Clinical and economic outcomes of serious postoperative infections following resection of common respiratory and gastrointestinal solid tumors

Unlike infections occurring during periods of chemotherapy-induced neutropenia, postoperative infections in patients with solid malignancy remain largely understudied. The purpose of this population-based study was to evaluate the clinical and economic burden, as well as the relationship of hospital surgical volume and outcomes associated with serious postoperative infection (SPI) – i.e., bacteremia/sepsis, pneumonia, and wound infection – following resection of common solid tumors.^ From the Texas Discharge Data Research File, we identified all Texas residents who underwent resection of cancer of the lung, esophagus, stomach, pancreas, colon, or rectum between 2002 and 2006. From their billing records, we identified ICD-9 codes indicating SPI and also subsequent SPI-related readmissions occurring within 30 days of surgery. Random-effects logistic regression was used to calculate the impact of SPI on mortality, as well as the association between surgical volume and SPI, adjusting for case-mix, hospital characteristics, and clustering of multiple surgical admissions within the same patient and patients within the same hospital. Excess bed days and costs were calculated by subtracting values for patients without infections from those with infections computed using multilevel mixed-effects generalized linear model by fitting a gamma distribution to the data using log link.^ Serious postoperative infection occurred following 9.4% of the 37,582 eligible tumor resections and was independently associated with an 11-fold increase in the odds of in-hospital mortality (95% Confidence Interval [95% CI], 6.7-18.5, P < 0.001). Patients with SPI required 6.3 additional hospital days (95% CI, 6.1 - 6.5) at an incremental cost of $16,396 (95% CI, $15,927–$16,875). There was a significant trend toward lower overall rates of SPI with higher surgical volume (P=0.037). ^ Due to the substantial morbidity, mortality, and excess costs associated with SPI following solid tumor resections and given that, under current reimbursement practices, most of this heavy burden is borne by acute care providers, it is imperative for hospitals to identify more effective prophylactic measures, so that these potentially preventable infections and their associated expenditures can be averted. Additional volume-outcomes research is also needed to identify infection prevention processes that can be transferred from higher- to lower-volume providers.^

Identfying predictors of St. Louis encephalitis activity in Houston Texas

Generalized linear Poisson and logistic regression models were utilized to examine the relationship between temperature and precipitation and cases of Saint Louis encephalitis virus spread in the Houston metropolitan area. The models were investigated with and without repeated measures, with a first order autoregressive (AR1) correlation structure used for the repeated measures model. The two types of Poisson regression models, with and without correlation structure, showed that a unit increase in temperature measured in degrees Fahrenheit increases the occurrence of the virus 1.7 times and a unit increase in precipitation measured in inches increases the occurrence of the virus 1.5 times. Logistic regression did not show these covariates to be significant as predictors for encephalitis activity in Houston for either correlation structure. This discrepancy for the logistic model could be attributed to the small data set.^ Keywords: Saint Louis Encephalitis; Generalized Linear Model; Poisson; Logistic; First Order Autoregressive; Temperature; Precipitation. ^

Motivational interviewing and feedback for college drinkers: Handling missing values with complete case analysis and multiple imputation

Objective: In this secondary data analysis, three statistical methodologies were implemented to handle cases with missing data in a motivational interviewing and feedback study. The aim was to evaluate the impact that these methodologies have on the data analysis. ^ Methods: We first evaluated whether the assumption of missing completely at random held for this study. We then proceeded to conduct a secondary data analysis using a mixed linear model to handle missing data with three methodologies (a) complete case analysis, (b) multiple imputation with explicit model containing outcome variables, time, and the interaction of time and treatment, and (c) multiple imputation with explicit model containing outcome variables, time, the interaction of time and treatment, and additional covariates (e.g., age, gender, smoke, years in school, marital status, housing, race/ethnicity, and if participants play on athletic team). Several comparisons were conducted including the following ones: 1) the motivation interviewing with feedback group (MIF) vs. the assessment only group (AO), the motivation interviewing group (MIO) vs. AO, and the intervention of the feedback only group (FBO) vs. AO, 2) MIF vs. FBO, and 3) MIF vs. MIO.^ Results: We first evaluated the patterns of missingness in this study, which indicated that about 13% of participants showed monotone missing patterns, and about 3.5% showed non-monotone missing patterns. Then we evaluated the assumption of missing completely at random by Little's missing completely at random (MCAR) test, in which the Chi-Square test statistic was 167.8 with 125 degrees of freedom, and its associated p-value was p=0.006, which indicated that the data could not be assumed to be missing completely at random. After that, we compared if the three different strategies reached the same results. For the comparison between MIF and AO as well as the comparison between MIF and FBO, only the multiple imputation with additional covariates by uncongenial and congenial models reached different results. For the comparison between MIF and MIO, all the methodologies for handling missing values obtained different results. ^ Discussions: The study indicated that, first, missingness was crucial in this study. Second, to understand the assumptions of the model was important since we could not identify if the data were missing at random or missing not at random. Therefore, future researches should focus on exploring more sensitivity analyses under missing not at random assumption.^

