Search

A Minimum Income Guarantee (MIG) ensures people have a minimum amount of income for essentials such as healthy food, housing, health care, social and digital networks to support health and well-being. MIGs could be a useful tool to reduce inequalities. A MIG will only be effective if those who are eligible take it up. The aim of this paper is to explore how individual characteristics were associated with take-up of a MIG for pensioners (aged 60+ for women and aged 65+ for men) in England. The data used is from the British Household Panel Survey including 9430 observations from 1,893 people, from 1999-2002. We estimated a random effects logistic regression. Results show that women were less likely to claim than men (OR ranging from 0.17 [95% CI 0.10-0.29]-0.73 [95% CI 0.40-1.34]), and couples were less likely to claim (OR ranging from 0.04 [95% CI 0.03-0.06]-0.01 [95%CI 0.01-0.02] ) than single person households. People with better mental health (OR 1.05 95% CI 1.02-1.08), older pensioners (75+) (OR ranging from 1.98 [95% CI 1.52-2.59]-2.81 [95%CI 2.16-3.67]), those who were registered disabled (OR 4.03 95% CI 2.50-6.52), and those with no formal qualification (OR ranging from 1.74 [95%CI 0.93-3.26]-2.07 [95% CI 1.22-3.51]) were more likely to claim. Understanding who is likely to claim MIGs is important to avoid social security policy inadvertently increasing inequalities.

The NHS Diabetes Prevention Programme (DPP) in England is a behavioural intervention for preventing type 2 diabetes mellitus (T2DM) among people with non-diabetic hyperglycaemia (NDH). How this programme affects inequalities by age, sex, limiting illnesses or disability, ethnicity or deprivation is not known. We used multinomial and binary logistic regression models to compare whether the population with NDH at different stages of the programme are representative of the population with NDH: stages include (1) prevalence of NDH (using survey data from UK Household Longitudinal Study (n=794) and Health Survey for England (n=1383)); (2) identification in primary care and offer of programme (using administrative data from the National Diabetes Audit (n=1 267 350)) and (3) programme participation (using programme provider records (n=98 024)). Predicted probabilities drawn from the regressions with demographics as each outcome and dataset identifier as predictors showed that younger adults (aged under 40) (4% of the population with NDH (95% CI 2.4% to 6.5%)) and older adults (aged 80 and above) (12% (95% CI 9.5% to 14.2%)) were slightly under-represented among programme participants (2% (95% CI 1.8% to 2.2%) and 8% (95% CI 7.8% to 8.2%) of programme participants, respectively). People living in deprived areas were under-represented in eight sessions (14% (95% CI 13.7% to 14.4%) vs 20% (95% CI 16.4% to 23.6%) in the general population). Ethnic minorities were over-represented among offers (35% (95% CI 35.1% to 35.6%) vs 13% (95% CI 9.1% to 16.4%) in general population), though the proportion dropped at the programme completion stage (19% (95% CI 18.5% to 19.5%)). The DPP has the potential to reduce ethnic inequalities, but may widen socioeconomic, age and limiting illness or disability-related inequalities in T2DM. While ethnic minority groups are over-represented at the identification and offer stages, efforts are required to support completion of the programme. Programme

Background: The NHS Diabetes Prevention Programme (DPP) in England is a behavioural intervention for preventing type 2 diabetes mellitus (T2DM) among people with non-diabetic hyperglycaemia (NDH). How this programme affects inequalities by age, sex, limiting illnesses or disability, ethnicity or deprivation is not known. Methods: We used multinomial and binary logistic regression models to compare whether the population with NDH at different stages of the programme are representative of the population with NDH: stages include (1) prevalence of NDH (using survey data from UK Household Longitudinal Study (n=794) and Health Survey for England (n=1383)); (2) identification in primary care and offer of programme (using administrative data from the National Diabetes Audit (n=1 267 350)) and (3) programme participation (using programme provider records (n=98 024)). Results: Predicted probabilities drawn from the regressions with demographics as each outcome and dataset identifier as predictors showed that younger adults (aged under 40) (4% of the population with NDH (95% CI 2.4% to 6.5%)) and older adults (aged 80 and above) (12% (95% CI 9.5% to 14.2%)) were slightly under-represented among programme participants (2% (95% CI 1.8% to 2.2%) and 8% (95% CI 7.8% to 8.2%) of programme participants, respectively). People living in deprived areas were under-represented in eight sessions (14% (95% CI 13.7% to 14.4%) vs 20% (95% CI 16.4% to 23.6%) in the general population). Ethnic minorities were over-represented among offers (35% (95% CI 35.1% to 35.6%) vs 13% (95% CI 9.1% to 16.4%) in general population), though the proportion dropped at the programme completion stage (19% (95% CI 18.5% to 19.5%)). Conclusion: The DPP has the potential to reduce ethnic inequalities, but may widen socioeconomic, age and limiting illness or disability-related inequalities in T2DM. While ethnic minority groups are over-represented at the identification and offer stages, efforts are required to supp

