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Defining 'actionable' high- costhealth care use: results using the Canadian Institute for Health Information population grouping methodology
A small proportion of the population consumes the majority of health care resources. High-cost health care users are a heterogeneous group. We aim to segment a provincial population into relevant homogenous sub-groups to provide actionable information on risk factors associated with high-cost health...
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Published in: | International journal for equity in health 2019-11, Vol.18 (1), p.171-171, Article 171 |
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description | A small proportion of the population consumes the majority of health care resources. High-cost health care users are a heterogeneous group. We aim to segment a provincial population into relevant homogenous sub-groups to provide actionable information on risk factors associated with high-cost health care use within sub-populations.
The Canadian Institute for Health Information (CIHI) Population Grouping methodology was used to define mutually exclusive and clinically relevant health profile sub-groups. High-cost users (> = 90th percentile of health care spending) were defined within each sub-group. Univariate analyses explored demographic, socio-economic status, health status and health care utilization variables associated with high-cost use. Multivariable logistic regression models were constructed for the costliest health profile groups.
From 2015 to 2017, 1,175,147 individuals were identified for study. High-cost users consumed 41% of total health care resources. Average annual health care spending for individuals not high-cost were $642; high-cost users were $16,316. The costliest health profile groups were 'long-term care', 'palliative', 'major acute', 'major chronic', 'major cancer', 'major newborn', 'major mental health' and 'moderate chronic'. Both 'major acute' and 'major cancer' health profile groups were largely explained by measures of health care utilization and multi-morbidity. In the remaining costliest health profile groups modelled, 'major chronic', 'moderate chronic', 'major newborn' and 'other mental health', a measure of socio-economic status, low neighbourhood income, was statistically significantly associated with high-cost use.
Model results point to specific, actionable information within clinically meaningful subgroups to reduce high-cost health care use. Health equity, specifically low socio-economic status, was statistically significantly associated with high-cost use in the majority of health profile sub-groups. Population segmentation methods, and more specifically, the CIHI Population Grouping Methodology, provide specificity to high-cost health care use; informing interventions aimed at reducing health care costs and improving population health. |
doi_str_mv | 10.1186/s12939-019-1074-3 |
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The Canadian Institute for Health Information (CIHI) Population Grouping methodology was used to define mutually exclusive and clinically relevant health profile sub-groups. High-cost users (> = 90th percentile of health care spending) were defined within each sub-group. Univariate analyses explored demographic, socio-economic status, health status and health care utilization variables associated with high-cost use. Multivariable logistic regression models were constructed for the costliest health profile groups.
From 2015 to 2017, 1,175,147 individuals were identified for study. High-cost users consumed 41% of total health care resources. Average annual health care spending for individuals not high-cost were $642; high-cost users were $16,316. The costliest health profile groups were 'long-term care', 'palliative', 'major acute', 'major chronic', 'major cancer', 'major newborn', 'major mental health' and 'moderate chronic'. Both 'major acute' and 'major cancer' health profile groups were largely explained by measures of health care utilization and multi-morbidity. In the remaining costliest health profile groups modelled, 'major chronic', 'moderate chronic', 'major newborn' and 'other mental health', a measure of socio-economic status, low neighbourhood income, was statistically significantly associated with high-cost use.
