Potential conflicts in the fight against counterfeit drugs

This analysis looks at the best way to deal with the proliferation of fake drugs, and considers the conflict that arises when government agencies aim to reduce the harmful effects of the fake medicine trade while the pharmaceutical firms seek profit maximization. It is demonstrated that the pharmaceutical industry might wish to encourage better law enforcement rather than improved information policies, even when the latter would lead to a greater reduction in the fake drug trade.fake medicine trade

Statistical evaluation of quality in healthcare

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonGovernance of the healthcare systems is one of the most important challenges forWestern countries. Within this, an accurate assessment of the quality is key to policy makers and public managers, in order to guarantee equity, effectiveness and efficiency. In this thesis, we investigate aspects and methods related to healthcare evaluation by focussing on the healthcare system in Lombardy (Italy), where public and private providers compete with each other, patients are free to choose where to be hospitalized, and a pay-for-performance program was recently implemented. The general aim of this thesis is to highlight the role of statistics within a quality evaluation framework, in the form of advancing the statistical methods used to measure quality, of evaluating the effectiveness of implemented policies, and of testing the effect that mechanisms of competition and cooperation can have on the quality of a healthcare system. We firstly advance a new methodological approach for measuring hospital quality, providing a new tool for managers involved in performance evaluations. Multilevel models are typically used in healthcare, in order to account for the hierarchical structure of the data. These models however do not account for unobserved heterogeneity. We therefore propose an extension of the cluster-weighted models to the multilevel framework and focus in particular on the case of a binary dependent variable, which is common in healthcare. The resulting multilevel logistic cluster-weighted model is shown to perform well in a healthcare evaluation context. Secondly, we evaluate the effectiveness of a pay-for-performance program. Differently from the existent literature, in this thesis we evaluate this program on the basis of five health outcomes and across a wide range of medical conditions. Availability of data pre and post-policy in Lombardy allows us to use a difference-in-differences approach. The statistical model includes multiple dependent outcomes, that allow quantifying the joint effect of the program, and random effects, that account for the heterogeneity of the data at the ward and hospital level. The results show that the policy has overall a positive effect on the hospitals’ performance. Thirdly, we study the effect of pro-competition reforms on the hospital quality. In Lombardy, competition between hospitals has been mostly driven by the adoption of a quasi-market system. Our results show that no association exists between hospital quality and competition. We speculate that this may be the result of asymmetric information, i.e. the lack of transparent information provided to citizens about the quality of hospitals. This is bound to reduce the impact of pro-competition reforms on quality and can in part explain the conflicting results found in the literature on this subject. Our results should motivate a public disclosure of quality evaluations. Regardless of the specifics of a system, hospitals are altruistic economic agents and they cooperate in order to improve their quality. In this work, we analyse the effect of cooperation on quality, taking the network of patients’ transfers between hospitals as a proxy of their level of cooperation. Using the latest network models, we find that cooperation does lead to an increase in quality and should therefore be encouraged by policy makers

Locational Error in the Estimation of Regional Discrete Choice Models Using Distance as a Regressor

In many microeconometric studies distance from a relevant point of interest (such as a hospital) is often used as a predictor in a regression framework. Confidentiality rules, often, require to geo-mask spatial micro-data, reducing the quality of such relevant information and distorting inference on models’ parameters. This paper extends previous literature, extending the classical results on the measurement error in a linear regression model to the case of hospital choice, showing that in a discrete choice model the higher is the distortion produced by the geo-masking, the higher will be the downward bias in absolute value toward zero of the coefficient associated to the distance in the models. Monte Carlo simulations allow us to provide evidence of theoretical hypothesis. Results can be used by the data producers to choose the optimal value of the parameters of geo-masking preserving confidentiality, not destroying the statistical information

Todos herimos, a veces. Resumen de HUHU en IberLEF 2023: Detección de Humor que Difunde Prejuicios en Twitter

