Efficient cloud tracing: From very high level to very low level

With the increase of cloud infrastructure complexity, the origin of service deterioration is difficult to detect because issues may occur at the different layer of the system. We propose a multi-layer tracing approach to gather all the relevant information needed for a full workflow analysis. The idea is to collect trace events from all the cloud nodes to follow users' requests from the cloud interface to their execution on the hardware. Our approach involves tracing OpenStack's interfaces, the virtualization layer, and the host kernel space to perform analysis and show abnormal tasks and the main causes of latency or failures in the system. Experimental results about virtual machines live migration confirm that we are able to analyse services efficiency by locating platforms' weakest links

Assessing of Preparedness for Disasters and Crisis in Centers of Trauma and Accidents of Kermanshah University of Medical Sciences in 2016

Background and aims: Natural and technologic disasters and accidents have great influence on people's lifestyle and their health. Main object of hospitals is providing fast and timely health care to reduce mortality and complications by the disaster. The aim of this study is to evaluate preparedness crisis and disasters in centers of trauma of Kermanshah University of Medical Sciences. Methods: The present descriptive, cross-sectional study was conducted in three hospitals (A,B,C) of Kermanshah university of medical sciences, Iran, 2016. Data were collected using a self-administered checklist and questioner through observation and interview. The checklist included 220 yes/no questions in 10 domains of emergency (30 questions), admission (24 questions), evacuation and transfer (30 questions), traffic (15 questions), communication (16 questions), security (17 questions), education (17 questions), support (28 questions), human workforce (21 questions), and leadership and management (22 items). Scores 0 and 1 were given to “No” and “Yes” choices, respectively. Data were analyzed using SPSS and descriptive statistics. Results: Overall, the relative mean of disaster preparedness in the study hospitals A, B and C was 99.1, 43.4and 84.7, respectively. Generally, the average readiness score for all hospitals was 75. The most and lowest preparedness was related to the management and traffic domains. Conclusion: According to the results, preparedness of hospitals was in the suitable level. Officials of medical centers have the necessary programs and educations in all areas of disaster preparedness for quick response and timely in hospitals

National and regional seasonal dynamics of all-cause and cause-specific mortality in the USA from 1980 to 2016

In temperate climates, winter deaths exceed summer ones. However, there is limited information on the timing and the relative magnitudes of maximum and minimum mortality, by local climate, age group, sex and medical cause of death. We used geo-coded mortality data and wavelets to analyse the seasonality of mortality by age group and sex from 1980 to 2016 in the USA and its subnational climatic regions. Death rates in men and women ≥ 45 years peaked in December to February and were lowest in June to August, driven by cardiorespiratory diseases and injuries. In these ages, percent difference in death rates between peak and minimum months did not vary across climate regions, nor changed from 1980 to 2016. Under five years, seasonality of all-cause mortality largely disappeared after the 1990s. In adolescents and young adults, especially in males, death rates peaked in June/July and were lowest in December/January, driven by injury deaths

Children’s height and weight in rural and urban populations in low-income and middle-income countries: a systematic analysis of population-representative data

Background Urban living aff ects children’s nutrition and growth, which are determinants of their survival, cognitive development, and lifelong health. Little is known about urban–rural diff erences in children’s height and weight, and how these diff erences have changed over time. We aimed to investigate trends in children’s height and weight in rural and urban settings in low-income and middle-income countries, and to assess changes in the urban–rural diff erentials in height and weight over time. Methods We used comprehensive population-based data and a Bayesian hierarchical mixture model to estimate trends in children’s height-for-age and weight-for-age Z scores by rural and urban place of residence, and changes in urban–rural diff erentials in height and weight Z scores, for 141 low-income and middle-income countries between 1985 and 2011. We also estimated the contribution of changes in rural and urban height and weight, and that of urbanisation, to the regional trends in these outcomes. Findings Urban children are taller and heavier than their rural counterparts in almost all low-income and middleincome countries. The urban–rural diff erential is largest in Andean and central Latin America (eg, Peru, Honduras, Bolivia, and Guatemala); in some African countries such as Niger, Burundi, and Burkina Faso; and in Vietnam and China. It is smallest in southern and tropical Latin America (eg, Chile and Brazil). Urban children in China, Chile, and Jamaica are the tallest in low-income and middle-income countries, and children in rural areas of Burundi, Guatemala, and Niger the shortest, with the tallest and shortest more than 10 cm apart at age 5 years. The heaviest children live in cities in Georgia, Chile, and China, and the most underweight in rural areas of Timor-Leste, India, Niger, and Bangladesh. Between 1985 and 2011, the urban advantage in height fell in southern and tropical Latin America and south Asia, but changed little or not at all in most other regions. The urban–rural weight diff erential also decreased in southern and tropical Latin America, but increased in east and southeast Asia and worldwide, because weight gain of urban children outpaced that of rural children.Interpretation Further improvement of child nutrition will require improved access to a stable and aff ordable food supply and health care for both rural and urban children, and closing of the the urban–rural gap in nutritional status

Accessibility and allocation of public parks and gardens in England and Wales: a COVID-19 social distancing perspective

