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...
Background Describing the prevalence and trends of cardiometabolic risk factors that are associated with noncommunicable diseases (NCDs) is crucial for monitoring progress, planning prevention, and providing evidence to support policy efforts. We aimed to analyse the transition in body-mass index (BMI), obesity, blood pressure, raised blood pressure, and diabetes in the Americas, between 1980 and 2014.Methods We did a pooled analysis of population-based studies with data on anthropometric measurements, biomarkers for diabetes, and blood pressure from adults aged 18 years or older. A Bayesian model was used to estimate trends in BMI, raised blood pressure (systolic blood pressure ≥140 mm Hg or diastolic blood pressure ≥90 mm Hg), and diabetes (fasting plasma glucose ≥7•0 mmol/L, history of diabetes, or diabetes treatment) from 1980 to 2014, in 37 countries and six subregions of the Americas. Findings 389 population-based surveys from the Americas were available. Comparing prevalence estimates from 2014 with those of 1980, in the non-English speaking Caribbean subregion, the prevalence of obesity increased from 3•9% (95% CI 2•2-6•3) in 1980, to 18•6% (14•3-23•3) in 2014, in men; and from 12•2% (8•2-17•0) in 1980, to 30•5% (25•7-35•5) in 2014, in women. The English-speaking Caribbean subregion had the largest increase in the prevalence of diabetes, from 5•2% (2•1-10•4) in men and 6•4% (2•6-10•4) in women in 1980, to 11•1% (6•4-17•3) in men and 13•6% (8•2-21•0) in women in 2014). Conversely, the prevalence of raised blood pressure has decreased in all subregions; the largest decrease was found in North America from 27•6% (22•3-33•2) in men and 19•9% (15•8-24•4) in women in 1980, to 15•5% (11•1-20•9) in men and 10•7% (7•7-14•5) in women in 2014. Interpretation Despite the generally high prevalence of cardiometabolic risk factors across the Americas, estimates also showed a high level of heterogeneity in the transition between countries. The increasing prevalence of obesity and diabetes observed over time requires appropriate measures to deal with these public health challenges. Our results support a diversification of health interventions across subregions and countries. Funding Wellcome Trust.
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.
Objetivos. Estimar la carga de enfermedad asociada al consumo de tabaco en Paraguay y evaluar el potencial efecto económico y sanitario del aumento de precio mediante impuestos. Materiales y métodos. Se diseñó un modelo de microsimulación de Monte Carlo que incorporó la historia natural, costos y calidad de vida de enfermedades asociadas al tabaquismo para el 2015. Asimismo, se estimó el impacto en varios escenarios de aumento de impuestos sobre la prevalencia de tabaquismo y la recaudación fiscal. Resultados. 3354 personas mueren al año en Paraguay por consecuencia del tabaquismo. El 19 % de las muertes son por enfermedad isquémica cardíaca, el 15 % por accidentes cerebrovasculares. El 77 % de las muertes por enfermedad pulmonar obstructiva crónica y el 83 % de cáncer de pulmón son atribuibles al tabaquismo. Estas enfermedades en Paraguay representan un costo médico directo anual de más de 1,5 x 10 6 millones de guaraníes, mientras la recaudación impositiva por la venta de cigarrillos apenas llega a cubrir un 20 % de este gasto. Un aumento en el precio de los cigarrillos del 50 % vía impuestos, podría llevar a evitar 2507 muertes en diez años y generar recursos por 2,4 x 10 6 millones por ahorro en gastos sanitarios y aumento de recaudación. Conclusiones. El costo y la carga de enfermedad asociado al consumo de tabaco en el sistema de salud es elevado en Paraguay. Un aumento del precio de los cigarrillos a través de los impuestos tendría importantes beneficios sanitarios y podría compensar parcialmente los costos sanitarios.
RESUMENIntroducción: Paraguay no escapa a la epidemia global de obesidad. Este estudio describió la prevalencia y los determinantes sociodemográficos, clínicos y conductuales asociados en el país. Métodos: Estudio transversal tipo encuesta poblacional con representatividad nacional, incluyó personas entre edades de 15 y 74 años. El muestreo fue probabilístico, trietápico sin reemplazo. STEPSwise fue la metodología y encuesta aplicada durante junio-setiembre 2011. Se consideró obesidad un índice de masa (IMC) corporal >=30. Este valor se distribuyó según las variables sociodemográficas, clínicas y conductuales. Por regresión logística se estimó asociación entre las variables, en odd ratios (OR) con intervalos de confianza del 95%(IC95%). Resultados: Se incluyeron 2501 participantes. Fueron obesos 23,5% de la población, 20,2% y 26,0% hombres y mujeres, respectivamente. Además del sexo, mostraron diferencias significativas: tener >35 años 3,17(2,11-4,76) que los menores; hombres con residencia urbana 1,94(1,35-2,79) veces más que los del área rural. Los hombres en pareja 2,52(1,80-3,53) veces más obesos que los solteros; funcionario público 2,57(1,57-4,26) veces más que otros trabajos. Baja actividad física presentó obesidad 1,75(1,19-2,57) veces más que tener una actividad física mínimamente aceptable. En hombres el quintil de ingreso superior presentó 3,87(2,17-6,92) más obesidad que el quintil inferior. En mujeres el bajo nivel educativo es 2,01(1,43-2,83) veces más que un mayor nivel. Conclusiones: El patrón observado de la distribución de la obesidad describe entornos y conductas más obesogénicos que otros en Paraguay. Estos resultados sirven para tomar decisiones e intervenciones específicas en salud pública, más allá de las medidas poblacionales.Palabras Clave: obesidad, estilo de vida, determinantes de la salud. 18 ABSTRACTIntroduction: Paraguay has not escaped from the global epidemic of obesity. This study described the prevalence and socio-demographic, clinical and behavioral determinants associated to obesity. Methods: Cross-sectional study with a nationally representative survey, included people among 15 and 74 years old. The sampling was probabilistic, three-stage without replacement. STEPSwise was the methodology and survey applied during June-September 2011. Obesity was considered a body mass index (BMI) >=30 kg/m2. This value was distributed by sociodemographic, clinical, and behavioral variables. A logistic regression among obesity and variables was estimated for obtain odd ratios (OR) with confidence intervals of 95% (95% CI) of association. Results: 2501 participants were included. Were obese 23.5%, men and women were 20.2% and 26.0%, respectively. In addition to gender, showed significant differences: >35 years-old 3.17(2.11-4.76) than younger; men in urban residence 1.94(1.35-2.79) odds more than those in rural areas. No single men 2.52(1.80-3.53) odds more obese than single; civil servants 2.57(1.57-4.26) odds more than other jobs. Low physical activity 1.75(1.19-2.57) odds were more obese t...
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