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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

Overview of attention for article published in Health and Quality of Life Outcomes, January 2017
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  • Good Attention Score compared to outputs of the same age (65th percentile)
  • High Attention Score compared to outputs of the same age and source (82nd percentile)

Mentioned by

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4 tweeters
facebook
1 Facebook page
reddit
1 Redditor

Citations

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26 Dimensions

Readers on

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86 Mendeley
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Title
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
Published in
Health and Quality of Life Outcomes, January 2017
DOI 10.1186/s12955-016-0580-x
Pubmed ID
Authors

Lili Wang, Andrew J Palmer, Fiona Cocker, Kristy Sanderson

Abstract

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. 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. 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). 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.

Twitter Demographics

The data shown below were collected from the profiles of 4 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 86 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Croatia 1 1%
Denmark 1 1%
Nigeria 1 1%
Unknown 83 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 16%
Student > Master 12 14%
Student > Postgraduate 11 13%
Researcher 9 10%
Student > Doctoral Student 8 9%
Other 16 19%
Unknown 16 19%
Readers by discipline Count As %
Medicine and Dentistry 28 33%
Nursing and Health Professions 12 14%
Agricultural and Biological Sciences 4 5%
Psychology 4 5%
Social Sciences 3 3%
Other 11 13%
Unknown 24 28%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 27 April 2018.
All research outputs
#4,795,474
of 15,442,255 outputs
Outputs from Health and Quality of Life Outcomes
#570
of 1,660 outputs
Outputs of similar age
#132,113
of 385,405 outputs
Outputs of similar age from Health and Quality of Life Outcomes
#44
of 259 outputs
Altmetric has tracked 15,442,255 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 1,660 research outputs from this source. They receive a mean Attention Score of 4.0. This one has gotten more attention than average, scoring higher than 65% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 385,405 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 65% of its contemporaries.
We're also able to compare this research output to 259 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.