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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]

Background: In the global aging, the coronavirus disease 2019 (COVID-19) pandemic may have affected the place of death (PoD) in Japan, where hospital deaths have dominated for decades. We analyzed the PoD trends before and during the COVID-19 pandemic in Japan. Methods: This nationwide observational study used vital statistics based on death certificates from Japan between 1951 and 2021. The proportion of PoD; deaths at home, hospitals, and nursing homes; and annual percentage change (APC) were estimated using joinpoint regression analysis. Analyses were stratified by age groups and causes of death. Results: After 2019, home deaths exhibited upward trends, while hospital death turned into downward trends. By age, no significant trend change was seen in the 0–19 age group, while hospital deaths decreased in the 20–64 age group in 2019. The trend change in home death in the ≥65 age group significantly increased since 2019 with an APC of 12.3% (95% confidence interval [CI]: 9.0 to 15.7), while their hospital death trends decreased by −4.0% (95% CI: −4.9 to −3.1) in 2019−2021. By cause of death, home death due to cancer and the old age increased since 2019 with an APC of 29.3% (95% CI: 25.4 to 33.2) and 8.8% (95% CI: 5.5 to 12.2), respectively. Conclusion: PoD has shifted from hospital to home during the COVID-19 pandemic in Japan. The majority of whom were older population with cancer or old age. [ABSTRACT FROM AUTHOR]

The clinical characteristics and long-term outcomes of patients with ischemic stroke (IS) and atrial fibrillation detected after stroke (AFDAS) have not been clearly established. Previous studies evaluating patients with AFDAS were limited by the low prescription rates of anticoagulants and short follow-up periods. Consecutive patients hospitalized for IS between 2014 and 2017 were identified from a National Health Insurance Research Database. The included patients were categorized into three groups: (1) known diagnosis of AF (KAF) before the index stroke, (2) AFDAS, and (3) without AF (non-AF). Univariable and multivariable Cox regression analyses were performed to estimate the hazard ratio (HR) for independent variables and recurrent IS, hemorrhagic stroke, or all-cause mortality. We identified 158,909 patients with IS of whom 16,699 (10.5%) had KAF and 7,826 (4.9%) had AFDAS. The patients with AFDAS were younger, more often male, and had lower CHA2DS2-VASc scores (3.8 ± 1.9 versus 4.9 ± 1.8, p < 0.001) than the patients with KAF. Anticoagulant treatment significantly reduced the risks of all outcomes. The standardized mortality rates were 40.4, 28.6, and 18.4 (per 100 person-years) for the patients with KAF, AFDAS, and non-AF, respectively. Compared with AFDAS, KAF was associated with lower risks of recurrent IS [hazard ratio (HR): 0.91, 95% confidence interval (CI): 0.86–0.97, p < 0.01] and hemorrhagic stroke (HR: 0.88, 95% CI: 0.79–0.99, p < 0.01) and a higher risk of all-cause mortality (HR: 1.11, 95% CI: 1.07–1.16, p < 0.001). The risks of recurrent IS and hemorrhagic stroke were higher and of all-cause mortality was lower for patients with AFDAS than with KAF. There is a strong need to refine treatment modalities to reduce the high mortality in patients with KAF and AFDAS. [ABSTRACT FROM AUTHOR]

COVID-19 is an infectious disease that continues to spread worldwide. A precise estimation of the cases and deaths due to COVID-19 would allow for appropriate consideration of healthcare resource allocation, public health response, and vaccination and economic planning, to minimize social damage. In this study, we analyzed the progression of COVID-19 cases and deaths until January 2022 in 156 countries using a nonlinear mixed-effect model based on the SIR framework. Given the major changes in mortality from infection, risk of re-infection and social responses, the analysis was limited to the period before the emergence of the Omicron variant. The impact of infection prevention measures in various countries was assessed, with a specific focus on estimating the effectiveness of lockdowns, where the effect was assumed to change over time. By accounting for excess mortality, our analysis allowed the estimation of unreported cases and deaths, and thus providing a more comprehensive understanding of the impact of pandemic. In the analysis, we identified gross domestic product (GDP), proportion of people aged 65 years or older, latitude of the capital city on transmissibility of infection, and city population and cardiovascular death rate on mortality rate as significant influencing factors. Furthermore, the differences in transmissibility and mortality rates by variants and the effect of vaccination on the mortality rate were assessed. The transmissibility has increased by odds ratios of 1.2 to 1.4 in Beta, Gamma, and Delta variants; mortality rate has increased by odds ratios of 1.7, 2.2, and 1.4 in Beta, Gamma, and Delta variants, respectively; and vaccination decreased the mortality rate by odds ratios of 0.4 and 0.1 in Delta and other variants, respectively. [ABSTRACT FROM AUTHOR]

Cervical cancer has high incidence and mortality rates, especially in less-developed countries. Prevention methods are well established, but there are still barriers preventing some Brazilian women from undergoing a Pap sample. The objective of the study was to evaluate the acceptance, preferences and completion of four screening methods. This has an experimental design (community trial). A total of 164 participants who had never had a Pap sample or had not had one for more than three years were included. The city's urban area was stratified by census tracts and divided according to income and education levels. Women belonging to the lower-income strata were considered in the study. Random blocks were numbered into five intervention groups (Group 1- Pap sample at the hospital; Group 2- Pap sample in the mobile unit; Group 3- urine self-collection; Group 4- vaginal self-collection; Group 5- woman's choice). Only 164 women met all of the eligibility criteria (15.3%). Most of them accepted the assigned method (92%), but only 84% of the women completed the collection step. The acceptance rates were as follows: Group 1 (100%), Group 2 (64.5%), Group 3 (100%) and Group 4 (91.4%). In Group 5, the women's preferences were distributed as follows: examination performed at the hospital, 13 women (33.3%); examination performed at the mobile unit, 11 women (28.2%); urine self-collection, 11 women (28.2%); and vaginal self-collection, 4 women (10.3%). This study suggests that methods that allow cervical sampling collected near the women's domicile might improve the acceptance and completion of preventive tests. This finding is relevant for the development of new cervical cancer screening strategies. [ABSTRACT FROM AUTHOR]

Background: Annual global data on mental disorders prevalence and firearm death rates for 2000–2019, enables the U.S. to be compared with comparable counties for these metrics. Methods: The Institute for Health Metrics and Evaluation (IHME) Global Health Burden data were used to compare the prevalence of mental disorders with overall, homicide and suicide firearm death rates including homicides and suicides, in high sociodemographic (SDI) countries. Results: Overall and in none of the nine major categories of mental disorders did the U.S. have a statistically-significant higher rate than any of 40 other high SDI countries during 2019, the last year of available data. During the same year, the U.S. had a statistically-significant higher rate of all deaths, homicides, and suicides by firearm (all p<<0.001) than all other 40 high SDI countries. Suicides accounted for most of the firearm death rate differences between the U.S. and other high SDI countries, and yet the prevalence of mental health disorders associated with suicide were not significantly difference between the U.S. and other high SDI countries. Conclusion: Mental disorder prevalence in the U.S. is similar in all major categories to its 40 comparable sociodemographic countries, including mental health disorders primarily associated with suicide. It cannot therefore explain the country's strikingly higher firearm death rate, including suicide. Reducing firearm prevalence, which is correlated with the country's firearm death rate, is a logical solution that has been applied by other countries. [ABSTRACT FROM AUTHOR]

Background: Globally, with a neonatal mortality rate of 27/1000 live births, Sub-Saharan Africa has the highest rate in the world and is responsible for 43% of all infant fatalities. In the first week of life, almost three-fourths of neonatal deaths occur and about one million babies died on their first day of life. Previous studies lack conclusive evidence regarding the overall estimate of early neonatal mortality in Sub-Saharan Africa. Therefore, this review aimed to pool findings reported in the literature on magnitude of early neonatal mortality in Sub-Saharan Africa. Methods: This review's output is the aggregate of magnitude of early neonatal mortality in sub-Saharan Africa. Up until June 8, 2023, we performed a comprehensive search of the databases PubMed/Medline, PubMed Central, Hinary, Google, Cochrane Library, African Journals Online, Web of Science, and Google Scholar. The studies were evaluated using the JBI appraisal check list. STATA 17 was employed for the analysis. Measures of study heterogeneity and publication bias were conducted using the I2 test and the Eggers and Beggs tests, respectively. The Der Simonian and Laird random-effect model was used to calculate the combined magnitude of early neonatal mortality. Besides, subgroup analysis, sensitivity analysis, and meta regression were carried out to identify the source of heterogeneity. Results: Fourteen studies were included from a total of 311 articles identified by the search with a total of 278,173 participants. The pooled magnitude of early neonatal mortality in sub-Saharan Africa was 80.3 (95% CI 66 to 94.6) per 1000 livebirths. Ethiopia had the highest pooled estimate of early neonatal mortality rate, at 20.1%, and Cameroon had the lowest rate, at 0.5%. Among the included studies, both the Cochrane Q test statistic (χ2 = 6432.46, P <0.001) and I2 test statistic (I2 = 99.80%, p <0.001) revealed statistically significant heterogeneity. Egger's weighted regression (p <0.001) and funnel plot show evidence of publication bias in this meta-analysis. Conclusion: This review demonstrated that the pooled magnitude of early neonatal mortality in sub-Saharan Africa is substantial. Therefore, governmental and nongovernmental agencies, international organizations, healthcare providers and institutions and academic and research institutions should give a due attention and design strategies to reduce early neonatal mortality in Sub-Saharan Africa. [ABSTRACT FROM AUTHOR]

