Search

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]

AbstractThe incredible mobilisation to welcome Ukrainian refugees following the start of the Russia-Ukraine war in 2022 demonstrated how housing is an affective sociomaterial infrastructure. In cruel contrast, such mobilisation also exposed the inherently racist and colonial face of accommodating refugees and migrants, and the structural inequalities that racial capitalism continues to reproduce. Drawing reflections across several geographies and temporalities through illustrative vignettes, this paper begs the questions under what conditions do we accept that some people are housed, others not? What kind of power structures allow certain bodies to be welcomed? This paper borrows from postcolonial, black and critical theory to examine refugee housing through the lens of necropolitical infrastructure. It connects Mbembe’s notion of necropower with Power and Mee’s notion of housing as infrastructure of care, and filters this through reflections across Calais, Brescia and Athens, to illustrate the bio- and necropolitical side of the refugee housing infrastructure in Europe. The paper ultimately argues that refugee housing, because it is an infrastructure and because it is part of the broader extractive and exploitative system of (humanitarian) care, is deeply imbricated in the production of racialised subjects. Specifically, it is complicit with the extraction, (re)incorporation, abandonment and slow death of Black and Brown refugee lives. [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]

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]

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]

Despite efforts to reduce stillbirths and neonatal deaths, inconsistent definitions and reporting practices continue to hamper global progress. Existing data frequently being limited in terms of quality and comparability across countries. This paper addresses this critical issue by outlining the new International Classification of Disease (ICD‐11) recommendations for standardized recording and reporting of perinatal deaths to improve data accuracy and international comparison. Key advancements in ICD‐11 include using gestational age as the primary threshold to for reporting, clearer guidance on measurement and recording of gestational age, and reporting mortality rates by gestational age subgroups to enable country comparisons to include similar populations (e.g., all births from 154 days [22+0 weeks] or from 196 days [28+0 weeks]). Furthermore, the revised ICD‐11 guidance provides further clarification around the exclusion of terminations of pregnancy (induced abortions) from perinatal mortality statistics. Implementing standardized recording and reporting methods laid out in ICD‐11 will be crucial for accurate global data on stillbirths and perinatal deaths. Such high‐quality data would both allow appropriate regional and international comparisons to be made and serve as a resource to improve clinical practice and epidemiological and health surveillance, enabling focusing of limited programmatic and research funds towards ending preventable deaths and improving outcomes for every woman and every baby, everywhere. [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]

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]

In this paper, higher-order numerical schemes to the McKendrick–Von Foerster equation are presented when the death rate has singularity at the maximum age. The third, fourth-order schemes that are proposed here are based on the characteristics which are non-intersecting lines in this case, and are multi-step methods with appropriate corrections at each step. In fact, the convergence analysis of these schemes is discussed in detail. Moreover, numerical experiments are provided to validate the orders of convergence of the proposed third-order and fourth-order schemes. • McKendrick-Von Foerster equation with singular mortality is considered. • Designed a higher order scheme. • Convergence of the proposed schemes is established. [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]

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]

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]

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]

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]

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]

Stroke is one of the main diseases endangering the health and life of the middle-aged and elderly, and in recent years it shows a younger trend.It has the characteristics of high incidence, mortality and disability rate. Atherosclerosis is the main intervention target for stroke prevention and treatment.CircRNA is highly expressed in the cerebrovascular system and plays an important regulatory role in the pathogenesis of ischemiareperfusion and ischemic stroke.This paper reviews the mechanism of circRNA and miRNA molecular network in the pathophysiological process of atherosclerosis, ischemic stroke and ischemia-reperfusion injury, so as to provide theoretical support for the application of circRNA in gene diagnosis and drug treatment of atherosclerotic ischemic stroke. [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]

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]

This paper presents a study on a Lotka-Volterra ammensalism model that incorporates the fear effect, which can potentially decrease the birth rate and raise the mortality rate of the species. For the autonomous case, equilibrium points' local and global stability are discussed. For the nonautonomous case, sufficient conditions which ensure the persistence and extinction, and global asymptotic stability of the positive solutions are obtained, respectively. The study has shown that with the increase of the fear effect, the final density of the affected population will decrease, and when the fear effect is large enough, it will cause population's extinction. [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]

