National Dataset Research Articles

National Trends and Outcomes of Acute Myocardial Infarction After Transcatheter Aortic Valve Replacement

BackgroundPrior studies have reported decreased use of an invasive approach for acute myocardial infarction (AMI) in patients undergoing transcatheter aortic valve replacement (TAVR). ObjectivesThe aim of this study was to determine whether prior TAVR affects the use of subsequent coronary revascularization and outcomes of AMI in a contemporary national data set. MethodsConsecutive TAVR patients from 2016 to 2022 were identified from the U.S. Vizient Clinical Data Base who were hospitalized after the index TAVR hospitalization with ST-segment elevation myocardial infarction (STEMI) or non–ST-segment elevation myocardial infarction (NSTEMI). Patients with STEMI or NSTEMI with or without prior TAVR from the same time period were compared for the use of coronary angiography, revascularization, and in-hospital outcomes. Propensity score matching was used to account for imbalances in patient characteristics. ResultsAmong 206,229 patients who underwent TAVR, the incidence of STEMI was 25 events per 100,000 person-years of follow-up, and that of NSTEMI was 229 events per 100,000 person-years. After propensity matching, the use of coronary revascularization was similar in the prior TAVR and no TAVR cohorts in both the STEMI (65.3% vs 63.9%; P = 0.81) and NSTEMI (41.4% vs 41.7%; P = 0.88) subgroups. Compared with patients without prior TAVR, in-hospital mortality was higher in the prior TAVR cohort in patients with STEMI (27.1% vs 16.7%; P = 0.03) and lower in those with NSTEMI (5.8% vs 8.2%; P = 0.02). ConclusionsIn this large, national retrospective study, AMI events after TAVR were infrequent. There were no differences in the use of coronary revascularization for STEMI or NSTEMI in TAVR patients compared with the non-TAVR population. In-hospital mortality for STEMI is higher in TAVR patients compared with those without prior TAVR.

Exploring Gender Moderation: The Impact of Neighborhood Factors on Adolescent Internalizing and Externalizing Symptoms.

Limited previous studies investigated the influences of various types of neighborhood factors on adolescent behavior problems. Meanwhile, although previous theoretical frameworks suggested that gender played a significant role in terms of neighborhood impacts on adolescent behavioral problems, few studies investigated the gender differences in such neighborhood influences. Using the year 9 and year 15 data of the national dataset Future of Families and Child Wellbeing Study (FFCWS, overly sampled participants from low-income families), this study examined how the neighborhood structural and process factors can affect adolescent behavioral problems (internalizing and externalizing symptoms) and whether gender worked as a significant moderator for such relationships in the U.S. Structural equation models and multigroup SEM were estimated (N = 3411). Findings suggested that residential instability was associated with increased levels of internalizing symptoms among adolescents at age 15, whereas neighborhood social cohesion was linked to reduced levels of externalizing symptoms throughout adolescence. Furthermore, the moderating effects of gender were found for the association between residential instability and internalizing symptoms. Implications of such findings are further discussed.

Incidental Prostate Cancer in Patients Treated for Benign Prostatic Hyperplasia: Analysis from a Contemporary National Dataset.

(1) Background: Prostate Cancer (PCa) may be incidentally diagnosed during the microscopic evaluation of resected tissue from BPH surgeries, characterizing the clinical condition known as incidental PCa (iPCa). This study aims to assess the prevalence of iPCa following BPH surgery to evaluate the associated surgical procedures and to scrutinize preoperative and postoperative management. (2) Methods: A retrospective analysis was conducted using the PearlDiver™ Mariner database, containing patient records compiled between 2011 and 2021. International Classification of Diseases (ICD) and Current Procedural Terminology (CPT) codes were employed to identify the population and outcomes. Our primary objective was to assess the prevalence of iPCa, categorized by the type of procedures, and to evaluate the subsequent treatment strategies. The secondary aim was to assess the impact of prostate biopsy (PB) and prostate MRI on iPCa detection. (3) Results: The overall cohort, accounting for 231,626 patients who underwent BPH surgery, exhibited a 2.2% prevalence rate of iPCa. The highest rate was observed for TURP (2.32%), while the lowest was recorded for RASP (1.18%). Preoperative MRI and PB demonstrated opposing trends over the years. Of the 5090 patients identified with iPCa, nearly 68% did not receive active treatment. The most common treatments were RT and ADT; 34.6% underwent RT, 31.75% received ADT, and 21.75% were treated with RT+ADT. RP was administered to approximately 9% of patients undergoing endoscopic procedures. Multivariate logistic regression analysis revealed age and openSP as additional risk factors for iPCa. Conversely, PB and MRI before surgery were linked to a decreased risk. (4) Conclusions: The contemporary prevalence of iPCa after BPH surgery is <3%. The increase in the use of prostate MRI mirrors a decline in the PB biopsy prior to BPH surgery but without resulting in an increased detection rate of iPCa. In contemporary routine clinical practice, iPCa is mostly managed in a different way when compared to biopsy-detected PCa.

