Crowding and Delivery of Healthcare in Emergency Departments: The European Perspective

Emergency department (ED) crowding is a multifactorial problem, resulting in increased ED waiting times, decreased patient satisfaction and deleterious domino effects on the entire hospital. Although difficult to define and once limited to anecdotal evidence, crowding is receiving more attention as attempts are made to quantify the problem objectively. It is a worldwide phenomenon with regional influences, as exemplified when analyzing the problem in Europe compared to that of the United States. In both regions, an aging population, limited hospital resources, staff shortages and delayed ancillary services are key contributors; however, because the structure of healthcare differs from country to country, varying influences affect the issue of crowding. The approach to healthcare delivery as a right of all people, as opposed to a free market commodity, depends on governmental organization and appropriation of funds. Thus, public funding directly influences potential crowding factors, such as number of hospital beds, community care facilities, and staffing. Ultimately ED crowding is a universal problem with distinctly regional root causes; thus, any approach to address the problem must be tailored to regional influences

Effect of a Medical Student Emergency Ultrasound Clerkship on Number of Emergency Department Ultrasounds

Objective: To determine whether a medical student emergency ultrasound clerkship has an effect on the number of patients undergoing ultrasonography and the number of total scans in the emergency department.Methods: We conducted a prospective, single-blinded study of scanning by emergency medicine residents and attendings with and without medical students. Rotating ultrasound medical students were assigned to work equally on all days of the week. We collected the number of patients scanned and the number of scans, as well as participation of resident and faculty.Results: In seven months 2,186 scans were done on the 109 days with students and 707 scans on the 72 days without them. Data on 22 days was not recorded. A median of 13 patients per day were scanned with medical students (CI 12-15) versus seven (CI 6-9) when not. In addition, the median number of scans was 18 per day with medical students (CI 16-20) versus eight (CI 6-10) without them.Conclusion: There were significantly more patients scanned and scans done when ultrasound medical students were present. [West J Emerg Med. 2010; 11:31-34]

Factors Associated with Complications in Older Adults with Isolated Blunt Chest Trauma

Objective: To determine the prevalence of adverse events in elderly trauma patients with isolated blunt thoracic trauma, and to identify variables associated with these adverse events.Methods: We performed a chart review of 160 trauma patients age 65 and older with significant blunt thoracic trauma, drawn from an American College of Surgeons Level I Trauma Center registry. Patients with serious injury to other body areas were excluded to prevent confounding the cause of adverse events. Adverse events were defined as acute respiratory distress syndrome or pneumonia, unanticipated intubation, transfer to the intensive care unit for hypoxemia, or death. Data collected included history, physical examination, radiographic findings, length of hospital stay, and clinical outcomes.Results: Ninety-nine patients had isolated chest injury, while 61 others had other organ systems injured and were excluded. Sixteen patients developed adverse events [16.2% 95% confidence interval (CI) 9.5-24.9%], including two deaths. Adverse events were experienced by 19.2%, 6.1%, and 28.6% of those patients 65-74, 75-84, and >85 years old, respectively. The mean length of stay was 14.6 days in patients with an adverse event and 5.8 days in patients without. Post hoc analysis revealed that all 16 patients with an adverse event had one or more of the following: age ≥85, initial systolic blood pressure <90 mmHg, hemothorax, pneumothorax, three or more unilateral rib fractures, or pulmonary contusion (sensitivity 100%, CI 79.4-100%; specificity 38.6%, CI 28.1-49.9%).Conclusion: Adverse events from isolated thoracic trauma in elderly patients complicate 16% of our sample. These criteria were 100% sensitive and 38.5% specific for these adverse events. This study is a first step to identifying variables that might aid in identifying patients at high risk for serious adverse events. [WestJEM. 2009;10:79-84.

Trauma and Emergency Anesthesia Checklists

This study aimed at investigating the anesthesiologist critical role in stabilizing the patient and maintaining safe conditions during this dynamic period and frequently will find it necessary to shift management strategies as the case evolves. Ant to analyze the followed checklist upon the arrival of trauma patients and the using of the emergency anesthesia procedures. Besides the attempt to justify the use of medical checklists, and following up the checklists’ protocols, especially in the field of emergency anesthesia procedures for trauma patients by analyzing the most used checklists worldwide, and demonstrates the importance of adherence to regulations in the checklists for trauma patients. The study concluded that trauma and emergency anesthesia checklist can improve communication in the care of critically ill patients requiring an anesthetic

