Mortality Dataset Research Articles

High sensitivity troponins and mortality in the population with metabolic dysfunction-associated steatotic liver disease

Given the global high prevalence of MASLD and its poor CVD prognosis, it is essential to perform risk stratification for MASLD patients. The specific impact of High Sensitivity Troponins (hs-cTn ) on mortality in MASLD patients remains unexplored. The NHANES databases from 1999 to 2004, which include data on high-sensitivity cardiac troponin (hs-cTn) levels and comorbidities, were linked with the most recent mortality dataset. Myocardial injury was determined using the 99th upper reference limits (URL) for hs-cTn. Our study included 3460 MASLD patients. The mean follow-up duration was 192 months, during which 1074 (23%) MASLD participants died from all-cause mortality, and 363 (7.3%) died from CVD mortality. Our findings indicate that MASLD patients with elevated levels of hs-cTnT (> 99th URL) exhibit increased risks of all-cause mortality [adjusted hazard ratio (aHR) = 1.93] and CVD mortality (aHR = 2.4). Similar results were observed for hs-cTnI, where the aHRs for all-cause mortality and CVD mortality were 2.03 and 2.97, respectively. Furthermore, we identified a nonlinear dose-response relationship between hs-cTn levels and the risk of mortality (P for nonlinearity < 0.001). Our findings suggest that hs-cTn can predict mortality risk in MASLD, aiding clinicians in risk-stratifying this population. Therefore, we recommend considering hs-cTn detection in individuals with MASLD to effectively assess their future mortality risk.

Specialist intellectual disability liaison nurses in general hospitals in England: cohort study using a large mortality dataset

ObjectiveIntellectual disability liaison nurses in general hospitals could enhance access to high-quality, adapted healthcare and improve outcomes. We aimed to explore associations between the input of intellectual disability liaison nurses...

Pulmonary hypertension-related deaths in patients with acute pulmonary embolism in the United States, 2003 to 2020.

Data regarding the mortality trends in pulmonary embolism (PE)-related mortality in patients with concomitant pulmonary hypertension (PH) are lacking. We assessed the trends in PE-related mortality in patients with concomitant PH in the United States (US) over the past 2 decades and during the first year of the COVID-19 pandemic using data from the Centers for Disease Control and Prevention's (CDC) Wide-ranging ONline Data for Epidemiologic Research (WONDER) dataset. Mortality data were retrieved from the publicly available CDC WONDER mortality dataset from 2003 to 2020. Age-adjusted mortality rates (AAMRs), per 100,000 population, were assessed using Joinpoint regression modelling and expressed as estimated average annual percentage change (AAPC) with relative 95% CIs and stratified by urbanicity, sex, age, and race/ethnicity. Over the study period, the AAMR for PE/PH-related mortality linearly increased (AAPC: +4.3% [95% CI: 3.7 to 4.9], p < 0.001) without sex differences. The AAMR increase was more pronounced in White individuals (AAPC: +4.8% [95% CI: 4.1 to 5.5], p < 0.001) and in subjects living in rural areas (AAPC: +5.1% [95% CI: 3.8 to 6.4], p < 0.001) compared to those living in urban areas. During the first year of the COVID-19 pandemic there was a significant excess in PE/PH-related mortality among women, older than 65 years and living in rural areas. The rate of PE/PH-related mortality in the US is increasing. Although the early diagnosis of PH in patients with acute PE has become easier with improved diagnostic modalities, the mortality rate of these patients remains high.

International Classification of Disease based Injury Severity Score (ICISS): a comparison of methodologies applied to linked data from New South Wales, Australia

BackgroundThe International Classification of Disease Injury Severity Score (ICISS) provides an efficient method to determine injury severity in hospitalised injury patients. Injury severity metrics are of particular interest for the...

Overdispersed Nonlinear Regression Models

In this paper we propose nonlinear regression models in the biparametric family of distributions. In this class of models we propose two new classes of overdispersed nonlinear regression models: the first, defined from the overdispersion family of distributionsproposed by Dey, Gelfand, and Peng (1994), and the second from a class of compound distributions. For these models, we develop a Bayesian method in which samples of the posterior distributions are obtained by applying an iterated Metropolis-Hastings algorithmobtained by assuming two groups of parameters, defined by the mean and dispersion regression structures. In the first subclass of models, to improve the performance of the iterated Metropolis-Hastings algorithm, we develop worked variables from the applicationof Fisher scoring algorithm, to build the kernel transition function. A Bayesian method to fit compound regression models is also proposed. Finally, we present an application to neonatal mortality dataset to illustrate the use of the proposed models and the perfor-mance of the Bayesian method to fit the proposed models.

