Prospective validation of a predictive model that identifies homeless people at risk of re-presentation to the emergency department.
OBJECTIVE
To prospectively evaluate the accuracy of a predictive model to identify homeless people at risk of representation to an emergency department.


METHODS
A prospective cohort analysis utilised one month of data from a Principal Referral Hospital in Melbourne, Australia. All visits involving people classified as homeless were included, excluding those who died. Homelessness was defined as living on the streets, in crisis accommodation, in boarding houses or residing in unstable housing. Rates of re-presentation, defined as the total number of visits to the same emergency department within 28 days of discharge from hospital, were measured. Performance of the risk screening tool was assessed by calculating sensitivity, specificity, positive and negative predictive values and likelihood ratios.


RESULTS
Over the study period (April 1, 2009 to April 30, 2009), 3298 presentations from 2888 individuals were recorded. The homeless population accounted for 10% (n=327) of all visits and 7% (n=211) of all patients. A total of 90 (43%) homeless people re-presented to the emergency department. The predictive model included nine variables and achieved 98% (CI, 0.92-0.99) sensitivity and 66% (CI, 0.57-0.74) specificity. The positive predictive value was 68% and the negative predictive value was 98%. The positive likelihood ratio 2.9 (CI, 2.2-3.7) and the negative likelihood ratio was 0.03 (CI, 0.01-0.13).


CONCLUSIONS
The high emergency department re-presentation rate for people who were homeless identifies unresolved psychosocial health needs. The emergency department remains a vital access point for homeless people, particularly after hours. The risk screening tool is key to identify medical and social aspects of a homeless patient's presentation to assist early identification and referral.
Melbourne School of Population Health, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Level 4, 207 Bouverie St, Carlton, Victoria 3010, Australia. dgmoore@optusnet.com.au
Gaye
G
Moore
Graham
G
Hepworth
Tracey
T
Weiland
Elizabeth
E
Manias
Marie Frances
MF
Gerdtz
Margaret
M
Kelaher
David
D
Dunt
D015331:Cohort Studies; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data*; D005260:Female; D006703:Ill-Housed Persons / Q000706:statistics & numerical data*; D006779:Hospitals, Public / Q000706:statistics & numerical data; D006786:Hospitals, Urban / Q000706:statistics & numerical data; D006801:Humans; D008297:Male; D001523:Mental Disorders / Q000453:epidemiology; D008603:Mental Health / Q000706:statistics & numerical data; D015233:Models, Statistical; D010342:Patient Acceptance of Health Care / Q000706:statistics & numerical data*; D010359:Patient Readmission / Q000706:statistics & numerical data*; D011446:Prospective Studies; D018570:Risk Assessment; D012307:Risk Factors; D012959:Socioeconomic Factors; D019966:Substance-Related Disorders / Q000453:epidemiology; D014505:Urban Population / Q000706:statistics & numerical data; D014739:Victoria / Q000453:epidemiology; D014947:Wounds and Injuries / Q000453:epidemiology