Body mass index or BMI per country and year. This is divided into two tables, mean-body-mass-index-bmi-in-adult-males.csv for males and mean-body-mass-index-bmi-in-adult-women.csv for females. Taken from https://ourworldindata.org/obesity

This statistic illustrates the top 10 countries with the lowest average body mass index among adults in 2014. Eritrea recorded the lowest average BMI in 2014 with ****, followed by Ethiopia with **** BMI.

This statistic depicts the top 10 countries with the highest average body mass index among adults in 2014. Nauru recorded the highest BMI in 2014 with around 32.5, followed by the Cook Islands with a 32.3 BMI.

BackgroundIn high-income countries, obesity prevalence (body mass index greater than or equal to 30 kg/m2) is highest among the poor, while overweight (body mass index greater than or equal to 25 kg/m2) is prevalent across all wealth groups. In contrast, in low-income countries, the prevalence of overweight and obesity is higher among wealthier individuals than among poorer individuals. We characterize the transition of overweight and obesity from wealthier to poorer populations as countries develop, and project the burden of overweight and obesity among the poor for 103 countries.Methods and findingsOur sample used 182 Demographic and Health Surveys and World Health Surveys (n = 2.24 million respondents) from 1995 to 2016. We created a standard wealth index using household assets common among all surveys and linked national wealth by country and year identifiers. We then estimated the changing probability of overweight and obesity across every wealth decile as countries’ per capita gross domestic product (GDP) rises using logistic and linear fixed-effect regression models. We found that obesity rates among the wealthiest decile were relatively stable with increasing national wealth, and the changing gradient was largely due to increasing obesity prevalence among poorer populations (3.5% [95% uncertainty interval: 0.0%–8.3%] to 14.3% [9.7%–19.0%]). Overweight prevalence among the richest (45.0% [35.6%–54.4%]) and the poorest (45.5% [35.9%–55.0%]) were roughly equal in high-income settings. At $8,000 GDP per capita, the adjusted probability of being obese was no longer highest in the richest decile, and the same was true of overweight at $10,000. Above $25,000, individuals in the richest decile were less likely than those in the poorest decile to be obese, and the same was true of overweight at $50,000. We then projected overweight and obesity rates by wealth decile to 2040 for all countries to quantify the expected rise in prevalence in the relatively poor. Our projections indicated that, if past trends continued, the number of people who are poor and overweight will increase in our study countries by a median 84.4% (range 3.54%–383.4%), most prominently in low-income countries. The main limitations of this study included the inclusion of cross-sectional, self-reported data, possible reverse causality of overweight and obesity on wealth, and the lack of physical activity and food price data.ConclusionsOur findings indicate that as countries develop economically, overweight prevalence increased substantially among the poorest and stayed mostly unchanged among the wealthiest. The relative poor in upper- and lower-middle income countries may have the greatest burden, indicating important planning and targeting needs for national health programs.

The European Health Interview Survey (EHIS) aims at measuring on a harmonised basis and with a high degree of comparability among Member States (MS) the health status (including disability), health determinants (lifestyle) of the EU citizens and use of health care services and limitations in accessing it.

The general coverage of the survey is the population aged 15 or over living in private households residing in the territory of the country.

EHIS was developed between 2003 and 2006. It consists of four modules on health status, health determinants, health care, and background variables (socio-demographic characteristics of the population).

Three waves of EHIS have currently been implemented. The first wave of EHIS (EHIS wave 1 or EHIS round 2008) was conducted between 2006 and 2009 in 17 EU Member States as well as Switzerland and Turkey.

The second wave (EHIS wave 2 or EHIS round 2014) was conducted between 2013 and 2015 in all EU Member States, Iceland, Norway and Turkey according to the Commission Regulation 141/2013.

The third wave of EHIS was conducted in 2019. All Member States participated in the EHIS wave 3 in accordance with the Commission Regulation (EU) No. 2018/255. A derogation regarding the data collection period was granted for some countries: the data collection period was 2018 for Belgium, 2018-2020 for Austria and Germany, and 2019-2020 for Malta.

The questionnaire consists of the same four modules for all the EHIS waves and over the years, some changes to the questionnaire have been implemented to satisfy specific users’ needs. Also, countries are allowed to include additional questions in the specific submodules or even specific sub-modules in the survey if this does not have an impact on the results of the compulsory variable

EHIS includes the following topics:

Health status
This topic includes different dimensions of health status and health-related activity limitations:

• General health status (Minimum European health module): self-perceived health, chronic morbidity and disability (activity limitation)
• Disease-specific morbidity
• Accidents and injuries
• Health-related absenteeism from work
• Physical and sensory functional limitations (also cognitive limitations in wave 3)
• Difficulties in personal care activities / activities of daily living (such as eating and washing) and help received/needed
• Household activities / Instrumental activities of daily living (such as preparing meals and shopping) and help received/needed
• Pain
• Aspect of mental health (psychological distress and mental well-being in the first wave, depressive symptoms in the second and third waves)
• Work-related health problems (only in the first wave).

