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dc.contributor.authorRomero-Ortuno, Roman
dc.contributor.authorWalsh, Cathal D
dc.contributor.authorLawlor, Brian A
dc.contributor.authorKenny, Rose Anne
dc.date.accessioned2012-05-22T15:35:37Z
dc.date.available2012-05-22T15:35:37Z
dc.date.issued2010-08-24
dc.identifier.citationBMC Geriatrics. 2010 Aug 24;10(1):57
dc.identifier.urihttp://dx.doi.org/10.1186/1471-2318-10-57
dc.identifier.urihttp://hdl.handle.net/10147/225306
dc.description.abstractAbstract Background A frailty paradigm would be useful in primary care to identify older people at risk, but appropriate metrics at that level are lacking. We created and validated a simple instrument for frailty screening in Europeans aged ≥50. Our study is based on the first wave of the Survey of Health, Ageing and Retirement in Europe (SHARE, http://www.share-project.org), a large population-based survey conducted in 2004-2005 in twelve European countries. Methods Subjects: SHARE Wave 1 respondents (17,304 females and 13,811 males). Measures: five SHARE variables approximating Fried's frailty definition. Analyses (for each gender): 1) estimation of a discreet factor (DFactor) model based on the frailty variables using LatentGOLD®. A single DFactor with three ordered levels or latent classes (i.e. non-frail, pre-frail and frail) was modelled; 2) the latent classes were characterised against a biopsychosocial range of Wave 1 variables; 3) the prospective mortality risk (unadjusted and age-adjusted) for each frailty class was established on those subjects with known mortality status at Wave 2 (2007-2008) (11,384 females and 9,163 males); 4) two web-based calculators were created for easy retrieval of a subject's frailty class given any five measurements. Results Females: the DFactor model included 15,578 cases (standard R 2 = 0.61). All five frailty indicators discriminated well (p < 0.001) between the three classes: non-frail (N = 10,420; 66.9%), pre-frail (N = 4,025; 25.8%), and frail (N = 1,133; 7.3%). Relative to the non-frail class, the age-adjusted Odds Ratio (with 95% Confidence Interval) for mortality at Wave 2 was 2.1 (1.4 - 3.0) in the pre-frail and 4.8 (3.1 - 7.4) in the frail. Males: 12,783 cases (standard R 2 = 0.61, all frailty indicators had p < 0.001): non-frail (N = 10,517; 82.3%), pre-frail (N = 1,871; 14.6%), and frail (N = 395; 3.1%); age-adjusted OR (95% CI) for mortality: 3.0 (2.3 - 4.0) in the pre-frail, 6.9 (4.7 - 10.2) in the frail. Conclusions The SHARE Frailty Instrument has sufficient construct and predictive validity, and is readily and freely accessible via web calculators. To our knowledge, SHARE-FI represents the first European research effort towards a common frailty language at the community level.
dc.titleA Frailty Instrument for primary care: findings from the Survey of Health, Ageing and Retirement in Europe (SHARE)
dc.typeJournal Article
dc.language.rfc3066en
dc.rights.holderRomero-Ortuno et al.; licensee BioMed Central Ltd.
dc.description.statusPeer Reviewed
dc.date.updated2012-05-22T15:02:56Z
refterms.dateFOA2018-08-22T17:18:24Z
html.description.abstractAbstract Background A frailty paradigm would be useful in primary care to identify older people at risk, but appropriate metrics at that level are lacking. We created and validated a simple instrument for frailty screening in Europeans aged &#8805;50. Our study is based on the first wave of the Survey of Health, Ageing and Retirement in Europe (SHARE, http://www.share-project.org), a large population-based survey conducted in 2004-2005 in twelve European countries. Methods Subjects: SHARE Wave 1 respondents (17,304 females and 13,811 males). Measures: five SHARE variables approximating Fried's frailty definition. Analyses (for each gender): 1) estimation of a discreet factor (DFactor) model based on the frailty variables using LatentGOLD&#174;. A single DFactor with three ordered levels or latent classes (i.e. non-frail, pre-frail and frail) was modelled; 2) the latent classes were characterised against a biopsychosocial range of Wave 1 variables; 3) the prospective mortality risk (unadjusted and age-adjusted) for each frailty class was established on those subjects with known mortality status at Wave 2 (2007-2008) (11,384 females and 9,163 males); 4) two web-based calculators were created for easy retrieval of a subject's frailty class given any five measurements. Results Females: the DFactor model included 15,578 cases (standard R 2 = 0.61). All five frailty indicators discriminated well (p &lt; 0.001) between the three classes: non-frail (N = 10,420; 66.9%), pre-frail (N = 4,025; 25.8%), and frail (N = 1,133; 7.3%). Relative to the non-frail class, the age-adjusted Odds Ratio (with 95% Confidence Interval) for mortality at Wave 2 was 2.1 (1.4 - 3.0) in the pre-frail and 4.8 (3.1 - 7.4) in the frail. Males: 12,783 cases (standard R 2 = 0.61, all frailty indicators had p &lt; 0.001): non-frail (N = 10,517; 82.3%), pre-frail (N = 1,871; 14.6%), and frail (N = 395; 3.1%); age-adjusted OR (95% CI) for mortality: 3.0 (2.3 - 4.0) in the pre-frail, 6.9 (4.7 - 10.2) in the frail. Conclusions The SHARE Frailty Instrument has sufficient construct and predictive validity, and is readily and freely accessible via web calculators. To our knowledge, SHARE-FI represents the first European research effort towards a common frailty language at the community level.


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