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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.


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Journal ArticleDOI
TL;DR: It is concluded that the thresholds proposed by Kozinn and Scott using weight, age, activity, the state of the patellofemoral joint and chondrocalcinosis should not be considered to be contraindications for the use of the Oxford UKR.
Abstract: The contraindications for unicompartmental knee replacement (UKR) remain controversial. The views of many surgeons are based on Kozinn and Scott's 1989 publication which stated that patients who weighed more than 82 kg, were younger than 60 years, undertook heavy labour, had exposed bone in the patellofemoral joint or chondrocalcinosis, were not ideal candidates for UKR. Our aim was to determine whether these potential contraindications should apply to patients with a mobile-bearing UKR. In order to do this the outcome of patients with these potential contraindications was compared with that of patients without the contraindications in a prospective series of 1000 UKRs. The outcome was assessed using the Oxford knee score, the American Knee Society score, the Tegner activity score, revision rate and survival. The clinical outcome of patients with each of the potential contraindications was similar to or better than those without each contraindication. Overall, 678 UKRs (68%) were performed in patients who had at least one potential contraindication and only 322 (32%) in patients deemed to be ideal. The survival at ten years was 97.0% (95% confidence interval 93.4 to 100.0) for those with potential contraindications and 93.6% (95% confidence interval 87.2 to 100.0) in the ideal patients. We conclude that the thresholds proposed by Kozinn and Scott using weight, age, activity, the state of the patellofemoral joint and chondrocalcinosis should not be considered to be contraindications for the use of the Oxford UKR.

180 citations

Journal ArticleDOI
TL;DR: Socioeconomic status differentials substantially account for the health inequalities between indigenous and non-indigenous groups in India, and a strong socioeconomic gradient in health is also evident within indigenous populations, reiterating the overall importance of socioeconomic status for reducing population-level health disparities.
Abstract: Background Systematic evidence on the patterns of health deprivation among indigenous peoples remains scant in developing countries. We investigate the inequalities in mortality and substance use between indigenous and non-indigenous, and within indigenous, groups in India, with an aim to establishing the relative contribution of socioeconomic status in generating health inequalities. Methods and Findings Cross-sectional population-based data were obtained from the 1998–1999 Indian National Family Health Survey. Mortality, smoking, chewing tobacco use, and alcohol use were four separate binary outcomes in our analysis. Indigenous status in the context of India was operationalized through the Indian government category of scheduled tribes, or Adivasis, which refers to people living in tribal communities characterized by distinctive social, cultural, historical, and geographical circumstances. Indigenous groups experience excess mortality compared to non-indigenous groups, even after adjusting for economic standard of living (odds ratio 1.22; 95% confidence interval 1.13–1.30). They are also more likely to smoke and (especially) drink alcohol, but the prevalence of chewing tobacco is not substantially different between indigenous and non-indigenous groups. There are substantial health variations within indigenous groups, such that indigenous peoples in the bottom quintile of the indigenous-peoples-specific standard of living index have an odds ratio for mortality of 1.61 (95% confidence interval 1.33–1.95) compared to indigenous peoples in the top fifth of the wealth distribution. Smoking, drinking alcohol, and chewing tobacco also show graded associations with socioeconomic status within indigenous groups. Conclusions Socioeconomic status differentials substantially account for the health inequalities between indigenous and non-indigenous groups in India. However, a strong socioeconomic gradient in health is also evident within indigenous populations, reiterating the overall importance of socioeconomic status for reducing population-level health disparities, regardless of indigeneity.

