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

Investigation of social, demographic and health variations in the usage of prescribed and over-the-counter medicines within a large cohort (South Yorkshire, UK)

Objectives Prescribed and over-the-counter (non-prescribed) medicine usage has increased in recent years; however, there has been less investigation of the socioeconomic predictors of use. This has been due to a lack of data, especially for over-the-counter medicines. Our study aims to understand how prescribed and over-the-counter medicine patterns vary by demographic, social and health characteristics within a large population cohort. Design Cross-sectional data analysis. Setting South Yorkshire, UK. Participants 27 806 individuals from wave 1 of the Yorkshire Health Study (2010–2012). Measures Individuals self-reported each medicine they were taking and whether each was prescribed or not. The medicines were grouped into 14 categories (eg, cardiovascular system, infection, contraception). Negative binomial regression models were used to analyse the count of medicine usage. We included demographic (age, gender, ethnicity), social (education), health-related (body mass index, smoking, alcohol consumption, physical activity) factors and chronic health conditions (eg, stroke, anxiety and heart disease) in our analyses. Results 49% of men and 62% of women were taking medicine with the majority of this prescribed (88% and 83%, respectively). Health conditions were found to be positively associated with prescribed medicine usage, but mixed in their associated with over-the-counter medicines. Educational attainment was negatively associated with prescribed and positively associated with over-the-counter usage. Conclusions Our study addresses a dearth of evidence to provide new insights into how behaviours in medicine usage vary by demographic, social and health-related factors. Differences in over-the-counter medicine usage by educational attainment may help our understanding of the determinants of health inequalities

Association between body mass index and health-related quality of life, and the impact of self-reported long-term conditions - cross-sectional study from the south Yorkshire cohort dataset

Background We sought to quantify the relationship between body mass index (BMI) and health-related quality (HRQoL) of life, as measured by the EQ-5D, whilst controlling for potential confounders. In addition, we hypothesised that certain long-term conditions (LTCs), for which being overweight or obese is a known risk factor, may mediate the association between BMI and HRQoL. Hence the aim of our study was to explore the association between BMI and HRQoL, first controlling for confounders and then exploring the potential impact of LTCs. Methods We used baseline data from the South Yorkshire Cohort, a cross-sectional observational study which uses a cohort multiple randomised controlled trial design. For each EQ-5D health dimension we used logistic regression to model the probability of responding as having a problem for each of the five health dimensions. All continuous variables were modelled using fractional polynomials. We examined the impact on the coefficients for BMI of removing LTCs from our model. We considered the self-reported LTCs: diabetes, heart disease, stroke, cancer, osteoarthritis, breathing problems and high blood pressure. Results The dataset used in our analysis had data for 19,460 individuals, who had a mean EQ-5D score of 0.81 and a mean BMI of 26.3 kg/m2. For each dimension, BMI and all of the LTCs were significant predictors. For overweight or obese individuals (BMI ≥ 25 kg/m2), each unit increase in BMI was associated with approximately a 3% increase in the odds of reporting a problem for the anxiety/depression dimension, a 8% increase for the mobility dimension, and approximately 6% for the remaining dimension s. Diabetes, heart disease, osteoarthritis and high blood pressure were identified as being potentially mediating variables for all of the dimensions. Conclusions Compared to those of a normal weight (18.5 < BMI < 25 kg/m2), overweight and obese individuals had a reduced HRQoL, with each unit increase in BMI associated with approximately a 6% increase in the odds of reporting a problem on any of the EQ-5D health dimensions. There was evidence to suggest that diabetes, heart disease, osteoarthritis and high blood pressure may mediate the association between being overweight and HRQoL

Who are the obese? A cluster analysis exploring subgroups of the obese

Background Body mass index (BMI) can be used to group individuals in terms of their height and weight as obese. However, such a distinction fails to account for the variation within this group across other factors such as health, demographic and behavioural characteristics. The study aims to examine the existence of subgroups of obese individuals. Methods Data were taken from the Yorkshire Health Study (2010–12) including information on demographic, health and behavioural characteristics. Individuals with a BMI of ≥30 were included. A two-step cluster analysis was used to define groups of individuals who shared common characteristics. Results The cluster analysis found six distinct groups of individuals whose BMI was ≥30. These subgroups were heavy drinking males, young healthy females; the affluent and healthy elderly; the physically sick but happy elderly; the unhappy and anxious middle aged and a cluster with the poorest health. Conclusions It is important to account for the important heterogeneity within individuals who are obese. Interventions introduced by clinicians and policymakers should not target obese individuals as a whole but tailor strategies depending upon the subgroups that individuals belong to

The long-term impact of folic acid in pregnancy on offspring DNA methylation : follow-up of the Aberdeen folic acid supplementation trial (AFAST)

Funding This work was supported by the NIHR Bristol Biomedical Research Centre at the University Hospitals Bristol NHS Foundation Trust and the University of Bristol. The views expressed in this publication are those of the authors and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health. R.C.R., G.C.S., N.K., T.G., G.D.S. and C.L.R. work in a unit that receives funds from the University of Bristol and the UK Medical Research Council (MC_UU_12013/1, MC_UU_12013/2 and MC_UU_12013/8). This work was also supported by CRUK (grant number C18281/A19169) and the ESRC (grant number ES/N000498/1). C.M.T. is supported by a Wellcome Trust Career Re-entry Fellowship (grant number 104077/Z/14/Z).Peer reviewedPublisher PD

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

Best (but oft-forgotten) practices:the design, analysis, and interpretation of Mendelian randomization studies

Mendelian randomization (MR) is an increasingly important tool for appraising causality in observational epidemiology. The technique exploits the principle that genotypes are not generally susceptible to reverse causation bias and confounding, reflecting their fixed nature and Mendel’s first and second laws of inheritance. The approach is, however, subject to important limitations and assumptions that, if unaddressed or compounded by poor study design, can lead to erroneous conclusions. Nevertheless, the advent of 2-sample approaches (in which exposure and outcome are measured in separate samples) and the increasing availability of open-access data from large consortia of genome-wide association studies and population biobanks mean that the approach is likely to become routine practice in evidence synthesis and causal inference research. In this article we provide an overview of the design, analysis, and interpretation of MR studies, with a special emphasis on assumptions and limitations. We also consider different analytic strategies for strengthening causal inference. Although impossible to prove causality with any single approach, MR is a highly cost-effective strategy for prioritizing intervention targets for disease prevention and for strengthening the evidence base for public health policy

Challenges and novel approaches for investigating molecular mediation

Understanding mediation is useful for identifying intermediates lying between anexposure and an outcome which, when intervened upon, will remove (some of) the causal pathway between the exposure and outcome. Mediation approaches used in conventional epidemiology have been adapted to understanding the role of molecular intermediates in situations of high-dimensional omics data with varying degrees of success. In particular, the limitations of observational epidemiological study including confounding, reverse causation and measurement error can afflict conventional mediation approaches and may lead to incorrect conclusions regarding causal effects. Solutions to analysing mediation which overcome these problems include the use of instrumental variable methods such as Mendelian randomization, which may be applied to evaluate causality in increasingly complex networks ofomics data