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George Davey Smith

Other affiliations: Keele University, Western Infirmary, Health Science University  ...read more
Bio: George Davey Smith is an academic researcher from University of Bristol. The author has contributed to research in topics: Population & Mendelian randomization. The author has an hindex of 224, co-authored 2540 publications receiving 248373 citations. Previous affiliations of George Davey Smith include Keele University & Western Infirmary.


Papers
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Journal ArticleDOI
TL;DR: Despite little support for a direct effect of income inequality on health per se, reducing income inequality by raising the incomes of the most disadvantaged will improve their health, help reduce health inequalities, and generally improve population health.
Abstract: This article reviews 98 aggregate and multilevel studies examining the associations between income inequality and health. Overall, there seems to be little support for the idea that income inequality is a major, generalizable determinant of population health differences within or between rich countries. Income inequality may, however, directly influence some health outcomes, such as homicide in some contexts. The strongest evidence for direct health effects is among states in the United States, but even that is somewhat mixed. Despite little support for a direct effect of income inequality on health per se, reducing income inequality by raising the incomes of the most disadvantaged will improve their health, help reduce health inequalities, and generally improve population health.

948 citations

Journal ArticleDOI
Daniel I. Swerdlow1, Michael V. Holmes1, Karoline Kuchenbaecker2, Engmann Jel.1, Tina Shah1, Reecha Sofat1, Yiran Guo, C Chung1, Anne Peasey1, Roman Pfister3, Simon P. Mooijaart4, Helen Ireland1, Maarten Leusink5, Claudia Langenberg3, KaWah Li1, Jutta Palmen1, Phil Howard1, Jackie A. Cooper1, Fotios Drenos1, John Hardy1, Mike A. Nalls6, Yun Li7, Gordon D.O. Lowe8, Marlene C. W. Stewart9, S. J. Bielinski10, Julian Peto11, Nicholas J. Timpson12, John Gallacher13, Malcolm G. Dunlop9, Richard S. Houlston, Ian Tomlinson14, Ioanna Tzoulaki15, Jian'an Luan2, Boer Jma.2, Nita G. Forouhi2, N. C. Onland-Moret5, Y. T. van der Schouw16, Renate B. Schnabel16, Jaroslav A. Hubacek, Růžena Kubínová, Migle Baceviciene17, Abdonas Tamosiunas17, Andrzej Pajak18, Roman Topor-Madry18, Sofia Malyutina19, Damiano Baldassarre, Bengt Sennblad20, Elena Tremoli, U de Faire21, Luigi Ferrucci21, S Bandenelli, Tetsu Tanaka21, James F. Meschia10, AB Singleton6, Gerjan Navis22, I. Mateo Leach22, Bakker Sjl.22, Ron T. Gansevoort, Ian Ford8, Stephen E. Epstein23, Mary-Susan Burnett23, Joe Devaney23, Johan Wouter Jukema4, Westendorp Rgj.5, G Jan de Borst5, Y. van der Graaf5, P A de Jong5, Mailand-van der Zee A-H.5, Olaf H. Klungel5, A. de Boer5, P. A. Doevendans5, Jeffrey W. Stephens24, Charles B. Eaton25, Jennifer G. Robinson26, JoAnn E. Manson27, F G Fowkes28, Timothy M. Frayling28, Jenna Price9, Peter H. Whincup11, Richard W Morris1, Debbie A Lawlor12, George Davey Smith12, Yoav Ben-Shlomo12, Susan Redline27, Leslie A. Lange29, Meena Kumari1, Nicholas J. Wareham2, Verschuren Wmm.30, Emelia J. Benjamin30, John C. Whittaker11, Anders Hamsten20, Frank Dudbridge11, Delaney Jac.31, Andrew Wong31, Diana Kuh31, Rebecca Hardy31, Berta Almoguera Castillo7, John Connolly7, P. van der Harst, Eric J. Brunner1, Michael Marmot1, Christina L. Wassel32, Steve E. Humphries1, P.J. Talmud1, Mika Kivimäki1, Folkert W. Asselbergs5, Mikhail I. Voevoda19, Martin Bobak1, Hynek Pikhart1, James G. Wilson33, Hakon Hakonarson7, Alexander P. Reiner34, Brendan J. Keating7, Naveed Sattar8, Aroon D. Hingorani1, Juan P. Casas11 
TL;DR: IL6R blockade could provide a novel therapeutic approach to prevention of coronary heart disease that warrants testing in suitably powered randomised trials and could help to validate and prioritise novel drug targets or to repurpose existing agents and targets for new therapeutic uses.