Introduction The UK social security system is being transformed by the implementation of Universal Credit (UC), which combines six existing benefits and tax credits into a single payment for low-income households. Despite extensive reports of hardship associated with the introduction of UC, no previous studies have comprehensively evaluated its impact on mental health. Because payments are targeted at low-income households, impacts on mental health will have important consequences for health inequalities. Methods and analysis We will conduct a mixed methods study. Work package (WP) 1 will compare health outcomes for new recipients of UC with outcomes for legacy benefit recipients in two large population surveys, using the phased rollout of UC as a natural experiment. We will also analyse the relationship between the proportion of UC claimants in small areas and a composite measure of mental health. WP2 will use data collected by Citizen's Advice to explore the sociodemographic and health characteristics of people who seek advice when claiming UC and identify features of the claim process that prompt advice-seeking. WP3 will conduct longitudinal in-depth interviews with up to 80 UC claimants in England and Scotland to explore reasons for claiming and experiences of the claim process. Up to 30 staff supporting claimants will also be interviewed. WP4 will use a dynamic microsimulation model to simulate the long-term health impacts of different implementation scenarios. WP5 will undertake cost-consequence analysis of the potential costs and outcomes of introducing UC and cost-benefit analyses of mitigating actions. Ethics and dissemination We obtained ethical approval for the primary data gathering from the University of Glasgow, College of Social Sciences Research Ethics Committee, application number 400200244. We will use our networks to actively disseminate findings to UC claimants, the public, practitioners and policy-makers, using a range of methods and formats. Tr

Introduction The UK social security system is being transformed by the implementation of Universal Credit (UC), which combines six existing benefits and tax credits into a single payment for low-income households. Despite extensive reports of hardship associated with the introduction of UC, no previous studies have comprehensively evaluated its impact on mental health. Because payments are targeted at low-income households, impacts on mental health will have important consequences for health inequalities. Methods and analysis We will conduct a mixed methods study. Work package (WP) 1 will compare health outcomes for new recipients of UC with outcomes for legacy benefit recipients in two large population surveys, using the phased rollout of UC as a natural experiment. We will also analyse the relationship between the proportion of UC claimants in small areas and a composite measure of mental health. WP2 will use data collected by Citizen's Advice to explore the sociodemographic and health characteristics of people who seek advice when claiming UC and identify features of the claim process that prompt advice-seeking. WP3 will conduct longitudinal in-depth interviews with up to 80 UC claimants in England and Scotland to explore reasons for claiming and experiences of the claim process. Up to 30 staff supporting claimants will also be interviewed. WP4 will use a dynamic microsimulation model to simulate the long-term health impacts of different implementation scenarios. WP5 will undertake cost-consequence analysis of the potential costs and outcomes of introducing UC and cost-benefit analyses of mitigating actions. Ethics and dissemination We obtained ethical approval for the primary data gathering from the University of Glasgow, College of Social Sciences Research Ethics Committee, application number 400200244. We will use our networks to actively disseminate findings to UC claimants, the public, practitioners and policy-makers, using a range of methods and formats. Tr

