Preoperative nutritional screening by the specialist instead of the nutritional risk score might prevent excess nutrition: a multivariate analysis of nutritional risk factors.

BACKGROUND: The aim of the current study was to assess whether widely used nutritional parameters are correlated with the nutritional risk score (NRS-2002) to identify postoperative morbidity and to evaluate the role of nutritionists in nutritional assessment. METHODS: A randomized trial on preoperative nutritional interventions (NCT00512213) provided the study cohort of 152 patients at nutritional risk (NRS-2002 ≥3) with a comprehensive phenotyping including diverse nutritional parameters (n=17), elaborated by nutritional specialists, and potential demographic and surgical (n=5) confounders. Risk factors for overall, severe (Dindo-Clavien 3-5) and infectious complications were identified by univariate analysis; parameters with P<0.20 were then entered in a multiple logistic regression model. RESULTS: Final analysis included 140 patients with complete datasets. Of these, 61 patients (43.6%) were overweight, and 72 patients (51.4%) experienced at least one complication of any degree of severity. Univariate analysis identified a correlation between few (≤3) active co-morbidities (OR=4.94; 95% CI: 1.47-16.56, p=0.01) and overall complications. Patients screened as being malnourished by nutritional specialists presented less overall complications compared to the not malnourished (OR=0.47; 95% CI: 0.22-0.97, p=0.043). Severe postoperative complications occurred more often in patients with low lean body mass (OR=1.06; 95% CI: 1-1.12, p=0.028). Few (≤3) active co-morbidities (OR=8.8; 95% CI: 1.12-68.99, p=0.008) were related with postoperative infections. Patients screened as being malnourished by nutritional specialists presented less infectious complications (OR=0.28; 95% CI: 0.1-0.78), p=0.014) as compared to the not malnourished. Multivariate analysis identified few co-morbidities (OR=6.33; 95% CI: 1.75-22.84, p=0.005), low weight loss (OR=1.08; 95% CI: 1.02-1.14, p=0.006) and low hemoglobin concentration (OR=2.84; 95% CI: 1.22-6.59, p=0.021) as independent risk factors for overall postoperative complications. Compliance with nutritional supplements (OR=0.37; 95% CI: 0.14-0.97, p=0.041) and supplementation of malnourished patients as assessed by nutritional specialists (OR=0.24; 95% CI: 0.08-0.69, p=0.009) were independently associated with decreased infectious complications. CONCLUSIONS: Nutritional support based upon NRS-2002 screening might result in overnutrition, with potentially deleterious clinical consequences. We emphasize the importance of detailed assessment of the nutritional status by a dedicated specialist before deciding on early nutritional intervention for patients with an initial NRS-2002 score of ≥3.

Global surveillance of cancer survival 1995-2009 : analysis of individual data for 25 676 887 patients from 279 population-based registries in 67 countries (CONCORD-2)

