In 2019, it was estimated that the additional annual disability and mortality costs per obese individual in Canada were around ***** Canadian dollars. This graph shows the additional annual disability and mortality costs per obese individual in Canada in 2019, by gender.

Panama PA: Prevalence of Overweight: Weight for Height: % of Children Under 5, Modeled Estimate data was reported at 10.900 % in 2024. This records a decrease from the previous number of 11.100 % for 2023. Panama PA: Prevalence of Overweight: Weight for Height: % of Children Under 5, Modeled Estimate data is updated yearly, averaging 10.900 % from Dec 2000 (Median) to 2024, with 25 observations. The data reached an all-time high of 11.500 % in 2019 and a record low of 8.300 % in 2000. Panama PA: Prevalence of Overweight: Weight for Height: % of Children Under 5, Modeled Estimate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Panama – Table PA.World Bank.WDI: Social: Health Statistics. Prevalence of overweight children is the percentage of children under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's 2006 Child Growth Standards.;UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME).;Weighted average;Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues. Estimates are modeled estimates produced by the JME. Primary data sources of the anthropometric measurements are national surveys. These surveys are administered sporadically, resulting in sparse data for many countries. Furthermore, the trend of the indicators over time is usually not a straight line and varies by country. Tracking the current level and progress of indicators helps determine if countries are on track to meet certain thresholds, such as those indicated in the SDGs. Thus the JME developed statistical models and produced the modeled estimates.

ObjectiveExploring changing trends in the burden caused by overweight and obesity among children and adolescents from 1990 to 2019 at the global, regional, and national levels, based on data from the Global Burden of Disease study (GBD) 2019.MethodsThe annual number and rate of deaths and disability-adjusted life years (DALYs) associated with a high BMI among children and adolescents at global, regional, and national levels by age groups, sexes, and the sociodemographic index from 1990 to 2019 were collected from the GBD study 2019. Change percentage for number, and the estimated annual percentage changes (EAPCs) for rate were calculated to determine the temporal trends.ResultsFrom 1990 to 2019, global high BMI-related deaths decreased by 34% but DALYs increased by 48%. Death rates in females were higher than in males, although both showed decreasing trends. For the rate of DALYs, both sexes showed increasing trends, but since 1999, the rate in males has surpassed that in females. A high BMI had the greatest impact on children under 5 years of age, and the burden in other age groups continued to increase. Regionally, High-income Asia Pacific experienced the fastest decrease in death rate (EAPC=−9.57), and East Asia saw the fastest increase in the DALYs rate (EAPC= 3.47). Globally, as age increases, the proportion of disease burden attributed to a high BMI in females generally increases.ConclusionsOur findings emphasize the urgent need to improve efforts to prevent children and adolescents becoming overweight and obese.

West Virginia, Mississippi, and Arkansas are the U.S. states with the highest percentage of their population who are obese. The states with the lowest percentage of their population who are obese include Colorado, Hawaii, and Massachusetts. Obesity in the United States Obesity is a growing problem in many countries around the world, but the United States has the highest rate of obesity among all OECD countries. The prevalence of obesity in the United States has risen steadily over the previous two decades, with no signs of declining. Obesity in the U.S. is more common among women than men, and overweight and obesity rates are higher among African Americans than any other race or ethnicity. Causes and health impacts Obesity is most commonly the result of a combination of poor diet, overeating, physical inactivity, and a genetic susceptibility. Obesity is associated with various negative health impacts, including an increased risk of cardiovascular diseases, certain types of cancer, and diabetes type 2. As of 2022, around 8.4 percent of the U.S. population had been diagnosed with diabetes. Diabetes is currently the eighth leading cause of death in the United States.