In this paper, we investigate the objective function and deflation process for sparse Partial Least Squares (PLS) regression with multiple components. While many have considered variations on the objective for sparse PLS, the deflation process for sparse PLS has not received as much attention. Our work highlights a flaw in the Statistically Inspired Modification of Partial Least Squares (SIMPLS) deflation method when applied in sparse PLS regression. We also consider the Nonlinear Iterative Partial Least Squares (NIPALS) deflation in sparse PLS regression. To remedy the flaw in the SIMPLS method, we propose a new sparse PLS method wherein the direction vectors are constrained to be sparse and lie in a chosen subspace. We give insight into this new PLS procedure and show through examples and simulation studies that the proposed technique can outperform alternative sparse PLS techniques in coefficient estimation. Moreover, our analysis reveals a simple renormalization step that can be used to improve the estimation of sparse PLS direction vectors generated using any convex relaxation method.

Integrative analysis of high dimensional omics datasets has been studied by many authors in recent years. By incorporating prior known relationships among the variables, these analyses have been successful in elucidating the relationships between different sets of omics data. In this article, our goal is to identify important relationships between genomic expression and cytokine data from a human immunodeficiency virus vaccine trial. We proposed a flexible partial least squares technique, which incorporates group and subgroup structure in the modelling process. Our new method accounts for both grouping of genetic markers (eg, gene sets) and temporal effects. The method generalises existing sparse modelling techniques in the partial least squares methodology and establishes theoretical connections to variable selection methods for supervised and unsupervised problems. Simulation studies are performed to investigate the performance of our methods over alternative sparse approaches.

We propose two multivariate extensions of the Bayesian group lasso for variable selection and estimation for data with high dimensional predictors and multi-dimensional response variables. The methods utilize spike and slab priors to yield solutions which are sparse at either a group level or both a group and individual feature level. The incorporation of group structure in a predictor matrix is a key factor in obtaining better estimators and identifying associations between multiple responses and predictors. The approach is suited to many biological studies where the response is multivariate and each predictor is embedded in some biological grouping structure such as gene pathways. Our Bayesian models are connected with penalized regression, and we prove both oracle and asymptotic distribution properties under an orthogonal design. We derive efficient Gibbs sampling algorithms for our models and provide the implementation in a comprehensive R package called MBSGS available on the Comprehensive R Archive Network (CRAN). The performance of the proposed approaches is compared to state-of-the-art variable selection strategies on simulated data sets. The proposed methodology is illustrated on a genetic dataset in order to identify markers grouping across chromosomes that explain the joint variability of gene expression in multiple tissues.

This paper examines the synthetic control method in contrast to commonly used difference-in-differences (DiD) estimation, in the context of a re-evaluation of a pay-for-performance (P4P) initiative, the Advancing Quality scheme. The synthetic control method aims to estimate treatment effects by constructing a weighted combination of control units, which represents what the treated group would have experienced in the absence of receiving the treatment. While DiD estimation assumes that the effects of unobserved confounders are constant over time, the synthetic control method allows for these effects to change over time, by re-weighting the control group so that it has similar pre-intervention characteristics to the treated group. We extend the synthetic control approach to a setting of evaluation of a health policy where there are multiple treated units. We re-analyse a recent study evaluating the effects of a hospital P4P scheme on risk-adjusted hospital mortality. In contrast to the original DiD analysis, the synthetic control method reports that, for the incentivised conditions, the P4P scheme did not significantly reduce mortality and that there is a statistically significant increase in mortality for non-incentivised conditions. This result was robust to alternative specifications of the synthetic control method

We study the long-term effects on hospital activity of a three-year national integration programme. We use administrative data spanning from 24 months before to 22 months after the programme, to estimate the effect of programme discontinuation using difference-in-differences method. Our results show that after programme discontinuation, emergency admissions were slower to increase in Vanguard compared to non-Vanguard sites. These effects were heterogeneous across sites, with greater reductions in care home Vanguard sites and concentrated among the older population. Care home Vanguards showed significant reductions beginning early in the programme but falling away more rapidly after programme discontinuation. Moreover, there were greater reductions for sites performing poorly before the programme. Overall, this suggests the effects of the integration programme might have been lagged but transitory, and more reliant on continued programme support.