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Background: The objectives of this investigation are to evaluate the use of health care services for non-communicable disease and prevention by children and adolescents and to identify the factors linked to the use of health care services., Methods: This cross-sectional survey was conducted between December 2014 and January 2015 among 1198 parents of students aged between 5 and 18 years attending 12 selected schools in the geographic area of Salerno and Naples, Italy, using a self-administered questionnarie., Results: 68.2% of parents stated that had visited their general practitioner (GP) or family pediatrician (FP) with their child in the last year. 66.2% of children had had at least one visit to a specialist and more than half (54.8%) had had preventive care visits in the last year. The use of preventive care visits within last year was significantly higher amongst female, among those who had visited their GP or FP and among those who had a parent with a college degree or higher. The proportion of emergency department visits and hospital admissions reported were 12.8% and 4.7% respectively., Conclusion: This results highlights the need of educational interventions for parents and adolescents in order to increase the utilization of preventive health services.

For most water treatment plants, a significant number of performance data variables are attained on a time series basis. Due to the interconnectedness of the variables, it is often difficult to assess over-arching trends and quantify operational performance. The objective of this study was to establish simple and reliable predictive models to correlate target variables with specific measured parameters. This study presents a multivariate analysis of the physicochemical parameters of municipal wastewater. Fifteen quality and quantity parameters were analyzed using data recorded from 2010 to 2016. To determine the overall quality condition of raw and treated wastewater, a Wastewater Quality Index (WWQI) was developed. The index summarizes a large amount of measured quality parameters into a single water quality term by considering pre-established quality limitation standards. To identify treatment process performance, the interdependencies between the variables were determined by using Principal Component Analysis (PCA). The five extracted components from the 15 variables accounted for 75.25% of total dataset information and adequately represented the organic, nutrient, oxygen demanding, and ion activity loadings of influent and effluent streams. The study also utilized the model to predict quality parameters such as Biological Oxygen Demand (BOD), Total Phosphorus (TP), and WWQI. High accuracies ranging from 71% to 97% were achieved for fitting the models with the training dataset and relative prediction percentage errors less than 9% were achieved for the testing dataset. The presented techniques and procedures in this paper provide an assessment framework for the wastewater treatment monitoring programs., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

The analysis of multiple outcomes is becoming increasingly common in modern biomedical studies. It is well-known that joint statistical models for multiple outcomes are more flexible and more powerful than fitting a separate model for each outcome; they yield more powerful tests of exposure or treatment effects by taking into account the dependence among outcomes and pooling evidence across outcomes. It is, however, unlikely that all outcomes are related to the same subset of covariates. Therefore, there is interest in identifying exposures or treatments associated with particular outcomes, which we term outcome-specific variable selection. In this work, we propose a variable selection approach for multivariate normal responses that incorporates not only information on the mean model, but also information on the variance-covariance structure of the outcomes. The approach effectively leverages evidence from all correlated outcomes to estimate the effect of a particular covariate on a given outcome. To implement this strategy, we develop a Bayesian method that builds a multivariate prior for the variable selection indicators based on the variance-covariance of the outcomes. We show via simulation that the proposed variable selection strategy can boost power to detect subtle effects without increasing the probability of false discoveries. We apply the approach to the Normative Aging Study (NAS) epigenetic data and identify a subset of five genes in the asthma pathway for which gene-specific DNA methylations are associated with exposures to either black carbon, a marker of traffic pollution, or sulfate, a marker of particles generated by power plants., (© 2016, The International Biometric Society.)

Background and Aims: Large metropolitan areas often exhibit multiple morbidity hotspots. However, the identification of specific health hazards, associated with the observed morbidity patterns, is not always straightforward. In this study, we suggest an empirical approach to the identification of specific health hazards, which have the highest probability of association with the observed morbidity patterns., Methods: The morbidity effect of a particular health hazard is expected to weaken with distance. To account for this effect, we estimate distance decay gradients for alternative locations and then rank these locations based on the strength of association between the observed morbidity and wind-direction weighted proximities to these locations. To validate this approach, we use both theoretical examples and a case study of the Greater Haifa Metropolitan Area (GHMA) in Israel, which is characterized by multiple health hazards., Results: In our theoretical examples, the proposed approach helped to identify correctly the predefined locations of health hazards, while in the real-world case study, the main health hazard was identified as a spot in the industrial zone, which hosts several petrochemical facilities., Conclusion: The proposed approach does not require extensive input information and can be used as a preliminary risk assessment tool in a wide range of environmental settings, helping to identify potential environmental risk factors behind the observed population morbidity patterns.

