90th Quantile Research Articles

Distributional Dynamics Amidst Resource Abundance: The Implications of Structural and Institutional Transformations

A critical evaluation of prior studies indicates that the roles of structural and institutional transformations in moderating natural resource-inequality dynamics is understudied. Moreover, very little is known about such dynamics in resource-rich sub-Saharan African (SSA) countries. To cover the literature gaps, this study harnesses annual panel datasets comprising natural resource rent, structural transformation, institutional quality and technology for the top 10 resource-rich SSA countries over 2000–2018 for empirical evaluations. After ascertaining long-run coevolution among the series, the estimates of the panel quantile regression depict that natural resource abundance is the leading cause of income disparities in these countries; hence, it promotes inequality significantly across all quantiles of the distributions. Furthermore, at the 5th quantile, both structural and institutional transformations deaccelerate inequality insignificantly, but at the 10th quantile, institutional reforms engender equitable income distributions. Notably, beyond the 10th and 50th quantiles, institutional and structural reforms significantly promote income disparities. Additionally, at the 10th quantile, technology marginally promotes equitable income distributions, but at the 60th, 70th and 80th quantiles, technology significantly promotes inequality. Implicatively, a blend of structural reforms and technological improvements within the thresholds around 10th and 50th quantiles could ensure equitable prosperities in the region. Among other policy options highlighted herein, pro-equity redistributive policies that ensure no one is left behind are expedient to eliminate income disparities in these countries.

Monthly streamflow forecasting by machine learning methods using dynamic weather prediction model outputs over Iran

Seasonal hydrological forecasts play a critical role in water resources management. The Copernicus Climate Change Service (C3S) data store provides open access to monthly hydrological forecasts for up to six-months. This study aims to evaluate, for the first time, 1- to 3-month runoff forecasts using the European Centre for Medium-Range Weather Forecasts (ECMWF) ensembles of precipitation, runoff, and temperature in 1981–2015 period over a total of 30 s-level basins in Iran. We adopted the 5th, 50th and 95th ECMWF ensemble quantiles for each variable that represent low, medium and high probability of occurrence, respectively. Pearson correlation analysis (Pca), Recursive Feature Elimination (RFE) via random forest (RF) model, and Bayesian Networks (BN) feature selection algorithms were used in order to reduce input variable dimension and select potential predictors to be fed to the machine learning models. Multiple Linear Regression (MLR), Artificial Neural Networks (ANN), Support Vector Regression (SVR), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) machine learning models were used with Repeated K-Fold cross validation (rK-Fold CV) while model efficiency was evaluated using modified Kling-Gupta efficiency coefficient (KGE’), Nash-Sutcliffe Efficiency coefficient (NSE), and Normalized Root Mean Square Error (NRMSE). Results of this study revealed that C3S runoff ensembles have the highest impact on forecast accuracy of streamflow, followed by precipitation and temperature. Overall, model performance yield a best-to-worst ranking of ANN, XGBoost, RF, MLR, and SVR with KGE’ values of 0.70, 0.68, 0.66, 0.57, and 0.41, respectively. The predictive performance of all models decreased with lead times beyond 1-month, where ANN and XGBoost outperformed other models with KGE’ of 0.65 for 2-month lead time and 0.60 for 3-month lead time. The three superior models of XGBoost, ANN, and RF, were employed with RFE and BN FSAs most frequently across Iran’s 30 s level basins in all lead times. Almost all models in the arid central region of Iran showed the lowest performance while highest skills were achieved in the western regions of Iran. Finally, for all models and over all regions, the model performance reduced by increase in lead-time.

Does economic complexity increase energy intensity?

Today, many countries in the world pay special attention to energy efficiency to improve environmental quality and reduce the waste of energy resources. For this purpose, this research has investigated the economic complexity of energy intensity (EI) from 1995 to 2019 using club convergence and panel quantile regression (PQR) models. The club convergence results showed that out of 62 countries, 42 converged in energy intensity during the period. Then, using the PQR model, the determining factors of EI for these 42 countries were evaluated: economic complexity, urbanization, trade openness, industrial production, foreign direct investment, oil price, and economic growth. The result of the PQR model confirmed that economic complexity has an inverted U-shaped relationship with EI. Urbanization and foreign direct investment have a positive and increasing effect on EI. In addition, trade openness positively affects EI, mainly in the 10th and 25th quantiles. On the other hand, economic growth has a decreasing effect on EI. Moreover, industrialization and oil prices can reduce EI, particularly in the 90th quantile. Given the inverted U-shaped effect of the energy complexity on energy intensity, where the lower and higher quantiles are negative, it is advised that policymakers ought to promote the leap from lower levels of economic complexity to higher levels by reducing intermediate levels as much as possible. Policymakers should also be conscious that promoting the diffusion of economic complexity also mitigates noxious gas emissions.