Patient Characteristics, Treatment Patterns, and Outcomes for Patients With Renal Cell Carcinoma in England: A Retrospective Cohort Study

Background and ObjectiveConsidering the rapidly evolving treatment landscape of renal cell carcinoma (RCC), recent descriptions of the RCC population in the UK are lacking, as are real-world data on treatment and patient outcomes. To analyse the demographic and clinical characteristics, treatment patterns, and overall survival of patients with RCC using national data sets in England. Patients and MethodsThis was a retrospective cohort study of patients diagnosed with RCC (all stages) between 2014-2018 using demographic, clinical, cancer registration, and treatment data. Patients were followed until death or study end (December 31, 2020). Treatments administered in each line were described to understand treatment sequencing. Kaplan–Meier methods were used for time-to-event analyses. Factors associated with discontinuation and survival were identified using Cox proportional hazard models. Results and LimitationsAmong 32,577 included patients, the median age at diagnosis was 66 years, 63.4% were male, and 6,786 (20.8%) had metastatic RCC at diagnosis. Tyrosine kinase inhibitor (TKI) monotherapy was the most common treatment class across lines. Over three quarters of patients (78.5% [95% CI: 78.0-78.9]) were alive one year after diagnosis (93.2% in the non-metastatic at diagnosis subgroup and 37.1% among patients with metastases at diagnosis). At three years post initial diagnosis, 18.0% patients were alive in the metastatic at diagnosis subgroup. Rapid evolution of the treatment landscape limits the results regarding lines of therapy. ConclusionThis large-scale study provides insight on characteristics of patients with RCC, and it highlights the need for better treatment options to improve survival.

Abstract 3554: Using All of Us cancer data on the Researcher Workbench to advance career development

Abstract This session will provide an overview of the All of Us Research Program (All of Us) dataset, discuss a mechanism to build the capacity of research institutions using the All of Us dataset, and outline the career benefits researchers experienced from using the dataset. All of Us is building a national health dataset to accelerate medical breakthroughs. It is used by cancer researchers to advance health research and career development. This dataset is large and diverse, both in the data types available as well as the demographics of participants: as of April 2023, health data from 413,450+ participants is available to researchers, from a variety of distinct data types, including survey responses, physical measurements, electronic health records, whole genome sequencing, wearable data, and genotyping arrays, with 75% of participants self-identifying as underrepresented in biomedical research and about 45% of participants self-identifying as racial and ethnic minorities. Researchers already leverage this dataset, with All of Us data used in more than 210 publications across peer-reviewed journals. In addition, this dataset is useful for cancer researchers - All of Us EHR data indicates over 60,700 participants are diagnosed with at least one type of cancer, and more than 10% of research projects underway (725/6,945 as of September 2023) using the All of Us dataset are cancer-related, making it the top condition studied by researchers. To help build capacity at research institutions, All of Us collaborated with the NCI Center to Reduce Cancer Health Disparities (CRCHD) Partnerships to Advance Cancer Health Equity (PACHE) to develop a Notice of Special Interest (NOSI) titled Administrative Supplements to Support "All of Us" and Health Disparities-Related Pilot Research Projects at NCI CRCHD-Funded PACHE Partnerships. The focus of this NOSI was on P20 and U54 awards associated with research projects led by PACHE researchers interested in exploring the connections between social determinants of health outcomes and populations experiencing cancer health disparities. NIH awarded investigators at the following institutions: City College of New York, University of California, Irvine, and Temple University. Principal investigators of these awards will provide an overview of their work, describe how they analyze the All of Us dataset, and discuss how their use of the dataset has helped advance their career as cancer researchers: Karen Hubbard (City College of New York) Project Title: Relative and Intersectional Analysis of social determinants of Lung Cancer Risk among Black and Hispanic All of Us respondents Hannah Lui Park (University of California, Irvine) Project Title: Improving breast cancer risk assessment and prevention in Asian American women Lin Zhu (Temple University) Project Title: Elucidating the Roles of Metabolic Syndrome and Non-Alcoholic Fatty Liver Disease among Asian American Patients with Chronic Hepatitis B Citation Format: Karriem S. Watson, Rubin Baskir, Division of Engagement and OutreachAll of Us Research Program, Karen Hubbard, Hannah L. Park, Lin Zhu. Using All of Us cancer data on the Researcher Workbench to advance career development [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 3554.

