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.

Around 44 percent of Russians aged 19 years and older had a body mass index (BMI) of 25 to 30, which corresponded to pre-obesity, according to a survey conducted in 2023. The BMI of the second-largest share of respondents was classified as normal, ranging between 18.5 and 25. The share of overweight population in the country was forecast to increase in the following years and exceed 64 percent in 2029.

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

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

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.

BackgroundIncreases in body mass index (BMI) and the prevalence of overweight in low- and middle income countries (LMICs) are often ascribed to changes in global trade patterns or increases in national income. These changes are likely to affect populations within LMICs differently based on their place of residence or socioeconomic status (SES).ObjectiveUsing nationally representative survey data from 38 countries and national economic indicators from the World Bank and other international organizations, we estimated ecological and multilevel models to assess the association between national levels of gross domestic product (GDP), foreign direct investment (FDI), and mean tariffs and BMI.DesignWe used linear regression to estimate the ecological association between average annual change in economic indicators and BMI, and multilevel linear or ordered multinomial models to estimate associations between national economic indicators and individual BMI or over- and underweight. We also included cross-level interaction terms to highlight differences in the association of BMI with national economic indicators by type of residence or socioeconomic status (SES).ResultsThere was a positive but non-significant association of GDP and mean BMI. This positive association of GDP and BMI was greater among rural residents and the poor. There were no significant ecological associations between measures of trade openness and mean BMI, but FDI was positively associated with BMI among the poorest respondents and in rural areas and tariff levels were negatively associated with BMI among poor and rural respondents.ConclusionMeasures of national income and trade openness have different associations with the BMI across populations within developing countries. These divergent findings underscore the complexity of the effects of development on health and the importance of considering how the health effects of “globalizing” economic and cultural trends are modified by individual-level wealth and residence.

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.

Approximately half of all people in the Netherlands had a normal body weight in 2022, measured by the industry-standard Body Mass Index method. Men were more likely to be overweight than women, whereas more women than men were underweight. Interestingly, obesity was found more often among women, with approximately ** percent of Dutch females suffering from being severely overweight. Looking at the overall population, more than half of the Dutch inhabitants aged 20 years and older were overweight.

Weight issues vary between generations

Age groups in the Netherlands suffered from several different health problems related to weight and body image. A recent study found that obesity occured in more than ** percent of Dutch inhabitants aged 50 to 64 years old, whereas only * percent of Gen Z and millennials (aged 18 to 34 years old) were obese. When confronted with the question of how they perceive their own bodies, nearly ** percent of the Dutch millennials think they are overweight. This may have something to do with the omnipresence of unattainable beauty ideals on social media, often portrayed by fitgirl/boy influencers.

Global perspective

When looking at adults, the share of obesity in the Netherlands was quite close to the global average, being much lower than in the United States, Russia, or Iceland, to name but a few examples. In contrast, the prominence of underweight issues among Dutch youth was disproportionate in an international context. Nearly ** percent of Dutch ** and 15-year-old boys were underweight, which was more than in any other European country. the aforementioned negative body image may have been part of the cause for this frequency of underweight issues.

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.

The double burden of malnutrition has assumed severer forms in Low and Middle Income Countries (LMICs) arising from sharper increases in prevalence rates of overweight and obesity in these countries compared to higher income countries. Considering that LMICs already have fragile health systems, the rising obesity levels may just be a ticking time bomb requiring expeditious implementation of priority actions by all global and national actors to prevent an explosion of cardiovascular disease related deaths. The aim of this systematic review and meta-analysis was to provide a current estimate of obesity and overweight prevalence among Ghanaian adults and assess socio-demographic disparities following the PRISMA guidelines. We searched Pubmed with Medline, Embase, Science direct and African Journals Online (AJOL) for studies on overweight and obesity published between 2013 and January 2023. Applying a quality effects model, pooled mean Body Mass Index (BMI) and prevalence of overweight and obesity were obtained from 42 studies conducted across all three geographical locations of Ghana with a combined sample size of 29137. From the analysis, the mean BMI of adults in Ghana was 24.7 kgm-2 while overweight and obesity prevalence was estimated as 23.1% and 13.3% respectively. Temporal analysis showed sharper increases in overweight and obesity prevalence from 2017/2018. Mean BMI (Females: 25.3kgm-2 vrs Males: 23.1 kgm-2), overweight (Females: 25.9% vrs Males: 16.5%) and obesity (Females: 17.4% vrs Males: 5.5%) prevalence were higher among females than males. Gender differences in mean BMI and obesity prevalence were both significant at p

Body mass index (BMI) mean values in relation to maternal height and country of birth.