891 citations

Journal ArticleDOI
TL;DR: Mendelian randomization is the term applied to the random assortment of alleles at the time of gamete formation that results in population distributions of genetic variants that are generally independent of behavioural and environmental factors that typically confound epidemiological associations between putative risk factors and disease.
Abstract: Mendelian randomization is the term applied to the random assortment of alleles at the time of gamete formation. This results in population distributions of genetic variants that are generally independent of behavioural and environmental factors that typically confound epidemiological associations between putative risk factors and disease. In some circumstances this can provide a study design akin to randomized comparisons. The principles of Mendelian randomization can serve to limit several potential problems in observational epidemiology (Table 1). The avoidance of confounding is clearly a key advantage, and in view of this, Martin Tobin and colleagues6 have suggested that the approach should be termed ‘Mendelian deconfounding’. However, there are several additional and perhaps equally important ways in which Mendelian randomization can strengthen inferences drawn from observational studies. In the example Katan originally presented—that of the association between low serum cholesterol levels and cancer—the most plausible bias would be introduced by reverse causation. The early stages of cancer could lead to a decrease in circulating cholesterol levels, generating an inverse association between cholesterol levels and cancer morbidity or mortality.1 Early stages of cancer will not, however, change inherited genetic variants that are associated with cholesterol levels. Thus if low cholesterol level were a cause of increased cancer risk then individuals with genetic variants associated with lower cholesterol levels should have a higher cancer risk. If, on the other hand, reverse causation is responsible for the association between cholesterol level and cancer, there should be no association between genetic variants related to cholesterol level and cancer risk. Biological forms of reverse causation may influence many epidemiological associations—for example, those between markers of inflammation and coronary heart disease, where existing atherosclerosis may influence the level of factors such as fibrinogen and C-reactive protein.13 Reverse causation can also occur through exposure assignment—for example, people with early stages of coronary heart disease may take vitamin supplements because they believe these will reduce their risk of cardiovascular events. This will tend to generate a positive association between vitamin intake and disease. A form of reverse causation can also occur through reporting bias, with the presence of disease influencing reporting disposition. In casecontrol studies people with the disease under investigation may report on their prior exposure history in a different way than do controls—perhaps because the former will think harder about potential reasons that account for why they have developed the disease. In this situation the association between genetic variants related to the exposure and disease outcome will not usually be biased. In observational studies associations between an exposure and disease will generally be biased if there is selection according to an exposure–disease combination in case-control studies, or according to an exposure–disease risk combination in prospective studies. If, for example, people with an exposure and at low risk of disease for other reasons were differentially excluded from a study the exposure would appear to be positively related to disease outcome, even if there were no such association in the underlying population. This is a form of ‘Berkson’s bias’, well known to epidemiologists.14 A possible example of such associative selection bias relates to the finding in the large American Cancer Society volunteer cohort that high alcohol consumption was associated with a reduced risk of stroke.15 This is somewhat counter-intuitive as the outcome category included haemorrhagic stroke (for which there is no obvious mechanism through which alcohol would reduce risk) and because alcohol is known to increase blood pressure16,17— a major causal factor for stroke.18 Population-based studies have found that alcohol tends to increase stroke risk.19–21 Heavy drinkers who volunteer for a study known to be about the health effects of their lifestyle are likely to be very unrepresentative of all heavy drinkers in the population, in ways that render them to be at low risk of stroke. Moderate and non-drinkers

881 citations

01 Jan 2015
TL;DR: The contribution of rare and low-frequency variants to human traits is largely unexplored as mentioned in this paper, but the contribution of these variants to the human traits has not yet been fully explored.
Abstract: The contribution of rare and low-frequency variants to human traits is largely unexplored. Here we describe insights from sequencing whole genomes (low read depth, 7×) or exomes (high read depth, 80×) of nearly 10,000 individuals from population-based and disease collections. In extensively phenotyped cohorts we characterize over 24 million novel sequence variants, generate a highly accurate imputation reference panel and identify novel alleles associated with levels of triglycerides (APOB), adiponectin (ADIPOQ) and low-density lipoprotein cholesterol (LDLR and RGAG1) from single-marker and rare variant aggregation tests. We describe population structure and functional annotation of rare and low-frequency variants, use the data to estimate the benefits of sequencing for association studies, and summarize lessons from disease-specific collections. Finally, we make available an extensive resource, including individual-level genetic and phenotypic data and web-based tools to facilitate the exploration of association results.

824 citations


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Journal ArticleDOI
04 Sep 2003-BMJ
TL;DR: A new quantity is developed, I 2, which the authors believe gives a better measure of the consistency between trials in a meta-analysis, which is susceptible to the number of trials included in the meta- analysis.
Abstract: Cochrane Reviews have recently started including the quantity I 2 to help readers assess the consistency of the results of studies in meta-analyses. What does this new quantity mean, and why is assessment of heterogeneity so important to clinical practice? Systematic reviews and meta-analyses can provide convincing and reliable evidence relevant to many aspects of medicine and health care.1 Their value is especially clear when the results of the studies they include show clinically important effects of similar magnitude. However, the conclusions are less clear when the included studies have differing results. In an attempt to establish whether studies are consistent, reports of meta-analyses commonly present a statistical test of heterogeneity. The test seeks to determine whether there are genuine differences underlying the results of the studies (heterogeneity), or whether the variation in findings is compatible with chance alone (homogeneity). However, the test is susceptible to the number of trials included in the meta-analysis. We have developed a new quantity, I 2, which we believe gives a better measure of the consistency between trials in a meta-analysis. Assessment of the consistency of effects across studies is an essential part of meta-analysis. Unless we know how consistent the results of studies are, we cannot determine the generalisability of the findings of the meta-analysis. Indeed, several hierarchical systems for grading evidence state that the results of studies must be consistent or homogeneous to obtain the highest grading.2–4 Tests for heterogeneity are commonly used to decide on methods for combining studies and for concluding consistency or inconsistency of findings.5 6 But what does the test achieve in practice, and how should the resulting P values be interpreted? A test for heterogeneity examines the null hypothesis that all studies are evaluating the same effect. The usual test statistic …

