journal of Evaluation,  https://doi-org.proxy.library.emory.edu/10.1177%2F10982140211016059

 

Jones ND, Azzam T, Wanzer DL, Skousen D, Knight C, Sabarre N (2020).  Enhancing the Effectiveness of Logic Models.  American Journal of Evaluation 4 (3), pg. 452-47 https://journals.sagepub.com/doi/abs/10.1177/1098214018824417

 


1 Kegler MC, Halpin SN, Butterfoss FD (2020).  Evaluation Methods Commonly Used to Assess the Effectiveness of Community Coalitions in Public Health:  Results from a Scoping Review.  New Directions for Evaluation Special Issue:  Evaluating Community Coalitions and Collaboratives. 165; pg.139-157 https://www.evaluationinnovation.org/wp-content/uploads/2017/12/Evaluating-Coalitions-and-Networks.pdf

 

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