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dc.contributor.authorJotheeswaran, A. T.
dc.contributor.authorBryce, R.
dc.contributor.authorPrina, A. Matthew
dc.contributor.authorAcosta, Daisy
dc.contributor.authorFerri, Cleusa P.
dc.contributor.authorGuerra, Mariella
dc.contributor.authorHuang, Yueqin
dc.contributor.authorLlibre Rodríguez, Juan J.
dc.contributor.authorSalas, Aquiles
dc.contributor.authorSosa, Ana Luisa
dc.contributor.authorWilliams, Joseph D.
dc.contributor.authorDewey, Michael E.
dc.contributor.authorAcosta, Isaac
dc.contributor.authorLiu, Zhaorui
dc.contributor.authorBeard, John
dc.contributor.authorPrince, Martin J.
dc.date.accessioned2018-11-23T22:12:20Z
dc.date.available2018-11-23T22:12:20Z
dc.date.issued2015
dc.identifier.citationJotheeswaran AT, Bryce R, Prina M, Acosta D, Ferri CP, Guerra M, et al. Frailty and the prediction of dependence and mortality in low- and middle-income countries: a 10/66 population based cohort study. BMC Medicine. 2015 ; 13: 1-12en
dc.identifier.issn17417015
dc.identifier.urihttps://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-015-0378-4
dc.description.abstractIn 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. Methods: 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. Results: 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 under nutrition predicted only mortality and sensory impairment only dependence. Exhaustion predicted neither outcome. Conclusions: 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.en
dc.language.isoenen
dc.publisherBMC Medicineen
dc.relation.ispartofseries13_(1);art no.138
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectDemenciaes_ES
dc.subjectPaíses en desarrolloes_ES
dc.subjectAccidente cerebrovasculares_ES
dc.subjectEstadísticas y datos numéricoses_ES
dc.subjectCuidadoreses_ES
dc.subjectPersonas con discapacidades_ES
dc.titleFrailty and the prediction of dependence and mortality in low- and middle-income countries: a 10/66 population based cohort study.en
dc.typeArticleen


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