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Severe traumatic brain injury (sTBI) is a silent epidemic, causing approximately 300,000 intensive care unit (ICU) admissions annually, with a 30% mortality rate. Despite worldwide efforts to optimize the management of patients and improve outcomes, the level of evidence for the treatment of these patients remains low. The concomitant occurrence of thromboembolic events, particularly pulmonary embolism (PE), remains a challenge for intensivists due to the risks of anticoagulation to the injured brain. We performed a literature review on sTBI and concomitant PE to identify and report the most recent advances on this topic. We searched PubMed and Scopus for papers published in the last five years that included the terms "pulmonary embolism" and "traumatic brain injury" in their title or abstract. Exclusion criteria were papers referring to children, non-sTBI populations, and post-acute care. Our search revealed 75 papers, of which 38 are included in this review. The main topics covered include the prevalence of and risk factors for pulmonary embolism, the challenges of timely diagnosis in the ICU, the timing of pharmacological prophylaxis, and the treatment of diagnosed PE. [ABSTRACT FROM AUTHOR]

Background: A nanny state imposes restrictions on people's liberty and freedom of action in order to advance their interest and welfare. The extent to which this is desirable, or even ethically acceptable, is debated in the literature. This paper formulates and tests the following hypothesis: the more of a nanny a state has been in the past, the more likely it is that the incumbent government will respond to a new, unknown threat with interventions of a paternalist nature, irrespective of other factors that might contribute to shaping government's response. This hypothesis is then taken to the data using the first wave of COVID-19 as an empirical test. Methods: Data are collected from secondary sources for a sample of 99 countries. Nanny statism is measured by the number of paternalist laws and regulations adopted by a country in the past. The response to COVID is proxied by the time of adoption of control and containment measures and their stringency. The public health outcome is measured by the COVID-19 death toll at the end of June 2020. These variables, plus several controls, are then used to estimate a set of linear and probit regressions and a proportional hazard model of the timing of adoption of control and containment measures. Results: An increase in nanny statism by 0.1 (on a scale from 0 to 10) on average increases the probability of adoption of control and containment measures by 0.077 (i.e. 7.7 percentage points). The central tenement of the hypothesis is therefore consistent with the empirical evidence. The linear and probit regressions also show that there is no evidence of a significant effect of nanny statism on the stringency of the measures adopted. Irrespective of stringency, however, early adoption of control and containment measures is found to reduce the death toll of COVID-19 in the first half of 2020: an increase in nanny statism by 0.1 reduces the COVID death toll by approximately 7%. Conclusions: A tradition of nanny statism potentially leads to a more timely and effective public policy response to a new, unknown crisis. Further tests of the hypothesis should look at the relationship between nanny statism and public health outcomes from natural disasters. [ABSTRACT FROM AUTHOR]

During the 2022 summer, Europe experienced heatwaves with record temperatures, and a study has argued that they caused about 62,000 deaths between 30 May and 4 September. The total number of excess deaths during the same period was about 137,000, indicating that the heatwaves were a substantial contributor. Not ruling out that explanation entirely, this paper argues that it was unlikely a strong cause. First, if the heatwaves were a strong cause of numerous deaths, one would assume that the older and deprived were relatively likely to die. However, during the 2022 summer heatwaves in England, which were claimed to have caused about 2900 deaths, the oldest age cohort did not have a higher excess death rate than the middle age cohort, and the excess death rate actually decreased with deprivation status. Moreover, Iceland had among Europe's highest excess death rates during the summer, which cannot be attributed to heatwaves. During June, July, and August 2022, comparable southern hemisphere countries furthermore had high excess death rates, which cannot be attributed to heatwaves either, as it was during their winter. Also, Europe's excess death rate was higher during the 2022–2023 winter than during the 2022 summer, and intuitively not attributed to heatwaves, but neither to cold weather, as that winter was abnormally mild. Finally, the paper discusses the puzzling issue that about 56% more women than men, relative to the population, presumably died from the heatwaves. [ABSTRACT FROM AUTHOR]

In early 2021, the United Kingdom (UK) had the highest per capita death rate from Covid-19 of any large country. Yet it had previously been ranked as one of the best prepared countries for a future pandemic. This gap between preparedness and performance has been the subject of intense debate, including as part of the UK Covid-19 Inquiry. In this paper, we contribute to this ongoing process of reflection by identifying the imagined public(s) within the UK's scientific advice system. Drawing on scholarship in Science and Technology Studies (STS) that critiques framings of a singular or homogeneous 'public', we review meeting minutes and media briefings to reveal two imagined publics, co-constructed by the UK's science advisors and policymakers in early 2020: first, a 'freedom-loving' public resistant to stringent policy interventions; and second, a public that was—in an echo of wartime rhetoric— 'all in it together'. These imagined publics reflect a series of framing assumptions that help to make sense of the UK's pandemic response. We focus particularly on the tensions between the homogeneous and multi-faceted imagined public, and the compound health and social inequalities that predated the pandemic but became starker and more visible as it unfolded. Our paper charts these tensions and demonstrates how these imagined publics went through stages of cohesion and fracture in the fraught early months of the pandemic. We conclude by considering the implications of this analysis for understanding the UK's response to Covid-19, and for the future of scientific advice and emergency preparedness. Why does this matter? Studies of scientific advice reveal that how scientists and decision makers imagine the public and their concerns affect the communication of scientific advice, and the construction and value placed on relevant knowledge. Advisory scientists frame their models and their advice in terms of what they regard as politically possible. [ABSTRACT FROM AUTHOR]

From the beginning of the Iraq war, in March of 2003, to the present day, controversy has swirled around the death toll of the war. This paper narrows down the range of uncertainty for the numbers and trends in violent deaths in the war. I assemble and appraise all primary sources that cover the period from March of 2003 onwards—six sample surveys plus a casualty recording project (Iraq Body Count [IBC]). Data permitting, I present cumulative monthly figures with, for the surveys, 95% bootstrapped uncertainty intervals. The analysis uncovers a core of high-quality mainstream sources that are highly consistent with each another. In addition, there are three outlier surveys that are compromised by serious flaws and produce estimates far outside the mainstream. Discarding the outlying and flawed surveys reveals a clear picture of the violent death toll from the Iraq war. IBC figures, extended to include combatants, occupy a central position within the mainstream range of estimates. The strong consistency across the high-quality sources provides a rare validation of three war-death-measurement methodologies—household-based surveys, sibling-based surveys, and casualty recording. Methodological success notwithstanding, we must transcend the numbers to truly comprehend the human costs of the war. [ABSTRACT FROM AUTHOR]