Bangladesh BD: Prevalence of Overweight: Weight for Height: % of Children Under 5, Modeled Estimate data was reported at 1.600 % in 2024. This records a decrease from the previous number of 1.700 % for 2023. Bangladesh BD: Prevalence of Overweight: Weight for Height: % of Children Under 5, Modeled Estimate data is updated yearly, averaging 1.600 % from Dec 2000 (Median) to 2024, with 25 observations. The data reached an all-time high of 2.000 % in 2019 and a record low of 0.700 % in 2000. Bangladesh BD: Prevalence of Overweight: Weight for Height: % of Children Under 5, Modeled Estimate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Bangladesh – Table BD.World Bank.WDI: Social: Health Statistics. Prevalence of overweight children is the percentage of children under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's 2006 Child Growth Standards.;UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME).;Weighted average;Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues. Estimates are modeled estimates produced by the JME. Primary data sources of the anthropometric measurements are national surveys. These surveys are administered sporadically, resulting in sparse data for many countries. Furthermore, the trend of the indicators over time is usually not a straight line and varies by country. Tracking the current level and progress of indicators helps determine if countries are on track to meet certain thresholds, such as those indicated in the SDGs. Thus the JME developed statistical models and produced the modeled estimates.

Poland PL: Prevalence of Overweight: Weight for Height: % of Children Under 5: Female data was reported at 7.100 % in 2019. This records an increase from the previous number of 4.700 % for 2011. Poland PL: Prevalence of Overweight: Weight for Height: % of Children Under 5: Female data is updated yearly, averaging 5.900 % from Dec 2011 (Median) to 2019, with 2 observations. The data reached an all-time high of 7.100 % in 2019 and a record low of 4.700 % in 2011. Poland PL: Prevalence of Overweight: Weight for Height: % of Children Under 5: Female data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Poland – Table PL.World Bank.WDI: Social: Health Statistics. Prevalence of overweight, female, is the percentage of girls under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's 2006 Child Growth Standards.;UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology.;;Estimates of overweight children are from national survey data. Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues.

Coronavirus disease 2019 (COVID-19) has already raised serious concern globally as the number of confirmed or suspected cases have increased rapidly. Epidemiological studies reported that obesity is associated with a higher rate of mortality in patients with COVID-19. Yet, to our knowledge, there is no comprehensive systematic review and meta-analysis to assess the effects of obesity and mortality among patients with COVID-19. We, therefore, aimed to evaluate the effect of obesity, associated comorbidities, and other factors on the risk of death due to COVID-19. We did a systematic search on PubMed, EMBASE, Google Scholar, Web of Science, and Scopus between January 1, 2020, and August 30, 2020. We followed Cochrane Guidelines to find relevant articles, and two reviewers extracted data from retrieved articles. Disagreement during those stages was resolved by discussion with the main investigator. The random-effects model was used to calculate effect sizes. We included 17 articles with a total of 543,399 patients. Obesity was significantly associated with an increased risk of mortality among patients with COVID-19 (RRadjust: 1.42 (95%CI: 1.24–1.63, p < 0.001). The pooled risk ratio for class I, class II, and class III obesity were 1.27 (95%CI: 1.05–1.54, p = 0.01), 1.56 (95%CI: 1.11–2.19, p < 0.01), and 1.92 (95%CI: 1.50–2.47, p < 0.001), respectively). In subgroup analysis, the pooled risk ratio for the patients with stroke, CPOD, CKD, and diabetes were 1.80 (95%CI: 0.89–3.64, p = 0.10), 1.57 (95%CI: 1.57–1.91, p < 0.001), 1.34 (95%CI: 1.18–1.52, p < 0.001), and 1.19 (1.07–1.32, p = 0.001), respectively. However, patients with obesity who were more than 65 years had a higher risk of mortality (RR: 2.54; 95%CI: 1.62–3.67, p < 0.001). Our study showed that obesity was associated with an increased risk of death from COVID-19, particularly in patients aged more than 65 years. Physicians should aware of these risk factors when dealing with patients with COVID-19 and take early treatment intervention to reduce the mortality of COVID-19 patients.

