Towards domestic cooking efficiency: A case study on burger pan frying using experimental and computational results

It is well known that the use of efficient domestic cooking appliances and equipment can not only save energy, but also improve the quality of the food being prepared. This work raises the question of whether cooking procedures can also contribute to this energy efficiency. Focusing on burger pan frying, experimental data were used to develop a model able to predict cooking outcomes under different power levels supplied by an induction hob. The proposed model takes into account not only the heat consumed by water evaporation in the contact region but also the shrinkage process of the hamburger. A new formulation based on the multiplicative decomposition of the strain deformation gradient is proposed to describe the observed decoupling between weight and volume loss during the process. The model properly predicts temperature, moisture loss and shrinkage, and allows elucidation of the effects of supplying different amounts of energy on the final water content

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

Background Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. Methods The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model—a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates—with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality—which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. Findings The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2–100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1–290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1–211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4–48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3–37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7–9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. Interpretation Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Prevalence of vertebral fracture and densitometric osteoporosis in Spanish adult men: The Camargo Cohort Study

The aim of this study was to assess the prevalence of densitometric osteoporosis and vertebral fractures in Spanish men aged ≥50 years, and to study how the relationship between them may change depending on how osteoporosis is diagnosed. A community-based population of 1003 men aged ≥50 years was studied. Bone mineral density (BMD) was measured by DXA at the lumbar spine, femoral neck and total hip. Vertebral fractures were assessed by lateral thoracic and lumbar spine radiographs. The prevalence of osteoporosis was estimated with both the World Health Organization (WHO) (T-score of &lt;−2.5 at the femoral neck, calculated using the young white female normal reference database) and the National Osteoporosis Foundation (NOF) criteria (T-score of &lt;−2.5 at the femoral neck, total hip or lumbar spine, calculated using the young white male normal reference database). The prevalence of osteoporosis using the WHO criterion was 1.1% and using the NOF criterion was 13%, while that of vertebral fractures was 21.3%. The area under the curve (AUC) for the relationship between BMD and vertebral fracture prevalence was 0.64. The odds ratio for osteoporosis using the WHO definition was 2.57 (p = 0.13), and 1.78 (p = 0.007) using the NOF definition. Vertebral fracture prevalence rose with age. The prevalence of osteoporosis increased only moderately in men aged &gt;70 years with the WHO criterion, and showed no change using the NOF definition. The prevalence of osteoporosis in Spanish men using the WHO definition is too small to have any meaningful clinical use. Although the figure is higher using the NOF definition, it would seem that population-based studies of BMD in men are of questionable value.</p

Prevalence of vertebral fracture and densitometric osteoporosis in Spanish adult men: The Camargo Cohort Study

<p>The aim of this study was to assess the prevalence of densitometric osteoporosis and vertebral fractures in Spanish men aged ≥50 years, and to study how the relationship between them may change depending on how osteoporosis is diagnosed. A community-based population of 1003 men aged ≥50 years was studied. Bone mineral density (BMD) was measured by DXA at the lumbar spine, femoral neck and total hip. Vertebral fractures were assessed by lateral thoracic and lumbar spine radiographs. The prevalence of osteoporosis was estimated with both the World Health Organization (WHO) (T-score of <−2.5 at the femoral neck, calculated using the young white female normal reference database) and the National Osteoporosis Foundation (NOF) criteria (T-score of <−2.5 at the femoral neck, total hip or lumbar spine, calculated using the young white male normal reference database). The prevalence of osteoporosis using the WHO criterion was 1.1% and using the NOF criterion was 13%, while that of vertebral fractures was 21.3%. The area under the curve (AUC) for the relationship between BMD and vertebral fracture prevalence was 0.64. The odds ratio for osteoporosis using the WHO definition was 2.57 (<em>p</em> = 0.13), and 1.78 (<em>p</em> = 0.007) using the NOF definition. Vertebral fracture prevalence rose with age. The prevalence of osteoporosis increased only moderately in men aged >70 years with the WHO criterion, and showed no change using the NOF definition. The prevalence of osteoporosis in Spanish men using the WHO definition is too small to have any meaningful clinical use. Although the figure is higher using the NOF definition, it would seem that population-based studies of BMD in men are of questionable value.</p

Assessment of bone health in patients with type 1 gaucher disease using impact microindentation

