Statistical Datasets Research Articles

Small Hydropower Assessment of Uganda Based on Multisource Geospatial Data

This article is based on the freely available data of the web-based hydropower map HYPOSO, which the authors compiled. Only the Ugandan river network and associated hydropower potential are highlighted here, using freely available geospatial datasets. The main objective was to assess Ugandan river and stream hydropower potential, compare it with previous assessments, and identify potential sites for small hydropower plant installation. GIS techniques were extensively used to analyse hydrological and other related geospatial data. The stream-reach power potential was determined based on channel slope, the length between tributaries, and the average flow derived from a specific runoff distribution map. Stream profiles extracted from the river network’s digital elevation model were validated against previous assessments. Uganda’s hydropower potential was determined in various patterns, and its values were compared with prior estimates. Around 500 potential high-energy intensity stream reaches and new potential areas for small hydropower plant development were identified in this country, considering a range of characteristics. Statistical datasets were analysed, and their straightforward summaries were presented. These summary characteristics of hydropower potential are necessary for decision-makers to foster hydropower development in this country.

STATISTICAL SECURITY OF ECONOMIC MANAGEMENT

Modern integrated economic statistics is a set of economic and statistical data that reflect a consistent and holistic picture of economic activity for political, commercial and other analytical purposes. In analyzing the historical aspect of the emergence and development of statistical practice, we have shown that its origin occurred around the time when the state emerged. There is information about the elementary recount (census) of population and land, which was conducted several millennia ago. With the formation of a centralized state and especially with its development, the scope of statistics has expanded significantly. It is established that the statistical support of effective development of enterprises of Ukraine in the scale of a particular industry can be realized according to official statistics, namely: statistical observations of the volume of sold industrial products and operating costs of sold products. It is emphasized that today the volume and structure of data presented on the official website of statistics of Ukraine, is considered insufficient for full information support of management processes of industrial enterprises. The approaches of scholars to the interpretation of the role of statistical methods of analysis for successful economic management are considered. The basic mathematical and statistical methods are listed and their short characteristic is given. It is established that the profitability indicator is chosen as a general indicator of efficiency and technological level of enterprises of the studied industries. Construction of the economic and mathematical model of profitability of products of separate industries made it possible to reveal production reserves to increase the efficiency of the enterprises. For mechanical engineering it means reducing the capital intensity of products used funds (depreciation). For metallurgy it means a reduction in material and labor intensity of products. For the production of chemical products it is reducing material consumption. When choosing statistical methods for studying socio-economic phenomena, it is necessary to take into account their interconnectedness and interdependence. Based on the results, the priority for the development of management decisions for the effective development of processing enterprises should be to increase the degree of processing of raw materials (to increase the technological level of production), increase productivity of fixed assets and increase final consumption.

Decision criteria for the classification of meteorological phenomena in the weather radar complex of the near-airfield zone

The increasing need to obtain data on the meteorological situation to ensure the safety of aircraft flight actualizes the development of radar systems for remote collection and processing of information, including for solving the problems of classifying dangerous weather phenomena. This determined the primary development of the domestic weather radar complex of the near airfield zone (WR BZ). The article presents the features of the construction of the WR BZ, as well as the main tasks it solves. To classify meteorological phenomena from cloudiness to squall in the weather radar complex of the near airfield zone, it is proposed to take as a basis the criteria tested by weather radar stations of previous generations. These criteria are based on an analysis of the height distribution of reflectivity, taking into account the vertical temperature profile. In addition, a criterion for classifying thunderstorms in the cold period of time has been additionally introduced in the WR BZ. To calibrate the values of the criteria, a mathematical apparatus and special software were developed. To collect statistical data, WR BZ were installed in various climatic regions: the Central and North-Western Federal Districts and the Republic of Crimea. Further, an effective validation of the information received was carried out. At present, WR BZ has passed preliminary, acceptance, certification tests, trial operation, while demonstrating an effective classification of meteorological phenomena, thanks to the correct selection of decision criteria. The article considers the possibility of increasing the reliability and justification of the classification of dangerous meteorological phenomena through the additional use of information on the distribution of altitudes of the specific rate of dissipation of turbulent energy of the atmosphere, as well as an additional set of statistical data in various climatic zones of the European territory of Russia – the Upper Volga Region and the Krasnodar Region.