178 citations

Journal ArticleDOI
TL;DR: Of the factors tested, relative social deprivation best captures the aspects of the obesogenic environment responsible for the risk of obesity in genetically susceptible adults.
Abstract: Previous studies have suggested that modern obesogenic environments accentuate the genetic risk of obesity. However, these studies have proven controversial as to which, if any, measures of the environment accentuate genetic susceptibility to high body mass index (BMI). We used up to 120 000 adults from the UK Biobank study to test the hypothesis that high-risk obesogenic environments and behaviours accentuate genetic susceptibility to obesity. We used BMI as the outcome and a 69-variant genetic risk score (GRS) for obesity and 12 measures of the obesogenic environment as exposures. These measures included Townsend deprivation index (TDI) as a measure of socio-economic position, TV watching, a 'Westernized' diet and physical activity. We performed several negative control tests, including randomly selecting groups of different average BMIs, using a simulated environment and including sun-protection use as an environment. We found gene-environment interactions with TDI (Pinteraction = 3 × 10 -10 ), self-reported TV watching (Pinteraction = 7 × 10 -5 ) and self-reported physical activity (Pinteraction = 5 × 10 -6 ). Within the group of 50% living in the most relatively deprived situations, carrying 10 additional BMI-raising alleles was associated with approximately 3.8 kg extra weight in someone 1.73 m tall. In contrast, within the group of 50% living in the least deprivation, carrying 10 additional BMI-raising alleles was associated with approximately 2.9 kg extra weight. The interactions were weaker, but present, with the negative controls, including sun-protection use, indicating that residual confounding is likely. Our findings suggest that the obesogenic environment accentuates the risk of obesity in genetically susceptible adults. Of the factors we tested, relative social deprivation best captures the aspects of the obesogenic environment responsible.

178 citations

Journal ArticleDOI
TL;DR: Hyperinsulinaemia is positively associated with breast cancer in this cohort of older women, and this effect may be mediated via a number of hormonal pathways acting at different stages of the life course.
Abstract: Objective: To assess the association between fasting insulin levels and breast cancer. Design: Cross sectional study. Participants: 3868 women aged 60-79 years. Main outcome measure: Prevalent breast cancer (151 cases). Results: Insulin levels were positively associated with breast cancer. The age adjusted odds ratio (95% confidence interval) for a one unit increase in log(e) insulin levels among women without diabetes was 1.34 (1.02, 1.77). This association was not substantively altered by adjustment for potential confounding factors (age of menopause, hysterectomy/oophorectomy, hormone replacement use, oral contraceptive use, parity, adult social class and smoking) or potential mediating factors (body mass index, waist to hip ratio, leg length, age at menarche and childhood social class). Women with both long legs and higher insulin levels were at particularly increased risk, with breast cancer prevalence being 5.7% among women in the highest thirds of both insulin levels and leg-length compared to 1.8% among those in the lowest thirds of both. Positive associations between insulin levels and breast cancer were found for both pre- and post-menopausal breast cancers. Fasting glucose levels, HOMA score, diabetes and a history of gestational glycosuria or diabetes were also positively associated with breast cancer. Conclusions: Hyperinsulinaemia is positively associated with breast cancer in this cohort of older women. This effect may be mediated via a number of hormonal pathways acting at different stages of the life course.