Humour is an efficient strategy to spread prejudice because, most of the time, it evades moral judgement. However, it perpetuates stereotypes and doing so justifies discriminatory acts. At HUHU we propose a frame to study how humour is used to discriminate minorities and to analyse their interplay with the degree of prejudice expressed against specific groups. To this end, we provide a corpus of prejudiced tweets in Spanish annotated with the presence of humour, its prejudice degree and the targeted groups: women and feminists, the LGBTI+ community, immigrants and racially discriminated people, and over-weighted people. This paper analyses the results achieved by the 46 teams that participated in HUHU.El humor es una estrategia eficiente para propagar prejuicios porque, la mayoría de las veces, elude el juicio moral. Sin embargo, perpetúa estereotipos y, al hacerlo, justifica actos discriminatorios. En HUHU proponemos un marco para estudiar cómo el humor se utiliza para discriminar a las minorías y analizar su interacción con el grado de prejuicio expresado contra grupos específicos. Con este fin, proporcionamos un corpus de tweets prejuiciosos en español anotados en cuanto a la presencia de humor, su grado de prejuicio y los grupos de: mujeres y feministas, comunidad LGBTI+, inmigrantes y personas discriminadas racialmente, así como personas con sobrepeso. Este artículo analiza los resultados obtenidos por los 46 equipo que participaron en HUHU.This work has been partially developed with the support of valgrAI - Valencian Graduate School and Research Network of Artificial Intelligence and the Generalitat Valenciana, and co-funded by the European Union. The work of Berta Chulvi and Paolo Rosso is supported by FairTransNLPStereotypes PID2021–124361OB-C31 funded by MCIN/AEI/10.13039/501100011033 and by ERDF, EU A way of making Europe

Getting multi-level governance wrong can be a matter of life and death during a pandemic

The COVID-19 pandemic is a global crisis, but it has had a highly varied impact on different regions across Europe. Marta Angelici, Paolo Berta (University of Milano-Bicocca), Joan Costa-Font (LSE) and Gilberto Turati (Università Cattolica del Sacro Cuore) argue that while centralised decision-making can help solve collective action problems like border closures, the management of the response to a health emergency is often more effective when it is implemented at the local level. Decentralised health care governance helps explain why mortality rates were lower in Italy than in Spain during the first wave of the pandemic

Affordance segmentation of hand-occluded containers from exocentric images

Visual affordance segmentation identifies the surfaces of an object an agent can interact with. Common challenges for the identification of affordances are the variety of the geometry and physical properties of these surfaces as well as occlusions. In this paper, we focus on occlusions of an object that is hand-held by a person manipulating it. To address this challenge, we propose an affordance segmentation model that uses auxiliary branches to process the object and hand regions separately. The proposed model learns affordance features under hand-occlusion by weighting the feature map through hand and object segmentation. To train the model, we annotated the visual affordances of an existing dataset with mixed-reality images of hand-held containers in third-person (exocentric) images. Experiments on both real and mixed-reality images show that our model achieves better affordance segmentation and generalisation than existing models.Comment: Paper accepted to Workshop on Assistive Computer Vision and Robotics (ACVR) in International Conference on Computer Vision (ICCV) 2023; 10 pages, 4 figures, 2 tables. Data, code, and trained models are available at https://apicis.github.io/projects/acanet.htm

Real Time Forecasting of Covid-19 Intensive Care Units demand

Response management to the SARS-CoV-2 outbreak requires to answer several forecasting tasks. For hospital managers, a major one is to anticipate the likely needs of beds in intensive care in a given catchment area one or two weeks ahead, starting as early as possible in the evolution of the epidemic. This paper proposes to use a bivariate Error Correction model to forecast the needs of beds in intensive care, jointly with the number of patients hospitalised with Covid-19 symptoms. Error Correction models are found to provide reliable forecasts that are tailored to the local characteristics both of epidemic dynamics and of hospital practice for various regions in Europe in Italy, France and Scotland, both at the onset and at later stages of the spread of the disease. This reasonable forecast performance suggests that the present approach may be useful also beyond the set of analysed regions.JRC.I.1-Monitoring, Indicators & Impact Evaluatio