Bayesian Regression Analysis Research Articles

HDL cholesterol: A potential mediator of the association between serum levels of a mixture of metals and the risk of aortic dissection in a Chinese population

Aortic dissection (AD) is a severe cardiovascular disease with a high mortality rate. However, the associations between the serum levels of metals and the risk of AD remain unclear. One hundred twenty-seven patients with AD (type A and B) identified from 2017 to 2019 at the Second Affiliated Hospital of Nanjing Medical University were included; 183 controls that were also included. A logistic regression analysis was performed to determine the associations between serum levels of metals and the risk of AD. Weighted Quantile Sum (WQS) regression and Bayesian Kernel Machine Regression (BKMR) analyses were performed to explore the effects of mixtures of metals on the risk of AD. A linear regression analysis was performed to evaluate the relationships between the serum levels of metals and the white blood cells (WBCs) count and serum lipid levels and blood glucose. We conducted a mediation analysis to explore the contribution rates of WBC counts or serum lipid levels and blood glucose to the association between metal levels and the risk of AD. Exposure to serum levels of Cu (coefficient = 6.33; 95 % CI = 2.52, 10.14; p trend < 0.001) were significantly and positively associated with the risk of AD. In the WQS analysis, Cu (50.3 %), Ni (32.7 %) and Mo (17.1 %) contributed to the AD risk. In the BKMR analysis, Cu and Mo were shown to play important roles in the association with the AD risk. Moreover, serum concentrations of Cu were significantly and inversely associated with HDL-cholesterol levels. HDL-cholesterol levels mediated 7.42 % of the association between serum Cu levels and the prevalence of AD. Our study provided the first evidence that serum levels of mixtures of metals are associated with the AD risk in a Chinese population. Increased concentrations of metals, particularly Cu, may increase the risk of AD by reducing HDL-cholesterol levels.

Effects of drought on groundwater-fed lake areas in the Nebraska Sand Hills

Study regionThe Nebraska Sand Hills (NSH) lies in the western part of Nebraska, United States. We chose the north-eastern, central, and western parts of NSH with distinct climate, topography, and hydrology. Study focusThe study assesses the response of hundreds of shallow groundwater-fed lakes to drought. Total lake area (TLA), determined by classifying Landsat satellite images from 1984 to 2018, was juxtaposed with published Palmer Drought Severity Index (PDSI) and detrended cumulative PDSI (DeCumPDSI) at monthly and annual timescales. The PDSI and DeCumPDSI were time lagged to incorporate the preceding climatic effect (groundwater time lag) and evaluated against TLA using Bayesian regression analysis. New hydrologic insight for the regionTLA in the NSH respond to the seasonal as well as long-term climatic effects moderated by topography, surface, and subsurface hydrology. A higher determination coefficient R2 and lower mean square error of TLA at annual PDSI and DeCumPDSI illustrate the effect of long-term climatic fluctuations and groundwater influence: the evaporative losses from lakes are modulated by the lake-groundwater exchange, but the groundwater recharge has a longer response time to the drought. The study provides a simple method of assessment of the climate impact that results from the satellite data, gridded climate observation, and statistics for sensitive landscape of the NSH.

The characteristics of Chinese anxiously withdrawn children's friendships: Quantity, quality, and protecting against loneliness

AbstractThe purpose of this study was to compare the friendship quantity and quality of urban Chinese anxiously withdrawn children with those of typical children, and to examine the moderating role of friendship in the relation between anxious withdrawal and feelings of loneliness. Anxiously withdrawn (n = 66) and typical children (n = 170) were identified from a complete sample (N = 572) using classroom nominations collected in grades 3–6 (Mage = 10.51, SD = 1.13). At the group level, compared with typical children, anxiously withdrawn children received fewer friendship nominations from their classmates, and particularly from male classmates. Anxiously withdrawn boys nominated fewer male classmates as friends than typical boys and anxiously withdrawn girls had fewer mutual friendships with girls than typical girls. Anxiously withdrawn children reported more conflicts within their friendships than typical children; and anxiously withdrawn girls reported lower positive friendship quality than typical girls. Using a variable‐centred approach with the complete sample, a Bayesian multilevel regression analysis revealed that positive friendship quality moderated the association between anxious withdrawal and feelings of loneliness.