General Anesthesia: Observing and Monitoring the Post-operative Complications

The aim of this research was to analyze the post-operative complications of general anesthesia and by utilizing that information, to plan and produce a comprehensive detailed layout of the literature related to the subject investigated. More than 200 studies were surveyed, and the data collected was organized with a systematic layout of the observing and monitoring processes of the post-operative general anesthesia complications. The study concluded that post anaesthetic observations and monitoring are an essential requirement for patient assessment and the recognition of clinical deterioration in post-operative patients. There is disparity in the literature as to what constitutes ‘standard’ routine post anaesthesia orders, so in line with the observation and continuous monitoring guidelines

Castor Leaves-Based Biochar for Adsorption of Safranin from Textile Wastewater

The prospect of synthesizing biochar from agricultural wastes or by-products to utilize them as a promising adsorbent material is increasingly gaining attention. This research work focuses on synthesizing biochar from castor biomass (CBM) and evaluating its potential as an adsorbent material. Castor biomass-based biochar (CBCs) prepared by the slow pyrolysis process at different temperatures (CBC400 °C, CBC500 °C, and CBC600 °C for 1 h) was investigated for the adsorption of textile dye effluents (safranin). The pyrolysis temperature played a key role in enhancing the morphology, and the crystallinity of the biochar which are beneficial for the uptake of safranin. The CBC600 adsorbent showed a higher safranin dye removal (99.60%) and adsorption capacity (4.98 mg/g) than CBC500 (90.50% and 4.52 mg/g), CBC400 (83.90% and 4.20 mg/g), and castor biomass (CBM) (64.40% and 3.22 mg/g). Adsorption data fitted better to the Langmuir isotherm model than to the Freundlich isotherm model. The kinetics of the adsorption process was described well using the pseudo-second-order kinetic model. The study on the effect of the contact time for the adsorption process indicated that for CBC600, 80% dye removal occurred in the first 15 min of the contact time. After three regeneration cycles, CBC600 exhibited the highest dye removal efficiency (64.10%), highlighting the enhanced reusability of CBCs. The crystalline patterns, functional binding sites, and surface areas of the prepared CBCs (CBC400, CBC500, CBC600) were characterized by X-ray diffraction, Fourier transform infrared spectroscopy, and Brunauer–Emmett–Teller surface area measurements, respectively

Global, regional, and national age-sex-specific mortality and life expectancy, 1950–2017: a systematic analysis for the Global Burden of Disease Study 2017

Background Assessments of age-specific mortality and life expectancy have been done by the UN Population Division, Department of Economics and Social Affairs (UNPOP), the United States Census Bureau, WHO, and as part of previous iterations of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD). Previous iterations of the GBD used population estimates from UNPOP, which were not derived in a way that was internally consistent with the estimates of the numbers of deaths in the GBD. The present iteration of the GBD, GBD 2017, improves on previous assessments and provides timely estimates of the mortality experience of populations globally. Methods The GBD uses all available data to produce estimates of mortality rates between 1950 and 2017 for 23 age groups, both sexes, and 918 locations, including 195 countries and territories and subnational locations for 16 countries. Data used include vital registration systems, sample registration systems, household surveys (complete birth histories, summary birth histories, sibling histories), censuses (summary birth histories, household deaths), and Demographic Surveillance Sites. In total, this analysis used 8259 data sources. Estimates of the probability of death between birth and the age of 5 years and between ages 15 and 60 years are generated and then input into a model life table system to produce complete life tables for all locations and years. Fatal discontinuities and mortality due to HIV/AIDS are analysed separately and then incorporated into the estimation. We analyse the relationship between age-specific mortality and development status using the Socio-demographic Index, a composite measure based on fertility under the age of 25 years, education, and income. There are four main methodological improvements in GBD 2017 compared with GBD 2016: 622 additional data sources have been incorporated; new estimates of population, generated by the GBD study, are used; statistical methods used in different components of the analysis have been further standardised and improved; and the analysis has been extended backwards in time by two decades to start in 1950.Background Assessments of age-specific mortality and life expectancy have been done by the UN Population Division, Department of Economics and Social Affairs (UNPOP), the United States Census Bureau, WHO, and as part of previous iterations of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD). Previous iterations of the GBD used population estimates from UNPOP, which were not derived in a way that was internally consistent with the estimates of the numbers of deaths in the GBD. The present iteration of the GBD, GBD 2017, improves on previous assessments and provides timely estimates of the mortality experience of populations globally. Methods The GBD uses all available data to produce estimates of mortality rates between 1950 and 2017 for 23 age groups, both sexes, and 918 locations, including 195 countries and territories and subnational locations for 16 countries. Data used include vital registration systems, sample registration systems, household surveys (complete birth histories, summary birth histories, sibling histories), censuses (summary birth histories, household deaths), and Demographic Surveillance Sites. In total, this analysis used 8259 data sources. Estimates of the probability of death between birth and the age of 5 years and between ages 15 and 60 years are generated and then input into a model life table system to produce complete life tables for all locations and years. Fatal discontinuities and mortality due to HIV/AIDS are analysed separately and then incorporated into the estimation. We analyse the relationship between age-specific mortality and development status using the Socio-demographic Index, a composite measure based on fertility under the age of 25 years, education, and income. There are four main methodological improvements in GBD 2017 compared with GBD 2016: 622 additional data sources have been incorporated; new estimates of population, generated by the GBD study, are used; statistical methods used in different components of the analysis have been further standardised and improved; and the analysis has been extended backwards in time by two decades to start in 1950

