Different Causes Of Death Research Articles

A retrospective analysis of post-mortem findings in New Zealand weka (Gallirallus australis), 1995–2022

ABSTRACT Aims To determine the major causes of mortality in weka (Gallirallus australis), and to investigate associations between causes of mortality and captivity status, age, sex, decade of submission, and season. Methods Necropsy records were obtained from the Massey University School of Veterinary Science/Wildbase Pathology database (Palmerston North, NZ) for weka submitted between 1 January 1995 and 22 March 2022. Causes of mortality were classified into categories based on aetiology. Frequency of diagnosis was tested for association with region of submission, captivity status, age, sex, decade, and season of death. Results A total of 156 necropsy reports were included in this study, of which 96 (61%) were from wild weka, 57 (36.5%) were captive, and three (1.9%) were of an unspecified captivity status. Weka were submitted from 12 regions across New Zealand. There were 65 (41.7%) adults, 16 (10.3%) juveniles, and 75 (48.1%) weka of an undetermined age among the 156 submissions. Of the weka with a known sex, there was a similar distribution between sexes with 27 (17.3%) males and 29 (18.6%) females. A cause of death was determined in 132/156 (84.6%) cases, with 24/156 (15.4%) cases having an unknown diagnosis. The leading cause of mortality in weka was traumatic injury, which occurred in 65/156 (41.7%), followed by infectious and/or inflammatory diseases in 26/156 (16.7%), and degenerative and/or nutritional conditions affecting 20/156 (12.8%) cases. The distribution of the primary causes of death was found to be dependent on captivity status (p < 0.001). Traumatic and toxic causes of death were more frequent in wild than captive weka. The cause of death was also dependent on season (p < 0.001). There was a significant difference in cause of death between summer and all other seasons (spring p = 0.008; autumn p < 0.001; winter p < 0.001) and between autumn and winter (p = 0.008). Conclusion Trauma was identified as the most significant cause of mortality in the free-living weka necropsied. The inherent and uncertain submissions biases, and low case numbers over a long period of time, means that temporal patterns and the effect of captivity status on causes of mortality should be interpreted with caution.

The gender difference in cause of death in Japanese atrial fibrillation patients: The Fushimi AF Registry

Abstract Background Previously we reported that incident rate of heart failure (HF) hospitalization was higher in female than male and stroke was comparable between female and male in Japanese atrial fibrillation (AF) patients. We also reported cardiovascular death was mainly related to HF but not stroke. However, the gender difference in cause of death in Japanese AF patients is little known. Method The Fushimi AF Registry is a community-based prospective survey of AF patients in Fushimi-ku, which is a typical urban district of Japan. We started to enroll patients from March 2011, and follow-up data were available for 4,496 patients by the end of February 2022. In the entire cohort, 2,677 were male and 1,819 were female. We compared clinical characteristics and cause and incidence rate of death between two groups. Result Female patients were older (female vs. male; 76.7 years vs. 71.5 years, p&amp;lt;0.01), less in body weight (51.2 kg vs. 65.0 kg, p&amp;lt;0.01), had higher pulse rate (79.4 bpm vs. 77.5 bpm, p&amp;lt;0.01), less often persistent/permanent type (48.2% vs. 52.0%, p=0.04), less likely to have previous stroke/systemic embolism (18.6% vs. 21.1%, p=0.04), diabetes mellitus (19.9% vs. 26.2%, p&amp;lt;0.01), coronary artery disease (12.3% vs. 16.0%, p&amp;lt;0.01) and more likely to have prior HF (32.2% vs. 24.0%, p&amp;lt;0.01), chronic kidney disease (39.3% vs. 33.7%, p&amp;lt;0.01)and had higher CHADS2 score (2.15 vs. 1.96, p&amp;lt;0.01). In prescription data, female patients less often received oral anticoagulant (52.1% vs. 58.2%, p&amp;lt;0.01) and antiplatelet drug (23.8% vs. 29.0%, p&amp;lt;0.01). Systolic blood pressure, hypertension and major bleeding were comparable between two groups. During the median follow-up of 2,093 days, the incidence rate of all cause death was comparable between female group and male group (5.20 vs. 4.86 % per person-year, log rank p=0.32). The incidence rate of cardiac death was significantly higher in female group than male group (0.98 vs. 0.70 % per person-year, log rank p=0.02) and the incidence rate of vascular death was also significantly higher in female group than male group (0.43 vs. 0.26 % per person-year, log rank p=0.03). The incidence rate of death due to HF, which was main cause of cardiac death, was higher in female group but not significant (0.86 vs. 0.66 % per person-year, log rank p=0.08). We analyzed the proportion of cause of death in each gender, cardiac death and stroke were higher and non-cardiovascular death was lower in female than male group. Conclusion The incidence rates of cardiac death and vascular death were significantly higher in female group than male group, and death due to HF was non-significantly higher in female group. Concerning the proportion of cause of death in each gender, cardiac death and stroke were higher in female than male. The impact of cardiovascular events may be stronger and prevention of them may be more important in female.

