title: Using latent class analysis to model prescription medications in the measurement of falling among a community elderly population.
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.
authors.affiliation: Department of Public Health, Nova Southeastern University, Health Professions Division, Ft. Lauderdale, FL 33328, USA. patrick@nova.edu
authors.forename: Patrick C
authors.initials: PC
authors.lastname: Hardigan
authors.forename: David C
authors.initials: DC
authors.lastname: Schwartz
authors.forename: William D
authors.initials: WD
authors.lastname: Hardigan
mesh_terms: D000058:Accidental Falls; D000368:Aged; D000369:Aged, 80 and over; D011307:Drug Prescriptions / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D008297:Male; D008962:Models, Theoretical; D055553:Prescription Drugs / Q000009:adverse effects*; D012111:Residence Characteristics; D012189:Retrospective Studies; D018570:Risk Assessment
chemical_list: D055553:Prescription Drugs