Health care
This topic covers the use of different types of medicines and formal and informal health and social care services, which are complemented by data on health-related expenditure, and limitations in access to and satisfaction with health care services:

• Hospitalisation (in-patient and day care)
• Consultations with doctors and dentists
• Visits to specific health professionals (such as physiotherapists or psychologists)
• Use of home care services
• Use of medicines (prescribed and non-prescribed)
• Healthcare preventive actions (such as influenza vaccination, breast examination, cervical smear test and blood tests)
• Unmet needs for health care
• Out-of-pocket payments for medical care (only in the first wave)
• Satisfaction with services provided by healthcare providers (only in the first wave)
• Visits to specific categories of alternative medicine practitioners (only in the first wave).

Health determinants
This topic includes various individual and environmental health determinants:

• Height and weight
• Physical activity/exercise
• Consumption of fruits, vegetables and juice
• Drinking sugar-sweetened soft drinks (only in the third wave)
• Tobacco smoking behaviour and exposure to tobacco smoke
• Use of e-cigarettes or similar electronic devices (only in the third wave)
• Alcohol consumption
• Social support
• Provision of informal care or assistance (only in the second and third waves)
• Illicit drug use (only in the first wave)
• Environment (home and workplace exposures, criminality exposure) (only in the first wave).

Background variables on demography and socio-economic characteristics.

All indicators are expressed as percentages within the population and statistics are broken down by age and sex and one other dimension such as educational attainment level, income quintile group, degree of urbanization, country of birth, country of citizenship, level of disability (activity limitation).

Body mass index (BMI) by sex, age and country of citizenship

In 2023, the distribution of body-mass-index (BMI) across Italy varied greatly by region. According to the data, southern regions had a higher share of overweight and obese people compared to the national average. Overall, the overweight population in Italy is projected to reach **** percent by 2029. The Italian regions with the highest share of people considered as having a normal weight in 2023 were Trentino-South Tyrol, Tuscany, and Marche. Conversely, the region of Aosta Valley hosted the most underweight people in the country, in relative terms, with *** percent.

Diabetes The number of individuals suffering from diabetes in Italy amounted to ***** in 2022. Although the risk factors related to type one diabetes are not fully known, among the risk factors for diabetes type 2, being overweight or obese are among the most common. Indeed, in 2021, almost ** percent of obese women were also diabetic. This rate lowers to **** percent for men. Obesity among children and adolescents Childhood obesity is becoming an issue in the country, with the share of overweight and obese children growing every year. Indeed, Italy has become one of the European countries with the highest obesity rate among children. This tendency is more prevalent among young boys, with **** percent of male minors overweight between 2020 and 2021, compared to ** percent of females.

Accountability for global health issues such as a pandemic and its devastating consequences are usually ascribed to a virus, but a comprehensive view should also take into account the state of the host. Data suggests that excessive nutrition is to blame for a yet unknown but not negligible portion of deaths attributed to severe acute respiratory syndrome coronavirus 2. We analyzed the correlation between mean body mass index (BMI) and 2-year coronavirus disease 2019 (COVID-19) mortality rates reported by 181 countries worldwide. Almost two thirds of the countries included had a mean BMI greater or equal to 25, with death rates ranging from 3 to 6,280 per million. Death rates in countries with a mean BMI below 25 ranged from 3 to 1,533. When the analysis was restricted to countries where the extent of testing was deemed more representative of actual mortality, only 20.1% had a mean BMI

Body mass index (BMI) by sex, age and country of birth

Between 2020 and 2035, the projected overweight and obese adult population (or adults with high body mass index, BMI) in Afghanistan was estimated to grow at an annual growth rate of *** percent, the highest in the Asia-Pacific region. In contrast, Japan's number of overweight and obese adults was projected to grow at an annual growth rate of less than *** percent.

