Abstracts

Deborah Cragun/Nina Sperber (Univ. of South Florida/Duke University): The value of contrapositive consistency and coverage in selecting among causal chain models explaining the adoption of pharmacogenetic testing for antidepressants

Testing for genes that affect how individuals respond to medications (i.e., pharmacogenetics) can help select antidepressants that may be more effective than alternatives or reduce the risk of side effects. Despite its potential benefits, pharmacogenetics has spread slowly in routine clinical care. To identify potential areas for intervention, we conducted a study to understand what factors differentiated adopters versus non-adopters of pharmacogenetic testing for two genes (CYP2D6 or CYP2C19) that metabolize antidepressants. This presentation will demonstrate our pragmatic application of CNA in which we used theory in ordering data from our mixed methods study to allow for causal chain models. We also demonstrate the value of using contrapositive consistency and contrapositive coverage in selecting among multiple models and how these measures of fit may ultimately help in reducing model ambiguity.

Pavel Svačina/Jonathan Freitas (VSE Prague/Univ. of Minas Gerais): Do transaction costs or transactional value matter in technology licensing? Lessons learned from configurational causal modelling

The royalty rate is a typical contractual arrangement between the licensor and the licensee in a technology licensing transaction. Technology royalty rates show high variability, and factors determining high or low rates are of interest for academics and professionals in technology valuation. Given the lack of academic studies in this area, we bring an exploratory analysis to shed light on causal dependencies between technology royalty rates and transaction characteristics that potentially make a difference to the final arranged royalty rate. We achieve this by using relatively new methods of configurational causal modelling (CCM) - coincidence analysis (CNA) and logic regression (LR). We analysed 375 technology licensing transactions from both healthcare and non-healthcare sectors. Our results possibly have important theoretical (reconciling two competing theories), practical (considering complex causal paths when benchmarking royalty rates) and methodological (interactive adjustments of minimum CNA's consistency and coverage thresholds, taking into account LR's solutions) implications.

Jessica Dodge (VA Pittsburgh): Difference making implementation strategies for significant implementation: Coincidence Analysis on a global systematic review of implementation studies 

Despite the presence of evidence-based treatments, consumers do not always receive evidence-based care or services. The field of implementation science attempts to address this gap by evaluating the best ways to help individuals, organizations, and communities use evidence-based interventions. Implementation strategies are the supportive approaches used to promote the uptake of evidence-based practices.  Not only do studies that assess the effectiveness of these strategies vary in rigor, design, and evaluated outcomes, but they also vary by how many strategies are used at one time. These “bundles” of strategies make it difficult to know which strategies make the difference in implementing an evidence-based practice. The goal of the current study was to identify which strategy or combination of strategies lead to implementation success. This study builds off a global systematic review of 129 implementation studies that organized implementation outcomes and strategies. While the systematic review was able to identify frequencies of strategies used across various implementation outcomes, it was not able to identify which particular strategies led to or made a difference in a study having significant implementation outcomes. Therefore, we used those same N=129 studies to apply Coincidence Analysis (CNA) to understand which implementation strategies are leading to significant implementation outcomes.  We leveraged a novel CNA consensus approach that has not yet been employed in the field of implementation science.

Jonathan Freitas (Univ. of Minas Gerais): Developing an online/hybrid course on CNA

Currently, to learn CNA, you either study the texts and packages by yourself (occasionally seeking specific assistance from experts); or find yourself fortunate enough to be in a university that offers a course on the subject; or, more likely, attend the annual in-person workshop. However, self-study can be challenging, and even with occasional help, grasping the details of the method can be daunting. On the other hand, while workshops offer high-quality instruction, they may not be accessible to all interested parties and could prove too intense for beginners. Therefore, aiming to supplement these current learning alternatives with an additional option, we are developing an online/hybrid course on configurational causal analysis, specifically tailored for those engaged in empirical research. This complementary approach builds on: visual learning; practical examples drawn from various fields; frequent exercises to reinforce learning; continuous updates; and modular structure, with optional deep dives based on learner interest, ranging from basic level to intermediate or advanced. Our goal is to make CNA accessible globally and foster high-quality research employing the method. In the presentation, we will provide an overview of the proposal, development status, and sample materials to gather feedback and make adjustments before the launch.

