Background: Depression is a prevalent and disabling condition in older persons (≥60 years) that increases the risk of mortality and negatively influences quality

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Depression and Quality of Life in Older Persons: A Review

In 2 meta-analyses on gender differences in depression in nationally representative samples, we advance previous work by including studies of depression diagnoses and symptoms to (a) estimate the magnitude of the gender difference in depression across a wide array of nations and ages; (b) use a developmental perspective to elucidate patterns of gender differences across the life span; and (c) incorporate additional theory-driven moderators (e.g., gender equity). For major depression diagnoses and depression symptoms, respectively, we meta-analyzed data from 65 and 95 articles and their corresponding national data sets, representing data from 1,716,195 and 1,922,064 people in over 90 different nations. Overall, odds ratio (OR) = 1.95, 95% confidence interval (CI) [1.88, 2.03], and d = 0.27 [0.26, 0.29]. Age was the strongest predictor of effect size. The gender difference for diagnoses emerged earlier than previously thought, with OR = 2.37 at age 12. For both meta-analyses, the gender difference peaked in adolescence (OR = 3.02 for ages 13-15, and d = 0.47 for age 16) but then declined and remained stable in adulthood. Cross-national analyses indicated that larger gender differences were found in nations with greater gender equity, for major depression, but not depression symptoms. The gender difference in depression represents a health disparity, especially in adolescence, yet the magnitude of the difference indicates that depression in men should not be overlooked. (PsycINFO Database Record

Gender differences in depression in representative national samples: Meta-analyses of diagnoses and symptoms.

Objectives: To operationalize frailty using eight scales and to compare their content validity, feasibility, prevalence estimates of frailty, and ability to predict all-cause mortality.
Design: Secondary analysis of the Survey of Health, Ageing and Retirement in Europe (SHARE).
Setting: Eleven European countries.
Participants: Individuals aged 50 to 104 (mean age 65.3 ± 10.5, 54.8% female, N = 27,527).
Measurements: Frailty was operationalized using SHARE data based on the Groningen Frailty Indicator, the Tilburg Frailty Indicator, a 70-item Frailty Index (FI), a 44-item FI based on a Comprehensive Geriatric Assessment (FI-CGA), the Clinical Frailty Scale, frailty phenotype (weighted and unweighted versions), the Edmonton Frail Scale, and the FRAIL scale.
Results: All scales had fewer than 6% of cases with at least one missing item, except the SHARE-frailty phenotype (11.1%) and the SHARE-Tilburg (12.2%). In the SHARE-Groningen, SHARE-Tilburg, SHARE-frailty phenotype, and SHARE-FRAIL scales, death rates were 3 to 5 times as high in excluded cases as in included ones. Frailty prevalence estimates ranged from 6% (SHARE-FRAIL) to 44% (SHARE-Groningen). All scales categorized 2.4% of participants as frail. Of unweighted scales, the SHARE-FI and SHARE-Edmonton scales most accurately predicted mortality at 2 (SHARE-FI area under the receiver operating characteristic curve (AUC) = 0.77, 95% confidence interval (CI) = 0.75�0.79); SHARE-Edmonton AUC = 0.76, 95% CI = 0.74�0.79) and 5 (both AUC = 0.75, 95% CI = 0.74�0.77) years. The continuous score of the weighted SHARE-frailty phenotype (AUC = 0.77, 95% CI = 0.75�0.78) predicted 5-year mortality better than the unweighted SHARE-frailty phenotype (AUC = 0.70, 95% CI = 0.68�0.71), but the categorical score of the weighted SHARE-frailty phenotype did not (AUC = 0.70, 95% CI = 0.68�0.72).
Conclusion: Substantive differences exist between scales in their content validity, feasibility, and ability to predict all-cause mortality. These frailty scales capture related but distinct groups. Weighting items in frailty scales can improve their predictive ability, but the trade-off between specificity, predictive power, and generalizability requires additional evaluation.

Operationalization of Frailty Using Eight Commonly Used Scales and Comparison of Their Ability to Predict All‐Cause Mortality

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://wwwshare-projectorg
 ), a large population-based survey conducted in 2004-2005 in twelve European countries 
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 (ie 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 
Females: the DFactor model included 15,578 cases (standard R
2 = 061) All five frailty indicators discriminated well (p < 0001) between the three classes: non-frail (N = 10,420; 669%), pre-frail (N = 4,025; 258%), and frail (N = 1,133; 73%) Relative to the non-frail class, the age-adjusted Odds Ratio (with 95% Confidence Interval) for mortality at Wave 2 was 21 (14 - 30) in the pre-frail and 48 (31 - 74) in the frail Males: 12,783 cases (standard R
2 = 061, all frailty indicators had p < 0001): non-frail (N = 10,517; 823%), pre-frail (N = 1,871; 146%), and frail (N = 395; 31%); age-adjusted OR (95% CI) for mortality: 30 (23 - 40) in the pre-frail, 69 (47 - 102) in the frail 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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A Frailty Instrument for primary care: findings from the Survey of Health, Ageing and Retirement in Europe (SHARE)

Objective Investigators within many disciplines are using measures of well-being, but it is not always clear what they are measuring, or which instruments may best meet their objectives. The aims of this review were to: systematically identify well-being instruments, explore the variety of well-being dimensions within instruments and describe how the production of instruments has developed over time.

Design Systematic searches, thematic analysis and narrative synthesis were undertaken.

Data sources MEDLINE, EMBASE, EconLit, PsycINFO, Cochrane Library and CINAHL from 1993 to 2014 complemented by web searches and expert consultations through 2015.

Eligibility criteria Instruments were selected for review if they were designed for adults (≥18 years old), generic (ie, non-disease or context specific) and available in an English version.

Results A total of 99 measures of well-being were included, and 196 dimensions of well-being were identified within them. Dimensions clustered around 6 key thematic domains: mental well-being, social well-being, physical well-being, spiritual well-being, activities and functioning, and personal circumstances. Authors were rarely explicit about how existing theories had influenced the design of their tools; however, the 2 most referenced theories were Diener's model of subjective well-being and the WHO definition of health. The period between 1990 and 1999 produced the greatest number of newly developed well-being instruments (n=27). An illustration of the dimensions identified and the instruments that measure them is provided within a thematic framework of well-being.

Conclusions This review provides researchers with an organised toolkit of instruments, dimensions and an accompanying glossary. The striking variability between instruments supports the need to pay close attention to what is being assessed under the umbrella of ‘well-being’ measurement.

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Review of 99 self-report measures for assessing well-being in adults: exploring dimensions of well-being and developments over time

BACKGROUND In an 11-country European collaboration, 14 population-based surveys included 21,724 subjects aged > or = 65 years. Most participating centres used the Geriatric Mental State (GMS), but other measures were also used.

AIMS To derive from these instruments a common depression symptoms scale, the EURO-D, to allow comparison of risk factor profiles between centres.

METHOD Common items were identified from the instruments. Algorithms for fitting items to GMS were derived by observation of item correspondence or expert opinion. The resulting 12-item scale was checked for internal consistency, criterion validity and uniformity of factor-analytic profile.

RESULTS The EURO-D is internally consistent, capturing the essence of its parent instrument. A two-factor solution seemed appropriate: depression, tearfulness and wishing to die loaded on the first factor (affective suffering), and loss of interest, poor concentration and lack of enjoyment on the second (motivation).

CONCLUSIONS The EURO-D scale should permit valid comparison of risk-factor associations between centres, even if between-centre variation remains difficult to attribute.

R. Fuhrer

Papers

Development of the EURO-D scale--a European, Union initiative to compare symptoms of depression in 14 European centres.