Who wants to cross borders in the EU for health care?: an analysis of the Eurobarometer data in 2007 and 2014

ABSTRACT - BACKGROUND The EU Directive on cross-border healthcare clarified the entitlements of EU citizens to medical care in other EU Member states. However, little is known about whether EU citizens have been travelling or are willing to travel to receive medical care. The aim of this study was to measure the determinants of cross-border patient mobility and willingness to travel to receive medical care in the EU, before and after the adoption of the Directive. METHODS We used individual data from the Eurobarometer 210 (2007) and 425 (2014). In the two years, 54,384 EU citizens were randomly selected for telephone and face-to-face interviews. We performed a logistic regression on the cross-border patient mobility and willingness to travel to other EU countries to use healthcare services as a function of the year (2007 or 2014), adjusting for age, gender, education, self perceived health (SPH), and country size. RESULTS In 2007, 3.3% of citizens reported cross-border mobility, and 4.6% in 2014. The odds of cross-border patients’ mobility was 15% higher in 2014, compared to 2007 (OR 1.15, 95%CI 1.05-1.26, p<.001). In addition, mobility was 15% higher in males (OR 1.15, 95%CI 1.05-1.3, p<0.001) and 20% amongst the more educated (OR 1.2, 95%CI 1.1- 1.3, p<.001). However, the odds decreased with age (OR 0.9 per decade, 95%CI 0.84- 0.92, p<.001), bad and very bad SPH, and country size. In 2014 the willingness to travel decreased by 22% compared to 2007. The other determinants of willingness to travel, namely gender, age, education, SHP, and country size, had a similar effect as in the cross-border mobility model. CONCLUSIONS Cross-border patient mobility and willingness to travel are more likely amongst younger, more educated, and healthier patients from smaller countries. The 2011 directive does not seem to have promoted mobility at a large scale among the neediest citizens.RESUMO - INTRODUÇÃO A diretiva da União Europeia (UE) referente ao exercício dos direitos dos pacientes em cuidados de saúde transfronteiriços clarificou os direitos dos cidadãos da UE. No entanto, pouco se sabe sobre a mobilidade transfronteiriça dos pacientes e a vontade de viajar para receber cuidados médicos. Desse modo, pretendemos estudar os determinantes da mobilidade transfronteiriça dos pacientes e a vontade de viajar para receber cuidados médicos na UE, especialmente após a adoção da diretiva. MÉTODOS Utilizamos dados do Eurobarómetro 210 (2007) e 425 (2014). Nos dois anos 54.384 cidadãos da UE foram selecionados aleatoriamente para entrevistas telefónicas e pessoalmente. Aplicámos uma regressão logística à mobilidade transfronteiriça dos pacientes e a vontade de viajar para usar os serviços de saúde noutros países da EU em função do ano (2007 ou 2014), idade, sexo, educação, saúde auto-reportada e tamanho do país. RESULTADOS Em 2007, 3,3% dos cidadãos relataram mobilidade transfronteiriça aumentando para 4,6% em 2014. A probabilidade de mobilidade transfronteiriça dos pacientes foi 15% maior em 2014, em comparação com 2007 (OR 1,15, IC 95% 1,05-1,26, p <.001). Além disso, a mobilidade foi 15% maior em homens (OR 1,15, IC 95% 1,05-1,3, p <0,001) e 20% em níveis mais elevados de educação (OR 1,2, 95% CI 1.1-1,3, p <0,001). No entanto, a probabilidade diminuí com a idade (OR 0,9 por década, IC 95% 0,84-0,92, p <0,001), má e muito má saúde auto-reportada e tamanho do país. Por outro lado, em 2014, a vontade de viajar diminuiu 22% em relação a 2007. Os outros determinantes da vontade de viajar, sexo, idade, educação, saúde auto-reportada e tamanho do país tiveram um efeito semelhante ao do modelo da mobilidade. CONCLUSÕES Entre 2007 e 2014, houve um ligeiro aumento da mobilidade transfronteiriça dos pacientes, que é, no entanto ainda baixo. A mobilidade transfronteiriça dos pacientes e a vontade de viajar são mais prováveis entre os pacientes mais jovens, mais educados, mais saudáveis, e de países mais pequenos. A diretiva de 2011 não parece ter promovido a mobilidade em grande escala entre os cidadãos mais necessitados