Population and fertility by age and sex for 195 countries and territories, 1950–2017: a systematic analysis for the Global Burden of Disease Study 2017

Background Population estimates underpin demographic and epidemiological research and are used to track progress on numerous international indicators of health and development. To date, internationally available estimates of population and fertility, although useful, have not been produced with transparent and replicable methods and do not use standardised estimates of mortality. We present single-calendar year and single-year of age estimates of fertility and population by sex with standardised and replicable methods. Methods We estimated population in 195 locations by single year of age and single calendar year from 1950 to 2017 with standardised and replicable methods. We based the estimates on the demographic balancing equation, with inputs of fertility, mortality, population, and migration data. Fertility data came from 7817 location-years of vital registration data, 429 surveys reporting complete birth histories, and 977 surveys and censuses reporting summary birth histories. We estimated age-specific fertility rates (ASFRs; the annual number of livebirths to women of a specified age group per 1000 women in that age group) by use of spatiotemporal Gaussian process regression and used the ASFRs to estimate total fertility rates (TFRs; the average number of children a woman would bear if she survived through the end of the reproductive age span [age 10–54 years] and experienced at each age a particular set of ASFRs observed in the year of interest). Because of sparse data, fertility at ages 10–14 years and 50–54 years was estimated from data on fertility in women aged 15–19 years and 45–49 years, through use of linear regression. Age-specific mortality data came from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 estimates. Data on population came from 1257 censuses and 761 population registry location-years and were adjusted for underenumeration and age misreporting with standard demographic methods. Migration was estimated with the GBD Bayesian demographic balancing model, after incorporating information about refugee migration into the model prior. Final population estimates used the cohort-component method of population projection, with inputs of fertility, mortality, and migration data. Population uncertainty was estimated by use of out-of-sample predictive validity testing. With these data, we estimated the trends in population by age and sex and in fertility by age between 1950 and 2017 in 195 countries and territories.Background Population estimates underpin demographic and epidemiological research and are used to track progress on numerous international indicators of health and development. To date, internationally available estimates of population and fertility, although useful, have not been produced with transparent and replicable methods and do not use standardised estimates of mortality. We present single-calendar year and single-year of age estimates of fertility and population by sex with standardised and replicable methods. Methods We estimated population in 195 locations by single year of age and single calendar year from 1950 to 2017 with standardised and replicable methods. We based the estimates on the demographic balancing equation, with inputs of fertility, mortality, population, and migration data. Fertility data came from 7817 location-years of vital registration data, 429 surveys reporting complete birth histories, and 977 surveys and censuses reporting summary birth histories. We estimated age-specific fertility rates (ASFRs; the annual number of livebirths to women of a specified age group per 1000 women in that age group) by use of spatiotemporal Gaussian process regression and used the ASFRs to estimate total fertility rates (TFRs; the average number of children a woman would bear if she survived through the end of the reproductive age span [age 10–54 years] and experienced at each age a particular set of ASFRs observed in the year of interest). Because of sparse data, fertility at ages 10–14 years and 50–54 years was estimated from data on fertility in women aged 15–19 years and 45–49 years, through use of linear regression. Age-specific mortality data came from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 estimates. Data on population came from 1257 censuses and 761 population registry location-years and were adjusted for underenumeration and age misreporting with standard demographic methods. Migration was estimated with the GBD Bayesian demographic balancing model, after incorporating information about refugee migration into the model prior. Final population estimates used the cohort-component method of population projection, with inputs of fertility, mortality, and migration data. Population uncertainty was estimated by use of out-of-sample predictive validity testing. With these data, we estimated the trends in population by age and sex and in fertility by age between 1950 and 2017 in 195 countries and territories

