Facility-level conditions leading to higher reach: a configurational analysis of national VA weight management programming

Abstract

Background While the Veterans Health Administration (VHA) MOVE! weight management program is effective in helping patients lose weight and is available at every VHA medical center across the United States, reaching patients to engage them in treatment remains a challenge. Facility-based MOVE! programs vary in structures, processes of programming, and levels of reach, with no single factor explaining variation in reach. Configurational analysis, based on Boolean algebra and set theory, represents a mathematical approach to data analysis well-suited for discerning how conditions interact and identifying multiple pathways leading to the same outcome. We applied configurational analysis to identify facility-level obesity treatment program arrangements that directly linked to higher reach. Methods A national survey was fielded in March 2017 to elicit information about more than 75 different components of obesity treatment programming in all VHA medical centers. This survey data was linked to reach scores available through administrative data. Reach scores were calculated by dividing the total number of Veterans who are candidates for obesity treatment by the number of “new” MOVE! visits in 2017 for each program and then multiplied by 1000. Programs with the top 40 % highest reach scores (n = 51) were compared to those in the lowest 40 % (n = 51). Configurational analysis was applied to identify specific combinations of conditions linked to reach rates. Results One hundred twenty-seven MOVE! program representatives responded to the survey and had complete reach data. The final solution consisted of 5 distinct pathways comprising combinations of program components related to pharmacotherapy, bariatric surgery, and comprehensive lifestyle intervention; 3 of the 5 pathways depended on the size/complexity of medical center. The 5 pathways explained 78 % (40/51) of the facilities in the higher-reach group with 85 % consistency (40/47). Conclusions Specific combinations of facility-level conditions identified through configurational analysis uniquely distinguished facilities with higher reach from those with lower reach. Solutions demonstrated the importance of how local context plus specific program components linked together to account for a key implementation outcome. These findings will guide system recommendations about optimal program structures to maximize reach to patients who would benefit from obesity treatment such as the MOVE! program.

Evaluation of Organizational Capacity in the Implementation of a Church-Based Cancer Education Program

“…Unlike traditional variable-oriented methods, configurational analysis retains a persistent link to individual cases, applying formal logic to develop models identifying the specific bundles of conditions that distinguish cases with an outcome of interest from those without. Configurational analysis in general-and Coincidence Analysis in particular-has appeared across a wide variety of health-related implementation contexts in the published literature since 2020 (Cohen et al, 2021;Coury et al, 2021;Hickman et al, 2020;Miech et al, 2021;Petrik et al, 2020;Whitaker et al, 2020;Yakovchenko et al, 2020).…”

Ready, set, go! The role of organizational readiness to predict adoption of a family caregiver training program using the Rogers’ diffusion of innovation theory

“…Specifically, facility complexity ranges from 1 (the most complex with the largest levels of patient volume, patient risk, teaching and research; largest number and breadth of physician specialists; and a level 5 intensive care unit), level 2 (medium complexity), and level 3 (lowest level of complexity). High-complexity hospitals are typically urban and have the largest volume of patients and medical services [ 50 ] and therefore may also have more caregivers to serve and more staff to support caregiver programming. In addition, complexity could be related to implementation climate, capacity, and successful change.…”

Modeling Contingency in Veteran Community Reintegration: A Mixed Methods Approach

“…These data were analyzed using the R package “cna” for multi-value configurational analysis ( Ambuhl & Baumgartner, 2018 ). Using a configurational approach to condition selection described in detail elsewhere ( Yakovchenko et al, 2020 ), we first applied the “minimally sufficient conditions” function within the R “cna” package to scan the entire dataset and identify configurations with the strongest connections to the outcome of interest ( Hickman et al, 2020 ; Miech et al, 2021 ; Petrik et al, 2020 ;). Analyses for the presence of the outcome and the absence of the outcome were conducted separately.…”