17 Data Loofah: A web-based app for efficiently identifying erroneous data

OBJECTIVES/GOALS: The goal was to create and deploy an intuitive, easy-to-use tool that clinical investigators can apply to their data to identify erroneous or inconsistent data entries. Investigators can then correct any errors prior to sharing the data with their statistician for analysis. METHODS/STUDY POPULATION: We developed an interactive shiny app, the Data Loofah, using R Studio that researchers or data analysts can use to examine data. After an investigator uploads data, the app reports which variables are numeric or categorical. Means, standard deviation, median, 25th and 75th quantiles, range and number of missing values are reported for numeric variables. Counts and percentages of categorical variables are summarized. Graphical displays further enhance identification of errors. Access to the Data Loofah is through a secure, university-maintained website with access restricted to university personnel. Supporting materials consisting of instructional step-by-step handouts and videos were developed to assist investigators in the use of the app. RESULTS/ANTICIPATED RESULTS: We will integrate use of the Data Loofah into our Clinical and Translational Science Program’s biostatistics consultative practice. Investigators will use the Data Loofah to pre-screen their data prior to sending it to a statistician, identify errors and inconsistencies, and facilitate making necessary corrections. Statisticians will also use the Data Loofah to review data with investigators prior to starting analyses. Through use of this app, investigators are expected to develop a better understanding of their data specifically and more generally about requirements for preparing data for statistical analysis. Most significantly, regular use of the Data Loofah is expected to result in higher quality data and more efficient use of statistician resources due to reduced effort for data cleaning. DISCUSSION/SIGNIFICANCE: Data cleaning is a time-consuming task and finding data errors can be difficult for data analysts not familiar with clinical variables under study. Further, failure to identify data errors can lead to erroneous results. By facilitating identification of data errors by clinical investigators, the Data Loofah will improve and enhance research output.

Estimated Probability Distribution of Bleeding After Pediatric Tonsillectomy

The American Academy of Otolaryngology-Head and Neck Surgery Foundation has recommended yearly surgeon self-monitoring of posttonsillectomy bleeding rates. However, the predicted distribution of rates to guide this monitoring remain unexplored. To use a national cohort of children to estimate the probability of bleeding after pediatric tonsillectomy to guide surgeons in self-monitoring of this event. This retrospective cohort study used data from the Pediatric Health Information System for all pediatric (<18 years old) patients who underwent tonsillectomy with or without adenoidectomy in a children's hospital in the US from January 1, 2016, through August 31, 2021, and were discharged home. Predicted probabilities of return visits for bleeding within 30 days were calculated to estimate quantiles for bleeding rates. A secondary analysis included logistic regression of bleeding risk by demographic characteristics and associated conditions. Data analyses were conducted from August 7, 2022 to January 28, 2023. Revisits to the emergency department or hospital (inpatient/observation) for bleeding (primary/secondary diagnosis) within 30 days after index discharge after tonsillectomy. Of the 96 415 children (mean [SD] age, 5.3 [3.9] years; 41 284 [42.8%] female; 46 954 [48.7%] non-Hispanic White individuals) who had undergone tonsillectomy, 2100 (2.18%) returned to the emergency department or hospital with postoperative bleeding. The predicted 5th, 50th, and 95th quantiles for bleeding were 1.17%, 1.97%, and 4.75%, respectively. Variables associated with bleeding after tonsillectomy were Hispanic ethnicity (OR, 1.19; 99% CI, 1.01-1.40), very high residential Opportunity Index (OR, 1.28; 99% CI, 1.05-1.56), gastrointestinal disease (OR, 1.33; 99% CI, 1.01-1.77), obstructive sleep apnea (OR, 0.85; 99% CI, 0.75-0.96), obesity (OR,1.24; 99% CI, 1.04-1.48), and being more than 12 years old (OR, 2.48; 99% CI, 2.12-2.91). The adjusted 99th percentile for bleeding after tonsillectomy was approximately 6.39%. This retrospective national cohort study predicted 50th and 95th percentiles for posttonsillectomy bleeding of 1.97% and 4.75%. This probability model may be a useful tool for future quality initiatives and surgeons who are self-monitoring bleeding rates after pediatric tonsillectomy.