The main concern of this paper is providing a flexible discrete model that captures every kind of dispersion (equi-, over- and under-dispersion). Based on the balanced discretization method, a new discrete version of Burr–Hatke distribution is introduced with the partial moment-preserving property. Some statistical properties of the new distribution are introduced, and the applicability of proposed model is evaluated by considering counting series. A new integer-valued autoregressive (INAR) process based on the mixing Pegram and binomial thinning operators with discrete Burr–Hatke innovations is introduced, which can model contagious data properly. The different estimation approaches of parameters of the new process are provided and compared through the Monte Carlo simulation scheme. The performance of the proposed process is evaluated by four data sets of the daily death counts of the COVID-19 in Austria, Switzerland, Nigeria and Slovenia in comparison with some competitor INAR(1) models, along with the Pearson residual analysis of the assessing model. The goodness of fit measures affirm the adequacy of the proposed process in modeling all COVID-19 data sets. The fundamental prediction procedures are considered for new process by classic, modified Sieve bootstrap and Bayesian forecasting methods for all COVID-19 data sets, which is concluded that the Bayesian forecasting approach provides more reliable results. [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]

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]

Policymakers attempt to foster entrepreneurship within European regions yet firm interrelationships across European regions remain understudied. This paper analyses the extent to which past firm births and deaths influence future firm births and deaths across European NUTS 2 regions through the lens of competition, multiplier and Marshall effects. Using a novel multilevel mixed effects regression model, we find evidence for an immediate multiplier effect changing to a competition effect as time passes across European regions and countries. We also identify that significant variations in firm birth and death rates exist across European regions validating the importance of more decentralized regional entrepreneurial policies which has implications for policies like the European Regional Development Fund's (ERDF) Cohesion Plan and the Smart Specialization Strategy. [ABSTRACT FROM AUTHOR]

Purpose: Access to the pineal region has always represented a fascinating challenge to the neurosurgeons. Almost equally thrilling is the historical evolution from the hard beginnings with extremely high mortality rates to the current surgical outcomes, with excellent resection rates without long-term morbidity for most of the patients. The purpose of this paper is to provide an overview of the historical evolution of the occipital interhemispheric transtentorial (OITT) approach and its role in the development of access to the pineal region. Methods: Review of the literature highlights the occipital transtentorial approach from the historical context prior its description and the beginning to the current modifications and new recent insights. Results: The occipital transtentorial approach described initially by Poppen in 1966 has played a key role in the progress and success accessing the pineal area. Conclusion: This historical review aims to highlight the extraordinary effort of those neurosurgeons who guided and led the development of one of the most important approaches to the pineal region. [ABSTRACT FROM AUTHOR]

Since Canada legalized medical assistance in dying (MAID) in 2016, it has become one of the most permissive regimes in the world for euthanasia and assisted suicide. The number of deaths has risen rapidly and the categories of eligibility continue to expand. The country is poised, as of March 2024, to allow MAID for those whose sole underlying condition is mental illness, generating considerable debate. Advocates of MAID for mental illness often frame it as a question of equal access, but this extension involves considerable complexities not present in other cases. This paper examines psychiatric MAID in the Canadian context, engaging directly with the most pertinent arguments of the practice􀂶s advocates. The paper argues that independent of any prior commitments vis-à-vis the permissibility of MAID per se, there is a clear ethical and legal necessity to oppose extending MAID on the grounds of mental illness if we follow the parameters set up within the Canadian regime. The paper advances three arguments: first, that mental illnesses cannot be deemed irremediable, as required by the Canadian law; second, that we cannot establish with adequate certainty that a mentally ill patient has the decision-making capacity to choose MAID; and third, that allowing psychiatric MAID will have a devastating impact on care and support of the mentally ill. [ABSTRACT FROM AUTHOR]