Pancreatic Perivascular Epithelial Cell Tumor (PEComa) with Liver Metastasis: a Case Report and Literature Review

Aim: to present a clinical case of pancreatic PEComa with liver metastases with an emphasis on morphological features, radiology diagnostic methods and features of treatment tactics, allowing practitioners to get an idea about this rare mesenchymal tumors composed of “perivascular epithelioid cells”, and a review of literature data on pancreatic PEComa, including 32 author's cases.General statements. A 22-year-old woman who was surgically treated for pancreatic head PEComa with infiltration of the duct of Wirsung, common bile duct, duodenal wall, focal invasion into the blood and lymphatic vessels, and perineural space infiltration. Synchronously, metastases were detected in both liver lobes, for which she took Everolimus for 6 years under the control of radiology methods with dose adjustment and frequency of administration. At the A.V. Vishnevsky National Medical Research Center of Surgery, the patient underwent right-sided hemihepatectomy and atypical resection of II-III liver segments.Conclusion. To determine clearer criteria for the diagnosis and differential diagnosis of PEComa, to identify criteria for the malignancy of these tumors, to develop treatment tactics and further dynamic monitoring, a set of statistical data of significant group and randomized clinical trials are needed. Pancreatic PEComas in this group are extremely rare and often have a benign course. The presented clinical case demonstrates the most malignant form of this tumor with localization in the pancreas and liver metastases.

Closing Data Gaps and Paving the Way for Pan-European Fire Safety Efforts: Part II—Terminology of Fire Statistical Variables

A well-defined terminology of fire-related variables is important for correct analyses and supporting knowledge-based decisions regarding the evaluation of building fires at the European level. After developing an overview of current practices for fire statistics in Part I, the terminology used and the data collected by the EU Member States and eight other countries regarding fire incidents, property damage and human losses were mapped to increase awareness of their practice and support a comprehensive assessment of several fire statistical datasets. A questionnaire was distributed to relevant authorities responsible for the collection, elaboration/analysis, and fire statistical data publications to define and select the essential variables for an appropriate fire assessment and fire incident description. Based on the results of the questionnaire able to identify the essential fire statistical variables and a detailed analysis of current definitions adopted in the fire statistics of the EU Member States and other countries, a common terminology is proposed to collect the necessary data in the EU Member States and obtain meaningful datasets based on standardised terms and definitions. The results will generate essential outputs to move towards harmonised fire statistics at the EU level and contribute to an appropriate analysis able to improve fire prevention and fire mitigation in building fires.

Constructing a knowledge graph for open government data: the case of Nova Scotia disease datasets

The majority of available datasets in open government data are statistical. They are widely published by various governments to be used by the public and data consumers. However, most open government data portals do not provide the five-star Linked Data standard datasets. The published datasets are isolated from one another while conceptually connected. This paper constructs a knowledge graph for the disease-related datasets of a Canadian government data portal, Nova Scotia Open Data. We leveraged the Semantic Web technologies to transform the disease-related datasets into Resource Description Framework (RDF) and enriched them with semantic rules. An RDF data model using the RDF Cube vocabulary was designed in this work to develop a graph that adheres to best practices and standards, allowing for expansion, modification and flexible re-use. The study also discusses the lessons learned during the cross-dimensional knowledge graph construction and integration of open statistical datasets from multiple sources.

Social media and political involvement in Cambodia

A total of 1,300 students in Cambodia were surveyed regarding their opinions on social media and political trust, their feelings regarding social media use, engagement on social media, and interactive relationships with social media. Also, it was conducted using a questionnaire-based quantitative approach. Additionally, it employed a statistical dataset analysis using bivariate and multivariate techniques to understand its operation better. The study founds a significant correlation between trust in the political organization and political engagement. Moreover, it suggests that promoting civic engagement in social media contributes to building trust between governments and citizens. It is also possible to express political views and discuss social issues on social media.