Right-left asymmetry in corticospinal tract microstructure and dexterity are uncoupled in late adulthood

Ageing leads to a decline in white matter microstructure and dexterous function of the hand. In adolescents, it has previously been shown that the degree of right-left asymmetry in the corticospinal tract (CST) is linearly related with right-left asymmetry in dexterity. Here, we tested whether this association is also expressed in older adults. Participants completed a simple circle drawing task with their right and left hand as a measure of dexterity and underwent whole-brain diffusion weighted imaging at 3 Tesla (n = 199; aged 60–72 years). Fractional anisotropy and mean diffusivity of right and left CST were extracted from a manually defined region-of-interest. Linear regression analyses were computed to replicate the analyses in adolescents. Frequentist analyses were complemented with a Bayesian analytical framework. Outcome measures were compared with those previously reported in adolescents (aged 11–16 years). Asymmetries in white matter microstructure of the CST were evident and comparable to the degree of lateralisation observed in adolescence. Similarly, asymmetries in dexterity were evident, but to a lesser degree than in adolescents. Unlike in adolescents, we found no evidence of a linear relationship between asymmetries in CST microstructure and dexterity. Complementary Bayesian regression analysis provided moderate evidence in favour of the null hypothesis, pointing towards a lack of association between the structural and functional measures of right-left asymmetry. Our findings are compatible with the notion that, by late adulthood, a diverging impact of age on white matter structure and dexterous hand function dilutes the structure-function relationship between CST microstructure and manual proficiency that has been reported in adolescents.

Simulation Study for Penalized Bayesian Elastic Net Quantile Regression

Bayesian regression analysis has great importance in recent years, especially in the Regularization method, Such as ridge, Lasso, adaptive lasso, elastic net methods, where choosing the prior distribution of the interested parameter is the main idea in the Bayesian regression analysis. By penalizing the Bayesian regression model, the variance of the estimators are reduced notable and the bias is getting smaller. The tradeoff between the bias and variance of the penalized Bayesian regression estimator consequently produce more interpretable model with more prediction accuracy. In this paper, we proposed new hierarchical model for the Bayesian quantile regression by employing the scale mixture of normals mixing with truncated gamma distribution that stated by (Li and Lin, 2010) as Laplace prior distribution. Therefore, new Gibbs sampling algorithms are introduced. A comparison has made with classical quantile regression model and with lasso quantile regression model by conducting simulations studies. Our model is comparable and gives better results.

Health expenditures by services and providers for 195 countries, 2000–2017

IntroductionNational Health Accounts are a significant source of health expenditure data, designed to be comprehensive and comparable across countries. However, there is currently no single repository of this data and...

Exploring the relationship between anthropomorphism and theory‐of‐mind in brain and behaviour

The process of understanding the minds of other people, such as their emotions and intentions, is mimicked when individuals try to understand an artificial mind. The assumption is that anthropomorphism, attributing human‐like characteristics to non‐human agents and objects, is an analogue to theory‐of‐mind, the ability to infer mental states of other people. Here, we test to what extent these two constructs formally overlap. Specifically, using a multi‐method approach, we test if and how anthropomorphism is related to theory‐of‐mind using brain (Experiment 1) and behavioural (Experiment 2) measures. In a first exploratory experiment, we examine the relationship between dispositional anthropomorphism and activity within the theory‐of‐mind brain network (n = 108). Results from a Bayesian regression analysis showed no consistent relationship between dispositional anthropomorphism and activity in regions of the theory‐of‐mind network. In a follow‐up, pre‐registered experiment, we explored the relationship between theory‐of‐mind and situational and dispositional anthropomorphism in more depth. Participants (n = 311) watched a short movie while simultaneously completing situational anthropomorphism and theory‐of‐mind ratings, as well as measures of dispositional anthropomorphism and general theory‐of‐mind. Only situational anthropomorphism predicted the ability to understand and predict the behaviour of the film's characters. No relationship between situational or dispositional anthropomorphism and general theory‐of‐mind was observed. Together, these results suggest that while the constructs of anthropomorphism and theory‐of‐mind might overlap in certain situations, they remain separate and possibly unrelated at the personality level. These findings point to a possible dissociation between brain and behavioural measures when considering the relationship between theory‐of‐mind and anthropomorphism.

Quality and Adoption of COVID-19 Tracing Apps and Recommendations for Development: Systematic Interdisciplinary Review of European Apps.