We conduct this research with a two-fold aim: providing a quantitative analysis of the opioid epidemic in the United States (U.S.), and exploring the impact of the COVID-19 pandemic on opioid-related mortality. The duration and persistence of the opioid epidemic lends itself to the need for an overarching analysis with extensive scope. Additionally, studying the ramifications of these concurrent severe public health crises is vital for informing policies to avoid preventable mortality. Using data from CDC WONDER, we consider opioid-related deaths grouped by Census Region spanning January 1999 to October 2022 inclusive, and later add on a demographic component with gender-stratification. Through the lens of key events in the opioid epidemic, we build an interrupted time series model to reveal statistically significant drivers of opioid-related mortality. We then employ a counterfactual to approximate trends in the absence of COVID-19, and estimate excess opioid-related deaths (defined as observed opioid-related deaths minus projected opioid-related deaths) associated with the pandemic. According to our model, the proliferation of fentanyl contributed to sustained increases in opioid-related death rates across three of the four U.S. census regions, corroborating existing knowledge in the field. Critically, each region has an immediate increase to its opioid-related monthly death rate of at least 0.31 deaths per 100,000 persons at the start of the pandemic, highlighting the nationwide knock-on effects of COVID-19. There are consistent positive deviations from the expected monthly opioid-related death rate and a sizable burden from cumulative excess opioid-related deaths, surpassing 60,000 additional deaths nationally from March 2020 to October 2022, ∼70% of which were male. These results suggest that robust, multi-faceted measures are even more important in light of the COVID-19 pandemic to prevent overdoses and educate users on the risks associated with potent synthetic opioids such as fentanyl. [ABSTRACT FROM AUTHOR]

The Gull Alpha Power Lomax distribution is a new extension of the Lomax distribution that we developed in this paper (GAPL). The proposed distribution's appropriateness stems from its usefulness to model both monotonic and non-monotonic hazard rate functions, which are widely used in reliability engineering and survival analysis. In addition to their special cases, many statistical features were determined. The maximum likelihood method is used to estimate the model's unknown parameters. Furthermore, the proposed distribution's usefulness is demonstrated using two medical data sets dealing with COVID-19 patients' mortality rates, as well as extensive simulated data applied to assess the performance of the estimators of the proposed distribution. [ABSTRACT FROM AUTHOR]

Video-microscopy is a technology widely used to follow, in a single cell manner, cell behavior. A number of new studies are searching a way to track these behaviors by artificial intelligence; unfortunately some real-time events still have to be track manually. For that reason, we developed a software that helps the experimenter to analyze collected data. Toto-cell is very simple to use and it can be adapted at different type of analyses or treatments. It allows a wide new range of parameters that were nearly impossible to calculate only by hand. We thus developed this new software using HEC-1-A endometrial cell line to track different cellular parameters such as: the number of normal/abnormal mitosis, the ratio per day of death, mitosis, cell fusions or finally the length between two mitosis cycles. We treated our cells with cisplatin, doxorubicin or AZD5363 (an Akt inhibitor) to obtain different cellular events. What emerged is a huge heterogeneity for these analyzed parameters between the cells in a single treatment which is clearly demonstrated by the results provided by Toto-Cell. In conclusion, our software is an important tool to facilitate the analysis of video-microscopy, in a quantifying and qualifying manner. It enables a higher accuracy when compared to manual calculations. [ABSTRACT FROM AUTHOR]

The real-time monitoring on the risk status of the vehicle and its driver can provide the assistance for the early detection and blocking control of single-vehicle accidents. However, complex risk coupling relationship is one of the main features of single-vehicle accidents with high mortality rate. On the basis of investigating the coupling effect among multi-risk factors and establishing a safety management database throughout the life cycle of vehicles, single-vehicle driving risk network (SVDRN) with a three-level threshold was developed, and its topology features were analyzed to assessment the importance of nodes. To avoid the one-sidedness of single indicator, the multi-attribute comprehensive evaluation model was applied to measure the comprehensive effect of characteristic indicators for nodes importance. A algorithm for real-time monitoring of vehicle driving risk status was proposed to identify key risk chains. The result revealed that improper operation, speeding, loss of vehicle control and inefficient driver management were the sequence of top four risk factors in the comprehensive evaluation result of nodes importance (mean value = 0.185, SD = 0.119). There were minor differences of 0.017 in the node importance among environmental factors, among which non-standard road alignment had the larger value. The improper operation and non-standard road alignment were the highest combination correlation of factors affecting road safety, with the support of 51.81% and the confidence of 69.35%. This identification algorithm of key risk chains that combines node importance and its risk state threshold can effectively determine the high-frequency risk transmission paths and risk factors through multi-vehicle test, providing a basis for centralization management of transport enterprises. [ABSTRACT FROM AUTHOR]

There are few studies comparing proportion, frequency, mortality and mortality rate following antimicrobial-resistant (AMR) infections between tertiary-care hospitals (TCHs) and secondary-care hospitals (SCHs) in low and middle-income countries (LMICs) to inform intervention strategies. The aim of this study is to demonstrate the utility of an offline tool to generate AMR reports and data for a secondary data analysis. We conducted a secondary-data analysis on a retrospective, multicentre data of hospitalised patients in Thailand. Routinely collected microbiology and hospital admission data of 2012 to 2015, from 15 TCHs and 34 SCHs were analysed using the AMASS v2.0 (www.amass.website). We then compared the burden of AMR bloodstream infections (BSI) between those TCHs and SCHs. Of 19,665 patients with AMR BSI caused by pathogens under evaluation, 10,858 (55.2%) and 8,807 (44.8%) were classified as community-origin and hospital-origin BSI, respectively. The burden of AMR BSI was considerably different between TCHs and SCHs, particularly of hospital-origin AMR BSI. The frequencies of hospital-origin AMR BSI per 100,000 patient-days at risk in TCHs were about twice that in SCHs for most pathogens under evaluation (for carbapenem-resistant Acinetobacter baumannii [CRAB]: 18.6 vs. 7.0, incidence rate ratio 2.77; 95%CI 1.72–4.43, p<0.001; for carbapenem-resistant Pseudomonas aeruginosa [CRPA]: 3.8 vs. 2.0, p = 0.0073; third-generation cephalosporin resistant Escherichia coli [3GCREC]: 12.1 vs. 7.0, p<0.001; third-generation cephalosporin resistant Klebsiella pneumoniae [3GCRKP]: 12.2 vs. 5.4, p<0.001; carbapenem-resistant K. pneumoniae [CRKP]: 1.6 vs. 0.7, p = 0.045; and methicillin-resistant Staphylococcus aureus [MRSA]: 5.1 vs. 2.5, p = 0.0091). All-cause in-hospital mortality (%) following hospital-origin AMR BSI was not significantly different between TCHs and SCHs (all p>0.20). Due to the higher frequencies, all-cause in-hospital mortality rates following hospital-origin AMR BSI per 100,000 patient-days at risk were considerably higher in TCHs for most pathogens (for CRAB: 10.2 vs. 3.6,mortality rate ratio 2.77; 95%CI 1.71 to 4.48, p<0.001; CRPA: 1.6 vs. 0.8; p = 0.020; 3GCREC: 4.0 vs. 2.4, p = 0.009; 3GCRKP, 4.0 vs. 1.8, p<0.001; CRKP: 0.8 vs. 0.3, p = 0.042; and MRSA: 2.3 vs. 1.1, p = 0.023). In conclusion, the burden of AMR infections in some LMICs might differ by hospital type and size. In those countries, activities and resources for antimicrobial stewardship and infection control programs might need to be tailored based on hospital setting. The frequency and in-hospital mortality rate of hospital-origin AMR BSI are important indicators and should be routinely measured to monitor the burden of AMR in every hospital with microbiology laboratories in LMICs. [ABSTRACT FROM AUTHOR]

Background: The regional disparity of heatwave-related mortality over a long period has not been sufficiently assessed across the globe, impeding the localisation of adaptation planning and risk management towards climate change. We quantified the global mortality burden associated with heatwaves at a spatial resolution of 0.5°×0.5° and the temporal change from 1990 to 2019. Methods and findings: We collected data on daily deaths and temperature from 750 locations of 43 countries or regions, and 5 meta-predictors in 0.5°×0.5° resolution across the world. Heatwaves were defined as location-specific daily mean temperature ≥95th percentiles of year-round temperature range with duration ≥2 days. We first estimated the location-specific heatwave-mortality association. Secondly, a multivariate meta-regression was fitted between location-specific associations and 5 meta-predictors, which was in the third stage used with grid cell-specific meta-predictors to predict grid cell-specific association. Heatwave-related excess deaths were calculated for each grid and aggregated. During 1990 to 2019, 0.94% (95% CI: 0.68–1.19) of deaths [i.e., 153,078 cases (95% eCI: 109,950–194,227)] per warm season were estimated to be from heatwaves, accounting for 236 (95% eCI: 170–300) deaths per 10 million residents. The ratio between heatwave-related excess deaths and all premature deaths per warm season remained relatively unchanged over the 30 years, while the number of heatwave-related excess deaths per 10 million residents per warm season declined by 7.2% per decade in comparison to the 30-year average. Locations with the highest heatwave-related death ratio and rate were in Southern and Eastern Europe or areas had polar and alpine climates, and/or their residents had high incomes. The temporal change of heatwave-related mortality burden showed geographic disparities, such that locations with tropical climate or low incomes were observed with the greatest decline. The main limitation of this study was the lack of data from certain regions, e.g., Arabian Peninsula and South Asia. Conclusions: Heatwaves were associated with substantial mortality burden that varied spatiotemporally over the globe in the past 30 years. The findings indicate the potential benefit of governmental actions to enhance health sector adaptation and resilience, accounting for inequalities across communities. Qi Zhao and colleagues modelled the global, regional and national burden of heatwave-related mortality from 1990 to 2019. Author summary: Why was this study done?: Although exposure to heatwave has been associated with increased risk of excess deaths and certain temporal attenuation observed, the evidence mainly comes from limited locations. The regional disparity in heatwave-related mortality burden over a long period has not been adequately quantified across the globe. What did the researchers do and find?: Heatwave-related excess deaths were predicted to each grid cell (0.5°×0.5°) across the globe between 1990 and 2019 using a three-stage modelling framework. During 1990 to 2019 warm seasons, 153,078 deaths were associated with heatwaves (nearly half in Asia), which accounted for 0.94% of all deaths and equated 236 deaths per 10 million residents. The global heatwave-related excess death rate declined by 7.2% per decade in comparison to the 30-year average. Heatwave-related mortality showed complex regional disparities, such that Southern and Eastern Europe or areas had polar and alpine climates, and/or their residents had high incomes had the greatest cumulative burden while certain locations in Oceania or those with tropical climate or low incomes were observed with the greatest decline over decades. What do these findings mean?: These regional disparities suggest the necessity of considering the cumulation and temporal change of mortality burden for evaluating the historical health impact of climate change and developing time-efficient adaptation strategies for a certain area. Localised adaptation planning and risk management should be encouraged across all levels of government, with considering subnational capacities and inequalities. Lack of time-series death data from certain regions, e.g., Arabian Peninsula and South Asia, may reduce the prediction accuracy in those regions. [ABSTRACT FROM AUTHOR]