IntroductionMetabolic risk factors (MRFs) predispose populations to a variety of chronic diseases with a huge burden globally. With the increasing burden of these risk factors in Iran, in this study, we aimed to report the estimated burden attributed to MRFs at national and subnational scales in Iran, from 1990 to 2019.MethodsBased on the comparative risk assessment method of the Global Burden of Disease (GBD) Study 2019, data of deaths and disability-adjusted life years (DALYs) attributable to four top MRFs in Iran including high systolic blood pressure (SBP), high fasting plasma glucose (FPG), high body mass index (BMI), and high low-density lipoprotein (LDL) for the 1990–2019 period, were extracted. The socio-demographic index (SDI) was used to report the data based on the corresponding socio-economic stratifications. The results were reported in national and subnational 31 provinces of Iran to discover disparities regarding the attributable burden to MRFs. Furthermore, we reported the causes of diseases to which the attributable burden to MRFs was related.ResultsOverall, the age-standardized high LDL, high SBP, high BMI, and high FPG-attributed death rate changed by −45.1, −35.6, +2.8, and +19.9% from 1990 to 2019, respectively. High SBP was the leading risk factor regarding attributed age-standardized death rates reaching 157.8 (95% uncertainty interval: 135.3–179.1) and DALY rates reaching 2973.4 (2652.2–3280.2) per 100,000 person-years, in 2019. All rates increased with aging, and men had higher rates except for the +70 years age group. At the subnational level, provinces in the middle SDI quintile had the highest death and DALY rates regarding all four MRFs. Total deaths, DALYs, YLLs and YLDs number by the causes of diseases linked to MRFs increased over the study period. Cardiovascular diseases, diabetes mellitus, and kidney diseases were the main causes of burden of disease attributable to MRFs.ConclusionHerein, we found divergent patterns regarding the burden of MRFs as well as disparities in different regions, sex, and age groups for each risk factor and related causes. This could provide policymakers with a clearer vision toward more appropriate decision-making and resource allocation to prevent the burden of MRFs in Iran.

BackgroundObesity affects the course of critical illnesses. We aimed to estimate the association of obesity with the severity and mortality in coronavirus disease 2019 (COVID-19) patients.Data SourcesA systematic search was conducted from the inception of the COVID-19 pandemic through to 13 October 2021, on databases including Medline (PubMed), Embase, Science Web, and Cochrane Central Controlled Trials Registry. Preprint servers such as BioRxiv, MedRxiv, ChemRxiv, and SSRN were also scanned.Study Selection and Data ExtractionFull-length articles focusing on the association of obesity and outcome in COVID-19 patients were included. Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines were used for study selection and data extraction. Our Population of interest were COVID-19 positive patients, obesity is our Intervention/Exposure point, Comparators are Non-obese vs obese patients The chief outcome of the study was the severity of the confirmed COVID-19 positive hospitalized patients in terms of admission to the intensive care unit (ICU) or the requirement of invasive mechanical ventilation/intubation with obesity. All-cause mortality in COVID-19 positive hospitalized patients with obesity was the secondary outcome of the study.ResultsIn total, 3,140,413 patients from 167 studies were included in the study. Obesity was associated with an increased risk of severe disease (RR=1.52, 95% CI 1.41-1.63, p<0.001, I2 = 97%). Similarly, high mortality was observed in obese patients (RR=1.09, 95% CI 1.02-1.16, p=0.006, I2 = 97%). In multivariate meta-regression on severity, the covariate of the female gender, pulmonary disease, diabetes, older age, cardiovascular diseases, and hypertension was found to be significant and explained R2 = 40% of the between-study heterogeneity for severity. The aforementioned covariates were found to be significant for mortality as well, and these covariates collectively explained R2 = 50% of the between-study variability for mortality.ConclusionsOur findings suggest that obesity is significantly associated with increased severity and higher mortality among COVID-19 patients. Therefore, the inclusion of obesity or its surrogate body mass index in prognostic scores and improvement of guidelines for patient care management is recommended.

The leading causes of death in the United States are, by far, cardiovascular diseases and cancer. However, the death rates from these diseases, as well as other leading causes of death, have decreased over the past few decades. The one major exception is deaths caused by Alzheimer’s disease, which have increased significantly. Cardiovascular disease deaths Although cardiovascular diseases are currently the leading cause of death in the United States, the death rate of these diseases has dropped significantly. In the year 1950, there were around *** deaths per 100,000 population due to cardiovascular diseases. In the year 2023, this number was ***** per 100,000 population. Risk factors for heart disease include smoking, poor diet, diabetes, obesity, stress, family history, and age. Alzheimer’s disease deaths While the death rates for cardiovascular disease, cancer, diabetes, and chronic lower respiratory diseases have all decreased, the death rate for Alzheimer’s disease has increased. In fact, from the year 2000 to 2022, the death rate from Alzheimer’s disease rose an astonishing *** percent. This increase is in part due to a growing aging population.