Building footprint data for countries in Africa: To what extent are existing data products comparable?

Growth and developments in computing power, machine-learning algorithms and satellite imagery spatiotemporal resolution have led to rapid developments in automated feature-extraction. These methods have been applied to create geospatial datasets of features such as roads, trees and building footprints, at a range of spatial scales, with national and multi-country datasets now available as open data from multiple sources. Building footprint data is particularly useful in a range of applications including mapping population distributions, planning resource distribution campaigns and in humanitarian response. In settings with well-developed geospatial data systems, such datasets may complement existing authoritative sources, but in data-scarce settings, they may be the only source of data. However, knowledge on the degree to which building footprint data products are comparable and can be used interchangeably, and the impact of selecting a particular dataset on subsequent analyses remains limited. For all countries in Africa, we review the available multi-country building footprint data products and analyse their similarities and differences in terms of building area and count metrics. We explore the variation between building footprint data products across a range of spatial scales, including sub-national administrative units and different settlement types. Our results show that the available building footprint data products are not interchangeable. There are clear differences in counts and total area of building footprints between the assessed data products, as well as considerable spatial heterogeneity in building footprint coverage and completeness.

Influence of local versus national datasets on seismic loss estimates: A case study for residential buildings in the metropolitan area of Montreal, Canada

Assessing seismic losses requires information on ground shaking levels, a comprehensive inventory of elements at risk and their asset values to evaluate repair/reconstruction costs. The uncertainties associated with these parameters are often significant and the available data are often incomplete. This study aims to assess the value and utility of detailed data in refining seismic risk estimates. Over several years, detailed site conditions and residential exposure datasets were compiled for Greater Montreal. These models were used to estimate building damage and associated losses for various seismic scenarios. Recently, Natural Resources Canada developed a national exposure model to provide a National Earthquake Risk Profile using data derived from existing sources such as topographic maps and census data. This paper compares seismic loss projections for residential buildings using the local dataset as a baseline with those from the national model using OpenQuake and the latest Seismic Hazard Model. The local site model is based on in situ data and site measurements, while the global, proxy-based model relies on USGS correlations between slope topography and site classification. Results show that the global model overestimates seismic hazards, resulting in 30% higher residential losses than the local model. Absolute losses are increased by a factor of 3.6–5.6 depending on the building classification when using the national exposure model compared to the local inventory while relative losses are increased by a factor of 1.1–1.5. These results emphasize the need to develop detailed seismic risk assessment databases for accurate risk assessments, and for selecting appropriate risk reduction measures.

Intimate partner violence, suicide and self-harm in Sri Lanka: Analysis of national data.