Income inequality, shadow economy and environmental degradation in Africa: quantile regression via moment’s approach

PurposeThe purpose of this examine the impact of income inequality and shadow economy on environmental degradation given the growing income inequality, shadow economy and ecological degradation in developing countries. Thus, this study is motivated to offer empirical insight into how income inequality and shadow economy influence the environment in African countries.Design/methodology/approachData from 29 countries in Africa between 2000 and 2017 were used, while the novel method of moments quantile regression of Machado and Silva (2019) and Dumitrescu and Hurlin (D-H) (2012) granger causality is used as the estimation techniques.FindingsThe results established the presence of cross-sectional dependence and slope heterogeneity in the panel, while Westerlund panel cointegration confirmed the long-run cointegration among the variables. The results from the quantile regression suggest that income inequality increases environmental degradation from the 5th to the 30th quantiles, while from the 70th quantiles, income inequality reduces ecological degradation. The shadow economy negatively influences environmental degradation across the quantiles, strengthening environmental quality. Per capita income (economic growth) and financial development positively impact environmental degradation throughout the quantiles. However, urbanization reduces environmental degradation from 60th to 95th quantiles. The D-H causality established a two-way relationship between income inequality and environmental degradation, while one-way from shadow economy, per capita income and urbanization to environmental degradation were established.Originality/valueThis study provides fresh insights into the nexus between shadow economy and environmental quality in the presence of higher levels of income inequality for the case of African region. The study applies quantile analysis via moment proposed by Machado and Silva (2019). This technique shows that the impact of income inequality and shadow economy on environmental degradation is heterogeneous across the quantiles of ecological footprints in Africa.

Urbanization, renewable energy, and carbon dioxide emissions: a pathway to achieving sustainable development goals (SDGs) in sub-Saharan Africa

PurposeThe purpose of this paper is to investigate sustainable green economy in sub-Saharan African (SSA) countries over the period 1990–2019 using a quantile regression approach, considering the nexus between urbanization, economic growth, renewable energy, trade and carbon dioxide (CO2) emissions.Design/methodology/approachThe study used a dynamic panel quantile regression to investigate the conditional distribution of CO2 emissions along the turn-points of urbanization, economic growth, renewable energy, trade and the regressors via quadratic modeling specifications.FindingsThe main findings are established as follows. There is strong evidence of the Kuznets curve in the nexus between urbanization, economic growth, renewable energy, trade and CO2 emissions, respectively. Second, urbanization thresholds that should not be exceeded for sustainability to reduce CO2 emissions are 0.21%, and 2.70% for the 20th and 75th quantiles of the CO2 emissions distribution. Third, growth thresholds of 3.64%, 3.84%, 4.01%, 4.36% and 5.87% across the quantiles of the CO2 emissions distribution. Fourth, energy thresholds of 3.64%, 3.61%, 3.70%, 4.02% and 4.34% across the quantiles of the CO2 emissions distribution. Fifth, trade thresholds of 3.37% and 4.47% for the 20th and median quantiles of the CO2 emissions distribution, respectively.Practical implicationsThe empirical shreds of evidence offer policy implications in such that building sustainable development and environment requires maintaining the critical mass, not beyond those insightful thresholds to achieving sustainable development and environmentally friendly SSA countries.Social implicationsSustainable cities and communities in an era of economic recovery path COVID-19 mitigate greenhouse gas. The policy relevance is of particular concern to the sustainable development goals.Originality/valueThe study is novel considering the extant literature by providing policymakers with avoidable thresholds for policy formulations and implementations in the nexus between urbanization, economic growth, renewable energy and trade openness.

Early Performance of the Patients Over Paperwork Initiative among Family Medicine Physicians.

