Multimorbidity and Hospital Admissions in High-Need, High-Cost Elderly Patients
Objective: The aim was to clarify which pairs or clusters of diseases predict the hospital-related events and death in a population of patients with complex health care needs (PCHCN). Method: Subjects classified in 2012 as PCHCN in a local health unit by ACG® (Adjusted Clinical Groups) System were l...
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| Veröffentlicht in: | Journal of aging and health 2020-06, Vol.32 (5-6), p.259-268 |
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| container_issue | 5-6 |
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| container_title | Journal of aging and health |
| container_volume | 32 |
| creator | Buja, Alessandra Rivera, Michele De Battisti, Elisa Corti, Maria Chiara Avossa, Francesco Schievano, Elena Rigon, Stefano Baldo, Vincenzo Boccuzzo, Giovanna Ebell, Mark H. |
| description | Objective: The aim was to clarify which pairs or clusters of diseases predict the hospital-related events and death in a population of patients with complex health care needs (PCHCN). Method: Subjects classified in 2012 as PCHCN in a local health unit by ACG® (Adjusted Clinical Groups) System were linked with hospital discharge records in 2013 to identify those who experienced any of a series of hospital admission events and death. Number of comorbidities, comorbidities dyads, and latent classes were used as exposure variable. Regression analyses were applied to examine the associations between dependent and exposure variables. Results: Besides the fact that larger number of chronic conditions is associated with higher odds of hospital admission or death, we showed that certain dyads and classes of diseases have a particularly strong association with these outcomes. Discussion: Unlike morbidity counts, analyzing morbidity clusters and dyads reveals which combinations of morbidities are associated with the highest hospitalization rates or death. |
| doi_str_mv | 10.1177/0898264318817091 |
| format | Article |
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Method: Subjects classified in 2012 as PCHCN in a local health unit by ACG® (Adjusted Clinical Groups) System were linked with hospital discharge records in 2013 to identify those who experienced any of a series of hospital admission events and death. Number of comorbidities, comorbidities dyads, and latent classes were used as exposure variable. Regression analyses were applied to examine the associations between dependent and exposure variables. Results: Besides the fact that larger number of chronic conditions is associated with higher odds of hospital admission or death, we showed that certain dyads and classes of diseases have a particularly strong association with these outcomes. Discussion: Unlike morbidity counts, analyzing morbidity clusters and dyads reveals which combinations of morbidities are associated with the highest hospitalization rates or death.</description><identifier>ISSN: 0898-2643</identifier><identifier>EISSN: 1552-6887</identifier><identifier>DOI: 10.1177/0898264318817091</identifier><identifier>PMID: 30522388</identifier><language>eng</language><publisher>Los Angeles, CA: SAGE Publications</publisher><subject>Aged ; Aged, 80 and over ; Chronic Disease - classification ; Chronic Disease - epidemiology ; Chronic Disease - mortality ; Comorbidity ; Female ; Frail Elderly - statistics & numerical data ; Health technology assessment ; Hospitalization - statistics & numerical data ; Humans ; Italy - epidemiology ; Latent Class Analysis ; Male ; Multimorbidity ; National Health Programs ; Older people ; Regression Analysis</subject><ispartof>Journal of aging and health, 2020-06, Vol.32 (5-6), p.259-268</ispartof><rights>The Author(s) 2018</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c407t-b833ff0e62298751a026da22476cd19e924839f8ba32cd9b4b188dfadf005e73</citedby><cites>FETCH-LOGICAL-c407t-b833ff0e62298751a026da22476cd19e924839f8ba32cd9b4b188dfadf005e73</cites><orcidid>0000-0003-2143-7730 ; 0000-0001-6217-9948</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://journals.sagepub.com/doi/pdf/10.1177/0898264318817091$$EPDF$$P50$$Gsage$$H</linktopdf><linktohtml>$$Uhttps://journals.sagepub.com/doi/10.1177/0898264318817091$$EHTML$$P50$$Gsage$$H</linktohtml><link.rule.ids>314,776,780,21800,27903,27904,43600,43601</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30522388$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Buja, Alessandra</creatorcontrib><creatorcontrib>Rivera, Michele</creatorcontrib><creatorcontrib>De Battisti, Elisa</creatorcontrib><creatorcontrib>Corti, Maria Chiara</creatorcontrib><creatorcontrib>Avossa, Francesco</creatorcontrib><creatorcontrib>Schievano, Elena</creatorcontrib><creatorcontrib>Rigon, Stefano</creatorcontrib><creatorcontrib>Baldo, Vincenzo</creatorcontrib><creatorcontrib>Boccuzzo, Giovanna</creatorcontrib><creatorcontrib>Ebell, Mark H.