Population-based screening leads to improved glaucoma diagnosis in Kazakhstan

To analyse the effect of the state screening program for glaucoma, funded by the Kazakhstan State programs for the health care system “Salamatty Kazakhstan” (2011–2015) and “Densaulyk” (2016–2019), on glaucoma diagnosis. This study involved 1,620,658 men and women 40–70 years of age. State screening for glaucoma includes measures of intraocular pressure (IOP) and corneal thickness. Data for glaucoma incidence were retrieved from the state statistics dataset. Studies of vulnerable age groups and ophthalmologists’ availabilities were assessed, and correlations with glaucoma detection were determined. For the statistical analysis, MATLAB application package version R2017b (MathWorks, USA) was used. The number of registered glaucoma patients was 43,337 in 2010. In 2016, following the screening program, this figure increased to 68,195. A 49% increase in newly identified glaucoma cases from 70.8 (in 2010) to 124 (in 2016) per 100,000 population was determined. Thus, the sufficiency and effectiveness of the screening program were confirmed by the increase in the number of patients diagnosed with glaucoma, which also depended on age. A correlation between suspected glaucoma and age groups was exposed with the observation of a high number of cases in the older population. The state screening programs for glaucoma in the Republic of Kazakhstan constitute initial steps in the prevention of irreversible blindness and disability due to glaucoma. Such screening seems to allow timely and adequate diagnosis of the disease in people>40 years of age.

Preparation for Entering a Higher Educational Institution as an Investment Project

The purpose of the study is to develop an algorithm for managing investments in preparation for entering a higher educational institution as an investment project.Materials and methods. The study includes a review of bibliographic sources on the existing methods and approaches to determining the economic efficiency of preparing for admission to a higher educational institution, as well as methods for assessing the effectiveness of investment projects. It also includes the collection and analysis of a statistical set of data and the development of a mechanism for choosing a training method with the available data about the applicant through neural network modeling.Results. A number of factors influencing the effectiveness of preparing an applicant for admission are considered. The effectiveness of the main trajectories of preparation for admission is evaluated by modeling the results of the collected statistical population, in particular: school preparation, tutoring, additional courses. Neural network models are created to determine the relationship between the factors influencing the result of preparation for admission and to make a choice to determine the significance of the applicant’s input parameters for various training trajectories. In accordance with the selected data, a neural network model is being developed to select the optimal trajectory for preparing for admission to a higher educational institution. As a result of creating a neural network model, a mathematical model is designed to determine the most optimal training method. Preparation for admission is considered as an investment project. By applying the method of evaluating the effectiveness of an investment project and the approach to managing it, based on the created model, an algorithm is described for choosing the best trajectory for preparing for admission to a higher educational institution, which allows applicants to independently evaluate investments in training as an investment project and choose the most cost-effective way of training. By adapting the method of assessing the economic efficiency of investments by discounting cash flows used in the business environment, a methodology was formulated for choosing the most optimal training trajectory.Conclusion. It can be argued that it makes sense to consider investments in additional preparation for a high school student to enter a higher educational institution as an investment project in which the parent acts as an investor, and the goal of the project is to enter a higher educational institution. To assess the effectiveness of investments in training and competent management of them, an algorithm has been developed for choosing the most cost-effective trajectory of preparation for entering a higher educational institution, using the example of admission to the Perm State National Research University. The algorithm is based on a neural network model that takes into account the input parameters of students: the duration of training, the frequency of additional classes, the level of motivation and average performance as factors influencing the probability of entering a higher educational institution. The algorithm is designed and can be used to manage investments in additional education of the child by determining the most cost-effective way to use financial resources. It is expedient to use the developed algorithm in those cases when the final goal involves the admission of a child to an educational institution. In this case, the use of the model will offer the best training path from an economic point of view for a particular applicant, taking into account the input parameters.

Retrospective study of more than 5 million emergency admissions to hospitals in England: Epidemiology and outcomes for people with dementia.