In 2019, the Centers for Medicare & Medicaid Services began implementing the Patients Over Paperwork (POP) initiative in response to clinicians reporting burdensome documentation regulations. To date, no study has evaluated how these policy changes have influenced documentation burden. Our data came from the electronic health records of an academic health system. Using quantile regression models, we assessed the association between the implementation of POP and clinical documentation word count using data from family medicine physicians in an academic health system from January 2017 to May 2021 inclusive. Studied quantiles included the 10th, 25th, 50th, 75th, and 90th quantiles. We controlled for patient-level (race/ethnicity, primary language, age, comorbidity burden), visit-level (primary payer, level of clinical decision making involved, whether a visit was done through telemedicine, whether a visit was for a new patient), and physician-level (sex) characteristics. We found that the POP initiative was associated with lower word counts across all of the quantiles. In addition, we found lower word counts among notes for private payers and telemedicine visits. Conversely, higher word counts were observed in notes that were written by female physicians, notes for new patient visits, and notes involving patients with greater comorbidity burden. Our initial evaluation suggests that documentation burden, as measured by word count, has declined over time, particularly following implementation of the POP in 2019. Additional research is needed to see whether the same occurs when examining other medical specialties, clinician types, and longer evaluation periods.

Novel approach to studying effects of inhalational exposure on lung function in civilians exposed to the World Trade Center disaster

It is increasingly important to study the impact of environmental inhalation exposures on human health in natural or man-made disasters in civilian populations. The members of the World Trade Center Environmental Health Center (WTC EHC; WTC Survivors) had complex exposures to environmental disaster from the destruction of WTC towers and can serve to reveal the effects of WTC exposure on the entire spectrum of lung functions. We aimed to investigate the associations between complex WTC exposures and measures of spirometry and oscillometry in WTC Survivors and included 3605 patients enrolled between Oct 1, 2009 and Mar 31, 2018. We performed latent class analysis and identified five latent exposure groups. We applied linear and quantile regressions to estimate the exposure effects on the means and various quantiles of pre-bronchodilator (BD) % predicted forced expiratory volume in one second (FEV1), forced vital capacity (FVC) and FEV1/FVC ratio, as well as the resistance at an oscillating frequency of 5 Hz (R5), frequency dependence of resistance R5–20, and reactance area (AX). Compared with Group 5, which had low or unknown exposure and was treated as the reference group, Group 1, the local workers with both acute and chronic exposures, had a lower median of % predicted FVC (−3.6; 95% CI: −5.4, −1.7) and higher (more abnormal) measures of AX at 10th quantile (0.77 cmH2O L–1 s; 95% CI: 0.41, 1.13) and 25th quantile (0.80 cmH2O L−1 s; 95% CI: 0.41, 1.20). Results suggested heterogeneous exposures to the WTC disaster had differential effects on the distributions of lung functions in the WTC Survivors. These findings could provide insights for future investigation of environmental disaster exposures.

Regional Differences, Dynamic Evolution and Driving Factors Analysis of PM2.5 in the Yangtze River Economic Belt

The proposal of a “dual-carbon” goal puts forward higher requirements for air pollution control. Identifying the spatial-temporal characteristics, regional differences, dynamic evolution, and driving factors of PM2.5 are the keys to formulating targeted haze reduction measures and ameliorating air quality. Therefore, adopting the Dagum Gini Coefficient and its decomposition method, the Kernel Density Estimation model, and spatial quantile regression model, this study analyzes the regional differences, dynamic evolution, and driving factors of PM2.5 concentrations (PM2.5) in the Yangtze River Economic Belt (YREB) and the upstream, midstream, and downstream (the three regions) from 2003 to 2018. The study shows that: (1) PM2.5 in the YREB was characterized by increasing first and then decreasing, with evident heterogeneity and spatial agglomeration characteristics. (2) Inter-regional differences and intensity of trans-variation were the primary sources of PM2.5 differences. (3) The density curve of PM2.5 shifted to the left in the YREB and the upstream, midstream, and midstream, suggesting that PM2.5 has declined. (4) Industrial service level (IS) and financial expenditure scale (FES) exerted a significant and negative effect on PM2.5 across the quantiles. On the contrary, population density (PD) showed a significant and positive influence. Except for the 75th quantile, the technology level (TEC) significantly inhibited PM2.5. The remaining variables had a heterogeneous impact on PM2.5 at different quantiles. The above results suggest that regional joint prevention and control mechanisms, collaborative governance mechanisms, and comprehensive policy mix mechanisms should be established to cope with PM2.5 pollution and achieve green, sustainable economic development of the YREB.