BackgroundSimulation study results suggest that COVID-19 contact tracing apps have the potential to achieve pandemic control. Concordantly, high app adoption rates were a stipulated prerequisite for success. Early studies on potential adoption were encouraging. Several factors predicting adoption rates were investigated, especially pertaining to user characteristics. Since then, several countries have released COVID-19 contact tracing apps.ObjectiveThis study’s primary aim is to investigate the quality characteristics of national European COVID-19 contact tracing apps, thereby shifting attention from user to app characteristics. The secondary aim is to investigate associations between app quality and adoption. Finally, app features contributing to higher app quality were identified.MethodsEligible COVID-19 contact tracing apps were those released by national health authorities of European Union member states, former member states, and countries of the European Free Trade Association, all countries with comparable legal standards concerning personal data protection and app use voluntariness. The Mobile App Rating Scale was used to assess app quality. An interdisciplinary team, consisting of two health and two human–computer interaction scientists, independently conducted Mobile App Rating Scale ratings. To investigate associations between app quality and adoption rates and infection rates, Bayesian linear regression analyses were conducted.ResultsWe discovered 21 national COVID-19 contact tracing apps, all demonstrating high quality overall and high-level functionality, aesthetics, and information quality. However, the average app adoption rate of 22.9% (SD 12.5%) was below the level recommended by simulation studies. Lower levels of engagement-oriented app design were detected, with substantial variations between apps. By regression analyses, the best-case adoption rate was calculated by assuming apps achieve the highest ratings. The mean best-case adoption rates for engagement and overall app quality were 39.5% and 43.6%, respectively. Higher adoption rates were associated with lower cumulative infection rates. Overall, we identified 5 feature categories (symptom assessment and monitoring, regularly updated information, individualization, tracing, and communication) and 14 individual features that contributed to higher app quality. These 14 features were a symptom checker, a symptom diary, statistics on COVID-19, app use, public health instructions and restrictions, information of burden on health care system, assigning personal data, regional updates, control over tracing activity, contact diary, venue check-in, chats, helplines, and app-sharing capacity.ConclusionsEuropean national health authorities have generally released high quality COVID-19 contact tracing apps, with regard to functionality, aesthetics, and information quality. However, the app’s engagement-oriented design generally was of lower quality, even though regression analyses results identify engagement as a promising optimization target to increase adoption rates. Associations between higher app adoption and lower infection rates are consistent with simulation study results, albeit acknowledging that app use might be part of a broader set of protective attitudes and behaviors for self and others. Various features were identified that could guide further engagement-enhancing app development.

Temporal trends in between and within-country inequalities in caesarean delivery in low- and middle-income countries: a Bayesian analysis.

To provide updated information about between-country variations, temporal trends and changes in inequalities within countries in caesarean delivery (CD) rates. Cross-sectional study of Demographic and Health Survey (DHS) during 1990-2018. 74 low- and middle-income countries (LMICs). Women 15-49years of age who had live births in the last 3years. Bayesian linear regression analysis was performed and absolute differences were calculated. Population-level CD by countries and sociodemographic characteristics of mothers over time. CD rates, based on the latest DHS rounds, varied substantially between the study countries, from 1.5% (95% CI 1.1-1.9%) in Madagascar to 58.9% (95% CI 56.0-61.6%) in the Dominican Republic. Of 62 LMICs with at least two surveys, 57 countries showed a rise in CD during 1990-2018, with the greatest increase in Sierra Leone (19.3%). Large variations in CD rates were observed across mother's wealth, residence, education and age, with a higher rate of CD by the richest and urban mothers. These inequalities have widened in many countries. Stratified analyses suggest greater provisioning of CD by the richest mothers in private facilities and poorest mothers in public facilities. CD rates varied substantially across geographical locations and over time, irrespective of public or private health facilities. Changes in CD rates continue across wealth, place of residence, education, and age of mother, and are widening in most study countries. Increasing caesarean delivery rates were greater among the richest and urban mothers than their counterparts, with widened gaps in LMICs.

Double burden of malnutrition among women of reproductive age in 55 low- and middle-income countries: progress achieved and opportunities for meeting the global target.

