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Synthetic Control Methods for the Evaluation of Single-Unit Interventions in Epidemiology: A Tutorial.

ABSTRACT: Evaluating the impacts of population-level interventions (e.g., changes to state legislation) can be challenging as conducting randomized experiments is often impractical and inappropriate, especially in settings where the intervention is implemented in a single, aggregate unit (e.g., a country or state). A common nonrandomized alternative is to compare outcomes in the treated unit(s) with unexposed controls both before and after the intervention. However, the validity of these designs depends on the use of controls that closely resemble the treated unit on before-intervention characteristics and trends on the outcome, and suitable controls may be difficult to find because the number of potential control regions is typically limited. The synthetic control method provides a potential solution to these problems by using a data-driven algorithm to identify an optimal weighted control unit-a "synthetic control"-based on data from before the intervention from available control units. While popular in the social sciences, the method has not garnered as much attention in health research, perhaps due to a lack of accessible texts aimed at health researchers. We address this gap by providing a comprehensive, nontechnical tutorial on the synthetic control method, using a worked example evaluating Florida's "stand your ground" law to illustrate methodological and practical considerations.

SUBMITTER: Bonander C

PROVIDER: S-EPMC8634614 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundCo-creation strategies, such as cooperative planning, are promising as a means to ensure that physical activity interventions address real-world problems and are tailored to the target group. This has already been validated in diverse settings. However, questions targeting the transferability of cooperative planning to new settings and the key factors influencing its success or failure remain unclear. At the same time, co-creation processes are complex, and evaluation can be challenging. Following calls for detailed reporting, this paper describes the programme activities, the underlying logic, and methodological design of a study that aims to evaluate the transfer of cooperative planning to new settings and to explore the associated key determinants.MethodsCooperative planning was utilized as a strategy to target physical activity promotion in three real-world German settings in the nursing care and automotive mechatronics sectors. This involved researchers working alongside stakeholders from practice and policy to conjointly develop new interventions to promote physical activity in physically demanding jobs. A pragmatic approach is used to evaluate both the transferability and key determinants of this strategy. We developed a logic model for this co-creation process that describes the underlying assumptions and guides the evaluation. The evaluation outcomes of this study include planning meetings, newly developed interventions, and the determinants that are likely to affect cooperative planning. Quantitative and qualitative data will be collected using questionnaires, documents, and interviews. The quantitative data will be analysed descriptively, while the qualitative data will mainly be analysed using qualitative content analysis, split by settings. Subsequently, data triangulation will be used to integrate the quantitative and qualitative findings, which will then be compared across all three settings.DiscussionThe study findings will contribute to a better understanding of co-creation strategies, their transferability, and key determinants. The practical implications can include a checklist for assessing key determinants and a guideline for transferring cooperative planning into new settings to benefit more people. Ultimately, this study will help to advance co-creation strategies and may be relevant for researchers, practitioners, and policy-makers targeting physical activity promotion in various contexts.Trial registrationOpen Science Framework: https://osf.io/r6xnt/ (retrospectively registered).

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Synthetic Control Methods for the Evaluation of Single-Unit Interventions in Epidemiology: A Tutorial.

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