People living with dementia (PwD) admitted in emergency to an acute hospital may be at higher risk of inappropriate care and poorer outcomes including longer hospitalisations and higher risk of emergency re-admission or death. Since 2009 numerous national and local initiatives in England have sought to improve hospital care for PwD. We compared outcomes of emergency admissions for cohorts of patients aged 65+ with and without dementia at three points in time. We analysed emergency admissions (EAs) from the Hospital Episodes Statistics datasets for England 2010/11, 2012/13 and 2016/17. Dementia upon admission was based on a diagnosis in the patient's hospital records within the last five years. Outcomes were length of hospital stays (LoS), long stays (> = 15 days), emergency re-admissions (ERAs) and death in hospital or within 30 days post-discharge. A wide range of covariates were taken into account, including patient demographics, pre-existing health and reasons for admission. Hierarchical multivariable regression analysis, applied separately for males and females, estimated group differences adjusted for covariates. We included 178 acute hospitals and 5,580,106 EAs, of which 356,992 (13.9%) were male PwD and 561,349 (18.6%) female PwD. Uncontrolled differences in outcomes between the patient groups were substantial but were considerably reduced after control for covariates. Covariate-adjusted differences in LoS were similar at all time-points and in 2016/17 were 17% (95%CI 15%-18%) and 12% (10%-14%) longer for male and female PwD respectively compared to patients without dementia. Adjusted excess risk of an ERA for PwD reduced over time to 17% (15%-18%) for males and 17% (16%-19%) for females, but principally due to increased ERA rates amongst patients without dementia. Adjusted overall mortality was 30% to 40% higher for PwD of both sexes throughout the time-period; however, adjusted in-hospital rates of mortality differed only slightly between the patient groups, whereas PwD had around double the risk of dying within 30 days of being discharged. Over the six-year period, covariate-adjusted hospital LoS, ERA rates and in-hospital mortality rates for PwD were only slightly elevated compared to similar patients without dementia and remaining differences potentially reflect uncontrolled confounding. PwD however, were around twice as likely to die shortly after discharge, the reasons for which require further investigation. Despite being widely used for service evaluation, LoS, ERA and mortality may lack sensitivity to changes in hospital care and support to PwD.

Effect of C-reactive protein on the risk of Heart failure: a mendelian randomization study

BackgroundTraditional observational studies have shown positive associations between c-reactive protein (CRP) and heart failure (HF) risk. However, this association has not been fully elucidated. Therefore, Mendelian randomization was used to examine CRP’s possible etiological roles with HF.MethodsWe implemented a two-sample Mendelian randomization framework to examine the causality of the association between CRP and HF based on summary statistics by large-scale genome-wide association studies (GWAS) datasets of European ancestry through inverse-variance weighted, weighted median, MREgger regression, and MR-PRESSO methods. The summary statistics dataset on the association of genetic variants with CRP was used from the published GWAS of European descent in UK Biobank participants (N = 427,367) and the CHARGE consortium (N = 575,531). The GWAS dataset used to identify genetic variants underlying HF from the HERMES consortium includes 977,323 participants (47,309 cases and 930,014 controls). The odds ratio (OR) with 95% confidence intervals (CIs) was employed to examine this association.ResultsThe results of our IVW indicated that CRP was strongly associated with HF (OR = 4.18, 95% CI = 3.40–5.13, p < 0.001). The Cochran heterogeneity test showed significant heterogeneity among SNPs of CRP (Q = 317.55, p < 0.001; I2 = 37.6%), and no considerable pleiotropy was detected for the association of CRP with HF [intercept = 0.003; p = 0.234]. This finding remained consistent using different Mendelian randomization methods and sensitivity analyses.ConclusionOur MR study did identify convincing evidence to support CRP associated with HF risk. Human genetic data suggest that CRP is a causative factor in HF. Hence, CRP assessment may offer additional prognostic information as an adjuvant to overall risk assessment in HF patients. These findings prompt significant questions about the function of inflammation in the progression of HF. More research into the role of inflammation in HF is needed to guide trials of anti-inflammation management.

Uranium and the Macro-Economy in Niger: An Empirical Investigation

Like many commodities, uranium price is notoriously volatile, and such volatility can have serious economic consequences for countries that export them. Niger, which is the fourth-largest producer of uranium in the world, has been dealing with uranium price fluctuations since the start of its production in 1972. Since 2011, Niger is also an oil-exporting country. However, Niger is still one of the poorest countries in the world, and policymakers are keen on learning the impact of natural resource revenue on the economy. Policymakers are particularly interested in knowing whether uranium export has been a curse instead of a blessing. As such, the present study seeks to analyze, empirically, the impact of uranium price fluctuations on key macroeconomic variables in Niger. More precisely, the objective of this article is to document the existence of Dutch "disease" or lack thereof in the economy. The study uses VAR and VECM methodologies and utilizes Granger causality tests, variance decomposition, and impulse response functions to guide the analysis. These approaches allow for an empirical deeper dive into the impact of the uranium sector on the economy. The article uses annual time series data covering the period from 1977 to 2015. The data comes from three main sources, including the World Development Indicators (WDI) database, the Central Bank of West African Countries dataset (EDEN), and Niger's National Institute of Statistics (INS) dataset. The results show that Niger's economy suffered from Dutch disease from uranium export. Indeed, the Granger causality tests show that uranium export revenue cannot be used to forecast real income in Niger. Furthermore, impulse response functions confirm the above result as a shock to uranium price and the resultant effects on money supply, inflation, and the real exchange rate is consistent with the Dutch "disease" theory. In general, uranium export revenue was the source of macroeconomic imbalances observed in the country during the period considered by the study. Now that Niger is also an exporter of oil in addition to uranium, policymakers should avoid the trap of Dutch "disease" by investing natural resources revenue strategically. Given Niger's development challenges, using natural resources revenue for public investment in infrastructure, health, and education should be a priority.