Background: Tuberculosis (TB) and COVID-19 co-infection poses a significant global health challenge with increased fatality rates and adverse outcomes. However, the existing evidence on the epidemiology and treatment of TB-COVID co-infection remains limited. Methods: This updated systematic review aimed to investigate the prevalence, fatality rates, and treatment outcomes of TB-COVID co-infection. A comprehensive search across six electronic databases spanning November 1, 2019, to January 24, 2023, was conducted. The Joanna Briggs Institute Critical Appraisal Checklist assessed risk of bias of included studies, and meta-analysis estimated co-infection fatality rates and relative risk. Results: From 5,095 studies screened, 17 were included. TB-COVID co-infection prevalence was reported in 38 countries or regions, spanning both high and low TB prevalence areas. Prevalence estimates were approximately 0.06% in West Cape Province, South Africa, and 0.02% in California, USA. Treatment approaches for TB-COVID co-infection displayed minimal evolution since 2021. Converging findings from diverse studies underscored increased hospitalization risks, extended recovery periods, and accelerated mortality compared to single COVID-19 cases. The pooled fatality rate among co-infected patients was 7.1% (95%CI: 4.0% ~ 10.8%), slightly lower than previous estimates. In-hospital co-infected patients faced a mean fatality rate of 11.4% (95%CI: 5.6% ~ 18.8%). The pooled relative risk of in-hospital fatality was 0.8 (95% CI, 0.18–3.68) for TB-COVID patients versus single COVID patients. Conclusion: TB-COVID co-infection is increasingly prevalent worldwide, with fatality rates gradually declining but remaining higher than COVID-19 alone. This underscores the urgency of continued research to understand and address the challenges posed by TB-COVID co-infection. Author summary: Tuberculosis (TB) and COVID-19, both highly infectious diseases, have posed significant global health challenges, particularly in low/middle-income countries (LMICs) with limited medical resources. Our research highlights that TB-COVID co-infection remains a substantial concern, impacting regions with varying TB burdens. The predominant treatment approach for TB-COVID co-infection has not notably evolved since our earlier study in 2021. It typically involves a combination of the recommended TB regimen and standard COVID-19 treatment. Our analysis consistently shows that individuals with TB-COVID co-infection are at heightened risk of hospitalization, protracted recovery periods, and accelerated mortality compared to those with sole COVID-19 infections. Remarkably, we found limited information on the post-COVID-19 condition of co-infected patients. One study indicated a higher prevalence of anxiety symptoms, highlighting the potential psychological toll of TB-COVID co-infection. Although the fatality rate has gradually decreased, it remains notably higher than that of COVID-19 alone. Our findings underscore the urgent need for global collaboration to address the complex challenges posed by TB-COVID co-infection, particularly in countries with limited medical resources. [ABSTRACT FROM AUTHOR]

The age-standardized incidence of head trauma in 2016 was 369 per 100,000 people worldwide. The Western Pacific region, including Japan, had the highest incidence. This study aimed to extract ICD-10 code data for intracranial injury (S06) and external causes of morbidity and mortality (V01–Y89), analyze their characteristics and interrelationships, and contribute to these diseases' prevention, treatment, and prognosis. The number of deaths according to injury type and external cause type of intracranial injury published by the Japanese government was statistically analyzed using JoinPoint, and univariate distribution and multivariate correlation were conducted using JMP Software. From 1999–2021, there was a downward trend in the number of deaths because of intracranial injuries: mortality from intracranial injuries was higher among those aged ≥65 years. Conversely, mortality from intracranial injuries was lower among those aged ≤14 years. Among deaths from intracranial injury, mortality from diffuse brain injury and traumatic subdural hemorrhage was more common. Among deaths from external causes of intracranial injury, mortality from falls, transport accidents, and other unforeseen accidents was more common. Mortality because of intracranial injuries increased significantly during the 2011 Great East Japan Earthquake. For some age groups and sexes, there were significant inverse correlations of mortality with traumatic subdural hemorrhage and traumatic subarachnoid hemorrhage for transport accidents, intentional self-harm and assault, and diffuse brain injury and focal brain injury for falls. We believe that the data presented in this study will be useful for preventing and treating intracranial injuries and for developing administrative measures to reduce intracranial injuries. [ABSTRACT FROM AUTHOR]

The U.S. Transuranium and Uranium Registries performs autopsies on each of its deceased Registrants as a part of its mission to follow up occupationally-exposed individuals. This provides a unique opportunity to explore death certificate misclassification errors, and the factors that influence them, among this small population of former nuclear workers. Underlying causes of death from death certificates and autopsy reports were coded using the 10th revision of the International Classification of Diseases (ICD-10). These codes were then used to quantify misclassification rates among 268 individuals for whom both full autopsy reports and death certificates with legible underlying causes of death were available. When underlying causes of death were compared between death certificates and autopsy reports, death certificates correctly identified the underlying cause of death's ICD-10 disease chapter in 74.6% of cases. The remaining 25.4% of misclassified cases resulted in over-classification rates that ranged from 1.2% for external causes of mortality to 12.2% for circulatory disease, and under-classification rates that ranged from 7.7% for external causes of mortality to 47.4% for respiratory disease. Neoplasms had generally lower misclassification rates with 4.3% over-classification and 13.3% under-classification. A logistic regression revealed that the odds of a match were 2.8 times higher when clinical history was mentioned on the autopsy report than when it was not. Similarly, the odds of a match were 3.4 times higher when death certificates were completed using autopsy findings than when autopsy findings were not used. This analysis excluded cases where it could not be determined if autopsy findings were used to complete death certificates. The findings of this study are useful to investigate the impact of death certificate misclassification errors on radiation risk estimates and, therefore, improve the reliability of epidemiological studies. [ABSTRACT FROM AUTHOR]

Introduction: Septic shock is a severe form of sepsis that has a high mortality rate, and a substantial proportion of these patients will develop cardiac dysfunction, often termed septic cardiomyopathy (SCM). Some SCM patients may develop frank cardiac failure, termed sepsis-related cardiogenic shock (SeRCS). Little is known of SeRCS. This study describes baseline characteristics of patients with SCM and SeRCS compared to patients with septic shock without cardiac dysfunction. We compare clinical outcomes among SCM, SeRCS, and septic shock, and identify risk factors for the development of SCM and SeRCS. Methods: Septic patients admitted to the ICU with an echocardiogram obtained within 72 hours were included. Left ventricular ejection fraction of ≤55% was used to define SCM, and cardiac index ≤2.1 L/min/m2 among patients with SCM defined SeRCS. Machine learning was used to identify risk factors for development of SCM and SeRCS. Logistic regression was used to compare mortality among groups. Results: Among 1229 patients, 977 patients had septic shock without cardiac dysfunction, 207 had SCM, and 45 had SeRCS. In patients with septic shock, the strongest predictor for developing SCM and SeRCs was a prior history of cardiac dysfunction. Mortality did not significantly differ among the three groups. Conclusions: SCM and SeRCS affect a minority of patients with septic shock, disproportionately affecting individuals with a history of cardiac disease. We did not identify a mortality difference associated with SCM or SeRCS. Additional work is needed to define further subtypes and treatment options for this patient population. [ABSTRACT FROM AUTHOR]

Background: Accurate estimates of the COVID-19 pandemic's indirect impacts are crucial, especially in low- and middle-income countries. This study aims to update estimates of excess maternal deaths in Brazil during the first two years of the COVID-19 pandemic. Methods: This was an exploratory mixed ecological study using the counterfactual approach. The observed maternal deaths were gathered from the Mortality Information System (SIM) for the period between March 2015 and February 2022. Expected deaths from March 2020 to February 2022 were estimated using quasipoisson generalized additive models, considering quadrimester, age group, and their interaction as predictor variables. Analyses were performed in R version 4.1.2, RStudio, version 2023.03.1+446 and carried out with support from the "mgcv" and "plot_model" libraries. Results: A total of 5,040 maternal deaths were reported, with varying excess mortality across regions and age groups, resulting in 69% excess maternal mortality throughout Brazil during the first two years of the pandemic. The Southeast region had 50% excess mortality throughout the first two years and 76% excess in the second year. The North region had 69% excess mortality, increasing in the second year, particularly among women aged 20–34. The Northeast region showed 80% excess mortality, with a significant increase in the second year, especially among women aged 35–49. The Central-West region had 75% excess mortality, higher in the second year and statistically significant among women aged 35–49. The South region showed 117% excess mortality, reaching 203% in the second year among women aged 20–34, but no excess mortality in the 10–19 age category. Conclusions: Over two years, Brazil saw a significant impact on maternal excess deaths, regardless of region and pandemic year. The highest peak occurred between March and June 2021, emphasizing the importance of timely and effective epidemic responses to prevent avoidable deaths and prepare for new crises. [ABSTRACT FROM AUTHOR]