A novel approach to the cause of death identification—multi-strategy integration of multi-organ FTIR spectroscopy information using machine learning

Identifying the cause of death has always been a major focus and challenge in forensic practice and research. Traditional techniques for determining the causes of death are time-consuming, labor-intensive, have high professional barriers, and are vulnerable to significant subjective bias. Additionally, most current studies on causes of death are limited to specific organs and single causes. To overcome these challenges, this study utilized simple and rapid fourier transform infrared spectroscopy (FTIR) detection technology, integrating data from six organs—heart, liver, spleen, lung, kidney, and brain. The optimum model for identifying seven different causes of death was determined by evaluating the performance of models developed using the model efficiencies of single-organ (SO), single-organ model fusion (SOMF), multi-organ data fusion (MODF), and multi-organ data model fusion (MODMF) modeling methods. Considering factors such as operational costs, model performance, and model complexity, the MODF artificial neural network (ANN) model was found to be the most suitable choice for constructing a cause of death identification model, with a cross-validation mean accuracy of 0.960 and a test set accuracy of 0.952. The heart and kidney contributed more spectral features to the construction of the cause of death identification model compared to other organs. This study not only demonstrated that data fusion and model fusion are effective strategies for improving model performance but also provided a comprehensive data analysis framework and process for modeling with small sample multi-modal data (multiple organ data). In conclusion, by exploring various approaches, this study offers new solutions for identifying the cause of death.

Complication of Grief and PTSD among bereaved siblings in Israel

This study explores the experiences of bereaved siblings in Israel, examining how different causes of death affect their psychological well-being. We recruited three groups of siblings who lost loved ones as a result of military service, terrorism, or civilian circumstances. A total of 159 bereaved siblings completed questionnaires measuring complications of grief (CG), posttraumatic stress disorder (PTSD), and world assumptions. Results indicated that siblings who experienced civilian loss exhibited the highest prevalence of CG, while no significant differences in PTSD levels were observed across the groups. Both CG and PTSD were associated with the world assumptions regarding the benevolence of the world and sense of self-worth. The study also examined how demographic variables influence siblings’ well-being and coping with loss. Overall, the findings emphasize the importance of recognizing the unique experiences of bereaved siblings and underscore the necessity of tailored support to address their individualized needs.

Artificial Intelligence in Forensic Sciences: A Systematic Review of Past and Current Applications and Future Perspectives.

