title: 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.
  
  
  OBJECTIVE
  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.
authors.affiliation: a South London and Maudsley NHS Foundation Trust , London , UK and.
authors.forename: Alison
authors.initials: A
authors.lastname: Beck
authors.affiliation: b Institute of Psychiatry, Psychology and Neuroscience, King's College London , London , UK.
authors.forename: Victoria
authors.initials: V
authors.lastname: Harris
authors.affiliation: a South London and Maudsley NHS Foundation Trust , London , UK and.
authors.forename: Loveday
authors.initials: L
authors.lastname: Newman
authors.affiliation: a South London and Maudsley NHS Foundation Trust , London , UK and.
authors.forename: Lauren Jayne
authors.initials: LJ
authors.lastname: Evans
authors.affiliation: a South London and Maudsley NHS Foundation Trust , London , UK and.
authors.forename: Helen
authors.initials: H
authors.lastname: Lewis
authors.affiliation: a South London and Maudsley NHS Foundation Trust , London , UK and.
authors.forename: Ruth
authors.initials: R
authors.lastname: Pegler
mesh_terms: D000293:Adolescent; D000328:Adult; D001509:Bed Occupancy / Q000706:statistics & numerical data; D005260:Female; D006778:Hospitals, Psychiatric / Q000706:statistics & numerical data*; D006801:Humans; D007297:Inpatients; D007902:Length of Stay / Q000706:statistics & numerical data; D008131:London; D008297:Male; D008605:Mental Health Services / Q000706:statistics & numerical data*; D010343:Patient Admission / Q000706:statistics & numerical data; D010359:Patient Readmission / Q000706:statistics & numerical data; D016012:Poisson Distribution; D055815:Young Adult
keywords: Inpatient services; heavy use; length of stay; readmission; statistical methodology