Continuity or change in business representation in Britain? An assessment of the Heseltine initiatives of the 1990s

Britain has a fragmented, overlapping, and underresourced system of business representation. Attempts at reform, however, have proved difficult and largely unsuccessful. A coherent and logical system is relevant, in terms of both an effective dialogue between government and business, and the promotion of competitiveness and productivity. Through interviews and archival evidence, I look at how government has attempted to reform business associations. The main focus is the Heseltine initiatives of the 1990s: I outline the various initiatives taken, reveal the extent to which policy represented continuity or change, and consider whether the initiatives were effective. I show that they had a degree of success but that they would have made greater impact if they had been sustained over a longer period of time. A consideration of the historical context, moreover, suggests there may be limits to the role of government intervention in business association reform

Joint Analysis for Genome-Wide Association Studies in Family-Based Designs

In family-based data, association information can be partitioned into the between-family information and the within-family information. Based on this observation, Steen et al. (Nature Genetics. 2005, 683–691) proposed an interesting two-stage test for genome-wide association (GWA) studies under family-based designs which performs genomic screening and replication using the same data set. In the first stage, a screening test based on the between-family information is used to select markers. In the second stage, an association test based on the within-family information is used to test association at the selected markers. However, we learn from the results of case-control studies (Skol et al. Nature Genetics. 2006, 209–213) that this two-stage approach may be not optimal. In this article, we propose a novel two-stage joint analysis for GWA studies under family-based designs. For this joint analysis, we first propose a new screening test that is based on the between-family information and is robust to population stratification. This new screening test is used in the first stage to select markers. Then, a joint test that combines the between-family information and within-family information is used in the second stage to test association at the selected markers. By extensive simulation studies, we demonstrate that the joint analysis always results in increased power to detect genetic association and is robust to population stratification

Maternal milk consumption, fetal growth, and the risks of neonatal complications: The Generation R Study

Background: Maternal cow-milk consumption may increase birth weight. Previous studies did not assess the association of maternal milk consumption with trimester-specific fetal growth. Objective: The objective was to assess associations of first-trimester maternal milk consumption with fetal growth characteristics in different trimesters and the risk of neonatal complications. Design: In total, 3405 mothers participating in a prospective cohort study completed a 293-item semiquantitative food-frequency questionnaire to obtain information about dairy consumption during the first trimester of pregnancy. Fetal head circumference, femur length, and weight were estimated in the second and third trimesters by ultrasonography. Results: Maternal milk consumption of >3 glasses/d was associated with greater fetal weight gain in the third trimester of pregnancy, which led to an 88-g (95% CI: 39, 135 g) higher birth weight than that with milk consumption of 0 to 1 glass/d. In addition, head circumference tended to be 2.3 cm (95% CI: -0.0, 4.6 cm) larger when mothers consumed >3 glasses/d. Maternal milk consumption was not associated with length growth. Maternal protein intake (P for trend = 0.01), but not fat or carbohydrate intake, from dairy products was associated with higher birth weight. This association appeared to be limited to milk (P for trend < 0.01), whereas protein intake from nondairy food or cheese was not associated with birth weight. Conclusions: Maternal milk consumption is associated with greater fetal weight gain. The association seems to be due to milk protein, or milk components closely associated with protein, rather than to the fat or carbohydrate fraction of milk

Development and preliminary testing of the new five-level version of EQ-5D (EQ-5D-5L)

This article introduces the new 5-level EQ-5D (EQ-5D-5L) health status measure. EQ-5D currently measures health using three levels of severity in five dimensions. A EuroQol Group task force was established to find ways of improving the instrument's sensitivity and reducing ceiling effects by increasing the number of severity levels. The study was performed in the United Kingdom and Spain. Severity labels for 5 levels in each dimension were identified using response scaling. Focus groups were used to investigate the face and content validity of the new versions, including hypothetical health states generated from those versions. Selecting labels at approximately the 25th, 50th, and 75th centiles produced two alternative 5-level versions. Focus group work showed a slight preference for the wording 'slight-moderate-severe' problems, with anchors of 'no problems' and 'unable to do' in the EQ-5D functional dimensions. Similar wording was used in the Pain/Discomfort and Anxiety/Depression dimensions. Hypothetical health states were well understood though participants stressed the need for the internal coherence of health states. A 5-level version of the EQ-5D has been developed by the EuroQol Group. Further testing is required to determine whether the new version improves sensitivity and reduces ceiling effects