The aim of this study is to review the available knowledge concerning the use of artificial Intelligence (AI) in general in different areas of Forensic Sciences from human identification to postmortem interval estimation and the estimation of different causes of death. This paper aims to emphasize the different uses of AI, especially in Forensic Medicine, and elucidate its technical part. This will be achieved through an explanation of different technologies that have been so far employed and through new ideas that may contribute as a first step to the adoption of new practices and to the development of new technologies. A systematic literature search was performed in accordance with the Preferred Reported Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines in the PubMed Database and Cochrane Central Library. Neither time nor regional constrictions were adopted, and all the included papers were written in English. Terms used were MACHINE AND LEARNING AND FORENSIC AND PATHOLOGY and ARTIFICIAL AND INTELIGENCEAND FORENSIC AND PATHOLOGY. Quality control was performed using the Joanna Briggs Institute critical appraisal tools. A search of 224 articleswas performed. Seven more articles were extracted from the references of the initial selection. After excluding all non-relevant articles, the remaining 45 articles were thoroughly reviewed through the whole text. A final number of 33 papers were identified as relevant to the subject, in accordance with the criteria previously established. It must be clear that AI is not meant to replace forensic experts but to assist them in their everyday work life.

Cause-specific excess mortality in Denmark, Finland, Norway, and Sweden during the COVID-19 pandemic 2020–2022: a study using nationwide population data

While there is substantial evidence on excess mortality in the first two years of the COVID-19 pandemic, no study has conducted a cause-specific analysis of excess mortality for the whole period 2020–2022 across multiple countries. We examined cause-specific excess mortality during 2020–2022 in Denmark, Finland, Norway, and Sweden—four countries with similar demographics and welfare provisions, which implemented different pandemic response policies. To this end, we utilized nationwide register-based information on annual cause-specific deaths stratified by age and sex, and applied linear regression models to predict mortality in 2020–2022 based on the reference period 2010–2019. Excess deaths were obtained by contrasting actual and expected deaths. Additional analyses employed standardization to a common population, as well as population adjustments to account for previous deaths. Our results showed that, besides deaths due to COVID-19 (a total of 32,491 during 2020–2022), all countries experienced excess deaths due to cardiovascular diseases (in total 11,610 excess deaths), and under-mortality due to respiratory diseases other than COVID-19 (in total 9878) and dementia (in total 8721). The excess mortality due to cardiovascular diseases was particularly pronounced in Finland and Norway in 2022, and the under-mortality due to dementia was particularly pronounced in Sweden in 2021–2022. In conclusion, while COVID-19 deaths emerge as the most apparent consequence of the pandemic, our findings suggest that mortality has also been influenced by substitutions between different causes of death and over time, as well as indirect consequences of COVID-19 infection and pandemic responses—albeit to different extents in the different countries.

Autopsy rates and the misclassification of suicide and accident deaths.

Mortality statistics are critical to determine the burden of disease. Certain causes of death are prone to being misclassified on cause of death certificates. This poses a serious risk for public health and safety, as accurate death certificates form the basis for mortality statistics, which in turn are crucial for research, funding allocation and health interventions. This study uses generalised estimating equations and regression modelling to investigate for which cause of death categories suicide and accident deaths are misclassified as. National mortality statistics and autopsy rates from North America and Europe covering the past forty years were analysed to determine the associations between the different causes of death in cross-sectional and longitudinal models. We find that suicides and deaths by accidents are frequently mutually misclassified. We also find that suicides are frequently misclassified as drug use disorder deaths, in contrast to accident deaths, which are not misclassified as drug use disorder deaths. Furthermore, suicides do not seem to be misclassified as undetermined deaths or ill-defined deaths. The frequency of misclassification shows that the quality of death certificates should be improved, and autopsies may be used systematically to control the quality of death certificates.

Trends in the impact of suicide compared to other external causes of death: From 1995 to 2020

BackgroundSuicide is one of the world's greatest public health problems. More than 700,000 people lose their lives to suicide every year. While funding for mental health waits to be increased, thousands of suicides occur every day. Material and methodsThis study aims to quantify the global impact of suicide compared to other external causes of death in terms of years of potential life lost (YPLL), and how this will change between 1995 and 2020. Our source of information is the World Health Organization (WHO) mortality database. We then use YPLL, a standard measure of premature mortality and burden of disease that brings precision to the assessment of the impact of different causes of death. This, combined with the compound annual growth rate (CAGR) as a way of expressing increase, gives us a better understanding of the real situation and trends of suicide compared to other external causes of death in different countries worldwide. ResultsBased on the available sources of information and the selection criteria, we obtained a sample of 69 countries. The CAGR for all causes per capita decreased over the observed period in 65 countries, and it increases in 4 countries. In contrast, the CAGR specifically for suicide decreased in 49 countries, while an increase was observed in 20 countries. ConclusionsPrevention of most external causes of mortality shows promising data in most countries. However, this is not the case for suicide. Thus, YPLL due to suicide have decreased to a comparatively lesser extent and have even increased in some countries, a very worrying situation that poses many clinical and epidemiological challenges.