</creatorcontrib><title>Multimorbidity and Hospital Admissions in High-Need, High-Cost Elderly Patients</title><title>Journal of aging and health</title><addtitle>J Aging Health</addtitle><description>Objective: The aim was to clarify which pairs or clusters of diseases predict the hospital-related events and death in a population of patients with complex health care needs (PCHCN). 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Rivera, Michele ; De Battisti, Elisa ; Corti, Maria Chiara ; Avossa, Francesco ; Schievano, Elena ; Rigon, Stefano ; Baldo, Vincenzo ; Boccuzzo, Giovanna ; Ebell, Mark H.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c407t-b833ff0e62298751a026da22476cd19e924839f8ba32cd9b4b188dfadf005e73</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Aged</topic><topic>Aged, 80 and over</topic><topic>Chronic Disease - classification</topic><topic>Chronic Disease - epidemiology</topic><topic>Chronic Disease - mortality</topic><topic>Comorbidity</topic><topic>Female</topic><topic>Frail Elderly - statistics & numerical data</topic><topic>Health technology assessment</topic><topic>Hospitalization - statistics & numerical data</topic><topic>Humans</topic><topic>Italy - epidemiology</topic><topic>Latent Class Analysis</topic><topic>Male</topic><topic>Multimorbidity</topic><topic>National Health Programs</topic><topic>Older people</topic><topic>Regression Analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Buja, Alessandra</creatorcontrib><creatorcontrib>Rivera, Michele</creatorcontrib><creatorcontrib>De Battisti, Elisa</creatorcontrib><creatorcontrib>Corti, Maria Chiara</creatorcontrib><creatorcontrib>Avossa, Francesco</creatorcontrib><creatorcontrib>Schievano, Elena</creatorcontrib><creatorcontrib>Rigon, Stefano</creatorcontrib><creatorcontrib>Baldo, Vincenzo</creatorcontrib><creatorcontrib>Boccuzzo, Giovanna</creatorcontrib><creatorcontrib>Ebell, Mark H.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Premium</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of aging and health</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Buja, Alessandra</au><au>Rivera, Michele</au><au>De Battisti, Elisa</au><au>Corti, Maria Chiara</au><au>Avossa, Francesco</au><au>Schievano, Elena</au><au>Rigon, Stefano</au><au>Baldo, Vincenzo</au><au>Boccuzzo, Giovanna</au><au>Ebell, Mark H.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Multimorbidity and Hospital Admissions in High-Need, High-Cost Elderly Patients</atitle><jtitle>Journal of aging and health</jtitle><addtitle>J Aging Health</addtitle><date>2020-06</date><risdate>2020</risdate><volume>32</volume><issue>5-6</issue><spage>259</spage><epage>268</epage><pages>259-268</pages><issn>0898-2643</issn><eissn>1552-6887</eissn><abstract>Objective: The aim was to clarify which pairs or clusters of diseases predict the hospital-related events and death in a population of patients with complex health care needs (PCHCN). Method: Subjects classified in 2012 as PCHCN in a local health unit by ACG® (Adjusted Clinical Groups) System were linked with hospital discharge records in 2013 to identify those who experienced any of a series of hospital admission events and death. Number of comorbidities, comorbidities dyads, and latent classes were used as exposure variable. Regression analyses were applied to examine the associations between dependent and exposure variables. Results: Besides the fact that larger number of chronic conditions is associated with higher odds of hospital admission or death, we showed that certain dyads and classes of diseases have a particularly strong association with these outcomes. 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| subjects | Aged Aged, 80 and over Chronic Disease - classification Chronic Disease - epidemiology Chronic Disease - mortality Comorbidity Female Frail Elderly - statistics & numerical data Health technology assessment Hospitalization - statistics & numerical data Humans Italy - epidemiology Latent Class Analysis Male Multimorbidity National Health Programs Older people Regression Analysis |
| title | Multimorbidity and Hospital Admissions in High-Need, High-Cost Elderly Patients |
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