The increase in the number of infections and the worrisome state of mortality linked to the COVID-19 pandemic demand an optimal statistical model and efficient monitoring scheme to analyze the deaths. This paper aims to model the COVID-19 mortality in Nigeria using four non-normal distributions grouped under the generalized gamma distribution, by specifying the best-fit distribution to model the number of deaths linked to the COVID-19 pandemic. In addition, a control chart to monitor the COVID-19 deaths based on the best-fit distribution is proposed. The performance of the proposed Gamma-CUSUM chart as a monitoring scheme was compared with the standard normal-CUSUM chart. The results revealed that the Gamma-CUSUM chart first signals a change in the number of deaths on day 68 while there was no change in the number of deaths for the standard normal-CUSUM chart. Also, the exact point of change was visible on the Gamma-CUSUM chart which was impossible on a standard normal-CUSUM control chart. [ABSTRACT FROM AUTHOR]

Background: Tuberculosis (TB) continues to pose a significant public health challenge in India, which is home to one of the highest TB burdens worldwide. This systematic review and meta-analysis will aim to synthesize the anticipated progress and potential challenges in achieving TB elimination in India by 2025. Methods: A comprehensive search will be conducted across multiple databases, including PubMed, Scopus, and Web of Science, to identify relevant studies. The eligibility criteria will encompass individuals diagnosed with TB in India, interventions targeting TB treatment, prevention, or control, and various comparator groups. Outcomes of interest will include incidence reduction, mortality rate, treatment success rate, barriers to TB care, and more. Both quantitative and qualitative data will be synthesized, and the risk of bias will be assessed using established tools. Outcomes: The review is expected to provide a holistic understanding of the TB landscape in India, highlighting the effective interventions and potential challenges in the journey towards TB elimination. Conclusions: While it is anticipated that significant progress will be made in the fight against TB in India, challenges are likely to persist. This review will offer a comprehensive roadmap for researchers, policymakers, and healthcare professionals, emphasizing the importance of continued efforts, innovative strategies, and a multi-pronged approach in achieving the goal of TB elimination in India by 2025. [ABSTRACT FROM AUTHOR]

Background: The role of hyperbaric oxygen therapy (HBOT) in necrotizing soft tissue infections (NSTI) is mainly based on small retrospective studies. A previous study using the 1998–2009 National Inpatient Sample (NIS) found HBOT to be associated with decreased mortality in NSTI. Given the argument of advancements in critical care, we aimed to investigate the continued role of HBOT in NSTI. Methods: The 2012–2020 National Inpatient Sample (NIS) was queried for NSTI admissions who received surgery. 60,481 patients between 2012–2020 were included, 600 (<1%) underwent HBOT. Primary outcome was in-hospital mortality. Secondary outcomes included amputation, hospital length of stay, and costs. A multivariate model was constructed to account for baseline differences in groups. Results: Age, gender, and comorbidities were similar between the two groups. On bivariate comparison, the HBOT group had lower mortality rate (<2% vs 5.9%, p<0.001) and lower amputation rate (11.8% vs 18.3%, p<0.001) however, longer lengths of stay (16.9 days vs 14.6 days, p<0.001) and higher costs ($54,000 vs $46,000, p<0.001). After multivariate analysis, HBOT was associated with decreased mortality (Adjusted Odds Ratio (AOR) 0.22, 95% CI 0.09–0.53, P<0.001) and lower risk of amputation (AOR 0.73, 95% CI 0.55–0.96, P = 0.03). HBO was associated with longer stays by 1.6 days (95% CI 0.4–2.7 days) and increased costs by $7,800 (95% CI $2,200-$13,300), they also had significantly lower risks of non-home discharges (AOR 0.79, 95%CI 0.65–0.96). Conclusions: After correction for differences, HBOT was associated with decreased mortality, amputations, and non-home discharges in NSTI with the tradeoff of increase to costs and length of stay. [ABSTRACT FROM AUTHOR]

Objective: Even though patients with sepsis and DIC have a higher mortality rate compared to those without DIC, screening for DIC is not currently part of sepsis management protocols. This may be due to a lack of literature on the frequency of DIC occurrence in sepsis patients, as well as the absence of evidence on the optimal DIC criteria to use for identifying DIC and predicting mortality among the five criteria available. To address this gap, this study investigates the predictive value of five different criteria for diagnosing DIC and its relationship to patient outcomes in our population of sepsis patients. Methods: In the Medicine department of Aga Khan University Hospital, a retrospective observational study was conducted, enrolling all adult patients with International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) coding of sepsis and clinical suspicion of DIC between January 2018 and December 2020. To diagnose DIC, five different criteria were utilized, namely the International Society of Thrombosis and Hemostasis (ISTH), the Korean Society on Thrombosis and Hemostasis (KSTH), the Japanese Association for Acute Medicine (JAAM), the revised-JAAM (RJAAM), and the Japanese Ministry of Health and Welfare (JMHW). The study analyzed the sensitivity, specificity, negative predictive value, positive predictive value, and accuracy of these five criteria, as well as the overall prediction of mortality. Results: Of 222 septic patients included in this study with clinical suspicion of DIC, 94.6% of patient had DIC according to KSTH criteria, followed by JAAM (69.4%), ISTH (64.0%), JMHW (53.2%) and lastly R-JAAM (48.6%). KSTH had sensitivity of 95.4% in diagnosing DIC and predicting mortality with a positive predictive value of 70% but specificity of 7.3% only. JAAM had sensitivity of 75.9%, positive predictive value of 75.9% with a specificity of 45.5%. ISTH had sensitivity of 69.4%, positive predictive value 75.3% and specificity of 48.5%. Conclusion: DIC can impose a significant burden on septic patients and its presence can lead to higher mortality rates. Early detection through screening for DIC in septic patients can potentially reduce mortality. However, it is necessary to identify the most appropriate diagnostic criteria for each population, as various criteria have demonstrated different performance in different populations. Establishing a gold standard for each population can aid in accurate diagnosis of DIC. [ABSTRACT FROM AUTHOR]

Background: Patients diagnosed with stage III breast cancer often undergo surgery, radiation therapy, and chemotherapy as part of their treatment. The choice of anesthesia technique during surgery has been a subject of interest due to its potential association with immune changes and prognosis. In this study, we aimed to compare the mortality rates between stage III breast cancer patients undergoing surgery with propofol-based intravenous general anesthesia and those receiving inhaled anesthetics. Methods: Using data from Taiwan's National Health Insurance Research Database and Taiwan Cancer Registry, we identified a cohort of 10,896 stage III breast cancer patients. Among them, 1,506 received propofol-based intravenous anesthetic maintenance, while 9,390 received inhaled anesthetic maintenance. To ensure comparability between the two groups, we performed propensity-score matching. Results: Our findings revealed a significantly lower mortality rate in patients who received inhaled anesthetics compared to those who received propofol-based intravenous anesthesia. Sensitivity analysis further confirmed the robustness of our results. Conclusions: This study suggests that inhaled anesthesia technique is associated with a lower mortality rate in clinical stage III breast cancer. Further research is needed to validate and expand upon these results. [ABSTRACT FROM AUTHOR]

Background: The most appropriate surgical method for patients with uncomplicated type B aortic dissection (UTBAD) in the chronic phase remains controversial. This study evaluated the outcomes of patients with UTBAD who needed aortic treatment as well as the impact of the treatment method or indication criteria on their prognosis. Methods: This retrospective review of 106 consecutive patients with aortic events in the chronic phase who underwent initial treatment for UTBAD between 2004 and 2021 comprised three groups: 19 patients who underwent endovascular repair (TEVAR), 38 who underwent open aortic repair and the medication group that included 49 patients. Aortic events were defined as a late operation or indication for operation for dissected aorta, aortic diameter (AD) ≥ 55 mm, rapid aortic enlargement (≥5 mm/6 months), and saccular aneurysmal change. The endpoint was all-cause death. We assessed the association between treatment methods or surgical indication criteria and mortality using a Cox regression analysis. Results: The 5-year actuarial mortality rates were 27.1% in the TEVAR group, 19.6% in the open aortic repair group, and 38.4% in the medication group (p = 0.86). Moreover, the 5-year actuarial mortality rates in patients who had AD ≥ 55 mm were significantly higher than those patients with other surgical indication criteria (41.2% vs. 18.7%, p < 0.01). Multivariable analysis revealed a significant difference in AD ≥ 55 mm (hazard ratio [HR]: 2.88, 95% confidence interval [CI] 1.38–6.02, p < 0.01) and age (HR: 1.09, 95% CI 1.05–1.13, p < 0.01). Conclusions: Under the existing surgical indication criteria, there was no difference in mortality rates among patients with UTBAD based on their surgical treatment. [ABSTRACT FROM AUTHOR]

Background: Covid-19 epidemics raged around the world in years 2020–2022. The dynamics of the epidemics and their mortality varied by country depending on prevention, treatments, vaccination and health status of the population. Objective: The study compares Covid-19 morbidity and mortality in South-Africa and in France, two countries with similar population size and with reliable reporting systems, in order to better understand the dynamics and impacts of the epidemics and the effects of health policies and programs. Data and methods: Data on cases, deaths, hospitalizations, vaccinations were drawn from national statistics. Published data on cases and deaths were corrected for undercount. Results: Results show a different epidemiology in the two countries in the first three years of the epidemic (2020–2022). Incidence was higher in South Africa, and some 44% more people were infected by December 2022 than in France. Mortality and case-fatality were also higher in South Africa despite a favourable age structure. The age pattern of mortality showed higher values in South Africa among the young adults. Young women appeared somewhat disadvantaged in South Africa. Lastly, vaccination appeared to have had no effect on incidence, but a large effect on case-fatality in France. Conclusions: Despite about the same population size and the same crude death rate at baseline, South Africa exhibited more cases and more deaths from Covid-19 over the 2020–2022 period. Prevention strategies (lockdown and vaccination) appear to have had large impacts on morbidity and mortality. [ABSTRACT FROM AUTHOR]

