An Application of Latent Class Analysis in the Measurement of Falling Among a Community Elderly Population

ABSTRACT

Latent Class Analysis (LCA) is a statistical method for finding subtypes of related cases (latent classes) from multivariate categorical data. LCA is well suited to many health applications where one wishes to identify disease subtypes or diagnostic subcategories. In this paper we demonstrate the utility of LCA for the prediction of falls among community dwelling elderly. Falls among the elderly are a major public health concern. Therefore the possibility of a modeling technique which could better estimate fall probability is both timely and needed.

A three-step modeling approach was employed. First, we looked for the optimal number of latent classes for the seven binary indicators: (1) arthritis, (2) high blood pressure, (3) diabetes, (4) heart disease, (5) foot disorders, (6) Parkinson’s disease, and (7) stroke. Second, we modeled two covariates (age and number of medications) on the latent class. Third, we modeled the appropriate latent class structure, with the covariates, on the distal outcome (fall/no fall). The default estimator is maximum likelihood with robust standard errors. The Pearson chi-square, likelihood ratio chi-square, BIC, Lo-Mendell-Rubin Adjusted Likelihood Ratio test and the bootstrap likelihood ratio test are used for model comparisons.

A review of the model fit indices with covariates shows that a five-class solution was preferred. The predictive probability for latent classes ranged from 60% to 72%. Persons in classes one, two and five possessed the greatest probability of falling.

In conclusion we found the LCA method effective for finding relevant subgroups with a heterogenous atrisk population for falling. This study demonstrated that LCA offers researchers a valuable tool to model medical data.

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