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

The Connecticut Collaboration for Fall Prevention (CCFP) is a longitudinal translational study seeking to demonstrate the feasibility of reducing the fall-related injury rate among older people through a professional behavioral change intervention based on risk factors identified in the Yale Frailty and Injuries: Cooperative Studies of Intervention Techniques and other clinical trials. [1, 2] Seven targeted risk factors included walking and transfer difficulty, balance impairment, postural hypotension, use of four or more medications, foot and vision problems and home fall hazards. Interventions known to rectify each targeted risk factor were included in dissemination and training materials. The CCFP employed professional behavior-change strategies to encourage healthcare providers and healthcare facilities throughout the intervention area to incorporate evidence-based fall assessment and management strategies into their practices were described previously. [3] Although the CCFP started to monitor injurious falls in October 1999, the professional change outreach efforts did not begin until October 2001. The intervention was not expected to be fully implemented until later on in the study. Like any large scale community health project, the design and analysis of this study entail a number of important statistical considerations. First is that for practical reasons translational studies often do not lend themselves to randomization, the statistical cornerstone of randomized controlled trials (RCTs). Second is that protection of participant confidentiality often mandates that these interventions are not assessed on an individual basis, but rather for a community or geographical area as a whole. A geographically aggregate measure of outcome potentially biases the estimated effect in a manner referred to as ecological fallacy. [4] To satisfy these constraints on randomization and outcome measure, observational studies based on quasi-experimental designs are often used. [5] Because such designs do not have all of the statistical strengths of an RCT, the final probability statements of the analysis are sometimes labeled “plausibility” statements. [6] This terminology is an acknowledgement of the potential for bias arising from non-randomized assignment and use of an aggregate measure of outcome. The translation of results from highly controlled RCTs to a more general setting is referred to either as second stage translational research or the T2 application phase and is a topic of great interest to the medical research community. [7–9] The first stage established the efficacy of the CCFP’s interventional components in an RCT. [1] We posit that the quasi-experimental design described here is a statistically valid approach for evaluating how well this clinically efficacious strategy translates into the setting of actual clinical practices. The hypothesis of this article is whether the pre-intervention rates of fall-related healthcare utilization of the study arms are significantly different. The CCFP employed a quasi-experimental design which compared a treatment area that received the intervention and a “usual care” control area which did not. Although both study arms are within Connecticut, they are geographically disjoint to limit contamination bias from the dissemination of interventional knowledge. The intervention arm was in north-central Connecticut while the usual care arm was located along the I-95 corridor from Norwalk to New London. To balance requirements for participant anonymity and an adequate number of sampling units, postal (i.e., ZIP) code tabulation areas (ZCTAs) were chosen as the experimental unit. ZCTAs were created by the US Census Bureau to satisfy the demand of researchers for a bounded areal unit corresponding to the ZIP codes used by the US Postal Service.