title: Using a multi-state model to enhance understanding of geriatric patient care.
abstract: OBJECTIVE
  To use multi-state Markov chain modelling to analyse data on geriatric patient care, and to make comparisons between male and female patients.
  
  
  METHODS
  Estimation, from observed data, of covariate (age of patient and date of admission to hospital or community care) dependent parameters of statistical models for time in care and subsequent events.
  
  
  RESULTS
  Differential effects of these covariates shown on the parameters of the models for female and male patients, where these parameters can be interpreted as affecting different features of the distributions of time in care.
  
  
  CONCLUSIONS
  Multi-state modelling is an appropriate means of analysing data on geriatric patient care and can reveal underlying patterns of differential effects, some of which may not be apparent from more routine data processing.
authors.affiliation: School of Mathematical Sciences, Queensland University of Technology, GPO Box 2434, Brisbane, QLD 4001, Australia. m.faddy@qut.edu.au
authors.forename: Malcolm J
authors.initials: MJ
authors.lastname: Faddy
authors.forename: Sally I
authors.initials: SI
authors.lastname: McClean
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D005260:Female; D005853:Geriatrics / Q000379:methods* / Q000706:statistics & numerical data; D006299:Health Services for the Aged / Q000706:statistics & numerical data* / Q000639:trends; D017052:Hospital Mortality / Q000639:trends; D006801:Humans; D007902:Length of Stay / Q000706:statistics & numerical data; D008131:London / Q000453:epidemiology; D008297:Male; D008390:Markov Chains; D015233:Models, Statistical; D010351:Patient Discharge / Q000706:statistics & numerical data; D010360:Patient Transfer / Q000706:statistics & numerical data; D011336:Probability; D012737:Sex Factors; D016008:Statistical Distributions