Modern spectroscopic and sensor technologies combined with multivariate modelling are increasingly used for the quantitative analysis of complex mixtures. Their performance depends directly on the data design chosen for model training and validation. A well-balanced calibration experiment with the fewest samples possible presents additional challenges when several mixture components (factors) need to be calibrated on the same dataset and subsequently quantified from the same multivariate measurement. This practically important problem stays poorly addressed by the theory of experimental design. This theoretical work systematically formulates the requirements to an optimal calibration/validation dataset and introduces a new family of calibration designs, where the samples are placed along the diagonals of an experimental space that is a hypercube. Such placement is appropriate due to reasonable assumptions about the linear nature of analytical response. Suggested filling schemes allow economical diagonal designs with intrinsic validation to be built for multiple factors presented in as many levels as the number of samples. The most important practical cases of two and three factors are considered in detail, and generalization to higher dimensions is outlined. Diagonal designs of any complexity can be generated using a simple geometrical scheme or with a supplied script., (Copyright © 2016 Elsevier B.V. All rights reserved.)

Aims: To determine whether elevations of uric acid levels are associated with the cluster of disorders described in metabolic syndrome and to evaluate whether hyperuricemia may be considered a component of this syndrome., Methods: One year case-control study was conducted in Bikaner, Rajasthan, India from January to December 2013. The study population consisted of 200 subjects, 100 with metabolic syndrome (case) and 100 without metabolic syndrome (control) aged between 18 and 80 years, attending OPD at PBM Hospital were studied. Controls were age and sex matched to the cases. Blood tests and all physical variables were examined using standard methods. Subjects were divided into 6 groups according to their possession of 0, 1, 2, 3, 4 or 5 components of the metabolic syndrome. Statistical analysis was done using ANOVA, linear regression analysis and multivariate linear regression model., Results: Mean serum UA level was significantly associated with all components of metabolic syndrome (p<0.001) and had strong positive correlation (r=+0.66 to +0.77, p<0.0001) with all of them except serum HDL with which it showed strong negative correlation(r=-0.71, p<0.0001). It increased as the number of metabolic factors increased showing a highly significant trend (p<0.0001). On multivariate regression analysis UA contributed to 66.84% variance of metabolic syndrome., Conclusion: The current multivariate regression analysis clearly infers that uric acid can be considered as a marker and potential modifier of metabolic syndrome., (Copyright © 2016 Diabetes India. Published by Elsevier Ltd. All rights reserved.)

In targeted proteomics using liquid chromatography-tandem triple quadrupole mass spectrometry (LC/MS/MS) in the selected reaction monitoring (SRM) mode, selecting the best observable or visible peptides is a key step in the development of SRM assay methods of target proteins. A direct comparison of signal intensities among all candidate peptides by brute-force LC/MS/MS analysis is a concrete approach for peptide selection. However, the analysis requires an SRM method with hundreds of transitions. This study reports on the development of a method for predicting and identifying hopeless peptides to reduce the number of candidate peptides needed for brute-force experiments. Hopeless peptides are proteotypic peptides that are unlikely to be selected for targets in SRM analysis owing to their poor ionization characteristics. Targeted proteomics data from Escherichia coli demonstrated that the relative ionization efficiency between two peptides could be predicted from sequences of two peptides, when a multivariate regression model is used. Validation of the method showed that >20% of the candidate peptides could be successfully eliminated as hopeless peptides with a false positive rate of less than 2%.