Supplementary files for article Supplementary information files for Height and body-mass index trajectories of school-aged children and adolescents from 1985 to 2019 in 200 countries and territories: a pooled analysis of 2181 population-based studies with 65 million participants.BackgroundComparable global data on health and nutrition of school-aged children and adolescents are scarce. We aimed to estimate age trajectories and time trends in mean height and mean body-mass index (BMI), which measures weight gain beyond what is expected from height gain, for school-aged children and adolescents.MethodsFor this pooled analysis, we used a database of cardiometabolic risk factors collated by the Non-Communicable Disease Risk Factor Collaboration. We applied a Bayesian hierarchical model to estimate trends from 1985 to 2019 in mean height and mean BMI in 1-year age groups for ages 5–19 years. The model allowed for non-linear changes over time in mean height and mean BMI and for non-linear changes with age of children and adolescents, including periods of rapid growth during adolescence.FindingsWe pooled data from 2181 population-based studies, with measurements of height and weight in 65 million participants in 200 countries and territories. In 2019, we estimated a difference of 20 cm or higher in mean height of 19-year-old adolescents between countries with the tallest populations (the Netherlands, Montenegro, Estonia, and Bosnia and Herzegovina for boys; and the Netherlands, Montenegro, Denmark, and Iceland for girls) and those with the shortest populations (Timor-Leste, Laos, Solomon Islands, and Papua New Guinea for boys; and Guatemala, Bangladesh, Nepal, and Timor-Leste for girls). In the same year, the difference between the highest mean BMI (in Pacific island countries, Kuwait, Bahrain, The Bahamas, Chile, the USA, and New Zealand for both boys and girls and in South Africa for girls) and lowest mean BMI (in India, Bangladesh, Timor-Leste, Ethiopia, and Chad for boys and girls; and in Japan and Romania for girls) was approximately 9–10 kg/m2. In some countries, children aged 5 years started with healthier height or BMI than the global median and, in some cases, as healthy as the best performing countries, but they became progressively less healthy compared with their comparators as they grew older by not growing as tall (eg, boys in Austria and Barbados, and girls in Belgium and Puerto Rico) or gaining too much weight for their height (eg, girls and boys in Kuwait, Bahrain, Fiji, Jamaica, and Mexico; and girls in South Africa and New Zealand). In other countries, growing children overtook the height of their comparators (eg, Latvia, Czech Republic, Morocco, and Iran) or curbed their weight gain (eg, Italy, France, and Croatia) in late childhood and adolescence. When changes in both height and BMI were considered, girls in South Korea, Vietnam, Saudi Arabia, Turkey, and some central Asian countries (eg, Armenia and Azerbaijan), and boys in central and western Europe (eg, Portugal, Denmark, Poland, and Montenegro) had the healthiest changes in anthropometric status over the past 3·5 decades because, compared with children and adolescents in other countries, they had a much larger gain in height than they did in BMI. The unhealthiest changes—gaining too little height, too much weight for their height compared with children in other countries, or both—occurred in many countries in sub-Saharan Africa, New Zealand, and the USA for boys and girls; in Malaysia and some Pacific island nations for boys; and in Mexico for girls.InterpretationThe height and BMI trajectories over age and time of school-aged children and adolescents are highly variable across countries, which indicates heterogeneous nutritional quality and lifelong health advantages and risks.

BackgroundThe incidence of Type 1 Diabetes (T1D) in children varies dramatically between countries. Part of the explanation must be sought in environmental factors. Increasingly, public databases provide information on country-to-country environmental differences.MethodsInformation on the incidence of T1D and country characteristics were searched for in the 194 World Health Organization (WHO) member countries. T1D incidence was extracted from a systematic literature review of all papers published between 1975 and 2014, including the 2013 update from the International Diabetes Federation. The information on country characteristics was searched in public databases. We considered all indicators with a plausible relation with T1D and those previously reported as correlated with T1D, and for which there was less than 5% missing values. This yielded 77 indicators. Four domains were explored: Climate and environment, Demography, Economy, and Health Conditions. Bonferroni correction to correct false discovery rate (FDR) was used in bivariate analyses. Stepwise multiple regressions, served to identify independent predictors of the geographical variation of T1D.FindingsT1D incidence was estimated for 80 WHO countries. Forty-one significant correlations between T1D and the selected indicators were found. Stepwise Multiple Linear Regressions performed in the four explored domains indicated that the percentages of variance explained by the indicators were respectively 35% for Climate and environment, 33% for Demography, 45% for Economy, and 46% for Health conditions, and 51% in the Final model, where all variables selected by domain were considered. Significant environmental predictors of the country-to-country variation of T1D incidence included UV radiation, number of mobile cellular subscriptions in the country, health expenditure per capita, hepatitis B immunization and mean body mass index (BMI).ConclusionsThe increasing availability of public databases providing information in all global environmental domains should allow new analyses to identify further geographical, behavioral, social and economic factors, or indicators that point to latent causal factors of T1D.

European Health Survey: Body Mass Index of the adult population, according to sex, country of birth and age group. Population aged 18 years old and over. National.