ObjectiveTo examine trends and projections of underweight (Body Mass Index, BMI < 18.5 kg/m2) and overweight (BMI ≥ 25.0 kg/m2) in women of reproductive age in 55 low- and middle-income countries (LMICs).MethodsWe used data from 2,337,855 women aged 15–49 years from nationally representative Demographic and Health Survey conducted between 1990 and 2018. Bayesian linear regression analyses were performed.ResultsDuring 1990–2018, the prevalence of underweight decreased in 35 countries and overweight increased in 50 countries. The highest underweight increase was in Morocco (5.5%) and overweight in Nepal (12.4%). In 2030, >20% of women in eight LMICs will be underweight, with Madagascar (36.8%), Senegal (32.2%), and Burundi (29.2%) projected to experience the highest burden of underweight. Whereas >50% of women in 22 LMICs are projected to be overweight, with Egypt (94.7%), Jordan (75.0%), and Pakistan (74.1%) projected to have the highest burden of overweight. 24 LMICs are projected to experience the double burden of malnutrition (both underweight and overweight >20%) in 2030. Noticeable variations in underweight and overweight were observed across wealth, residence, education, and age of women, with a higher rate of overweight in high-income, high-education, and urban women. These inequalities have widened in many countries and are projected to continue. The probability of eradicating overweight and underweight is nearly 0% for all countries by 2030, except Egypt is on track to eradicate underweight.ConclusionsAlthough the prevalence of underweight declined, this decline has been superseded by the dramatic increase of overweight. None of the 55 LMICs is likely to eradicate malnutrition in women by 2030.

Complex interactions of in-stream dissolved organic matter and nutrient spiralling unravelled by Bayesian regression analysis

Abstract. Uptake and release patterns of dissolved organic matter (DOM) compounds and co-transported nutrients are entangled, and the current literature does not provide a consistent picture of the interactions between the retention processes of DOM fractions. We performed plateau addition experiments with five different complex DOM leachates in a small experimental stream impacted by diffuse agricultural pollution. The study used a wide range of DOM qualities by including leachates of cow dung, pig dung, corn leaves, leaves from trees, and whole nettle plants. We measured changes in nutrient and dissolved organic carbon (DOC) concentrations along the stream course and determined DOM fractions by fluorescence measurements and parallel factor (PARAFAC) decomposition. To assess the influences of hydrological transport processes, we used a 1D hydrodynamic model. We developed a non-linear Bayesian approach based on the nutrient spiralling concept, which we named the “interactions in nutrient spirals using Bayesian regression” (INSBIRE) approach. This approach can disentangle complex interactions of biotic and abiotic drivers of reactive solutes' uptake in multi-component DOM sources. It can show the variability of the uptake velocities and quantify their uncertainty distributions. Furthermore, previous knowledge of nutrient spiralling can be included in the model using prior probability distributions. We used INSBIRE to assess interactions of compound-specific DOM and nutrient spiralling metrics in our experiment. Bulk DOC uptake varied among sources, showing decreasing uptake velocities in the following order: corn &gt; pig dung &gt; leaves &gt; nettles &gt; cow dung. We found no correlations between bulk DOC uptake and the amounts of protein-like compounds or co-leached soluble reactive phosphorus (SRP). The fastest uptake was observed for SRP and the tryptophan-like component, while the other DOM components' uptake velocities more or less resembled that of the bulk DOC. Almost all DOM components showed a negative relationship between uptake and concentration, known as efficiency loss. Furthermore, we observed a few negative and (weak) positive interactions between the uptake and the concentration of different components, such as a decreased uptake of protein-like compounds at high concentrations of a high-molecular-weight humic-like compound. We also found an influence of the wetted width on the uptake of SRP and a microbially derived humic substance, which indicates the importance of the sediment–water interface for P and humic C cycling in the studied stream. Overall, we show that bulk DOC is a weak predictor of DOC uptake behaviour for complex DOM leachates. Individual DOM compound uptake, including co-leached nutrients, is controlled by both internal (quality-related) and external (environmental) factors within the same aquatic ecosystem. We conclude that the cycling of different C fractions and their mutual interaction with N and P uptake in streams is a complex, non-linear problem, which can only be assessed with advanced non-linear approaches, such as the presented INSBIRE approach.

Comprehensive molecular pharmacodynamic assessment identifies response markers of intermediary metabolism associated with BPM 31510-IV treatment in advanced glioblastoma multiforme patients.

