Evidence of bias in the Eurovision song contest: modelling the votes using Bayesian hierarchical models

The Eurovision Song Contest is an annual musical competition held among active members of the European Broadcasting Union since 1956. The event is televised live across Europe. Each participating country presents a song and receive a vote based on a combination of tele-voting and jury. Over the years, this has led to speculations of tactical voting, discriminating against some participants and thus inducing bias in the final results. In this paper we investigate the presence of positive or negative bias (which may roughly indicate favouritisms or discrimination) in the votes based on geographical proximity, migration and cultural characteristics of the participating countries through a Bayesian hierarchical model. Our analysis found no evidence of negative bias, although mild positive bias does seem to emerge systematically, linking voters to performers.Comment: 16 pages, 3 figure

Statistical tools for synthesizing lists of differentially expressed features in related experiments

A novel approach for finding a list of features that are commonly perturbed in two or more experiments, quantifying the evidence of dependence between the experiments by a ratio

Missing data analysis and imputation via latent Gaussian Markov random fields

Acknowledgements. V. Gomez-Rubio has been supported by grants MTM2016-77501-P and PID2019-106341GB-I00 from the Spanish Ministry of Economy and Competitiveness co-fnanced with FEDER funds, grant SBPLY/17/180501/000491 and SBPLY/21/180501/000241 funded by Consejería de Educacion, Cultura y Deportes (JCCM, Spain) and FEDER. Marta Blangiardo acknowledges partial support through the grant R01HD092580 funded by the National Institute of Health and from the MRC Centre for Environment and Health, which is currently funded by the Medical Research Council (MR/S019669/1).This paper recasts the problem of missing values in the covariates of a regression model as a latent Gaussian Markov random field (GMRF) model in a fully Bayesian framework. The proposed approach is based on the definition of the covariate imputation sub-model as a latent effect with a GMRF structure. This formulation works for continuous covariates but for categorical covariates a typical multiple imputation approach is employed. Both techniques can be easily combined for the case in which continuous and categorical variables have missing values. The resulting Bayesian hierarchical model naturally fts within the integrated nested Laplace approximation (INLA) framework, which is used for model fitting. Hence, this work fills an important gap in the INLA methodology as it allows to treat models with missing values in the covariates. As in any other fully Bayesian framework, by relying on INLA for model fitting it is possible to formulate a joint model for the data, the imputed covariates and their missingness mechanism. In this way, it is possible to tackle the more general problem of assessing the missingness mechanism by conducting a sensitivity analysis on the different alternatives to model the non-observed covariates. Finally, the proposed approach is illustrated in two examples on modeling health risk factors and disease mapping

Bayesian hierarchical model for the prediction of football results

The problem of modelling football data has become increasingly popular in the last few years and many different models have been proposed with the aim of estimating the characteristics that bring a team to lose or win a game, or to predict the score of a particular match. We propose a Bayesian hierarchical model to fulfil both these aims and test its predictive strength based on data about the Italian Serie A 1991-1992 championship. To overcome the issue of overshrinkage produced by the Bayesian hierarchical model, we specify a more complex mixture model that results in a better fit to the observed data. We test its performance using an example of the Italian Serie A 2007-2008 championship

Bayesian Interrupted Time Series for evaluating policy change on mental well-being: an application to England's welfare reform

Factors contributing to social inequalities are also associated with negative mental health outcomes leading to disparities in mental well-being. We propose a Bayesian hierarchical model which can evaluate the impact of policies on population well-being, accounting for spatial/temporal dependencies. Building on an interrupted time series framework, our approach can evaluate how different profiles of individuals are affected in different ways, whilst accounting for their uncertainty. We apply the framework to assess the impact of the United Kingdoms welfare reform, which took place throughout the 2010s, on mental well-being using data from the UK Household Longitudinal Study. The additional depth of knowledge is essential for effective evaluation of current policy and implementation of future policy.Comment: 13 pages, 5 figures, 2 table

The effect of immigration policy reform on mental health in people from minoritised ethnic groups in England: an interrupted time series analysis of longitudinal data from the UK Household Longitudinal Study cohort

Background: In 2012, the UK Government announced a series of immigration policy reforms known as the hostile environment policy, culminating in the Windrush scandal. We aimed to investigate the effect of the hostile environment policy on mental health for people from minoritised ethnic backgrounds. We hypothesised that people from Black Caribbean backgrounds would have worse mental health relative to people from White ethnic backgrounds after the Immigration Act 2014 and the Windrush scandal media coverage in 2017, since they were particularly targeted. Methods: Using data from the UK Household Longitudinal Study, we performed a Bayesian interrupted time series analysis, accounting for fixed effects of confounders (sex, age, urbanicity, relationship status, number of children, education, physical or mental health impairment, housing, deprivation, employment, place of birth, income, and time), and random effects for residual temporal and spatial variation. We measured mental ill health using a widely used, self-administered questionnaire on psychological distress, the 12-item General Health Questionnaire (GHQ-12). We compared mean differences (MDs) and 95% credible intervals (CrIs) in mental ill health among people from minoritised ethnic groups (Black Caribbean, Black African, Indian, Bangladeshi, and Pakistani) relative to people of White ethnicity during three time periods: before the Immigration Act 2014, after the Immigration Act 2014, and after the start of the Windrush scandal media coverage in 2017. Findings: We included 58 087 participants with a mean age of 45·0 years (SD 34·6; range 16–106), including 31 168 (53·6%) female and 26 919 (46·3%) male participants. The cohort consisted of individuals from the following ethnic backgrounds: 2519 (4·3%) Black African, 2197 (3·8%) Black Caribbean, 3153 (5·4%) Indian, 1584 (2·7%) Bangladeshi, 2801 (4·8%) Pakistani, and 45 833 (78·9%) White. People from Black Caribbean backgrounds had worse mental health than people of White ethnicity after the Immigration Act 2014 (MD in GHQ-12 score 0·67 [95% CrI 0·06–1·28]) and after the 2017 media coverage (1·28 [0·34–2·21]). For Black Caribbean participants born outside of the UK, mental health worsened after the Immigration Act 2014 (1·25 [0·11–2·38]), and for those born in the UK, mental health worsened after the 2017 media coverage (2·00 [0·84–3·15]). We did not observe effects in other minoritised ethnic groups. Interpretation: Our finding that the hostile environment policy worsened the mental health of people from Black Caribbean backgrounds in the UK suggests that sufficient, appropriate mental health and social welfare support should be provided to those affected. Impact assessments of new policies on minority mental health should be embedded in all policy making. Funding: Wellcome Trust