3104 — Capturing the Central Line Bundle Infection Prevention Interventions: Comparison of Reflective and Composite Modeling Methods
Gilmartin HM, Denver/Seattle COIN; Sousa KH, University of Colorado;
Decades of work on healthcare-associated infection (HAI) prevention has led to a greater understanding of the relationships between infection prevention interventions and HAI outcomes, though little is known of the influence of organizational context on these relationships. The Quality Health Outcomes Model (QHOM) suggests that interventions are affected by organizational context in reducing adverse outcomes. To empirically test these relationships, a measurement model that represents an intervention is needed. Interventions can be modeled as reflective if the indicators are influenced by the factor or as a composite if the indicators influence the factor. Due to a lack of theoretical guidance, this study tested the central line (CL) bundle interventions in reflective (RF) and composite (CM) models to assess the impact of the modeling methods within the QHOM.
Data from 614 U.S. hospitals that participated in the Prevention of Nosocomial Infection and Cost-effectiveness-Refined study were obtained for secondary analysis. The RF model was developed using factor analysis. The CM model was developed using guidance from the literature. Both models were tested in a structural equation model.
The two modeling approaches resulted in adequate fitting models (RMSEA = .04; CFI = .95) and supported similar relationships within the QHOM. Context, as a mediating influence between interventions and outcomes, was not supported in either model (RF = .05 p = .48 and CM = .03; p = .71). A direct relationship was noted between the CL bundle interventions and organizational context (RF = .23; CM = .19; p = .01), and lower CL infections (RF = -.28; CM = -.25; p = .01).
There was little difference between the results of the two modeling approaches. Both models fit the data, yet the relationships within the QHOM were not supported. Due to the difference in direction of influence in the RF and CM models, interpretation of the findings is challenging. At this time, a composite approach appears the most appropriate.
Bundled interventions better represent the multi-factorial nature of infection prevention work. Due to a lack of clarity on how to capture bundled healthcare interventions in measurement models, the comparison of modeling approaches, with guidance from subject experts, is recommend to increase confidence in research findings.