George Davey Smith

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.

Is epidemiology ready for epigenetics

Abstract: The revellers depicted in the painting by the Dutch artist Judith Leyster (1609–60) (Figure 1) will not have given epigenetics a passing thought. Little were they to know that indulgences, such as drinking alcohol and smoking, would be contributing to their ‘exposome’1 and marking their epigenome to potentially compromise their future health. The skeleton proffering an hourglass is perhaps a portent of the perils of such indulgence. Epigenetic alterations have been linked—sometimes tentatively—to a wide array of exposures and health outcomes, from smoking2 and alcohol3,4 to lung cancer5 and psychoses,6 and the field will surely witness a glut of further literature in the near future. Figure 1 ‘The Last Drop’. Judith Leyster, c.1630–1631. Philadelphia Museum of Art, reproduced by kind permission of Philadelphia Museum of Art Epigenetics has undoubtedly recently taken the world of medical research by storm,7 offering the promise of prediction, prevention and treatment of a wide spectrum of common complex diseases.8 The current special issue brings together a collection of reviews and articles with epigenetics as a common theme to consider the contribution that epidemiology can make in defining the role of epigenetics in common complex disease, or conversely, to ask the question—is epidemiology ready for epigenetics?

Commentary: Behind the Broad Street pump: aetiology, epidemiology and prevention of cholera in mid-19th century Britain

Abstract: Introductory epidemiology text books and courses generally contain little epidemiological history, but an exception is made for the story of John Snow, the water-born transmission of cholera, and the handle of the Broad Street pump.1–5 Snow’s 1855 book, On The Mode Of Communication Of Cholera,6 is indeed a beautiful demonstration of ‘the epidemiological imagination’7 in action, and continues to provide example and inspiration to people entering the discipline. However, it appeared amidst a veritable spate of speculation, experiment, investigation and recommendations regarding cholera, and some of these less celebrated (at least now) contributions remain instructive. Therefore, in the current issue of the International Journal of Epidemiology we reprint a section of Dr John Sutherland’s report for the General Board of Health on the 1848–1849 British cholera epidemic (Figure 1), together with a series of commentaries.8–10 The extracts from Sutherland’s report include his investigation of the effect of water source on cholera risk in Salford, Manchester, which was briefly referred to by Snow6 and has occasionally been recognized as a seminal investigation.11,12 The discussion by Sutherland of the implications of his finding are clearly at variance with those of Snow, who more strongly emphasized the necessary transmissible element in generating cholera (and thus in triggering epidemics), but Sutherland’s utilization of quantitative data is striking. As with (virtually) all scientific advances, Snow’s work did not emerge from a vacuum, and this background has been explored from various perspectives.13–23 The proto-epidemiological approaches to cholera in the mid-19th century have continuing implications for epidemiological theory and practice, and this does not only apply to the investigations now seen to have contributed to us reaching the correct conclusions. The efforts of many of Snow’s predecessors and contemporaries were seen, at the time, as of at least (and often greater) importance than those of Snow.13,14,24,25 The contributions of those who are now excluded from potted histories of epidemiology are certainly worth revisiting.

Response to Hartwig and Davies.

Abstract: and indeed many of the other analyses reported in our paper do not suffer from potential bias due to weak instruments. Hartwig and Davies did, however, suggest that we could have used estimates from an external source to obtain less biased results in our MR-Egger analyses. Whereas we agree that this would be good practice in most situations, we do not feel that it would have been appropriate in our study, for two reasons. First, the focus of our article was not on a possible causal relationship between body mass index and BMD (which is well-known and widely accepted), but rather on a possible causal relationship between adiposity [as operationalized as fat mass calculated from total body dual-energy X-ray absorptiometry (DXA)] and BMD. There are no publicly available genome-wide association studies of total body fat mass as measured by total body DXA, and therefore no external estimates that we could have applied in our analyses (i.e. as far as we are aware, we are currently the largest such study). We could have used external estimates for analyses involving body mass index, but this would have been of limited utility since body mass index is a far from perfect measure of adiposity. Second, our study involved 9-year-old children from the Avon Longitudinal Study of Parents and Children. It is unclear the extent to which effect sizes of adiposity-associated variants in adults reflect effect sizes of adiposity-associated variants in children (as Hartwig and Davies recognize), and we therefore feel it would have been inappropriate to use adult-derived external estimates in our study of children.

Measuring inconsistency in meta-analyses

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 …

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

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

The PRISMA Statement for Reporting Systematic Reviews and Meta-Analyses of Studies That Evaluate Health Care Interventions: Explanation and Elaboration

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.

The Cochrane Collaboration’s tool for assessing risk of bias in randomised trials

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