BACKGROUND: Worldwide data for cancer survival are scarce. We aimed to initiate worldwide surveillance of cancer survival by central analysis of population-based registry data, as a metric of the effectiveness of health systems, and to inform global policy on cancer control. METHODS: Individual tumour records were submitted by 279 population-based cancer registries in 67 countries for 25·7 million adults (age 15-99 years) and 75 000 children (age 0-14 years) diagnosed with cancer during 1995-2009 and followed up to Dec 31, 2009, or later. We looked at cancers of the stomach, colon, rectum, liver, lung, breast (women), cervix, ovary, and prostate in adults, and adult and childhood leukaemia. Standardised quality control procedures were applied; errors were corrected by the registry concerned. We estimated 5-year net survival, adjusted for background mortality in every country or region by age (single year), sex, and calendar year, and by race or ethnic origin in some countries. Estimates were age-standardised with the International Cancer Survival Standard weights. FINDINGS: 5-year survival from colon, rectal, and breast cancers has increased steadily in most developed countries. For patients diagnosed during 2005-09, survival for colon and rectal cancer reached 60% or more in 22 countries around the world; for breast cancer, 5-year survival rose to 85% or higher in 17 countries worldwide. Liver and lung cancer remain lethal in all nations: for both cancers, 5-year survival is below 20% everywhere in Europe, in the range 15-19% in North America, and as low as 7-9% in Mongolia and Thailand. Striking rises in 5-year survival from prostate cancer have occurred in many countries: survival rose by 10-20% between 1995-99 and 2005-09 in 22 countries in South America, Asia, and Europe, but survival still varies widely around the world, from less than 60% in Bulgaria and Thailand to 95% or more in Brazil, Puerto Rico, and the USA. For cervical cancer, national estimates of 5-year survival range from less than 50% to more than 70%; regional variations are much wider, and improvements between 1995-99 and 2005-09 have generally been slight. For women diagnosed with ovarian cancer in 2005-09, 5-year survival was 40% or higher only in Ecuador, the USA, and 17 countries in Asia and Europe. 5-year survival for stomach cancer in 2005-09 was high (54-58%) in Japan and South Korea, compared with less than 40% in other countries. By contrast, 5-year survival from adult leukaemia in Japan and South Korea (18-23%) is lower than in most other countries. 5-year survival from childhood acute lymphoblastic leukaemia is less than 60% in several countries, but as high as 90% in Canada and four European countries, which suggests major deficiencies in the management of a largely curable disease. INTERPRETATION: International comparison of survival trends reveals very wide differences that are likely to be attributable to differences in access to early diagnosis and optimum treatment. Continuous worldwide surveillance of cancer survival should become an indispensable source of information for cancer patients and researchers and a stimulus for politicians to improve health policy and health-care systems. FUNDING: Canadian Partnership Against Cancer (Toronto, Canada), Cancer Focus Northern Ireland (Belfast, UK), Cancer Institute New South Wales (Sydney, Australia), Cancer Research UK (London, UK), Centers for Disease Control and Prevention (Atlanta, GA, USA), Swiss Re (London, UK), Swiss Cancer Research foundation (Bern, Switzerland), Swiss Cancer League (Bern, Switzerland), and University of Kentucky (Lexington, KY, USA).

Improved predictive mapping of indoor radon concentrations using ensemble regression trees based on automatic clustering of geological units.

PURPOSE: According to estimations around 230 people die as a result of radon exposure in Switzerland. This public health concern makes reliable indoor radon prediction and mapping methods necessary in order to improve risk communication to the public. The aim of this study was to develop an automated method to classify lithological units according to their radon characteristics and to develop mapping and predictive tools in order to improve local radon prediction. METHOD: About 240 000 indoor radon concentration (IRC) measurements in about 150 000 buildings were available for our analysis. The automated classification of lithological units was based on k-medoids clustering via pair-wise Kolmogorov distances between IRC distributions of lithological units. For IRC mapping and prediction we used random forests and Bayesian additive regression trees (BART). RESULTS: The automated classification groups lithological units well in terms of their IRC characteristics. Especially the IRC differences in metamorphic rocks like gneiss are well revealed by this method. The maps produced by random forests soundly represent the regional difference of IRCs in Switzerland and improve the spatial detail compared to existing approaches. We could explain 33% of the variations in IRC data with random forests. Additionally, the influence of a variable evaluated by random forests shows that building characteristics are less important predictors for IRCs than spatial/geological influences. BART could explain 29% of IRC variability and produced maps that indicate the prediction uncertainty. CONCLUSION: Ensemble regression trees are a powerful tool to model and understand the multidimensional influences on IRCs. Automatic clustering of lithological units complements this method by facilitating the interpretation of radon properties of rock types. This study provides an important element for radon risk communication. Future approaches should consider taking into account further variables like soil gas radon measurements as well as more detailed geological information.

The Feminization of Occupations and Change in Wages : a Panel Analysis of Britain, Germany, and Switzerland

In the past four decades, women have made major inroads into occupations previously dominated by men. This paper examines whether occupational feminization is accompanied by a decline in wages: Do workers suffer a wage penalty if they remain in, or move into, feminizing occupations? We analyze this question over the 1990s and 2000s in Britain, Germany, and Switzerland, using longitudinal panel data to estimate individual fixed effects for men and women. Moving from an entirely male to an entirely female occupation entails a loss in individual earnings of 13 percent in Britain, 7 percent in Switzerland, and 3 percent in Germany. The impact of occupational feminization on wages is not linear, but sets apart occupations holding more than 60 percent of women. Moving into such female occupations incurs a wage penalty. Contrary to the prevailing idea in economics, differences in productivity-human capital, job-specific skills, and time investment-do not fully explain the wage gap between male and female occupations. The wage penalty associated with working in a female occupation is also much larger where employer discretion is greater-in the private sector-than where wagesetting is guided by formal rules-the public sector. These findings suggest that wage disparities across male and female occupations are due to gender devaluation.