Introduction: The aim of the study was to investigate the significance of factors associated with minimal hepatic encephalopathy (MHE) in cirrhotic patients., Material and Methods: Between September 2012 and August 2013, 60 cirrhotic patients, including MHE and non-MHE (NMHE) patients, were included in the study. Associated factors and clinical factors were analyzed to see if they were significantly different between MHE and non-MHE patients. Upon identifying the factors showing differences, we applied multivariate regression analysis to further decide which were the most significant ones to differentiate MHE from NMHE patients., Results: There were 26 patients diagnosed with MHE and 34 with NMHE. Our results demonstrated that the prevalence rate of small intestinal bacterial overgrowth (SIBO) was highly associated with patients with MHE (65.4%, 17 out of 26), in contrast to the rate in NMHE patients (8.8%, 3 out of 34). We also found that factors including age, education level, intelligent test results, plasma albumin level and plasma ammonia levels were significantly different between MHE and NMHE patients. Ultimately, with logistic regression analysis, we found that SIBO was the most significant factor differentiating MHE patients from NMHE patients (p < 0.05)., Conclusions: In cirrhotic patients, SIBO was highly associated with MHE. This may further our understanding of the mechanisms of MHE and help to develop potential therapeutic interventions to treat cirrhotic patients with MHE.

Background & Aims: Malnutrition is an important issue in patient outcome. Screening tools to find risk patients need to be evaluated. This study looks at the validity and reliability of nutritional risk screening (named NRS-2002) in hospitalized patients with chronic heart failure., Methods: In this cross-sectional study nutritional screening was performed using NRS-2002 in 131 patients with chronic heart failure. The predictive validity was evaluated in relation to whether NRS-2002 predicted the incidence of complications and length of hospital stay. NRS-2002's ability to locate nutritional risk in patients with edema was evaluated. The inter-rater reliability was measured between three investigators screening 45 patients each., Results: The prevalence of nutritional risk was 57%. The incidence of complications and the median length of hospital stay were significantly higher in patients at nutritional risk compared to patients not at nutritional risk. Only the component of severity of disease in NRS-2002 and not the component of the nutritional status was associated with increased length of hospital stay in multivariate analysis. Patients with edema were classified correctly regarding nutritional risk status by NRS-2002 in all but one occasion. The inter-rater reliability was documented, kappa >0.60., Conclusion: NRS-2002 was a reliable screening tool in an in-patient sample with chronic heart failure. The validity of NRS-2002 needs further investigation in a larger sample of hospitalized patients with chronic heart failure., (Copyright © 2014 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.)

The aim of the present study was to reveal the metabolic disorders most commonly associated with nonalcoholic fatty liver disease (NAFLD). Triglyceride (TG), high-density lipoprotein cholesterol (HDL), low-density lipoprotein cholesterol (LDL), blood glucose (BG) and hemoglobin A1c (HbA1c) were analyzed. NAFLD was diagnosed using abdominal ultrasound (US), and TG, HDL, LDL, BG and HbA1c were immediately collected on the same day and subjected to multivariate regression analysis. Stepwise analysis was performed to select the variables that were closely associated with NAFLD. The patients who were positive for the hepatitis B antigen and hepatitis C antibody were excluded from the study. Additionally, the patients who were prescribed prednisolone or methotrexate were excluded from the study as these agents may cause NAFLD or liver toxicity. The study included 168 and 125 patients with and without NAFLD, respectively. TG, BG and HbA1c were strongly correlated with NAFLD. Among these parameters, TG was the strongest predictor of NAFLD (χ 2 =9.89, P=0.0017). TG was the parameter that was most strongly associated with NAFLD. In conclusion, elevated TG was a marker of NAFLD.

Background: Extravascular lung water (EVLW), as measured by the thermodilution method, reflects the extent of pulmonary edema. Currently, there are no clinically effective treatments for preventing increases in pulmonary vascular permeability, a hallmark of lung pathophysiology, in patients with acute lung injury/acute respiratory distress syndrome (ALI/ARDS). In this study, we examined the contributions of hemodynamic and osmolarity factors, for which appropriate interventions are expected in critical care, to EVLW in patients with ALI/ARDS., Methods: We performed a subgroup analysis of a multicenter observational study of patients with acute pulmonary edema. Overall, 207 patients with ALI/ARDS were enrolled in the study. Multivariate regression analysis was used to evaluate the associations of hemodynamic and serum osmolarity parameters with the EVLW index (EVLWI; calculated as EVLW/Ideal body weight). We analyzed factors measured on the day of enrollment (day 0), and on days 1 and 2 after enrollment., Results: Multivariate regression analysis showed that global end-diastolic volume index (GEDVI) was significantly associated with EVLWI measured on days 0, 1, and 2 (P = 0.002, P < 0.001, and P = 0.003, respectively), whereas other factors were not significantly associated with EVLWI measured on all 3 days., Conclusions: Among several hemodynamic and serum osmolarity factors that could be targets for appropriate intervention, GEDVI appears to be a key contributor to EVLWI in patients with ALI/ARDS., Trial Registration: University Hospital Medical Information Network (UMIN) Clinical Trials Registry UMIN000003627.

