176
Views
0
CrossRef citations to date
0
Altmetric
Articles

Using healthcare encounter data to identify high-cost users among adults with a history of homelessness: a validation study

, , , , , & show all
Pages 343-351 | Received 13 Aug 2021, Accepted 17 Apr 2022, Published online: 09 May 2022
 

ABSTRACT

People experiencing homelessness are often considered frequent healthcare users. Although their service use is not uniform, it can be difficult to identify the highest-cost users without access to comprehensive cost data. This study validated a set of algorithms that apply healthcare encounter data to identify high-cost users among adults with a history of homelessness. Administrative healthcare cost data were compared across common frequent user definitions for emergency department (ED) visits and hospitalizations. Sensitivity, specificity, positive predictive values, and negative predictive values were derived for a set of seven algorithms. Twenty-three percent of the cohort was high-cost users. Optimal algorithms to identify high-cost users were ≥1 hospitalization with 78% sensitivity and 96% specificity and ≥1 hospitalization or ≥6 ED visits with 82% sensitivity and 89% specificity. The positive predictive values indicate that 85% of people with ≥1 hospitalization in a year and 69% of people with ≥1 hospitalization or ≥6 ED visits in a year were correctly classified as high-cost users. This study offers a straightforward method to identify high-cost users among adults with a history of homelessness. The optimal algorithms can be used to inform resource planning and service evaluation to ensure high-needs groups receive appropriate and tailored interventions.

Acknowledgement

This study was supported by ICES, which is funded by an annual grant from the Ontario Ministry of Health (MOH) and the Ministry of Long-Term Care (MLTC). We thank IQVIA Solutions Canada Inc. for use of their Drug Information File. The At Home/Chez Study was supported by the Mental Health Commission of Canada, MOHLTC, and the Canadian Institute of Health Research (CIHR). The Health and Housing in Transition study was supported by an operating grant and an Interdisciplinary Capacity Enhancement Grant on Homelessness, Housing and Health from CIHR. This research was also supported by the CIHR foundation grant held by SWH (FDN-167263). Parts of this material are based on data and information compiled and provided by Ontario Ministry of Health (MOH). The analyses, conclusions, opinions and statements expressed herein are solely those of the authors and do not reflect those of the funding or data sources; no endorsement is intended or should be inferred.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work and associated studies [At Home/Chez Soi and Health and Housing in Transition] were supported by Canadian Institutes of Health Research [grant numbers CIHR MOP-130405, CIHR FDN-167263, FDN-167263, MOP-86765, HOA-80066] and Ontario Ministry of Health and Long-Term Care [grant number HSRF-259].

Notes on contributors

Kathryn Wiens

Kathryn Wiens completed this work as a doctoral student at the Dalla Lana School of Public Health at the University of Toronto.

Laura C. Rosella

Laura C. Rosella is an Associate Professor in the Dalla Lana School of Public Health at the University of Toronto where she holds Canada Research Chair in Population Health Analytics. Dr. Rosella’s research interests include population health and health equity, data science, predictive models to support public health planning, knowledge translation and evaluation, and population health management.

Paul Kurdyak

Paul Kurdyak is a Professor and Chair, Addictions and Mental Health Policy at CAMH and the Dalla Lana School of Public Health and Mental Health, and Lead of the Mental Health and Addictions Research Program at ICES. Dr. Kurdyak’s work focuses on understanding the determinants of and barriers to treatment for mental illnesses and explores the interaction between chronic medical and mental illnesses.

Simon Chen

Simon Chen was a senior research analyst at ICES for the duration of this project. He has a master’s degree in Public Health from the Geisel School of Medicine at Dartmouth.

Tim Aubry

Tim Aubry is a Professor in the School of Psychology and a Senior Researcher at the Centre for Research on Educational and Community Services at the University of Ottawa. Dr. Aubry has collaborated on research projects with community organizations and governments at all levels, contributing to the development of effective social programs and policies.

Vicky Stergiopoulos

Vicky Stergiopoulos is a Clinician Scientist and the Physician-in-Chief at the Centre for Addiction and Mental Health. She is also a Professor and Vice-Chair, Clinical and Innovation in the Department of Psychiatry at the University of Toronto. Her research focuses on the design, implementation and evaluation of interventions to improve the health and social outcomes of adults experiencing mental health and addiction challenges and social disadvantage.

Stephen W. Hwang

Stephen W. Hwang is a Professor of Medicine at the University of Toronto and Staff Physician in the Division of General Internal Medicine at St. Michael’s Hospital. He is the Director of the MAP Centre for Urban Health Solutions and holds the Chair in Homelessness, Housing, and Health at St. Michael’s Hospital. Dr. Hwang’s research program focuses on the connections between housing and health, access to health care for people experiencing homelessness, and interventions to improve the health of people who are homeless.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 65.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 381.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.