This paper considers the distribution of mortality across social groups classified by caste, gender and sector of origin in India in the mid-2010s: as such, the essay is intended to be both a methodological/measurement-oriented study and a substantive empirical assessment of an important dimension of human functioning in India. The analysis is carried out employing micro-data on the age-distributions of population and death-rates available in the National Family Health Survey of 2015–16 (NFHS-4). Mortality in the paper is measured in terms of the crude death rate, an indicator of "inefficiency" in the age-distribution of deaths, and an "age-adjusted" death rate which takes account of both the mean and the dispersion of a distribution. The last-mentioned indicator is taken to be the preferred measure of mortality. The analysis in the paper suggests that mortality outcomes across castes replicate the caste hierarchy and that there is a sharp rural-urban divide in the distribution of death. Mortality sex-ratios are found to be relatively more favourable for the lower than the higher castes. The results presented in the paper are not unexpected, but they provide quantitative confirmation of one's worst suspicions regarding the skewed distribution of mortality across social groups in India. [ABSTRACT FROM AUTHOR]

Presently, 15 US states require that passenger vehicles undergo periodic safety inspections. Past studies estimating the effectiveness of these safety inspection and maintenance programs (I/M programs) in their stated aim of mitigating road accidents and fatalities have tended to rely on outdated data sets or to focus on specific geographic regions. Since inspection program effectiveness continues to be deliberated in legislative bodies across the country, this paper aims to present a replicable and data-driven quantification of the effects of I/M programs on road fatalities, applying the largest available data set, covering all 50 US states over a 44-year period. Numerous panel data regressions showed a negative correlation between the presence of state I/M programs and the fleet size–adjusted roadway fatality rate. Fixed-effects (FE) estimates suggest that states with I/M programs had 5.5% fewer roadway fatalities per 100,000 registered passenger vehicles—95% confidence interval (CI): 0.4% to 10.6%—nationwide based on nearly exhaustive data for fatal passenger vehicle accidents from 1980 to 2015. These results are complemented by an additional FE specification with fewer variables, but over a longer period. A two-stage least-squares specification is also presented that not only supports this finding but also implies a causal relationship between the presence of I/M programs and lower road fatality rates. The convergent results of the regressions presented in this paper provide compelling evidence that jurisdictions experience lower roadway fatality rates due to the presence of an active safety I/M program for passenger vehicles. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to assess the economic impact of uniform COVID-controlling policies that were implemented by the US government in 2020 and compare it with hypothetical targeted policies that consider the heterogenous effect of COVID-19 on different age groups. Design/methodology/approach: The author began by showing that the adjusted SEQIHR model is a good fit to the US COVID-induced daily death data in that it can capture the nonlinearities of the data very well. Then, he used this model with extra parameters to evaluate the economic effects of COVID-19 through its impact on the job market. Findings: The results show that targeted COVID-controlling policies could reduce the US death rate and GDP loss to 0.03% and 2%, respectively. By comparing these results with uniform COVID-controlling policies, which led to a 0.1% death rate and 3.5% GDP loss, we could conclude that the death rate reduction is 0.07%. Approximately 378,000 Americans died because of COVID-19 during 2020, therefore, reducing the death rate to 0.03% means saving a significant proportion of the COVID-19 casualties, around 280,000 lives. Originality/value: To the best of the author's knowledge, this paper is the first study to assess the economic impacts of COVID-controlling policies by using the multirisk SEQIHR model. [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]

This paper introduces a Nearly Unstable INteger‐valued AutoRegressive Conditional Heteroscedastic (NU‐INARCH) process for dealing with count time series data. It is proved that a proper normalization of the NU‐INARCH process weakly converges to a Cox–Ingersoll–Ross diffusion in the Skorohod topology. The asymptotic distribution of the conditional least squares estimator of the correlation parameter is established as a functional of certain stochastic integrals. Numerical experiments based on Monte Carlo simulations are provided to verify the behavior of the asymptotic distribution under finite samples. These simulations reveal that the nearly unstable approach provides satisfactory and better results than those based on the stationarity assumption even when the true process is not that close to nonstationarity. A unit root test is proposed and its Type‐I error and power are examined via Monte Carlo simulations. As an illustration, the proposed methodology is applied to the daily number of deaths due to COVID‐19 in the United Kingdom. [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]

