As of 2024, approximately ** percent of men and ** percent of women in Romania considered themselves overweight or obese. Across the European countries featured, being overweight or obese was more prevalent among males.

This statistis displays the growth in obesity rates in selected European countries in 1975 and 2014. The obesity rate in Ireland grew from *** percent in 1975 to **** percent in 2014.

By 2025, the prevalence of obese women in the United Kingdom is projected to be at ** percent, this would be the highest obesity prevalence in women compared to EU countries. Ireland with **** percent was forecast to have the second highest prevalence of obese women in Europe, followed by Malta at ** percent.

This statistic displays the obesity prevalence in selected European countries by 2025. Great Britain is set to be the most obese nation in Europe by 2025 with an obesity prevalence of **** percent, followed closely by Ireland and Lithuania with an expected prevalence rate of ***** percent respectively.

Introduction: Obesity constitutes a major public health problem in Europe, but how the obesity epidemic in European countries will evolve remains unknown. Most previous obesity projections considered the short-term future only, focused on single non-European countries, and projected ongoing increases foremost. We comparatively project obesity prevalence into the long-term future for 18 European countries and the USA. Data: We used national age-specific (20–84 years) and sex-specific obesity prevalence estimates (1975–2016) from the NCD Risk Factor Collaboration (NCD-RisC) 2017 study, which are based on available measured height and weight data, supplemented with estimates from a Bayesian hierarchical model. Methods: We projected age- and sex-specific obesity prevalence up to the year 2100 by integrating the notion of a wave-shaped obesity epidemic into conventional age-period projections. Results: In 1990–2016, the increasing trends in obesity prevalence were decelerating. Obesity is expected to reach maximum levels between 2030 and 2052 among men, and between 2026 and 2054 among women. The maximum levels will likely be reached first in The Netherlands, USA, and UK, and last in Switzerland; and are expected to be highest in the USA and UK, and lowest in The Netherlands for men and Denmark for women. In 2060, obesity prevalence is expected to be lowest among Dutch men and highest among Swiss men. The projected age-specific obesity prevalence levels have an inverse U-shape, peaking at around the age of 60–69 years. Discussion: Applying our novel approach to the NCD-RisC 2017 data, obesity prevalence is expected to reach maximum levels between 2026 and 2054, with the USA and UK reaching the highest maximum levels first, followed by other European countries.

Historical dataset showing European Union obesity rate by year from N/A to N/A.

By 2030, Malta, Hungary, and the United Kingdom were projected to have the highest prevalence of obesity in their populations among men in Europe. Across the whole of Europe, approximately ** percent of men were forecast to be classified as obese in 2030.

This indicator is derived from the body mass index (BMI), which is defined as the weight in kilograms divided by the square of the height in metres. People aged 18 years or over are considered obese if their BMI is equal to or greater than 30. The category ‘pre-obese’ refers to people with a BMI between 25 and less than 30. The category ‘overweight’ (BMI equal or greater than 25) combines the two categories pre-obese and obese. The data presented in this section stem from the European Health Interview Survey (EHIS) and the EU Statistics on Income and Living Conditions (EU-SILC).

Introduction: Using data from the TackSHS survey, we aim to provide updated estimates on the prevalence and determinants of overweight and obesity in Europe. Methods: A face-to-face survey was conducted in 2017-2018 in 12 European countries (Bulgaria, England, France, Germany, Greece, Ireland, Italy, Latvia, Poland, Portugal, Romania, and Spain). Overall, 10,810 participants, representative in each country of the general adult population, provided information on self-reported height and weight. Results: Almost half of participants (48.1%; 95% confidence interval, CI: 47.2-49.1) reported to be overweight or obese (54.1% in men and 42.5% in women), and 12.6% (95% CI: 12.0-13.2) obese (11.3% in men and 13.8% in women). Obesity prevalence was lowest in Italy (7.5%) and France (8.8%), and highest in Greece (19.7%) and Romania (21.1%). Multilevel logistic random-effects analyses showed that prevalence of obesity was related with higher age and lower level of education and socioeconomic status. As compared to northern European countries, Western and Southern European ones showed a significantly lower obesity prevalence. When compared to a companion study conducted in 2010, Eastern and Northern European countries showed an increased trend in obesity prevalence. Conversely, countries with the lowest obesity prevalence (less than 10%), such as Italy and France, showed a decreased trend. Conclusions: Despite a large heterogeneity across countries, overweight and obesity prevalence estimates in Europe are alarming, with most of the countries reporting obesity prevalence approaching 20% or more, particularly in Eastern and Northern Europe. Since 2010, obesity prevalence increased in most of these countries.

