Suchergebnisse

Objectives: Our aim was to determine clusters of non-communicable diseases (NCDs) in a very large, population-based sample of middle-aged and older adults from low- and middle-income (LMICs) and high-income (HICs) regions. Additionally, we explored the associations with several covariates. Design: The total sample was 72¿140 people aged 50+ years from three population-based studies (English Longitudinal Study of Ageing, Survey of Health, Ageing and Retirement in Europe Study and Study on Global Ageing and Adult Health) included in the Ageing Trajectories of Health: Longitudinal Opportunities and Synergies (ATHLOS) project and representing eight regions with LMICs and HICs. Variables were previously harmonised using an ex-post strategy. Eight NCDs were used in latent class analysis. Multinomial models were made to calculate associations with covariates. All the analyses were stratified by age (50–64 and 65+ years¿old). Results Three clusters were identified: 'cardio-metabolic' (8.93% in participants aged 50–64 years and 27.22% in those aged 65+ years), 'respiratory-mental-articular' (3.91% and 5.27%) and 'healthy' (87.16% and 67.51%). In the younger group, Russia presented the highest prevalence of the 'cardio-metabolic' group (18.8%) and England the 'respiratory-mental-articular' (5.1%). In the older group, Russia had the highest proportion of both classes (48.3% and 9%). Both the younger and older African participants presented the highest proportion of the 'healthy' class. Older age, being woman, widowed and with low levels of education and income were related to an increased risk of multimorbidity. Physical activity was a protective factor in both age groups and smoking a risk factor for the 'respiratory-mental-articular'. Conclusion: Multimorbidity is common worldwide, especially in HICs and Russia. Health policies in each country addressing coordination and support are needed to face the complexity of a pattern of growing multimorbidity. ; This work was supported by the 5-year Ageing Trajectories of Health: Longitudinal Opportunities and Synergies (ATHLOS) project and the Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM). The ATHLOS project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 635 316. DF's work has been supported by grant RTI2018-100927-J-I00 Retos Investigación from Ministerio de Ciencia e Innovación (MCI), by Marsden grant E2987-3648 (Royal Society of New Zealand), and by grant 2017 SGR 622 (GRBIO) from the Departament d'Economia i Coneixement de la Generalitat de Catalunya (Spain). This work, grant number RTI2018-100927-J-I00, is supported by the Ministerio de Ciencia e Innovación (MCI, Spain), by the Agencia Estatal de Investigación (AEI, Spain) and by the European Regional Development Fund FEDER (FEDER, UE). BO's work is supported by the PERIS programme 2016–2020 'Ajuts per a la Incorporació de Científics i Tecnòlegs' (grant number SLT006/17/00066), with the support of the Health Department from the Generalitat de Catalunya. ; Peer Reviewed ; Postprint (published version)

Delphine Saragoussi,1 Maëlys Touya,2 Josep Maria Haro,3 Bengt Jönsson,4 Martin Knapp,5 Bastien Botrel,6 Ioana Florea,7 Henrik Loft,8 Benoît Rive9 1Real-World Evidence and Epidemiology, Lundbeck SAS, Issy-les-Moulineaux, France; 2Health Economics and Outcomes Research, Lundbeck, Deerfield, IL, US; 3Research and Teaching Unit, Parc Sanitari Sant Joan de Deu, CIBERSAM, University of Barcelona, Sant Boi de Llobregat, Barcelona, Spain; 4Department of Economics, Stockholm School of Economics, Stockholm, Sweden; 5Department of Health Policy, London School of Economics and Political Science, London, UK; 6Biostatistics, Inferential, Paris, France; 7Clinical Research Paediatrics, H. Lundbeck A/S, Valby, Denmark; 8Biometrics, H. Lundbeck A/S, Valby, Denmark; 9Global Analytics, Lundbeck SAS, Issy-les-Moulineaux, France Background: The Prospective Epidemiological Research on Functioning Outcomes Related to Major Depressive Disorder (PERFORM) study has been initiated to better understand the course of a depressive episode and its impact on patient functioning. This analysis aimed to identify sociodemographic and clinical factors associated with failure to achieve remission at month 2 after initiating or switching antidepressant monotherapy and with subsequent relapse at month 6 for patients in remission at month 2. Materials and methods: This was a 2-year observational cohort study in 1,159 outpatients aged 18–65 years with major depressive disorder initiating or undergoing the first switch of antidepressant monotherapy. Factors with P8 weeks (OR 0.51), being in psychotherapy (OR 0.51), sexual dysfunction (OR 0.62), and severity of depression (OR 0.87). Factors significantly associated with relapse at month 6 were male sex (OR 2.47), being married or living as a couple (OR 2.73), residual patient-reported cognitive symptoms at 2 months (OR 1.12 per additional unit of Perceived Deficit Questionnaire-5 score) and residual depressive symptoms at 2 months (OR 1.27 per additional unit of Patient Health Questionnaire-9 score). Conclusion: Different factors appear to be associated with failure to achieve remission in patients with major depressive disorder and with subsequent relapse in patients who do achieve remission. Patient-reported cognitive dysfunction is an easily measurable and treatable characteristic that may be associated with an increased likelihood of relapse at 6 months in patients who have achieved remission. Keywords: major depressive disorder, remission, relapse, patient-reported cognitive dysfunction