There is increasing evidence from South Asia and internationally that intimate partner violence (IPV) is strongly associated with self-harm, however its association with suicide and self-harm has not been extensively examined, nor has this relationship been explored at a national level. Using national datasets, area-level variation in IPV, suicide and self-harm in Sri Lanka were examined. In addition, the association between individual level exposure to past-year IPV and non-fatal self-harm by any household member were explored in a series of multi-level logistic regression models, adjusting for age. Similar patterns in the distribution of suicide and IPV were found, with higher rates evident in post-conflict districts, specifically Batticaloa, Kilinochchi, and Mullaitivu. Experience of past year IPV and its various forms were strongly associated with household-level self-harm in the past year (adjusted odds ratio [AOR] = 3.83 95% CI 2.27-6.46). A similar magnitude was found for physical/sexual abuse (AOR 5.17 95% CI 2.95-9.05) and psychological abuse (AOR 4.64 95% CI 2.50-7.00). A dose-response association was also evident for frequency of abuse, with an increasing risk of household-level self-harm for women reporting abuse 'less often' (AOR 2.95 95% CI 1.46-5.92), and abuse experienced 'daily, weekly, or monthly' (AOR 4.83 95% CI 2.59-9.00), compared to no abuse. This study contributes to a growing body of evidence on the relationship between IPV and suicidal behaviour in South Asia. Addressing IPV and its various forms should be a priority for suicide prevention in Sri Lanka, alongside trauma-informed approaches in post-conflict settings.

Effectiveness of County Communication Approaches in Facilitating Public Participation in Kenya’s Devolved Governments

Purpose: The aim of this research was to evaluate the efficacy of county communication frameworks in fostering public participation in Kenya’s 47 devolved governments. Effective communication is both a function and proof of good governance. Thus the significance of county communication facilitating enduring citizen participation in policy making and implementation processes cannot be overstated. The evaluation of county communication approaches was conducted in alignment with overall objectives outlined in the County Government Act of 2012. These objectives include: creating awareness on devolution and governance; promotion of peace and cohesion; advocacy for development, and promotion of the freedom of the media.&#x0D; Methodology: The present study adopted a qualitative research approach. The research targeted 8 counties clustered in county economic blocs of North Rift Economic Bloc (NOREB), Lake Region Economic Bloc LREB, Frontier Counties Development Council (FCDC) , South Eastern Kenya Economic Block (SEKEB), Mt Kenya and Aberdares Economic Bloc, Jumuiya ya Kaunti za Pwani( JKP), Narok and Kajiado Economic Block(NAKAEB) and Nairobi City County. The counties purposely selected to represent each of these economic blocs include: Trans Nzoia, Kisumu, Mandera, Makueni, Meru, Kwale, Narok and Nairobi. This study used both primary and secondary methods to collect data. In-depth interviews were used to collect primary data from 26 participants, specifically, from Council of Governors, Sub-County Administrators, County Assemblies Forum, County Directors of Communication and Community Based Organizations. They were purposively chosen for their role in communicating, facilitating, and monitoring public participation. Secondary data was collected mainly from national dataset, surveys, and reports from ministries, departments, and agencies.&#x0D; Findings: The findings show that concept of public participation is deeply rooted in the Constitution of Kenya 2010. County communication frameworks embed strategic communication approaches for policy making and implementation in devolved governments. The finds show that the level of understanding of devolution among Kenyans varies. Those in urban and city counties have a greater appreciation of decentralization while those in rural counties have limited understanding of devolution and its implication. Majority of Kenyans have difficulty distinguishing functions of devolved governments from those assigned to the national government by the CoK 2010. Additionally, county governments are required to promote peace and cohesion among the residents. The findings show that this objective receives less emphasis and is not a priority of counties, with the exception of frontier counties. However, counties promote development in their jurisdiction recognizing that they have a responsibility to stimulate investments that create jobs for unemployed and generally improve living standards of the residents. Finally, county governments have a role to play in ensuring media practice remain free from any interference even though this responsibility lie with the national government. &#x0D; Implications to Theory, Practice and Policy: Based on the review of the literature and findings of this study, the study recommends that county governments should meaningfully engage citizens in governance by first, empowering them to understand their responsibility and secondly, allowing them to engage meaningfully in issues that affect them. To achieve this, county governments should establish mechanisms that promote civic education, public participation and access to information as provided in the County Government Act 2012.

Impact of national commissioning of pre-exposure prophylaxis (PrEP) on equity of access in England: a PrEP-to-need ratio investigation

ObjectivesHIV pre-exposure prophylaxis (PrEP) is highly effective in preventing HIV acquisition. In England, NHS availability was limited to participants of the PrEP Impact Trial until late 2020. Some key populations...