Measuring population health using health expectancy estimates from morbidity and mortality databases.

The progressive incorporation of quality of life indicators in health planning meets a critical need: The evaluation of the performance of health services, which are under stress by multiple causes, but in particular by an ageing population. In general, national health plans rely on health expectancies obtained using the Sullivan method. The Sullivan health expectancy index combines age-specific mortality rates and age-specific prevalence of healthy life, obtained from health surveys. The objective of this work is to investigate an equivalent estimation, using available information from morbidity and mortality datasets. Mortality and morbidity information, corresponding to years 2016 and 2017, was obtained for the population of the county of Baix Empordà (Catalonia), N = 91,130. Anonymized individual information on diagnoses, procedures and pharmacy consumption contained in the individual clinical record (ICD and ATC codes), were classified into health states. Based on the observed health transitions and mortality, life expectancies by health state were obtained from a multistate microsimulation model. Healthy life expectancies at birth and 65 years for females and males were respectively HLE0female = 39.94, HLE0male = 42.87, HLE65female = 2.43, HLE65male = 2.17. These results differed considerably from the Sullivan equivalents, e.g., 8.25 years less for HLE65female, 9.26 less for HLE65male. Point estimates for global life expectancies at birth and 65 years of age: LE0female = 85.82, LE0male = 80.58, LE65female = 22.31, LE65male = 18.86. Health indicators can be efficiently obtained from multistate models based on mortality and morbidity information, without the use of health surveys. This alternative method could be used for monitoring populations in the context of health planning. Life Expectancy results were consistent with the standard government reports. Due to the different approximation to the concept of health (data-based versus self-perception), healthy life expectancies obtained from multistate micro simulation are consistently lower than those calculated with the standard Sullivan method.

Hypoxic indices for obstructive sleep apnoea severity and cardiovascular disease risk prediction: A comparison and application in a community population.

The apnoea-hypopnoea index (AHI) and oxygen desaturation index (ODI) encounter challenges in capturing the intricate relationship between obstructive sleep apnoea (OSA) and cardiovascular disease (CVD) risks. Although novel hypoxic indices have been proposed to tackle these limitations, there remains a gap in comprehensive validation and comparisons across a unified dataset. Samples were derived from the Sleep Heart Health Study (SHHS), involving 4485 participants aged over 40 years after data quality screening. The study compared several key indices, including AHI, ODI, the reconstructed hypoxic burden (rHB), the percentage of sleep time with the duration of respiratory events causing desaturation (pRED_3p) and the sleep breathing impairment index (SBII), in relation to CVD mortality and morbidity risks. Adjusted Cox proportional models were employed to calculate hazard ratios (HRs) for each index, and comparisons were performed. SBII and pRED_3p exhibited significant correlations with both CVD mortality and morbidity, with SBII showing the highest adjusted HR (95% confidence interval) for mortality (2.04 [1.25, 3.34]) and pRED_3p for morbidity (1.43 [1.09-1.88]). In contrast, rHB was only significant in predicting CVD mortality (1.63 [1.05-2.53]), while AHI and ODI did not show significant correlations with CVD outcomes. The adjusted models based on SBII and pRED_3p exhibited optimal performance in the CVD mortality and morbidity datasets, respectively. This study identified the optimal indices for OSA-related CVD risks prediction, SBII for mortality and pRED_3p for morbidity. The open-source online platform provides the computation of the indices.

Estimated pulse wave velocity in metabolic dysfunction-associated steatotic liver disease and all-cause/cause-specific mortality.