The aim of this study was to identify latent groups of similar trajectories in processing speed through aging, as well as factors that are associated with these trajectories. In the context of the Ageing Trajectories of Health: Longitudinal Opportunities and Synergies (ATHLOS) project, data from the English Longitudinal Study of Aging (ELSA) (n = 12099) were analyzed. Latent groups of similar trajectories in the processing scores as well as their predictors and covariates were investigated, using group-based trajectory models (GBTM). The coefficient estimates for potential group predictors correspond to parameters of multinomial logit functions that are integrated in the model. Potential predictors included sex, level of education, marital status, level of household wealth, level of physical activity, and history of smoking, while time-varying covariates included incidence of cardiovascular disease (CVD), diabetes mellitus, depressive symptoms, and sleep disturbances. Four trajectories were identified and named after their baseline scores and shapes: High (4.4%), Middle/Stable (31.5%), Low/Stable (44.5%), and Low Decline (19.6%). Female sex, higher levels of education, mild level of physical activity, having been married, and higher level of wealth were associated with a higher probability of belonging to any of the higher groups compared to the Low/Decline that was set as reference, while presence of CVD, diabetes mellitus, and depressive symptoms were associated with lower processing speed scores within most trajectories. All the aforementioned factors might be valid targets for interventions to reduce the burden of age-related cognitive impairment. ; This work was supported by the five-year Ageing Trajectories of Health: Longitudinal Opportunities and Synergies (ATHLOS) project. The ATHLOS project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 635316.ELSA is supported by NationalInstitute on Aging Grants 2R01AG7644–01A1 and2R01AG017644. BO's work is supported by the PERIS program 2016–2020 "Ajuts per a la Incorporació de Científics i Tecnòlegs" [grant number SLT006/17/00066], with the support of the Health Department of the Generalitat de Catalunya

On March 12th, 2020, the WHO declared COVID-19 as a pandemic. The collective impact of environmental and ecosystem factors, as well as biodiversity, on the spread of COVID-19 and its mortality evolution remain empirically unknown, particularly in regions with a wide ecosystem range. The aim of our study is to assess how those factors impact on the COVID-19 spread and mortality by country. This study compiled a global database merging WHO daily case reports with other publicly available measures from January 21st to May 18th, 2020. We applied spatio-temporal models to identify the influence of biodiversity, temperature, and precipitation and fitted generalized linear mixed models to identify the effects of environmental variables. Additionally, we used count time series to characterize the association between COVID-19 spread and air quality factors. All analyses were adjusted by social demographic, country-income level, and government policy intervention confounders, among 160 countries, globally. Our results reveal a statistically meaningful association between COVID-19 infection and several factors of interest at country and city levels such as the national biodiversity index, air quality, and pollutants elements (PM10, PM2.5, and O3). Particularly, there is a significant relationship of loss of biodiversity, high level of air pollutants, and diminished air quality with COVID-19 infection spread and mortality. Our findings provide an empirical foundation for future studies on the relationship between air quality variables, a country's biodiversity, and COVID-19 transmission and mortality. The relationships measured in this study can be valuable when governments plan environmental and health policies, as alternative strategy to respond to new COVID-19 outbreaks and prevent future crises. ; Temple University. College of Public Health ; Epidemiology and Biostatistics