FCTNet: Fusion of 3D CNN and transformer dance action recognition network

Folk dance is an important intangible cultural heritage in China. In the environment where movement recognition technology is widely used, there is still no research field on the protection and inheritance of folk dance culture. In order to better protect and inherit the minority dance, screening the typical movements of 5 types of minority dance, through the dance video frame processing, obtain the key movements of 19 class dance sequence, build the national dance typical action data set, put forward a 3D CNN fusion Transformer national dance recognition network model (FCTNet), the recognition rate of 96.7% in the experiment. The results show that the construction method of the folk dance data set is reasonable, the identification model has good performance for the classification of folk dance, and can effectively identify and record the folk dance movements, which also makes new contributions to the digital protection of folk dance.

Abstract P390: Cardiometabolic Disease Prevalence Are Inconsistent by Racial Group Across Three National United States Datasets From 2015 to 2021

Introduction: The United States uses the National Health Interview Survey (NHIS), Behavioral Risk Factor Surveillance System (BRFSS), and National Health and Nutrition Examination Survey (NHANES) to inform disease trends and preventative research. However, the consistency in prevalence of cardiometabolic disorders in these datasets by race is understudied. We examined differences in the crude and age/sex adjusted prevalence of self-reported doctor diagnosis of myocardial infarction (MI), stroke (CVA), coronary heart disease (CHD), any cardiovascular disease (CVD), and diabetes (DM) by racial group across the datasets. Methods: We harmonized available NHIS (N = 179,593), BRFSS (N = 2,592,050), and NHANES (N = 12,506) datasets of adults aged ≥ 30 from 2015-2021. We first calculated crude prevalence by race and survey. We then used multivariable logistic regression to calculate age and sex adjusted prevalence by race and survey, accounting for survey weights. Results: The majority of participants were female (52.3%) and 53.6 years old (SD=14.4). Crude prevalence was most similar within a given race group for CVA (Table). Prevalence across surveys varied up to 4.6% by race and was most variable on average for DM. For each race group, prevalence estimates were generally higher in BRFSS than NHIS. Prevalence estimates varied most in people of Other/Multiple race. Racial differences in DM were significant in all datasets (p&lt;0.001). Results were similar after adjustment for age and sex. Conclusion: While prevalence estimates for our national datasets are consistent for the NH-White population, estimates are less consistent for other racial groups. Additional work is needed to ensure consistency of cardiometabolic surveillance both nationally and among minoritized groups.

Short-term load forecasting method based on secondary decomposition and improved hierarchical clustering

In the context of large-scale grid connection of new energy, short-term load forecasting is a vital and challenging task for power system to balance supply and demand. To effectively improve the forecasting accuracy, a new load forecasting method is proposed aiming to mine the characteristics of load data and study the application of artificial intelligence algorithms. In this paper, the seasonal and trend decomposition using loess (STL) method is firstly applied to decompose the load data into the trend, seasonal and residual components and the residual component with the highest complexity is further decomposed by the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) approach. Secondly, in order to reduce the number of components, the improved hierarchical clustering technique is proposed to cluster all intrinsic mode functions (IMFs) obtained by CEEMDAN into high-frequency and low-frequency components. Then, different network models are trained to get the prediction results for different components, and the total load prediction value is achieved by stacking all of them. Finally, the national demand dataset of Great Britain in 2021–2022 is used to conduct the ablation and comparative experiments. The mean absolute percentage error (MAPE) and the root mean square error (RMSE) of the proposed method are 2.064% and 724.01 MW, respectively, which verified the effectiveness and advancement of the proposed method.

Monitoring solutions for remote locations: A data gathering approach for remote nature-based solution sites

Nature-based solutions have gained popularity as an approach to tackling hydro-meteorological hazards (HMHs) in both urban and rural settings. Despite this popularity, challenges persist regarding the evidence base for their effectiveness and data scarcity at the feature or site scale. Flood modelling is a common approach to quantifying the effectiveness of NbS; however, the accuracy of these models heavily depends on the accuracy of the DEM, land cover, and hydraulic/hydrological data utilised. Remote and rural settings often face data scarcity due to the challenging nature of data collection, and insufficient funding for monitoring. Additionally, NbS features vary in size and scale, with many being small (<1 m in width), posing challenges for accurate representation in national LiDAR datasets. Technological advancements in remote sensing technologies, such as unmanned aerial vehicles, handheld LiDAR, and GPS-GNSS, offer opportunities to gather high-resolution, high-accuracy data in these challenging locations. This article proposes a methodological framework for collecting elevation data at remote NbS sites that can tackle areas affected by both sparse and dense vegetation cover. This approach proves valuable in both pre-NbS implementation, through facilitating NbS opportunity and environmental risk identification, and post-NbS implementation, through aiding in geo-spatial feature location, improving existing DEM data for flood modelling, and monitoring temporal changes.

