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      Life Course Socioeconomic Conditions and Frailty at Older Ages


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          This article aimed to assess associations of childhood socioeconomic conditions (CSC) with the risk of frailty in old age and whether adulthood socioeconomic conditions (ASC) influence this association.


          Data from 21,185 individuals aged 50 years and older included in the longitudinal Survey of Health, Ageing, and Retirement in Europe were used. Frailty was operationalized as a sum of presenting weakness, shrinking, exhaustion, slowness, or low activity. Confounder-adjusted multilevel logistic regression models were used to analyze associations of CSC and ASC with frailty.


          While disadvantaged CSC was associated with higher odds of (pre-)frailty in women and men (odds ratio [OR] = 1.73, 95% confidence interval [CI] 1.34, 2.24; OR = 1.84, 95% CI 1.27, 2.66, respectively), this association was mediated by ASC. Personal factors and demographics, such as birth cohort, chronic conditions, and difficulties with activities of daily living, increased the odds of being (pre-)frail.


          Findings suggest that CSC are associated with frailty at old age. However, when taking into account ASC, this association no longer persists. The results show the importance of improving socioeconomic conditions over the whole life course in order to reduce health inequalities in old age.

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          Most cited references51

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          Fitting Linear Mixed-Effects Models Usinglme4

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            Frailty in Older Adults: Evidence for a Phenotype

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              Fitting Linear Mixed-Effects Models Using lme4

              Maximum likelihood or restricted maximum likelihood (REML) estimates of the parameters in linear mixed-effects models can be determined using the lmer function in the lme4 package for R. As for most model-fitting functions in R, the model is described in an lmer call by a formula, in this case including both fixed- and random-effects terms. The formula and data together determine a numerical representation of the model from which the profiled deviance or the profiled REML criterion can be evaluated as a function of some of the model parameters. The appropriate criterion is optimized, using one of the constrained optimization functions in R, to provide the parameter estimates. We describe the structure of the model, the steps in evaluating the profiled deviance or REML criterion, and the structure of classes or types that represents such a model. Sufficient detail is included to allow specialization of these structures by users who wish to write functions to fit specialized linear mixed models, such as models incorporating pedigrees or smoothing splines, that are not easily expressible in the formula language used by lmer. Journal of Statistical Software, 67 (1) ISSN:1548-7660

                Author and article information

                Role: Decision Editor
                J Gerontol B Psychol Sci Soc Sci
                J Gerontol B Psychol Sci Soc Sci
                The Journals of Gerontology Series B: Psychological Sciences and Social Sciences
                Oxford University Press (US )
                June 2020
                07 February 2019
                07 February 2019
                : 75
                : 6
                : 1348-1357
                [1 ] Swiss NCCR “LIVES - Overcoming Vulnerability: Life Course Perspectives”Arve , Geneva, Switzerland
                [2 ] Center for the Interdisciplinary Study of Gerontology and Vulnerability, University of Geneva , Switzerland
                [3 ] Unit of Population Epidemiology, Department of Community Medicine, Primary Care and Emergency Medicine, Geneva University Hospitals , Switzerland
                [4 ] Department of Epidemiology, Emory University , Atlanta, Georgia
                [5 ] Institute of Social and Preventive Medicine, Lausanne University Hospital , Switzerland
                [6 ] Department of Ambulatory Care and Community Medicine, University of Lausanne , Switzerland
                [7 ] ZHAW, Zurich University of Applied Sciences , Switzerland
                [8 ] NOVA - Norwegian Social Research, Center for Welfare and Labor Research , Oslo, Norway
                [9 ] International Centre for Life Course Studies in Society and Health, Department of Epidemiology and Public Health, University College London , UK
                [10 ] Department of General Internal Medicine, Rehabilitation and Geriatrics, University of Geneva , Switzerland
                Author notes
                Author information
                © The Author(s) 2019. Published by Oxford University Press on behalf of The Gerontological Society of America.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

                : 13 April 2018
                : 01 February 2019
                : 12 March 2019
                Page count
                Pages: 10
                Funded by: European Union, DOI 10.13039/501100000780;
                Award ID: 676060
                Funded by: Swiss National Science Foundation, DOI 10.13039/501100001711;
                Award ID: 51NF40-160590
                Funded by: European Commission, DOI 10.13039/501100000780;
                Award ID: FP5 (QLK6-CT-2001-00360)
                Award ID: FP6 (SHARE-I3: RII-CT-2006-062193, COMPARE: CIT5-CT-2005-028857, SHARELIFE: CIT4-CT-2006-028812)
                Award ID: FP7 (SHARE-PREP: N°211909, SHARE-LEAP: N°227822, SHARE M4: N°261982
                Funded by: German Ministry of Education and Research;
                Funded by: Max Planck Society for the Advancement of Science;
                Funded by: U.S. National Institute on Aging, DOI 10.13039/100000049;
                Award ID: U01_AG09740-13S2
                Award ID: P01_AG005842
                Award ID: P01_AG08291
                Award ID: P30_AG12815
                Award ID: R21_AG025169
                Award ID: Y1-AG-4553-01
                Award ID: IAG_BSR06-11
                Award ID: OGHA_04-064
                Award ID: HHSN271201300071C
                The Journal of Gerontology: Social Sciences
                Cumulative Dis/Advantage

                Geriatric medicine
                health outcomes,socioeconomic status,successful aging
                Geriatric medicine
                health outcomes, socioeconomic status, successful aging


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