On March 12th, 2020, the WHO declared COVID-19 as a pandemic. The collective impact of environ-mental and ecosystem factors, as well as biodiversity, on the spread of COVID-19 and its mortalityevolution remain empirically unknown, particularly in regions with a wide ecosystem range. The aim ofour study is to assess how those factors impact on the COVID-19 spread and mortality by country. Thisstudy compiled a global database merging WHO daily case reports with other publicly available measuresfrom January 21st to May 18th, 2020. We applied spatio-temporal models to identify the influence ofbiodiversity, temperature, and precipitation andfitted generalized linear mixed models to identify theeffects of environmental variables. Additionally, we used count time series to characterize the associationbetween COVID-19 spread and air quality factors. All analyses were adjusted by social demographic,country-income level, and government policy intervention confounders, among 160 countries, globally.Our results reveal a statistically meaningful association between COVID-19 infection and several factorsof interest at country and city levels such as the national biodiversity index, air quality, and pollutantselements (PM10,PM2.5,and O3). Particularly, there is a significant relationship of loss of biodiversity, highlevel of air pollutants, and diminished air quality with COVID-19 infection spread and mortality. Ourfindings provide an empirical foundation for future studies on the relationship between air qualityvariables, a country's biodiversity, and COVID-19 transmission and mortality. The relationships measuredin this study can be valuable when governments plan environmental and health policies, as alternativestrategy to respond to new COVID-19 outbreaks and prevent future crises ; Daniel Fernández supported by grant 2017 SGR 622 (GRBIO) administrated by the Departament d'Economia i Coneixement de la Generalitat de Catalunya (Spain) ; Peer Reviewed ; Postprint (author's final draft)

(1) Cognitive decline differs among individuals and cognition function domains. We sought to identify distinct groups of immediate and delayed verbal memory in two age subsamples (50–64, 65+ years), and to analyze associated factors. (2) Latent class mixed models were used to identify verbal memory trajectories in a sample of Spanish community-dwelling individuals over 8 years' follow up. Chi-square and Kruskal–Wallis tests were used to assess differences among trajectories. (3) Different trajectories were identified. In the case of immediate verbal memory, these were: very low/decline (6.3%), low/stable (38.2%), medium/slow decline (43.4%), and high/slow decline (12.2%) in the middle-aged group, and low/decline (20.4%), medium/slow decline (60.4%), and high/slow decline (19.2%) in the older subsample. In delayed verbal memory, more distinct patterns were found: very low/decline (12.4%), low/stable (51.4%), medium/accelerated decline (24.7%), and high/slow increase (11.4%) in the younger group, and low/slow decline (34.4%), medium/decline (52.7%), and high/slow decline (12.9%) in the older group. (4) Overall, low initial performance and decline were associated with older age, lower education, and higher diabetes/stroke prevalence. Differences found suggests heterogeneity in cognitive ageing. The high prevalence of cardiovascular diseases in those with worse cognition suggests that early interventions to prevent those conditions should be targeted in midlife to delay cognitive decline. ; This work was supported by the European Community's Seventh Framework Programme [grant number 223071-COURAGE Study]; the Instituto de Salud Carlos III-FIS [grant numbers PS09/00295, PS09/01845, PI12/01490, and PI13/00059. Projects PI12/01490 and PI13/00059 have been co-funded by the European Union European Regional Development Fund (ERDF) "AWay to Build Europe"]; the European Union's Horizon 2020 Research and Innovation Programme [grant agreement 635316]; and the Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III. NMM is supported by the programme 'Contratos predoctorales para Formación de Personal Investigador, FPI-UAM', Universidad Autónoma de Madrid, Spain. EL's work is supported by the Sara Borrell postdoctoral program (CD18/00099) of the Instituto de Salud Carlos III (Spain) and co-funded by the European Union (ERDF/ESF, "Investing in your future"). DM's work is supported by the Spanish Ministry of Education, Culture, and Sports [grant number FPU15/02634]. DF's work has been supported by grant RTI2018-100927-J-I00 administrated by Ministerio de Ciencia e Innovación (MCI, Spain), by the Agencia Estatal de Investigación (AEI, Spain), and by the European Regional Development Fund (FEDER, UE), by Marsden grant E2987-3648 administrated by the Royal Society of New Zealand), and by grant 2017 SGR 622 (GRBIO) administrated by the Departament d'Economia i Coneixement de la Generalitat de Catalunya (Spain).