A robust discretization method of factor screening for landslide susceptibility mapping using convolution neural network, random forest, and logistic regression models

ABSTRACT The selection of discretization criteria and interval numbers of landslide-related environmental factors generally fails to quantitatively determine or filter, resulting in uncertainties and limitations in the performance of machine learning (ML) methods for landslide susceptibility mapping (LSM). The aim of this study is to propose a robust discretization criterion (RDC) to quantify and explore the uncertainty and subjectivity of different discretization methods. The RDC consists of two steps: raw classification dataset generation and optimal dataset extraction. To evaluate the robustness of the proposed RDC method, Lushan County of Sichuan Province in China was chosen as the study area to generate the LSM based on three datasets (optimal dataset, original dataset with continuous values, and statistical dataset) using three popular ML methods, namely, convolution neural network, random forest, and logistic regression. The results show that the areas under the receiver operating characteristic curve (AUCs) of the optimal dataset for the abovementioned ML models are 0.963, 0.961, and 0.930 which are higher than those of the original dataset (0.938, 0.947, and 0.900) and statistical dataset (0.948, 0.954, and 0.897). In conclusion, the RDC method can extract the more representative features from environmental factors and outperform the other conventional discretization methods.

Environmental assessment of the Ecuadorian cocoa value chain with statistics-based LCA

IntroductionCocoa is one of the main crops grown in Ecuador. The agricultural area dedicated to cocoa represents the largest area dedicated to a permanent crop the country. Dry bean production has grown at an average annual rate of 15% since 2014, mainly due to yield improvements and replacement of other crops. Several varieties of cocoa are grown, but production is dominated by two main varieties: “Cacao Fino y de Aroma” and clonal varieties (dominated by CCN-51). Cocoa, mainly in monocrop systems, is mainly produced on the Ecuadorian Coast (but also in the Highlands and Amazonia). This study presents a statistics-based LCA of the Ecuadorian cocoa value chain.Material and methodsLCIs representing the various types of systems in each link of the value chain—i.e. the various types of farming systems, processing and distribution—were constructed in terms of representative production units. Sub-chains centred on different cocoa varieties and value-adding strategies were identified. Primary and secondary data were collected for the most representative system types, as defined in the actor typologies. Primary data were obtained via field visits and surveys, while secondary data were obtained mainly from statistical datasets of the National Institute of Statistics and Census. Impacts were computed following the European Commission's Product Environmental Footprint, while soil carbon turnover was modelled using RothC.Results and discussionIdentified types of producers are subsistence and entrepreneurial small, medium, and large. Two post-harvest strategies were modelled: a volume-oriented one and a quality-oriented one. The main sub-chains identified are the volume/commodity-oriented one (which is dominantly based on cocoa which either does not undergo post-harvest, or which undergoes volume-oriented post-harvest activities) and the quality-oriented one. Across producer types, irrigation and negative direct field emissions are the most important factors, followed in importance by total energy consumption. Post-harvest and processing activities are dominated by energy expenditures. Sub-chains feature significantly different intensity of impacts, with the volume-oriented sub-chain (i.e. those privileging quantity over quality) featuring lower impacts than the quality-oriented ones.ConclusionsThe impacts of the value chain are comparatively lower, at least regarding climate change, than in other producing countries. Its agricultural phase generally exhibits low input pressure, contributes to climate change mitigation through high C sequestration in biomass that exceeds C losses due to land use change (e.g. deforestation), and does not seem to pose an immediate threat to biodiversity. Improvement initiatives do not necessarily imply intensification of production.

Who Will Care for Rural Older Adults? Measuring the Direct Care Workforce in Rural Areas.