Measuring progress from 1990 to 2017 and projecting attainment to 2030 of the health-related Sustainable Development Goals for 195 countries and territories: a systematic analysis for the Global Burden of Disease Study 2017

Background Efforts to establish the 2015 baseline and monitor early implementation of the UN Sustainable Development Goals (SDGs) highlight both great potential for and threats to improving health by 2030. To fully deliver on the SDG aim of “leaving no one behind”, it is increasingly important to examine the health-related SDGs beyond national-level estimates. As part of the Global Burden of Diseases, Injuries, and Risk Factors Study 2017 (GBD 2017), we measured progress on 41 of 52 health-related SDG indicators and estimated the health-related SDG index for 195 countries and territories for the period 1990–2017, projected indicators to 2030, and analysed global attainment. Methods We measured progress on 41 health-related SDG indicators from 1990 to 2017, an increase of four indicators since GBD 2016 (new indicators were health worker density, sexual violence by non-intimate partners, population census status, and prevalence of physical and sexual violence [reported separately]). We also improved the measurement of several previously reported indicators. We constructed national-level estimates and, for a subset of health-related SDGs, examined indicator-level differences by sex and Socio-demographic Index (SDI) quintile. We also did subnational assessments of performance for selected countries. To construct the health-related SDG index, we transformed the value for each indicator on a scale of 0–100, with 0 as the 2·5th percentile and 100 as the 97·5th percentile of 1000 draws calculated from 1990 to 2030, and took the geometric mean of the scaled indicators by target. To generate projections through 2030, we used a forecasting framework that drew estimates from the broader GBD study and used weighted averages of indicator-specific and country-specific annualised rates of change from 1990 to 2017 to inform future estimates. We assessed attainment of indicators with defined targets in two ways: first, using mean values projected for 2030, and then using the probability of attainment in 2030 calculated from 1000 draws. We also did a global attainment analysis of the feasibility of attaining SDG targets on the basis of past trends. Using 2015 global averages of indicators with defined SDG targets, we calculated the global annualised rates of change required from 2015 to 2030 to meet these targets, and then identified in what percentiles the required global annualised rates of change fell in the distribution of country-level rates of change from 1990 to 2015. We took the mean of these global percentile values across indicators and applied the past rate of change at this mean global percentile to all health-related SDG indicators, irrespective of target definition, to estimate the equivalent 2030 global average value and percentage change from 2015 to 2030 for each indicator.Background Efforts to establish the 2015 baseline and monitor early implementation of the UN Sustainable Development Goals (SDGs) highlight both great potential for and threats to improving health by 2030. To fully deliver on the SDG aim of “leaving no one behind”, it is increasingly important to examine the health-related SDGs beyond national-level estimates. As part of the Global Burden of Diseases, Injuries, and Risk Factors Study 2017 (GBD 2017), we measured progress on 41 of 52 health-related SDG indicators and estimated the health-related SDG index for 195 countries and territories for the period 1990–2017, projected indicators to 2030, and analysed global attainment. Methods We measured progress on 41 health-related SDG indicators from 1990 to 2017, an increase of four indicators since GBD 2016 (new indicators were health worker density, sexual violence by non-intimate partners, population census status, and prevalence of physical and sexual violence [reported separately]). We also improved the measurement of several previously reported indicators. We constructed national-level estimates and, for a subset of health-related SDGs, examined indicator-level differences by sex and Socio-demographic Index (SDI) quintile. We also did subnational assessments of performance for selected countries. To construct the health-related SDG index, we transformed the value for each indicator on a scale of 0–100, with 0 as the 2·5th percentile and 100 as the 97·5th percentile of 1000 draws calculated from 1990 to 2030, and took the geometric mean of the scaled indicators by target. To generate projections through 2030, we used a forecasting framework that drew estimates from the broader GBD study and used weighted averages of indicator-specific and country-specific annualised rates of change from 1990 to 2017 to inform future estimates. We assessed attainment of indicators with defined targets in two ways: first, using mean values projected for 2030, and then using the probability of attainment in 2030 calculated from 1000 draws. We also did a global attainment analysis of the feasibility of attaining SDG targets on the basis of past trends. Using 2015 global averages of indicators with defined SDG targets, we calculated the global annualised rates of change required from 2015 to 2030 to meet these targets, and then identified in what percentiles the required global annualised rates of change fell in the distribution of country-level rates of change from 1990 to 2015. We took the mean of these global percentile values across indicators and applied the past rate of change at this mean global percentile to all health-related SDG indicators, irrespective of target definition, to estimate the equivalent 2030 global average value and percentage change from 2015 to 2030 for each indicator