12 research outputs found
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
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/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
Rising rural body-mass index is the main driver of the global obesity epidemic in adults
Body-mass index (BMI) has increased steadily in most countries in parallel with a rise in the proportion of the population who live in cities(.)(1,2) This has led to a widely reported view that urbanization is one of the most important drivers of the global rise in obesity(3-6). Here we use 2,009 population-based studies, with measurements of height and weight in more than 112 million adults, to report national, regional and global trends in mean BMI segregated by place of residence (a rural or urban area) from 1985 to 2017. We show that, contrary to the dominant paradigm, more than 55% of the global rise in mean BMI from 1985 to 2017-and more than 80% in some low- and middle-income regions-was due to increases in BMI in rural areas. This large contribution stems from the fact that, with the exception of women in sub-Saharan Africa, BMI is increasing at the same rate or faster in rural areas than in cities in low- and middle-income regions. These trends have in turn resulted in a closing-and in some countries reversal-of the gap in BMI between urban and rural areas in low- and middle-income countries, especially for women. In high-income and industrialized countries, we noted a persistently higher rural BMI, especially for women. There is an urgent need for an integrated approach to rural nutrition that enhances financial and physical access to healthy foods, to avoid replacing the rural undernutrition disadvantage in poor countries with a more general malnutrition disadvantage that entails excessive consumption of low-quality calories.Peer reviewe
Heterogeneous contributions of change in population distribution of body mass index to change in obesity and underweight NCD Risk Factor Collaboration (NCD-RisC)
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
Assessment of the psychometric properties of the Adaptive Behavior Scale with psychiatric patients.
Findings and Guidelines on Provider Technology, Fatigue, and Well-being: Scoping Review
BackgroundVideo and other technologies are reshaping the delivery of health care, yet barriers related to workflow and possible provider fatigue suggest that a thorough evaluation is needed for quality and process improvement.
ObjectiveThis scoping review explored the relationship among technology, fatigue, and health care to improve the conditions for providers.
MethodsA 6-stage scoping review of literature (from 10 databases) published from 2000 to 2020 that focused on technology, health care, and fatigue was conducted. Technologies included synchronous video, telephone, informatics systems, asynchronous wearable sensors, and mobile health devices for health care in 4 concept areas related to provider experience: behavioral, cognitive, emotional, and physical impact; workplace at the individual, clinic, hospital, and system or organizational levels; well-being, burnout, and stress; and perceptions regarding technology. Qualitative content, discourse, and framework analyses were used to thematically analyze data for developing a spectrum of health to risk of fatigue to manifestations of burnout.
ResultsOf the 4221 potential literature references, 202 (4.79%) were duplicates, and our review of the titles and abstracts of 4019 (95.21%) found that 3837 (90.9%) were irrelevant. A full-text review of 182 studies revealed that 12 (6.6%) studies met all the criteria related to technology, health care, and fatigue, and these studied the behavioral, emotional, cognitive, and physical impact of workflow at the individual, hospital, and system or organizational levels. Video and electronic health record use has been associated with physical eye fatigue; neck pain; stress; tiredness; and behavioral impacts related to additional effort owing to barriers, trouble with engagement, emotional wear and tear and exhaustion, cognitive inattention, effort, expecting problems, multitasking and workload, and emotional experiences (eg, anger, irritability, stress, and concern about well-being). An additional 14 studies that evaluated behavioral, emotional, and cognitive impacts without focusing on fatigue found high user ratings on data quality, accuracy, and processing but low satisfaction with clerical tasks, the effort required in work, and interruptions costing time, resulting in more errors, stress, and frustration. Our qualitative analysis suggests a spectrum from health to risk and provides an outline of organizational approaches to human factors and technology in health care. Business, occupational health, human factors, and well-being literature have not studied technology fatigue and burnout; however, their findings help contextualize technology-based fatigue to suggest guidelines. Few studies were found to contextually evaluate differences according to health professions and practice contexts.
ConclusionsHealth care systems need to evaluate the impact of technology in accordance with the Quadruple Aim to support providers’ well-being and prevent workload burden, fatigue, and burnout. Implementation and effectiveness approaches and a multilevel approach with objective measures for clinical, human factors, training, professional development, and administrative workflow are suggested. This requires institutional strategies and competencies to integrate health care quality, technology and well-being outcomes