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Eur Geriatr Med. Author manuscript; available in PMC Dec 1, 2012.
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PMCID: PMC3237697

The Frailty Instrument of the Survey of Health, Ageing and Retirement in Europe (SHARE-FI) predicts mortality beyond age, comorbidities, disability, self-rated health, education and depression


Frailty in older adults is a key clinical concept characterised by dysregulation of multiple biological systems, accumulation of deficits, vulnerability to stressors and increased risk of adverse outcomes such as falls, disability, hospitalisation, institutionalisation and death (13). Although there is no international consensus on the definition of frailty (4), one of the most accepted operationalisations is that of Fried et al., who defined it as a clinical syndrome in which three or more of the following are present: unintentional weight loss, exhaustion, weakness, slow walking speed, and low physical activity (5).

Frailty is an emerging concept in General Practice and has the potential to provide commissioners of health care with a clinical focus for targeting resources at an ageing population (6, 7). However, operationalising Fried’s frailty phenotype on an individual patient requires complex calculations on a reference sample, which is not practical in the context of primary care. Indeed, family physicians and community practitioners are in need of easy instruments for frailty (8).

To provide European community practitioners with an easy frailty metric, we created and validated a Frailty Instrument for primary care (SHARE-FI) based on the Survey of Health, Ageing and Retirement in Europe (9). The SHARE-FI calculators (one for each gender) are freely accessible on BMC Geriatrics (http://www.biomedcentral.com/1471-2318/10/57) and their use is intended for community-dwelling adults aged 50 and over. In our main study, we demonstrated that SHARE-FI was a powerful predictor of mortality over a mean follow-up of 2.4 years, even after adjusting for baseline age (9).

Ageing, comorbidity, disability and frailty are distinct (albeit causally related) clinical entities (1014). Our main study (9) did not report how SHARE-FI predicts mortality after adjusting for those and other potential confounders such as self-rated health (15, 16), education level (17, 18) and depression (19). The present study investigates the robustness SHARE-FI to predict mortality in the face of those important covariates.



17,304 females and 13,811 males included in the first wave of the Survey of Health, Aging and Retirement in Europe (SHARE, release 2.3.0 of November 13th, 2009), corresponding to nationally representative samples of 12 European countries (Austria, Germany, Sweden, Netherlands, Spain, Italy, France, Denmark, Greece, Switzerland, Belgium and Israel).

Creation of SHARE-FI

As detailed in our main study (9), SHARE-FI was created via estimation of a discrete factor (DFactor) model based on five SHARE frailty items (Figure 1), using LatentGOLD® (version 4.5.0). The five frailty items were proposed by Santos-Eggimann et al. (20). A single DFactor with three ordered levels or latent classes (non-frail, pre-frail and frail) was obtained for each gender. Figure 2 summarises the original development and validation of SHARE-FI (9).

Figure 2
Development and validation of SHARE-FI (separately for each gender). The illustration has been reproduced with permission of the authors (copyright holders) from the original SHARE-FI study (http://www.biomedcentral.com/1471-2318/10/57). The latter is ...

Mortality prediction

Wave 2 established whether wave 1 participants had died, were still alive, or had been lost to follow-up. For those who had died, the exact time to death since the initial interview was not collected. Wave 1 data were collected between 2004–2006 and wave 2 between 2006–2007. The mean individual follow up period between Wave 1 and Wave 2 was 2.4 years.


  • Age (years).
  • Number of years of education. Based on respondents’ self-report of their highest level of education achieved, years of education were derived from the 1997 International Standard Classification of Education (ISCED-97) (21).
  • Self-rated health on the following scale: 1: excellent; 2: very good; 3: good; 4: fair; or 5: poor.
  • Number of chronic diseases (active or in the past) diagnosed by a doctor, from the following list:
    a) Heart attack or myocardial infarction or coronary thrombosis or any other heart problem including congestive heart failure; b) High blood pressure or hypertension; c) High blood cholesterol; d) Stroke or cerebral vascular disease; e) Diabetes or high blood sugar; f) Chronic lung disease such as chronic bronchitis or emphysema; g) Asthma; h) Arthritis, including osteoarthritis, or rheumatism; i) Osteoporosis; j) Cancer or malignant tumour, including leukaemia or lymphoma, but excluding minor skin cancers; k) Stomach or duodenal ulcer, peptic ulcer; l) Parkinson disease; m) Cataracts; n) Hip fracture or femoral fracture; o) Other fractures; p) Alzheimer's disease, dementia or senility; q) Benign tumour, and r) Other conditions.
  • Number of limitations with activities of daily living (ADL), from the following list (one point per each limitation):
    a) Dressing, including putting on shoes and socks; b) Walking across a room; c) Bathing or showering; d) Eating, such as cutting up the food; e) Getting in and out of bed, and f) Using the toilet, including getting up or down.
  • EURO-D depression scale (22).