An analysis of the Eurobarometer data in 2007 and 2014

Background: The European Union (EU) Directive on Patients' Rights in Cross-border Healthcare clarified the entitlements to medical care in other EU Member states. However, little is known about whether EU citizens have been travelling or are willing to travel to receive care. This study aimed to measure the determinants of cross-border patient mobility and willingness to travel to receive medical care in the EU, before and after the adoption of the Directive. Methods: We used individual data from the Eurobarometer 210 (2007) and 425 (2014). In the 2 years, 53 439 EU citizens were randomly selected. We performed a logistic regression on the cross-border patient mobility and willingness to travel to other EU countries to use healthcare services as a function of the year (2007 or 2014), adjusting for age, gender, education and country size. Results: In 2007, 3.3% of citizens reported cross-border mobility and 4.6% in 2014. The odds of cross-border patients' mobility were 11% higher in 2014, compared with 2007 [odds ratio (OR) 1.11, 95% confidence interval (CI) 1.02-1.21]. Also, mobility was 19% higher in males (OR 1.19, 95% CI 1.08-1.30) and 20% higher amongst the more educated (OR 1.20, 95% CI 1.09-1.31). However, the odds decreased 11% per decade of age (OR 0.89 per decade, 95% CI 0.85-0.93) and country size. In 2014, the willingness to travel decreased by 20% compared with 2007. Conclusions: Cross-border patient mobility is more likely amongst the younger, the more educated and those from smaller countries. The directive does not seem to have promoted mobility at a large scale among the neediest citizens.publishersversionpublishe

A tale of two pandemics in three countries: Portugal, Spain, and Italy

This chapter explores the structural similarities and differences between these three countries: on the one hand, in their respective health sectors’ capacities and reorganization; and on the other hand, in the different degrees of state capacity to respond to the pressing needs of their populations. In the last great epidemic, the 1918 flu, there was a transparent north-south gradient in the extent to which European countries were hit by the pandemic, with Portugal, Spain, and Italy among those that were hit the hardest (Ansart et al., 2009). How was it this time? To what extent does the impact of COVID-19 reflect resilient societal and institutional vulnerabilities in these countries? And to what extent have national specificities interacted with those shared vulnerabilities, leading to different outcomes?info:eu-repo/semantics/publishedVersio

desafios para a saúde

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a functional data analysis from August 2020 to March 2022

Publisher Copyright: © 2024 Ribeiro et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.During the SARS-CoV-2 pandemic, governments and public health authorities collected massive amounts of data on daily confirmed positive cases and incidence rates. These data sets provide relevant information to develop a scientific understanding of the pandemic's spatiotemporal dynamics. At the same time, there is a lack of comprehensive approaches to describe and classify patterns underlying the dynamics of COVID-19 incidence across regions over time. This seriously constrains the potential benefits for public health authorities to understand spatiotemporal patterns of disease incidence that would allow for better risk communication strategies and improved assessment of mitigation policies efficacy. Within this context, we propose an exploratory statistical tool that combines functional data analysis with unsupervised learning algorithms to extract meaningful information about the main spatiotemporal patterns underlying COVID-19 incidence on mainland Portugal. We focus on the timeframe spanning from August 2020 to March 2022, considering data at the municipality level. First, we describe the temporal evolution of confirmed daily COVID-19 cases by municipality as a function of time, and outline the main temporal patterns of variability using a functional principal component analysis. Then, municipalities are classified according to their spatiotemporal similarities through hierarchical clustering adapted to spatially correlated functional data. Our findings reveal disparities in disease dynamics between northern and coastal municipalities versus those in the southern and hinterland. We also distinguish effects occurring during the 2020-2021 period from those in the 2021-2022 autumn-winter seasons. The results provide proof-of-concept that the proposed approach can be used to detect the main spatiotemporal patterns of disease incidence. The novel approach expands and enhances existing exploratory tools for spatiotemporal analysis of public health data.publishersversionpublishe

A stochastic model of an early warning system for detecting anomalous incidence values of COVID-19