BackgroundHigh body mass index (BMI) plays a critical role in the initiation and development of type 2 diabetes (T2D). Up to now, far too little attention has been paid to the global burden of T2D attributable to high BMI. This study aims to report the deaths and disability-adjusted life years (DALYs) of T2D related to high BMI in 204 countries and territories from 1990 to 2019.MethodsData on T2D burden attributable to high BMI were retrieved from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019. The global cases, age-standardized rates of mortality (ASMR), and disability-adjusted life years (ASDR) attributable to high BMI were estimated by age, sex, geographical location, and socio-demographic index (SDI). The estimated annual percentage change (EAPC) was calculated to quantify the trends of ASMR and ASDR during the period 1990–2019.ResultsGlobally, there were 619,494.8 deaths and 34,422,224.8 DALYs of T2D attributed to high BMI in 2019, more than triple in 1990. Moreover, the pace of increase in ASMR and ASDR accelerated during 1990–2019, with EAPC of 1.36 (95% CI: 1.27 to 1.45) and 2.13 (95% CI: 2.10 to 2.17) separately, especially in men, South Asia, and low-middle SDI regions. Oceania was the high-risk area of standardized T2D deaths and DALYs attributable to high BMI in 2019, among which Fiji was the country with the heaviest burden. In terms of SDI, middle SDI regions had the biggest T2D-related ASMR and ASDR in 2019.ConclusionThe global deaths and DALYs of T2D attributable to high BMI substantially increased from 1990 to 2019. High BMI as a major public health problem needs to be tackled properly and timely in patients with T2D.

United States Prevalence of Overweight: % of Adults data was reported at 67.900 % in 2016. This records an increase from the previous number of 67.400 % for 2015. United States Prevalence of Overweight: % of Adults data is updated yearly, averaging 55.200 % from Dec 1975 (Median) to 2016, with 42 observations. The data reached an all-time high of 67.900 % in 2016 and a record low of 41.000 % in 1975. United States Prevalence of Overweight: % of Adults data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Social: Health Statistics. Prevalence of overweight adults is the percentage of adults ages 18 and over whose Body Mass Index (BMI) is more than 25 kg/m2. Body Mass Index (BMI) is a simple index of weight-for-height, or the weight in kilograms divided by the square of the height in meters.;World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).;;

Table of INEBase Body Mass Index of the adult population by sex, country of birth, and age group. Population aged 18 years old and over. National. European Health Survey

This data set summarizes obesity and the number of deaths caused by it in each country

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2993575%2Fb55c8c53db1eb6809cc0fb6b5a081195%2F2024-05-25%20093352.png?generation=1716597253375211&alt=media" alt="">

💡I have already divided these into TRAIN data, TEST data, and ANSWER data so you guys can start working on the regression problem right away.

• train.csv: Obesity and deaths data from 1990 to 2013
• test.csv: The explanatory variable in 2014
• answer.csv: The objective variable in 2014

These data were created with the assumption that the number of deaths due to obesity in 2014 will be estimated from data from 1990 to 2013.

There is also something called HINT data(hint.csv). This is data for 2015 and beyond. I have left it out of the train or test data because it has many missing values, but it may be useful for forecasting and for those who are interested in more recent data.

This statistic shows a ranking of the estimated overweight population share in 2020 in Latin America and the Caribbean, differentiated by country. Obesity is defined as a body mass index (BMI) of more than **.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than *** countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).

Patient demographics and clinical data of the three study groups defined by the BMI range.

The adult obesity rate, or the percentage of the county population (age 18 and older*) that is obese, or has a Body Mass Index (BMI) equal to or greater than 30 [kg/m2], is illustrative of a serious health problem, in Champaign County, statewide, and nationally.

The adult obesity rate data shown here spans from Reporting Years (RY) 2015 to 2024. Champaign County’s adult obesity rate fluctuated during this time, peaking in RY 2022. The adult obesity rates for Champaign County, Illinois, and the United States were all above 30% in RY 2024, but the Champaign County rate was lower than the state and national rates. All counties in Illinois had an adult obesity rate above 30% in RY 2024, but Champaign County's rate is one of the lowest among all Illinois counties.

Obesity is a health problem in and of itself, and is commonly known to exacerbate other health problems. It is included in our set of indicators because it can be easily measured and compared between Champaign County and other areas.

This data was sourced from the University of Wisconsin’s Population Health Institute’s and the Robert Wood Johnson Foundation’s County Health Rankings & Roadmaps. Each year’s County Health Rankings uses data from the most recent previous years that data is available. Therefore, the 2024 County Health Rankings (“Reporting Year” in the table) uses data from 2021 (“Data Year” in the table). The survey methodology changed in Reporting Year 2015 for Data Year 2011, which is why the historical data shown here begins at that time. No data is available for Data Year 2018. The County Health Rankings website notes to use caution if comparing RY 2024 data with prior years.

*The percentage of the county population measured for obesity was age 20 and older through Reporting Year 2021, but starting in Reporting Year 2022 the percentage of the county population measured for obesity was age 18 and older.

Source: University of Wisconsin Population Health Institute. County Health Rankings & Roadmaps 2024. www.countyhealthrankings.org.