Uganda UG: Prevalence of Overweight: Weight for Height: % of Children Under 5, Modeled Estimate data was reported at 4.200 % in 2024. This records an increase from the previous number of 3.900 % for 2023. Uganda UG: Prevalence of Overweight: Weight for Height: % of Children Under 5, Modeled Estimate data is updated yearly, averaging 4.200 % from Dec 2000 (Median) to 2024, with 25 observations. The data reached an all-time high of 5.300 % in 2004 and a record low of 3.300 % in 2019. Uganda UG: Prevalence of Overweight: Weight for Height: % of Children Under 5, Modeled Estimate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Uganda – Table UG.World Bank.WDI: Social: Health Statistics. Prevalence of overweight children is the percentage of children under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's 2006 Child Growth Standards.;UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME).;Weighted average;Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues. Estimates are modeled estimates produced by the JME. Primary data sources of the anthropometric measurements are national surveys. These surveys are administered sporadically, resulting in sparse data for many countries. Furthermore, the trend of the indicators over time is usually not a straight line and varies by country. Tracking the current level and progress of indicators helps determine if countries are on track to meet certain thresholds, such as those indicated in the SDGs. Thus the JME developed statistical models and produced the modeled estimates.

Additional file 2: Table S1. Study design.

Additional inpatient cases/hospitalizations, ICU admissions and deaths (per 10,000 total population in country and as percentage of total COVID-19 outcomes) related to overweight and obesity in 2020 and 2021.

This repository accompanies the doctoral thesis titled: "The Effect of Health System, Health Risk Factors and Health Service Coverage on Fertility, Morbidity and Mortality in HDI Countries: An Econometric Analysis." Given the complexity of the data and methodological procedures, key supplementary materials detailing the data sources, processing techniques, analytical scripts, and extended results are provided in the present Mendeley Data Repository. These materials are intended to promote transparency, reproducibility, and further research. The repository includes the following supplementary files: Supplementary File 1: Contains detailed information on all indicators used in the study, including those from the Global Reference List (GRL) and control variables. It specifies the definition, unit of measurement, data source, missing data proportions, inclusion status, and whether the indicator is positively or negatively associated with the outcome. Supplementary File 2: Provides the R script used to perform Multiple Imputation by Chained Equations (MICE) to handle missing data across indicators. Supplementary File 3: Describes the imputed dataset generated using the MICE method for both pre-COVID (2015–2019) and post-COVID (2020–2021) periods. Supplementary File 4: Contains the R script used to construct the composite and sub-indices for Health System, Health Risk Factors, Service Coverage, and Health Status. Supplementary File 5: Provides the R script used to compute Compound Annual Growth Rates (CAGR) for all indices and component indicators. Supplementary File 6: Includes the Stata Do-file used to run panel data regression models, estimating the impact of Health System, Health Risk Factors, and Service Coverage on fertility, morbidity, and mortality. Supplementary File 7: Contains the Stata Do-file used for conducting the Phillips and Sul Convergence Analysis to assess convergence/divergence trends among countries toward selected health-related SDG targets. Supplementary File 8: Provides descriptive statistics—including mean, standard deviation, and coefficient of variation—for selected health indicators across 100 HDI countries during the study period (2015–2021). Supplementary File 9: Presents the CAGR estimates of all constructed indices, separately reported for pre-COVID (2015–2019) and post-COVID (2020–2021) phases. Supplementary File 10: Provides the forecasted values for 57 indicators across 100 countries up to the year 2025, supporting the study’s predictive analysis.

