METHODS: This study has an ecological design. As a measure of socioeconomic status, we used principal component analysis to construct a socioeconomic index using census data. Districts were ranked according to the standardised median index of households and assigned to each individual in the 5-year mortality data. The mortality indicators of interest were potential years of life lost (PYLL), standardised mortality ratio (SMR), infant mortality rate (IMR) and under-5 mortality rate (U5MR). Both socioeconomic status and mortality outcomes were used compute the concentration index which provided the summary measure of the magnitude of inequality.

RESULTS: Socially disadvantaged districts were found to have worse mortality outcomes compared to more advantaged districts. The values of the concentration index for the overall population of the Peninsula are C = -0.1334 (95% CI: -0.1605 to -0.1063) for the PYLL, C = -0.0685 (95% CI: -0.0928 to -0.0441) for the SMR, C = -0.0997 (95% CI: -0.1343 to -0.0652) for the IMR and C = -0.1207 (95% CI: -0.1523 to -0.0891) for the U5MR. Mortality outcomes within ethnic groups were also found to be less favourable among the poor.

METHODS: For 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.

FINDINGS: We 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.

INTERPRETATION: The 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.

Materials and Methods: The 500 individuals of both males and females aged 40 years and older with missing posterior teeth and not rehabilitated with any prosthesis were gone through a clinical history, intraoral examination, and anthropometric measurement to get information regarding age, sex, socioeconomic status, missing posterior teeth, and body mass index (BMI). Subjects were divided into five groups according to BMI (underweight > 18.5 kg/m2, normal weight 18.5-23 kg/m2, overweight 23-25 kg/m2, obese without surgery 25-32.5 kg/m2, obese with surgery < 32.5 kg/m2). Multivariate logistic regression was used to adjust data according to age, sex, number of missing posterior teeth, and socioeconomic status.

Results: People with a higher number of tooth loss were more obese. Females with high tooth loss were found to be more obese than male. Low socioeconomic group obese female had significantly higher tooth loss than any other group. No significant relation between age and obesity was found with regard to tooth loss.

Methods: We adopted a comparative cross-sectional study on pre-clinical medical students who appeared in two different admission tests. The stress, anxiety, and depression levels of students were measured by the depression, anxiety, stress scale (DASS-21), and their burnout level was measured by the Copenhagen Burnout Inventory.

Results: The stress, anxiety, and depression scores between MMI and PI were not significantly different (p-value > 0.05). The personal, work and client burnout scores between MMI and PI were not significantly different (p-value > 0.05). The prevalence of stress (MMI = 39%, PI = 36.9%), anxiety (MMI = 78%, PI = 67.4%), depression (MMI = 41%, PI = 36.2%) and burnout (MMI = 29%, PI = 31.9%) between MMI and PI cohorts was not significantly different (p-value > 0.05). These results showed similar levels of stress, anxiety, depression, and burnout in students at the end of the pre-clinical phase.