The indicator measures the share of obese people based on their body mass index (BMI). BMI is defined as the weight in kilos divided by the square of the height in meters. People aged 18 years or over are considered obese with a BMI equal or greater than 30. Other categories are: underweight (BMI less than 18.5), normal weight (BMI between 18.5 and less than 25), and pre-obese (BMI between 25 and less than 30). The category overweight (BMI equal or greater than 25) combines the two categories pre-obese and obese.

BackgroundThe comorbidities associated with overweight and obesity have been well researched and scientifically proven while their relationship to mental health is still not verified.MethodsThis study is aimed at investigating reciprocal associations between obesity and mental health, and is intended to further analyze possible long-term effects using data from the Survey of Health, Ageing and Retirement in Europe (SHARE). In order to do that, waves 4 and 8, conducted in 2010 and 2019/20 of this survey, were analyzed in a cross-lagged panel approach including 16,184 adult Europeans (50+) using multiple linear regression analysis focusing on the Body Mass Index (BMI), depression status and quality of life (QoL).ResultsFindings yield significant cross-lagged effects in one direction regarding BMI predicting QoL and depression state, whereas depression state and QoL do not significantly predict BMI. Findings include people living with obesity, overweight, and underweight showing significantly decreased levels of QoL as well as increased depression scores compared to people of normal weight over a lag time of 10 years, where people living with obesity indicate the strongest effect.ConclusionsHowever, results do not confirm reciprocal associations in the long term. Hence, there is a strong need to carry out further research on this issue.

As of 2022, over ** percent of men and ** percent of women in Greece considered themselves as overweight. Furthermore, approximately ** percent of men and ** percent of women in Czechia were classed as overweight. Across Europe, levels of overweight were higher among men compared to women.

Overweight and obesity prevalence rates estimates (1975-2016)

Europe Obesity Management Market growth is driven by rising obesity rates, technological advancements, and supportive government initiatives.

This statistical report presents a range of information on obesity, physical activity and diet, drawn together from a variety of sources. The topics covered include: - Overweight and obesity prevalence among adults and children - Physical activity levels among adults and children - Trends in purchases and consumption of food and drink and energy intake - Health outcomes of being overweight or obese. This report contains seven chapters which consist of the following: - Chapter 1: Introduction; this summarises government policies, targets and outcome indicators in this area, as well as providing sources of further information and links to relevant documents. - Chapters 2 to 6 cover obesity, physical activity and diet and provides an overview of the key findings from these sources, whilst maintaining useful links to each section of these reports. - Chapter 7: Health Outcomes; presents a range of information about the health outcomes of being obese or overweight which includes information on health risks, hospital admissions and prescription drugs used for treatment of obesity. - Figures presented in this report have been obtained from a number of sources and presented in a user-friendly format. Some of the data contained in the chapter have been published previously by the Health and Social Care Information Centre (HSCIC). Previously unpublished figures on obesity-related Finished Hospital Episodes and Finished Consultant Episodes for 2012-13 are presented using data from the HSCIC's Hospital Episode Statistics as well as data from the Prescribing Unit at the HSCIC on prescription items dispensed for treatment of obesity.

Europe Obesity Intervention Devices market USD 76.86 million in 2024 and will grow at a compound annual growth rate (CAGR) of 4.5% from 2024 to 2031. Increasing obesity rates and a rising emphasis on preventive healthcare is expected to aid the sales to USD 109.4 million by 2031

By 2030, Hungary, Turkey, and Croatia were projected to have the highest prevalence of childhood obesity (between 5 and 19 years of age) across Europe at ** percent. Across the whole of Europe, it was projected that just over ** percent of children aged between five and nine years of age would be classed as obese by 2030.