Influence of Clean Energy and Financial Structure on China’s Provincial Carbon Emission Efficiency—Empirical Analysis Based on Spatial Spillover Effects

Clean energy is an essential means to limiting carbon emissions and improving economic transformation, and a market-oriented financial structure is the inevitable result of the deepening of supply-side financial reforms. Exploring whether clean energy enhances carbon emission efficiency (CEE) through financial structural adjustment is essential in formulating policies intended to achieve the dual goals of “carbon peaking” and “carbon neutrality”. As part of the evaluation of China’s provincial CEE using panel data of 30 provinces from 2000 to 2019, this paper adopts an improved nonradial directional distance function (NDDF), while empirically analyzing the influence of clean energy and a market-oriented financial structure on CEE using a spatial econometric model. The results indicate the following findings: (1) The provincial CEE in China is characterized by significant spatial autocorrelation. (2) A 1% increase in the integration of clean energy and a market-oriented financial structure leads to a 0.0032% increase in the local CEE and a 0.0076% increase in neighboring regions’ CEE through the spatial spillover effect. Clean energy can efficiently enhance CEE through the stock market, while it has a passive impact through bank credit. (3) The interactive effect between clean energy and a market-oriented financial structure varies according to the provincial CEE. From the 25th to the 90th quantiles, the role of clean energy in promoting CEE through the capital market is very significant, while clean energy inhibits CEE through bank credit in most provinces. Therefore, China’s clean energy development will bolster its competitiveness in the global market through a market-oriented financial structure that will bring economic development and environmental pollution into balance and provide a theoretical foundation for China’s double carbon reduction.

The rainbow reality: income difference and discrimination based on sexual orientation and occupations

PurposeThe article aimed to identify and analyze the income differences across the income distribution between homosexuals and heterosexuals by occupational groups in Brazil.Design/methodology/approachPNAD-C microdata was used in two periods (2013–2015 and 2016 to 2019), highlighting the different economic, social and political contexts in Brazil. Recentered influence function and quantile income decomposition were estimated to verify the difference and income discrimination according to the guideline the worker’s sexuality.FindingsFor some cases homosexual workers earn more and in others, homosexuals earn less than heterosexuals. The differences in remuneration according to sexual orientation were smaller in positions that demand low qualification and competence. The quantile income decomposition between 2013 and 2015 revealed the positive effect of discrimination was the generator of income disparities between homosexuals and heterosexuals, with greater impact for the 10th and 90th quantiles and on groups that require small levels of complexity and education. Between 2016 and 2019, there was the presence of both effects, but the explained effect was the promoter of wage disparities in most occupational groups.Research limitations/implicationsGiven the dataset, it was only possible to work with proxies of homosexual couples.Originality/valueThere has been little research linking the themes of discrimination based on sexual orientation and occupational groups, and so far, it does not exist similar in Latin America. This study found that sexual orientation influences remuneration according to the occupational group that the worker belongs to, affecting income and, consequently, occupational choice. This connection of issues will contribute to new insights into discrimination based on sexual orientation, as well as more effective public policies aimed at reducing discrimination against homosexuals.

Prevalence and risk predictors of childhood stunting in Bangladesh.

