Please use this identifier to cite or link to this item: http://hdl.handle.net/11434/1159
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dc.contributor.authorMcKenzie, Dean-
dc.contributor.authorFahey, Michael-
dc.contributor.authorGwini, Stella-
dc.contributor.authorPonsford, Jennie-
dc.contributor.authorDowning, Marina-
dc.contributor.authorSultana, Ron-
dc.contributor.authorBarrett, Jonathan-
dc.contributor.authorMcConchie, Steven-
dc.contributor.otherDipnall, Joanna-
dc.date.accessioned2017-07-19T01:37:06Z-
dc.date.available2017-07-19T01:37:06Z-
dc.date.issued2017-06-
dc.identifier.citationEpworth Research Institute Research Week 2017; Poster 26: pp 50en_US
dc.identifier.urihttp://hdl.handle.net/11434/1159-
dc.description.abstractINTRODUCTION: Coined in the1970s by physician, epidemiologist and applied statistician Prof. Alvan Feinstein MD (1925-2001), the term "comorbidity" may be defined as the presence of two or more disorders, such as alcohol use disorder and depression, or colorectal cancer and chronic obstructive pulmonary disease. Comorbidity or "multimorbidity" is generally associated with increased use of hospital services and poorer prognosis, due to additive (higher number of disorders generally indicates poorer health) and multiplicative (synergistic or interactive) relationships between the disorders. An example of such a synergistic relationship is patients with lower limb injuries and comorbid diabetes mellitus not being able to manage and control the latter, due to restricted movement. Instruments based upon weighted counts of chronic disorders such as the Charlson, and Elixhauser indices are predictive of mortality and other adverse outcomes but provide little information on specific synergistic co-relationships between comorbid disorders. Various regression methods, as well as decision trees (a favourite tool of Dr. Feinstein), can quantify such relationships and predict outcome, however there are very few methods of graphically representing co-morbidity. AIMS: To present a simple and easy to understand method of graphing comorbidity. METHADOLOGY: "Heat maps", a twentieth century development of a nineteenth century concept, can show in vivid colour or in monochrome the overall strength of relationships between two or more disorders in a sample of patients. Heat maps are less complex than network graphs, provide more information than pie charts and Venn diagrams and are readily implemented in R, SPSS, Stata and Microsoft Excel. Graphs such as heat maps allow effective communication with clinicians and may act as a conduit to further analysis, including factor analysis, latent class analysis, and self-organizing maps. RESULTS: Illustrative published medical data and custom charts and graphs will be presented. [See Poster].en_US
dc.subjectComorbidityen_US
dc.subjectMultimorbidityen_US
dc.subjectComorbid Disordersen_US
dc.subjectSynergistic Relationshipsen_US
dc.subjectGraphic Representationen_US
dc.subjectGraphsen_US
dc.subjectStatisticsen_US
dc.subjectRegression Methodsen_US
dc.subjectDecisions Treesen_US
dc.subjectHeat Mapsen_US
dc.subjectCharlson Comorbidity Indexen_US
dc.subjectElixhauser Comorbidity Indexen_US
dc.subjectSPSSen_US
dc.subjectStata Softwareen_US
dc.subjectMicrosoft Excelen_US
dc.subjectR (Programming Language)en_US
dc.subjectCommunicationen_US
dc.subjectFactor Analysisen_US
dc.subjectLatent Class Analysisen_US
dc.subjectSelf-Organising Mapsen_US
dc.subjectIllustrative Medical Dataen_US
dc.subjectIntensive Care Unit, Epworth HealthCare, Victoria, Australia.en_US
dc.subjectClinical Institutes and Medical Audit, Epworth HealthCare, Victoria, Australiaen_US
dc.subjectEpworth Prostate Centre, Epworth Healthcare, Victoria, Australiaen_US
dc.subjectEmergency Department, Epworth HealthCare, Melbourne, Victoria, Australiaen_US
dc.subjectEpworth Research Institute, Epworth HealthCare, Victoria, Australiaen_US
dc.titleGraphing comorbidity.en_US
dc.typeConference Posteren_US
dc.description.affiliatesDepartment of Statistics, Data Science and Epidemiology at Swinburne University of Technologyen_US
dc.description.affiliatesMonash Universityen_US
dc.description.affiliatesDeakin Universityen_US
dc.type.studyortrialReviewen_US
dc.description.conferencenameEpworth Research Institute Research Week 2017en_US
dc.description.conferencelocationEpworth HealthCare Research Institute, Victoria, Australiaen_US
dc.type.contenttypeTexten_US
Appears in Collections:Health Informatics
Research Week

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