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Multimorbidity patterns in the elderly: a prospective cohort study with cluster analysis

Overview of attention for article published in BMC Geriatrics, January 2018
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (90th percentile)

Mentioned by

32 tweeters


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100 Mendeley
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Multimorbidity patterns in the elderly: a prospective cohort study with cluster analysis
Published in
BMC Geriatrics, January 2018
DOI 10.1186/s12877-018-0705-7
Pubmed ID

Marina Guisado-Clavero, Albert Roso-Llorach, Tomàs López-Jimenez, Mariona Pons-Vigués, Quintí Foguet-Boreu, Miguel Angel Muñoz, Concepción Violán


Multimorbidity is the coexistence of more than two chronic diseases in the same individual; however, there is no consensus about the best definition. In addition, few studies have described the variability of multimorbidity patterns over time. The aim of this study was to identify multimorbidity patterns and their variability over a 6-year period in patients older than 65 years attended in primary health care. A cohort study with yearly cross-sectional analysis of electronic health records from 50 primary health care centres in Barcelona. Selected patients had multimorbidity and were 65 years of age or older in 2009. Diagnoses (International Classification of Primary Care, second edition) were extracted using O'Halloran criteria for chronic diseases. Multimorbidity patterns were identified using two steps: 1) multiple correspondence analysis and 2) k-means clustering. Analysis was stratified by sex and age group (65-79 and ≥80 years) at the beginning of the study period. Analysis of 2009 electronic health records from 190,108 patients with multimorbidity (59.8% women) found a mean age of 71.8 for the 65-79 age group and 84.16 years for those over 80 (Standard Deviation [SD] 4.35 and 3.46, respectively); the median number of chronic diseases was seven (Interquartil range [IQR] 5-10). We obtained 6 clusters of multimorbidity patterns (1 nonspecific and 5 specifics) in each group, being the specific ones: Musculoskeletal, Endocrine-metabolic, Digestive/Digestive-respiratory, Neurological, and Cardiovascular patterns. A minimum of 42.5% of the sample remained in the same pattern at the end of the study, reflecting the stability of these patterns. This study identified six multimorbidity patterns per each group, one nonnspecific pattern and five of them with a specific pattern related to an organic system. The multimorbidity patterns obtained had similar characteristics throughout the study period. These data are useful to improve clinical management of each specific subgroup of patients showing a particular multimorbidity pattern.

Twitter Demographics

The data shown below were collected from the profiles of 32 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 100 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 100 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 19 19%
Student > Ph. D. Student 18 18%
Student > Master 15 15%
Other 8 8%
Student > Doctoral Student 6 6%
Other 19 19%
Unknown 15 15%
Readers by discipline Count As %
Medicine and Dentistry 31 31%
Nursing and Health Professions 11 11%
Social Sciences 5 5%
Agricultural and Biological Sciences 4 4%
Neuroscience 2 2%
Other 15 15%
Unknown 32 32%

Attention Score in Context

This research output has an Altmetric Attention Score of 21. 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 06 May 2018.
All research outputs
of 12,896,547 outputs
Outputs from BMC Geriatrics
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Outputs of similar age
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Outputs of similar age from BMC Geriatrics
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Altmetric has tracked 12,896,547 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,329 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.2. This one has done particularly well, scoring higher than 92% 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 344,274 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 90% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them