45,105 citations

Journal ArticleDOI
13 Sep 1997-BMJ
TL;DR: Funnel plots, plots of the trials' effect estimates against sample size, are skewed and asymmetrical in the presence of publication bias and other biases Funnel plot asymmetry, measured by regression analysis, predicts discordance of results when meta-analyses are compared with single large trials.
Abstract: Objective: Funnel plots (plots of effect estimates against sample size) may be useful to detect bias in meta-analyses that were later contradicted by large trials. We examined whether a simple test of asymmetry of funnel plots predicts discordance of results when meta-analyses are compared to large trials, and we assessed the prevalence of bias in published meta-analyses. Design: Medline search to identify pairs consisting of a meta-analysis and a single large trial (concordance of results was assumed if effects were in the same direction and the meta-analytic estimate was within 30% of the trial); analysis of funnel plots from 37 meta-analyses identified from a hand search of four leading general medicine journals 1993-6 and 38 meta-analyses from the second 1996 issue of the Cochrane Database of Systematic Reviews . Main outcome measure: Degree of funnel plot asymmetry as measured by the intercept from regression of standard normal deviates against precision. Results: In the eight pairs of meta-analysis and large trial that were identified (five from cardiovascular medicine, one from diabetic medicine, one from geriatric medicine, one from perinatal medicine) there were four concordant and four discordant pairs. In all cases discordance was due to meta-analyses showing larger effects. Funnel plot asymmetry was present in three out of four discordant pairs but in none of concordant pairs. In 14 (38%) journal meta-analyses and 5 (13%) Cochrane reviews, funnel plot asymmetry indicated that there was bias. Conclusions: A simple analysis of funnel plots provides a useful test for the likely presence of bias in meta-analyses, but as the capacity to detect bias will be limited when meta-analyses are based on a limited number of small trials the results from such analyses should be treated with considerable caution. Key messages Systematic reviews of randomised trials are the best strategy for appraising evidence; however, the findings of some meta-analyses were later contradicted by large trials Funnel plots, plots of the trials9 effect estimates against sample size, are skewed and asymmetrical in the presence of publication bias and other biases Funnel plot asymmetry, measured by regression analysis, predicts discordance of results when meta-analyses are compared with single large trials Funnel plot asymmetry was found in 38% of meta-analyses published in leading general medicine journals and in 13% of reviews from the Cochrane Database of Systematic Reviews Critical examination of systematic reviews for publication and related biases should be considered a routine procedure

37,989 citations

Journal ArticleDOI
TL;DR: In this review the usual methods applied in systematic reviews and meta-analyses are outlined, and the most common procedures for combining studies with binary outcomes are described, illustrating how they can be done using Stata commands.

31,656 citations

Journal ArticleDOI
TL;DR: An Explanation and Elaboration of the PRISMA Statement is presented and updated guidelines for the reporting of systematic reviews and meta-analyses are presented.
Abstract: Systematic reviews and meta-analyses are essential to summarize evidence relating to efficacy and safety of health care interventions accurately and reliably. The clarity and transparency of these reports, however, is not optimal. Poor reporting of systematic reviews diminishes their value to clinicians, policy makers, and other users. Since the development of the QUOROM (QUality Of Reporting Of Meta-analysis) Statement—a reporting guideline published in 1999—there have been several conceptual, methodological, and practical advances regarding the conduct and reporting of systematic reviews and meta-analyses. Also, reviews of published systematic reviews have found that key information about these studies is often poorly reported. Realizing these issues, an international group that included experienced authors and methodologists developed PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) as an evolution of the original QUOROM guideline for systematic reviews and meta-analyses of evaluations of health care interventions. The PRISMA Statement consists of a 27-item checklist and a four-phase flow diagram. The checklist includes items deemed essential for transparent reporting of a systematic review. In this Explanation and Elaboration document, we explain the meaning and rationale for each checklist item. For each item, we include an example of good reporting and, where possible, references to relevant empirical studies and methodological literature. The PRISMA Statement, this document, and the associated Web site (http://www.prisma-statement.org/) should be helpful resources to improve reporting of systematic reviews and meta-analyses.

25,711 citations

Journal ArticleDOI
18 Oct 2011-BMJ
TL;DR: The Cochrane Collaboration’s tool for assessing risk of bias aims to make the process clearer and more accurate.
Abstract: Flaws in the design, conduct, analysis, and reporting of randomised trials can cause the effect of an intervention to be underestimated or overestimated. The Cochrane Collaboration’s tool for assessing risk of bias aims to make the process clearer and more accurate

22,227 citations