Bathing facilities and health phronesis: tackling English obesity. Mixed methods sequential research in five phases.
Research questions and hypotheses • RQ1: Does the geospatial distribution of swimming facilities impact health? (Nomothetic). (H10: Pools is insignificant vs. H1A: Pools is significant) • RQ2: Is the construction of swimming pools adequate for national health need? (Nomothetic). (H20: Forecast pool construction stable vs. H2A: Forecast pool construction increases) • RQ3: What policy learning emerges from idiosyncratic cases? (Idiographic & qualitative) Approach After problematisation (1) and structured literature review (2), the study conducted cross-sectional analysis of excess mortality and swimming pools (3a & 3b) and longitudinal analysis of pool construction (3c-e). Cross-sectional investigation involved factor analysis (3a) to explore and regression to analysis (3b) to investigate English mortality and its covariates (3b). The For the time series analysis, the study analysed 120 years of English pool construction data using autoregressive distributed lag models - ARIMA (3c), ADL (3d) and ECM (3e).
Data Cross sectional analysis Deaths (DV, Yd): ONS standardised mortality ratio (2013-2017). Observed total deaths from all causes (by five year age and gender band) as a percentage of expected deaths.
Access Leisure (IV, X1): reflects accessibility to 727 leisure centres, swimming baths or 2,738 health clubs in kilometres. Liverpool University’s Consumer Data Research Centre, Access to Healthy Assets and Hazards (AHAH) index. Obesity (IV, X2): percentage of adult population with a body mass index (BMI) of 30 kg/m2 or higher, age-standardized, WHO 2389 NCD_BMI_30 (2020). Deprivation (IV, X3): deprivation score for English small areas, sourced from Index of Multiple Deprivation (2019). Environment (IV, X4) measures accessible blue and green space, sourced via SE (2020), data constitutes an element of AHAH (2017).
Pools (IV, X5): reflects pools per 10,000 in 2018. Data extracted from SE Active Places Power (APP) Time series analysis Pools constructed (PC & ∆PC): English swimming pools constructed each year during a 120 year period since 1900, SE Active Places Power (2020) database. English output (GDP & ∆GDP): Bank of England millennium of macroeconomic data UK (2017) provides historical macroeconomic and financial statistics.
English population (Pop & ∆Pop): English population and population growth 1900-2020, Office for National Statistics (ONS): Total population (2018).

The number of deaths caused by heart disease has decreased in the United States from ***** per 100,000 population in 1990 to ***** deaths per 100,000 population in 2019. Nevertheless, heart disease is still the leading cause of death in the country, followed closely by cancer, which has a mortality rate of ***** per 100,000 people. Heart disease in the U.S.Diseases of the heart and blood vessels are often associated with atherosclerosis, which occurs when plaque builds up along arterial walls. This can limit the flow of blood and can lead to blood clots, a common cause of stroke or heart attacks. Other types of heart disease include arrhythmia (abnormal heart rhythms) and heart valve problems. Many of these diseases can be treated with medication, although many complications will still remain. One of the leading cholesterol lowering drugs in the United States, Crestor, generated around **** billion U.S. dollars of revenue in 2024. Risk Factors for heart disease There are many risk factors associated with the development of heart disease, including family history, ethnicity, and age. However, there are other factors that can be modified through lifestyle changes such as physical inactivity, smoking, and unhealthy diets. Obesity has also been commonly associated with risk factors like hypertension and diabetes type II. In the United States, some ** percent of white adults are currently obese.

Samoa WS: Prevalence of Overweight: Weight for Height: % of Children Under 5: Male data was reported at 9.000 % in 2019. This records an increase from the previous number of 6.200 % for 2014. Samoa WS: Prevalence of Overweight: Weight for Height: % of Children Under 5: Male data is updated yearly, averaging 7.600 % from Dec 2014 (Median) to 2019, with 2 observations. The data reached an all-time high of 9.000 % in 2019 and a record low of 6.200 % in 2014. Samoa WS: Prevalence of Overweight: Weight for Height: % of Children Under 5: Male data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Samoa – Table WS.World Bank.WDI: Social: Health Statistics. Prevalence of overweight, male, is the percentage of boys under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's 2006 Child Growth Standards.;UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology.;;Estimates of overweight children are from national survey data. Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues.