BACKGROUND: Gaucher disease (GD), one of the commonest lysosomal disorders (a global population incidence of 1:50,000), is characterized by beta-glucocerebrosidase deficiency. Some studies have demonstrated bone infiltration in up to 80% of patients, even if asymptomatic. Bone disorder remains the main cause of morbidity in these patients, along with osteoporosis, avascular necrosis, and bone infarcts. Enzyme replacement therapy (ERT) has been shown to improve these symptoms. METHODS: This cross-sectional study included patients with type 1 Gaucher disease (GD1) selected from the Catalan Study Group on GD. Clinical data were collected and a general laboratory workup was performed. Bone mineral density (BMD) was measured at the lumbar spine and hip using dual energy X-ray absorptiometry (DXA). Patients with bone infarcts or any other focal lesion in the area of indentation visible on imaging were excluded. Bone Material Strength index (BMSi) was measured by bone impact microindentation using an Osteoprobe instrument. ANCOVA models were fitted to adjust for age, sex, weight and height. RESULTS: Sixteen patients with GD1 and 29 age- and sex-matched controls were included. GD1 was associated with significantly lower BMSi (adjusted beta -9.30 [95%CI -15.18 to -3.42]; p =0.004) and reduced lumbar (adjusted beta -0.14 [95%CI -0.22 to -0.06]; p = 0.002) and total hip BMD (adjusted beta -0.09 [95%CI -0.15 to -0.03]; p= 0.006), compared to GD1-free controls. Chitotriosidase levels were negatively correlated with BMSi (linear R(2 ) = 51.6%, p= 0.004). CONCLUSION: Bone tissue mechanical characteristics were deteriorated in patients with GD1. BMSi was correlated with chitotriosidase, the marker of GD activity. Bone disorder requires special consideration in this group of patients, and microindentation could be an appropriate tool for assessing and managing their bone health. This article is protected by copyright. All rights reserved.</p

Assessment of bone health in patients with type 1 gaucher disease using impact microindentation

BACKGROUND: Gaucher disease (GD), one of the commonest lysosomal disorders (a global population incidence of 1:50,000), is characterized by beta-glucocerebrosidase deficiency. Some studies have demonstrated bone infiltration in up to 80% of patients, even if asymptomatic. Bone disorder remains the main cause of morbidity in these patients, along with osteoporosis, avascular necrosis, and bone infarcts. Enzyme replacement therapy (ERT) has been shown to improve these symptoms. METHODS: This cross-sectional study included patients with type 1 Gaucher disease (GD1) selected from the Catalan Study Group on GD. Clinical data were collected and a general laboratory workup was performed. Bone mineral density (BMD) was measured at the lumbar spine and hip using dual energy X-ray absorptiometry (DXA). Patients with bone infarcts or any other focal lesion in the area of indentation visible on imaging were excluded. Bone Material Strength index (BMSi) was measured by bone impact microindentation using an Osteoprobe instrument. ANCOVA models were fitted to adjust for age, sex, weight and height. RESULTS: Sixteen patients with GD1 and 29 age- and sex-matched controls were included. GD1 was associated with significantly lower BMSi (adjusted beta -9.30 [95%CI -15.18 to -3.42]; p =0.004) and reduced lumbar (adjusted beta -0.14 [95%CI -0.22 to -0.06]; p = 0.002) and total hip BMD (adjusted beta -0.09 [95%CI -0.15 to -0.03]; p= 0.006), compared to GD1-free controls. Chitotriosidase levels were negatively correlated with BMSi (linear R(2 ) = 51.6%, p= 0.004). CONCLUSION: Bone tissue mechanical characteristics were deteriorated in patients with GD1. BMSi was correlated with chitotriosidase, the marker of GD activity. Bone disorder requires special consideration in this group of patients, and microindentation could be an appropriate tool for assessing and managing their bone health. This article is protected by copyright. All rights reserved.</p

Bone mineral density and body composition among athletes: Lightweight versus heavyweight sports

Objectives: Energy restriction and weight loss techniques are associated with adverse effects on bone mineral density (BMD) whilst participation in sports is known to be beneficial for skeletal health. However, it is not entirely clear the skeletal health status in lightweight sports where participants often use weight management techniques to attain relatively low mass. Therefore, the aim of this study is to evaluate the differences in BMD and body composition among athletes engaged in weight restricted and non-weight restricted sports.Published versio

The impact of hip fracture on health-related quality of life and activities of daily living: the SPARE-HIP prospective cohort study

PURPOSE: The medical morbidity and mortality associated with neck of femur fractures is well-documented, whereas there is limited data for patient-reported outcomes. The aim of this study was to characterize the impact of neck of femur fractures on activities of daily living and patient-reported health-related quality of life. METHODS: Design and participants: Multicentric prospective cohort study. Consecutive sample patients with fragility hip fracture over 50 years old admitted in 48 hospitals in Spain. OUTCOMES: daily living activity function (Barthel Index) and health-related quality of life (EQ-5D) pre-fracture, admission to hospital and at 1- and 4-month follow-up post-fracture. STATISTICS: Barthel and EQ-5D over time are described as mean (SD) and median (interquartile range). RESULTS: A total of 997 patients were recruited at baseline with 4-month outcomes available for, and 856 patients (89.5%). Barthel Index fell from 78.77 (23.75) at baseline to 43.62 (19.86) on admission to hospital with the fracture. Scores partially recovered to 54.89 (25.40) and 64.09 (21.35) at 1- and 4-month post-fracture, respectively. EQ-5D fell from a median of 0.75 (0.47-0.91) to - 0.01 (- 0.03 to 0.51) on admission. Partial recovery was observed again to (0.51 (- 0.06 to 0.67)) and (0.60 (0.10 to 0.80)) at 1- and 4-month post-fracture, respectively. CONCLUSIONS: Hip fracture results in a large decline in the ability to perform activities of daily living and patient-reported health-related quality of life with only partial recovery amongst survivors 4-month post-fracture