Skin cancer is the predominant form of cancer worldwide, including 75% of all cancer cases. This study aims to evaluate the effectiveness of the spectrum-aided visual enhancer (SAVE) in detecting skin cancer. This paper presents the development of a novel algorithm for snapshot hyperspectral conversion, capable of converting RGB images into hyperspectral images (HSI). The integration of band selection with HSI has facilitated the identification of a set of narrow band images (NBI) from the RGB images. This study utilizes various iterations of the You Only Look Once (YOLO) machine learning (ML) framework to assess the precision, recall, and mean average precision in the detection of skin cancer. YOLO is commonly preferred in medical diagnostics due to its real-time processing speed and accuracy, which are essential for delivering effective and efficient patient care. The precision, recall, and mean average precision (mAP) of the SAVE images show a notable enhancement in comparison to the RGB images. This work has the potential to greatly enhance the efficiency of skin cancer detection, as well as improve early detection rates and diagnostic accuracy. Consequently, it may lead to a reduction in both morbidity and mortality rates. [ABSTRACT FROM AUTHOR]

In one of the first papers on the impact of early-life conditions on individuals' health in older age, Barker and Osmond [Lancet, 327, 1077-1081 (1986)] show a strong positive relationship between infant mortality rates in the 1920s and ischemic heart disease in the 1970s. We merge historical data on infant mortality rates to 370,000 individual records in the UK Biobank using information on local area and year of birth. We replicate the association between the early-life infant mortality rate and laterlife ischemic heart disease in our sample. We then go "beyond Barker," by showing considerable genetic heterogeneity in this association that is robust to within-area as well as within-family analyses. We find no association between the polygenic index and heart disease in areas with the lowest infant mortality rates, but a strong positive relationship in areas characterized by high infant mortality. These findings suggest that advantageous environments can cushion one's genetic disease risk. [ABSTRACT FROM AUTHOR]

Earthquake death tolls are a basic statistical measure of the capability of a country to manage seismic risk. The extremely high Turkish death toll of 50,000 from the Kahramanmaras earthquake doublet of 6 February 2023 is the product of a cascade of detrimental factors. These need to be explained if lessons from this disaster are to be learned. This is the purpose and objective of this paper, which is a contribution to the interdisciplinary Frontiers research topic on integrated perspectives on the 2023 Turkey and Syria earthquakes: advancing understanding and preparedness across earth sciences, engineering and public health. This paper covers these three disciplines by focusing on casualties, and identifying crucial aspects of earth sciences and engineering which contributed to the high death toll. First, there was a surprising combination of multiple fault segment ruptures, and a high level of ground motion relative to the risk-based Turkish code, indicative of the under-representation of the M7.5+ earthquake doublet event in the national probabilistic seismic hazard model. This combination of fault segment ruptures was missing from all seismic source models. Furthermore, the capability of buildings to cope with strong ground motion was much reduced by informal construction methods, which eroded the margin of safety needed to avoid building collapse. The extent of building code non-compliance was widely underestimated in seismic risk models. Noncompliance is often hard to identify, but construction amnesties make noncompliance more transparent and trackable. The disastrous outcome of the Kahramanmaras earthquake doublet of 6 February 2023 has drawn global attention to systemic building code non-compliance, and the open official acceptance of informal housing. To demonstrate that this key systemic risk is far from being just a Turkish problem, notably in Istanbul, the challenge of Italian informal housing is highlighted within the context of international building code non-compliance. [ABSTRACT FROM AUTHOR]

In this paper, we consider the influence of a nonlinear contact rate caused by multiple contacts in classical SIR model. In this paper, we unversal unfolding a nilpotent cusp singularity in such systems through normal form theory, we reveal that the system undergoes a Bogdanov-Takens bifurcation with codimension 2. During the bifurcation process, numerous lower codimension bifurcations may emerge simultaneously, such as saddle-node and Hopf bifurcations with codimension 1. Finally, employing the Matcont and Phase Plane software, we construct bifurcation diagrams and topological phase portraits. Additionally, we emphasize the role of symmetry in our analysis. By considering the inherent symmetries in the system, we provide a more comprehensive understanding of the dynamical behavior. Our findings suggest that if this occurrence rate is applied to the SIR model, it would yield different dynamical phenomena compared to those obtained by reducing a 3-dimensional dynamical model to a planar system by neglecting the disease mortality rate, which results in a stable nilpotent cusp singularity with codimension 2. We found that in SIR models with the same occurrence rate, both stable and unstable Bogdanov-Takens bifurcations occur, meaning both stable and unstable limit cycles appear in this system. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) have the trouble of limited battery power, and wireless charging provides a promising solution to this problem, which is not easily affected by the external environment. In this paper, we study the recharging of sensors in wireless rechargeable sensor networks (WRSNs) by scheduling two mobile chargers (MCs) to collaboratively charge sensors. We first formulate a novel sensor charging scheduling problem with the objective of maximizing the number of surviving sensors, and further propose a collaborative charging scheduling algorithm(CCSA) forWRSNs. In the scheme, the sensors are divided into important sensors and ordinary sensors. TwoMCs can adaptively collaboratively charge the sensors based on the energy limit of MCs and the energy demand of sensors. Finally, we conducted comparative simulations. The simulation results showthat the proposed algorithm can effectively reduce the death rate of the sensor. The proposed algorithm provides a solution to the uncertainty of node charging tasks and the collaborative challenges posed by multiple MCs in practical scenarios. [ABSTRACT FROM AUTHOR]

The goal of this paper is to understand the interplay between seasonality and diapause on the population dynamics of ticks. To analyse this challenge, we study a mathematical model in which the population is divided into two groups: individuals with regular development and individuals undergoing diapause. A key ingredient in the model is that the common birth and death rates depend on time in order to describe the seasonal fluctuations of the environment. From a mathematical point of view, this paper offers a novel methodology to derive criteria of global attraction. In some classical models, we give delay-dependent conditions that cover the best delay-independent criteria of global attraction. We discuss how our approach improves existing results in relevant literature. Moreover, our results are simple and versatile enough to match experimental observations. To illustrate this potential, we analyse our model with parameters coming from real observations. A message of this paper is that seasonality plays a critical role in the population dynamics of ticks. [ABSTRACT FROM AUTHOR]

As a no-disturbance integrated-retrofitting technique, an external rocking frame was widely used on reinforced concrete (RC) structures. Yet, with the increasing demand for seismic strengthening of existing buildings, it has become a concern to evaluate the seismic strengthening schemes based on seismic resilience. Firstly, the dynamic equation of the structural system was derived, and the deformation control mechanism was revealed; thus, the corresponding design method was put forward for the rocking frame reinforcement. Secondly, after soft-first-floor structures were reinforced by rocking frames, the evaluation method of the reinforcement scheme was investigated based on seismic resilience. Finally, the feasibility of the assessment method was verified by a soft-first-floor frame structure, and a comparison was made between the method proposed in this paper and the conventional method. The results find that the soft-first-floor structure reinforced by the rocking frame increased by 10% in the inter-layer displacement and improved by 55.6% and 63.0% in the injury and mortality rates, compared to the buckling-restrained brace scheme. This indicates that the reinforcement scheme of soft layer structures with rocking frames is feasible and effective, and the reinforcement evaluation method proposed in this paper can quantitatively reflect the improvement in seismic performance. [ABSTRACT FROM AUTHOR]

