Do aftercare services reduce inpatient psychiatric readmissions?
OBJECTIVE
To determine whether aftercare services reduce the likelihood that children and adolescents will be readmitted to inpatient psychiatric facilities.


METHODS
Analyses of data from the Fort Bragg Demonstration. Data were based on 204 sample individuals (children and adolescents), all of whom were discharged from inpatient facilities during the study period.


METHODS
These analyses use hazard modeling to examine the impact of aftercare services on the likelihood of readmission. Comparisons of individuals for whom the timing of aftercare services differ are adjusted for a wide range of individual characteristics, including client demographics, diagnosis, symptomatology, and psychosocial functioning.


METHODS
Detailed data on psychopathology, symptomatology, and psychosocial functioning were collected on individuals included in these analyses. This information was taken from structured diagnostic interviews and behavior checklists, including the Child Behavior Checklist and Diagnostic Interview Schedule for Children, completed by the child and his or her caretaker. Information on the use of mental health services was taken from insurance claims and a management information system, and was used to identify the period from discharge to readmission and to describe the client's use of outpatient therapy, case management, intermediate (or stepdown) services, and residential treatment centers during this period.


CONCLUSIONS
Using Cox models that allow for censoring and that include the use of aftercare services as time-varying covariates, we find that aftercare services generally do not influence the likelihood of inpatient readmission. For the lower middle class families included in this study, the estimated effect of aftercare is not statistically significant and has limited practical significance. When we look at specific forms of aftercare, we find that outpatient therapy has the largest effect and that stepdown services in intermediate settings have the smallest. We also identify family and individual characteristics that influence the likelihood of readmission.
Foster
E M
EM
School of Policy Studies, Applied Research Center, Georgia State University, Atlanta 30302-4039, USA.
D000293:Adolescent; D000359:Aftercare / Q000706:statistics & numerical data*; D002648:Child; D003266:Continuity of Patient Care / Q000706:statistics & numerical data; D005260:Female; D005845:Georgia; D006264:Health Benefit Plans, Employee / Q000706:statistics & numerical data; D006772:Hospitals, Military; D006778:Hospitals, Psychiatric / Q000706:statistics & numerical data*; D006801:Humans; D007629:Kentucky; D008297:Male; D009657:North Carolina; D010359:Patient Readmission / Q000706:statistics & numerical data*; D016016:Proportional Hazards Models