Dual activation of pathways regulated by steroid receptors and peptide growth factors in primary prostate cancer revealed by Factor Analysis of microarray data

Background: We use an approach based on Factor Analysis to analyze datasets generated for transcriptional profiling. The method groups samples into biologically relevant categories, and enables the identification of genes and pathways most significantly associated to each phenotypic group, while allowing for the participation of a given gene in more than one cluster. Genes assigned to each cluster are used for the detection of pathways predominantly activated in that cluster by finding statistically significant associated GO terms. We tested the approach with a published dataset of microarray experiments in yeast. Upon validation with the yeast dataset, we applied the technique to a prostate cancer dataset. Results: Two major pathways are shown to be activated in organ-confined, non-metastatic prostate cancer: those regulated by the androgen receptor and by receptor tyrosine kinases. A number of gene markers (HER3, IQGAP2 and POR1) highlighted by the software and related to the later pathway have been validated experimentally a posteriori on independent samples. Conclusion: Using a new microarray analysis tool followed by a posteriori experimental validation of the results, we have confirmed several putative markers of malignancy associated with peptide growth factor signalling in prostate cancer and revealed others, most notably ERRB3 (HER3). Our study suggest that, in primary prostate cancer, HER3, together or not with HER4, rather than in receptor complexes involving HER2, could play an important role in the biology of these tumors. These results provide new evidence for the role of receptor tyrosine kinases in the establishment and progression of prostate cancer.

Study of tergal glands morphogenesis through an integrative analysis of genomic data

About 50% of living species are holometabolan insects. Therefore, unraveling the ori- gin of insect metamorphosis from the hemimetabolan (gradual metamorphosis) to the holometabolan (sudden metamorphosis at the end of the life cycle) mode is equivalent to explaining how all this biodiversity originated. One of the problems with studying the evolution from hemimetaboly to holometaboly is that most information is available only in holometabolan species. Within the hemimetabolan group, our model, the cock- roach Blattella germanica, is the most studied species. However, given that the study of adult morphogenesis at organismic level is still complex, we focused on the study of the tergal gland (TG) as a minimal model of metamorphosis. The TG is formed in tergites 7 and 8 (T7-8) in the last days of the last nymphal instar (nymph 6). The comparative study of four T7-T8 transcriptomes provided us with crucial keys of TG formation, but also essential information about the mechanisms and circuitry that allows the shift from nymphal to adult morphogenesis.

Statistical Analysis of an SEIR Epidemic Model

This thesis was focussed on statistical analysis methods and proposes the use of Bayesian inference to extract information contained in experimental data by estimating Ebola model parameters. The model is a system of differential equations expressing the behavior and dynamics of Ebola. Two sets of data (onset and death data) were both used to estimate parameters, which has not been done by previous researchers in (Chowell, 2004). To be able to use both data, a new version of the model has been built. Model parameters have been estimated and then used to calculate the basic reproduction number and to study the disease-free equilibrium. Estimates of the parameters were useful to determine how well the model fits the data and how good estimates were, in terms of the information they provided about the possible relationship between variables. The solution showed that Ebola model fits the observed onset data at 98.95% and the observed death data at 93.6%. Since Bayesian inference can not be performed analytically, the Markov chain Monte Carlo approach has been used to generate samples from the posterior distribution over parameters. Samples have been used to check the accuracy of the model and other characteristics of the target posteriors.