Using the 2021 Occupational Employment and Wage Statistics (OEWS) dataset, we calculate the ratio of direct care workers relative to the population of older adults (ages 65+) across rural and urban areas in the US. We find that there are, on average, 32.9 home health aides per 1000 older adults (age 65+) in rural areas and 50.4 home health aides per 1000 older adults in urban areas. There are, on average, 20.9 nursing assistants per 1000 older adults in rural areas and 25.3 nursing assistants per 1000 older adults in urban areas. There is substantial regional variation. Greater investment needs to be made in improving wages and job quality for direct care workers to attract workers to these critical occupations, especially in rural areas where the need for direct care is greater.

Uterine-preserving treatments or hysterectomy reintervention after myomectomy or uterine artery embolisation: A retrospective cohort study of long-term outcomes.

To assess comparative rates of further uterine-preserving procedures (UPP) or hysterectomy reintervention, after myomectomy or uterine artery embolisation (UAE). Population-based, retrospective cohort study. England. Women who underwent myomectomy or UAE between 2010 and 2015 under the NHS. Data was abstracted from NHS Health Episode Statistics datasets. Hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated using Cox proportional-hazards regression. 30-day readmission, UPP and hysterectomy reintervention rates. 9443 and 6224 women underwent elective myomectomy or UAE, respectively. After 118 136 total person-years of follow-up, the rate of hysterectomy was 8.34 and 20.98 per 1000 patient years for myomectomy or UAE, respectively. There was a 2.4-fold increased risk of undergoing hysterectomy after UAE when compared with myomectomy in adjusted models (HR 2.38 [95% CI 2.10-2.66]) [adjusted for age, ethnicity, multiple deprivation index, geographical region and comorbidities]. The HR for undergoing a UPP reintervention was 1.44 (95% CI 1.29-1.60) in favour of myomectomy. The rate of hysterectomy was increased 22% following UAE compared with laparoscopic myomectomy (0.97-1.52). Age may influence reintervention rates, and there was variation in hysterectomy risk when stratified by geographical region. After a median of 7 years of follow-up, there is a 2.4-fold increased rate of hysterectomy and 44% increased risk of UPPs as reintervention after UAE, relative to myomectomy. These findings will aid pre-procedure counselling for women with fibroids. Future work should investigate the effect of other outcome modifiers, such as fertility intentions and fibroid anatomical characteristics.

Phenotypic and Genetic Links between Body Fat Measurements and Primary Open-Angle Glaucoma.

The phenotypic and genetic links between body fat phenotypes and primary open-angle glaucoma (POAG) are unclear. We conducted a meta-analysis of relevant longitudinal epidemiological studies to evaluate the phenotypic link. To identify genetic links, we performed genetic correlation analysis and pleiotropy analysis of genome-wide association study summary statistics datasets of POAG, intraocular pressure (IOP), vertical cup-to-disc ratio, obesity, body mass index (BMI), and waist-to-hip ratio. In the meta-analysis, we first established that obese and underweight populations have a significantly higher risk of POAG using longitudinal data. We also discovered positive genetic correlations between POAG and BMI and obesity phenotypes. Finally, we identified over 20 genomic loci jointly associated with POAG/IOP and BMI. Among them, the genes loci CADM2, RP3-335N17.2, RP11-793K1.1, RPS17P5, and CASC20 showed the lowest false discovery rate. These findings support the connection between body fat phenotypes and POAG. The newly identified genomic loci and genes render further functional investigation.

The association between maternal insurance status on maternal and neonatal outcomes in women with hypertensive disorders of pregnancy.

The objective of this study was to examine the association between maternal insurance status and maternal and neonatal adverse outcomes in women who had hypertensive disorders in pregnancy. A population-based retrospective cohort study was undertaken using the US Vital Statistics dataset on Period Linked Birth-Infant Data from 2016-2020. The study population was restricted to non-anomalous births from women whose pregnancies were complicated by hypertensive disorders. Insurance status was categorized as private, Medicaid, self-pay and other. The primary outcome was a composite of maternal adverse outcomes, which included admission to the intensive care unit, unplanned hysterectomy, maternal blood transfusion or uterine rupture. We examined the role of prenatal care in these relationships using a mediation analysis with Kotelchuck's Adequacy of Prenatal Care Utilization Index. Multivariable logistic regression models were used to estimate the association between maternal insurance status and adverse outcomes (using adjusted odds ratios [aOR] and 99% confidence interval [CI]). Of the 18,999,865 live births in the five-year study, 1,642,654 (8.6%) met the inclusion criteria. The frequency of the composite maternal adverse outcome was 1.3%. The maternal composite occurred more frequently in women with Medicaid (aOR = 1.11, 99% CI: 1.06, 1.16) or self-pay (aOR = 1.40, 99% CI: 1.25, 1.55) when compared to private insurance. Adjusting for prenatal care slightly attenuated this association, but remained significant. Among women with hypertensive disorders in pregnancy, women with Medicaid insurance or self-pay were more likely to experience maternal and neonatal adverse outcomes than women with private insurance.