The growth and fertility rates of past populations can be estimated by analyzing the age-at-death distribution of skeletal samples. The procedure involves regressing growth or fertility rate on the age-at-death ratio, which is a proxy that captures the number of skeletons in two broad age-at-death categories (e.g., D5+/D20+). Galeta and Pankowská (2023, doi: 10.1371/journal.pone.0286580) recently developed a new prediction algorithm. They proposed to estimate growth and fertility rates using a unique prediction formula for each skeletal sample. Each formula is based on a unique reference set of simulated skeletal samples that match the size of the target real skeletal sample. The simulated skeletal samples are generated from populations with similar mortality levels to those assumed in the time period represented by the target skeletal sample. A correct setting of the sample size and the level of mortality increases the accuracy of the estimate. The approach, however, is computationally intensive because it involves generating many simulated reference skeletal samples. In this paper, we present the demrat package, written in the R programming language, which automates the simulation. The functions of the package provide a complete workflow from a real skeletal sample to the prediction of demographic rates. In addition, we offer a web application that allows non-R users to deploy predictions using the demrat package with a user-friendly, point-and-click graphical interface. Although the demrat package allows for estimating demographic rates for a single skeletal sample, we recommend predicting demographic rates in a larger set of skeletal samples and producing smoothed general demographic trends over large areas and/or long periods of time. [ABSTRACT FROM AUTHOR]

By January 2024, the COVID-19 pandemic claimed more than 1.1 million deaths in the United States (U.S.). People in prison are particularly vulnerable to COVID-19 as they have no ability to socially distance, secure masks, disinfect their environment or have as much access to tests or vaccinations as is available in the community. In addition, many of these individuals reside in crowded conditions with little ventilation, which makes the spread of the virus more likely. In this paper, we used data from two projects, including the UCLA Law COVID Behind Bars Data Project and the COVID Prison Project, and supplemented these with publicly available data to examine the number of deaths and infection rates caused by COVID-19 among people in prison and prison staff in the U.S., as reported by the population of those facilities. We found that the incidence of infections and death rates in prisons were affected by crowding, prison security type (maximum, medium, minimum, or mixed) and level of prison (state or federal). People in prison who were less likely to have as much human contact (e.g., maximum-security prisons) were also less likely to be afflicted with COVID-19. People in prison were twice as likely to be infected by COVID-19 but had a similar death rate compared to the general public. Prison overcrowding increased the infection rate. The most effective state health policy was to quarantine people who had close contact with confirmed, positive cases. Further, state prisons demonstrated a higher death rate compared to federal prisons. Greater efforts to ameliorate COVID-19 and similar pathogens should be directed at state prisons with lower-level security and prisons with closer contact with the community. Quarantining close-contacts and restricting movements were the most effective state-level responses to reduce infections in prisons during April 2020 to April 2022. [ABSTRACT FROM AUTHOR]

This paper concerns a diffusive predation model with nonlinear growth, cross-diffusion and protection zone terms. The main purpose is to investigate the effects of nonlinear growth and cross-diffusion on the coexistent solution when protection zone is present. Firstly, a priori estimate and the existence of positive solutions are discussed, including local and global existence. Then, some asymptotic properties of coexistent solutions induced by the mortality rate, nonlinear growth of predator and cross-diffusion are analyzed. It is revealed that there exist critical values related to certain principal eigenvalues such that the nonlinear growth, cross-diffusion and protection zone all have significant effects on the coexistent solutions; as far as the nonlinear growth concerned, we find that it has important influences on the coexistence region of two species undoubtedly. Biologically, this implies that these critical values greatly affect the survival of species. [ABSTRACT FROM AUTHOR]

Prostate cancer remains a prevalent health concern, emphasizing the critical need for early diagnosis and precise treatment strategies to mitigate mortality rates. The accurate prediction of cancer grade is paramount for timely interventions. This paper introduces an approach to prostate cancer grading, framing it as a classification problem. Leveraging ResNet models on multi-scale patch-level digital pathology and the Diagset dataset, the proposed method demonstrates notable success, achieving an accuracy of 0.999 in identifying clinically significant prostate cancer. The study contributes to the evolving landscape of cancer diagnostics, offering a promising avenue for improved grading accuracy and, consequently, more effective treatment planning. By integrating innovative deep learning techniques with comprehensive datasets, our approach represents a step forward in the pursuit of personalized and targeted cancer care. [ABSTRACT FROM AUTHOR]

Reliable estimates of age-specific vital rates are crucial in demographic studies, while ages are, in most cases, commonly grouped in bins of five years. Indeed, public health and national systems require single age-specific data to achieve accurate social planning. This paper introduces a deep learning approach for splitting the abridged death rates, providing a more comprehensive perspective on the indirect age-specific vital rates estimation from grouped data. Additionally, we contribute to the existing literature by introducing a multi-population (countries and genders) approach, providing reliable estimates considering the heterogeneity of longevity dynamics over age, years, and across populations. We also contribute to the state of the art in indirect estimation by introducing, for the first time, a multi-population indirect estimation leveraging subnational data. Our model accurately captures mortality dynamics by age over time and among different populations. We prove the model's ability to estimate reliable predictions of age-specific mortality rates by also studying how the hyperparameters' choice affects the model reliability and analyzing the age-specific relative differences between the real and the estimated mortality rates. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic led to 231,841 deaths and 940,243 hospitalisations in England, by the end of March 2023. This paper calculates the real-time infection hospitalisation risk (IHR) and infection fatality risk (IFR) using the Office for National Statistics Coronavirus Infection Survey (ONS CIS) and the Real-time Assessment of Community Transmission Survey between November 2020 to March 2023. The IHR and the IFR in England peaked in January 2021 at 3.39% (95% Credible Intervals (CrI): 2.79, 3.97) and 0.97% (95% CrI: 0.62, 1.36), respectively. After this time, there was a rapid decline in the severity from infection, with the lowest estimated IHR of 0.32% (95% CrI: 0.27, 0.39) in December 2022 and IFR of 0.06% (95% CrI: 0.04, 0.08) in April 2022. We found infection severity to vary more markedly between regions early in the pandemic however, the absolute heterogeneity has since reduced. The risk from infection of SARS-CoV-2 has changed substantially throughout the COVID-19 pandemic with a decline of 86.03% (80.86, 89.35) and 89.67% (80.18, 93.93) in the IHR and IFR, respectively, since early 2021. From April 2022 until March 2023, the end of the ONS CIS study, we found fluctuating patterns in the severity of infection with the resumption of more normative mixing, resurgent epidemic waves, patterns of waning immunity, and emerging variants that have shown signs of convergent evolution. The severity of SARS-CoV-2 infection varied over the course of the pandemic due to factors such as changes in variant characteristics and population immunity from previous infection or vaccination. Here, the authors estimate infection hospitalisation and infection fatality rates in England over time from the start of the pandemic until March 2023. [ABSTRACT FROM AUTHOR]