Several reports show a significant association between metabolic dysfunction-associated steatotic liver disease (MASLD) and arterial stiffness (estimated pulse wave velocity [ePWV]) as a surrogate marker of vascular age. We investigate whether ePWV as arterial stiffness in MASLD is associated with all-cause/cause-specific mortality. This cohort study was based on the third National Health and Nutrition Examination Survey (NHANES, 1988-1994) and NHANES 2007-2014 and linked mortality datasets through 2019. Cox regression models assessed the association between ePWV categorized by quartile and all-cause/cause-specific mortality among individuals with MASLD. During the follow-up of a median of 26.3 years (interquartile range: 19.9-27.9), higher levels of ePWV among individuals with MASLD were associated with increased all-cause mortality, which remained significant after adjusting for demographic, lifestyle, clinical, and metabolic risk factors. Furthermore, higher ePWV in MASLD was associated with higher cardiovascular mortality. There was a 44% (hazard ratio: 1.44, 95% confidence interval: 1.32-1.58) increase in all-cause mortality and a 53% (hazard ratio: 1.53, 95% confidence interval: 1.32-1.77) increase in cardiovascular mortality for every 1 m/s increase in ePWV in MASLD. However, there was no significant association between ePWV and cancer-related mortality. Sensitivity analyses using the NHANES 2007-2014 dataset showed results identical to the original analysis. Higher ePWV in MASLD was associated with a higher risk of all-cause and cardiovascular mortality beyond traditional cardiovascular risk factors. Screening for ePWV in individuals with MASLD may be an effective and beneficial approach to reducing all-cause and cardiovascular mortality.

FIT calculator: a multi-risk prediction framework for medical outcomes using cardiorespiratory fitness data

Accurately predicting patients' risk for specific medical outcomes is paramount for effective healthcare management and personalized medicine. While a substantial body of literature addresses the prediction of diverse medical conditions, existing models predominantly focus on singular outcomes, limiting their scope to one disease at a time. However, clinical reality often entails patients concurrently facing multiple health risks across various medical domains. In response to this gap, our study proposes a novel multi-risk framework adept at simultaneous risk prediction for multiple clinical outcomes, including diabetes, mortality, and hypertension. Leveraging a concise set of features extracted from patients' cardiorespiratory fitness data, our framework minimizes computational complexity while maximizing predictive accuracy. Moreover, we integrate a state-of-the-art instance-based interpretability technique into our framework, providing users with comprehensive explanations for each prediction. These explanations afford medical practitioners invaluable insights into the primary health factors influencing individual predictions, fostering greater trust and utility in the underlying prediction models. Our approach thus stands to significantly enhance healthcare decision-making processes, facilitating more targeted interventions and improving patient outcomes in clinical practice. Our prediction framework utilizes an automated machine learning framework, Auto-Weka, to optimize machine learning models and hyper-parameter configurations for the simultaneous prediction of three medical outcomes: diabetes, mortality, and hypertension. Additionally, we employ a local interpretability technique to elucidate predictions generated by our framework. These explanations manifest visually, highlighting key attributes contributing to each instance's prediction for enhanced interpretability. Using automated machine learning techniques, the models simultaneously predict hypertension, mortality, and diabetes risks, utilizing only nine patient features. They achieved an average AUC of 0.90 ± 0.001 on the hypertension dataset, 0.90 ± 0.002 on the mortality dataset, and 0.89 ± 0.001 on the diabetes dataset through tenfold cross-validation. Additionally, the models demonstrated strong performance with an average AUC of 0.89 ± 0.001 on the hypertension dataset, 0.90 ± 0.001 on the mortality dataset, and 0.89 ± 0.001 on the diabetes dataset using bootstrap evaluation with 1000 resamples.

Using the dead to infer about the living: Amphibian roadkill spatiotemporal dynamics suggest local populations' reduction

Roads represent one of the main sources of wildlife mortality, population decline, and isolation, especially for low-vagility animal groups. It is still not clearly understood how wildlife populations respond to these negative effects over space and time. Most studies on wildlife road mortality do not consider the spatial and temporal components simultaneously, or the imperfect roadkill detection, both of which could lead to inaccurate assumptions and unreliable mitigation actions. In this study, we applied a multi-season occupancy model to a 14-year amphibian mortality dataset collected along 120 km of roads, combined with freely available landscape and remote sensing metrics, to identify the spatiotemporal patterns of amphibian roadkill in a Mediterranean landscape in Southern Portugal. Our models showed an explicit general decrease in amphibian roadkill. The Iberian painted frog (Discoglossus galganoi) experienced roadkill declines over time of ∼70 %, while the spiny common toad (Bufo spinosus) and the fire salamander (Salamandra salamandra) had a loss of nearly 50 %, and the Southern marbled newt (Triturus pygmaeus) had 40 %. Despite the decreasing trend in roadkill, spatial patterns seem to be rather stable from year to year.Multi-season occupancy models, when combined with relevant landscape and remote sensing predictors, as well as long-term monitoring data, can describe dynamic changes in roadkill over space and time. These patterns are valuable tools for understanding roadkill patterns and drivers in Mediterranean landscapes, enabling the differentiation of road sections with varying roadkill over time. Ultimately, this information may contribute to the development of effective conservation measures.