COVID-19 mortality rates increase rapidly with age, are higher among men than women, and vary across racial/ethnic groups, but this is also true for other natural causes of death. Prior research on COVID-19 mortality rates and racial/ethnic disparities in those rates has not considered to what extent disparities reflect COVID-19-specific factors, versus preexisting health differences. This study examines both questions. We study the COVID-19-related increase in mortality risk and racial/ethnic disparities in COVID-19 mortality, and how both vary with age, gender, and time period. We use a novel measure validated in prior work, the COVID Excess Mortality Percentage (CEMP), defined as the COVID-19 mortality rate (Covid-MR), divided by the non-COVID natural mortality rate during the same time period (non-Covid NMR), converted to a percentage. The CEMP denominator uses Non-COVID NMR to adjust COVID-19 mortality risk for underlying population health. The CEMP measure generates insights which differ from those using two common measures–the COVID-MR and the all-cause excess mortality rate. By studying both CEMP and COVID-MRMR, we can separate the effects of background health from Covid-specific factors affecting COVID-19 mortality. We study how CEMP and COVID-MR vary by age, gender, race/ethnicity, and time period, using data on all adult decedents from natural causes in Indiana and Wisconsin over April 2020-June 2022 and Illinois over April 2020-December 2021. CEMP levels for racial and ethnic minority groups can be very high relative to White levels, especially for Hispanics in 2020 and the first-half of 2021. For example, during 2020, CEMP for Hispanics aged 18–59 was 68.9% versus 7.2% for non-Hispanic Whites; a ratio of 9.57:1. CEMP disparities are substantial but less extreme for other demographic groups. Disparities were generally lower after age 60 and declined over our sample period. Differences in socio-economic status and education explain only a small part of these disparities. [ABSTRACT FROM AUTHOR]

Emergomyces africanus is a recently identified thermally-dimorphic fungal pathogen that causes disseminated infection in people living with advanced HIV disease. Known as emergomycosis, this disseminated disease is associated with very high case fatality rates. Over the last decade, improved diagnostics and fungal identification in South Africa resulted in a dramatic increase in the number of reported cases. Although the true burden of disease is still unknown, emergomycosis is among the most frequently diagnosed dimorphic fungal infections in Southern Africa; and additional species in the genus have been identified on four continents. Little is known about the pathogenesis and the host's immune response to this emerging pathogen. Therefore, we established a murine model of pulmonary infection using a clinical isolate, E. africanus (CBS 136260). Both conidia and yeast forms caused pulmonary and disseminated infection in mice with organisms isolated in culture from lung, spleen, liver, and kidney. Wild-type C57BL/6 mice demonstrated a drop in body weight at two weeks post-infection, corresponding to a peak in fungal burden in the lung, spleen, liver, and kidney. An increase in pro-inflammatory cytokine production was detected in homogenized lung supernatants including IFN-γ, IL-1β, IL-6, IL12-p40 and IL-17 at three- and four-weeks post-infection. No significant differences in TNF, IL-12p70 and IL-10 were observed in wild-type mice between one and four-weeks post-infection. Rag-1-deficient mice, lacking mature T-and B-cells, had an increased fungal burden associated with reduced IFN-γ production. Together our data support a protective T-helper type-1 immune response to E. africanus infection. This may provide a possible explanation for the susceptibility of only a subset of people living with advanced HIV disease despite hypothesized widespread environmental exposure. In summary, we have established a novel murine model of E. africanus disease providing critical insights into the host immune components required for eliminating the infection. Author summary: Emergomyces africanus is a thermally-dimorphic fungus that was recently described as the cause of disseminated infections in persons living with advanced HIV disease in South Africa, where emergomycosis is among the most frequently diagnosed dimorphic fungal diseases. Four additional Emergomyces species have been described causing serious infections around the world, and the infection has a high case fatality rate. Despite the seriousness of this infection, the pathophysiology, immunology, diagnosis, and optimal management of emergomycosis remain largely unknown, hampered by the absence of an animal model. Here, we established a mouse model of pulmonary and disseminated emergomycosis using a clinical isolate of E. africanus with the aim of investigating the host immune response. We found that infection spread from the lungs to other organs within two weeks. We describe the effector immune responses in immune-competent mice aiding in controlled infection. We also highlight the immune-modulating components lacking in immunocompromised mice that promote susceptibilty to infection. Our study provides new insights into the components of the host immune response required for controlling Emergomyces spp. infection. [ABSTRACT FROM AUTHOR]

Purpose: Although the outbreak of the Corona Virus Disease 2019 (COVID-19) occurred on a global scale, governments from different countries adopted different policies and achieved different anti-epidemic effects. The purpose of this study is to investigate whether and how the government response affected the transmission scale of COVID-19 on the dynamic perspective. Methodology: This paper uses a dynamic generalized moment method to research the relationship between the government response and COVID-19 case fatality rate by using panel data from eight countries: China, United States, Canada, Australia, Italy, France, Japan, and South Korea. Findings: We have the following findings: 1. Government responses have a significant impact on the scale of COVID-19 transmission. 2. The rate of increase of government responses on the growth rate of COVID-19 case fatality rate has the characteristics of cyclicity and repeatability, that is, with the increase in the growth rate of government responses, the COVID-19 case fatality rate shows the following cyclical motion law: increasing first, reaching the maximum point, and then declining, and finally reaching the minimum point and then rising; ultimately, its convergence becomes 0. The cyclical fluctuations of COVID-19 in the long term may be caused by the decline in the level of government response, the mutation of the virus, and the violation of restrictive policies by some citizens. 3. The government response has a lag in controlling the spread of COVID-19. Originality/Value: Since there is a lack of literature on the impact of government responses on the development of COVID-19 from a long-term and dynamic perspective. This paper fills this gap in empirical research. We provide and expand new empirical evidence based on the current literature. This paper provides the basis for government decision-making and will help to formulate the response to other major public health events that may occur in the future. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic required significant public health interventions from local governments. Although nonpharmaceutical interventions often were implemented as decision rules, few studies evaluated the robustness of those reopening plans under a wide range of uncertainties. This paper uses the Robust Decision Making approach to stress-test 78 alternative reopening strategies, using California as an example. This study uniquely considers a wide range of uncertainties and demonstrates that seemingly sensible reopening plans can lead to both unnecessary COVID-19 deaths and days of interventions. We find that plans using fixed COVID-19 case thresholds might be less effective than strategies with time-varying reopening thresholds. While we use California as an example, our results are particularly relevant for jurisdictions where vaccination roll-out has been slower. The approach used in this paper could also prove useful for other public health policy problems in which policymakers need to make robust decisions in the face of deep uncertainty. [ABSTRACT FROM AUTHOR]

The need to control the sanitary situation during the COVID-19 pandemic has led governments to implement several restrictions with substantial social and economic impacts. We explored people's trade-offs in terms of their income, life restrictions, education, and poverty in the society, compared to their willingness to avoid deaths. We applied a web-based discrete choice experiment to elicit preferences of the Portuguese citizens for these attributes and computed the marginal rate of substitution in terms of avoided deaths. We recorded 2,191 responses that faced the possibility of having 250 COVID-19 related deaths per day as the worst possible outcome from the choice levels presented. Estimates suggested that individuals would be willing to sacrifice 30% instead of 10% of their income to avoid approximately 47 deaths per day during the first six months of 2021. For the same period, they would also accept 30% of the students' population to become educationally impaired, instead of 10%, to avoid approximately 25 deaths; a strict lockdown, instead of mild life restrictions, to avoid approximately 24 deaths; and 45% of the population to be in risk of poverty, instead of 25%, to avoid approximately 101 deaths. Our paper shows that avoiding deaths was strongly preferred to the remaining societal impacts; and that being a female, as well as working on site, led individuals to be more averse to such health hazards. Furthermore, we show how a DCE can be used to assess the societal support to decision-making during times of crisis. [ABSTRACT FROM AUTHOR]

Socioeconomic factors have exacerbated the impact of COVID–19 worldwide. Brazil, already marked by significant economic inequalities, is one of the most affected countries, with one of the highest mortality rates. Understanding how inequality and income segregation contribute to excess mortality by COVID–19 in Brazilian cities is essential for designing public health policies to mitigate the impact of the disease. This paper aims to fill in this gap by analyzing the effect of income inequality and income segregation on COVID–19 mortality in large urban centers in Brazil. We compiled weekly COVID–19 mortality rates from March 2020 to February 2021 in a longitudinal ecological design, aggregating data at the city level for 152 Brazilian cities. Mortality rates from COVID-19 were compared across weeks, cities and states using mixed linear models. We estimated the associations between COVID-19 mortality rates with income inequality and income segregation using mixed negative binomial models including city and week-level random intercepts. We measured income inequality using the Gini index and income segregation using the dissimilarity index using data from the 2010 Brazilian demographic census. We found that 88.2% of COVID–19 mortality rates variability was between weeks, 8.5% between cities, and 3.3% between states. Higher-income inequality and higher-income segregation values were associated with higher COVID–19 mortality rates before and after accounting for all adjustment factors. In our main adjusted model, rate ratios (RR) per 1 SD increases in income inequality and income segregation were associated with 17% (95% CI 9% to 26%) and 11% (95% CI 4% to 19%) higher mortality. Income inequality and income segregation are long-standing hallmarks of large Brazilian cities. Risk factors related to the socioeconomic context affected the course of the pandemic in the country and contributed to high mortality rates. Pre-existing social vulnerabilities were critical factors in the aggravation of COVID–19, as supported by the observed associations in this study. [ABSTRACT FROM AUTHOR]