The child nutritional status of a country is a potential indicator of socioeconomic development. Child malnutrition is still the leading cause of severe health and welfare problems across Bangladesh. The most prevalent form of child malnutrition, stunting, is a serious public health issue in many low and middle-income countries. This study aimed to investigate the heterogeneous effect of some child, maternal, household, and health-related predictors, along with the quantiles of the conditional distribution of Z-score for height-for-age (HAZ) of under five children in Bangladesh. In this study, a sample of 8,321 children under five years of age was studied from BDHS-2017-18. The chi-square test was mainly used to identify the significant predictors of the HAZ score and sequential quantile regression was used to estimate the heterogeneous effect of the significant predictors at different quantiles of the conditional HAZ distribution. The findings revealed that female children were significantly shorter than their male counterparts except at the 75th quantile. It was also discovered that children aged 7-47 months were disadvantaged, but children aged 48-59 months were advantaged in terms of height over children aged 6 months or younger. Moreover, children with a higher birth order had significantly lower HAZ scores than 1st birth order children. In addition, home delivery, the duration of breastfeeding, and the BCG vaccine and vitamin A received status were found to have varied significant negative associations with the HAZ score. As well, seven or fewer antenatal care visits was negatively associated with the HAZ score, but more than seven antenatal care visits was positively associated with the HAZ score. Additionally, children who lived in urban areas and whose mothers were over 18 years and either normal weight or overweight had a significant height advantage. Furthermore, parental secondary or higher education had a significant positive but varied effect across the conditional HAZ distribution, except for the mother's education, at the 50th quantile. Children from wealthier families were also around 0.30 standard deviations (SD) taller than those from the poorest families. Religion also had a significant relationship with the conditional HAZ distribution in favor of non-Muslim children. To enhance children's nutritional levels, intervention measures should be designed considering the estimated heterogeneous effect of the risk factors. This would accelerate the progress towards achieving the targets of Sustainable Development Goals (SDGs) related to child and maternal health in Bangladesh by 2030.

The heterogeneous impact of the environmental policy stringency on premature indoor and outdoor deaths from air pollution in the G7 countries: Do economic complexity and green innovation matter?

This study investigates the effect of green innovation initiatives, environmental policy stringency, and economic complexity on mortality from air pollution. The G7 countries during 1990–2019 and the moments' quantile regression econometric method were applied. The results support that CO2 emissions per capita and economic complexity positively impact the death rates from indoor and outdoor air pollution. Furthermore, green innovation initiatives, environmental policy stringency, real GDP per capita, renewable energy consumption per capita, and urbanization mitigate/reduce the deaths caused by indoor and outdoor air pollution. The impact of green innovation initiatives decreases as quantiles go up, but only the 10th, 25th, and 50th quantiles are statistically significant. Likewise, environmental policy stringency decreases as quantiles go up, but only the 25th and 50th quantiles are statistically significant. Finally, economic complexity increases as quantiles go up and are statistically significant except for the 10th quantile. The results support that policymakers should wisely consider the strength of environmental policy stringency and stimulate efficient green innovation initiatives to reduce mortality by air pollution. They also should monitor economic complexity evolvement, identify the activities that most contribute to the mortality caused by air pollution, and intervene accordingly.

Determinants of continuum of care for maternal, newborn, and child health services in Ethiopia: Analysis of the modified composite coverage index using a quantile regression approach.

Maternal and child mortality remain unacceptably high in the Sustainable Development Goals era. Continuum of care has become a key strategy for improving the health of mothers and newborns. Previous research on the continuum of care in Ethiopia is often limited to maternal health services. Maternal and child health services are inseparably linked, and an integrated approach to care is essential. This study assessed the continuum of maternal, newborn, and child health care and associated factors in Ethiopia. The analysis was based on the 2016 Ethiopian Demographic and Health Survey data. We restricted our analysis to women with their most recent children-alive and living with their mother- aged 12-23 months at the time of the survey (n = 1891). The modified composite coverage index, constructed from twelve maternal and child health services, was calculated as an indicator of the continuum of care. Bivariable and multivariable quantile regression were used to analyse the relationship between the predictors and specific quantiles of the composite coverage index. The effect of each variable was examined at the 10th, 25th, 50th, 75th, and 95th quantiles. The results showed that the average composite coverage index value was 39%. The overall completion rate of the continuum of care was low (2%). Four % of the women did not receive any of the services along the continuum of care. Postnatal care for newborns had the lowest coverage (12%). This study provides evidence that factors such as the educational status of women, region, residence, socio-economic status, perceived distance to a health facility, pregnancy intention, mode of delivery, parity, and early antenatal care initiation influence the continuum of care differently across levels of the composite coverage index. The findings call for integrated and targeted strategies that aim to improve the continuum of care considering the determinants.