The Association of Weight With Surgical Morbidity in Infants Undergoing Enterostomy Reversal: A Study of the NSQIP-Pediatrics Database

IntroductionOptimal criteria and timing for enterostomy closure (EC) in neonates is largely based on clinical progression and adequate weight, with most institutions using 2.0–2.5 kg as the minimum acceptable weight. It is unclear how the current weight cutoff affects post-operative morbidity. AimTo determine how infant weight at the time of EC influences 30-day complications. MethodsInfants weighing ≤4000 g who underwent EC were identified in the 2012–2019 ACS NSQIP-P database. Demographics, comorbidities, and 30-day outcomes were assessed using univariate analysis. Multivariable logistic regression controlling for ASA score, nutritional support, and ventilator support was used to estimate the independent association of weight on risk of 30-day complications. ResultsA total of 1692 neonates from the NSQIP-P database during the years 2012–2019 met inclusion criteria. Neonates weighing <2.5 kg were significantly more likely to have a younger gestational age, require ventilator support, and have concurrent comorbidities. Major morbidity, a composite outcome of the individual postoperative complications, was observed in 283 (16.7%) infants. ASA classifications 4 and 5, dependence on nutritional support, and ventilator support were independently associated with increased risk of 30-day complications. With respect to weight, we found no significant difference in major morbidity between infants weighing <2.5 kg and infants weighing ≥2.5 kg. ConclusionDespite using a robust, national dataset, we could find no evidence that a defined weight cut-off was associated with a reduction in major morbidity, indicating that weight should not be a priority factor when determining eligibility for neonatal EC. Level of EvidenceIII.

Sex differences in trends and in-hospital outcomes of acute myocardial infarction in patients with familial hypercholesterolemia: insights from a large national database

ABSTRACT Background Sex differences in clinical outcomes following acute myocardial infarction (AMI) are well known. However, data on sex differences among patients with familial hypercholesterolemia (FH) are limited. We aimed to explore sex differences in outcomes of AMI among patients with FH from a national administrative dataset. Research design and methods We utilized the National Inpatient Sample to identify admissions with a primary diagnosis of AMI and a secondary diagnosis of FH. Our primary outcome of interest was in-hospital mortality; secondary outcomes were performance of percutaneous coronary intervention (PCI), coronary artery bypass grafting (CABG), respiratory complications, use of inotropes, use of mechanical circulatory support (MCS), bleeding complications, transfusion and facility discharge. We adjusted for demographics (model A), comorbidities (model B), and intervention (model C). Results Between October 2016 and December 2020, 5,714,993 admissions with a primary diagnosis of AMI were identified, of which 3,035 (0.05%) had a secondary diagnosis of FH. In-hospital mortality did not differ between men and women (Model C, adjusted OR = 0.85; 95% CI 0.28–2.60, p = 0.773). There was no sex difference in the secondary outcomes. Conclusion Despite generally being older and having more comorbidities, women with FH fair equally with men with FH in terms of mortality during AMI admission.

The Value of Emergency Care Data Set (ECDS) Presentation Codes for Predicting Mortality and Inpatient Admission.

Early identification of patients at higher risk of death and hospital admission is an important problem in Emergency Departments (ED). Most triage scales were developed before current electronic healthcare records were developed.The implementation of a national Emergency Care Data Set (ECDS) allows for the standardised recording of presenting complaints and the use of Electronic Patient Records (EPR) offers the potential for automated triage. The mortality risk and need for hospital admission associated with the different presenting complaints in a standardised national data set has not been previously reported. This study aimed to quantify the risks of death and hospitalisation from presenting complaints. This would be valuable in developing automated triage tools and decision support software. We conducted an observational retrospective cohort study on patients who visited a single ED in 2021. The presenting complaints related to subsequent attendances were excluded. This patient list was then manually matched with a routinely collected list of deaths. All deaths that occurred within 30 days of attendance were included. Data was collected from 84,999 patients, of which 1,159 people died within 30 days of attendance.The mortality rate was the highest in cardiac arrest [32(78.1%)], cardiac arrest due to trauma [2(50%)] and respiratory arrest [3(50%)]. Drowsy [17(12%)], hypothermia [3(13%)] and cyanosis [1(10%)] were also high-risk categories. Chest pain [34(0.6%)] was not a high-risk presenting complaint. The initial presenting complaint in ECDS may be usefulto identify people at higher and lower risk of death. This information is useful for building automated triage models.