Cerebral infarction is a common neurological disease with high rates of morbidity, mortality, and recurrence, posing a great threat to human life and health. Cerebral infarction is the second leading cause of death in the world and the leading cause of long-term disability in humans. The results of the third national retrospective sampling survey on causes of death in 2008 showed that cerebral infarction has become the leading cause of death in China and its mortality rate is 4–5 times that of European and American countries. Therefore, this article proposed a study on the predictive value of Cmmi-MHR combined with thromboelastography parameters that was performed for acute cerebral infarction. This paper mainly proposed a high frame rate imaging technology and analyzed its algorithm. In this article, in the experimental part, an in-depth analysis of the predictive value of the Monocyte-to-high-density lipoprotein cholesterol ratio (MHR) combined with thromboelastography parameters was performed for acute cerebral infarction. The final experimental results showed that HDL (OR = 1.695%, P-trend = 0.049) had a probability of death within 90 days of hospitalization (OR = 0.81, 95% CI = 1.06–3.11, P-trend = 0.523). There were no significant differences in mortality rate after 90 days. Regardless of adjusting for confounders such as age, gender, and NIHSS score, there was no significant difference in the risk of MHR or monocyte count within 90 days of hospitalization. The conclusion indicates that the combination of Cmmi-MHR and thromboelastography parameters provides a new perspective and method for the diagnosis and treatment of cerebral infarction, and provides important support for personalized treatment and management of cerebral infarction. [ABSTRACT FROM AUTHOR]

Key Clinical Message: Transverse colonic volvulus (TCV) is a serious condition with a mortality rate of up to 33%. It is very rare, especially in children. Despite its rarity, surgeons should have a high index of suspicion and include it in the list of differential diagnoses, especially in patients with developmental delays and associated uncommon syndromes. Resection and anastomosis, whether as a one‐stage or two‐stage procedure, proved to be the best treatment options for children. Since prompt identification and management are vital, this paper presents useful information on the presentation, treatment, and outcome of this case report. [ABSTRACT FROM AUTHOR]

Botulism is a priority disease worldwide because it has a very severe course of evolution that can lead to death. This paper aims to describe the main epidemiological characteristics of botulism cases confirmed in Romania over 14 years (2007–2020). We performed a retrospective study using the publicly available national surveillance data and reported to the National Institute of Public Health. A total of 325 cases of foodborne botulism were reported in Romania, with no infant or wound botulism. Most of the cases (125, 38.5%) were reported among young adults (25–44 years old), over half (205, 63%) of them living in rural areas. The incriminated food item was identified in 161 cases; in most cases (145, 90%) the food item was prepared in the household. The main food category was represented by meat and meat-based products (94, 68.6%). In almost all cases the identified type was BoNT/B (230/231, 99.5%). Fifteen deaths were recorded, and the case fatality rate was 4.6%. Botulism cases were reported annually in Romania. Surveillance data are essential for implementing control measures and adapting educational campaigns according to existing needs. [ABSTRACT FROM AUTHOR]

Intracranial aneurysms (IAs) occur in 3–5% of the general population and are characterized by localized structural deterioration of the arterial wall with loss of internal elastic lamina and disruption of the media. The risk of incidence and rupture of aneurysms depends on age, sex, ethnicity, and other different factors, indicating the influence of genetic and environmental factors. When an aneurysm ruptures, there is an estimated 20% mortality rate, along with an added 30–40% morbidity in survivors. The alterations in hormonal levels can influence IAs, while the rupture of an aneurysm can have various impacts on endocrine pathways and affect their outcome. This review explores the reciprocal relationship between endocrinological changes (estrogen, growth hormone, and thyroid hormones) and IAs, as well as the effects of aneurysm ruptures on endocrine fluctuations. Based on the data presented in this paper, we recommend further exploration into the influence of hormones on aneurysm formation and rupture. Additionally, we propose conducting endocrine assessments for patients who have experienced a rupture of IAs. Monitoring hormonal changes in patients with IAs could serve as a potential risk factor for rupture, leading to interventions in the approach to managing IAs. [ABSTRACT FROM AUTHOR]

This paper aims to introduce a novel family of probability distributions by the well-known method of the T–X family of distributions. The proposed family is called a "Novel Generalized Exponent Power X Family" of distributions. A three-parameters special sub-model of the proposed method is derived and named a "Novel Generalized Exponent Power Weibull" distribution (NGEP-Wei for short). For the proposed family, some statistical properties are derived including the hazard rate function, moments, moment generating function, order statistics, residual life, and reverse residual life. The well-known method of estimation, the maximum likelihood estimation method is used for estimating the model parameters. Besides, a comprehensive Monte Carlo simulation study is conducted to assess the efficacy of this estimation method. Finally, the model selection criterion such as Akaike information criterion (AINC), the correct information criterion (CINC), the Bayesian information criterion (BINC), the Hannan–Quinn information criterion (HQINC), the Cramer–von-Misses (CRMI), and the ANDA (Anderson–Darling) are used for comparison purpose. The comparison of the NGEP-Wei with other rival distributions is made by Two COVID-19 data sets. In terms of performance, we show that the proposed method outperforms the other competing methods included in this study. [ABSTRACT FROM AUTHOR]