Background: Breast-conserving therapy, consisting of lumpectomy and adjuvant radiotherapy, is considered standard treatment for early-stage breast cancer. One of the most important risk factors of local recurrence is the presence of positive surgical margins following lumpectomy. We aimed to develop and validate a predictive model (nomogram) to predict for positive margins following the first attempt at lumpectomy as a preoperative tool for clinical decision-making., Methods: Patients with clinical T1-2N0-1Mx-0 histology-proven invasive breast carcinoma who underwent BCT throughout the North-East region of The Netherlands between June 2008 and July 2009 were selected from the Netherlands Cancer Registry (n = 1185). Results from multivariate logistic regression analyses served as the basis for development of the nomogram. Nomogram calibration and discrimination were assessed graphically and by calculation of a concordance index, respectively. Nomogram performance was validated on an external independent dataset (n = 331) from the University Medical Center Groningen., Results: The final multivariate regression model included clinical, radiological, and pathological variables. Concordance indices were calculated of 0.70 (95% CI: 0.66-0.74) and 0.69 (95% CI: 0.63-0.76) for the modeling and the validation group, respectively. Calibration of the model was considered adequate in both groups. A nomogram was developed as a graphical representation of the model. Moreover, a web-based application (http://www.breastconservation.com) was build to facilitate the use of our nomogram in a clinical setting., Conclusion: We developed and validated a nomogram that enables estimation of the preoperative risk of positive margins in breast-conserving surgery. Our nomogram provides a valuable tool for identifying high-risk patients who might benefit from preoperative MRI and/or oncoplastic surgery., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

Low birth weight (LBW) is an important risk factor for neonatal and infant mortality and morbidity in adults.. However, no large scale study on the prevalence of LBW and related maternal risk factors in China has been published. To explore the effects of maternal factors on LBW for term birth in China, we conducted a hospital-based retrospective study of 55, 633 Chinese pregnancy cases between 2001 and 2008. Maternal sociodemographic data, history of infertility and contraceptive use were obtained. Their medical status and diseases during pre-pregnancy were examined by physical examination at the first antenatal care visit. Maternal medical status before childbirth and pregnancy outcomes, including body weight, infant gender, multiple pregnancy and congenital anomalies, were recorded. Univariate and multivariate logistic regression, and linear regression were used to investigate the relationship between maternal factors and term LBW. The general incidence of term LBW was 1.70% in the developed area of China. After preliminary analysis using the univariate model, low primary education, anemia, hypertensive disorders, placental previa, oligohydramnios and premature rupture of membrane were predicted as independent factors of term LBW in the multivariate model. Furthermore, the decrease in annual frquencies of these risk factors were major causes of gradual decline in the incidence of LBW (from 2.43% in 2001 to 1.21% in 2008). The study demonstrated that among maternal factors, primary education, anemia and hypertensive disorders could contribute to LBW for term birth even in the most developed area of China.

Background: This study aimed to determine whether modified shock index (MSI) is associated with mortality that is superior to heart rate, blood pressure, or the shock index (SI) in emergency patients., Methods: A retrospective database review was performed on 22 161 patients who presented to Peking Union Medical College Hospital Emergency Department and received intravenous fluids from January 1 to December 31, 2009. We gathered data of the patients on age, gender, vital signs, levels of consciousness, presenting complaints, and SI and MSI were calculated for all patients., Results: Multivariate regression analysis was performed to determine the correlation between risk factors and outcome. There is a significant correlation between emergency patient mortality rate and patient's vital signs obtained at the triage desk (HR>120 beats/min, systolic BP<90 mmHg, diastolic BP<60 mmHg). MSI is a stronger predictor of emergency patient mortality compared to heart rate and blood pressure alone, whereas SI does not have a significant correlation with emergency patient mortality rate., Conclusion: MSI is a clinically significant predictor of mortality in emergency patients. It may be better than using heart rate and blood pressure alone. SI is not significantly correlated with the mortality rate of the emergency patient.