OBJECTIVE: To quantitatively identify predictors and determinants of length of psychiatric hospital stay for children. METHOD: Forty-seven demographic, psychosocial stressor, psychopathology, and disposition variables were statistically reviewed as correlates of length of stay in 100 consecutive discharges from a child psychiatry inpatient service (age range 4-12) in a private hospital. Those with strong statistical significance were then analyzed by multiple regression. RESULTS: Greater severity of psychopathology (measured by the Children's Global Assessment Scale), greater severity of psychosocial stressors (by Axis IV scale), diagnosis of post-traumatic stress disorder, special educational and out-of-home dispositions, and severe tantrums in hospital all strongly predicted longer hospital stay. Diagnosis of adjustment disorder predicted shorter stay. Together these variables explained 57% of the total variance in length of stay. CONCLUSIONS: The most powerful of these predictor variables could potentially be measured at the time of admission, thus permitting accurate prediction of length of stay. A set of models was generated for this purpose.
OBJECTIVE: To quantitatively identify predictors and determinants of length of psychiatric hospital stay for children. METHOD: Forty-seven demographic, psychosocial stressor, psychopathology, and disposition variables were statistically reviewed as correlates of length of stay in 100 consecutive discharges from a child psychiatry inpatient service (age range 4-12) in a private hospital. Those with strong statistical significance were then analyzed by multiple regression. RESULTS: Greater severity of psychopathology (measured by the Children's Global Assessment Scale), greater severity of psychosocial stressors (by Axis IV scale), diagnosis of post-traumatic stress disorder, special educational and out-of-home dispositions, and severe tantrums in hospital all strongly predicted longer hospital stay. Diagnosis of adjustment disorder predicted shorter stay. Together these variables explained 57% of the total variance in length of stay. CONCLUSIONS: The most powerful of these predictor variables could potentially be measured at the time of admission, thus permitting accurate prediction of length of stay. A set of models was generated for this purpose.
Authors: Claude M Chemtob; Omar G Gudiño; Rohini Luthra; Rachel Yehuda; James Schmeidler; Brian Auslander; Hillel Hirshbein; Alan Schoor; Rick Greenberg; Jeffrey Newcorn; Paula G Panzer; Todd Schenk; Paul Levine; Robert Abramovitz Journal: Evid Based Pract Child Adolesc Ment Health Date: 2016-08-26
Authors: Greg Haggerty; Nicholas Forlenza; Charlotte Poland; Sagarika Ray; Jennifer Zodan; Ashwin Mehra; Ajay Goyal; Matthew R Baity; Caleb J Siefert; Sean Sobin; David Leite; Samuel J Sinclair Journal: J Nerv Ment Dis Date: 2014-11 Impact factor: 2.254