2059 Background: BPM 31510-IV is a drug-lipid conjugate nanodispersion containing oxidized Coenzyme Q10 (CoQ10) in clinical development for glioblastoma multiforme (GBM). In a recently concluded Phase 1 study of BPM 31510-IV (NCT03020602), in addition to safety and tolerability, longitudinal pharmacodynamic samples (20 samples/cycle of 28 days) were collected at various times in patient’s refractory to radiation, temozolomide, and bevacizumab. Methods: Comprehensive multi-omic (proteomic, lipidomic, metabolomic) profiles were generated from buffy coat (proteomics only), plasma, and urine matrices. These data were further analyzed using bAIcis, a Bayesian statistics based artificial intelligence (AI) software, creating causal networks linking clinical information and endpoints to molecular composition of diverse biomatrices of patients prior to, as well as during, treatment with BPM 31510-IV. Twelve subjects comprised the intent to treat population (ITT) which were stratified across days of treatment (DR1; ≤28 days; DLT period; n=6) and (DR2, OS; &gt;28 days; n=6). Bayesian networks and regression analysis were performed on the outputs of the analysis. Molecular analyte panels (combination of proteins, lipids, and metabolites) descriptive of progression free survival (PFS), adverse events (possibly/probably related to BPM 31510-IV), and of overall survival (OS) were generated. Results: Significant alteration (p&lt;0.05) of metabolically associated protein and critical metabolite drivers of intermediary metabolism were identified as causally related to PFS. Significant quantitative changes in levels of several proteins (buffy coat) and metabolites (urine) were identified with probable or possible associations to adverse events in BPM 31510-IV treated subjects. Conclusions: Overall, alterations in proteins and metabolites influencing mitochondrial function and intermediary metabolism that differentiated responders versus non-responders and identified potential markers of adverse events associated with BPM 31510-IV exposure were identified and will be further explored for complementary diagnostic utility.

Quantifying bankfull flow width using preserved bar clinoforms from fluvial strata

AbstractReconstruction of active channel geometry from fluvial strata is critical to constrain the water and sediment fluxes in ancient terrestrial landscapes. Robust methods—grounded in extensive field observations, numerical simulations, and physical experiments—exist for estimating the bankfull flow depth and channel-bed slope from preserved deposits; however, we lack similar tools to quantify bankfull channel widths. We combined high-resolution lidar data from 134 meander bends across 11 rivers that span over two orders of magnitude in size to develop a robust, empirical relation between the bankfull channel width and channel-bar clinoform width (relict stratigraphic surfaces of bank-attached channel bars). We parameterized the bar cross-sectional shape using a two-parameter sigmoid, defining bar width as the cross-stream distance between 95% of the asymptotes of the fit sigmoid. We combined this objective definition of the bar width with Bayesian linear regression analysis to show that the measured bankfull flow width is 2.34 ± 0.13 times the channel-bar width. We validated our model using field measurements of channel-bar and bankfull flow widths of meandering rivers that span all climate zones (R2 = 0.79) and concurrent measurements of channel-bar clinoform width and mud-plug width in fluvial strata (R2 = 0.80). We also show that the transverse bed slopes of bars are inversely correlated with bend curvature, consistent with theory. Results provide a simple, usable metric to derive paleochannel width from preserved bar clinoforms.

A Bayesian Regression Analysis of the Effects of Alert Presence and Scenario Criticality on Automated Vehicle Takeover Performance.

This study investigates the impact of silent and alerted failures on driver performance across two levels of scenario criticality during automated vehicle transitions of control. Recent analyses of automated vehicle crashes show that many crashes occur after a transition of control or a silent automation failure. A substantial amount of research has been dedicated to investigating the impact of various factors on drivers' responses, but silent failures and their interactions with scenario criticality are understudied. A driving simulator study was conducted comparing scenario criticality, alert presence, and two driving scenarios. Bayesian regression models and Fisher's exact tests were used to investigate the impact of alert and scenario criticality on takeover performance. The results show that silent failures increase takeover times and the intensity of posttakeover maximum accelerations and decrease the posttakeover minimum time-to-collision. While the predicted average impact of silent failures on takeover time was practically low, the effects on minimum time-to-collision and maximum accelerations were safety-significant. The analysis of posttakeover control interaction effects shows that the effect of alert presence differs by the scenario criticality. Although the impact of the absence of an alert on takeover performance was less than that of scenario criticality, silent failures seem to play a substantial role-by leading to an unsafe maneuver-in critical automated vehicle takeovers. Understanding the implications of silent failure on driver's takeover performance can benefit the assessment of automated vehicles' safety and provide guidance for fail-safe system designs.

