Statistical approaches for identifying heavy users of inpatient mental health services

Abstract

Background: A lack of consensus exists concerning how to identify "heavy users" of inpatient mental health services.

Aim: To identify a statistical approach that captures, in a clinically meaningful way, "heavy" users of inpatient services using number of admissions and total time spent in hospital.

Methods: "Simple" statistical methods (e.g. top 2%) and data driven methods (e.g. the Poisson mixture distribution) were applied to admissions made to adult acute services of a London mental health trust.

Results: The Poisson mixture distribution distinguished "frequent users" of inpatient services, defined as having 4 + admissions in the study period. It also distinguished "high users" of inpatient services, defined as having 52 + occupied bed days. Together "frequent" and "high" users were classified as "heavy users".

Conclusions: Data driven criteria such as the Poisson mixture distribution can identify "heavy" users of inpatient services. The needs of this group require particular attention.

Keywords: Inpatient services; heavy use; length of stay; readmission; statistical methodology.