Deep learning-based prediction of one-year mortality in Finland is an accurate but unfair aging marker

Short-term mortality risk, which is indicative of individual frailty, serves as a marker for aging. Previous age clocks focused on predicting either chronological age or longer-term mortality. Aging clocks predicting short-term mortality are lacking and their algorithmic fairness remains unexamined. We developed a deep learning model to predict 1-year mortality using nationwide longitudinal data from the Finnish population (FinRegistry; n = 5.4 million), incorporating more than 8,000 features spanning up to 50 years. We achieved an area under the curve (AUC) of 0.944, outperforming a baseline model that included only age and sex (AUC = 0.897). The model generalized well to different causes of death (AUC > 0.800 for 45 of 50 causes), including coronavirus disease 2019, which was absent in the training data. Performance varied among demographics, with young females exhibiting the best and older males the worst results. Extensive prediction fairness analyses highlighted disparities among disadvantaged groups, posing challenges to equitable integration into public health interventions. Our model accurately identified short-term mortality risk, potentially serving as a population-wide aging marker.

Contributions of causes of death to differentials in life expectancy by internal migrant status in the Netherlands. A population register based study, 2015–2019

Important health differences exist in the context of international migration and residential mobility. Less is known about health differences regarding the medium-distance level of internal migration. This study examines life expectancy gaps between internal movers and stayers in the Netherlands and their underlying processes by assessing the contribution of different causes of death by age and sex. It uses individually-linked death counts and population exposures extracted from population registers, covering the native Dutch population aged 10+ from 2015 to 2019. The pooled data were disaggregated by causes-of-death group (neurodegenerative diseases, cardiovascular diseases, lifestyle-related mortality, external causes, and other causes), internal migrant status (movers and stayers, based on past 10-year residence in the 40 NUTS-3 [Nomenclature of Territorial Units for Statistics, level 3] regions), age, and sex. Comparing movers and stayers, we computed life expectancy at age 10 (e10), age- and cause-specific mortality risks, and applied decomposition methods to assess contributions of causes of death to e10 gaps. In the Netherlands in 2015–2019, e10 was lower for movers between NUTS-3 regions than stayers (males: 2.49 years; females: 3.51 years), due to excess mortality for movers at most ages. Movers only had a lower mortality than stayers at younger working ages (males: ages 20–44; females: ages 20–34). Mortality from neurodegenerative diseases and cardiovascular diseases were the largest contributors to the e10 gap, especially at ages 75+ and for females. Mortality from lifestyle-related and external causes of death contributed less, with the largest contributions for females aged 75–89 and males aged 45–69. The lower e10 of movers in the Netherlands is likely explained by health selection effects–in particular care-related moves as coping behaviour–rather than by causal effects through risk accumulation. Research focusing on regional or spatial heterogeneity of the mover-stayer health gap would be insightful to further understand these processes.