Among other diseases, Covid 19 creates a critical situation around the world. Five layers have been recorded so far, resulting in the loss of millions of lives in different countries. The virus was thought to be contagious, so the government initially severely forced citizens to keep a distance from each other. Since then, several vaccines have been developed that play an important role in controlling mortality. In the case of Covid-19 mortality, the government should be forced to take significant steps in the form of lockdown, keeping you away or forcing citizens to vaccinate. In this paper, modeling of Covid-19 death rates is discussed via probability distributions. To delineate the performance of the best fitted model, the mortality rate of Pakistan and Afghanistan is considered. Numerical results conclude that the NFW model can be used to predict the mortality rate for Covid-19 patients more accurately than other probability models. [ABSTRACT FROM AUTHOR]

The absence of reliable registration of Covid-19 deaths in India has prevented proper assessment and monitoring of the coronavirus pandemic. In addition, India's relatively young age structure tends to conceal the severity of Covid-19 mortality, which is concentrated in older age groups. In this paper, we present four different demographic samples of Indian populations for which we have information on both their demographic structures and death outcomes. We show that we can model the age distribution of Covid-19 mortality in India and use this modeling to estimate Covid-19 mortality in the country. Our findings point to a death toll of approximately 3.2–3.7 million persons by early November 2021. Once India's age structure is factored in, these figures correspond to one of the most severe cases of Covid-19 mortality in the world. India has recorded after February 2021 the second outbreak of coronavirus that has affected the entire country. The accuracy of official statistics of Covid-19 mortality has been questioned, and the real number of Covid-19 deaths is thought to be several times higher than reported. In this paper, we assembled four independent population samples to model and estimate the level of Covid-19 mortality in India. We first used a population sample with the age and sex of Covid-19 victims to develop a Gompertz model of Covid-19 mortality in India. We applied and adjusted this mortality model on two other national population samples after factoring in the demographic characteristics of these samples. We finally derive from these samples the most reasonable estimate of Covid-19 mortality level in India and confirm this result using a fourth population sample. Our findings point to a death toll of about 3.2–3.7 million persons by late May 2021. This is by far the largest number of Covid-19 deaths in the world. Once standardized for age and sex structure, India's Covid-19 mortality rate is above Brazil and the USA. Our analysis shows that existing population samples allow an alternative estimation of deaths due to Covid-19 in India. The results imply that only one out of 7–8 deaths appear to have been recorded as a Covid-19 death in India. The estimates also point to a very high Covid-19 mortality rate, which is even higher after age and sex standardization. The magnitude of the pandemic in India requires immediate attention. In the absence of effective remedies, this calls for a strong response based on a combination of non-pharmaceutical interventions and the scale-up of vaccination to make them accessible to all, with an improved surveillance system to monitor the progression of the pandemic and its spread across India's regions and social groups. [ABSTRACT FROM AUTHOR]

Mathematical models of vector-borne infections, including malaria, often assume age-independent mortality rates of vectors, despite evidence that many insects senesce. In this study we present survival data on insecticide-resistant Anopheles gambiae s.l. from experiments in Côte d'Ivoire. We fit a constant mortality function and two age-dependent functions (logistic and Gompertz) to the data from mosquitoes exposed (treated) and not exposed (control) to insecticide-treated nets (ITNs), to establish biologically realistic survival functions. This enables us to explore the effects of insecticide exposure on mosquito mortality rates, and the extent to which insecticide resistance might impact the effectiveness of ITNs. We investigate this by calculating the expected number of infectious bites a mosquito will take in its lifetime, and by extension the vectorial capacity. Our results show that the predicted vectorial capacity is substantially lower in mosquitoes exposed to ITNs, despite the mosquitoes in the experiment being highly insecticide-resistant. The more realistic age-dependent functions provide a better fit to the experimental data compared to a constant mortality function and, hence, influence the predicted impact of ITNs on malaria transmission potential. In models with age-independent mortality, there is a great reduction for the vectorial capacity under exposure compared to no exposure. However, the two age-dependent functions predicted an even larger reduction due to exposure, highlighting the impact of incorporating age in the mortality rates. These results further show that multiple exposures to ITNs had a considerable effect on the vectorial capacity. Overall, the study highlights the importance of including age dependency in mathematical models of vector-borne disease transmission and in fully understanding the impact of interventions. Author summary: Interventions against malaria are most commonly targeted on the adult mosquitoes, which transmit the infection from person to person. One of the most important interventions are bed-nets, treated with insecticides. Unfortunately, extensive exposure of mosquitoes to insecticide has led to widespread evolution of insecticide resistance, which might threaten control strategies. Piecing together the overall impact of resistance on the efficacy of insecticide-treated nets is complex, but can be informed by the use of mathematical models. However, there are some assumptions that the models frequently use which are not realistic in terms of the mosquito biology. In this paper, we formulate a model that includes age-dependent mortality rates, an important parameter in vector control since control strategies most commonly aim to reduce the lifespan of the mosquitoes. By using novel data collected using field-derived insecticide-resistant mosquitoes, we explore the effects that the presence of insecticides on nets have on the mortality rates, as well as the difference incorporating age dependency in the model has on the results. We find that including age-dependent mortality greatly alters the anticipated effects of insecticide-treated nets on mosquito transmission potential, and that ignoring this realism potentially overestimates the negative impact of insecticide resistance. [ABSTRACT FROM AUTHOR]

Background: COVID-19 has been the most important public health concern worldwide since 2020. Several vaccines are now available to help in controlling COVID-19 associated morbidity and mortality. This study will aim to provide the global and regional prevalence of SARS-CoV-2 infection as well as an estimate of disease severity among COVID-19 vaccinated individuals. Materials and methods: In order to determine the global burden of SARS-CoV-2 infection among vaccinated individuals, we will systematically extract and review papers from PubMed/MEDLINE, Excerpta Medica database (EMBASE), Cochrane Central Register of Controlled Trials (CENTRAL), Science direct and Cumulative Index to Nursing and Allied Health Literature (CINAHL). All the studies describing the prevalence and/or disease severity (hospitalization and case fatality rate) data of COVID-19 among individuals who received a partial or complete dose of WHO-approved COVID-19 vaccines will be eligible. A random effect model will be used to calculate the pooled prevalence and to estimate the disease severity. Subgroup analysis will be performed to explore the association between the number of vaccine doses received and the COVID-19 burdens. Discussion: This systematic review and meta-analysis will provide the global estimate data on pooled prevalence, hospitalization and case fatality rates of COVID-19 among vaccinated individuals. Moreover, the factors associated with reinfection and disease severity will be equally investigated in the meta-analysis. The results of this study will contribute in the understanding and estimation of the global burden of COVID-19 among vaccinated individuals. Findings will provide meaningful information for the success of the current global rollout of COVID-19 vaccination strategies and pave the way for future interventions. Systematic review registration: CRD42021273074. [ABSTRACT FROM AUTHOR]

Introduction: This paper describes how mortality among hospitalised COVID-19 patients changed during the first three waves of the epidemic in Italy. Methods: This prospective cohort study used the Kaplan-Meier method to analyse the time-dependent probability of death of all of the patients admitted to a COVID-19 referral centre in Milan, Italy, during the three consecutive periods of: 21 February-31 July 2020 (first wave, W1), 1 August 2020–31 January 2021 (second wave, W2), and 1 February-30 April 2021 (third wave, W3). Cox models were used to examine the association between death and the period of admission after adjusting for age, biological sex, the time from symptom onset to admission, disease severity upon admission, obesity, and the comorbidity burden. Results: Of the 2,023 COVID-19 patients admitted to our hospital during the study period, 553 (27.3%) were admitted during W1, 838 (41.5%) during W2, and 632 (31.2%) during W3. The crude mortality rate during W1, W2 and W3 was respectively 21.3%, 23.7% and 15.8%. After adjusting for potential confounders, hospitalisation during W2 or W3 was independently associated with a significantly lower risk of death than hospitalisation during W1 (adjusted hazard ratios [AHRs]: 0.75, 95% confidence interval [CI] 0.59–0.95, and 0.58, 95% CI 0.44–0.77). Among the patients aged >75 years, there was no significant difference in the probability of death during the three waves (AHRs during W2 and W3 vs W1: 0.93, 95% CI 0.65–1.33, and 0.88, 95% CI 0.59–1.32), whereas those presenting with critical disease during W2 and W3 were at significantly lower risk of dying than those admitted during W1 (AHRs 0.61, 95% CI 0.43–0.88, and 0.44, 95% CI 0.28–0.70). Conclusions: Hospitalisation during W2 and W3 was associated with a reduced risk of COVID-19 death in comparison with W1, but there was no difference in survival probability in patients aged >75 years. [ABSTRACT FROM AUTHOR]

Models have gained the spotlight in many discussions surrounding COVID-19. The urgency for timely decisions resulted in a multitude of models as informed policy actions must be made even when so many uncertainties about the pandemic still remain. In this paper, we use machine learning algorithms to build intuitive country-level COVID-19 motion models described by death toll velocity and acceleration. Model explainability techniques provide insightful data-driven narratives about COVID-19 death toll motion models—while velocity is explained by factors that are increasing/reducing death toll pace now, acceleration anticipates the effects of public health measures on slowing the death toll pace. This allows policymakers and epidemiologists to understand factors driving the outbreak and to evaluate the impacts of different public health measures. [ABSTRACT FROM AUTHOR]

