Bayesian hierarchical modeling for a non-randomized, longitudinal fall prevention trial with spatially correlated observations

Because randomization of participants is often not feasible in community-based health interventions, non-randomized designs are commonly employed. Non-randomized designs may have experimental units that are spatial in nature, such as zip codes that are characterized by aggregate statistics from sources like the U.S. census and the Centers for Medicare and Medicaid Services. A perennial concern with non-randomized designs is that even after careful balancing of influential covariates, bias may arise from unmeasured factors. In addition to facilitating the analysis of interventional designs based on spatial units, Bayesian hierarchical modeling can quantify unmeasured variability with spatially correlated residual terms. Graphical analysis of these spatial residuals demonstrates whether variability from unmeasured covariates is likely to bias the estimates of interventional effect. The Connecticut Collaboration for Fall Prevention is the first large-scale longitudinal trial of a community-wide healthcare intervention designed to prevent injurious falls in older adults. Over a two-year evaluation phase, this trial demonstrated a rate of fall-related utilization at hospitals and emergency departments by persons 70 years and older in the intervention area that was 11 per cent less than that of the usual care area, and a 9 per cent lower rate of utilization from serious injuries. We describe the Bayesian hierarchical analysis of this non-randomized intervention with emphasis on its spatial and longitudinal characteristics. We also compare several models, using posterior predictive simulations and maps of spatial residuals.