Optimizing Falls-related Planning and Intervention for Nursing Facilities by Ownership Type.

Falls among older adults are a major public health concern: They result in $30 billion in direct US healthcare costs annually and take an immense psychological and physical toll on older adults. Particularly concerning are falls in nursing home settings, which account for three times as many falls in adults over 65 than in any other setting. We hypothesized that tailoring falls prevention and response plans to nursing home profit model (for- or nonprofit) and ownership type (public, private, franchise) would greatly improve effectiveness of general plans. To this end, we extracted data from existing government databases, collected qualitative data through structured interviews with home employees, and collected novel quantitative data through web surveys from a representative sample of 40 Pennsylvania nursing homes about prevention and mitigation protocols, population, and facility characteristics, and falls outcome metrics. We analyzed fall-related risk factors that we scored and used to build multivariate logistic regression models to predict falls rates, and subsequently used to build multilevel logistic regression multivariate models to pinpoint the influence of facility type. We found a significant correlation between facility ownership and profit type and falls rates and outcomes. Armed with these analytical insights, we formulated improved falls prevention plans targeted to home types to achieve better falls outcomes as predicted by the models. Finally, we quantify the predicted impact of implementing these targeted plans on fall rates and outcomes in the homes in our study.