Data set from Ranucci M, de Vincentiis C, Menicanti L, La Rovere MT, Pistuddi V. A gender-based analysis of the obesity paradox in cardiac surgery: height for women, weight for men? Eur J Cardiothorac Surg. 2019 Jul 1;56(1):72-78. doi: 10.1093/ejcts/ezy454. PMID: 30657927.

This is the abstract:

Objectives: In cardiac surgery, obesity is associated with a lower mortality risk. This study aims to investigate the association between body mass index (BMI) and operative mortality separately in female patients and male patients undergoing cardiac surgery and to separate the effects of weight and height in each gender-based cohort of patients.

Methods: A retrospective cohort study including 7939 consecutive patients who underwent cardiac surgery was conducted. The outcome measure was the operative mortality.

Results: In men, there was a U-shaped relationship between the BMI and the operative mortality, with the lower mortality rate at a BMI of 35 kg/m2. In women, the relationship is J-shaped, with the lower mortality at a BMI of 22 kg/m2. Female patients with obesity class II-III had a relative risk for operative mortality of 2.6 [95% confidence interval (CI) 1.37-4.81, P = 0.002]. The relationship between weight and mortality rate is a U-shaped bot in men and women, with the lower mortality rate at 100 kg for men and 70 kg for women. Height was linearly and inversely associated with the operative mortality in men and women. After correction for the potential confounders, height, but not weight, was independently associated with operative mortality in women (odds ratio 0.949, 95% CI 0.915-0.983; P = 0.004); conversely, in men, this association exists for weight (odds ratio 1.017, 95% CI 1.001-1.032; P = 0.034), but not height.

Conclusions: Contrary to men, in women obesity does not reduce the operative mortality in cardiac surgery, whereas the height seems to be associated with a lower mortality.

Background and AimFew studies assessed the associations of overweight and obesity with severe outcomes of coronavirus disease 2019 (COVID-19) among elderly patients. This study was conducted to assess overweight and obesity in relation to risk of mortality, delirium, invasive mechanical ventilation (IMV) requirement during treatment, re-hospitalization, prolonged hospitalization, and ICU admission among elderly patients with COVID-19.MethodsThis was a single-center prospective study that was done on 310 elderly patients with COVID-19 hospitalized in the intensive care unit (ICU). We collected data on demographic characteristics, laboratory parameters, nutritional status, blood pressure, comorbidities, medications, and types of mechanical ventilation at baseline. Patients were followed up during ICU admission and until 45 days after the first visit, and data on delirium incidence, mortality, need for a form of mechanical ventilation, discharge day from ICU and hospital, and re-hospitalization were recorded for each patient.ResultsDuring the follow-up period, we recorded 190 deaths, 217 cases of delirium, and 35 patients who required IMV during treatment. After controlling for potential confounders, a significant association was found between obesity and delirium such that obese patients with COVID-19 had a 62% higher risk of delirium compared with normal-weight patients (HR: 1.62, 95% CI: 1.02–2.57). This association was not observed for overweight. In terms of other outcomes including ICU/45-day mortality, IMV therapy during treatment, re-hospitalization, prolonged hospitalization, and ICU admission, we found no significant association with overweight and obesity either before or after controlling for potential confounders.ConclusionWe found that obesity may be a risk factor for delirium among critically ill elderly patients with COVID-19.

Tonga TO: Prevalence of Overweight: Weight for Height: % of Children Under 5: Female data was reported at 9.900 % in 2019. This records a decrease from the previous number of 16.000 % for 2012. Tonga TO: Prevalence of Overweight: Weight for Height: % of Children Under 5: Female data is updated yearly, averaging 12.950 % from Dec 2012 (Median) to 2019, with 2 observations. The data reached an all-time high of 16.000 % in 2012 and a record low of 9.900 % in 2019. Tonga TO: Prevalence of Overweight: Weight for Height: % of Children Under 5: Female data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Tonga – Table TO.World Bank.WDI: Social: Health Statistics. Prevalence of overweight, female, is the percentage of girls under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's 2006 Child Growth Standards.;UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology.;;Estimates of overweight children are from national survey data. Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues.