Markov chain modelling for geriatric patient care.
OBJECTIVE
To show that Markov chain modelling can be applied to data on geriatric patients and use these models to assess the effects of covariates.


METHODS
Phase-type distributions were fitted by maximum likelihood to data on times spent by the patients in hospital and in community-based care. Data on the different events that ended the patients' periods of care were used to estimate the dependence of the probabilities of these events on the phase from which the time in care ended. The age of the patients at admission to care and the year of admission were also included as covariates.


RESULTS
Differential effects of these covariates were shown on the various parameters of the fitted model, and interpretations of these effects made.


CONCLUSIONS
Models based on phase-type distributions were appropriate for describing times spent in care, as the ordered phases had an interpretable structure corresponding to increasing amounts of care being given.
Faddy
M J
MJ
Queensland University of Technology, Brisbane, Australia.
McClean
S I
SI
D000368:Aged; D000369:Aged, 80 and over; D003153:Community Health Services / Q000706:statistics & numerical data*; D017143:Fuzzy Logic; D005853:Geriatrics / Q000458:organization & administration* / Q000706:statistics & numerical data; D006779:Hospitals, Public / Q000706:statistics & numerical data*; D006801:Humans; D007902:Length of Stay / Q000706:statistics & numerical data; D016013:Likelihood Functions; D008131:London; D008297:Male; D008390:Markov Chains; D015233:Models, Statistical; D009026:Mortality; D012720:Severity of Illness Index