Model results point to specific, actionable information within clinically meaningful subgroups to reduce high-cost health care use. Health equity, specifically low socio-economic status, was statistically significantly associated with high-cost use in the majority of health profile sub-groups. Population segmentation methods, and more specifically, the CIHI Population Grouping Methodology, provide specificity to high-cost health care use; informing interventions aimed at reducing health care costs and improving population health.</description><identifier>ISSN: 1475-9276</identifier><identifier>EISSN: 1475-9276</identifier><identifier>DOI: 10.1186/s12939-019-1074-3</identifier><identifier>PMID: 31707981</identifier><language>eng</language><publisher>England: BioMed Central Ltd</publisher><subject>Adolescent ; Adult ; Aged ; Aged, 80 and over ; Analysis ; Canada ; Demography ; Female ; Health Care Costs - statistics & numerical data ; Health care reform ; Health Status ; Humans ; Long term care ; Male ; Medical care utilization ; Medical economics ; Methods ; Middle Aged ; Morbidity ; Newborn infants ; Patient Acceptance of Health Care - statistics & numerical data ; Population health ; Risk Factors ; Social class ; Socioeconomic Factors ; Young Adult</subject><ispartof>International journal for equity in health, 2019-11, Vol.18 (1), p.171-171, Article 171</ispartof><rights>COPYRIGHT 2019 BioMed Central Ltd.</rights><rights>The Author(s). 2019</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c566t-f625a0459b29ba07757431280b08773c83656a9606d120f9a722487e352e2053</citedby><cites>FETCH-LOGICAL-c566t-f625a0459b29ba07757431280b08773c83656a9606d120f9a722487e352e2053</cites><orcidid>0000-0002-6487-2620</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6842471/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6842471/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,27924,27925,37013,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31707981$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Anderson, Maureen</creatorcontrib><creatorcontrib>Revie, Crawford W</creatorcontrib><creatorcontrib>Stryhn, Henrik</creatorcontrib><creatorcontrib>Neudorf, Cordell</creatorcontrib><creatorcontrib>Rosehart, Yvonne</creatorcontrib><creatorcontrib>Li, Wenbin</creatorcontrib><creatorcontrib>Osman, Meriç</creatorcontrib><creatorcontrib>Buckeridge, David L</creatorcontrib><creatorcontrib>Rosella, Laura C</creatorcontrib><creatorcontrib>Wodchis, Walter P</creatorcontrib><title>Defining 'actionable' high- costhealth care use: results using the Canadian Institute for Health Information population grouping methodology</title><title>International journal for equity in health</title><addtitle>Int J Equity Health</addtitle><description>A small proportion of the population consumes the majority of health care resources. High-cost health care users are a heterogeneous group. We aim to segment a provincial population into relevant homogenous sub-groups to provide actionable information on risk factors associated with high-cost health care use within sub-populations.
The Canadian Institute for Health Information (CIHI) Population Grouping methodology was used to define mutually exclusive and clinically relevant health profile sub-groups. High-cost users (> = 90th percentile of health care spending) were defined within each sub-group. Univariate analyses explored demographic, socio-economic status, health status and health care utilization variables associated with high-cost use. Multivariable logistic regression models were constructed for the costliest health profile groups.
From 2015 to 2017, 1,175,147 individuals were identified for study. High-cost users consumed 41% of total health care resources. Average annual health care spending for individuals not high-cost were $642; high-cost users were $16,316. The costliest health profile groups were 'long-term care', 'palliative', 'major acute', 'major chronic', 'major cancer', 'major newborn', 'major mental health' and 'moderate chronic'. Both 'major acute' and 'major cancer' health profile groups were largely explained by measures of health care utilization and multi-morbidity. In the remaining costliest health profile groups modelled, 'major chronic', 'moderate chronic', 'major newborn' and 'other mental health', a measure of socio-economic status, low neighbourhood income, was statistically significantly associated with high-cost use.
