List Size, Standards and Perfromance in General Practice - A Pilot Study in the South East Thames Region

This is a report of a piolt study carried out among 155 general practitioner trainers in South East Thames region. Similar pilot studies have been carried out among trainers in four other regions on England. Separate reports have been prepared for each of the five regions, together with a summary report comparing the findings among the regions. The pilot studies were funded by the DHSS and carried out by staff of the Health Services Research Unit at the University of Kent at Camterbury

Ranking the importance of nuclear reactions for activation and transmutation events

Pathways-reduced analysis is one of the techniques used by the Fispact-II nuclear activation and transmutation software to study the sensitivity of the computed inventories to uncertainties in reaction cross-sections. Although deciding which pathways are most important is very helpful in for example determining which nuclear data would benefit from further refinement, pathways-reduced analysis need not necessarily define the most critical reaction, since one reaction may contribute to several different pathways. This work examines three different techniques for ranking reactions in their order of importance in determining the final inventory, comparing the pathways based metric (PBM), the direct method and one based on the Pearson correlation coefficient. Reasons why the PBM is to be preferred are presented.Comment: 30 pages, 10 figure

Valuing Breastfeeding: Health Care Professionals’ Experiences of Delivering a Conditional Cash Transfer Scheme for Breastfeeding in Areas With Low Breastfeeding Rates

Alongside a randomized controlled trial testing the effectiveness of offering a cash transfer scheme (shopping vouchers) to mothers in areas with low breastfeeding rates, qualitative interviews were conducted with health care professionals delivering the scheme to explore their experiences. Health care professionals (n = 34; mainly midwives and health visitors) were interviewed in depth. Transcripts from recorded interviews were analyzed using a Framework Analysis approach. There was widespread acceptance of the scheme by health care professionals, with prior concerns regarding bribery and coercion being quickly allayed. Health care professionals reported that the scheme fitted in well with their routine ways of promoting and endorsing breastfeeding. They described their experiences of women’s positive reaction toward the scheme and how the scheme encouraged breastfeeding and gave breastfeeding higher value. Health care professionals reported that the incentives helped them engage women and promote and support breastfeeding in areas with low breastfeeding rates

Examining the incremental impact of long-standing health conditions on subjective well-being alongside the EQ-5D

Background: Generic preference-based measures such as the EQ-5D and SF-6D have been criticised for being narrowly focused on a sub-set of dimensions of health. Our study aims to explore whether long-standing health conditions have an incremental impact on subjective well-being alongside the EQ-5D. Methods: Using data from the South Yorkshire Cohort study (N = 13,591) collected between 2010 and 2012 on the EQ-5D, long-standing health conditions (self-reported), and subjective well-being measure – life satisfaction using a response scale from 0 (completely dissatisfied) to 10 (completely satisfied), we employed generalised logit regression models. We assessed the impact of EQ-5D and long-standing health conditions together on life satisfaction by examining the size and significance of their estimated odds ratios. Results: The EQ-5D had a significant association with life satisfaction, in which anxiety/depression and then self-care had the largest weights. Some long-standing health conditions were significant in some models, but most did not have an independent impact on life satisfaction. Overall, none of the health conditions had a consistent impact on life satisfaction alongside the EQ-5D. Conclusions: Out study suggests that the impact of long-standing health conditions on life satisfaction is adequately captured by the EQ-5D, although the findings are limited by reliance on self-reported conditions and a single item life satisfaction measure

Epigenetic modelling of former, current and never smokers

BACKGROUND: DNA methylation (DNAm) performs excellently in the discrimination of current and former smokers from never smokers, where AUCs > 0.9 are regularly reported using a single CpG site (cg05575921; AHRR). However, there is a paucity of DNAm models which attempt to distinguish current, former and never smokers as individual classes. Derivation of a robust DNAm model that accurately distinguishes between current, former and never smokers would be particularly valuable to epidemiological research (as a more accurate smoking definition vs. self-report) and could potentially translate to clinical settings. Therefore, we appraise 4 DNAm models of ternary smoking status (that is, current, former and never smokers): methylation at cg05575921 (AHRR model), weighted scores from 13 CpGs created by Maas et al. (Maas model), weighted scores from a LASSO model of candidate smoking CpGs from the literature (candidate CpG LASSO model), and weighted scores from a LASSO model supplied with genome-wide 450K data (agnostic LASSO model). Discrimination is assessed by AUC, whilst classification accuracy is assessed by accuracy and kappa, derived from confusion matrices. RESULTS: We find that DNAm can classify ternary smoking status with reasonable accuracy, including when applied to external data. Ternary classification using only DNAm far exceeds the classification accuracy of simply assigning all classes as the most prevalent class (63.7% vs. 36.4%). Further, we develop a DNAm classifier which performs well in discriminating current from former smokers (agnostic LASSO model AUC in external validation data: 0.744). Finally, across our DNAm models, we show evidence of enrichment for biological pathways and human phenotype ontologies relevant to smoking, such as haemostasis, molybdenum cofactor synthesis, body fatness and social behaviours, providing evidence of the generalisability of our classifiers. CONCLUSIONS: Our findings suggest that DNAm can classify ternary smoking status with close to 65% accuracy. Both the ternary smoking status classifiers and current versus former smoking status classifiers address the present lack of former smoker classification in epigenetic literature; essential if DNAm classifiers are to adequately relate to real-world populations. To improve performance further, additional focus on improving discrimination of current from former smokers is necessary. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13148-021-01191-6

The association between long-term conditions and uptake of population-based screening for colorectal cancer: results from two English cohort studies