Background: Mortality estimates at the subnational level are of urgent need in India for the formulation of policies and programmes at the district level. This is the first-ever study which used survey data for the estimation of life expectancy at birth () for the 640 districts from NFHS-4 (2015-16) and 707 districts from NFHS-5 (2019-21) for the total, male and female population in India. Methods: This study calculated annual age-specific mortality rates from NFHS-4 and NFHS-5 for India and all 36 states for the total, male and female population. This paper constructed the abridged life tables and estimated life expectancy at birth and further estimated the model parameters for all 36 states. This study linked state-specific parameters to the respective districts for the estimation of life expectancy at birth for 640 districts from NFHS-4 and 707 districts from NFHS-5 for the total, male and female population in India. Results: Findings at the state level showed that there were similarities between the estimated and calculated in most of the states. The results of this article observed that the highest varies in the ranges of 70 to 90 years among the districts of the southern region. falls below 70 years among most of the central and eastern region districts. In the northern region districts lies in the range of 70 years to 75 years. The estimates of life expectancy at birth shows the noticeable variations at the state and district levels for the person, male, and female populations from the NFHS (2015-16) and NFHS (2019-21). In the absence of age-specific mortality data at the district level in India, this study used the indirect estimation method of relating state-specific model parameters with the IMR of their respective districts and estimated across the 640 districts from NFHS-4 (2015-16) and 707 districts from NFHS-5 (2019-21). The findings of this study have similarities with the state-level estimations of from both data sources of SRS and NFHS and found the highest in the southern region and the lowest in the eastern and central region districts. Conclusions: In the lack of estimates at the district level in India, this study could be beneficial in providing timely life expectancy estimates from the survey data. The findings clearly shows variations in the district level . The districts from the southern region show the highest and districts from the central and eastern region has lower . Females have higher as compared to the male population in most of the districts in India. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive analysis of flood frequency and a spatio-temporal characterization of historical torrential floods in the Šumadija region using water discharge datasets and documented events. A chronology of 344 recorded torrential flood events, spanning from 1929 to 2020, illustrates the region's vulnerability, with a death toll exceeding 43. The study defines the intra-annual primary and secondary peaks of torrential flood occurrences and explains their spatial distribution. Furthermore, the identification of suitable probability distribution functions underscores the necessity of tailored approaches for effective flood risk management in this diverse geographical environment. The study employed Flood Frequency Analysis (FFA) and goodness-of-fit tests, including the Kolmogorov–Smirnov (K-S) and Cramér–von Mises (CvM) tests, to assess the frequency and magnitude of flood events and evaluate diverse distribution functions. The main results include the identification of suitable probability distribution functions for each river within the region, emphasizing the need for tailored approaches in flood risk management. Additionally, discharge values for various return periods offer crucial insights for informed decision-making in flood risk management and infrastructure planning. [ABSTRACT FROM AUTHOR]

This paper uses panel data for Canada from 1976 to 2018, across 10 provinces, to reassess the relationship between mortality rates and economic fluctuations. The key contribution of our paper lies in examining the extent to which this relationship is driven by the employment rate (extensive margin) versus average hours worked (intensive margin). We find evidence of procyclical mortality for females at the aggregate level; aggregate male mortality remains largely unaffected by economic fluctuations. Our findings also reveal temporal heterogeneity, as the extensive margin becomes the driving force for female mortality rates during the more recent period (1990 onwards). These findings remain robust when accounting for personal income and pollution. Finally, we find some support for a procyclical relationship for individuals in the working age groups, while mortality exhibits a countercyclical pattern for children (age 0 to 14) and the elderly (age 65 and above). [ABSTRACT FROM AUTHOR]

In this paper, we propose a neural network (NN) architecture of mortality improvement model with cohort effect. We then extend the mortality improvement NN model to consider autoregressive effects, which allows mortality improvement to depend on the lagged mortality rates. The advantage of our NN model setup is that the parameters of period and cohort effects are implicitly estimated by the NN models, and hence, the mortality projection can be obtained without taking the extra steps of selecting and estimating the suitable time-series model for period and cohort effects. Our empirical results suggests that, based on 48 populations in the Human Mortality Database with complete sets of observations from 1950 with the age span of 55–90, the NN models with cohort and autoregressive effects improve the forecast accuracy of mortality rate projections and provide better prediction performance. [ABSTRACT FROM AUTHOR]

For the life insurance industry and pension schemes, mortality projections are critical for accurately managing exposure to longevity risk in terms of both premium setting and reserving. Frailty has been identified as an important latent factor underpinning the evolution of mortality rates. It represents the comorbidities that drive the deterioration of the human body's physiological capacity. In this paper, we propose a stochastic mortality model that incorporates the trend in frailty, and we analyse the gap between the actuarial evaluations of premiums and technical provisions calculated under frailty-based and traditional stochastic mortality models. We observe that the frailty-based model leads to higher levels of uncertainty in estimates and projections (compared to a traditional stochastic mortality model), which is attributed to the explicit modelling of the comorbidities. This leads to proposing a potentially important policy-oriented recommendation: the incorporation of frailty in mortality modelling would allow for the profiling of mortality according to the portfolio in force for the insurer (or pension scheme), thereby mitigating the problem of adverse selection. [ABSTRACT FROM AUTHOR]

In this paper, with the method of epidemic dynamics, we assess the spread and prevalence of COVID-19 after the policy adjustment of prevention and control measure in December 2022 in Taiyuan City in China, and estimate the excess population deaths caused by COVID-19. Based on the transmission mechanism of COVID-19 among individuals, a dynamic model with heterogeneous contacts is established to describe the change of control measures and the population's social behavior in Taiyuan city. The model is verified and simulated by basing on reported case data from November 8th to December 5th, 2022 in Taiyuan city and the statistical data of the questionnaire survey from December 1st to 23rd, 2022 in Neijiang city. Combining with reported numbers of permanent residents and deaths from 2017 to 2021 in Taiyuan city, we apply the dynamic model to estimate theoretical population of 2022 under the assumption that there is no effect of COVID-19. In addition, we carry out sensitivity analysis to determine the propagation character of the Omicron strain and the effect of the control measures. As a result of the study, it is concluded that after adjusting the epidemic policy on December 6th, 2022, three peaks of infection in Taiyuan are estimated to be from December 22nd to 31st, 2022, from May 10th to June 1st, 2023, and from September 5th to October 13th, 2023, and the corresponding daily peaks of new cases can reach 400 000, 44 000 and 22 000, respectively. By the end of 2022, excess deaths can range from 887 to 4887, and excess mortality rate can range from 3.06% to 14.82%. The threshold of the infectivity of the COVID-19 variant is estimated 0.0353, that is if the strain infectivity is above it, the epidemic cannot be control with the previous normalization measures. [ABSTRACT FROM AUTHOR]

This paper incorporates mortality shocks in the scenarios for future mortality rates produced by a stochastic multi‐population mortality model. Hereto, the proposed model combines a decreasing stochastic mortality trend with a mechanism that switches between regimes of low and high volatility. During the high volatility regimes, mortality shocks occur that last from one to several years and temporarily impact the mortality rates before returning to the overall mortality trend. Furthermore, we account for the age‐specific impact of these mortality shocks on mortality rates. Actuaries and risk managers can tailor this scenario generator to their specific needs, risk management objectives, or supervisory requirements. The generated scenarios allow (re)insurers, policymakers, or actuaries to evaluate the effects of different catastrophe risk scenarios on, for example, the calculation of solvency capital requirements. [ABSTRACT FROM AUTHOR]