Extreme-wind events in China in the past 50 years and their impacts on sandstorm variations

Extreme-wind events not only cause disasters by themselves but can also trigger sandstorms, resulting in significant social and economic losses. Since recent years have experienced more frequent and severe extreme weather events, it is worth to explore how extreme-wind events response to recent climate change and how they impact the sandstorm variation. This work established two indices, EW90 and EW95, to identify extreme-wind events based on the relation between extreme-wind intensity and local-source sandstorm. EW90 and EW95 extreme-wind indices are defined as the daily maximum wind speeds greater than the 90th and 95th quantiles of local long-term historical records. The spatial distributions of EW90 and EW95 extreme-wind events are similar, which is higher in arid and semi-arid and coastal regions, and lower in southern China. Seasonally, extreme-wind events mainly occur in April and May for most areas over China, while they are more frequent from July to August for humid regions. During 1971–2020, both the EW90 and EW95 extreme-wind speeds and annual frequencies have significant decreasing trend (p &amp;lt; 0.01), while the daily mean wind speed does not decrease significantly. Extreme-wind events do not have significant abrupt change as well, while a turning positive trend after 2003 is found for mean wind speed. Moreover, sandstorm days are highly correlated with the EW90 and EW95 extreme-wind events, with linear and partial correlation coefficients around 0.95 and 0.5, respectively, while they do not have significant correlations with mean wind speed. Therefore, the significant reduction of sandstorms over northern China in the past 50 years is mainly due to the substantially decreasing extreme-wind events.

The association among SES, screen time, and outdoor play in children at different ages: The GECKO Drenthe study.

This study examined the association among socioeconomic status (SES), screen time, and outdoor play in children at different ages in the GECKO Drenthe birth cohort study. Valid data were obtained from two surveys at ages 3-4 years and 10-11 years. Screen time (TV watching and computer use) and outdoor play were reported by parents. Childhood SES was derived by a synthetic "Equivalized Household Income Indicator," an estimated disposable income. Quantile regression models (cross-sectional analysis) and linear regression models (change between 3-4 and 10-11 years) were used. In general, screen time increased strongly from a median of 51 min/day at 3-4 years (n = 888) to 122 min/day at 10-11 years (n = 1023), whereas time spent on outdoor play remained stable over age (77 min/day at 3-4 years and 81 min/day at 10-11 years). More time spent on outdoor play (50th quantile) was found in children with low SES families at 3-4 years, while at 10-11 years, more outdoor play was found in the high SES group. At 10-11 years, in the higher ranges of screen time, children from high SES had relatively lower screen time [50th quantile: -10.7 (-20.8; -0.6); 75th quantile: -13.6 (-24.4; -2.8)]. In the longitudinal analysis (n = 536), high SES was associated with an increasing time spent on outdoor play [11.7 (2.7; 20.8)]. Socioeconomic disparities in children's outdoor play and screen behavior may be more obvious with increasing age. Low SES may facilitate both outdoor play (at 3-4 years) and screen time (at 10-11 years); however, children from high SES families develop slightly more favorable behavior patterns with age.

Identifying Factors Related to Serum Lipids Using Multilevel Quantile Model: Analysis of Nationwide STEPs Survey 2016

Background: Lipid disorder is a modifiable risk factor for diseases related to plaque formation in arteries such as heart attack, stroke, and peripheral vascular diseases. Identifying related factors and diagnosis and treatment in time reduces the incidence of non-communicable diseases (NCDs). The aim of this study was to determine factors associated with lipids based on a national survey data. Methods: Data of 16757 individuals aged 25–64 years obtained from the Iranian STEPwise approach to NCD risk factor surveillance (STEPs) performed in 2016, through multistage random sampling, were analyzed. Because of clustered, hierarchical, and skewed form of the data, factors related to total holesterol (TC), triglycerides (TG), low-density lipoprotein-cholesterol) (LDL-C), high-density lipoprotein-cholesterol) (HDL-C), TG/HDL-C, TC/HDL-C, and LDL-C/HDL-C were determined applying multilevel quantile mixed model. Parameters of the model were estimated on the basis of random effect of the province as well as urban or rural area for 10th, 25th, 50th, 75th, and 90th quantiles. Statistical analyses were performed by R software version 4.0.2. Results: Significant relationship was found between age, body mass index (BMI), waist circumference (WC), diabetes, hypertension, smoking, physical activity, education level, and marital status with TC, LDL-C, HDL-C, LDL-C, and LDL-C/HDL-C. With increasing BMI and WC, subjects had higher levels of serum lipids, especially in higher quantiles of lipid levels. Lipid levels were significantly increased among smokers and those with diabetes or hypertension. The random effects were also significant showing that there is a correlation between the level of lipids in provincial habitants as well as urban and rural areas. Conclusions: This study showed that the effect of each factor varies depending on the centiles of the lipids. Significant relationship was found between sociodemographic, behaviors, and anthropometric indices with lipid parameters.