Background: Cardiovascular diseases, including ischemic heart disease, are the leading cause of premature death and disability worldwide. While traditional risk factors such as smoking, obesity, and diabetes have been thoroughly investigated, non-traditional risk factors like high-altitude exposure remain underexplored. This study aims to examine the incidence and mortality rates of ischemic heart disease over the past decade in Ecuador, a country with a diverse altitude profile spanning from 0 to 4,300 meters. Methods: We conducted a geographic distribution analysis of ischemic heart disease in Ecuador, utilizing hospital discharge and mortality data from the National Institute of Census and Statistics for the years 2011–2021. Altitude exposure was categorized according to two distinct classifications: the traditional division into low (< 2,500 m) and high (> 2,500 m) altitudes, as well as the classification proposed by the International Society of Mountain Medicine, which delineates low (< 1,500 m), moderate (1,500–2,500 m), high (2,500–3,500 m), and very high (3,500–5,800 m) altitudes. Findings: From 2011–2021, we analyzed 49,765 IHD-related hospital admissions and 62,620 deaths. Men had an age-adjusted incidence rate of 55.08/100,000 and a mortality rate of 47.2/100,000, compared to 20.77/100,000 and 34.8/100,000 in women. Incidence and mortality surged in 2020 by 83% in men and 75% in women. Altitudinal stratification revealed higher IHD rates at lower altitudes (<2500 m), averaging 61.65 and 121.8 per 100,000 for incidence and mortality, which declined to 25.9 and 38.5 at elevations >2500 m. Men had more pronounced rates across altitudes, exhibiting 138.7% and 150.0% higher incidence at low and high altitudes respectively, and mortality rates increased by 48.3% at low altitudes and 23.2% at high altitudes relative to women. Conclusion: Ecuador bears a significant burden of ischemic heart disease (IHD), with men being more affected than women in terms of incidence. However, women have a higher percentage of mortality post-hospital admission. Regarding elevation, our analysis, using two different altitude cutoff points, reveals higher mortality rates in low-altitude regions compared to high-altitude areas, suggesting a potential protective effect of high elevation on IHD risk. Nevertheless, a definitive dose-response relationship between high altitude and reduced IHD risk could not be conclusively established. [ABSTRACT FROM AUTHOR]

The most common reported epidemic time series in epidemiological surveillance are the daily or weekly incidence of new cases, the hospital admission count, the ICU admission count, and the death toll, which played such a prominent role in the struggle to monitor the Covid-19 pandemic. We show that pairs of such curves are related to each other by a generalized renewal equation depending on a smooth time varying delay and a smooth ratio generalizing the reproduction number. Such a functional relation is also explored for pairs of simultaneous curves measuring the same indicator in two neighboring countries. Given two such simultaneous time series, we develop, based on a signal processing approach, an efficient numerical method for computing their time varying delay and ratio curves, and we verify that its results are consistent. Indeed, they experimentally verify symmetry and transitivity requirements and we also show, using realistic simulated data, that the method faithfully recovers time delays and ratios. We discuss several real examples where the method seems to display interpretable time delays and ratios. The proposed method generalizes and unifies many recent related attempts to take advantage of the plurality of these health data across regions or countries and time, providing a better understanding of the relationship between them. An implementation of the method is publicly available at the EpiInvert CRAN package. Author summary: To monitor an epidemic, it is crucial to understand the relationship between the incidence of new cases, the hospital admission count, the ICU admission count, and the death toll time series. The relationship between any pair of such indicators can be formulated in terms of temporal delays and ratios which evolve across time. Given two such time series, we develop, based on a signal processing approach, an efficient numerical method for computing their time varying delay and ratio curves. Using realistic simulated data, we show that the method faithfully recovers time delays and ratios. In addition, we discuss several applications to real epidemic data where the method seems to output interpretable time delays and ratios. The obtained relationship between these epidemic time series is a key issue in epidemiological surveillance. The proposed technique provides a new tool to visualize, compare, and understand the evolution of key epidemiological time series. An implementation of the method is publicly available at the EpiInvert CRAN package. [ABSTRACT FROM AUTHOR]

In late 2019, the emergence of COVID-19 in Wuhan, China, led to the implementation of stringent measures forming the zero-COVID policy aimed at eliminating transmission. Zero-COVID policy basically aimed at completely eliminating the transmission of COVID-19. However, the relaxation of this policy in late 2022 reportedly resulted in a rapid surge of COVID-19 cases. The aim of this work is to investigate the factors contributing to this outbreak using a new SEIR-type epidemic model with time-dependent level of immunity. Our model incorporates a time-dependent level of immunity considering vaccine doses administered and time-post-vaccination dependent vaccine efficacy. We find that vaccine efficacy plays a significant role in determining the outbreak size and maximum number of daily infected. Additionally, our model considers under-reporting in daily cases and deaths, revealing their combined effects on the outbreak magnitude. We also introduce a novel Physics Informed Neural Networks (PINNs) approach which is extremely useful in estimating critical parameters and helps in evaluating the predictive capability of our model. [ABSTRACT FROM AUTHOR]

Background: Regular evaluation of caesarean section (CS) is required due to their rising trend and outcomes. Many women recently opt for elective CS, even in resource-constrained settings. Data evaluating the outcomes of CS is however sparse. Hence, this study sought to determine the rate of fetal mortalities and their determinants following CS in the Tatale District Hospital of the Northern Region, Ghana. Methods: A retrospective cross-sectional study was employed to analyze the medical records of 275 women who underwent CS from 2019 to 2021. Data were collected from the hospital's record of CS cases from 2019 to 2021. Descriptive statistics were used to summarize the data and Pearson's chi-square/Fisher's exact test was used to examine the relationship between maternal and obstetric characteristics and fetal mortality. At a 95% confidence interval (95% CI), logistic regression was fitted to assess significant variables and reported the results using odds ratio. Results: Of 1667 deliveries, 16.5% of the mothers gave birth by CS. A fetal mortality rate of 76.4 per 1000 total births was recorded following CS. Babies born with low Appearance, Pulse, Grimace, Activity and Respiration (APGAR) scores (0–3) at fifth-minute had an increased risk of fetal mortality (AOR = 523.19, 95%CI: 49.24–5559.37, p = <0.001). Having a history of previous CS, cephalopelvic disproportion and delayed labour were the major indications for CS. Conclusion: Overall, this study found a high rate of CS based on the World Health Organization's recommended CS rate. Interventions such as reducing the waiting time for surgery and early diagnosis of the need for CS, and ensuring the availability of modern equipment to resuscitate infants with low APGAR scores can significantly improve fetal outcomes following CS. [ABSTRACT FROM AUTHOR]

Background: Ectopic pregnancy (EP) is one of the leading causes of death in women in early pregnancy, and the mortality of EP have gradually decreased over time in developed countries such as the United Kingdom and the United States. However, epidemiological information on EP has been lacking in recent years, so we analyzed EP data over a thirty-year period from 1990–2019 with the help of Global Burden of Disease study (GBD) data to fill this gap. Methods: According to the EP data in GBD for the three decades from 1990 to 2019, we used estimated annual percentage changes (EAPC) to assess the trend of age-standardized incidence rate (ASIR), age-standardized death rate (ASDR) and age-standardized disability adjusted life years (AS-DALYs) trends in EP and to explore the correlation between socio-demographic index (SDI) stratification, age stratification and EP. Results: Global ASIR, ASDR, AS-DALYs for EP in 2019 are 170.33/100,000 persons (95% UI: 133.18 to 218.49), 0.16/100,000 persons (95% UI, 0.14 to 0.19) and 9.69/100,000 persons (95% UI, 8.27 to 11.31), respectively. At the overall level, ASDR is significantly negatively correlated with SDI values (R = -0.699, p < 0.001). Besides that, ASDR and AS-DALYs have basically the same pattern. In addition, iron deficiency is one of the risk factors for EP. Conclusions: In the past three decades, the morbidity, mortality and disease burden of EP have gradually decreased. It is noteworthy that some economically disadvantaged areas are still experiencing an increase in all indicators, therefore, it is more important to strengthen the protection of women from ethnic minorities and low-income groups. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 epidemic in Italy has severely affected people aged more than 80, especially socially isolated. Aim of this paper is to assess whether a social and health program reduced mortality associated to the epidemic. Methods: An observational retrospective cohort analysis of deaths recorded among >80 years in three Italian cities has been carried out to compare death rate of the general population and "Long Live the Elderly!" (LLE) program. Parametric and non-parametric tests have been performed to assess differences of means between the two populations. A multivariable analysis to assess the impact of covariates on weekly mortality has been carried out by setting up a linear mixed model. Results: The total number of services delivered to the LLE population (including phone calls and home visits) was 34,528, 1 every 20 day per person on average, one every 15 days during March and April. From January to April 2019, the same population received one service every 41 days on average, without differences between January-February and March-April. The January-April 2020 cumulative crude death rate was 34.8‰ (9,718 deaths out of 279,249 individuals; CI95%: 34.1–35.5) and 28.9‰ (166 deaths out of 5,727 individuals; CI95%:24.7–33.7) for the general population and the LLE sample respectively. The general population weekly death rate increased after the 11th calendar week that was not the case among the LLE program participants (p<0.001). The Standardized Mortality Ratio was 0.83; (CI95%: 0.71–0.97). Mortality adjusted for age, gender, COVID-19 weekly incidence and prevalence of people living in nursing homes was lower in the LLE program than in the general population (p<0.001). Conclusions: LLE program is likely to limit mortality associated with COVID-19. Further studies are needed to establish whether it is due to the impact of social care that allows a better clients' adherence to the recommendations of physical distancing or to an improved surveillance of older adults that prevents negative outcomes associated with COVID-19. [ABSTRACT FROM AUTHOR]