#2608 Trends over time in the causes of death with a functioning graft among kidney transplant recipients

Abstract Background and Aims Cardiovascular diseases are traditionally considered as the leading cause of death in kidney transplant recipients. Significant improvements in immunosuppressive strategies but also in the prevention and management of cardiovascular diseases have been made over the last decades. Therefore, we aimed to explore the evolution over time of the causes of death with a functioning graft in kidney transplant recipients. Method We performed a retrospective study including all patients transplanted with a kidney between January 1995 and December 2009 at Cliniques Universitaires Saint-Luc in Brussels who died with a functioning graft. Patients younger than 18 years old and those who received bone marrow or other solid organ transplant were excluded. Clinical data were collected by electronic medical chart review. Differences in causes of death were assessed between two eras of transplantation (first era [1995-2001] vs second era [2002-2009]). Results A total of 256 patients (aged 56 ± 11.18 years, 164 (64.9%) man) were included. Among those patients, 65 (25.39%) died from infectious causes, 64 (25%) from neoplastic causes, 62 (24.22%) from cardiovascular causes and 24 (9.38%) from other causes. The cause of death was unknown for 42 (16.41%) patients. Patients who received a kidney graft during the first era mostly died from cardiovascular causes (33%), while those who received a graft during the second era mostly died from infectious causes (31%). Death from neoplastic causes remained similar across eras (32 (26%) versus 32 (24%), p-value &amp;gt; 0.1). Patients grafted in the more recent era have threefold higher odds of dying from infectious than cardiovascular causes compared to those grafted in the former graft era (OR 3.1, p=0.002). Infectious causes of death were the most prevalent early cause of death in both eras i.e. 3 patients (60%) in the first era and 6 patients (50%) in the second era. Conclusion We observed a significant shift from cardiovascular deaths to infectious deaths between the former and the more recent transplantation era. Neoplastic causes of death remained stable over time.

Floods and cause-specific mortality in the UK: a nested case-control study

BackgroundFloods are the most frequent weather-related disaster, causing significant health impacts worldwide. Limited studies have examined the long-term consequences of flooding exposure.MethodsFlood data were retrieved from the Dartmouth Flood Observatory and linked with health data from 499,487 UK Biobank participants. To calculate the annual cumulative flooding exposure, we multiplied the duration and severity of each flood event and then summed these values for each year. We conducted a nested case-control analysis to evaluate the long-term effect of flooding exposure on all-cause and cause-specific mortality. Each case was matched with eight controls. Flooding exposure was modelled using a distributed lag non-linear model to capture its nonlinear and lagged effects.ResultsThe risk of all-cause mortality increased by 6.7% (odds ratio (OR): 1.067, 95% confidence interval (CI): 1.063–1.071) for every unit increase in flood index after confounders had been controlled for. The mortality risk from neurological and mental diseases was negligible in the current year, but strongest in the lag years 3 and 4. By contrast, the risk of mortality from suicide was the strongest in the current year (OR: 1.018, 95% CI: 1.008–1.028), and attenuated to lag year 5. Participants with higher levels of education and household income had a higher estimated risk of death from most causes whereas the risk of suicide-related mortality was higher among participants who were obese, had lower household income, engaged in less physical activity, were non-moderate alcohol consumers, and those living in more deprived areas.ConclusionsLong-term exposure to floods is associated with an increased risk of mortality. The health consequences of flooding exposure would vary across different periods after the event, with different profiles of vulnerable populations identified for different causes of death. These findings contribute to a better understanding of the long-term impacts of flooding exposure.

Non-stationary Bayesian spatial model for disease mapping based on sub-regions.

This paper aims to extend the Besag model, a widely used Bayesian spatial model in disease mapping, to a non-stationary spatial model for irregular lattice-type data. The goal is to improve the model's ability to capture complex spatial dependence patterns and increase interpretability. The proposed model uses multiple precision parameters, accounting for different intensities of spatial dependence in different sub-regions. We derive a joint penalized complexity prior to the flexible local precision parameters to prevent overfitting and ensure contraction to the stationary model at a user-defined rate. The proposed methodology can be used as a basis for the development of various other non-stationary effects over other domains such as time. An accompanying R package fbesag equips the reader with the necessary tools for immediate use and application. We illustrate the novelty of the proposal by modeling the risk of dengue in Brazil, where the stationary spatial assumption fails and interesting risk profiles are estimated when accounting for spatial non-stationary. Additionally, we model different causes of death in Brazil, where we use the new model to investigate the spatial stationarity of these causes.

