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dc.contributor.authorWang, L
dc.contributor.authorPalmer, AJ
dc.contributor.authorCocker, F
dc.contributor.authorSanderson, K
dc.date.accessioned2020-12-21T04:30:21Z
dc.date.available2020-12-21T04:30:21Z
dc.date.issued2017-01-09
dc.identifierpii: 10.1186/s12955-016-0580-x
dc.identifier.citationWang, L., Palmer, A. J., Cocker, F. & Sanderson, K. (2017). Multimorbidity and health-related quality of life (HRQoL) in a nationally representative population sample: implications of count versus cluster method for defining multimorbidity on HRQoL.. Health Qual Life Outcomes, 15 (1), pp.7-. https://doi.org/10.1186/s12955-016-0580-x.
dc.identifier.issn1477-7525
dc.identifier.urihttp://hdl.handle.net/11343/257651
dc.description.abstractBACKGROUND: No universally accepted definition of multimorbidity (MM) exists, and implications of different definitions have not been explored. This study examined the performance of the count and cluster definitions of multimorbidity on the sociodemographic profile and health-related quality of life (HRQoL) in a general population. METHODS: Data were derived from the nationally representative 2007 Australian National Survey of Mental Health and Wellbeing (n = 8841). The HRQoL scores were measured using the Assessment of Quality of Life (AQoL-4D) instrument. The simple count (2+ & 3+ conditions) and hierarchical cluster methods were used to define/identify clusters of multimorbidity. Linear regression was used to assess the associations between HRQoL and multimorbidity as defined by the different methods. RESULTS: The assessment of multimorbidity, which was defined using the count method, resulting in the prevalence of 26% (MM2+) and 10.1% (MM3+). Statistically significant clusters identified through hierarchical cluster analysis included heart or circulatory conditions (CVD)/arthritis (cluster-1, 9%) and major depressive disorder (MDD)/anxiety (cluster-2, 4%). A sensitivity analysis suggested that the stability of the clusters resulted from hierarchical clustering. The sociodemographic profiles were similar between MM2+, MM3+ and cluster-1, but were different from cluster-2. HRQoL was negatively associated with MM2+ (β: -0.18, SE: -0.01, p < 0.001), MM3+ (β: -0.23, SE: -0.02, p < 0.001), cluster-1 (β: -0.10, SE: 0.01, p < 0.001) and cluster-2 (β: -0.36, SE: 0.01, p < 0.001). CONCLUSIONS: Our findings confirm the existence of an inverse relationship between multimorbidity and HRQoL in the Australian population and indicate that the hierarchical clustering approach is validated when the outcome of interest is HRQoL from this head-to-head comparison. Moreover, a simple count fails to identify if there are specific conditions of interest that are driving poorer HRQoL. Researchers should exercise caution when selecting a definition of multimorbidity because it may significantly influence the study outcomes.
dc.languageeng
dc.publisherSpringer Science and Business Media LLC
dc.titleMultimorbidity and health-related quality of life (HRQoL) in a nationally representative population sample: implications of count versus cluster method for defining multimorbidity on HRQoL.
dc.typeJournal Article
dc.identifier.doi10.1186/s12955-016-0580-x
melbourne.affiliation.departmentMelbourne School of Population and Global Health
melbourne.source.titleHealth and Quality of Life Outcomes
melbourne.source.volume15
melbourne.source.issue1
melbourne.source.pages7-
dc.rights.licenseCC BY
melbourne.elementsid1285937
melbourne.openaccess.pmchttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC5223532
melbourne.contributor.authorPalmer, Andrew
dc.identifier.eissn1477-7525
melbourne.accessrightsOpen Access


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