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The interaction between individualism and wellbeing in predicting mortality: Survey of Health Ageing and Retirement in Europe.

ABSTRACT: The link between greater wellbeing and longevity is well documented. The aim of the current study was to test whether this association is consistent across individualistic and collectivistic cultures. The sample consisted of 13,596 participants from 11 European countries, each of which was assigned an individualism score according to Hofstede et al.'s (Cultures and organizations: software of the mind, McGraw Hill, New York, 2010) cultural dimension of individualism. We tested whether individualism moderated the cross-sectional association between wellbeing and self-rated health or the longitudinal association between wellbeing and mortality risk. Our analysis revealed a significant interaction between individualism and wellbeing such that the association between wellbeing and self-rated health or risk of mortality from cardiovascular disease was stronger in more individualistic countries. However, the interaction between wellbeing and individualism was not significant in analysis predicting all-cause mortality. Further prospective studies are needed to confirm our finding and to explore the factors responsible for this culturally dependent effect.

SUBMITTER: Okely JA

PROVIDER: S-EPMC5765189 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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The interaction between individualism and wellbeing in predicting mortality: Survey of Health Ageing and Retirement in Europe.

Okely Judith A JA Weiss Alexander A Gale Catharine R CR

Journal of behavioral medicine 20170715 1

The link between greater wellbeing and longevity is well documented. The aim of the current study was to test whether this association is consistent across individualistic and collectivistic cultures. The sample consisted of 13,596 participants from 11 European countries, each of which was assigned an individualism score according to Hofstede et al.'s (Cultures and organizations: software of the mind, McGraw Hill, New York, 2010) cultural dimension of individualism. We tested whether individuali ...[more]

PMID: 28712010

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Project description:BackgroundA 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.MethodsSubjectsSHARE Wave 1 respondents (17,304 females and 13,811 males).Measuresfive 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.ResultsFemales: the DFactor model included 15,578 cases (standard R2 = 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 R2 = 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.ConclusionsThe 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.

Project description:BackgroundActive and Healthy Ageing (AHA) is the process of optimizing opportunities related to health, participation, and safety in order to improve quality of life. The approach most often used to measure AHA is Rowe and Kahn's Satisfactory Ageing model. Nonetheless, this model has limitations. One of the strategic objectives of the WHO Global Strategy and Action Plan (2016) is to improve Healthy Ageing measurement. Our objectives were to compare two models of assessing AHA and further compare the results by country and sociodemographic variables.MethodsThis was a cross-sectional, observational analysis of a representative sample of the general population aged 50 years and older in Europe. The data analysed were obtained by the Study of Health, Ageing and Retirement in Europe (SHARE). The dependent variable was AHA and its dimensions, measured using the Rowe and Kahn AHA model (AHA-B) and the authors' model based on the WHO definition (AHA-BPS). A descriptive analysis and multivariate models of binary logistical regression were developed.ResultsThe sample consisted of 52,641 participants (mean age 65.24 years [SD = 10.18; Range = 50-104], 53.2% women). Healthy Ageing prevalence in the AHA-B model was 23.5% (95%CI = 23.1%-23.9%). In the AHA-BPS model, this prevalence was 38.9%. In both models, significant variations were observed between countries, and were distributed along a north-western to south-eastern gradient. The sociodemographic variables associated with the absence of AHA were advanced age, female sex, death of spouse, low educational level, lack of employment, and low financial status. Comparing the two models, the strength of association between absence of AHA and advanced age (85 years and older) was four times greater in the AHA-B model.ConclusionsOur results showing differences between these two models provide evidence that the AHA-BPS model does not penalize older age and is more likely to characterize AHA from a health promotion perspective.

Dataset Information

The interaction between individualism and wellbeing in predicting mortality: Survey of Health Ageing and Retirement in Europe.

Publications

The interaction between individualism and wellbeing in predicting mortality: Survey of Health Ageing and Retirement in Europe.

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