An episode-based framework for analyzing health care expenditures: an application of reward renewal models.
OBJECTIVE
To illustrate an episode-based framework for analyzing health care expenditures based on reward renewal models, a stochastic process used in engineering for describing processes that cycle on and off with "rewards" (or costs) occurring at the end of each cycle.


METHODS
Data used in the illustration were collected as part of an evaluation of a national initiative to improve mental health services for children and youth. Participants were enrolled in a longitudinal study at a demonstration site and in a comparison community between 1997 and 1999. The illustration involves analyses of mental health expenditures at the two sites and of the dynamics of service use behind those expenditures.


METHODS
Services data were derived from management information systems as well as patient records at inpatient facilities in the two communities. These data cover services received between 1997 and 2003. The analysis focuses on the year following study entry.


RESULTS
Between-site differences in expenditures reflect complex between-site differences in the timing of service use. In particular, children at the demonstration stayed in treatment longer but were less likely to return for treatment later. In contrast, children at the comparison site experienced substantially less continuity of care. Costs per day of treatment within an episode were comparable at the two sites.


CONCLUSIONS
Reward renewal models offer a promising means for integrating research on service episodes and the dynamics of service use with that on health care expenditures.
Foster
E Michael
EM
School of Public Health, University of North Carolina, 27599, USA.
Xuan
Fengjuan
F
D000293:Adolescent; D002648:Child; D005260:Female; D005102:Health Expenditures; D006302:Health Services Research; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D008605:Mental Health Services / Q000191:economics* / Q000706:statistics & numerical data*; D013269:Stochastic Processes