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

Contemp Clin Trials. 2008 May;29(3):343-50. doi: 10.1016/j.cct.2007.10.004. Epub 2007 Oct 26.

Abstract

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.

Publication types

  • Controlled Clinical Trial
  • Evaluation Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Accidental Falls / prevention & control*
  • Accidental Falls / statistics & numerical data*
  • Aged
  • Causality
  • Connecticut / epidemiology
  • Delivery of Health Care / statistics & numerical data*
  • Evidence-Based Medicine / methods
  • Feasibility Studies
  • Female
  • Humans
  • Longitudinal Studies
  • Male
  • Models, Organizational*
  • Patient Education as Topic / methods
  • Patient Education as Topic / organization & administration*
  • Patient Education as Topic / statistics & numerical data
  • Research Design
  • Risk Factors
  • Socioeconomic Factors