Statistical analyses

Binary logistic regressions were conducted with SPSS 16.0 to assess whether the frailty classes at Wave 1 significantly predicted whether or not a subject was dead by Wave 2. In the models, the frailty class variable was entered as a categorical predictor, using the non-frail class as reference category, and simple contrasts were requested. The dependent variable (i.e. dead at Wave 2, coded 0 = no and 1 = yes) included non-missing data only. The odds ratio (OR) for mortality was indicated by the Exp(B) statistic in the binary logistic regression model. Ninety-five percent confidence intervals for ORs were requested.

Four logistic regression models were computed: unadjusted (model 1) and age-adjusted (model 2), as previously reported (9); model 3 was adjusted by age, number of chronic diseases and number of ADL limitations; and model 4 was adjusted by age, number of chronic diseases, number of ADL limitations, self-rated health, years of education, and EURO-D score.

The logistic regression assumption of independent variables being linearly related to the logit was checked, and the fit of the models was checked with the Hosmer and Lemeshow test. Given the large sample size and consistent with our main publication (9), the level of significance was set at 0.01.


The mean age (standard deviation) of females was 63.6 (11.1), and that of males was 64.1 (9.9). Complete data for assessing frailty according to the approach by Santos-Eggimann et al. (20) were available for 15,578 females and 12,783 males.

Of the females with data available (N = 15,578), 66.9% were non-frail (N = 10,420), 25.8% were pre-frail (N = 4,025) and 7.3% were frail (N = 1,133). Of the males (N = 12,783), 82.3% were non-frail (N = 10,517), 14.6% were pre-frail (N = 1,871) and 3.1% were frail (N = 395).

By wave 2, information on the vital status was available for 11,384 females and 9,163 males. The crude mortality rate in females was 0.7% (non-frail), 2.6% (pre-frail) and 9.2% (frail). In males, the mortality was 2.0% (non-frail), 8.8% (pre-frail) and 22.6% (frail). The mortality effect was clearly incremental across frailty categories (9).

Table 1 shows the results of the binary logistic regression models. In model 4 (females), significant predictors were age (OR: 1.1, P < 0.001) and frail class (OR: 2.9, 95% CI: 1.3 – 6.2, P = 0.006). In males, significant predictors were age (OR: 1.1, P < 0.001), self-rated health (OR: 2.0, 95% CI: 1.5 – 2.5, P < 0.001) and frail class (OR: 2.5, 95% CI: 1.3 – 4.9, P = 0.007). Both models met the assumption of independent variables being linearly related to the logit and had a favourable Hosmer and Lemeshow test: χ2 = 7.671, df = 8, P = 0.466 (females) and χ2 = 6.329, df = 8, P = 0.610 (males).

Table 1
SHARE-FI categories: results of the logistic regression models for the prediction of mortality.


This study showed that SHARE-FI is a robust predictor of mortality even after adjusting for age, comorbidity, disability, self-rated health, education and depression. The OR for the frail class was more than twice as high as that of age alone, with age per se having a modest (OR: 1.1) effect. Indeed, frailty is more related to the biological than to the chronological age of individuals (12,14).

In model 4, the pre-frail class disappeared as a significant predictor of mortality (P ≥ 0.01 in both genders). Interestingly, the pre-frail state has been referred to in the literature as a state of less ‘inevitability’ that may be more amenable to interventions than the frail state (2325). Therefore, it is important that frailty interventions focus on minimising transitions from the pre-frail (which may be more benign from a mortality point of view) to the frail state (which has clear mortality implications), and maximise transitions from the frail to the pre-frail (or even non-frail) states.

The finding of a significant effect of self-reported health in males (but not in females) is in keeping with previous research that there may be gender differences in the association between self-rated health and mortality (15).

A limitation of this study is the significant proportion of missing mortality data, which was available for only 66% of the baseline sample. However, in our main study we conducted a sensitivity analysis suggesting that there was a higher burden of frailty at baseline in those for whom mortality information could not be obtained at follow-up, which could have resulted in underestimation of the age-adjusted mortality odds ratios (9).


This paper uses data from SHARE release 2.3.0, as of November 13th 2009. SHARE data collection in 2004–2007 was primarily funded by the European Commission through its 5th and 6th framework programmes (project numbers QLK6-CT-2001- 00360; RII-CT- 2006-062193; CIT5-CT-2005-028857). Additional funding by the US National Institute on Aging (grant numbers U01 AG09740-13S2; P01 AG005842; P01 AG08291; P30 AG12815; Y1-AG-4553-01; OGHA 04-064; R21 AG025169) as well as by various national sources is gratefully acknowledged (see http://www.share-project.org for a full list of funding institutions).


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Conflicts of interest



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