Funding Information: This work was supported by the Spatial Data Sciences for COVID-19 Pandemic (SCOPE) project funded by Fundação para a Ciência e a Tecnologia under the call AI 4 COVID-19: Data Science and Artificial Intelligence in the Public Administration to strengthen the fight against COVID-19 and future pandemics – 2020 (DSAIPA/DS/0115/2020). The authors gratefully acknowledge the support of the CERENA (strategic project FCT-UIDB/04028/2020), DGS, for making the data available. The authors acknowledge the important contributions of the reviewers that improved the quality of the final paper. Funding Information: This work was supported by the Spatial Data Sciences for COVID-19 Pandemic (SCOPE) project funded by Fundação para a Ciência e a Tecnologia under the call AI 4 COVID-19: Data Science and Artificial Intelligence in the Public Administration to strengthen the fight against COVID-19 and future pandemics – 2020 (DSAIPA/DS/0115/2020). The authors gratefully acknowledge the support of the CERENA (strategic project FCT-UIDB/04028/2020), DGS, for making the data available. The authors acknowledge the important contributions of the reviewers that improved the quality of the final paper. Publisher Copyright: © 2023, The Author(s).The ability to identify and predict outbreaks during epidemic and pandemic events is critical to the development and implementation of effective mitigation measures by the relevant health and political authorities. However, the spatiotemporal prediction of such diseases is not straightforward due to the highly non-linear behaviour of its evolution in both space and time. The methodology proposed herein is the basis of an early warning system to predict short-term anomalous values (i.e., high and low values) of the incidence of COVID-19 at the municipality level for mainland Portugal. The proposed modelling tool combines stochastic sequential simulation and machine learning, namely symbolic regression, to model the spatiotemporal evolution of the disease. The machine learning component is used to model the 14-day incidence rate curves of COVID-19, as provided by the Portuguese Directorate-General for Health, while the geostatistical simulation component models the spatial distribution of these predictions, for a simulation grid comprising the metropolitan area of Lisbon, following a pre-defined spatial continuity pattern. The method is illustrated for a period of 5 months during 2021, and considering the entire set of 19 municipalities belonging to the metropolitan area of Lisbon, Portugal. The results show the ability of the early warning system to predict and detect anomalous high and low incidence rate values for different periods of the pandemic event during this period.publishersversionpublishe

interrupted time series analysis

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a modelling study for Portugal

Funding Information: The authors acknowledge financial support from the Fundação para a Ciência e Tecnologia - FCT through project “Projection of the Impact of Non-pharmacological real-time Control and mitigation measures for the COVID-19 epidemic” (COVID-19 in-CTRL) - project n° 692 from the 2nd edition of RESEARCH 4 COVID-19. The first author also acknowledges FCT within the PhD grants program “DOCTORATES 4 COVID-19”, Grant No 2020.10172.BD. The second author also acknowledges FCT within projects UIDB/04621/2020 and UIDP/04621/2020. The third author also acknowledges FCT within the Strategic Project UIDB/00297 /2020 (Centro de Matemática e Aplicações, Universidade Nova de Lisboa ). Publisher Copyright: © 2022 The Author(s)Vaccination strategies to control COVID-19 have been ongoing worldwide since the end of 2020. Understanding their possible effect is key to prevent future disease spread. Using a modelling approach, this study intends to measure the impact of the COVID-19 Portuguese vaccination strategy on the effective reproduction number and explore three scenarios for vaccine effectiveness waning. Namely, the no-immunity-loss, 1-year and 3-years of immunity duration scenarios. We adapted an age-structured SEIR deterministic model and used Portuguese hospitalisation data for the model calibration. Results show that, although the Portuguese vaccination plan had a substantial impact in reducing overall transmission, it might not be sufficient to control disease spread. A significant vaccination coverage of those above 5 years old, a vaccine effectiveness against disease of at least 80% and softer non-pharmaceutical interventions (NPIs), such as mask usage and social distancing, would be necessary to control disease spread in the worst scenario considered. The immunity duration scenario of 1-year displays a resurgence of COVID-19 hospitalisations by the end of 2021, the same is observed in 3-year scenario although with a lower magnitude. The no-immunity-loss scenario presents a low increase in hospitalisations. In both the 1-year and 3-year scenarios, a vaccination boost of those above 65 years old would result in a 53% and 38% peak reduction of non-ICU hospitalisations, respectively. These results suggest that NPIs should not be fully phased-out but instead be combined with a fast booster vaccination strategy to reduce healthcare burden.publishersversionpublishe

risk factors associated with road traffic injuries in Mozambique: results of the 2016 INCOMAS study

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sensitivity analysis

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