Background: Studies on the effects of sociodemographic factors on health in aging now include the use of statistical models and machine learning. The aim of this study was to evaluate the determinants of health in aging using machine learning methods and to compare the accuracy with traditional methods. Material/Methods: The health status of 6,209 adults, age 80 years (n=1,357) were measured using an established health metric (0–100) that incorporated physical function and activities of daily living (ADL). Data from the English Longitudinal Study of Ageing (ELSA) included socio-economic and sociodemographic characteristics and history of falls. Health-trend and personal-fitted variables were generated as predictors of health metrics using three machine learning methods, random forest (RF), deep learning (DL) and the linear model (LM), with calculation of the percentage increase in mean square error (%IncMSE) as a measure of the importance of a given predictive variable, when the variable was removed from the model. Results: Health-trend, physical activity, and personal-fitted variables were the main predictors of health, with the%incMSE of 85.76%, 63.40%, and 46.71%, respectively. Age, employment status, alcohol consumption, and household income had the%incMSE of 20.40%, 20.10%, 16.94%, and 13.61%, respectively. Performance of the RF method was similar to the traditional LM (p=0.7), but RF significantly outperformed DL (p=0.006). Conclusions: Machine learning methods can be used to evaluate multidimensional longitudinal health data and may provide accurate results with fewer requirements when compared with traditional statistical modeling. ; The ATHLOS project has received funding from the European Union Horizon 2020 Research and Innovation Program under grant agreement No. 635316 (EU HORIZON2020-PHC-635316)

Structural and intercultural competence approaches have been widely applied to fields such as medical training, healthcare practice, healthcare policies and health promotion. Nevertheless, their systematic implementation in epidemiological research is absent. Based on a scoping review and a qualitative analysis, in this article we propose a checklist to assess cultural and structural competence in epidemiological research: the Structural and Intercultural Competence for Epidemiological Studies guidelines. These guidelines are organised as a checklist of 22 items and consider four dimensions of competence (awareness and reflexivity, cultural and structural validation, cultural and structural sensitivity, and cultural and structural representativeness), which are applied to the different stages of epidemiological research: (1) research team building and research questions; (2) study design, participant recruitment, data collection and data analysis; and (3) dissemination. These are the first guidelines addressing structural and cultural competence in epidemiological inquiry ; This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement no 825884 (SYNergies for Cohorts in Health: integrating the ROle of all Stakeholders, SYNCHROS). AM-H acknowledges the financial support from Institut Català de la Recerca i Estudis Avançats (ICREA) under the ICREA Academia Award

(1) Cognitive decline differs among individuals and cognition function domains. We sought to identify distinct groups of immediate and delayed verbal memory in two age subsamples(50–64, 65+years), and to analyze associated factors. (2) Latent class mixed models were used to identify verbal memory trajectories in a sample of Spanish community-dwelling individuals over 8 years' follow up. Chi-square and Kruskal–Wallis tests were used to assess differences among trajectories. (3) Different trajectories were identified. In the case of immediate verbal memory, these were: very low/decline (6.3%), low/stable (38.2%), medium/slow decline (43.4%), and high/slowdecline (12.2%) in the middle-aged group, and low/decline (20.4%), medium/slow decline (60.4%),and high/slow decline (19.2%) in the older subsample. In delayed verbal memory, more distinct patterns were found: very low/decline (12.4%), low/stable (51.4%), medium/accelerated decline (24.7%),and high/slow increase (11.4%) in the younger group, and low/slow decline (34.4%), medium/decline(52.7%), and high/slow decline (12.9%) in the older group. (4) Overall, low initial performance and decline were associated with older age, lower education, and higher diabetes/stroke prevalence.Differences found suggests heterogeneity in cognitive ageing. The high prevalence of cardiovascular diseases in those with worse cognition suggests that early interventions to prevent those conditions should be targeted in midlife to delay cognitive decline. ; This work was supported by the European Community's Seventh Framework Programme [grant number223071-COURAGE Study]; the Instituto de Salud Carlos III-FIS [grant numbers PS09/00295, PS09/01845, PI12/01490,and PI13/00059. Projects PI12/01490 and PI13/00059 have been co-funded by the European Union EuropeanRegional Development Fund (ERDF) "A Way to Build Europe"]; the European Union's Horizon 2020 Research andInnovation Programme [grant agreement 635316]; and the Centro de Investigación Biomédica en Red de SaludMental (CIBERSAM), Instituto de Salud Carlos III. NMM is supported by the programme 'Contratos predoctoralespara Formación de Personal Investigador, FPI-UAM', Universidad Autónoma de Madrid, Spain. EL's work issupported by the Sara Borrell postdoctoral program (CD18/00099) of the Instituto de Salud Carlos III (Spain)and co-funded by the European Union (ERDF/ESF, "Investing in your future"). DM's work is supported by theSpanish Ministry of Education, Culture, and Sports [grant number FPU15/02634]. DF's work has been supportedby grant RTI2018-100927-J-I00 administrated by Ministerio de Ciencia e Innovación (MCI, Spain), by the AgenciaEstatal de Investigación (AEI, Spain), and by the European Regional Development Fund (FEDER, UE), by Marsdengrant E2987-3648 administrated by the Royal Society of New Zealand), and by grant 2017 SGR 622 (GRBIO)administrated by the Departament d'Economia i Coneixement de la Generalitat de Catalunya (Spa ; Peer Reviewed ; Postprint (published version)