Association of myocardial injury with adverse long-term survival among cancer patients.

Over time, cardiovascular disease (CVD) deaths increasingly exceed those from malignancy among cancer survivors. However, the association of myocardial injury with long-term survival (beyond three years) in cancer patients has not been previously described. The National Health and Nutrition Examination Survey high-sensitivity cardiac troponin (hs-cTn) and morbidities databases (1999-2004) were linked with the latest mortality dataset isolating records were respondents reported cancer diagnosis by a healthcare professional. Myocardial injury was then determined by elevated hs-cTn. 16,225,560 weighted records (1,058 unweighted) were included in this observational study, with myocardial injury identified in 14·2%. Those with myocardial injury had progressively worse survival at 5 (51·6% vs. 89·5%), 10 (28·3% vs. 76·0%), and 15 years (12·6% vs. 61·4%) compared to those without myocardial injury. After adjusting for baseline characteristics, those with myocardial injury had an adjusted hazard ratio (aHR) of 2·10 (95% CI 2·09-2·10, p<0·001) for all-cause mortality, 2·23 (2·22-2·24, p<0·001) for cardiovascular mortality, and 1·59 (95% CI 1·59-1·60, p<0·001) for cancer mortality compared to those without myocardial injury. Among patients with no pre-existing CVD, the hs-cTn I Ortho assay was a strong independent predictor of all cause (aHR 6·29, 95% CI 6·25-6·33, p<0·001), CVD (aHR 11·38, 95% CI 11·23-11·54, p<0·001), and cancer (aHR 5·02, 95% CI 4·96-5·07, p<0·001) mortality. As a marker for myocardial injury, hs-cTn/s were independently associated with worse long-term survival among cancer patients with a stronger relationship with all-cause, cardiovascular, and cancer mortality using hs-cTn I ortho assay.

Expressive mortality models through Gaussian process kernels

AbstractWe develop a flexible Gaussian process (GP) framework for learning the covariance structure of Age- and Year-specific mortality surfaces. Utilizing the additive and multiplicative structure of GP kernels, we design a genetic programming algorithm to search for the most expressive kernel for a given population. Our compositional search builds off the Age–Period–Cohort (APC) paradigm to construct a covariance prior best matching the spatio-temporal dynamics of a mortality dataset. We apply the resulting genetic algorithm (GA) on synthetic case studies to validate the ability of the GA to recover APC structure and on real-life national-level datasets from the Human Mortality Database. Our machine learning-based analysis provides novel insight into the presence/absence of Cohort effects in different populations and into the relative smoothness of mortality surfaces along the Age and Year dimensions. Our modeling work is done with the PyTorch libraries in Python and provides an in-depth investigation of employing GA to aid in compositional kernel search for GP surrogates.

Trends in atrial fibrillation-related mortality in Europe, 2008-2019.