Introduction: Viral outbreaks present a particular challenge in countries in Africa where there is already a high incidence of other infectious diseases, including malaria which can alter immune responses to secondary infection. Ebola virus disease (EVD) is one such problem; understanding how Plasmodium spp. and Ebolavirus (EBOV) interact is important for future outbreaks. Methods: We conducted a systematic review in PubMed and Web of Science to find peer-reviewed papers with primary data literature to determine 1) prevalence of EBOV/Plasmodium spp. coinfection, 2) effect of EBOV/Plasmodium spp. coinfection on EVD pathology and the immune response, 3) impact of EBOV/Plasmodium spp. coinfection on the outcome of EVD-related mortality. Random effects meta-analyses were conducted with the R package meta to produce overall proportion and effect estimates as well as measure between-study heterogeneity. Results: From 322 peer-reviewed papers, 17 were included in the qualitative review and nine were included in a meta-analysis. Prevalence of coinfection was between 19% and 72%. One study reported significantly lower coagulatory response biomarkers in coinfected cases but no difference in inflammatory markers. Case fatality rates were similar between EBOV(+)/Pl(+) and EBOV(+)/Pl(-) cases (62.8%, 95% CI 49.3–74.6 and 56.7%, 95% CI 53.2–60.1, respectively), and there was no significant difference in risk of mortality (RR 1.09, 95% CI 0.90–1.31) although heterogeneity between studies was high. One in vivo mouse model laboratory study found no difference in mortality by infection status, but another found prior acute Plasmodium yoeli infection was protective against morbidity and mortality via the IFN-γ signalling pathway. Conclusion: The literature was inconclusive; studies varied widely and there was little attempt to adjust for confounding variables. Laboratory studies may present the best option to answer how pathogens interact within the body but improvement in data collection and analysis and in diagnostic methods would aid patient studies in the future. [ABSTRACT FROM AUTHOR]

Objective: The Spanish health system is made up of seventeen regional health systems. Through the official reporting systems, some inconsistencies and differences in case fatality rates between Autonomous Communities (CC.AA.) have been observed. Therefore the objective of this paper is to compare COVID-19 case fatality rates across the Spanish CC.AA. Material and methods: Observational descriptive study. The COVID-19 case fatality rate (CFR) was estimated according to the official records (CFR-PCR+), the daily mortality monitory system (MoMo) record (CFR-Mo), and the seroprevalence study ENE-COVID-19 (Estudio Nacional de sero Epidemiologia Covid-19) according to sex, age group and CC.AA. between March and June 2020. The main objective is to detect whether there are any differences in CFR between Spanish Regions using two different register systems, i. e., the official register of the Ministry of Health and the MoMo. Results: Overall, the CFR-Mo was higher than the CFR-PCR+, 1.59% vs 0.98%. The differences in case fatality rate between both methods were significantly higher in Castilla La Mancha, Castilla y León, Cataluña, and Madrid. The difference between both methods was higher in persons over 74 years of age (CFR-PCR+ 7.5% vs 13.0% for the CFR-Mo) but without statistical significance. There was no correlation of the estimated prevalence of infection with CFR-PCR+, but there was with CFR-Mo (R2 = 0.33). Andalucía presented a SCFR below 1 with both methods, and Asturias had a SCFR higher than 1. Cataluña and Castilla La Mancha presented a SCFR greater than 1 in any scenario of SARS-CoV-2 infection calculated with SCFR-Mo. Conclusions: The PCR+ case fatality rate underestimates the case fatality rate of the SARS-CoV- 2 virus pandemic. It is therefore preferable to consider the MoMo case fatality rate. Significant differences have been observed in the information and registration systems and in the severity of the pandemic between the Spanish CC.AA. Although the infection prevalence correlates with case fatality rate, other factors such as age, comorbidities, and the policies adopted to address the pandemic can explain the differences observed between CC.AA. [ABSTRACT FROM AUTHOR]

This paper analyses the effect of mortality rates (under-five and adult mortality) and population growth on the population ageing in a pooled sample of nine lower and upper middle European countries. Therefore, the main goal of this research is to investigate the ageing process of the population in the context of mortality mechanisms (under five and adult mortality) and of population growth in nine European LUMIs. The analysis is implemented in terms of Pooled least squares with cross-section fixed effects methodology. The novelty used within this research is White two-way cluster standard errors & covariance. This study is based on a database from the World Bank and UN covering the period 1995–2019. The expected results are making available quantitative analysis and insights in the context of mechanisms between the ageing process of population, mortality and population growth across these European LUMIs. Results are consistent with the notion that the increasing ageing process within these countries may be a consequence of the negative impact of population growth and from the influence of adult mortality for both sexes. The research results confirm the presence of solid ties of the mechanism between mortality, population growth and population ageing. Therefore, a clear point was provided that mortality acceleration will depend primarily on the level of population growth. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic stimulated the interest of scientists, decision makers and the general public in short-term mortality fluctuations caused by epidemics and other natural or man-made disasters. To address this interest and provide a basis for further research, in May 2020, the Short-term Mortality Fluctuations data series was launched as a new section of the Human Mortality Database. At present, this unique data resource provides weekly mortality death counts and rates by age and sex for 38 countries and regions. The main objective of this paper is to detail the web-based application for visualizing and analyzing the excess mortality based on the Short-term Mortality Fluctuation data series. The application yields a visual representation of the database that enhances the understanding of the underlying data. Besides, it enables the users to explore data on weekly mortality and excess mortality across years and countries. The contribution of this paper is twofold. First, to describe a visualization tool that aims to facilitate research on short-term mortality fluctuations. Second, to provide a comprehensive open-source software solution for demographic data to encourage data holders to promote their datasets in a visual framework. [ABSTRACT FROM AUTHOR]

The number of COVID-19 infections is key for accurately monitoring the pandemics. However, due to differential testing policies, asymptomatic individuals and limited large-scale testing availability, it is challenging to detect all cases. Seroprevalence studies aim to address this gap by retrospectively assessing the number of infections, but they can be expensive and time-intensive, limiting their use to specific population subgroups. In this paper, we propose a complementary approach that combines estimated (1) infection fatality rates (IFR) using a Bayesian melding SEIR model with (2) reported case-fatality rates (CFR) in order to indirectly estimate the fraction of people ever infected (from the total population) and detected (from the ever infected). We apply the technique to the U.S. due to their remarkable regional diversity and because they count with almost a quarter of all global confirmed cases and deaths. We obtain that the IFR varies from 1.25% (0.39–2.16%, 90% CI) in Florida, the most aged population, to 0.69% in Utah (0.21–1.30%, 90% CI), the youngest population. By September 8, 2020, we estimate that at least five states have already a fraction of people ever infected between 10% and 20% (New Jersey, New York, Massachussets, Connecticut, and District of Columbia). The state with the highest estimated fraction of people ever infected is New Jersey with 17.3% (10.0, 55.8, 90% CI). Moreover, our results indicate that with a probability of 90 percent the fraction of detected people among the ever infected since the beginning of the epidemic has been less than 50% in 15 out of the 20 states analyzed in this paper. Our approach can be a valuable tool that complements seroprevalence studies and indicates how efficient have testing policies been since the beginning of the outbreak. [ABSTRACT FROM AUTHOR]

In addition to fundamental mortality metrics such as mortality rates and mortality rate ratios, life expectancy is also commonly used to investigate excess mortality among a group of individuals diagnosed with specific diseases or conditions. However, as an average measure, life expectancy ignores the heterogeneity in lifespan. Interestingly, the variation in lifespan–a measure commonly used in the field of demography–has not been estimated for people with a specific condition. Based on recent advances in methodology in research within epidemiology and demography, we discuss two metrics, namely, the average life disparity and average lifetable entropy after diagnosis, which estimate the variation in lifespan for time-varying conditions in both absolute and relative aspects. These metrics are further decomposed into early and late components, separated by their threshold ages. We use mortality data for women with mental disorders from Danish registers to design a population-based study and measure such metrics. Compared with women from the general population, women with a mental disorder had a shorter average remaining life expectancy after diagnosis (37.6 years vs. 44.9 years). In addition, women with mental disorders also experienced a larger average lifespan variation, illustrated by larger average life disparity (9.5 years vs 9.1 years) and larger average lifetable entropy (0.33 vs 0.27). More specifically, we found that women with a mental disorder had a larger early average life disparity but a smaller late average life disparity. Unlike the average life disparity, both early and late average lifetable entropy were higher for women with mental disorders compared to the general population. In conclusion, the metric proposed in our study complements the current research focusing merely on life expectancy and further provides a new perspective into the assessment of people's health associated with time-varying conditions. [ABSTRACT FROM AUTHOR]

Studies evaluating the local quality of death certification in Brazil focused on completeness of death reporting or inappropriate coding of causes of death, with few investigating missing data. We aimed to use missing and unexpected values in core topics to assess the quality of death certification in Brazilian municipalities, to evaluate its correlation with the percentage of garbage codes, and to employ a data-driven approach with non-linear models to investigate the association of the socioeconomic and health infrastructure context with quality of death statistics among municipalities. This retrospective study used data from the Mortality Information System (2010–2017), and municipal data regarding healthcare infrastructure, socioeconomic characteristics, and death rates. Quality of death certification was assessed by missing or unexpected values in the following core topics: dates of occurrence, registration, and birth, place of occurrence, certifier, sex, and marital status. Models were fit to classify municipalities according to the quality of death certification (poor quality defined as death records with missing or unexpected values in core topics ≥ 80%). Municipalities with poor quality of death certification (43.9%) presented larger populations, lower death rates, lower socioeconomic index, healthcare infrastructure with fewer beds and physicians, and higher proportion of public healthcare facilities. The correlation coefficients between quality of death certification assessed by missing or unexpected values and the proportion of garbage codes were weak (0.11–0.49), but stronger for municipalities with lower socioeconomic scores. The model that best fitted the data was the random forest classifier (ROC AUC = 0.76; precision-recall AUC = 0.78). This innovative way of assessing the quality of death certification could help quality improvement initiatives to include the correctness of essential fields, in addition to garbage coding or completeness of records, especially in municipalities with lower socioeconomic status where garbage coding and the correctness of core topics appear to be related issues. [ABSTRACT FROM AUTHOR]