The aim of this study is to analyse the time trends in the European Union (EU) before and during the economic crisis in 1) the energy poverty (EP) prevalence; 2) the association between EP and health and 3) the impact of EP on health. We analyse trends among women and men in two EU macro regions, defined by a novel index of structural vulnerability to EP. The study shows how EP and its impact on health worsened during the economic crisis and identifies groups at higher risk such as women and people living in Mediterranean and Eastern European countries, which have been found to be countries with higher structural vulnerability to EP.
Objective: To analyse socioeconomic inequalities in all-cause mortality among men and women in nine European urban areas during the recent economic crisis, and to compare the results to those from two periods before the crisis. Method: This is an ecological study of trends based on three time periods (2000-2003, 2004-2008 and 2009-2014). The units of analysis were the small areas of nine European urban areas. We used a composite deprivation index as a socioeconomic indicator, along with other single indicators. As a mortality indicator, we used the smoothed standardized mortality ratio, calculated using the hierarchical Bayesian model proposed by Besag, York and Mollié. To analyse the evolution of socioeconomic inequalities, we fitted an ecological regression model that included the socioeconomic indicator, the period of time, and the interaction between these terms. Results: We observed significant inequalities in mortality among men for almost all the socioeconomic indicators, periods, and urban areas studied. However, no significant changes occurred during the period of the economic crisis. While inequalities among women were less common, there was a statistically significant increase in inequality during the crisis period in terms of unemployment and the deprivation index in Prague and Stockholm, respectively. Conclusions: Future analyses should also consider time-lag in the effect of crises on mortality and specific causes of death, and differential effects between genders. ; Objetivo: Analizar las desigualdades socioeconómicas en la mortalidad por todas las causas en hombres y mujeres de nueve áreas urbanas europeas durante la reciente crisis económica, y comparar los resultados con dos periodos previos a la crisis. Método: Estudio ecológico de tendencias basado en tres periodos (2000-2003, 2004-2008 y 2009-2014). Las unidades de análisis fueron las áreas pequeñas de nueve zonas urbanas europeas. Se utilizaron un índice compuesto de privación socioeconómica como indicador socioeconómico y otros indicadores simples. Como indicador de mortalidad se usó la razón de mortalidad estandarizada suavizada, calculada utilizando el modelo jerárquico bayesiano propuesto por Besag, York y Mollié. Para analizar la evolución de las desigualdades socioeconómicas se utilizó un modelo de regresión ecológico que incluía el indicador socioeconómico, el periodo y la interacción de ambos. Resultados: Se observaron desigualdades significativas en la mortalidad en los hombres para casi todos los indicadores socioeconómicos, periodos y áreas urbanas. Sin embargo, no hubo cambios significativos en las desigualdades en el periodo de crisis. Aunque las desigualdades entre las mujeres fueron menos comunes, hubo un incremento significativo en las desigualdades en mortalidad en el periodo de crisis en términos de desempleo y del índice de privación en Praga y Estocolmo, respectivamente. Conclusiones: Futuros análisis deberán tener en cuenta el tiempo transcurrido entre la crisis y su efecto en la mortalidad, así como diferentes causas de mortalidad y el efecto diferencial entre géneros. ; This study is a part of the EURO-HEALTHY project (Shaping EUROpean policies to promote HEALTH equity) and has received funding from the European Union's Horizon 2020 research and innovation programme (Grant Agreement No 643398). Dagmar Dzúrová and Michala Lustigova were also supported by Charles University (UNCE/HUM 018).
Numerous studies have demonstrated the relationship between summer temperatures and increased heat-related deaths. Epidemiological analyses of the health effects of climate exposures usually rely on observations from the nearest weather station to assess exposure-response associations for geographically diverse populations. Urban climate models provide high-resolution spatial data that may potentially improve exposure estimates, but to date, they have not been extensively applied in epidemiological research. We investigated temperature-mortality relationships in the city of Barcelona, and whether estimates vary among districts. We considered georeferenced individual (natural) mortality data during the summer months (June-September) for the period 1992-2015. We extracted daily summer mean temperatures from a 100-m resolution simulation of the urban climate model (UrbClim). Summer hot days (above percentile 70) and reference (below percentile 30) temperatures were compared by using a conditional logistic regression model in a case crossover study design applied to all districts of Barcelona. Relative Risks (RR), and 95% Confidence Intervals (CI), of all-cause (natural) mortality and summer temperature were calculated for several population subgroups (age, sex and education level by districts). Hot days were associated with an increased risk of death (RR = 1.13; 95% CI = 1.10-1.16) and were significant in all population subgroups compared to the non-hot days. The risk ratio was higher among women (RR = 1.16; 95% CI= 1.12-1.21) and the elderly (RR = 1.18; 95% CI = 1.13-1.22). Individuals with primary education had similar risk (RR = 1.13; 95% CI = 1.08-1.18) than those without education (RR = 1.10; 95% CI= 1.05-1.15). Moreover, 6 out of 10 districts showed statistically significant associations, varying the risk ratio between 1.12 (95% CI = 1.03-1.21) in Sants-Montjuïc and 1.25 (95% CI = 1.14-1.38) in Sant Andreu. Findings identified vulnerable districts and suggested new insights to public health policy makers on how to develop district-specific strategies to reduce risks. ; J.B. gratefully acknowledges funding from the European Union's Horizon 2020 research and innovation programme under grant agreements No 865564 (European Research Council Consolidator Grant EARLY-ADAPT), 727852 (project Blue-Action) and 730004 (project PUCS), and from the Ministry of Science and Innovation (MCIU) under grant agreements No RYC2018-025446-I (programme Ramón y Cajal) and EUR2019-103822 (project EURO-ADAPT). V.I. acknowledges funding from the European Union's Horizon 2020 research and innovation programme under grant agreements 730004 (project PUCS). H.A. gratefully acknowledges funding from the Secretariat for Universities and Research of the Ministry of Business and Knowledge of the Government of Catalonia (grant numbers B00391 [FI-2018], B100180 [FI-2019] and B200139 [FI-2020]).
The different geographical contexts seen in European metropolitan areas are reflected in the uneven distribution of health risk factors for the population. Accumulating evidence on multiple health determinants point to the importance of individual, social, economic, physical and built environment features, which can be shaped by the local authorities. The complexity of measuring health, which at the same time underscores the level of intra-urban inequalities, calls for integrated and multidimensional approaches. The aim of this study is to analyse inequalities in health determinants and health outcomes across and within nine metropolitan areas: Athens, Barcelona, Berlin-Brandenburg, Brussels, Lisbon, London, Prague, Stockholm and Turin. We use the EURO-HEALTHY Population Health Index (PHI), a tool that measures health in two components: Health Determinants and Health Outcomes. The application of this tool revealed important inequalities between metropolitan areas: Better scores were found in Northern cities when compared with their Southern and Eastern counterparts in both components. The analysis of geographical patterns within metropolitan areas showed that there are intra-urban inequalities, and, in most cities, they appear to form spatial clusters. Identifying which urban areas are measurably worse off, in either Health Determinants or Health Outcomes, or both, provides a basis for redirecting local action and for ongoing comparisons with other metropolitan areas. ; This research was conducted under the EURO-HEALTHY project, which was funded by the European Union's Horizon 2020 research and innovation programme, Grant Agreement No 643398, and received support from the Centre of Studies in Geography and Spatial Planning (CEGOT), funded by national funds through the Foundation for Science and Technology (FCT) under the reference UID/GEO/04084/2013.
