European Parliament Electoral Turnout in Post-Communist Europe

The relatively low voter turnout rates in the June 2004 European Parliamentary elections in many of the post-communist states surprised observers. While the average turnout rate for these new-EU member states barely surpassed 30%, turnout exhibited much variance at the national and sub-national levels. In this article, we study the determinants of European Parliamentary election voter turnout rates in the post-communist countries at the regional level. Our central hypothesis is that regional turnout rates may be related to regional economic conditions and that in areas experiencing economic hardship, turnout will be lower. We also assess the extent that EU attitudes matter for turnout. A unique data set, compiled at the NUTS-3.Economics of voting;participation;European Parliamentary election; post-communist countries.

US voter registration data is poor. But election officials are working to address the weak spots.

Amid allegations of widespread voter fraud from the Trump campaign, the American public has turned its gaze to the maintenance of voter registration lists. John Lindback and Mary Stegmaier provide an overview of the challenges posed by America’s decentralized voter registration system, and discuss reforms that are already underway to improve the accuracy of voter rolls

European Parliament Electoral Turnout in Post-Communist Europe

URL des Cahiers : https://halshs.archives-ouvertes.fr/CAHIERS-MSECahiers de la Maison des Sciences Economiques 2006.04 - ISSN 1624-0340The relatively low voter turnout rates in the June 2004 European Parliamentary elections in many of the post-communist states surprised observers. While the average turnout rate for these new-EU member states barely surpassed 30%, turnout exhibited much variance at the national and sub-national levels. In this article, we study the determinants of European Parliamentary election voter turnout rates in the post-communist countries at the regional level. Our central hypothesis is that regional turnout rates may be related to regional economic conditions and that in areas experiencing economic hardship, turnout will be lower. We also assess the extent that EU attitudes matter for turnout. A unique data set, compiled at the NUTS-3.Le niveau de participation relativement faible lors des élections parlementaires européennes de juin 2004 dans les pays d'Europe de l'Est a surpris de nombreux observateurs. Le taux de participation moyen dans ces nouveaux Etats membres dépassait à peine 30% et affichait de plus une forte variance aussi bien au niveau national que régional. Cet article analyse les déterminants du taux de participation lors des élections parlementaires européennes dans les régions des anciens pays communistes. Notre hypothèse centrale est que le taux de participation régional est fonction des conditions économiques régionales et que, dans les zones connaissant des difficultés économiques, le taux de participation sera plus bas. Nous évaluons aussi dans quelle mesure l'opinion à l'égard de l'Union Européenne influence la participation électorale. Ces hypothèses sont restées sur une base de données au niveau statistique régional NUTS 3

To improve their predictions, election forecasters should look to other disciplines like meteorology

The recent surge in public attention to election predictions has generated much discussion about how to improve forecasting model accuracy. Michael S. Lewis-Beck and Mary Stegmaier argue that advances in weather forecasting hold lessons for election forecasting. First, like weather models, election models should be based on sound theory. Second, more intensive data gathering, especially at the state level with repeated measurements over time, will capture the dynamics of the campaign and ultimately enhance the accuracy of predictions. Third, ensemble forecasting and applying expertise to adjust forecasts are other methods to consider for reducing forecast error

Using citizen forecasts we predict that with 362 electoral votes, Hillary Clinton will be the next president

Who will be the next US President? Some commentators have argued that voter intention polls are flawed because it is difficult to know who will actually turn out to vote. To get around this problem, Andreas Murr, Mary Stegmaier, and Michael S. Lewis-Beck use citizen forecasts, a “who do you think will win” survey question, to predict the election result

Vote expectations versus vote intentions : rival forecasting strategies

Are ordinary citizens better at predicting election results than conventional voter intention polls? We address this question by comparing eight forecasting models for British general elections: one based on voters’ expectations of who will win and seven based on who voters themselves intend to vote for (including “uniform national swing model” and “cube rule” models). The data come from ComRes and Gallup polls as well as the Essex Continuous Monitoring Surveys, 1950 – 2017, yielding 449 months with both expectation and intention polls. The large sample size allows us to compare the models’ prediction accuracy not just in the months prior to the election, but over the years leading up to it. In predicting both the winning party and parties’ seat shares, we find that vote expectations outperform vote intent ions models. Vote expectations thus appear an excellent tool for predicting the winning party and its seat share

