Coincidence Analysis: A New Method for Causal Inference in Implementation Science

Abstract

Background: Implementation of multifaceted interventions typically involves many diverse elements working together in interrelated ways, including intervention components, implementation strategies, and features of local context. Given this real-world complexity, implementation researchers may be interested in a new mathematical, cross-case method called Coincidence Analysis (CNA) that has been designed explicitly to support causal inference, answer research questions about combinations of conditions that are minimally necessary or sufficient for an outcome, and identify the possible presence of multiple causal paths to an outcome. CNA can be applied as a standalone method or in conjunction with other approaches, and can reveal new empirical findings related to implementation that might otherwise have gone undetected. Methods: We applied CNA to a publicly available dataset from Sweden with county-level data on human papillomavirus (HPV) vaccination campaigns and vaccination uptake in 2012 and 2014 and then compared CNA results to the published regression findings. Results: The original regression analysis found vaccination uptake was positively associated only with the availability of vaccines in schools. CNA produced different findings and uncovered an additional solution path: high vaccination rates were achieved by either (1) offering the vaccine in all schools or (2) a combination of offering the vaccine in some schools and media coverage. Conclusions: CNA offers a new comparative approach for researchers seeking to understand how implementation conditions work together and link to outcomes.Contributions to the literature· Coincidence Analysis (CNA) represents a new mathematical, cross-case method for researchers evaluating the implementation of complex interventions in dynamic settings.· CNA can address multiple dimensions of real-world complexity, including conjunctivity (where several conditions must be jointly present to bring about an outcome) and equifinality (where different paths can lead to the same outcome). CNA can also detect causal chains, where conditions lead to an intermediary outcome, which then leads to the final outcome. · Intentionally designed to investigate different hypotheses and uncover different properties of causal structures than more traditional approaches, CNA can identify implementation-related findings that might otherwise go undetected.

Improving Smoking and Blood Pressure Outcomes: The Interplay Between Operational Changes and Local Context

“…CCMs, by contrast, examine specific values of factors (ie, conditions) that are consistently necessary or sufficient for an outcome to appear, and rely on a "regularity" model of causality. [26][27][28] The regularity analytic framework fits our research question particularly well in that it allows for the evaluation of both causal complexity (ie, the joint presence of conditions) and equifinality (ie, multiple solution paths to the same outcome), and is robust with smaller sample sizes. 29,30 We linked this analytic framework with the theoretical framework of the Practice Change Model, which identifies critical elements for guiding practice change and emphasizes the importance of evolving interrelationships among elements, including stakeholder motivation, practice resources for change, external motivators, and options for change.…”

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“…To best understand the multilevel and interdependence of factors that might influence implementation, sophisticated quantitative and qualitative methods are required. 123 124 Lewis and colleagues suggest that common quantitative approaches to mediation testing in implementation trials are suboptimal, and that the product of coefficients approach might be preferable given its capacity to examine single level and multilevel mediation and maximise power. 122 Further, qualitative approaches have been suggested to be particularly useful in the absence of established quantitative measures, and structured qualitative inquiry can help deepen an understanding of mechanistic processes.…”

“…Reviews, however, suggest that implementation mechanisms are rarely tested in trials of implementation strategies,121122 and where testing has occurred, often it is undertaken inappropriately. To best understand the multilevel and interdependence of factors that might influence implementation, sophisticated quantitative and qualitative methods are required 123124. Lewis and colleagues suggest that common quantitative approaches to mediation testing in implementation trials are suboptimal, and that the product of coefficients approach might be preferable given its capacity to examine single level and multilevel mediation and maximise power 122.…”

Towards a neo-configurational theory of intrinsic motivation

“…in health science (e.g. Whitaker et al, 2020;Yakovchenko et al, 2020), CNA is still an innovative methodological approach in behavioural science. It applies a minimization algorithm custom-built for causal modelling based on a regularity theory of causation, i.e.…”