Cognition and its association with the factors of the EURO-D: Suffering and Motivation. Findings from SHARE Wave 6

Psychometric properties of the EURO-D scale of depressive symptomatology: Evidence from SHARE wave 8

BACKGROUND

The EURO-D is a short scale to measure symptoms of depression, very used in large population surveys. Although there are numerous validation studies, its psychometric properties remain unclear. The two-factor structure (Affective Suffering and Lack of Motivation) is replicated in several studies but with different item compositions, and none reported reliability indices for both factors. For that reason, the aim of this study is to examine the factorial validity of the scale, the reliability of the dimensions, the gender differential item functioning (DIF), and the nomological validity.

METHODS

46,317 participants aged 50 and over (M = 71.33), from which 57.4 % were females, in Wave 8 of the Survey of Health, Aging and Retirement in Europe (SHARE) were included.

INSTRUMENTS

EURO-D, R-UCLA, Self-perceived health index, and indicator of taking drugs for anxiety or depression. Factor Analyses, DIF, Reliability Index and Spearman correlations were estimated.

RESULTS

Factor analysis identified a bifactor structure: a general factor of Depression and two specific factors of Affective Suffering and Lack of Motivation, which reliabilities were 0.83, 0.83 and 0.79 respectively. No relevant DIF item by gender was found, but higher scores were found in women in all factors. Both factors had positive relations with loneliness, taking drugs and self-perceived health.

LIMITATIONS

this study has a cross-sectional design; future research may consider the longitudinal stability of the scale.

CONCLUSIONS

EURO-D shows adequate psychometric properties when a general factor of depression and two specific factors are considered. Women have higher scores on all dimensions.

The association of socio-economic and psychological factors with limitations in day-to-day activity over 7 years in newly diagnosed osteoarthritis patients

Previous research has established links between chronic pain and impaired cognitive ability, as well as between chronic pain and anxiety, in osteoarthritis. Furthermore, there is evidence linking risk of osteoarthritis to lower educational attainment. However, the inter-play of these factors with key social factors (e.g., social deprivation) at the early stages of osteoarthritis are not understood. Here, we used data from waves 4, 5, 6 and 7 of the Survey of Health, Ageing and Retirement in Europe (SHARE) (n = 971) and selected a subsample of respondents who initially did not report a diagnosis of osteoarthritis until wave 6. We used path models to test how social deprivation, education and anxiety, before diagnosis (waves 4 and 5), affect the relationship between cognitive ability, pain and limitations in activities of daily living following diagnosis (waves 6 and 7). We show that high social deprivation before diagnosis predicts greater limitations in activities of daily living after diagnosis, with this effect partly mediated by impaired cognitive ability. We also find that higher educational attainment before diagnosis may protect against limitations in activities of daily living after diagnosis via better cognitive ability and lower anxiety. Therefore, improving cognitive ability and managing anxiety may mitigate the associations of social deprivation and low educational attainment with limitations in activities of daily living.

A model-driven approach to better identify older people at risk of depression

Depression in later life is one of the most common mental disorders. Several instruments have been developed to detect the presence or the absence of certain symptoms or emotional disorders, based on cut-off points. However, the use of a cut-off does not allow identification of depression sub-types or distinguish between mild and severe depression. As a result, depression may be under- or over-diagnosed in older people. This paper aims to apply a model-driven approach to classify individuals into distinct sub-groups, based on different combinations of depressive and emotional conditions. This approach is based on two distinct statistical solutions: first, a latent class analysis is applied to the items collected by the depression scale and, according to the final model, the probability of belonging to each class is calculated for every individual. Second, a factor analysis of these classes is performed to obtain a reduced number of clusters for easy interpretation. We use data collected through the EURO-D scale in a large sample of older individuals, participants of the sixth wave of the Survey of Health, Ageing and Retirement in Europe. We show that by using such a model-based approach it is possible to classify individuals in a more accurate way than the simple dichotomisation ‘depressed’ versus ‘non-depressed’.