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Frailty and the prediction of dependence and mortality in low- and middle-income countries: a 10/66 population-based cohort study

Overview of attention for article published in BMC Medicine, June 2015
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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 (84th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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2 policy sources
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2 X users
facebook
1 Facebook page
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1 Wikipedia page

Citations

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110 Dimensions

Readers on

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235 Mendeley
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Title
Frailty and the prediction of dependence and mortality in low- and middle-income countries: a 10/66 population-based cohort study
Published in
BMC Medicine, June 2015
DOI 10.1186/s12916-015-0378-4
Pubmed ID
Authors

Jotheeswaran AT, Renata Bryce, Matthew Prina, Daisy Acosta, Cleusa P Ferri, Mariella Guerra, Yueqin Huang, Juan J. Llibre Rodriguez, Aquiles Salas, Ana Luisa Sosa, Joseph D. Williams, Michael E. Dewey, Isaac Acosta, Zhaorui Liu, John Beard, Martin Prince

Abstract

In countries with high incomes, frailty indicators predict adverse outcomes in older people, despite a lack of consensus on definition or measurement. We tested the predictive validity of physical and multidimensional frailty phenotypes in settings in Latin America, India, and China. Population-based cohort studies were conducted in catchment area sites in Cuba, Dominican Republic, Venezuela, Mexico, Peru, India, and China. Seven frailty indicators, namely gait speed, self-reported exhaustion, weight loss, low energy expenditure, undernutrition, cognitive, and sensory impairment were assessed to estimate frailty phenotypes. Mortality and onset of dependence were ascertained after a median of 3.9 years. Overall, 13,924 older people were assessed at baseline, with 47,438 person-years follow-up for mortality and 30,689 for dependence. Both frailty phenotypes predicted the onset of dependence and mortality, even adjusting for chronic diseases and disability, with little heterogeneity of effect among sites. However, population attributable fractions (PAF) summarising etiologic force were highest for the aggregate effect of the individual indicators, as opposed to either the number of indicators or the dichotomised frailty phenotypes. The aggregate of all seven indicators provided the best overall prediction (weighted mean PAF 41.8 % for dependence and 38.3 % for mortality). While weight loss, underactivity, slow walking speed, and cognitive impairment predicted both outcomes, whereas undernutrition predicted only mortality and sensory impairment only dependence. Exhaustion predicted neither outcome. Simply assessed frailty indicators identify older people at risk of dependence and mortality, beyond information provided by chronic disease diagnoses and disability. Frailty is likely to be multidimensional. A better understanding of the construct and pathways to adverse outcomes could inform multidimensional assessment and intervention to prevent or manage dependence in frail older people, with potential to add life to years, and years to life.

X Demographics

X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 235 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 3 1%
India 1 <1%
United Kingdom 1 <1%
Unknown 230 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 38 16%
Student > Master 34 14%
Student > Ph. D. Student 26 11%
Unspecified 17 7%
Student > Bachelor 14 6%
Other 57 24%
Unknown 49 21%
Readers by discipline Count As %
Medicine and Dentistry 65 28%
Nursing and Health Professions 23 10%
Unspecified 17 7%
Social Sciences 16 7%
Psychology 12 5%
Other 34 14%
Unknown 68 29%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 17 June 2022.
All research outputs
#3,122,970
of 23,881,329 outputs
Outputs from BMC Medicine
#1,852
of 3,613 outputs
Outputs of similar age
#40,444
of 268,861 outputs
Outputs of similar age from BMC Medicine
#39
of 69 outputs
Altmetric has tracked 23,881,329 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,613 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 44.6. This one is in the 48th percentile – i.e., 48% of its peers scored the same or lower than it.
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 268,861 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 84% of its contemporaries.
We're also able to compare this research output to 69 others from the same source and published within six weeks on either side of this one. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.