Model results point to specific, actionable information within clinically meaningful subgroups to reduce high-cost health care use. Health equity, specifically low socio-economic status, was statistically significantly associated with high-cost use in the majority of health profile sub-groups. Population segmentation methods, and more specifically, the CIHI Population Grouping Methodology, provide specificity to high-cost health care use; informing interventions aimed at reducing health care costs and improving population health.</description><subject>Adolescent</subject><subject>Adult</subject><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Analysis</subject><subject>Canada</subject><subject>Demography</subject><subject>Female</subject><subject>Health Care Costs - statistics & numerical data</subject><subject>Health care reform</subject><subject>Health Status</subject><subject>Humans</subject><subject>Long term care</subject><subject>Male</subject><subject>Medical care utilization</subject><subject>Medical economics</subject><subject>Methods</subject><subject>Middle Aged</subject><subject>Morbidity</subject><subject>Newborn infants</subject><subject>Patient Acceptance of Health Care - statistics & numerical data</subject><subject>Population health</subject><subject>Risk Factors</subject><subject>Social class</subject><subject>Socioeconomic Factors</subject><subject>Young Adult</subject><issn>1475-9276</issn><issn>1475-9276</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>DOA</sourceid><recordid>eNptkl1r1jAYhosobk5_gCcS8GB60JmvJq0Hwpgfe2Eg6M7D0zRtM9rkNUnF_Qd_tKmdYy9IDvIkz31f5OMuipcEnxFSi3eR0IY1JSZNSbDkJXtUHBMuq7KhUjx-UB8Vz2K8wZjIWsinxREjEsumJsfF74-mt866AZ2CTtY7aCdzikY7jCXSPqbRwJRGpCEYtETzHgUTlynFvFhduY8uwEFnwaGdi8mmJRnU-4AuN-fO5cUMKxvt_X6ZtnIIftmvhNmk0Xd-8sPt8-JJD1M0L-7mk-L686fri8vy6uuX3cX5VakrIVLZC1oB5lXT0qYFLGUlOSO0xi2upWS6ZqIS0AgsOkJx34CklNfSsIoaiit2Uuw2bOfhRu2DnSHcKg9W_d3wYVAQktWTURXrWtH2spe05aTWraa1oZ3sOBCBKc-sDxtrv7Sz6bRxKcB0AD3sODuqwf9UouaUS5IBb-4Awf9YTExqtlGbaQJn_BIVZYQJgbnEWfp6kw6Qj2bzu2aiXuXqXGDJBG64yKqz_6jy6MxstXf5v_P-geHtgSFrkvmVBlhiVLvv3w61ZNPq4GMMpr-_KcFqzaTaMqlyJtWaScWy59XDJ7p3_Ash-wME1dwP</recordid><startdate>20191110</startdate><enddate>20191110</enddate><creator>Anderson, Maureen</creator><creator>Revie, Crawford W</creator><creator>Stryhn, Henrik</creator><creator>Neudorf, Cordell</creator><creator>Rosehart, Yvonne</creator><creator>Li, Wenbin</creator><creator>Osman, Meriç</creator><creator>Buckeridge, David L</creator><creator>Rosella, Laura C</creator><creator>Wodchis, Walter P</creator><general>BioMed Central Ltd</general><general>BioMed Central</general><general>BMC</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>ISR</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-6487-2620</orcidid></search><sort><creationdate>20191110</creationdate><title>Defining 'actionable' high- costhealth care use: results using the Canadian Institute for Health Information population grouping methodology</title><author>Anderson, Maureen ; Revie, Crawford W ; Stryhn, Henrik ; Neudorf, Cordell ; Rosehart, Yvonne ; Li, Wenbin ; Osman, Meriç ; Buckeridge, David L ; Rosella, Laura C ; Wodchis, Walter P</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c566t-f625a0459b29ba07757431280b08773c83656a9606d120f9a722487e352e2053</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Adolescent</topic><topic>Adult</topic><topic>Aged</topic><topic>Aged, 80 and over</topic><topic>Analysis</topic><topic>Canada</topic><topic>Demography</topic><topic>Female</topic><topic>Health Care Costs - statistics & numerical data</topic><topic>Health care reform</topic><topic>Health Status</topic><topic>Humans</topic><topic>Long term care</topic><topic>Male</topic><topic>Medical care utilization</topic><topic>Medical economics</topic><topic>Methods</topic><topic>Middle Aged</topic><topic>Morbidity</topic><topic>Newborn infants</topic><topic>Patient Acceptance of Health Care - statistics & numerical data</topic><topic>Population health</topic><topic>Risk Factors</topic><topic>Social class</topic><topic>Socioeconomic Factors</topic><topic>Young Adult</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Anderson, Maureen</creatorcontrib><creatorcontrib>Revie, Crawford