Introduction: Uptake of screening for colorectal cancer (CRC) can reduce mortality, and population-based screening is offered in England. To date, there is little evidence on the association between having a long-term condition (LTC) and CRC screening uptake. The objective of this study was to examine the association between having an LTC and uptake of CRC screening in England with the guaiac fecal occult blood test, with a particular focus on common mental disorders. Methods: The study was a preregistered secondary analysis of two cohorts: first, a linked data set between the regional Yorkshire Health Study (YHS) and the National Health Service National Bowel Cancer Screening Program (BCSP, years 2006–2014); second, the national English Longitudinal Study of Ageing (ELSA, years 2014–2015). Individuals eligible for BCSP screening who participated in either the YHS (7,142) or ELSA Wave 7 (4,099) were included. Study registration: ClinicalTrials.gov, number NCT02503969. Results: In both the cohorts, diabetes was associated with lower uptake (YHS odds ratio [OR] for non-uptake 1.35, 95% CI 1.03–1.78; ELSA 1.33, 1.03–1.72) and osteoarthritis was associated with increased uptake (YHS 0.75, 0.57–0.99; ELSA 0.76, 0.62–0.93). After controlling for broader determinants of health, there was no evidence of significantly different uptake for individuals with common mental disorders. Conclusion: Two large independent cohorts provided evidence that uptake of CRC screening is lower among individuals with diabetes and higher among individuals with osteoarthritis. Further work should compare barriers and facilitators to screening among individuals with either of these conditions. This study also demonstrates the benefits of data linkage for improving clinical decision-making

Bayesian network analysis incorporating genetic anchors complements conventional Mendelian randomization approaches for exploratory analysis of causal relationships in complex data

Mendelian randomization (MR) implemented through instrumental variables analysis is an increasingly popular causal inference tool used in genetic epidemiology. But it can have limitations for evaluating simultaneous causal relationships in complex data sets that include, for example, multiple genetic predictors and multiple potential risk factors associated with the same genetic variant. Here we use real and simulated data to investigate Bayesian network analysis (BN) with the incorporation of directed arcs, representing genetic anchors, as an alternative approach. A Bayesian network describes the conditional dependencies/independencies of variables using a graphical model (a directed acyclic graph) with an accompanying joint probability. In real data, we found BN could be used to infer simultaneous causal relationships that confirmed the individual causal relationships suggested by bi-directional MR, while allowing for the existence of potential horizontal pleiotropy (that would violate MR assumptions). In simulated data, BN with two directional anchors (mimicking genetic instruments) had greater power for a fixed type 1 error than bi-directional MR, while BN with a single directional anchor performed better than or as well as bi-directional MR. Both BN and MR could be adversely affected by violations of their underlying assumptions (such as genetic confounding due to unmeasured horizontal pleiotropy). BN with no directional anchor generated inference that was no better than by chance, emphasizing the importance of directional anchors in BN (as in MR). Under highly pleiotropic simulated scenarios, BN outperformed both MR (and its recent extensions) and two recently-proposed alternative approaches: a multi-SNP mediation intersection-union test (SMUT) and a latent causal variable (LCV) test. We conclude that BN incorporating genetic anchors is a useful complementary method to conventional MR for exploring causal relationships in complex data sets such as those generated from modern "omics" technologies

Alternative Covid-19 mitigation measures in school classrooms:analysis using an agent-based model of SARS-CoV-2 transmission

The SARS-CoV-2 epidemic has impacted children's education, with schools required to implement infection control measures that have led to periods of absence and classroom closures. We developed an agent-based epidemiological model of SARS-CoV-2 transmission in a school classroom that allows us to quantify projected infection patterns within primary school classrooms, and related uncertainties. Our approach is based on a contact model constructed using random networks, informed by structured expert judgement. The effectiveness of mitigation strategies in suppressing infection outbreaks and limiting pupil absence are considered. COVID-19 infections in primary schools in England in autumn 2020 were re-examined and the model was then used to estimate infection levels in autumn 2021, as the Delta variant was emerging and it was thought likely that school transmission would play a major role in an incipient new wave of the epidemic. Our results were in good agreement with available data. These findings indicate that testing-based surveillance is more effective than bubble quarantine, both for reducing transmission and avoiding pupil absence, even accounting for insensitivity of self-administered tests. Bubble quarantine entails large numbers of absences, with only modest impact on classroom infections. However, maintaining reduced contact rates within the classroom can have a major benefit for managing COVID-19 in school settings

Alternative Covid-19 mitigation measures in school classrooms:analysis using an agent-based model of SARS-CoV-2 transmission

The SARS-CoV-2 epidemic has impacted children's education, with schools required to implement infection control measures that have led to periods of absence and classroom closures. We developed an agent-based epidemiological model of SARS-CoV-2 transmission in a school classroom that allows us to quantify projected infection patterns within primary school classrooms, and related uncertainties. Our approach is based on a contact model constructed using random networks, informed by structured expert judgement. The effectiveness of mitigation strategies in suppressing infection outbreaks and limiting pupil absence are considered. COVID-19 infections in primary schools in England in autumn 2020 were re-examined and the model was then used to estimate infection levels in autumn 2021, as the Delta variant was emerging and it was thought likely that school transmission would play a major role in an incipient new wave of the epidemic. Our results were in good agreement with available data. These findings indicate that testing-based surveillance is more effective than bubble quarantine, both for reducing transmission and avoiding pupil absence, even accounting for insensitivity of self-administered tests. Bubble quarantine entails large numbers of absences, with only modest impact on classroom infections. However, maintaining reduced contact rates within the classroom can have a major benefit for managing COVID-19 in school settings