Abstract Background: The present study aims to estimate childhood overweight and obesity prevalence and their association with individual and population-level correlates in Eastern and Western European countries. Methods: Data were obtained from the School Children Mental Health in Europe, a cross-sectional survey conducted in 2010 in Italy, Germany, the Netherlands, Romania, Bulgaria, Lithuania and Turkey. The sample consists of 5,206 school children aged 6 to 11 years old. Information on socio-demographics, children's height and weight, life-style and parental attitude were reported by the mothers. Country-level indicators were obtained through several data banks. Overweight and obesity in children were calculated according to the international age and gender-specific child Body Mass Index cut-off points. Multivariable logistic regression models included socio-demographic, lifestyle, mothers' attitude, and country-level indicators to examine the correlates of overweight. Results: Overall prevalence was 15.6% (95% CI = 19.3-21.7%) for overweight and 4.9% (95% CI = 4.3-5.6%) for obesity. In overweight (including obesity), Romanian children had the highest prevalence (31.4%, 95% CI = 28.1-34.6%) and Italian the lowest (10.4%, 95% CI = 8.1-12.6%). Models in the pooled sample showed that being younger (aOR = 0.93, 95% = CI 0.87-0.97), male (aOR = 1.24, 95% CI = 1.07-1.43), an only child (aOR = 1.40, 95% CI = 1.07-1.84), spending more hours per week watching TV (aOR = 1.01, 95% CI =1.002-1.03), and living in an Eastern Country were associated with greater risk of childhood overweight (including obesity). The same predictors were significantly associated with childhood overweight in the model conducted in the Eastern region, but not in the West. Higher Gross Domestic Product and Real Domestic Product, greater number of motor and passenger vehicles, higher percentage of energy available from fat, and more public sector expenditure on health were also associated with lower risk for childhood overweight after adjusting for covariables in the pooled sample and in the east of Europe, but not in the West. Conclusions: Prevalence rates of overweight and obesity in school children is still high, especially in Eastern regions, with some socio-demographic factors and life-styles associated with being overweight. It is also in the Eastern region itself where better macro-economic indicators are related with lower rates of childhood overweight. This represents a public health concern that deserves special attention in those countries undertaking economic and political transitions.