Recently, accurate forecasting of mortality rates with deep learning models has been investigated in several papers in the actuarial literature. Most of the models proposed to date are not explainable, making it difficult to communicate the basis on which mortality forecasts have been made. We adapt the LocalGLMnet of Richman, R. & Wüthrich, M. V. (2023). [LocalGLMnet: Interpretable deep learning for tabular data. Scandinavian Actuarial Journal2023(1), 71–95] to produce explainable forecasts of mortality rates using locally connected neural networks, and we show that these can be interpreted as autoregressive time-series models of mortality rates. These forecasts are shown to be highly accurate on the Human Mortality Database and the United States Mortality Database. Finally, we show how regularizing the LocalGLMnet can produce improved forecasts, and that by applying auto-encoders, observations of mortality rates can be denoised to improve forecasts even further. [ABSTRACT FROM AUTHOR]

Recently, there is a new stream of mortality forecasting research using the vector autoregressive model with different sparse model specifications. They have been shown to be able to overcome some of the limitations of the more traditional factor models such as the Lee–Carter model. In this paper, we propose a more generalized systematic vector autoregressive framework for modeling and forecasting mortality. Under this framework, we progressively increase the sophistication of the diagonal parameters in the autoregressive matrix and formulate a range of model structures in a systematic fashion. They offer much flexibility for capturing the mortality patterns of different populations. The resulting models produce age coherent forecasts, and their parameters are reasonably interpretable for modelers, demographers, and industry practitioners. Using the mortality data of Australia, Japan, New Zealand, and Taiwan, we demonstrate that the proposed approach generates appropriate forecasts of mortality rates and life expectancies and produces very good performance in the fitting and out‐of‐sample analysis. [ABSTRACT FROM AUTHOR]

Forecasting mortality is challenging. In general, mortality rate forecasting exercises have been based on the supposition that predictors' residuals are random noise. However, issues regarding model selection, misspecification, or the dynamic behavior of the temporal phenomenon lead to biased or underperformed single models. Residual series might present temporal patterns that can still be used to improve the forecasting system. This paper proposes a new recursive direct multi-step Hybrid System for Mortality Forecasting (HyS-MF) that combines the Autoregressive Integrated Moving Average (ARIMA) with Neural Basis Expansion for Time Series Forecasting (N-BEATS). HyS-MF employs (i) ARIMA to model and forecast the mortality rate time series with a recursive approach and (ii) N-BEATS with the direct multi-step approach to learn and forecast the residuals of the linear predictor. The final output is generated by summing ARIMA with the N-BEATS forecasts in each time horizon. HyS-MF achieved an average Mean Absolute Percentage Error (MAPE) less than 1.34% considering all prediction horizons, beating statistical techniques, machine learning, deep learning models, and hybrid systems considering 101 different time series from the French population mortality rate. [ABSTRACT FROM AUTHOR]

Malaria is a significant global health issue, responsible for the highest rates of morbidity and mortality globally. This paper introduces a very effective and precise convolutional neural network (CNN) method that employs advanced deep learning techniques to automate the detection of malaria in images of red blood cells (RBC). Furthermore, we present an emerging and efficient deep learning method for differentiating between cells infected with malaria and those that are not infected. To thoroughly evaluate the efficiency of our approach, we do a meticulous assessment that involves comparing different deep learning models, such as ResNet-50, MobileNet-v2, and Inception-v3, within the domain of malaria detection. Additionally, we conduct a thorough comparison of our proposed approach with current automated methods for malaria identification. An examination of the most current techniques reveals differences in performance metrics, such as accuracy, specificity, sensitivity, and F1 score, for diagnosing malaria. Moreover, compared to existing models for malaria detection, our method is the most successful, achieving an accurate score of 1.00 in all statistical matrices, confirming its promise as a highly efficient tool for automating malaria detection. [ABSTRACT FROM AUTHOR]

Background: In Japan, comprehensive cancer statistics are collected through cancer registries. However, data on urological cancers are rarely summarized or published in research papers. Methods: This retrospective study was performed using publicly available statistical data on urological cancers (prostate cancer [PCa], bladder cancer [BCa], and cancers of kidney and urinary tract [except urinary bladder]) in Japan, including a summary of the Ministry's mortality statistics, cancer incidence statistics from the Regional Cancer Registries through 2015, and the National Cancer Registry statistics from 2016. We examined the incidence and mortality rates of urological cancers stratified by age groups. Results: The number of new cases of PCa, BCa, and cancers of kidney and urinary tract (except urinary bladder) in 2019 was 94,748, 23,383, and 30,458, respectively, and the number of deaths in 2022 was 13,439, 9,598, and 9,795, respectively. The incidence and mortality rates of urological cancers have consistently increased. Since 2000, there has been a noteworthy increase in the mortality rate of urological cancers among individuals aged > 85 years. The incidence and mortality rates of BCa and cancers of kidney and urinary tract (except urinary bladder) were significantly higher in males than in females. Conclusions: Urological cancers in very elderly patients (> 85 years) will become increasingly important in the future. [ABSTRACT FROM AUTHOR]

Breast cancer is a widespread health threat for women globally, often difficult to detect early due to its asymptomatic nature. As the disease advances, treatment becomes intricate and costly, ultimately resulting in elevated fatality rates. Currently, despite the widespread use of advanced machine learning (ML) and deep learning (DL) techniques, a comprehensive diagnosis of breast cancer remains elusive. Most of the existing methods primarily utilize either attention‐based deep models or models based on handcrafted features to capture and gather local details. However, both of these approaches lack the capability to offer essential local information for precise tumor detection. Additionally, the available breast cancer datasets suffer from class imbalance issue. Hence, this paper presents a novel weighted average ensemble network (WA‐ENet) designed for early‐stage breast cancer detection that leverages the ability of ensemble technique over single classifier‐based models for more robust and accurate prediction. The proposed model employs a weighted average‐based ensemble technique, combining predictions from three diverse classifiers. The optimal combination of weights is determined using the hill climbing (HC) algorithm. Moreover, the proposed model enhances overall system performance by integrating deep features and handcrafted features through the use of HOG, thereby providing precise local information. Additionally, the proposed work addresses class imbalance by incorporating borderline synthetic minority over‐sampling technique (BSMOTE). It achieves 99.65% accuracy on BUSI and 97.48% on UDIAT datasets. [ABSTRACT FROM AUTHOR]

This paper examines the impact of Medicaid expansions to parents and childless adults on adult mortality. Specifically, we evaluate the long‐run effects of eight state Medicaid expansions from 1994 through 2005 on all‐cause, healthcare‐amenable, non‐healthcare‐amenable, and HIV‐related mortality rates using state‐level data. We utilize the synthetic control method to estimate effects for each treated state separately and the generalized synthetic control method to estimate average effects across all treated states. Using a 5% significance level, we find no evidence that Medicaid expansions affect any of the outcomes in any of the treated states or all of them combined. Moreover, there is no clear pattern in the signs of the estimated treatment effects. These findings imply that evidence that pre‐ACA Medicaid expansions to adults saved lives is not as clear as previously suggested. [ABSTRACT FROM AUTHOR]