Update data regarding the atrial fibrillation (AF)-related mortality trend in Europe remain scant. We assess the age- and sex-specific trends in AF-related mortality in the European states between the years 2008 and 2019. Data on cause-specific deaths and population numbers by sex for European countries were retrieved through the publicly available World Health Organization mortality dataset for the years 2008-2019. Atrial fibrillation-related deaths were ascertained when the International Classification of Diseases, 10th Revision code I48 was listed as the underlying cause of death in the medical death certificate. To calculate annual trends, we assessed the average annual % change (AAPC) with relative 95% confidence intervals (CIs) using Joinpoint regression. During the study period, 773 750 AF-related deaths (202 552 males and 571 198 females) occurred in Europe. The age-adjusted mortality rate (AAMR) linearly increased from 12.3 (95% CI: 11.2-12.9) per 100 000 population in 2008 to 15.3 (95% CI: 14.7-15.7) per 100 000 population in 2019 [AAPC: +2.0% (95% CI: 1.6-3.5), P<0.001] with a more pronounced increase among men [AAPC: +2.7% (95% CI: 1.9-3.5), P<0.001] compared with women [AAPC: +1.7% (95% CI: 1.1-2.3), P<0.001] (P for parallelism 0.01). Higher AAMR increases were observed in some Eastern European countries such as Latvia, Lithuania, and Poland, while the lower increases were mainly clustered in Central Europe. Over the last decade, the age-adjusted AF-related mortality has increased in Europe, especially among males. Disparities still exist between Western and Eastern European countries.

Impact of socioeconomic status on presentation, care quality and outcomes of patients attended by emergency medical services for dyspnoea: a population-based cohort study

BackgroundLow socioeconomic status (SES) has been linked to poor outcomes in many conditions. It is unknown whether these disparities extend to individuals presenting with dyspnoea. We aimed to evaluate the...

Identifying heat thresholds for South Africa towards the development of a heat-health warning system.

Exposure to heatwaves may result in adverse human health impacts. Heat alerts in South Africa are currently based on defined temperature-fixed threshold values for large towns and cities. However, heat-health warning systems (HHWS) should incorporate metrics that have been shown to be effective predictors of negative heat-related health outcomes. This study contributes to the development of a HHWS for South Africa that can potentially minimize heat-related mortality. Distributed lag nonlinear models (DLNM) were used to assess the association between maximum and minimum temperature and diurnal temperature range (DTR) and population-adjusted mortality during summer months, and the effects were presented as incidence rate ratios (IRR). District-level thresholds for the best predictor from these three metrics were estimated with threshold regression. The mortality dataset contained records of daily registered deaths (n = 8,476,532) from 1997 to 2013 and data for the temperature indices were for the same period. Maximum temperature appeared to be the most statistically significant predictor of all-cause mortality with strong associations observed in 40 out of 52 districts. Maximum temperature was associated with increased risk of mortality in all but three of the districts. Our results also found that heat-related mortality was influenced by regional climate because the spatial distribution of the thresholds varied according to the climate zones across the country. On average, districts located in the hot, arid interior provinces of the Northern Cape and North West experienced some of the highest thresholds compared to districts located in temperate interior or coastal provinces. As the effects of climate change become more significant, population exposure to heat is increasing. Therefore, evidence-based HHWS are required to reduce heat-related mortality and morbidity. The exceedance of the maximum temperature thresholds provided in this study could be used to issue heat alerts as part of effective heat health action plans.

Predicting risk of endometrial cancer in asymptomatic women (PRECISION): Model development and external validation.

Develop an endometrial cancer risk prediction model and externally validate it for UK primary care use. Cohort study. The UK Biobank was used for model development and a linked primary (Clinical Practice Research Datalink, CPRD) and secondary care (HES), mortality (ONS) and cancer register (NRCAS) dataset was used for external validation. Women aged 45-60 years with no history of endometrial cancer or hysterectomy. Model development was performed using a flexible parametric survival model and stepwise backward selection aiming to minimise the Akaike information criterion. Model performance on external validation was assessed through flexible calibration plots, calculation of the expected to observed ratio and C-statistic and decision curve analysis. Endometrial cancer diagnosis within 1-10 years of the index date. Model development included 222 031 women (902 incident endometrial cancer cases) and external validation 3 094 371 women (8585 endometrial cancer cases). The final model (with equation provided) incorporated age, body mass index, waist circumference, age at menarche, menopause and last birth, hormone replacement, tamoxifen and oral contraceptive pill use, type 2 diabetes, smoking and family history of bowel cancer. It was well calibrated on external validation (calibration slope 1.14, 95% confidence interval [CI] 1.11-1.17, E/O 1.03, 95% CI 1.01-1.05), with moderate/good discrimination (C-statistic 0.70, 95% CI 0.69-0.70) and had improved net benefit compared with previously developed models. The Predicting risk of endometrial cancer in asymptomatic women model (PRECISION), using easily measurable anthropometric, reproductive, personal and family history, accurately quantifies a woman's 10-year risk of endometrial cancer. Its use could determine eligibility for primary endometrial cancer prevention trials and for targeted resource allocation in UK general practices.