Correction of Population Stratification in Large Multi-Ethnic Association Studies

The vast majority of genetic risk factors for complex diseases have, taken individually, a small effect on the end phenotype. Population-based association studies therefore need very large sample sizes to detect significant differences between affected and non-affected individuals. Including thousands of affected individuals in a study requires recruitment in numerous centers, possibly from different geographic regions. Unfortunately such a recruitment strategy is likely to complicate the study design and to generate concerns regarding population stratification.We analyzed 9,751 individuals representing three main ethnic groups - Europeans, Arabs and South Asians - that had been enrolled from 154 centers involving 52 countries for a global case/control study of acute myocardial infarction. All individuals were genotyped at 103 candidate genes using 1,536 SNPs selected with a tagging strategy that captures most of the genetic diversity in different populations. We show that relying solely on self-reported ethnicity is not sufficient to exclude population stratification and we present additional methods to identify and correct for stratification.Our results highlight the importance of carefully addressing population stratification and of carefully “cleaning” the sample prior to analyses to obtain stronger signals of association and to avoid spurious results

Gas generation and wind power: A review of unlikely allies in the United Kingdom and Ireland

No single solution currently exists to achieve the utopian desire of zero fossil fuel electricity generation. Until such time, it is evident that the energy mix will contain a large variation in stochastic and intermittent sources of renewable energy such as wind power. The increasing prominence of wind power in pursuit of legally binding European energy targets enables policy makers and conventional generating companies to plan for the unique challenges such a natural resource presents. This drive for wind has been highly beneficial in terms of security of energy supply and reducing greenhouse gas emissions. However, it has created an unusual ally in natural gas. This paper outlines the suitability and challenges faced by gas generating units in their utilisation as key assets for renewable energy integration and the transition to a low carbon future. The Single Electricity Market of the Republic of Ireland and Northern Ireland and the British Electricity Transmission Trading Agreement Market are the backdrop to this analysis. Both of these energy markets have a reliance on gas generation matching the proliferation of wind power. The unlikely and mostly ignored relationship between natural gas generation and wind power due to policy decisions and market forces is the necessity of gas to act as a bridging fuel. This review finds gas generation to be crucially important to the continued growth of renewable energy. Additionally, it is suggested that power market design should adequately reward the flexibility required to securely operate a power system with high penetrations of renewable energy, which in most cases is provided by gas generation

REDUCE (Reviewing long-term antidepressant use by careful monitoring in everyday practice) internet and telephone support to people coming off long-term antidepressants: protocol for a randomised controlled trial.

BACKGROUND: Around one in ten adults take antidepressants for depression in England, and their long-term use is increasing. Some need them to prevent relapse, but 30-50% could possibly stop them without relapsing and avoid adverse effects and complications of long-term use. However, stopping is not always easy due to withdrawal symptoms and a fear of relapse of depression. When general practitioners review patients on long-term antidepressants and recommend to those who are suitable to stop the medication, only 6-8% are able to stop. The Reviewing long-term antidepressant use by careful monitoring in everyday practice (REDUCE) research programme aims to identify safe and cost-effective ways of helping patients taking long-term antidepressants taper off treatment when appropriate. METHODS: Design: REDUCE is a two-arm, 1:1 parallel group randomised controlled trial, with randomisation clustered by participating family practices. SETTING: England and north Wales. POPULATION: patients taking antidepressants for longer than 1 year for a first episode of depression or longer than 2 years for repeated episodes of depression who are no longer depressed and want to try to taper off their antidepressant use. INTERVENTION: provision of 'ADvisor' internet programmes to general practitioners or nurse practitioners and to patients designed to support antidepressant withdrawal, plus three patient telephone calls from a psychological wellbeing practitioner. The control arm receives usual care. Blinding of patients, practitioners and researchers is not possible in an open pragmatic trial, but statistical and health economic data analysts will remain blind to allocation. OUTCOME MEASURES: the primary outcome is self-reported nine-item Patient Health Questionnaire at 6 months for depressive symptoms. SECONDARY OUTCOMES: depressive symptoms at other follow-up time points, anxiety, discontinuation of antidepressants, social functioning, wellbeing, enablement, quality of life, satisfaction, and use of health services for costs. SAMPLE SIZE: 402 patients (201 intervention and 201 controls) from 134 general practices recruited over 15-18 months, and followed-up at 3, 6, 9 and 12 months. A qualitative process evaluation will be conducted through interviews with 15-20 patients and 15-20 practitioners in each arm to explore why the interventions were effective or not, depending on the results. DISCUSSION: Helping patients reduce and stop antidepressants is often challenging for practitioners and time-consuming for very busy primary care practices. If REDUCE provides evidence showing that access to internet and telephone support enables more patients to stop treatment without increasing depression we will try to implement the intervention throughout the National Health Service, publishing practical guidance for professionals and advice for patients to follow, publicised through patient support groups. TRIAL REGISTRATION: ISRCTN:12417565. Registered on 7 October 2019

