Summary Background Comparable 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. 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/m 2 . 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, the...

Physical activity, sedentary behavior, and sleep are important predictors of children's health. This paper aimed to investigate socioeconomic disparities in physical activity, sedentary behavior, and sleep across the WHO European region. This cross-sectional study used data on 124,700 children aged 6 to 9 years from 24 countries participating in the WHO European Childhood Obesity Surveillance Initiative between 2015 and 2017. Socioeconomic status (SES) was measured through parental education, parental employment status, and family perceived wealth. Overall, results showed different patterns in socioeconomic disparities in children's movement behaviors across countries. In general, high SES children were more likely to use motorized transportation. Low SES children were less likely to participate in sports clubs and more likely to have more than 2 h/day of screen time. Children with low parental education had a 2.24 [95% CI 1.94-2.58] times higher risk of practising sports for less than 2 h/week.In the pooled analysis, SES was not significantly related to active play. The relationship between SES and sleep varied by the SES indicator used. Importantly, resultsshowed that low SES is not always associated with a higher prevalence of "less healthy" behaviors. There is a great diversity in SES patterns across countries which supports the need for country-specific, targeted public health interventions.

The aim of this study was to investigate the association of alcohol consumption and problem drinking on transitions between work, unemployment, sickness absence and social benefits. Participants were 86,417 men and women aged 18–60 years who participated in the Danish National Health Survey in 2010. Information on alcohol consumption (units per week) and problem drinking (CAGE-C score of 4–6) was obtained by questionnaire. The primary outcome was labour market attachment. Information on labour market attachment was obtained from the national administrative registers during a 5-year follow-up period. Using Cox proportional hazards models, we estimated hazard ratios (HR) for transitions between work, unemployment, sickness absence and social benefits. Analyses were adjusted for potential confounders associated with demography, health, and socio-economy. High alcohol consumption and problem drinking was associated with higher probability of unemployment, sickness absence and social benefits among participants employed at baseline compared with participants who consumed 1–6 drinks/week. High alcohol consumption and problem drinking was associated with lower probability of returning to work among participants receiving sickness absence at baseline compared with participants who consumed 1–6 drinks/week and with non-problem drinkers: HRs were 0.75 (0.58–0.98) for 35+ drinks per week and 0.81 (0.65–1.00) for problem drinking (CAGE-C score of 4–6). Similar trends for weekly alcohol consumption and problem drinking were observed among participants who were unemployed at baseline. In summary, problem drinking has adverse consequences for labour market participation and is associated with higher probability of losing a job and a lower chance of becoming employed again. Electronic supplementary material The online version of this article (10.1007/s10654-018-0476-7) contains supplementary material, which is available to authorized users.

From 1985 to 2016, the prevalence of underweight decreased, and that of obesity and severe obesity increased, in most regions, with significant variation in the magnitude of these changes across regions. We investigated how much change in mean body mass index (BMI) explains changes in the prevalence of underweight, obesity, and severe obesity in different regions using data from 2896 population-based studies with 187 million participants. Changes in the prevalence of underweight and total obesity, and to a lesser extent severe obesity, are largely driven by shifts in the distribution of BMI, with smaller contributions from changes in the shape of the distribution. In East and Southeast Asia and sub-Saharan Africa, the underweight tail of the BMI distribution was left behind as the distribution shifted. There is a need for policies that address all forms of malnutrition by making healthy foods accessible and affordable, while restricting unhealthy foods through fiscal and regulatory restrictions.

Objective This study aimed to test the hypotheses that individuals with obesity are at higher risk of unemployment and sickness absence and have a lower chance of getting employed compared with individuals with normal weight. Methods Data on weight and height were collected at baseline from 87,796 participants in the Danish National Health Survey 2010. Participants were then followed in national registers for 5 years. Outcome measures were transitions from employment to unemployment and sickness absence and the transitions from unemployment or sickness absence to employment. Data were analyzed by Cox proportional hazards models adjusted for potential confounders. Results Hazard ratios for unemployment were 1.18 (95% CI: 1.10‐1.26) for individuals with obesity and 1.27 (95% CI: 1.14‐1.41) for individuals with severe obesity compared with individuals with normal weight. Participants with obesity also had a higher risk of sickness absence. Additionally, participants with obesity who were unemployed at baseline had a lower chance of becoming employed compared with participants with normal weight. Conclusions Obesity was associated with a higher risk of unemployment and sickness absence compared with individuals with normal weight. Additionally, obesity was associated with a lower chance of employment.