Citizen forecasting 2019: a big win for the Conservatives

The recent failures of voter intention polls to predict UK election results has led to public scepticism about the usefulness of polls. Andreas Murr, Mary Stegmaier, and Michael S. Lewis-Beck deploy an alternative approach, which focuses on which party opinion poll respondents expect to win the election (rather than just on their voting intentions). This ‘voter expectations’ model predicts a solid Johnson majority, with the Conservatives gaining 360 seats, and Labour only 190

Height, selected genetic markers and prostate cancer risk:Results from the PRACTICAL consortium

Background: Evidence on height and prostate cancer risk is mixed, however, recent studies with large data sets support a possible role for its association with the risk of aggressive prostate cancer. Methods: We analysed data from the PRACTICAL consortium consisting of 6207 prostate cancer cases and 6016 controls and a subset of high grade cases (2480 cases). We explored height, polymorphisms in genes related to growth processes as main effects and their possible interactions. Results: The results suggest that height is associated with high-grade prostate cancer risk. Men with height 4180cm are at a 22% increased risk as compared to men with height o173cm (OR 1.22, 95% CI 1.01–1.48). Genetic variants in the growth pathway gene showed an association with prostate cancer risk. The aggregate scores of the selected variants identified a significantly increased risk of overall prostate cancer and high-grade prostate cancer by 13% and 15%, respectively, in the highest score group as compared to lowest score group. Conclusions: There was no evidence of gene-environment interaction between height and the selected candidate SNPs. Our findings suggest a role of height in high-grade prostate cancer. The effect of genetic variants in the genes related to growth is seen in all cases and high-grade prostate cancer. There is no interaction between these two exposures.</p

Refined histopathological predictors of BRCA1 and BRCA2 mutation status: A large-scale analysis of breast cancer characteristics from the BCAC, CIMBA, and ENIGMA consortia

Introduction: The distribution of histopathological features of invasive breast tumors in BRCA1 or BRCA2 germline mutation carriers differs from that of individuals with no known mutation. Histopathological features thus have utility for mutation prediction, including statistical modeling to assess pathogenicity of BRCA1 or BRCA2 variants of uncertain clinical significance. We analyzed large pathology datasets accrued by the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA) and the Breast Cancer Association Consortium (BCAC) to reassess histopathological predictors of BRCA1 and BRCA2 mutation status, and provide robust likelihood ratio (LR) estimates for statistical modeling. Methods: Selection criteria for study/center inclusion were estrogen receptor (ER) status or grade data available for invasive breast cancer diagnosed younger than 70 years. The dataset included 4,477 BRCA1 mutation carriers, 2,565 BRCA2 mutation carriers, and 47,565 BCAC breast cancer cases. Country-stratified estimates of the

Polygenic Risk Scores for Prediction of Breast Cancer and Breast Cancer Subtypes

Stratification of women according to their risk of breast cancer based on polygenic risk scores (PRSs) could improve screening and prevention strategies. Our aim was to develop PRSs, optimized for prediction of estrogen receptor (ER)-specific disease, from the largest available genome-wide association dataset and to empirically validate the PRSs in prospective studies. The development dataset comprised 94,075 case subjects and 75,017 control subjects of European ancestry from 69 studies, divided into training and validation sets. Samples were genotyped using genome-wide arrays, and single-nucleotide polymorphisms (SNPs) were selected by stepwise regression or lasso penalized regression. The best performing PRSs were validated in an independent test set comprising 11,428 case subjects and 18,323 control subjects from 10 prospective studies and 190,040 women from UK Biobank (3,215 incident breast cancers). For the best PRSs (313 SNPs), the odds ratio for overall disease per 1 standard deviation in ten prospective studies was 1.61 (95%CI: 1.57-1.65) with area under receiver-operator curve (AUC) = 0.630 (95%CI: 0.628-0.651). The lifetime risk of overall breast cancer in the top centile of the PRSs was 32.6%. Compared with women in the middle quintile, those in the highest 1% of risk had 4.37- and 2.78-fold risks, and those in the lowest 1% of risk had 0.16- and 0.27-fold risks, of developing ER-positive and ER-negative disease, respectively. Goodness-of-fit tests indicated that this PRS was well calibrated and predicts disease risk accurately in the tails of the distribution. This PRS is a powerful and reliable predictor of breast cancer risk that may improve breast cancer prevention programs.NovartisEli Lilly and CompanyAstraZenecaAbbViePfizer UKCelgeneEisaiGenentechMerck Sharp and DohmeRocheCancer Research UKGovernment of CanadaArray BioPharmaGenome CanadaNational Institutes of HealthEuropean CommissionMinistère de l'Économie, de l’Innovation et des Exportations du QuébecSeventh Framework ProgrammeCanadian Institutes of Health Researc