Visiting parks and gardens supports physical and mental health. We quantified access to public parks and gardens in urban areas of England and Wales, and the potential for park crowdedness 22during periods of high use. We combined data from the Office for National Statistics and Ordnance Survey to quantify(i) the number of parks within 500and 1,000metresof urban postcodes (i.e., availability), (ii) the distance of postcodes to the nearest park (i.e., accessibility), and (iii) per-capita space in each park for people living within 1,000m.We26examined variability by city and share of flats. Around 25.4 million people(~87%) can access public parks or gardens within a ten-minute walk, while 3.8 million residents (~13%) live farther away; of these 21% are children and 13% are elderly. Areas with a higher share of flats on average are closer to a park but people living in these areas visit parks that are potentially overcrowded during periods of high use. Such disparity in urban areas of England and Wales becomes particularly evident during COVID-19 pandemic and lockdown when local parks, the only available out-of-home space option, hinder social distancing requirements. Cities aiming to facilitate social distancing while keeping public green spaces safe might require implementing measures such as dedicated park times for different age groups or entry allocation systems that, combined with smartphone apps or drones, can monitor and manage the total number of people using the park

Characterizing the Epidemiological Transition in Mexico: National and Subnational Burden of Diseases, Injuries, and Risk Factors

Gretchen Stevens and colleagues estimate deaths and loss of healthy life years (measured in disability-adjusted life years, DALYs) for Mexico as a whole and its 32 states

Contributions of diseases and injuries to widening life expectancy inequalities in England from 2001 to 2016: population-based analysis of vital registration data

Background Life expectancy inequalities in England have increased steadily since the 1980s. Our aim was to investigate how much deaths from different diseases and injuries and at different ages have contributed to this rise to inform policies that aim to reduce health inequalities. Methods We used vital registration data from the Office for National Statistics on population and deaths in England, by underlying cause of death, from 2001 to 2016, stratified by sex, 5-year age group, and decile of the Index of Multiple Deprivation (based on the ranked scores of Lower Super Output Areas in England in 2015). We grouped the 7·65 million deaths by their assigned International Classification of Diseases (10th revision) codes to create categories of public health and clinical relevance. We used a Bayesian hierarchical model to obtain robust estimates of cause-specific death rates by sex, age group, year, and deprivation decile. We calculated life expectancy at birth by decile of deprivation and year using life-table methods. We calculated the contributions of deaths from each disease and injury, in each 5-year age group, to the life expectancy gap between the most deprived and affluent deciles using Arriaga's method. Findings The life expectancy gap between the most affluent and most deprived deciles increased from 6·1 years (95% credible interval 5·9–6·2) in 2001 to 7·9 years (7·7–8·1) in 2016 in females and from 9·0 years (8·8–9·2) to 9·7 years (9·6–9·9) in males. Since 2011, the rise in female life expectancy has stalled in the third, fourth, and fifth most deprived deciles and has reversed in the two most deprived deciles, declining by 0·24 years (0·10–0·37) in the most deprived and 0·16 years (0·02–0·29) in the second-most deprived by 2016. Death rates from every disease and at every age were higher in deprived areas than in affluent ones in 2016. The largest contributors to life expectancy inequalities were deaths in children younger than 5 years (mostly neonatal deaths), respiratory diseases, ischaemic heart disease, and lung and digestive cancers in working ages, and dementias in older ages. From 2001 to 2016, the contributions to inequalities declined for deaths in children younger than 5 years, ischaemic heart disease (for both sexes), and stroke and intentional injuries (for men), but increased for most other causes. Interpretation Recent trends in life expectancy in England have not only resulted in widened inequalities but the most deprived communities are now seeing no life expectancy gain. These inequalities are driven by a diverse group of diseases that can be effectively prevented and treated. Adoption of the principle of proportionate universalism to prevention and health and social care can postpone deaths into older ages for all communities and reduce life expectancy inequalities

Long-term exposure to air-pollution and COVID-19 mortality in England: a hierarchical spatial analysis

Recent studies suggested a link between long-term exposure to air-pollution and COVID-19 mortality. However, due to their ecological design based on large spatial units, they neglect the strong localised air-pollution patterns, and potentially lead to inadequate confounding adjustment. We investigated the effect of long-term exposure to NO2 and PM2.5 on COVID-19 mortality in England using high geographical resolution. In this nationwide cross-sectional study in England, we included 38,573 COVID-19 deaths up to June 30, 2020 at the Lower Layer Super Output Area level (n = 32,844 small areas). We retrieved averaged NO2 and PM2.5 concentration during 2014–2018 from the Pollution Climate Mapping. We used Bayesian hierarchical models to quantify the effect of air-pollution while adjusting for a series of confounding and spatial autocorrelation. We find a 0.5% (95% credible interval: −0.2%, 1.2%) and 1.4% (95% CrI: −2.1%, 5.1%) increase in COVID-19 mortality risk for every 1 μg/m3 increase in NO2 and PM2.5 respectively, after adjusting for confounding and spatial autocorrelation. This corresponds to a posterior probability of a positive effect equal to 0.93 and 0.78 respectively. The spatial relative risk at LSOA level revealed strong patterns, similar for the different pollutants. This potentially captures the spread of the disease during the first wave of the epidemic. Our study provides some evidence of an effect of long-term NO2 exposure on COVID-19 mortality, while the effect of PM2.5 remains more uncertain