The burden of premature adult mortality associated with lack of access to electricity in India.

The impact of electricity access on all-cause premature mortality is unknown. We use a national dataset from India to compare districts with high access to electricity (>90% of households) to districts with middle (50-90%) and low (<50%) access to electricity and estimate the effect of lack of electricity access on all-cause premature mortality. In 2014, out of 597 districts in India, 174 districts had high access, 228 had middle access, and 195 had low access to electricity. When compared to districts with high access, districts with low access had higher rates of age-standardized premature mortality in both women (2.09, 95% CI: 1.43-2.74) and men (0.99, 0.10-1.87). Similarly, these districts had higher rates of conditional probability of premature death in both women (9.16, 6.19-12.13) and men (4.04, 0.77-7.30). Middle access districts had higher rates of age-standardized premature mortality and premature death in women, but not men. The total excess deaths attributable to reduced electricity access were 444,225 (45,195 in middle access districts and 399,030 in low access districts). In low access districts, the proportion of premature adult deaths attributable to low electricity access was 21.3% (14.4%- 28.1%) in women and 7.9% (1.5%- 14.3%) in men. Poor access to electricity is associated with nearly half a million premature adult deaths. One out of five premature deaths in adult women were linked to low electricity access making it a major social determinant of health.

Time-first approach for land cover mapping using big Earth observation data time-series in a data cube – a case study from the Lake Geneva region (Switzerland)

ABSTRACT Accurate, consistent, and high-resolution Land Use & Cover (LUC) information is fundamental for effectively monitoring landscape dynamics and better apprehending drivers, pressures, state, and impacts on land systems. Nevertheless, the availability of such national products with high thematic accuracy is still limited and consequently researchers and policymakers are constrained to work with data that do not necessarily reflect on-the-ground realities impending to correctly capture details of landscape features as well as limiting the identification and quantification of drivers and rate of change. Hereafter, we took advantage of the Switzerland’s official LUC statistical sampling survey and dense time-series of Sentinel-2 data, combining them with Machine and Deep Learning methods to produce an accurate and high spatial resolution land cover map over the Lake Geneva region. Findings suggest that time-first approach is a valuable alternative to space-first approaches, accounting for the intra-annual variability of classes, hence improving classification performances. Results demonstrate that Deep Learning methods outperform more traditional Machine Learning ones such as Random Forest, providing more accurate predictions with lower uncertainty. The produced land cover map has a high accuracy, an improved spatial resolution, while at the same time preserving the statistical significance (i.e. class proportion) of the official national dataset. This work paves the way towards the objective to produce a yearly high resolution land cover map of Switzerland and potentially implement a continuous land change monitoring capability. However further work is required to properly address challenges such as the need for increased temporal resolution for LUC information or the quality of training samples.

The changing social structure of housing tenures through China’s successive urban housing reforms

Housing system transitions often involve successive but overlapping periods of reform with crucial implications for housing inequality dynamics. Building on welfare regime and market transition discussions, this paper considers how the shifting roles of the state, market, and family in housing provision in China have led to changing housing tenures and differentiated access to them between populations with varying socio-economic resources. Drawing on two national datasets, our analyses reveal that the four decades of reforms dramatically upended urban China’s tenure structure with market homeownership becoming the dominant tenure. Nevertheless, we find a substantial and increasing role of the family in housing provision, especially for people with fewer socio-economic resources. The advantages associated with better institutional resources under the previous socialist housing system not only persist in accessing state-provided tenures but have also extended their influence into market housing. Meanwhile, homeownership outcomes have become increasingly polarized among groups with different income and education levels. Our findings expose the reproduction and apparent amplification of tenure-related housing inequalities over the period of housing regime transformation.