Polyphenol intake and mortality risk: a re-analysis of the PREDIMED trial

Background: Polyphenols may lower the risk of cardiovascular disease (CVD) and other chronic diseases due to their antioxidant and anti-inflammatory properties, as well as their beneficial effects on blood pressure, lipids and insulin resistance. However, no previous epidemiological studies have evaluated the relationship between the intake of total polyphenols intake and polyphenol subclasses with overall mortality. Our aim was to evaluate whether polyphenol intake is associated with all-cause mortality in subjects at high cardiovascular risk. Methods: We used data from the PREDIMED study, a 7,447-participant, parallel-group, randomized, multicenter, controlled five-year feeding trial aimed at assessing the effects of the Mediterranean Diet in primary prevention of cardiovascular disease. Polyphenol intake was calculated by matching food consumption data from repeated food frequency questionnaires (FFQ) with the Phenol-Explorer database on the polyphenol content of each reported food. Hazard ratios (HR) and 95% confidence intervals (CI) between polyphenol intake and mortality were estimated using time-dependent Cox proportional hazard models. Results: Over an average of 4.8 years of follow-up, we observed 327 deaths. After multivariate adjustment, we found a 37% relative reduction in all-cause mortality comparing the highest versus the lowest quintiles of total polyphenol intake (hazard ratio (HR) = 0.63; 95% CI 0.41 to 0.97; P for trend = 0.12). Among the polyphenol subclasses, stilbenes and lignans were significantly associated with reduced all-cause mortality (HR =0.48; 95% CI 0.25 to 0.91; P for trend = 0.04 and HR = 0.60; 95% CI 0.37 to 0.97; P for trend = 0.03, respectively), with no significant associations apparent in the rest (flavonoids or phenolic acids). Conclusions: Among high-risk subjects, those who reported a high polyphenol intake, especially of stilbenes and lignans, showed a reduced risk of overall mortality compared to those with lower intakes. These results may be useful to determine optimal polyphenol intake or specific food sources of polyphenols that may reduce the risk of all-cause mortality.

Panel Data Stochastic Convergence Analysis of the Mexican Regions

The stochastic convergence amongst Mexican Federal entities is analyzed in panel data framework. The joint consideration of cross-section dependence and multiple structural breaks is required to ensure that the statistical inference is based on statistics with good statistical properties. Once these features are accounted for, evidence in favour of stochastic convergence is found. Since stochastic convergence is a necessary, yet insufficient condition for convergence as predicted by economic growth models, the paper also investigates whether-convergence process has taken place. We found that the Mexican states have followed either heterogeneous convergence patterns or divergence process throughout the analyzed period.

Compositional data analysis of vote shares in the 2001 Australian Federal election

First application of compositional data analysis techniques to Australian election data

Regression analysis of compositional data when both the dependent variable and independent variable are components

It is well known that regression analyses involving compositional data need special attention because the data are not of full rank. For a regression analysis where both the dependent and independent variable are components we propose a transformation of the components emphasizing their role as dependent and independent variables. A simple linear regression can be performed on the transformed components. The regression line can be depicted in a ternary diagram facilitating the interpretation of the analysis in terms of components. An exemple with time-budgets illustrates the method and the graphical features

A panel data analysis of FDI and informal labor markets

The objective of this paper is to examine whether informal labor markets affect the flows of Foreign Direct Investment (FDI), and also whether this effect is similar in developed and developing countries. With this aim, different public data sources, such as the World Bank (WB), and the United Nations Conference on Trade and Development (UNCTAD) are used, and panel econometric models are estimated for a sample of 65 countries over a 14 year period (1996-2009). In addition, this paper uses a dynamic model as an extension of the analysis to establish whether such an effect exists and what its indicators and significance may be.

Sampling in chemical analysis

The uncertainty of any analytical determination depends on analysis and sampling. Uncertainty arising from sampling is usually not controlled and methods for its evaluation are still little known. Pierre Gy’s sampling theory is currently the most complete theory about samplingwhich also takes the design of the sampling equipment into account. Guides dealing with the practical issues of sampling also exist, published by international organizations such as EURACHEM, IUPAC (International Union of Pure and Applied Chemistry) and ISO (International Organization for Standardization). In this work Gy’s sampling theory was applied to several cases, including the analysis of chromite concentration estimated on SEM (Scanning Electron Microscope) images and estimation of the total uncertainty of a drug dissolution procedure. The results clearly show that Gy’s sampling theory can be utilized in both of the above-mentioned cases and that the uncertainties achieved are reliable. Variographic experiments introduced in Gy’s sampling theory are beneficially applied in analyzing the uncertainty of auto-correlated data sets such as industrial process data and environmental discharges. The periodic behaviour of these kinds of processes can be observed by variographic analysis as well as with fast Fourier transformation and auto-correlation functions. With variographic analysis, the uncertainties are estimated as a function of the sampling interval. This is advantageous when environmental data or process data are analyzed as it can be easily estimated how the sampling interval is affecting the overall uncertainty. If the sampling frequency is too high, unnecessary resources will be used. On the other hand, if a frequency is too low, the uncertainty of the determination may be unacceptably high. Variographic methods can also be utilized to estimate the uncertainty of spectral data produced by modern instruments. Since spectral data are multivariate, methods such as Principal Component Analysis (PCA) are needed when the data are analyzed. Optimization of a sampling plan increases the reliability of the analytical process which might at the end have beneficial effects on the economics of chemical analysis,