W</creatorcontrib><creatorcontrib>Stryhn, Henrik</creatorcontrib><creatorcontrib>Neudorf, Cordell</creatorcontrib><creatorcontrib>Rosehart, Yvonne</creatorcontrib><creatorcontrib>Li, Wenbin</creatorcontrib><creatorcontrib>Osman, Meriç</creatorcontrib><creatorcontrib>Buckeridge, David L</creatorcontrib><creatorcontrib>Rosella, Laura C</creatorcontrib><creatorcontrib>Wodchis, Walter P</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Science</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>International journal for equity in health</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Anderson, Maureen</au><au>Revie, Crawford W</au><au>Stryhn, Henrik</au><au>Neudorf, Cordell</au><au>Rosehart, Yvonne</au><au>Li, Wenbin</au><au>Osman, Meriç</au><au>Buckeridge, David L</au><au>Rosella, Laura C</au><au>Wodchis, Walter P</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Defining 'actionable' high- costhealth care use: results using the Canadian Institute for Health Information population grouping methodology</atitle><jtitle>International journal for equity in health</jtitle><addtitle>Int J Equity Health</addtitle><date>2019-11-10</date><risdate>2019</risdate><volume>18</volume><issue>1</issue><spage>171</spage><epage>171</epage><pages>171-171</pages><artnum>171</artnum><issn>1475-9276</issn><eissn>1475-9276</eissn><abstract>A small proportion of the population consumes the majority of health care resources. High-cost health care users are a heterogeneous group. We aim to segment a provincial population into relevant homogenous sub-groups to provide actionable information on risk factors associated with high-cost health care use within sub-populations.
The Canadian Institute for Health Information (CIHI) Population Grouping methodology was used to define mutually exclusive and clinically relevant health profile sub-groups. High-cost users (> = 90th percentile of health care spending) were defined within each sub-group. Univariate analyses explored demographic, socio-economic status, health status and health care utilization variables associated with high-cost use. Multivariable logistic regression models were constructed for the costliest health profile groups.
From 2015 to 2017, 1,175,147 individuals were identified for study. High-cost users consumed 41% of total health care resources. Average annual health care spending for individuals not high-cost were $642; high-cost users were $16,316. The costliest health profile groups were 'long-term care', 'palliative', 'major acute', 'major chronic', 'major cancer', 'major newborn', 'major mental health' and 'moderate chronic'. Both 'major acute' and 'major cancer' health profile groups were largely explained by measures of health care utilization and multi-morbidity. In the remaining costliest health profile groups modelled, 'major chronic', 'moderate chronic', 'major newborn' and 'other mental health', a measure of socio-economic status, low neighbourhood income, was statistically significantly associated with high-cost use.
Model results point to specific, actionable information within clinically meaningful subgroups to reduce high-cost health care use. Health equity, specifically low socio-economic status, was statistically significantly associated with high-cost use in the majority of health profile sub-groups. Population segmentation methods, and more specifically, the CIHI Population Grouping Methodology, provide specificity to high-cost health care use; informing interventions aimed at reducing health care costs and improving population health.</abstract><cop>England</cop><pub>BioMed Central Ltd</pub><pmid>31707981</pmid><doi>10.1186/s12939-019-1074-3</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0002-6487-2620</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Adolescent Adult Aged Aged, 80 and over Analysis Canada Demography Female Health Care Costs - statistics & numerical data Health care reform Health Status Humans Long term care Male Medical care utilization Medical economics Methods Middle Aged Morbidity Newborn infants Patient Acceptance of Health Care - statistics & numerical data Population health Risk Factors Social class Socioeconomic Factors Young Adult |
title | Defining 'actionable' high- costhealth care use: results using the Canadian Institute for Health Information population grouping methodology |
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