Development and validation of a model for predicting 18-month mortality in type 2 myocardial infarction

Despite the poor prognosis in patients with type 2 myocardial infarction (MI), no prospective data on risk stratification exists. The aim of this study was to develop and validate a model for prediction of 18-month mortality of among patients with type 2 MI (T2MI) and compare its performance with GRACE and TARRACO scores. The prospective observational study included 712 consecutive patients diagnosed with MI undergoing coronary angiography <24 h between January 2017 and December 2018. Diagnosis of T2MI was adjusted according to Third universal definition. A prognostic model was developed by using Bayesian approach and logistic regression analysis with identifying predictors for mortality. The model was validated by bootstrap validation. Comparison performance between scores using Delong test. T2MI was identified in 174 (24.4%) patients. The median age of patients was 69 years, 52% were female. The mortality rate was 20.1% at 18 months. Prior MI, presence of ST elevation, hemoglobin level at admission, Charlson comorbidity index and were independently associated with 18-month mortality. The model to predict 18-month mortality showed excellent discrimination (optimism corrected c-statistic = 0.822) and calibration (corrected slope = 0.893). GRACE and TARRACO scores had moderate discrimination [c-statistic = 0.748 (95% CI 0.652-0.843) and 0.741, 95% CI 0.669-0.805), respectively] and inferior compared with model (p = 0.043 and 0.037, respectively). The risk of mortality among T2MI patients could be accurately predicted by using common clinical characteristics and laboratory tests. Further studies are required with external validation of nomogram prior to clinical implementation.

Complex environmental contaminant mixtures and their associations with thyroid hormones using supervised and unsupervised machine learning techniques

Evaluating complex mixtures and their associated health effects poses a challenge in human populations. Herein, we assess the association between 17 organic and metal contaminants in blood with thyroid hormones in a remote Indigenous (First Nations) region from Quebec, Canada (n=526). Using principal component analysis (PCA) to reduce the number of variables, we generated varimax rotated principal component (PC) loadings of contaminants on these uncorrelated synthetic axes. Associations with levels of thyroid hormones (TSH, free T4, and total T3) were conducted using multivariable linear regression methods with the participant PC loadings and adjusting for covariates. Additionally, Bayesian kernel machine regression (BKMR) analysis was used to evaluate the univariate contaminant exposure effect as well as the contaminant mixture effects on levels of thyroid hormones. Significant and positive associations were found between total T3 and PC-2 (high positive nickel and cadmium loadings), total T3 and PC-3 (negative association with negative loading for nickel and positive loading for cadmium) and TSH and PC-1 (high positive loadings for organic contaminants). No significant observations were observed for free T4. BKMR provided additional insight into the PCA results and suggested that nickel, and not cadmium, was responsible for driving the observed effects with this effect remaining when evaluating the entire mixture. BKMR analysis did not support the association of TSH with organic contaminants that were found in the PCA regression. Our findings reinforced other studies which showed that metals such as nickel may alter thyroid hormone levels and highlighted how complex environmental mixtures interact with each other. These observations represent an important step to determining how complex mixtures of contaminants can be assessed in human populations, especially those living a subsistence lifestyle who may have high body burdens of contaminants, and to help understand the resultant net effect of exposures on endogenous thyroid hormones utilizing novel machine learning statistical methods.

Metaheuristic model for the interface shear strength between granular soil and structure considering surface morphology

A complete set of 13 morphological parameters in accordance with standard ISO 4287 was applied to quantifying a series of random profiles. These profiles were imported into a discrete numerical model to perform 480 interface shear tests on coarse-grained soils. The relevant morphological parameters were selected using Spearman’s rank correlation coefficient for model selection. An optimal metaheuristic model was developed using a genetic algorithm and was further compared with the existing predicted formulas. The 2D discrete element method (DEM) results indicate that the highest correlation with shear strength was obtained for the hybrid parameter Pdq which represents not only the amplitude information but also the surface slope information on a random surface. The optimal model with one significant input variable (Pdq) was effectively selected through the Bayesian nonparametric general regression analysis. For irregular interface shearing widely existing in most geotechnical engineering, Pdq is more efficient and accurate to quantify the surface morphology or estimate the interface shear strength compared with relative roughness.