Copula Models of COVID-19 Mortality in Minnesota and Wisconsin

In this study, we assess COVID-19-related mortality in Minnesota and Wisconsin with the aim of demonstrating both the temporal dynamics and the magnitude of the pandemic’s influence from an actuarial risk standpoint. In the initial segment of this paper, we discuss the methodology successfully applied to describe associations in financial and engineering time series. By applying time series analysis, specifically the autoregressive integrated with moving average methods (ARIMA), to weekly mortality figures at the national or state level, we subsequently delve into a marginal distribution examination of ARIMA residuals, addressing any deviation from the standard normality assumption. Thereafter, copulas are utilized to architect joint distribution models across varied geographical domains. The objective of this research is to offer a robust statistical model that utilizes observed mortality datasets from neighboring states and nations to facilitate precise short-term mortality projections. In the subsequent section, our focus shifts to a detailed scrutiny of the statistical interdependencies manifesting between Minnesota and Wisconsin’s weekly COVID-19 mortality figures, adjusted for the time series structure. Leveraging open-source data made available by the CDC and pertinent U.S. state government entities, we apply the ARIMA methodology with subsequent residual distribution modeling. To establish dependence patterns between the states, pair copulas are employed to articulate the relationships between the ARIMA residuals, drawing from fully parametric models. We explore several classes of copulas, comprising both elliptic and Archimedean families. Emphasis is placed on copula model selection. Student t-copula with the marginals modeled by non-standard t-distribution is suggested for ARIMA residuals of Minnesota and Wisconsin COVID mortality as the model of choice based on information criteria and tail cumulation. The copula approach is suggested for the construction of short-term prediction intervals for COVID-19 mortality based on publicly available data.

Prevalence and Impact of COVID-19 in Young Women of Reproductive Age [18-44 Years] Hospitalized with Pulmonary Embolism in the US in 2020 Using National Inpatient Sample Dataset, in-Hospital Mortality, Racial Disparity and Predictors of Mortality

Title: Prevalence and Impact of COVID -19 on In-hospital Outcomes and Associated Racial Disparities in Young Women of Reproductive Age Group (18-44 years) Hospitalized with Pulmonary Embolism in the US in 2020 Background: Given the potential of thrombotic events such as pulmonary embolism in COVID-19, it is essential to comprehend the specific risks and effects on young women of reproductive age, a high-risk group for venous thromboembolism. There is a paucity of data regarding the prevalence and impact of COVID-19 in young women within the reproductive age group who were hospitalized with pulmonary embolism (PE) in the United States during the year 2020. Methods: Utilizing the National Inpatient Sample (NIS) 2020 and relevant ICD-10-CM codes, we identified admissions for PE with COVID-19 vs. without COVID-19 among Young Women [18-44 years] hospitalized with PE. We compared the baseline characteristics and comorbidities between the two groups with and without COVID-19. Furthermore, we analyzed the multivariable adjusted odds ratio (OR) of all-cause mortality in COVID-19 with the non-COVID-19 cohort as a referent, subgroup odds for white vs. other races, and effect modification by diabetes mellitus (DM) as a comorbidity. Results: We identified 34,730 hospitalizations in young female patients of the reproductive age group (18 to 44 years) in 2020 with the diagnosis of PE, of which 2345 (6.8%) had COVID-19. The prevalence of PE was higher in COVID-19 cohort (1.6% vs. 0.6%, p&amp;lt;0.001). The majority of the patients with PE were Caucasian (58.9%) and in the lowest median income quartile (36.9%). Analysis of various comorbidities in both cohorts revealed a statistically significant higher prevalence of hypertension (29.6% in those with COVID-19 vs. 24.3% in those without COVID-19, p &amp;lt;0.001), Similarly, hyperlipidemia (11.7% vs. 7.3%, p &amp;lt;0.001), DM (20.9% vs. 12.4%, p &amp;lt;0.001), and obesity (50.3% vs. 32.7%, p &amp;lt;0.001), were more prevalent in COVID-19, indicating that patients who were admitted with PE with concomitant COVID-19 were of poorer overall cardiovascular health than those with COVID-19. Unadjusted all-cause mortality odds ratio was 2.41 (1.71-3.38, p &amp;lt;0.001) in PE with COVID-19 than non-COVID-19. Adjusted all-cause mortality for demographics, hospital level covariates, and pre-existing conditions revealed similar statistically significant findings, with the odds ratio being 2.8 (1.94-4.04, p &amp;lt;0.001). There was no racial disparity observed in all-cause mortality for non-whites compared to white patients admitted with PE and COVID-19 (p=0.064). Furthermore, all-cause mortality in patients with COVID-19 and PE was found to have an odds ratio of 3.04 (1.05-8.81, p=0.041) in a subset of DM compared to non-DM. The results have confirmed a further robust positive association with increasing mortality risk with DM in young women hospitalized with PE and COVID-19. Conclusion and Relevance: There was a nearly three times higher prevalence of PE and 2.5 times higher odds of mortality in Young Women of Reproductive age [18-44 years] hospitalized with PE with COVID-19 as compared to without COVID-19. The effect of PE and a higher risk of mortality are already established with COVID-19; however, there is a paucity of data in young women, which warrants more prospective studies to evaluate the long-term thromboembolic risks of PE and associated events in this population. The findings of this research could have significant implications for understanding the relationship between COVID-19 and PE in this patient population. Special emphasis needs to be laid on improved healthcare strategies, targeted interventions, and patient education in this cohort to establish future research agendas and provide better health care outcomes for this vulnerable group with established risk factors like diabetes. Key Words: COVID-19, Young Women, Pulmonary Embolism, In-Hospital Mortality, Racial Disparity, Predictors of Mortality, Diabetes Mellitus, National Inpatient Sample

