Hierarchical models to evaluate translational research: Connecticut collaboration for fall prevention

Background and objective: Evidence-based second stage translational studies are necessary and difficult to evaluate. A quasi-experimental design is used to compare the rate of fall-related health care utilization of two geographically disparate areas in Connecticut, a small state in the northeastern United States, to evaluate an intervention designed to reduce fall-related injuries among older persons. This evaluation examines the two years immediately prior to intervention. Methods: The experimental units are postal (i.e., zip) code tabulation areas (ZCTAs) in which counts of fall-related health care utilization and demographic characteristics can be gathered from local and federal public health sources. We employ hierarchical modeling to determine whether there was a difference in fall-related health care utilization between the study arms prior to initiating the intervention. Geographic information systems are used to characterize neighboring ZCTAs and to graph model-adjusted rates of fall-related utilization. Results: After adjustment for covariates and spatial variation, we observed no significant difference between rates or temporal trends of fall-related health care utilization in the study arms over the two year pre-intervention period. Conclusion: The study arms of the Connecticut Collaboration for Falls Prevention have equivalent rates and temporal trends of fall-related utilization over the two year pre-intervention period.