Can 'health resilience' explain international differences in COVID‐19 mortality? This paper aims to understand the significant diversity in mortality rates between countries in terms of their degree of preparedness for the crisis and the underlying health conditions of the population. We integrate COVID‐19 data from the first year of the pandemic with panel data from 2009 to 2017 for 188 countries around the world in order to investigate international variation in COVID‐19 mortality rates. Country‐level data on health, medical, social and policy variables are compared with COVID‐19 mortality rates, with further controls imposed to adjust for infection rate, population and health spending. The results show that prior health conditions, social deprivation and the demography of the country all have significant effects on the mortality rates associated with the virus. The evidence also suggests that countries with higher levels of health‐related policy targets demonstrated lower levels of mortality during the crisis. Finally, we confirm that social habits such as smoking, alcohol consumption and over‐eating create a highly vulnerable group of individuals who were exposed to a greater risk of mortality during the outbreak. [ABSTRACT FROM AUTHOR]

The early identification of gastric cancer holds considerable importance within medicine due to its crucial role in mitigating fatality rates. Currently, artificial identification and annotations using gastroscopic images are the main methods of evaluation. Nevertheless, doctors have significant difficulties implementing these techniques due to the considerable heterogeneity in the visual characteristics of early cancer tumors. Weakness in segmentation remains the biggest obstacle to accurate detection and extraction of the main lesion from the tumor. In this paper, we propose a deep model combining two networks encoded: Unet++ and the feature pyramid network. The encoder backbone on first detection is based on ResNet34, which feeds the feature extraction to the next step. The second step is adding an enhanced feature pyramid network by merging blocks and final segmentation heads. The decoder improves the model's capacity to collect hierarchical characteristics at many levels, resulting in enhanced segmentation performance that adapts to changes in illness symptoms. The proposed model achieved a segmentation accuracy of 96.8%, a dice-score of 86.6%, and an F1-score of 85.3% when using the EDD2020 dataset. While the accuracy for DCSA-Unet achieved 92%, Unet++ 90%, 77% for FPN, and DeepLabv3+ 94.2%, We trained the proposed model on two different datasets, the CVC-ClinicDB and Kvasir-Seg datasets. For CVC-ClinicDB, the results metrics registered a Dice-Score 91.64%, an IoU of 84.63%, and an accuracy of 98.55%. For the Kvasir-Seg dataset, the Dice score is 92.54%, the IoU is 87.57%, and the accuracy is 96.62%. [ABSTRACT FROM AUTHOR]

Purpose: To synthesize the available evidence on the extent to which area-level socioeconomic conditions are associated with drug overdose deaths in the United States. Methods: We performed a systematic review (in MEDLINE, EMBASE, PsychINFO, Web of Science, EconLit) for papers published prior to July 2022. Eligible studies quantitatively estimated the association between an area-level measure of socioeconomic conditions and drug overdose deaths in the US, and were published in English. We assessed study quality using the Effective Public Health Practice Project Quality Assessment Tool. The protocol was preregistered at Prospero (CRD42019121317). Results: We identified 28 studies that estimated area-level effects of socioeconomic conditions on drug overdose deaths in the US. Studies were scored as having moderate to serious risk of bias attributed to both confounding and in analysis. Socioeconomic conditions and drug overdose death rates were moderately associated, and this was a consistent finding across a large number of measures and differences in study designs (e.g., cross-sectional versus longitudinal), years of data analyzed, and primary unit of analysis (e.g., ZIP code, county, state). Conclusions: This review highlights the evidence for area-level socioeconomic conditions are an important factor underlying the geospatial distribution of drug overdose deaths in the US and the need to understand the mechanisms underlying these associations to inform future policy recommendations. The current evidence base suggests that, at least in the United States, employment, income, and poverty interventions may be effective targets for preventing drug overdose mortality rates. [ABSTRACT FROM AUTHOR]

Introduction: Avoidable mortality is widely used by public health researchers to measure population health, and many related methodologies have been proposed for doing so. This scoping review presents a comprehensive view of global peer-reviewed and grey literature exploring the association between socioeconomic status (SES) and avoidable mortality. Methods: We searched Ovid Medline, Scopus, and Web of Science to find articles that investigated SES inequalities in avoidable mortality. We limited our review to articles in English published between 2000 and 2020. For grey literature, we searched leading global and Canadian health information websites. We extracted data on different study characteristics, avoidable mortality definition, SES indicator, method of analysis of the association between avoidable mortality and SES, and main findings of the studies. Results: We identified 34 articles to review, including 29 scientific papers and 5 grey literature documents. The findings of the selected articles consistently indicate a negative association between SES and avoidable mortality rates. Studies have not all used the same definitions of avoidable mortality or SES nor operationalized them in the same way. Conclusion: Our review highlights the absence of a globally standard definition in avoidable mortality health equity research. Additional work to establish a standardized definition is crucial for supporting global comparability. [ABSTRACT FROM AUTHOR]

Background. Intra-arterial chemotherapy (IAC) is considered a unique technique for retinoblastoma (Rb) management and has widespread applicability as a first-line or second-line treatment due to the high globe survival rates. Objectives. This meta-analysis aimed to assess the efficacy of IAC approach among patients with Rb. Materials and methods. This study outlined the most recent research on IAC effectiveness in Rb treatment. We carried out a systematic search for published papers examining IAC treatment among patients with Rb using electronic search engines, including Embase, Web of Science (WoS), PubMed, OVID, and Google Scholar, until October 2021. Results. This meta-analysis included 39 observational studies with 2604 treated eyes and 3112 individuals who were eligible for inclusion. Enucleation rates varied from 0% to 43.7% in the chosen trials, with an odds ratio (OR) of 0.52 (95% confidence interval (95% CI): 0.41-0.66, p < 0.0001). A range of 30-100% was reported for globe salvage across 27 investigations involving 2310 eyes. The estimated OR of globe salvage was 2.41, with 95% CI of 1.6-3.63 and a p-value <0.0001. The combined total effect sizes and the death rate for the proportion of cases with metastatic Rb were as follows: OR = 0.03 (95% CI: 0.03-0.03) and OR = 0.05 (95% CI: 0.04-0.05, p < 0.0001), respectively. Conclusions. Retrospective trials have shown that intra-arterial-based therapy for Rb is an effective choice. Intra-arterial chemotherapy also reduced enucleation and metastasis incidence rates. The paucity of evidence in the literature necessitates further high-level studies. [ABSTRACT FROM AUTHOR]