BELMM: Bayesian model selection and random walk smoothing in time-series clustering.

Due to advances in measuring technology, many new phenotype, gene expression, and other omics time-course datasets are now commonly available. Cluster analysis may provide useful information about the structure of such data. In this work, we propose BELMM (Bayesian Estimation of Latent Mixture Models): a flexible framework for analysing, clustering, and modelling time-series data in a Bayesian setting. The framework is built on mixture modelling: first, the mean curves of the mixture components are assumed to follow random walk smoothing priors. Second, we choose the most plausible model and the number of mixture components using the Reversible-jump Markov chain Monte Carlo. Last, we assign the individual time series into clusters based on the similarity to the cluster-specific trend curves determined by the latent random walk processes. We demonstrate the use of fast and slow implementations of our approach on both simulated and real time-series data using widely available software R, Stan, and CU-MSDSp. The French mortality dataset is available at http://www.mortality.org, the Drosophila melanogaster embryogenesis gene expression data at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE121160. Details on our simulated datasets are available in the Supplementary Material, and R scripts and a detailed tutorial on GitHub at https://github.com/ollisa/BELMM. The software CU-MSDSp is available on GitHub at https://github.com/jtchavisIII/CU-MSDSp.

Greenspace exposure may increase life expectancy of elderly adults, especially for those with low socioeconomic status

With an increasing aging population in many cities worldwide, promoting and maintaining the health of elderly individuals has become a pressing public health issue. Although greenspaces may deliver many health outcomes for the elderly population, existing evidence remains inconsistent, partly due to discrepancies in the measure of greenspace and health outcomes. In addition, few studies examined the effect of greenspace exposure on life expectancy at the individual level. Thus, this study comprehensively investigated the association between greenspace exposure and life expectancy among elderly adults in Guangzhou, China, based on the individual-level mortality dataset. The data were analyzed at both the individual level and aggregate level, and two types of buffers (straight-line vs. street-network buffer) were used to define individual greenspace exposure. After controlling for the random effects and multiple types of covariates, we found that 1) elderly individuals with higher greenspace exposure were associated with an increased life expectancy; 2) elderly individuals with lower socioeconomic status benefit more from greenspace (i.e., equigenesis hypothesis); 3) different greenspace measurements lead to different results; 4) greenspace had the highest effects on life expectancy and equigenesis within the street-network buffer distances of 3000 m and 2500 m, respectively. This study underscores the potential health benefits of greenspace exposure on elderly individuals and the importance of provision and upkeep of greenspace, especially among socially disadvantaged groups.