College of Osteopathic Medicine Faculty Articles

Title

Using Latent Class Analysis to Model Prescription Medications in the Measurement of Falling Among a Community Elderly Population

ISBN or ISSN

1472-6947

Publication Title

BMC Medical Informatics and Decision Making

Date of original Performance / Presentation

May 2013

Publication Date / Copyright Date

5-25-2013

First Page

13

Last Page

60

DOI Number

10.1186/1472-6947-13-60

Abstract

BACKGROUND: 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. Using biomedical, pharmacological and demographic variables as predictors, latent class analysis (LCA) is demonstrated as a tool for the prediction of falls among community dwelling elderly. METHODS: Using a retrospective data-set a two-step LCA modeling approach was employed. First, we looked for the optimal number of latent classes for the seven medical indicators, along with the patients' prescription medication and three covariates (age, gender, and number of medications). Second, the appropriate latent class structure, with the covariates, were modeled on the distal outcome (fall/no fall). The default estimator was 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 were used for model comparisons. RESULTS: A review of the model fit indices with covariates shows that a six-class solution was preferred. The predictive probability for latent classes ranged from 84% to 97%. Entropy, a measure of classification accuracy, was good at 90%. Specific prescription medications were found to strongly influence group membership. CONCLUSIONS: In conclusion the LCA method was effective at finding relevant subgroups within a heterogenous at-risk population for falling. This study demonstrated that LCA offers researchers a valuable tool to model medical data.

Disciplines

Medical Specialties | Medicine and Health Sciences | Osteopathic Medicine and Osteopathy

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