BADM-Net: Hierarchical Classification Network for Identifying Anomalous Trends in Bridge Monitoring Data Patterns

SHM systems have been widely implemented in long-span bridges, and seas of field measurement data have been accumulated. Due to the imperfect sensors, data transmission and acquisition, various anomalies inevitably exist in the SHM data, which may lead to unreliable structural condition assessment. Thus, an effective approach for detecting data anomalies is highly desirable. Due to the imbalanced data, some anomalous patterns are undertrained in popular end-to-end deep neural network models, resulting in a reduction in detection precision. In this paper, a hierarchical classification model with deep neural network tree is proposed for imbalanced data. The DNN tree contains three levels: (1) CNN to divide seven types of data into four categories (134, 2, 5, 67), denoted as C4; (2) two DNNs to classify to two classes separately (1, 34, 6, 7), denoted as D2D2; (3) DNNs to classify to two classes (3, 4). So, the DNN tree is presented as C4_D2D2_D2. The DNN tree is an open framework and can be defined based on the data characteristics. In the data processing, three data sets are built for training, namely single-channel data set, dual-channel data set and statistical data set. To validate our work, we considered the effects of balanced and imbalanced training sets and training ratios. The results show that our model can detect the multi-pattern anomalies of SHM data efficiently with 95.5% high accuracy. Besides, the proportion of abnormal data classified to normal data has been reduced, especially 3-minor. This model successfully solves the problem in a simple and easy to understand way, which has certain reference significance for the bridge structure anomaly judgment in the future.

Racial and Ethnic Disparities among Pregnancies with Chronic Hypertension and Adverse Outcomes.

We aimed to ascertain whether the risk of adverse pregnancy outcomes in the United States among individuals with chronic hypertension differed by maternal race and ethnicity and to assess the temporal trend. Population-based retrospective study using the U.S. Vital Statistics datasets evaluated pregnancies with chronic hypertension, singleton live births that delivered at 24 to 41 weeks. The coprimary outcomes were a composite maternal adverse outcome (preeclampsia, primary cesarean delivery, intensive care unit admission, blood transfusion, uterine rupture, or unplanned hysterectomy) and a composite neonatal adverse outcome (preterm birth, small for gestational age, Apgar's score <5 at 5 minutes, assisted ventilation> 6 hours, seizure, or death). Multivariable Poisson regression models were used to estimate adjusted relative risks (aRRs) and 95% confidence intervals (CIs). Between 2014 and 2019, the rate of chronic hypertension in pregnancy increased from 1.6 to 2.2%. After multivariable adjustment, an increased risk for the composite maternal adverse outcome was found in Black (aRR = 1.10, 95% CI = 1.09-1.11), Hispanic (aRR = 1.04, 95% CI = 1.02-1.05), and Asian/Pacific Islander (aRR = 1.07, 95% CI = 1.05-1.10), compared with White individuals. Compared with White individuals, the risk of the composite neonatal adverse outcome was higher in Black (aRR = 1.39, 95% CI = 1.37-1.41), Hispanic (aRR = 1.15, 95% CI = 1.13-1.16), Asian/Pacific Islander (aRR = 1.34, 95% CI = 1.31-1.37), and American Indian (aRR = 1.12, 95% CI = 1.07-1.17). The racial and ethnic disparity remained unchanged during the study period. We found a racial and ethnic disparity with maternal and neonatal adverse outcomes in pregnancies with chronic hypertension that remained unchanged throughout the study period. · Between 2014 and 2019, the rate of chronic hypertension in pregnancy increased.. · Among people with chronic hypertension, there are racial and ethnic disparities in adverse outcomes.. · Black, Hispanic, and Asian/Pacific Islander have a higher risk of the adverse neonatal outcomes..