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      Associations between socioeconomic status and chronic kidney disease: a meta-analysis

      , , , , ,
      Journal of Epidemiology and Community Health
      BMJ

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          Abstract

          Background

          Socioeconomic status (SES) has long been conjectured to be associated with the incidence and progression of chronic kidney disease (CKD), but few studies have examined this quantitatively. This meta-analysis aims to fill this gap.

          Methods

          A systematic literature review was performed using Medline and EMBASE to identify observational studies on associations between SES and incidence and progression of CKD, published between 1974 and March 2017. Individual results were meta-analysed using a random effects model, in line with Meta-analysis of Observational Studies in Epidemiology guidelines.

          Results

          In total, 43 articles met our inclusion criteria. CKD prevalence was associated with several indicators of SES, particularly lower income (OR 1.34, 95% CI (1.18 to 1.53), P<0.001; I 2=73.0%, P=0.05); lower education (OR 1.21, 95% CI (1.11 to 1.32), P<0.001; I 2=45.20%, P=0.034); and lower combined SES (OR 2.18, 95% CI (1.64 to 2.89), P<0.001; I 2=0.0%, P=0.326). Lower levels of income, occupation and combined SES were also significantly associated with progression to end-stage renal disease (risk ratio (RR) 1.24, 95% CI (1.12 to 1.37), P<0.001; I 2=66.6%, P=0.006; RR 1.05, 95% CI (1.01 to 1.09), P=0.012; I 2=0.0%, P=0.796; and RR 1.39, 95% CI (1.09 to 1.79), P=0.009; I 2=74.2%, P=0.009). Subgroup analyses generally confirmed these results, except in a few cases, such as an inverse association related to particular socioeconomic backgrounds and where results were adjusted by more disease-related risk factors.

          Conclusion

          Lower income was most closely associated with prevalence and progression of CKD, and lower education was significantly associated with its prevalence. Evidence for other indicators was inconclusive.

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          Most cited references79

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          Measuring inconsistency in meta-analyses.

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            Bias in meta-analysis detected by a simple, graphical test

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              Quantifying heterogeneity in a meta-analysis.

              The extent of heterogeneity in a meta-analysis partly determines the difficulty in drawing overall conclusions. This extent may be measured by estimating a between-study variance, but interpretation is then specific to a particular treatment effect metric. A test for the existence of heterogeneity exists, but depends on the number of studies in the meta-analysis. We develop measures of the impact of heterogeneity on a meta-analysis, from mathematical criteria, that are independent of the number of studies and the treatment effect metric. We derive and propose three suitable statistics: H is the square root of the chi2 heterogeneity statistic divided by its degrees of freedom; R is the ratio of the standard error of the underlying mean from a random effects meta-analysis to the standard error of a fixed effect meta-analytic estimate, and I2 is a transformation of (H) that describes the proportion of total variation in study estimates that is due to heterogeneity. We discuss interpretation, interval estimates and other properties of these measures and examine them in five example data sets showing different amounts of heterogeneity. We conclude that H and I2, which can usually be calculated for published meta-analyses, are particularly useful summaries of the impact of heterogeneity. One or both should be presented in published meta-analyses in preference to the test for heterogeneity. Copyright 2002 John Wiley & Sons, Ltd.
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                Author and article information

                Journal
                Journal of Epidemiology and Community Health
                J Epidemiol Community Health
                BMJ
                0143-005X
                1470-2738
                March 08 2018
                April 2018
                April 2018
                February 02 2018
                : 72
                : 4
                : 270-279
                Article
                10.1136/jech-2017-209815
                29437863
                a1f673f0-d90a-40fa-8b27-c167e6bb2b4d
                © 2018
                History

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