Predicting mortality rates is a crucial issue in life insurance pricing and demographic statistics. Traditional approaches, such as the Lee-Carter model and its variants, predict the trends of mortality rates using factor models, which explain the variations of mortality rates from the perspective of ages, gender, regions, and other factors. Recently, deep learning techniques have achieved great success in various tasks and shown strong potential for time-series forecasting. In this paper, we propose a modified Transformer architecture for predicting mortality rates in major countries around the world. Through the multi-head attention mechanism and positional encoding, the proposed Transformer model extracts key features effectively and thus achieves better performance in time-series forecasting. By using empirical data from the Human Mortality Database, we demonstrate that our Transformer model has higher prediction accuracy of mortality rates than the Lee-Carter model and other classic neural networks. Our model provides a powerful forecasting tool for insurance companies and policy makers. [ABSTRACT FROM AUTHOR]

In many environments, predators have significantly longer lives and meet several generations of prey, or the prey population reproduces rapidly. The slow–fast effect can best describe such predator–prey interactions. The slow–fast effect ε can be considered as the ratio between the predator's linear death rate and the prey's linear growth rate. This paper examines a slow–fast, discrete predator–prey interaction with prey refuge and herd behavior to reveal its complex dynamics. Our methodology employs the eigenvalues of the Jacobian matrix to examine the existence and local stability of fixed points in the model. Through the utilization of bifurcation theory and center manifold theory, it is demonstrated that the system undergoes period-doubling bifurcation and Neimark–Sacker bifurcation at the positive fixed point. The hybrid control method is utilized as a means of controlling the chaotic behavior that arises from these bifurcations. Moreover, numerical simulations are performed to demonstrate that they are consistent with analytical conclusions and to display the complexity of the model. At the interior fixed point, it is shown that the model undergoes a Neimark–Sacker bifurcation for larger values of the slow–fast effect parameter by using the slow–fast effect parameter ε as the bifurcation parameter. This is reasonable since a large ε implies an approximate equality in the predator's death rate and the prey's growth rate, automatically leading to the instability of the positive fixed point due to the slow–fast impact on the predator and the presence of prey refuge. [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]

Computer vision methods for non-contact detection of herd pigs could help detect early disease and reduce mortality rates by analyzing pig behavior. Due to the limitation of breeding space and cost, the unit breeding area is relatively dense, making it difficult to detect all pigs for a long time without interruption. In order to improve the detection performance, this paper proposes an end-to-end efficient and accurate herd pig detection framework based on YOLOv7 target detection model, which is named Ghost-YOLOv7-SIoU. In this framework, the feature extraction backbone network consists of a series of directly connected efficient layer aggregation networks (ELAN) and downsampling modules. The neck network contains a feature pyramid network and path aggregation network. Ghost convolution is adopted to replace the 3 × 3 standard convolution of the ELAN module in backbone network and the scaled-up ELAN module in neck network to obtain rich features while reducing the parameter number and computational effort. Furthermore, to speed up the model convergence and improve the model robustness and accuracy, SIoU loss is used for bounding box regression in the training stage. On the VOC2012 dataset, the number of parameters and floating-point operations decreased by 13.4% and 15.7% compared to YOLOv7, with comparable detection accuracy. Additionally, the number of parameters and floating-point operations decreased by 13.7% and 16.1% on our pig dataset. Ghost-YOLOv7-SIoU is superior to YOLOV4-CSP and YOLOR-CSP in accuracy. Experimental results demonstrate the effectiveness of the proposed method in improving the efficiency of model detection while ensuring detection accuracy. [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]

In this paper, the Bayesian inference of the model of dependent lives is considered. We use the bivariate Gompertz (BGP) distribution. This new bivariate survival model and its extension have recently been proposed by Shoaee and Khorram. This model is more flexible and can be applied in the actuarial science of life insurance, and reliability theory in competing risks and shock models. As we know, the maximum likelihood estimates do not always exist and therefore cannot always be calculated. So, the Bayesian estimations are considered using the squared error loss function and a priori distributions that create a dependency between the hyper-parameters. But given the assumptions, one can see that explicit expressions cannot be obtained for Bayesian estimations. The importance sampling method is proposed to calculate the Bayes estimations and also to create the corresponding HPD credible intervals of the unknown parameters. The Monte Carlo simulation studies and analysis of a real data set are also performed to evaluate the proposed method for estimating parameters. Finally, a generalization of the dependent lives models is presented and the Bayesian estimate for this new bivariate family distribution is introduced and a comparison is made between the models of this new family. [ABSTRACT FROM AUTHOR]