The analyses of geographic variations in the prevalence of major chronic conditions, such as overweight and obesity, are an important public health tool to identify “hot spots” and inform allocation of funding for policy and health promotion campaigns, yet rarely performed. Here we aimed at exploring, for the first time in Luxembourg, potential geographic patterns in overweight/obesity prevalence in the country, adjusted for several demographic, socioeconomic, behavioural and health status characteristics. Data came from 720 men and 764 women, 25–64 years old, who participated in the European Health Examination Survey in Luxembourg (2013–2015). To investigate the geographical variation, geo-additive semi-parametric mixed model and Bayesian modelisations based on Markov Chain Monte Carlo techniques for inference were performed. Large disparities in the prevalence of overweight and obesity were found between municipalities, with the highest rates of obesity found in 3 municipalities located in the South-West of the country. Bayesian approach also underlined a nonlinear effect of age on overweight and obesity in both genders (significant in men) and highlighted the following risk factors: 1. country of birth for overweight in men born in a non-European country (Posterior Odds Ratio (POR): 3.24 [1.61–8.69]) and women born in Portugal (POR: 2.44 [1.25–4.43]), 2. low educational level (secondary or below) for overweight (POR: 1.66 (1.06–2.72)] and obesity (POR:2.09 [1.05–3.65]) in men, 3. single marital status for obesity in women (POR: 2.20 [1.24–3.91]), 4.fair (men: POR: 3.19 [1.58–6.79], women: POR: 2.24 [1.33–3.73]) to very bad health perception (men: POR: 15.01 [2.16–98.09]) for obesity, 5. sleeping more than 6 hours for obesity in unemployed men (POR: 3.66 [2.02–8.03]). Protective factors highlighted were: 1. single marital status against overweight (POR: [0.60 (0.38–0.96)]) and obesity (POR: 0.39 [0.16–0.84]) in men, 2. the fact to be widowed against overweight in women (POR: [0.30 (0.07–0.86)], as well as a non European country of birth (POR: 0.49 [0.19–0.98]), tertiary level of education (POR: 0.34 [0.18–0.64]), moderate alcohol consumption (POR: 0.54 [0.36–0.90]) and aerobic physical activity practice (POR: 0.44 [0.27–0.77]) against obesity in women. A double burden of environmental exposure due to historic mining and industrial activities and past economic vulnaribility in the South-West of the country may have participated to the higher prevalence of obesity found in this region. Other demographic, socioeconomic, behavioural and health status covariates could have been involved as well.

Comparisons among countries can help to identify opportunities for the reduction of inequalities in cardiometabolic health. The present cross-sectional analysis and meta-analysis aim to address to what extent obesity traits, socioeconomic, and behavioral factors determine poor metabolic health across body mass index (BMI) categories in two urban population-based samples from Central Europe. Data from the CoLaus (~6,000 participants; Lausanne, Switzerland) and the Kardiovize Brno 2030 (~2,000 participants; Brno, Czech Republic) cohorts. For each cohort, logistic regression analyses were performed to identify the main determinants of poor metabolic health overall and stratified by body mass index (BMI) categories. The results of each cohort were then combined in a meta-analysis. We first observed that waist circumference and body fat mass were associated with metabolic health, especially in non-obese individuals. Moreover, increasing age, being male, having low-medium educational level, abdominal obesity, and high body fat mass were the main determinants of the metabolically unhealthy profile in both cohorts. Meta-analysis stratified by BMI categories confirmed the previous results with slight differences across BMI categories. In fact, increasing age and being male were the main determinants of poor metabolic health independent of obesity status. In contrast, low educational level and current smoking were associated with poor metabolic health only in non-obese individuals. In line, public health strategies against obesity and related comorbidities should aim to improve social conditions and to promote healthy lifestyles before the progression of metabolic disorders.

Background: Obesity is a risk factor for many chronic diseases and the prevalence is increasing worldwide. Research suggests that sedentary behaviour (sitting) may be related to obesity. Aim: To examine the association between sitting time and obesity, while controlling for physical activity, in a large international sample. Subjects and methods: In total, 5338 adults from the UK, USA, Germany, Spain, Italy, France, Portugal, Austria and Switzerland self-reported their total daily sitting time, physical activity, age, height and weight. BMI (kg/m2), total physical activity (MET-minutes/week) and sitting time (hours/day) were derived. Participants were grouped into quartiles based on their daily sitting time (<4, 4–≤6, 6–≤8 and >8 hours/day) and logistic regression models explored the odds of being obese vs normal weight for each sitting time quartile. Results: Participants in the highest sitting time quartile (≥8 hours/day) had 62% higher odds of obesity compared to participants in the lowest quartile (<4 hours/day) after adjustment for physical activity and other confounding variables (OR = 1.62, 95% CI = 1.24–2.12, p < .01). Conclusion: Sitting time is associated with obesity in adults, independent of physical activity. Future research should clarify this association using objective measures of sitting time and physical activity to further inform health guidelines.