Christina M van der Feltz-Cornelis,1–3 Jim van Os,4,6 Susanne Knappe,5 Gunter Schumann,6 Eduard Vieta,7 Hans-Ulrich Wittchen,5 Shôn W Lewis,8 Iman Elfeddali,2,9 Kristian Wahlbeck,10,11 Donald Linszen,4 Carla Obradors-Tarragó,12,13 Josep Maria Haro12–141Trimbos Instituut, Utrecht, 2Tilburg University, Tranzo Department, Tilburg, 3GGz Breburg, Tilburg, 4Department of Psychiatry and Psychology, South Limburg Mental Health Research and Teaching Network, Euron, Maastricht University Medical Center, Maastricht, the Netherlands; 5Institute of Clinical Psychology and Psychotherapy and Center for Epidemiology and Longitudinal Studies, Technische Universität Dresden, Dresden, Germany; 6Institute of Psychiatry, King's College London, London, UK; 7Institute of Neuroscience, Hospital Clinic, University of Barcelona, IDIBAPS, CIBERSAM, Barcelona, Catalonia, Spain; 8School of Community-Based Medicine, University of Manchester, Manchester, UK; 9Department of Health Promotion/School of Public Health and Primary Care, Maastricht University, Maastricht, the Netherlands; 10The Nordic School of Public Health, Gothenburg, Sweden; 11National Institute for Health and Welfare, Helsinki, Finland; 12Centro de Investigación Biomédica en Red de Salud Mental, Madrid, 13Research and Development Unit, Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, Sant Boi de Llobregat, 14Universitat de Barcelona, Barcelona, SpainBackground: The size and increasing burden of disease due to mental disorders in Europe poses substantial challenges to its population and to the health policy of the European Union. This warrants a specific research agenda concerning clinical mental health research as one of the cornerstones of sustainable mental health research and health policy in Europe. The aim of this research was to identify the top priorities needed to address the main challenges in clinical research for mental disorders.Methods: The research was conducted as an expert survey and expert panel discussion during a scientific workshop.Results: Eighty-nine experts in clinical research and representing most European countries participated in this survey. Identified top priorities were the need for new intervention studies, understanding the diagnostic and therapeutic implications of mechanisms of disease, and research in the field of somatic-psychiatric comorbidity. The "subjectivity gap" between basic neuroscience research and clinical reality for patients with mental disorders is considered the main challenge in psychiatric research, suggesting that a shift in research paradigms is required.Conclusion: Innovations in clinical mental health research should bridge the gap between mechanisms underlying novel therapeutic interventions and the patient experience of mental disorder and, if present, somatic comorbidity. Clinical mental health research is relatively underfunded and should receive specific attention in Horizon 2020 funding programs.Keywords: clinical research, mental health, randomized clinical trials, Horizon 2020, expert survey, challenge, research agenda, Europe

Background: The present study aims to estimate childhood overweight and obesity prevalence and their association with individual and population-level correlates in Eastern and Western European countries. Methods: Data were obtained from the School Children Mental Health in Europe, a cross-sectional survey conducted in 2010 in Italy, Germany, the Netherlands, Romania, Bulgaria, Lithuania and Turkey. The sample consists of 5,206 school children aged 6 to 11 years old. Information on socio-demographics, children's height and weight, life-style and parental attitude were reported by the mothers. Country-level indicators were obtained through several data banks. Overweight and obesity in children were calculated according to the international age and gender-specific child Body Mass Index cut-off points. Multivariable logistic regression models included socio-demographic, lifestyle, mothers' attitude, and country-level indicators to examine the correlates of overweight. Results: Overall prevalence was 15.6% (95% CI = 19.3-21.7%) for overweight and 4.9% (95% CI = 4.3-5.6%) for obesity. In overweight (including obesity), Romanian children had the highest prevalence (31.4%, 95% CI = 28.1-34.6%) and Italian the lowest (10.4%, 95% CI = 8.1-12.6%). Models in the pooled sample showed that being younger (aOR = 0.93, 95% = CI 0.87-0.97), male (aOR = 1.24, 95% CI = 1.07-1.43), an only child (aOR = 1.40, 95% CI = 1.07-1.84), spending more hours per week watching TV (aOR = 1.01, 95% CI =1.002-1.03), and living in an Eastern Country were associated with greater risk of childhood overweight (including obesity). The same predictors were significantly associated with childhood overweight in the model conducted in the Eastern region, but not in the West. Higher Gross Domestic Product and Real Domestic Product, greater number of motor and passenger vehicles, higher percentage of energy available from fat, and more public sector expenditure on health were also associated with lower risk for childhood overweight after adjusting for covariables in the pooled sample and in the east of Europe, but not in the West. Conclusions: Prevalence rates of overweight and obesity in school children is still high, especially in Eastern regions, with some socio-demographic factors and life-styles associated with being overweight. It is also in the Eastern region itself where better macro-economic indicators are related with lower rates of childhood overweight. This represents a public health concern that deserves special attention in those countries undertaking economic and political transitions.

