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.

IMProving care After inherited Cancer Testing (IMPACT) study: protocol of a randomized trial evaluating the efficacy of two interventions designed to improve cancer risk management and family communication of genetic test results

“…Lastly, survey data will be analyzed using coincidence analysis (CNA) and the CNA package for R, following best practices outlined in a recently published study [90]. CNA will be used to reveal patterns of behavioral and contextual factors that systematically differ across individuals who did and did not achieve the primary outcomes for Arms A and B, respectively.…”

The Influence of Community and Leadership Support on Implementation Research Behaviors

“…The cross-sectional nature of this study and the use of regression analysis limited the ability to assess causal relationships. To gain further insight into how the various elements of support are interrelated, future studies using causal-inference methods such as qualitative comparative analysis or coincidence analysis may be useful for developing targeted implementation promotion interventions (Berg-Schlosser et al, 2009;Whitaker et al, 2020). In addition, prospective studies examining the impact of implementation promotion activities on researcher behavior can be used to verify hypothesized causal relationships.…”

Four strategic areas to advance equitable implementation of evidence-based practices in cancer care

“…CCMs evaluate which combinations of these conditions are required for implementation success. The two most common types of CCMs are qualitative comparative analysis (QCA) and coincidence analysis [ 98–100 ]. QCA and coincidence analysis (CNA) are both case-based analytic methods that draw upon notions of complex causality [ 97 ].…”