Low-level exposure to lead, mercury, arsenic, and cadmium, and blood pressure among 8-17-year-old participants of the 2009–2016 National Health and Nutrition Examination Survey

BackgroundDysregulation of systolic, diastolic blood pressure (SBP, DBP), and pulse pressure (PP) in children may predict elevated blood pressure (BP) in adulthood. Toxicant exposure is widely studied as a risk factor for high BP in adults, but not in children. We assessed the joint associations between lead (Pb), mercury (Hg), arsenic (As), and cadmium (Cd) exposure and SBP, DBP, and PP among 8-17 year-old participants (n = 1642) of the 2009–2016 National Health and Nutrition Examination Survey (NHANES). MethodsParticipants with at least two BP measures were included. Urinary As and Cd were adjusted for urinary creatinine concentrations. Blood Pb, Hg, and urinary As, Cd were natural log-transformed. Bayesian Kernel Machine Regression (BKMR) analyses were conducted to assess the associations between the toxicant mixture and BP measures. Multivariable regression models assessed the associations between individual toxicants, and the four toxicants simultaneously with each of the outcomes. Interactions with sodium intake were tested. ResultsExposure to all toxicants was low, with median (5%, 95%) level: Pb, 0.57 (0.26, 1.60) μg/dL; Hg, 0.37 (0.19, 2.12) μg/L; As, 5.61 (1.37, 33.2) μg/g creatinine, Cd, 0.06 (0.03, 0.23) μg/g creatinine. Toxicant mixture showed a statistically significant, inverse association with DBP, but not other BP measures. Linear regressions revealed no association between toxicants, individually or together, and BP measures. No evidence of interaction of sodium intake with any of the toxicants was observed. ConclusionsIn a nationally representative sample of 8–17 year-olds, we found suggestive inverse association of the mixture of low-level Pb, Hg, As, and Cd, with DBP. Longitudinal studies with multiple toxicants are needed to understand the interactive effects of toxicants on children's BP.

How does the self-reported health of undergraduate nursing students change during their degree programme? Survey results from a Scottish University

BackgroundThe lifestyle behaviours, and the physical and mental health of nurses, are poorer than those of other allied health professionals, and of the general population. However, these were no less favourable among first year undergraduate nursing students at a Scottish Higher Education Institution (HEI) than among similar people of the same age. We compared health and health behaviours among the same cohort of undergraduate nursing students over the course of their degree.MethodsAn anonymous self-complete repeat cross-sectional survey was administered during a timetabled teaching session at three time-points to undergraduate nursing students at the start of Years 1, 2 and 3 of their programme. They had received written information about the study previously.ResultsSelf-reported health did not change significantly over time, but there was a clear decline over the 3 years in the proportions of students rating their mental health as excellent/very good/good and a concomitant increase in those rating their mental health as fair/poor. Correspondingly, the mean WEMWBS wellbeing score declined over the 3 years, with the proportion of students with a score of < 46 (indicating either high risk of major depression, or in high risk of psychological distress and increased risk of depression) increasing from one quarter to one half. This effect was captured and described using a Bayesian regression analysis. The most noticeable change in health behaviours was a decline in physical activity levels over the study period. The proportion of students managing 150 min of weekly physical activity decreased from three quarters to two thirds. This was reflected in higher self-reported sedentary time, although there were no observable trends over time in mean BMI, or proportions of students categorised as overweight or obese.ConclusionsThis paper suggests that there may be a decline in mental health and in participation in physical activity among nursing students over the course of their degree. We recommend the incorporation of an intervention into the undergraduate nursing curriculum that promotes and encourages regular physical activity, offering students the opportunity to learn about health promotion and lifestyle change in practice, to improve their own physical health, and to address mental wellbeing.

Maternal asthma is associated with reduced lung function in male infants in a combined analysis of the BLT and BILD cohorts

RationaleAsthma in pregnancy is associated with respiratory diseases in the offspring.ObjectiveTo investigate if maternal asthma is associated with lung function in early life.MethodsData on lung function measured at 5–6 weeks...