Abstract 836: Long-term particulate matter and mortality in cancer patients and survivors compared to general population

Abstract Background and Aim Exposure to particulate matter (PM) has been reported as a major environmental hazard to human health. Although many studies found the associations with mortality and its susceptibility in patients with diabetes and heart diseases, few studies paid attention to cancer. In addition, there were no studies that compared these associations in cancer patients and survivors with those in healthy people from the same population. The aim of this study is to compare the association between long-term exposure to PM with all-cause and cause-specific mortality between the general population and cancer patients and survivors both of which were selected from the same cohort in the Seoul Metropolitan Area (SMA), South Korea. Methods Using the National Health Insurance Services-National Sample cohort, we selected the subjects who received health screening during 2005-2007, had full address information, and were aged 30-80 without severe disability. We defined cause-specific mortality based on the International Classification of Disease, 10th reversion from 2007 to 2015. Using a previously-validated prediction model, we estimated annual-average PM concentrations at participants’ district-level addresses and assessed individual long-term exposure as the average concentrations in the previous 5 years on each year during the follow-up. Then, we performed time-varying Cox proportional hazards model to estimate hazard ratios (HRs) and 95% confidence intervals (95%CI) per 10 ug/m3 increase in PM2.5 and PM10 adjusting for sociodemographic characteristics, family history of cancer, and health behaviors. Results Our study population of 98,493 adults include 94,494 (95.9%) general population and 3,999 (4.1%) cancer patients and survivors in the SMA at baseline. In the general population, we observed the associations with deaths from all causes (PM2.5: HR=1.31, 95% CI: 1.10-1.55; PM10: 1.07, 1.00-1.15), non-accidental causes (PM2.5: 1.51, 1.24-1.84; PM10: 1.10, 1.02-1.20), and cardiopulmonary diseases (PM2.5: 1.57, 1.07-2.31); PM10: 1.22, 1.04-1.43). In contrast, although all HRs of all-cause and cause-specific mortality were not statistically significant, cancer patients and survivors showed much higher HRs for all-cancer and lung-cancer mortality (PM2.5: 1.57 and 1.99; PM10: 1.01 and 1.33) than those for all or other cause-specific mortality as well as those of cancer-related mortality in the general population. Conclusions Our findings provided suggestive evidence of the different causes of deaths associated with long-term exposure to air pollution between the general population and cancer patients and survivors. While PM could behave as a risk factor of cardiopulmonary deaths in the general population, after diagnosed with cancer, exposure to PM may increase the risk of dying of cancer. Citation Format: Miyoun Shin, Ok-Jin Kim, Seung-Ah Choe, Yoon-Jung Choi, Ho Kim, Woojoo Lee, Ho Lee, Sun-Young Kim. Long-term particulate matter and mortality in cancer patients and survivors compared to general population [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 836.

“Immunohistochemical analysis of Sigma-1 receptor (σ-1R) expression in human pineal gland in relation to different causes of death”

Sigma-1 receptor (σ-1R) modulates cellular signaling pathways, probably acting as a ligand operated chaperone. When activated, the receptor translocates from the interface mitochondrion associated membrane of the endoplasmic reticulum to the cell membrane. σ-1R was demonstrated in some brain regions, including the pineal gland, and was proposed to be involved in several cerebral processes, including neuroprotective responses against homeostasis alterations. On this basis, the immunohistochemical expression of σ-1R in human pineal glands was evaluated, with particular regard to the different causes of death. Thirty-eight pineal glands obtained from forensic autopsies were divided into five groups according to the cause of death: sudden death, drowning, fire fatality, hanging, and hemorrhagic shock, and examined with hematoxylin-eosin stain and immunohistochemistry for σ-1R. Both pinealocytes and perivascular spaces were evaluated. The pineal glands from sudden death were only mildly positive for σ-1R, while a more evident immunopositivity was observed in hanging, fire fatality, hemorrhagic shock, and drowning. These results were confirmed in a two-by-two comparison between the sudden death group and other groups. Our data demonstrate for the first time with immunohistochemical techniques the presence of σ-1R expression in the human pineal gland and propose a direct correlation between σ-1R expression and duration of the death process, in particular when hypoxic conditions and/or excessive psychological stress are present.