The Brain Atlas Concordance Problem: Quantitative Comparison of Anatomical Parcellations

Many neuroscientific reports reference discrete macro-anatomical regions of the brain which were delineated according to a brain atlas or parcellation protocol. Currently, however, no widely accepted standards exist for partitioning the cortex and subcortical structures, or for assigning labels to the resulting regions, and many procedures are being actively used. Previous attempts to reconcile neuroanatomical nomenclatures have been largely qualitative, focusing on the development of thesauri or simple semantic mappings between terms. Here we take a fundamentally different approach, discounting the names of regions and instead comparing their definitions as spatial entities in an effort to provide more precise quantitative mappings between anatomical entities as defined by different atlases. We develop an analytical framework for studying this brain atlas concordance problem, and apply these methods in a comparison of eight diverse labeling methods used by the neuroimaging community. These analyses result in conditional probabilities that enable mapping between regions across atlases, which also form the input to graph-based methods for extracting higher-order relationships between sets of regions and to procedures for assessing the global similarity between different parcellations of the same brain. At a global scale, the overall results demonstrate a considerable lack of concordance between available parcellation schemes, falling within chance levels for some atlas pairs. At a finer level, this study reveals spatial relationships between sets of defined regions that are not obviously apparent; these are of high potential interest to researchers faced with the challenge of comparing results that were based on these different anatomical models, particularly when coordinate-based data are not available. The complexity of the spatial overlap patterns revealed points to problems for attempts to reconcile anatomical parcellations and nomenclatures using strictly qualitative and/or categorical methods. Detailed results from this study are made available via an interactive web site at http://obart.info

Accounting for Population Stratification in Practice: A Comparison of the Main Strategies Dedicated to Genome-Wide Association Studies

Genome-Wide Association Studies are powerful tools to detect genetic variants associated with diseases. Their results have, however, been questioned, in part because of the bias induced by population stratification. This is a consequence of systematic differences in allele frequencies due to the difference in sample ancestries that can lead to both false positive or false negative findings. Many strategies are available to account for stratification but their performances differ, for instance according to the type of population structure, the disease susceptibility locus minor allele frequency, the degree of sampling imbalanced, or the sample size. We focus on the type of population structure and propose a comparison of the most commonly used methods to deal with stratification that are the Genomic Control, Principal Component based methods such as implemented in Eigenstrat, adjusted Regressions and Meta-Analyses strategies. Our assessment of the methods is based on a large simulation study, involving several scenarios corresponding to many types of population structures. We focused on both false positive rate and power to determine which methods perform the best. Our analysis showed that if there is no population structure, none of the tests led to a bias nor decreased the power except for the Meta-Analyses. When the population is stratified, adjusted Logistic Regressions and Eigenstrat are the best solutions to account for stratification even though only the Logistic Regressions are able to constantly maintain correct false positive rates. This study provides more details about these methods. Their advantages and limitations in different stratification scenarios are highlighted in order to propose practical guidelines to account for population stratification in Genome-Wide Association Studies