(1) Background: Among Korean research papers there have been studies on the correlation between tuberculosis-hypertension and diabetes and the correlation between dementia-hypertension and diabetes, but there were no analysis data specifically on tuberculosis and dementia. (2) Methods: A total of 2992 tuberculosis patients in the Gyeongbuk region were analyzed through a final analysis of integrated disease and health management system data collected from 2021 to 2022. In this selection, patients with tuberculosis under 50 years of age and 368 people diagnosed with tuberculosis were excluded. (3) Results: From 2021 to 2022, among the 2992 tuberculosis patients in Gyeongsangbuk-do aged 50 or older, 2722 (91.0%) belonged to the general tuberculosis patient group, while 270 (9.0%) belonged to the dementia–tuberculosis patient group. The average age in the dementia–tuberculosis group was 81.4 years, significantly higher than the general group's average of 75.7 years. Within the dementia–tuberculosis patient group, 235 patients (87.0%) had underlying medical conditions in addition to dementia and tuberculosis. The tuberculosis treatment cure rate was 56.3% (1477 patients) in the general group and 38.9% (105 patients) in the dementia–tuberculosis patient group. (4) Conclusions: The cure rate was notably higher in the general group. Similarly, the mortality rate (deaths due to tuberculosis) was significantly higher in the dementia–tuberculosis patient group (7.0%, 19 patients) compared to the normal group (3.0%, 81 patients). The mortality rate in the dementia group was more than twice that of the general group. [ABSTRACT FROM AUTHOR]

To describe the natural history of spinal gangliogliomas (GG) in order to determine the most appropriate neuro-oncological management. A Medline search for relevant publications up to July 2023 using the key phrase "ganglioglioma spinal" and "ganglioglioma posterior fossa" led to the retrieval of 178 studies. This corpus provided the basis for the present review. As an initial selection step, the following inclusion criteria were adopted: (i) series and case reports on spinal GG; (ii) clinical outcomes were reported specifically for GG; (iii) GG was the only pathological diagnosis for the evaluation of the tumor; (iv) papers written only in English was evaluated; and (v) papers describing each case in the series were included. The World Health Organization (WHO) 2021 grading criteria for gangliogliomas were applied. A total of 107 tumors were evaluated (63 from male patients and 44 from female patients; 1.43 male/1.0 female ratio, mean age 18.34 ± 15.84 years). The most common site was the cervical spine, accounting for 43 cases (40.18%); GTR was performed in 35 cases (32.71%) and STR in 71 cases (66.35%), while this information was not reported in 1 case (0.94%). 8 deaths were reported (7.47%) involving 2 males (25%) and 6 females (75%) aged 4–78 years (mean 34.27 ± 18.22) years. GGs located on the spine displayed the same gender ratio as these tumors in general. The most frequent symptom was pain and motor impairment, while the most prevalent location was the cervical spinal cord. GTR of the tumor posed a challenge for neurosurgeons, due to the difficulty of resecting the lesion without damaging the spinal eloquent area, explaining the lower rate of cure for this tumor type. [ABSTRACT FROM AUTHOR]

In this paper, we propose a simple dynamic mortality model to fit and forecast mortality rates for measuring longevity and mortality risks. This proposal is based on a methodology for modelling interest rates, which assumes that changes in spot interest rates depend linearly on a small number of factors. These factors are identified as interest rates with a given maturity. Similarly, we assume that changes in mortality rates depend linearly on changes in a specific mortality rate, which we call the key mortality rate. One of the main advantages of this model is that it allows the development of an easy to implement methodology to measure longevity and mortality risks using simulation techniques. Particularly, we employ the model to calculate the Value-at-Risk and Conditional-Value-at-Risk of an insurance product testing the accuracy and robustness of our proposal using out-of-sample data from six different populations. [ABSTRACT FROM AUTHOR]

In this paper, we propose a diffusive susceptible-infected-susceptible epidemic model in a spatiotemporally heterogeneous environment. We consider a saturated incidence function of the form S I m + S + I , where m is a nonnegative function. When there is a positive disease-induced mortality rate everywhere, we demonstrate that the disease will always become extinct and the susceptible population will stabilize at zero or a positive constant. In the case where the disease-induced mortality rate is negligible, we examine the model in a time-periodic and spatially heterogeneous environment and establish the threshold dynamics between disease extinction and persistence in terms of the basic reproduction number. Under certain conditions, we show the global attractivity of both the disease-free equilibrium and endemic equilibrium by constructing appropriate Lyapunov functions. Moreover, we determine the spatial distribution of the disease when the diffusion rate of the susceptible or infected population (or both) is sufficiently small. Our findings suggest that the presence of a saturation effect reduces the transmission risk, allows for the total population size to have a substantial impact on disease dynamics, and may significantly alter the spatial distribution of the disease under certain circumstances. [ABSTRACT FROM AUTHOR]

Background A paucity of evidence exists regarding the optimal management for abdominal aortic graft infection. The aim of this paper was to assess short- and long-term outcomes following different surgical options in aortic graft infection patients. Methods Medline, Embase and the Cochrane Library were searched from inception to February 2023. Network meta-analysis was performed using a frequentist method. Patients were divided into four treatment groups: complete graft removal with in situ repair, complete graft removal with extra-anatomic repair, partial graft removal with in situ repair and partial graft removal with extra-anatomic repair. The mortality rate at 30-days and 1-year was the primary outcome. Secondary outcomes were longer-term mortality rate, primary patency and reinfections. For included RCTs, the Cochrane risk-of-bias tool was utilized to assess the risk of bias. The methodological quality of cohort studies was evaluated using the Newcastle–Ottawa scale. Results Among 4559 retrieved studies, 22 studies with 1118 patients (11 multi-arm and 11 single-arm studies) were included. Patients received complete graft removal with in situ repair (N = 852), partial graft removal with in situ repair (N = 36), complete graft removal with extra-anatomic repair (N = 228) and partial graft removal with extra-anatomic repair (N = 2). Both network meta-analysis results and pooled results of multi- and single-arm cohorts indicated that partial graft removal with in situ repair has the lowest 30-day and 1-year mortality rates (0% and 6.1% respectively), followed by complete graft removal with in situ repair (11.9% and 23.8% respectively) and complete graft removal with extra-anatomic repair (16.6% and 41.4% respectively). In addition, complete graft removal with in situ repair had a lower 3-year (complete graft removal with in situ repair versus complete graft removal with extra-anatomic repair: 32.1% versus 90%) and 5-year (complete graft removal with in situ repair versus complete graft removal with extra-anatomic repair: 45.6% versus 67.9%) mortality rate when compared with complete graft removal with extra-anatomic repair. Patients in the complete graft removal with in situ repair group had the lowest reinfections (8%), followed by partial graft removal with in situ repair (9.3%) and complete graft removal with extra-anatomic repair (22.4%). Conclusion Partial graft removal with in situ repair was associated with lower 30-day and 1-year mortality rates when compared with complete graft removal with in situ repair and complete graft removal with extra-anatomic repair. Partial graft removal with in situ repair might be a feasible treatment for specific aortic graft infection patients. [ABSTRACT FROM AUTHOR]