Deaths among adults under supervision of the England and Wales’ probation services: variation in individual and criminal justice-related factors by cause of death

BackgroundThe mortality rate among people under probation supervision in the community is greater than that among incarcerated people and that among the general population. However, there is limited research on the distinct vulnerabilities and risks underlying the causes of death in this population. In this retrospective cohort study, we examined the individual and criminal justice-related factors associated with different causes of death. Factors were assessed in relation to the type of supervision, distinguishing between those under post-custodial release and those serving a community sentence.ResultsThe study utilised the official data held by His Majesty’s Prison and Probation Service in England and Wales on the deaths of men and women under probation supervision between 01 April 2019 and 31 March 2021 where the cause of death had been definitively recorded (n = 1770). The high risk of deaths primarily caused by external factors (i.e., suspected suicide (10%), homicide (5%), and drug-related death (26%)) in this population was confirmed. A Gaussian Graphical Model (GGM) demonstrated unique relationships with suspected suicide and drug-related deaths for known suicide risk, history of drug use and recent (< 28 days of death) enforcement action due to a breach of probation conditions. Our findings suggest that that familial violence and abuse may be relevant in suicide and drug-related deaths and that minority groups may experience disproportional risk to certain types of death.ConclusionsThis study identified unique risk indicators and modifiable factors for deaths primarily caused by external factors in this population within the health and justice spheres. It emphasised the importance of addressing health inequalities in this population and improved joint-working across health and justice. This involves ensuring that research, policies, training, and services are responsive to the complex needs of those under probation supervision, including those serving community sentences. Only then can we hope to see lower rates of death within this population.

Automated detection of fatal cerebral haemorrhage in postmortem CT data

During the last years, the detection of different causes of death based on postmortem imaging findings became more and more relevant. Especially postmortem computed tomography (PMCT) as a non-invasive, relatively cheap, and fast technique is progressively used as an important imaging tool for supporting autopsies. Additionally, previous works showed that deep learning applications yielded robust results for in vivo medical imaging interpretation. In this work, we propose a pipeline to identify fatal cerebral haemorrhage on three-dimensional PMCT data. We retrospectively selected 81 PMCT cases from the database of our institute, whereby 36 cases suffered from a fatal cerebral haemorrhage as confirmed by autopsy. The remaining 45 cases were considered as neurologically healthy. Based on these datasets, six machine learning classifiers (k-nearest neighbour, Gaussian naive Bayes, logistic regression, decision tree, linear discriminant analysis, and support vector machine) were executed and two deep learning models, namely a convolutional neural network (CNN) and a densely connected convolutional network (DenseNet), were trained. For all algorithms, 80% of the data was randomly selected for training and 20% for validation purposes and a five-fold cross-validation was executed. The best-performing classification algorithm for fatal cerebral haemorrhage was the artificial neural network CNN, which resulted in an accuracy of 0.94 for all folds. In the future, artificial neural network algorithms may be applied by forensic pathologists as a helpful computer-assisted diagnostics tool supporting PMCT-based evaluation of cause of death.

Alcohol use, economic development and health burden: A conceptual framework

Economic development has been identified as an important influencing contributor to life expectancies: wealthier countries have lower mortality rates and different causes of death. Economic development also impacts alcohol consumption, as upper-middle and high-income countries, on average, have higher levels of consumption and less abstention. This often leads to a paradoxical situation whereby for low- and middle income countries increases in alcohol consumption are associated with decreases in alcohol-attributable mortality rates. These increases in consumption may diminish the benefits of economic development. Alcohol control policies can reduce the health and social burdens of increased alcohol use that result from economic development. Two case examples of Thailand and Vietnam which are presented. From 2010 to 2019 Thailand experienced a 3.7% increase in APC, a 9.8% decrease in all-cause mortality per 100,000 people, and a 1.4% decrease in alcohol-attributable mortality per 100,000 people. From 2010 to 2019 Vietnam experienced a 26.8% increase in APC, a 6.7% decrease in all-cause mortality per 100,000 people, and a 3.7% increase in alcohol-attributable mortality per 100,000 people. Due to a failure to implement strong alcohol control policies Vietnam has experienced an increase in alcohol-attributable mortality despite decreases in all-cause mortality. Accordingly, the implementation of alcohol control policies, can diminish the increases in alcohol use for low- and middle-income countries that accompany economic development.