178 citations

Journal ArticleDOI
Toby Johnson1, Tom R. Gaunt2, Stephen Newhouse3, Stephen Newhouse1, Sandosh Padmanabhan4, Marciej Tomaszewski5, Marciej Tomaszewski6, Meena Kumari7, Richard W Morris7, Ioanna Tzoulaki8, Ioanna Tzoulaki9, Eoin O'Brien10, Neil R Poulter9, Peter S. Sever9, Denis C. Shields10, Simon A. McG. Thom9, SG Wannamethee7, Peter H. Whincup11, Morris J. Brown12, John M. C. Connell13, Richard Dobson14, Philip Howard1, Charles A. Mein1, Abiodun Onipinla1, Sue Shaw-Hawkins1, Yun Zhang1, George Davey Smith2, Ian N M Day2, Debbie A Lawlor2, Alison H. Goodall6, Alison H. Goodall5, F. Gerald R. Fowkes15, Gonçalo R. Abecasis16, Paul Elliott17, Paul Elliott9, Vesela Gateva16, Peter S. Braund5, Peter S. Braund6, Paul Burton5, Paul Burton6, Christopher P. Nelson5, Christopher P. Nelson6, Martin D. Tobin6, Pim van der Harst18, Nicola Glorioso19, Hani Neuvrith20, Erika Salvi21, Jan A. Staessen22, Andrea Stucchi21, Nabila Devos23, Xavier Jeunemaitre24, Xavier Jeunemaitre23, Pierre-François Plouin24, Pierre-François Plouin23, Jean Tichet, Peeter Juhanson25, Elin Org25, Margus Putku25, Siim Sõber25, Gudrun Veldre25, Margus Viigimaa26, Anna Levinsson27, Annika Rosengren27, Dag S. Thelle28, Claire E. Hastie4, Thomas Hedner27, Wai K. Lee4, Olle Melander29, Björn Wahlstrand27, Rebecca Hardy, Andrew Wong, Jackie A. Cooper7, Jutta Palmen7, Li Chen30, Alexandre F.R. Stewart30, George A. Wells30, Harm-Jan Westra18, Marcel G. M. Wolfs18, Robert Clarke31, Maria Grazia Franzosi, Anuj Goel32, Anuj Goel33, Anders Hamsten34, Mark Lathrop, John F. Peden32, John F. Peden33, Udo Seedorf35, Hugh Watkins33, Hugh Watkins32, Willem H. Ouwehand36, Willem H. Ouwehand12, Jennifer G. Sambrook12, Jonathan Stephens12, Juan-Pablo Casas7, Juan-Pablo Casas37, Fotios Drenos7, Michael V. Holmes7, Mika Kivimäki7, Sonia Shah7, Tina Shah7, Philippa J. Talmud7, John C. Whittaker37, John C. Whittaker38, Chris Wallace12, Christian Delles4, Maris Laan25, Diana Kuh, Steve E. Humphries7, Fredrik Nyberg27, Fredrik Nyberg39, Daniele Cusi21, Robert Roberts30, Christopher Newton-Cheh40, Lude Franke18, Alive V. Stanton41, Anna F. Dominiczak4, Martin Farrall32, Martin Farrall33, Aroon D. Hingorani7, Nilesh J. Samani6, Nilesh J. Samani5, Mark J. Caulfield1, Patricia B. Munroe1 
TL;DR: An analysis of combined discovery and follow-up data identified SNPs significantly associated with BP at p < 8.56 × 10(-7) at four further loci and highlighted the utility of studying SNPs and samples that are independent of those studied previously even when the sample size is smaller than that in previous studies.
Abstract: Raised blood pressure (BP) is a major risk factor for cardiovascular disease. Previous studies have identified 47 distinct genetic variants robustly associated with BP, but collectively these explain only a few percent of the heritability for BP phenotypes. To find additional BP loci, we used a bespoke gene-centric array to genotype an independent discovery sample of 25,118 individuals that combined hypertensive case-control and general population samples. We followed up four SNPs associated with BP at our p < 8.56 × 10(-7) study-specific significance threshold and six suggestively associated SNPs in a further 59,349 individuals. We identified and replicated a SNP at LSP1/TNNT3, a SNP at MTHFR-NPPB independent (r(2) = 0.33) of previous reports, and replicated SNPs at AGT and ATP2B1 reported previously. An analysis of combined discovery and follow-up data identified SNPs significantly associated with BP at p < 8.56 × 10(-7) at four further loci (NPR3, HFE, NOS3, and SOX6). The high number of discoveries made with modest genotyping effort can be attributed to using a large-scale yet targeted genotyping array and to the development of a weighting scheme that maximized power when meta-analyzing results from samples ascertained with extreme phenotypes, in combination with results from nonascertained or population samples. Chromatin immunoprecipitation and transcript expression data highlight potential gene regulatory mechanisms at the MTHFR and NOS3 loci. These results provide candidates for further study to help dissect mechanisms affecting BP and highlight the utility of studying SNPs and samples that are independent of those studied previously even when the sample size is smaller than that in previous studies.

178 citations


Cited by
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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