Risk Factors of Thrombocytopenia After Cardiac Surgery with Cardiopulmonary Bypass.

Postoperative thrombocytopenia is common in cardiac surgery with cardiopulmonary bypass, and its risk factors are unclear. This retrospective study enrolled 3,175 adult patients undergoing valve surgeries with cardiopulmonary bypass from January 1, 2017 to December 30, 2018 in our institute. Postoperative thrombocytopenia was defined as the first postoperative platelet count below the 10th quantile in all the enrolled patients. Outcomes between patients with and without postoperative thrombocytopenia were compared. The primary outcome was in-hospital mortality. Risk factors of postoperative thrombocytopenia were assessed by logistic regression analysis. The 10th quantile of all enrolled patients (75×109/L) was defined as the threshold for postoperative thrombocytopenia. In-hospital mortality was comparable between thrombocytopenia and non-thrombocytopenia groups (0.9% vs. 0.6%, P=0.434). Patients in the thrombocytopenia group had higher rate of postoperative blood transfusion (5.9% vs. 3.2%, P=0.014), more chest drainage volume (735 [550-1080] vs. 560 [430-730] ml, P<0.001), and higher incidence of acute kidney injury (12.3% vs. 4.2%, P<0.001). Age > 60 years (odds ratio [OR] 2.25, 95% confidence interval [CI] 1.345-3.765, P=0.002], preoperative thrombocytopenia (OR 18.671, 95% CI 13.649-25.542, P<0.001), and cardiopulmonary bypass time (OR 1.088, 95% CI 1.059-1.117, P<0.001) were positively independently associated with postoperative thrombocytopenia. Body surface area (BSA) (OR 0.247, 95% CI 0.114-0.538, P<0.001) and isolated mitral valve surgery (OR 0.475, 95% CI 0.294-0.77) were negatively independently associated with postoperative thrombocytopenia. Positive predictors for thrombocytopenia after valve surgery included age > 60 years, small BSA, preoperative thrombocytopenia, and cardiopulmonary bypass time. BSA and isolated mitral valve surgery were negative predictors.

Differentiating renal cell carcinoma and oncocytoma with volumetric MRI histogram analysis.

In this study, the utility of histogram parameters derived from diffusion-weighted imaging for differentiate renal cell carcinoma (RCC) from oncocytoma was investigated. This research tracked 126 individuals who were diagnosed with RCC and oncocytoma through histopathological analysis, using magnetic resonance imaging (MRI) assessments from 2015 to 2023. We observed various attributes of these patients, including demographic details, surgical records, pre-surgery MRI results, MRI apparent diffusion coefficient (ADC) histogram analysis, and post-surgery histopathological outcomes. Calculations of ADC measurements such as mean, minimum, and maximum in conjunction with the 5th, 10th, 25th, 50th, 75th, 90th, and 95th quantile points were made. In addition, we also noted the skewness, kurtosis, and variance of these data points. The focus group for this investigation consisted of 75 male and 51 female patients. Out of these, 82 were diagnosed with RCC and 44 with oncocytoma. All ADC parameters including ADCmin, ADCmedian, ADCmean, and ADCmax, including the 5th, 10th, 25th, 50th, 75th, 90th, and 95th quantile divisions among the oncocytoma cohort were observed to be higher than the corresponding ones in the RCC group. A statistically meaningful difference was discovered between the minimum ADC value along with the 5th ranking of ADC measurements (p<0.001), in addition to mean of ADC (p=0.050), and the 10th (p=0.002) and 25th (p=0.015) quantiles of ADC data. When considering the region below the curve (AUC) in ROC analysis, the value of ADCmin was recorded as 0.739, with a sensitivity of 75.0%, and specificity of 68.2%. To distinguish oncocytoma from RCC, it may be useful to conduct a whole-tumor histogram and textural analysis of ADC values.