Modeling arsenic in European topsoils with a coupled semiparametric (GAMLSS-RF) model for censored data.

Arsenic (As) is a versatile heavy metalloid trace element extensively used in industrial applications. As is carcinogen, poses health risks through both inhalation and ingestion, and is associated with an increased risk of liver, kidney, lung, and bladder tumors. In the agricultural context, the repeated application of arsenical products leads to elevated soil concentrations, which are also affected by environmental and management variables. Since exposure to As poses risks, effective assessment tools to support environmental and health policies are needed. However, the most comprehensive soil As data available, the Land Use/Cover Area frame statistical Survey (LUCAS) database, contains severe limitations due to high detection limits. Although within International Organization for Standardization standards, the detection limits preclude the adoption of standard methodologies for data analysis. The present work focused on developing a new method to model As contamination in European soils using LUCAS soil samples. We introduce the GAMLSS-RF model, a novel approach that couples Random Forests with Generalized Additive Models for Location, Scale, and Shape. The semiparametric model can capture non-linear interactions among input variables while accommodating censored and non-censored observations and can be calibrated to include information from other campaign databases. After fitting and validating a spatial model, we produced European-scale As concentration maps at a 250 m spatial resolution and evaluated the patterns against reference values (i.e., two action levels and a background concentration). We found a significant variability of As concentration across the continent, with lower concentrations in Northern countries and higher concentrations in Portugal, Spain, Austria, France and Belgium. By overcoming limitations in existing databases and methodologies, the present approach provides an alternative way to handle highly censored data. The model also consists of a valuable probabilistic tool for assessing As contamination risks in soils, contributing to informed policy-making for environmental and health protection.

Age-Related Electrocardiographic Characteristics of Male Junior Soccer Athletes.

INTRODUCTION: Very limited data exist on normal age-related ECG variations in adolescents and no data have been published regarding the ECG anomalies induced by intensive training, which are relevant in pre-participation screening for sudden cardiac death prevention in the adolescent athletic population. The purpose of this study was to establish normal age-related electrocardiographic measurements (P wave duration, PR interval, QRS duration, QT, and QTc interval) grouped according to 2-year age intervals. METHODS: A total of 2,151 consecutive healthy adolescent Soccer athletes (trained for a mean of 7.2 ± 1.1 h per week, 100% male Caucasians, mean age 12.4 ± 1.4 years, range 7-18) underwent pre-participation screening, which included ECG and transthoracic echocardiography in a single referral center. RESULTS: Their heart rate progressively slowed as age increased (p < 0.001, ranging from 80.8 ± 13.2 to 59.5 ± 10.2 bpm), as expected. The P wave, PR interval, and QRS duration significantly increased in older age classes (p = 0.019, p = 0.001, and p < 0.001, respectively), and after Bonferroni's correction, the difference remained significant in all age classes for QRS duration. The QTc interval diminished progressively with increasing age (p = 0.003) while the QT interval increased progressively (p < 0.001). CONCLUSIONS: Significant variations in the normal ECG characteristics of young athletes exist between different age groups related to increasing age and training burden, thus, age-specific reference values could be adopted, as already done for echocardiographic measurements, and may help to further discriminate potentially pathologic conditions.

Reference values for the incremental shuttle walk test in patients with cardiovascular disease entering exercise-based cardiac rehabilitation.

The incremental shuttle walk test (ISWT) is used to assess functional capacity of patients entering cardiac rehabilitation. Factors such as age and sex account for a proportion of the variance in test performance in healthy individuals but there are no reference values for patients with cardiovascular disease. The aim of this study was to produce reference values for the ISWT. Participants were n = 548 patients referred to outpatient cardiac rehabilitation who underwent a clinical examination and performed the ISWT. We used regression to identify predictors of performance and produced centile values using the generalised additive model for location, scale and shape model. Men walked significantly further than women (395 ± 165 vs. 269 ± 118 m; t = 9.5, P < 0.001) so data were analysed separately by sex. Age (years) was the strongest predictor of performance in men (ß = -5.9; 95% CI: -7.1 to -4.6 m) and women (ß = -4.8; 95% CI: -6.3 to 3.3). Centile curves demonstrated a broadly linear decrease in expected ISWT values in males (25-85 years) and a more curvilinear trend in females. Patients entering cardiac rehabilitation present with highly heterogeneous ISWT values. Much of the variance in performance can be explained by patients' age and sex. Comparing absolute values with age-and sex-specific reference values may aid interpretation of ISWT performance during initial patient assessment at entry to cardiac rehabilitation.

Automatic smoothing parameter selection in GAMLSS with an application to centile estimation.

A method for automatic selection of the smoothing parameters in a generalised additive model for location, scale and shape (GAMLSS) model is introduced. The method uses a P-spline representation of the smoothing terms to express them as random effect terms with an internal (or local) maximum likelihood estimation on the predictor scale of each distribution parameter to estimate its smoothing parameters. This provides a fast method for estimating multiple smoothing parameters. The method is applied to centile estimation where all four parameters of a distribution for the response variable are modelled as smooth functions of a transformed explanatory variable x This allows smooth modelling of the location, scale, skewness and kurtosis parameters of the response variable distribution as functions of x.