As of 2022, 40 percent of men and 30 percent of women in Ireland were classed as overweight according to the results of a survey on body mass index (BMI) in the country. Furthermore, 23 percent of men and 20 percent of women were classed as obese.

In 2016, the mean body mass index of adults in Vietnam increased to 21.9. Vietnam belongs to one of the countries worldwide with the lowest obesity rate.

Oman Prevalence of Overweight: % of Adults data was reported at 62.600 % in 2016. This records an increase from the previous number of 61.900 % for 2015. Oman Prevalence of Overweight: % of Adults data is updated yearly, averaging 48.050 % from Dec 1975 (Median) to 2016, with 42 observations. The data reached an all-time high of 62.600 % in 2016 and a record low of 26.300 % in 1975. Oman 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 Oman – Table OM.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/).;;

Unweighted description of the study sample by survey.

Bivariate analysis showing the association between socio-demographic characteristics of students and overweight & obesity.

Low birth weight is a significant risk factor associated with high rates of neonatal and infant mortality, particularly in developing countries. However, most studies conducted on this topic in Ethiopia have small sample sizes, often focusing on specific areas and using standard models employing maximum likelihood estimation, leading to potential bias and inaccurate coverage probability. This study used a novel approach, the Bayesian rank likelihood method, within a latent traits model, to estimate parameters and provide a nationwide estimate of low birth weight and its risk factors in Ethiopia. Data from the Ethiopian Demographic and Health Survey (EDHS) of 2016 were used as a data source for the study. Data stratified all regions into urban and rural areas. Among 15, 680 representative selected households, the analysis included complete cases from 10, 641 children. The evaluation of model performance considered metrics such as the root mean square error, the mean absolute error, and t..., , , # Rank likelihood-based estimation of low birth weight in Ethiopia

Low birth weight data was obtained from the Ethiopian Demographic and Health Survey (EDHS).

Raw data: Lowbirthweight.sav

Description of the data and file structure

Lowbirthweightdata_data

childweight: categorical weight of the child at birth motherage: age of the mothers ancvisti: number of antenatal care visits that the mothers attended birthorder: order of birth for the child birthinterval: time between successive births (months) bmi: body mass index of the mothers Regions: the region where the child born CLID: cluster-level ID that indicates from which cluster the information is obtained

Sharing or accessing information

Our data is taken from the DHS website (http://dhsprogram.com. Low birth weight data was extracted from the 2016 EDHS. EDHS 2016 was conducted using standardized survey design and data collection procedures.

BackgroundLimited research addressed links between nutritional status, dietary habits, and cognitive functions in young children. This study assessed the status of cognitive functions and their association with nutritional status and dietary habits of school age children of Bangladesh.MethodsThis cross-sectional multi-centre study was conducted on 776 participants in 11 conveniently selected educational institutions. A printed questionnaire with three sections (Section 1: background information, section 2: PedsQL™ Cognitive Functioning Scale, and section 3: semi-quantitative food-frequency questionnaire) was utilized for the data collection purpose. Sections 1 and 3 were self-reported by parents, and trained volunteers completed section 2 in-person along with the anthropometric measurements. Statistical analyses were done in Stata (v.16). Mean with standard deviation and frequencies with percentages were used to summarize quantitative and qualitative variables, respectively. Pearson’s chi-square test and Spearman’s rank correlation coefficient were used to explore bivariate relationships.ResultsThe mean age of the participants was 12.02±1.88 years, and the majority (67%) were females. The prevalence of poor cognitive function was 46.52%, and among them, 66.02% were females. In terms of body mass index (BMI), 22.44% possessed normal weight, 17.51% were overweight, and 5.19% were obese. This study found a statistically significant relationship between BMI and cognitive functions. Furthermore, different dietary components (e.g., protein, carbohydrate, fat, fiber, iron, magnesium) showed a significant (p

In 2020, Tonga was the country with the highest share of women who were overweight or obese worldwide, with around 87 percent of women with a high body mass index (BMI). This statistic shows the countries with the highest share of women who were overweight or obese in 2020.