Background: The present study aims to estimate childhood overweight and obesity prevalence and their association with individual and population-level correlates in Eastern and Western European countries. Methods: Data were obtained from the School Children Mental Health in Europe, a cross-sectional survey conducted in 2010 in Italy, Germany, the Netherlands, Romania, Bulgaria, Lithuania and Turkey. The sample consists of 5,206 school children aged 6 to 11 years old. Information on socio-demographics, children's height and weight, life-style and parental attitude were reported by the mothers. Country-level indicators were obtained through several data banks. Overweight and obesity in children were calculated according to the international age and gender-specific child Body Mass Index cut-off points. Multivariable logistic regression models included socio-demographic, lifestyle, mothers' attitude, and country-level indicators to examine the correlates of overweight. Results: Overall prevalence was 15.6% (95% CI = 19.3-21.7%) for overweight and 4.9% (95% CI = 4.3-5.6%) for obesity. In overweight (including obesity), Romanian children had the highest prevalence (31.4%, 95% CI = 28.1-34.6%) and Italian the lowest (10.4%, 95% CI = 8.1-12.6%). Models in the pooled sample showed that being younger (aOR = 0.93, 95% = CI 0.87-0.97), male (aOR = 1.24, 95% CI = 1.07-1.43), an only child (aOR = 1.40, 95% CI = 1.07-1.84), spending more hours per week watching TV (aOR = 1.01, 95% CI =1.002-1.03), and living in an Eastern Country were associated with greater risk of childhood overweight (including obesity). The same predictors were significantly associated with childhood overweight in the model conducted in the Eastern region, but not in the West. Higher Gross Domestic Product and Real Domestic Product, greater number of motor and passenger vehicles, higher percentage of energy available from fat, and more public sector expenditure on health were also associated with lower risk for childhood overweight after adjusting for covariables in the pooled sample and in the east of Europe, but not in the West. Conclusions: Prevalence rates of overweight and obesity in school children is still high, especially in Eastern regions, with some socio-demographic factors and life-styles associated with being overweight. It is also in the Eastern region itself where better macro-economic indicators are related with lower rates of childhood overweight. This represents a public health concern that deserves special attention in those countries undertaking economic and political transitions.

Background: The present study aims to estimate childhood overweight and obesity prevalence and their association with individual and population-level correlates in Eastern and Western European countries. Methods: Data were obtained from the School Children Mental Health in Europe, a cross-sectional survey conducted in 2010 in Italy, Germany, the Netherlands, Romania, Bulgaria, Lithuania and Turkey. The sample consists of 5,206 school children aged 6 to 11 years old. Information on socio-demographics, children's height and weight, life-style and parental attitude were reported by the mothers. Country-level indicators were obtained through several data banks. Overweight and obesity in children were calculated according to the international age and gender-specific child Body Mass Index cut-off points. Multivariable logistic regression models included socio-demographic, lifestyle, mothers' attitude, and country-level indicators to examine the correlates of overweight. Results: Overall prevalence was 15.6% (95% CI = 19.3-21.7%) for overweight and 4.9% (95% CI = 4.3-5.6%) for obesity. In overweight (including obesity), Romanian children had the highest prevalence (31.4%, 95% CI = 28.1-34.6%) and Italian the lowest (10.4%, 95% CI = 8.1-12.6%). Models in the pooled sample showed that being younger (aOR = 0.93, 95% = CI 0.87-0.97), male (aOR = 1.24, 95% CI = 1.07-1.43), an only child (aOR = 1.40, 95% CI = 1.07-1.84), spending more hours per week watching TV (aOR = 1.01, 95% CI =1.002-1.03), and living in an Eastern Country were associated with greater risk of childhood overweight (including obesity). The same predictors were significantly associated with childhood overweight in the model conducted in the Eastern region, but not in the West. Higher Gross Domestic Product and Real Domestic Product, greater number of motor and passenger vehicles, higher percentage of energy available from fat, and more public sector expenditure on health were also associated with lower risk for childhood overweight after adjusting for covariables in the pooled sample and in the east of Europe, but not in the West. Conclusions: Prevalence rates of overweight and obesity in school children is still high, especially in Eastern regions, with some socio-demographic factors and life-styles associated with being overweight. It is also in the Eastern region itself where better macro-economic indicators are related with lower rates of childhood overweight. This represents a public health concern that deserves special attention in those countries undertaking economic and political transitions.