Sergei Muratov1, Justin Lee2, Anne Holbrook2, J Michael Paterson2, Jason R Guertin2, Lawrence Mbuagbaw2, Tara Gomes2, Wayne Khuu2, Priscila Pequeno2, Jean-Eric Tarride2. 1. Department of Health Research Methods, Evidence, and Impact (Muratov, Lee, Holbrook, Mbuagbaw, Tarride) and Divisions of Geriatric Medicine (Lee) and Clinical Pharmacology and Toxicology (Holbrook), Department of Medicine, McMaster University, Hamilton, Ont.; ICES (Paterson, Gomes, Khuu, Pequeno), Toronto, Ont.; Département de médecine sociale et préventive (Guertin), Faculté de médecine, and Centre de recherche du Centre hospitalier universitaire de Québec (Guertin), Axe Santé des populations et pratiques optimales en santé, Université Laval, Québec, Que.; Li Ka Shing Knowledge Institute (Gomes), St. Michael's Hospital, Toronto, Ont.; Centre for Health Economics and Policy Analysis (Tarride) and Department of Family Medicine (Paterson), McMaster University, Hamilton, Ont. muratos@mcmaster.ca. 2. Department of Health Research Methods, Evidence, and Impact (Muratov, Lee, Holbrook, Mbuagbaw, Tarride) and Divisions of Geriatric Medicine (Lee) and Clinical Pharmacology and Toxicology (Holbrook), Department of Medicine, McMaster University, Hamilton, Ont.; ICES (Paterson, Gomes, Khuu, Pequeno), Toronto, Ont.; Département de médecine sociale et préventive (Guertin), Faculté de médecine, and Centre de recherche du Centre hospitalier universitaire de Québec (Guertin), Axe Santé des populations et pratiques optimales en santé, Université Laval, Québec, Que.; Li Ka Shing Knowledge Institute (Gomes), St. Michael's Hospital, Toronto, Ont.; Centre for Health Economics and Policy Analysis (Tarride) and Department of Family Medicine (Paterson), McMaster University, Hamilton, Ont.
Abstract
BACKGROUND: Most health care spending is concentrated within a small group of high-cost health care users. To inform health policies, we examined the characteristics of index hospital admissions and their predictors among incident older high-cost users compared to older non-high-cost users in Ontario. METHODS: Using Ontario administrative data, we identified incident high-cost users aged 66 years or more and matched them 1:3 on age, gender and Local Health Integration Network with non-high-cost users aged 66 years or more. We defined high-cost users as patients within the top 5% most costly high-cost users during fiscal year 2013/14 but not during 2012/13. An index hospital admission, the main outcome, was defined as the first unplanned hospital admission during 2013/14, with no hospital admissions in the preceding 12 months. Descriptively, we analyzed the attributes of index hospital admissions, including costs. We identified predictors of index hospital admissions using stratified logistic regression. RESULTS: Over half (95 375/175 847 [54.2%]) of all high-cost users had an unplanned index hospital admission, compared to 8838/527 541 (1.7%) of non-high-cost users. High-cost users had a poorer health status, longer acute length of stay (mean 7.5 d v. 2.9 d) and more frequent designation as alternate level of care before discharge (20.8% v. 1.7%) than did non-high-cost users. Ten diagnosis codes accounted for roughly one-third of the index hospital admission costs in both cohorts. Although many predictors were similar between the cohorts, a lower risk of an index hospital admission was associated with residence in long-term care, attachment to a primary care provider and recent consultation by a geriatrician among high-cost users. INTERPRETATION: The high prevalence of index hospital admissions and the corresponding costs are a distinctive feature of incident older high-cost users. Improved access to specialist outpatient care, home-based social care and long-term care when required are worth further investigation. Copyright 2019, Joule Inc. or its licensors.
BACKGROUND: Most health care spending is concentrated within a small group of high-cost health care users. To inform health policies, we examined the characteristics of index hospital admissions and their predictors among incident older high-cost users compared to older non-high-cost users in Ontario. METHODS: Using Ontario administrative data, we identified incident high-cost users aged 66 years or more and matched them 1:3 on age, gender and Local Health Integration Network with non-high-cost users aged 66 years or more. We defined high-cost users as patients within the top 5% most costly high-cost users during fiscal year 2013/14 but not during 2012/13. An index hospital admission, the main outcome, was defined as the first unplanned hospital admission during 2013/14, with no hospital admissions in the preceding 12 months. Descriptively, we analyzed the attributes of index hospital admissions, including costs. We identified predictors of index hospital admissions using stratified logistic regression. RESULTS: Over half (95 375/175 847 [54.2%]) of all high-cost users had an unplanned index hospital admission, compared to 8838/527 541 (1.7%) of non-high-cost users. High-cost users had a poorer health status, longer acute length of stay (mean 7.5 d v. 2.9 d) and more frequent designation as alternate level of care before discharge (20.8% v. 1.7%) than did non-high-cost users. Ten diagnosis codes accounted for roughly one-third of the index hospital admission costs in both cohorts. Although many predictors were similar between the cohorts, a lower risk of an index hospital admission was associated with residence in long-term care, attachment to a primary care provider and recent consultation by a geriatrician among high-cost users. INTERPRETATION: The high prevalence of index hospital admissions and the corresponding costs are a distinctive feature of incident older high-cost users. Improved access to specialist outpatient care, home-based social care and long-term care when required are worth further investigation. Copyright 2019, Joule Inc. or its licensors.
Authors: Justin Lee; Sergei Muratov; Jean-Eric Tarride; J Michael Paterson; Kednapa Thavorn; Lawrence Mbuagbaw; Tara Gomes; Wayne Khuu; Hsien Seow; Lehana Thabane; Anne Holbrook Journal: CMAJ Open Date: 2021-01-11
Authors: Sergei Muratov; Justin Lee; Anne Holbrook; Jason Robert Guertin; Lawrence Mbuagbaw; John Michael Paterson; Tara Gomes; Priscila Pequeno; Jean-Eric Tarride Journal: BMJ Open Date: 2019-10-28 Impact factor: 2.692
Authors: Claudia Dziegielewski; Robert Talarico; Haris Imsirovic; Danial Qureshi; Yasmeen Choudhri; Peter Tanuseputro; Laura H Thompson; Kwadwo Kyeremanteng Journal: BMC Health Serv Res Date: 2021-12-06 Impact factor: 2.655
Authors: Shelley-Ann M Girwar; Robert Jabroer; Marta Fiocco; Stephen P Sutch; Mattijs E Numans; Marc A Bruijnzeels Journal: Health Sci Rep Date: 2021-07-23