Yinfei Duan (University of Alberta): Process and context factors contributing to or impeding successful implementation of a complex team-based behavioral intervention: a configurational analysis of the INFORM trial data

Care aides, unregulated workers in long-term care (LTC) homes, provide point-of-care to older residents with functional and cognitive impairments. Integrating care aides into formal team communications is crucial for quality of care, yet it presents a challenge. The INFORM (Improving Nursing Home Care Through Feedback On perfoRMance data) study, a cluster- randomized, three-arm trial, aimed to increase care aides' involvement in formal team communications through a complex behavioral intervention targeted at care unit managerial teams. The main components of the INFORM intervention included the dissemination (feedback) of baseline data in early 2016 and three workshops from June 2016 to April 2017. These workshops focused on developing strategies for setting and achieving goals for improving care aides' involvement (facilitated by the research team), and offering progress reporting and networking opportunities among units. A total of 201 care units from 67 Western Canadian LTC homes were randomized into three groups: control (baseline feedback), basic-assisted feedback (baseline feedback plus one in-person and two virtual workshops), and enhanced-assisted feedback (baseline feedback plus three in-person workshops). The primary findings of the INFORM trial indicated a statistically significant improvement in care aides' involvement in communications in both intervention groups compared to the control group, with no difference between the intervention groups. Process evaluation revealed moderate to high levels of fidelity delivery, receipt, and enactment; teams with higher enactment experienced a significantly larger improvement in care aides' involvement than lower enactment teams. Yet, it remains unclear why some teams were more successful (higher enactment) than others (lower enactment) in implementing the intervention, and how this success (or lack thereof) relates to factors of the implementation process and local context where strategies to enhance care aides' involvement were put into practice. This study sought to uncover difference-making factors linked to units' successful implementation of the INFORM intervention. We focused on factors related to implementation processes and local context, as well as their interplay in influencing implementation success (or lack thereof).

Dennis H. Li/Alithia Zamantakis/Reiping Huang (Northwestern University): Contextual factors predicting reach of a digital HIV intervention delivered by community-based organizations across the US

HIV researchers have long championed digital interventions as a key modality to reach cisgender young men who have sex with men (YMSM), who account for nearly half of new HIV infections in the US, with critical HIV prevention education and support. Yet, few such programs have been implemented outside of clinical trials, leaving open questions around how to best deliver them. To address this gap, we facilitated implementation of Keep It Up! (KIU!), a self-guided, web-based, evidence-based intervention (EBI), by HIV community-based organizations (CBOs) across the country. We aimed to identify configurations of contextual factors that could predict greater implementation success.

Luna De Souter/Michael Baumgartner (Univ. of Bergen): Evaluating sufficiency and necessity in model building with Coincidence Analysis

Coincidence Analysis (CNA) is a configurational comparative method of causal learning that has seen a significant uptick in applications in public health in recent years. To build its models, CNA exploits relations of sufficiency and necessity in data and relies on two evaluation measures called consistency and coverage, which are equivalent to positive predictive value and sensitivity. This paper argues that consistency and coverage have severe limitations for binary, so-called crisp-set data. In particular, they do not yield reliable evaluations when the relative frequencies of crisp-set causes and outcomes are at high or low extremes. We propose two alternative evaluation measures that are not affected by these limitations and benchmark them against standard consistency and coverage in an extended simulation experiment. It turns out that, across a wide range of data scenarios, the overall quality of CNA models built by means of the new measures is more than 20% higher than when models are built using the standard measures. In conclusion, we advocate replacing consistency and coverage in crisp-set model building by the new measures proposed in this paper.

Martyna Swiatczak (Univ. of Bergen): A Comparative Configurational Analysis on the Introduction of Statutory Minimum Wage

Since World War II, an increasing number of rich democracies have introduced statutory minimum wages. What are the political and economic conditions behind these labour market interventions? The presented research project employs a comparative configurational approach to investigate various combinations of conditions that explain the introduction of minimum wages. We use data for 19 countries from 1960 to 2017. Our findings indicate that either the combination of low bargaining coverage and left government or Christian Democratic participation in government and high inflation can explain the introduction of a statutory minimum wage. As a follow-up, we conduct case studies to examine each disjunct more closely.

Meghan Lane-Fall/Sapna Mendon-Plasek (University of Pennsylvania/Rand) Systematic review of the use of Configurational Comparative Methods (including QCA and CNA) in the field of implementation science