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      Self-Rated Health in the Last 12 Years of Life Compared to Matched Surviving Controls: The Health and Retirement Study

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          Abstract

          Self-rated health (SRH) is a valid measure of health status and associated with mortality. Based on individual-level biannual repeat data on SRH we sought to characterize the natural history of poor SRH during the 12 years prior to death in men and women in different age groups. We conducted a retrospective analysis of the Health and Retirement Study participants who died between 1998 and 2010 and had at least two SRH measurements in the 12 years prior to death. We used a nested case-control design to compare SRH trajectories of deceased men and women aged 30–64, 65–79 and 80 years versus surviving participants. The cases comprised 3,350 deceased participants who were matched to surviving controls (n = 8,127). SRH was dichotomized into good vs. poor health. Men and women dying at age 65–79 and ≥80 years had 1.5 to 3 times higher prevalence of poor SRH already 11–12 years prior to death compared to surviving controls. The risk estimates remained statistically significant even after adjusting for life-style related risk factors and diagnosed diseases. Prevalence of poor SRH before death was lowest among those aged ≥80 years and highest in 30–64 year-olds. In conclusion, men and women who subsequently die perceive their health worse already 11–12 years prior to death compared to their surviving controls.

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          Most cited references30

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          Self-rated health and mortality: a review of twenty-seven community studies.

          We examine the growing number of studies of survey respondents' global self-ratings of health as predictors of mortality in longitudinal studies of representative community samples. Twenty-seven studies in U.S. and international journals show impressively consistent findings. Global self-rated health is an independent predictor of mortality in nearly all of the studies, despite the inclusion of numerous specific health status indicators and other relevant covariates known to predict mortality. We summarize and review these studies, consider various interpretations which could account for the association, and suggest several approaches to the next stage of research in this field.
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            Longitudinal data analysis for discrete and continuous outcomes.

            Longitudinal data sets are comprised of repeated observations of an outcome and a set of covariates for each of many subjects. One objective of statistical analysis is to describe the marginal expectation of the outcome variable as a function of the covariates while accounting for the correlation among the repeated observations for a given subject. This paper proposes a unifying approach to such analysis for a variety of discrete and continuous outcomes. A class of generalized estimating equations (GEEs) for the regression parameters is proposed. The equations are extensions of those used in quasi-likelihood (Wedderburn, 1974, Biometrika 61, 439-447) methods. The GEEs have solutions which are consistent and asymptotically Gaussian even when the time dependence is misspecified as we often expect. A consistent variance estimate is presented. We illustrate the use of the GEE approach with longitudinal data from a study of the effect of mothers' stress on children's morbidity.
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              • Record: found
              • Abstract: found
              • Article: not found

              Longitudinal Data Analysis for Discrete and Continuous Outcomes

              Longitudinal data sets are comprised of repeated observations of an outcome and a set of covariates for each of many subjects. One objective of statistical analysis is to describe the marginal expectation of the outcome variable as a function of the covariates while accounting for the correlation among the repeated observations for a given subject. This paper proposes a unifying approach to such analysis for a variety of discrete and continuous outcomes. A class of generalized estimating equations (GEEs) for the regression parameters is proposed. The equations are extensions of those used in quasi-likelihood (Wedderburn, 1974, Biometrika 61, 439-447) methods. The GEEs have solutions which are consistent and asymptotically Gaussian even when the time dependence is misspecified as we often expect. A consistent variance estimate is presented. We illustrate the use of the GEE approach with longitudinal data from a study of the effect of mothers' stress on children's morbidity.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2014
                19 September 2014
                : 9
                : 9
                : e107879
                Affiliations
                [1 ]Department of Public Health, University of Turku, Turku, Finland
                [2 ]Finnish Institute of Occupational Health, Helsinki, Finland
                [3 ]Department of Society, Human Development, and Health, Harvard School of Public Health, Boston, Massachusetts, United States of America
                [4 ]Stress Research Institute, Stockholm University, Stockholm, Sweden
                [5 ]Department of Epidemiology and Public Health, University College London, London, United Kingdom
                [6 ]Hjelt Institute, Medical Faculty, University of Helsinki, Helsinki, Finland
                [7 ]Turku University Hospital, Turku, Finland
                Oregon Health & Science University, United States of America
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Analyzed the data: SS JP JV. Wrote the paper: SS JP IK HW MK JV.

                Article
                PONE-D-14-21989
                10.1371/journal.pone.0107879
                4169624
                25237814
                4b633379-144f-422c-bdee-8053baaca36b
                Copyright @ 2014

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 20 May 2014
                : 23 August 2014
                Page count
                Pages: 7
                Funding
                This work was supported by the EU's Era-Age 2 program (Academy of Finland (264944) and the Swedish Research Council for Health, Working Life and Welfare (Forte, 2012-1661)). Mika Kivimäki is supported by a professorial fellowship from the Economic and Social Research Council. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Medicine and Health Sciences
                Epidemiology
                Epidemiology of Aging
                Social Epidemiology
                Health Care
                End of Life
                Quality of Life
                Custom metadata
                The authors confirm that all data underlying the findings are fully available without restriction. The RAND HRS Data file is an open access data set based on the HRS data. It was developed at RAND with funding from the National Institute on Aging and the Social Security Administration. The research paper is based on the Health and Retirement Study (HRS). The data products are available without cost to registered users. More information can be found at ( http://hrsonline.isr.umich.edu).

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