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

Area of Science:

• Gerontology
• Public Health
• Healthcare Management

Background:

• Falls among older adults represent a significant public health issue, incurring substantial healthcare costs and causing physical and psychological harm.
• Nursing home residents experience falls at a rate three times higher than community-dwelling older adults, highlighting the critical need for effective interventions.
• Current general fall prevention plans may not adequately address the unique characteristics of different nursing home operational models.

Purpose of the Study:

• To investigate whether customizing fall prevention and response plans based on nursing home profit models (for-profit vs. nonprofit) and ownership types (public, private, franchise) enhances their effectiveness.
• To identify specific facility characteristics and operational factors that correlate with fall rates and outcomes in nursing homes.
• To develop and quantify the predicted impact of targeted fall prevention strategies for different nursing home types.

Main Methods:

• Data extraction from government databases, qualitative data from employee interviews, and quantitative data from web surveys of 40 Pennsylvania nursing homes.
• Analysis of fall-related risk factors, development of multivariate logistic regression models to predict fall rates, and multilevel logistic regression to assess facility type influence.
• Formulation of improved, targeted fall prevention plans based on analytical insights and prediction models.

Main Results:

• A significant correlation was identified between nursing home ownership/profit type and fall rates/outcomes.
• Specific risk factors and facility characteristics influencing fall rates were pinpointed through advanced statistical modeling.
• The study successfully predicted improved fall outcomes through the implementation of tailored prevention plans.

Conclusions:

• Customizing fall prevention strategies to nursing home operational models is crucial for improving resident safety and reducing fall incidents.
• The developed targeted plans offer a data-driven approach to enhance fall prevention effectiveness in diverse nursing home settings.
• Quantifiable predictions demonstrate the potential of these tailored plans to significantly reduce fall rates and improve outcomes.