Restrictive vs Liberal Blood Transfusions for Patients With Acute Myocardial Infarction and Anemia by Heart Failure Status: An RCT Subgroup Analysis

BackgroundRed blood cell transfusion can cause fluid overload. We evaluated the interaction between heart failure (HF) at baseline and transfusion strategy on outcomes in acute myocardial infarction (AMI). MethodsWe used data from the randomized REALITY trial. HF was defined as history of HF or Killip class > 1 at randomization. Primary outcome was major adverse cardiovascular events (MACE): composite of all-cause death, nonrecurrent AMI, stroke, or emergency revascularization prompted by ischemia at 30 days. ResultsAmong 658 randomized patients, 311 (47.3%) had HF. Patients with HF had higher rates of MACE at 30 days and 1 year and higher rates of nonfatal new-onset HF. There was no interaction between HF and effect of randomized assignment on the primary outcome or nonfatal new-onset HF. A liberal transfusion strategy was associated with increased all-cause death at 30 days and at 1 year in patients with HF (Pinteraction = 0.009 and P = 0.049, respectively). The main numerical difference in cause of death between restrictive and liberal strategies was death by HF at 30 days (4 vs 11). ConclusionsHF is frequent in patients with AMI and anemia and is associated with higher risk of MACE (including all-cause death) and nonfatal new-onset HF. Although there was no interaction of HF with effect of transfusion strategy on MACE, a liberal transfusion strategy was associated with higher all-cause death that appears driven by a higher risk of early death caused by HF. Clinical Trial RegistrationNCT02648113.

2000 Cause of death amongst frail and non-frail older people post-hospital admission in northern Tanzania

Abstract Introduction Verbal autopsy (VA) is a tool used to determine cause of death (COD) in regions lacking routine medical certification. Automated algorithms are widely used to interpret VA data. This study aimed to investigate potential differences in COD between frail and non-frail older people in northern Tanzania. Method This work forms part of a longitudinal study investigating the clinical outcomes of 308 consecutive adults aged ≥60 years following admission to four hospitals in northern Tanzania. Frailty status was established on admission using the Clinical Frailty Scale (CFS) and dichotomised with CFS ≥5 indicating frailty. For participants who passed away in the 10-12 months following admission, VA data were collected through telephone interview using the 2022 World Health Organization (WHO) VA instrument. COD estimates were established using the SmartVA-Analyze program, implementing the Tariff 2.0 Method. Results After a mean follow-up period of 10.8 (±0.9) months, VA data were available for 69 participants. At admission screening, 51 (73.9%) were frail, while 18 (26.1%) were non-frail. SmartVA produced COD estimates for 42 (60.9%) participants, while 27 (39.1%) remained undetermined. Cause Specific Mortality Fractions (CSMFs) for non-communicable disease were higher for those with frailty than the non-frail group (70.6% vs. 54.0%, respectively). An undetermined COD was more likely in those with frailty. Of the 27 undetermined COD, 22 (81.5%) were attributed to frail individuals and 5 (18.5%) to non-frail. These undetermined COD represent 43.1% of frailty-related deaths and 27.8% of non-frailty-related deaths. Conclusions Older people with frailty living in Tanzania are more likely to die from non-communicable diseases compared to non-frail older people. Although employing SmartVA to analyse VA data from this population was feasible, it faced challenges ascribing COD to all participants. This limitation may be due to the multi-morbidity often present in older populations where multiple factors combine to cause mortality.