Does the UK minimum wage reduce employment? A meta-regression analysis

The employment effect from raising the minimum wage has long been studied but remains in dispute. Our meta-analysis of 236 estimated minimum wage elasticities and 710 partial correlation coefficients from 16 UK studies finds no overall practically significant adverse employment effect. Unlike US studies, there seems to be little, if any, overall reporting bias. Multivariate meta-regression analysis identifies several research dimensions that are associated with differential employment effects. In particular, the residential home care industry may exhibit a genuinely adverse employment effect.

Disease mapping and regression with count data in the presence of overdispersion and spatial autocorrelation: a Bayesian model averaging approach

This paper applies the generalised linear model for modelling geographical variation to esophageal cancer incidence data in the Caspian region of Iran. The data have a complex and hierarchical structure that makes them suitable for hierarchical analysis using Bayesian techniques, but with care required to deal with problems arising from counts of events observed in small geographical areas when overdispersion and residual spatial autocorrelation are present. These considerations lead to nine regression models derived from using three probability distributions for count data: Poisson, generalised Poisson and negative binomial, and three different autocorrelation structures. We employ the framework of Bayesian variable selection and a Gibbs sampling based technique to identify significant cancer risk factors. The framework deals with situations where the number of possible models based on different combinations of candidate explanatory variables is large enough such that calculation of posterior probabilities for all models is difficult or infeasible. The evidence from applying the modelling methodology suggests that modelling strategies based on the use of generalised Poisson and negative binomial with spatial autocorrelation work well and provide a robust basis for inference.

Stabilized sparse ordinal regression for medical risk stratification

The recent wide adoption of electronic medical records (EMRs) presents great opportunities and challenges for data mining. The EMR data are largely temporal, often noisy, irregular and high dimensional. This paper constructs a novel ordinal regression framework for predicting medical risk stratification from EMR. First, a conceptual view of EMR as a temporal image is constructed to extract a diverse set of features. Second, ordinal modeling is applied for predicting cumulative or progressive risk. The challenges are building a transparent predictive model that works with a large number of weakly predictive features, and at the same time, is stable against resampling variations. Our solution employs sparsity methods that are stabilized through domain-specific feature interaction networks. We introduces two indices that measure the model stability against data resampling. Feature networks are used to generate two multivariate Gaussian priors with sparse precision matrices (the Laplacian and Random Walk). We apply the framework on a large short-term suicide risk prediction problem and demonstrate that our methods outperform clinicians to a large margin, discover suicide risk factors that conform with mental health knowledge, and produce models with enhanced stability. © 2014 Springer-Verlag London.

Adoption of obesity prevention policies and practices by Australian primary schools : 2006 to 2013

Despite significant investment in many countries, the extent of schools' adoption of obesity prevention policies and practices has not been widely reported. The aims of this article are to describe Australian schools' adoption of healthy eating and physical activity policies and practices over an 8-year period and to determine if their adoption varies according to schools' size, geographic or socio-economic location. Between 2006 and 2013, a representative randomly selected cohort of primary schools (n = 476) in New South Wales, Australia, participated in four telephone interviews. Repeated measures logistic regression analyses using a Generalised Estimating Equation (GEE) framework were undertaken to assess change over time. The prevalence of all four of the healthy eating practices and one physical activity practice significantly increased, while the prevalence of one physical activity practice significantly decreased. The adoption of practices did not differ by school characteristics. Government investment can equitably enhance school adoption of some obesity prevention policies and practices on a jurisdiction-wide basis. Additional and/or different implementation strategies may be required to facilitate greater adoption of physical activity practices. Ongoing monitoring of school adoption of school policies and practices is needed to ensure the intended benefits of government investment are achieved.

Does education reduce income inequality? A meta-regression analysis

This paper reexamines the effects of education on inequality through a comprehensive meta-regression analysis of the extant empirical literature. We find that education affects the two tails of the distribution of income: Education reduces the income share of top earners and increases the share of the bottom earners. Education has been particularly effective in reducing inequality in Africa. Some of the results suggest that secondary schooling appears to have a stronger effect than primary schooling, though this finding is not always robust. The heterogeneity in reported estimates can be largely explained by differences in the specification of the econometric model and measure of inequality and education. © 2013 John Wiley & Sons Ltd.

3D Intervertebral disc localization and segmentation from MR images by data-driven regression and classification

In this paper we propose a new fully-automatic method for localizing and segmenting 3D intervertebral discs from MR images, where the two problems are solved in a unified data-driven regression and classification framework. We estimate the output (image displacements for localization, or fg/bg labels for segmentation) of image points by exploiting both training data and geometric constraints simultaneously. The problem is formulated in a unified objective function which is then solved globally and efficiently. We validate our method on MR images of 25 patients. Taking manually labeled data as the ground truth, our method achieves a mean localization error of 1.3 mm, a mean Dice metric of 87%, and a mean surface distance of 1.3 mm. Our method can be applied to other localization and segmentation tasks.