Spatial distribution of orofacial cleft birth defects and uranium-radium concentrations in tap water in Atascosa, Bee, Brooks, Calhoun, Duval, Goliad, Hidalgo, Jim Hogg, Jim Wells, Karnes, Kleberg, Live Oak, McMullen, Nueces, San Patricio, Refugio, Starr, Victoria, Webb, and Zavala Counties, Texas

Background Past and recent evidence shows that radionuclides in drinking water may be a public health concern. Developmental thresholds for birth defects with respect to chronic low level domestic radiation exposures, such as through drinking water, have not been definitely recognized, and there is a strong need to address this deficiency in information. In this study we examined the geographic distribution of orofacial cleft birth defects in and around uranium mining district Counties in South Texas (Atascosa, Bee, Brooks, Calhoun, Duval, Goliad, Hidalgo, Jim Hogg, Jim Wells, Karnes, Kleberg, Live Oak, McMullen, Nueces, San Patricio, Refugio, Starr, Victoria, Webb, and Zavala), from 1999 to 2007. The probable association of cleft birth defect rates by ZIP codes classified according to uranium and radium concentrations in drinking water supplies was evaluated. Similar associations between orofacial cleft birth defects and radium/radon in drinking water were reported earlier by Cech and co-investigators in another of the Gulf Coast region (Harris County, Texas).50, 55 Since substantial uranium mining activity existed and still exists in South Texas, contamination of drinking water sources with radiation and its relation to birth defects is a ground for concern. ^ Methods Residential addresses of orofacial cleft birth defect cases, as well as live births within the twenty Counties during 1999-2007 were geocoded and mapped. Prevalence rates were calculated by ZIP codes and were mapped accordingly. Locations of drinking water supplies were also geocoded and mapped. ZIP codes were stratified as having high combined uranium (≥30μg/L) vs. low combined uranium (<30μg/L). Likewise, ZIP codes having the uranium isotope, Ra-226 in drinking water, were also stratified as having elevated radium (≥3 pCi/L) vs. low radium (<3 pCi/L). A linear regression was performed using STATA® generalized linear model (GLM) program to evaluate the probable association between cleft birth defect rates by ZIP codes and concentration of uranium and radium via domestic water supply. These rates were further adjusted for potentially confounding variables such as maternal age, education, occupation, and ethnicity. ^ Results This study showed higher rates of cleft births in ZIP codes classified as having high combined uranium versus ZIP codes having low combined uranium. The model was further improved by adding radium stratified as explained above. Adjustment for maternal age and ethnicity did not substantially affect the statistical significance of uranium or radium concentrations in household water supplies. ^ Conclusion Although this study lacks individual exposure levels, the findings suggest a significant association between elevated uranium and radium concentrations in tap water and high orofacial birth defect rates by ZIP codes. Future case-control studies that can measure individual exposure levels and adjust for contending risk factors could result in a better understanding of the exposure-disease association.^

Associations between teacher support and students' fruit and vegetable consumption in low-income elementary schools in central Texas

Background. Similar to parent support in the home environment, teacher support at school may positively influence children's fruit and vegetable (FV) consumption. This study assessed the relationship between teacher support for FV consumption and the FV intake of 4th and 5th grade students in low-income elementary schools in central Texas. Methods. A secondary analysis was performed on baseline data collected from 496 parent-child dyads during the Marathon Kids study carried out by the Michael & Susan Dell Center for Healthy Living at the University of Texas School of Public Health. A hierarchical linear regression analysis adjusting for key demographic variables, parent support, and home FV availability was conducted. In addition, separate linear regression models stratified by quartiles of home FV availability were conducted to assess the relationship between teacher support and FV intake by level of home FV availability. Results. Teacher support was not significantly related to students' FV intake (p = .44). However, the interaction of teacher support and home FV availability was positively associated with students' FV consumption (p < .05). For students in the lowest quartile of home FV availability, teacher support accounted for approximately 6% of the FV intake variance (p = .02). For higher levels of FV availability, teacher support and FV intake were not related. Conclusions. For lower income elementary school-aged children with low FV availability at home, greater teacher support may lead to modest increases in FV consumption.^

Regression with autocorrelated data: A study of time trend of global infant mortality rate

The infant mortality rate (IMR) is considered to be one of the most important indices of a country's well-being. Countries around the world and other health organizations like the World Health Organization are dedicating their resources, knowledge and energy to reduce the infant mortality rates. The well-known Millennium Development Goal 4 (MDG 4), whose aim is to archive a two thirds reduction of the under-five mortality rate between 1990 and 2015, is an example of the commitment. ^ In this study our goal is to model the trends of IMR between the 1950s to 2010s for selected countries. We would like to know how the IMR is changing overtime and how it differs across countries. ^ IMR data collected over time forms a time series. The repeated observations of IMR time series are not statistically independent. So in modeling the trend of IMR, it is necessary to account for these correlations. We proposed to use the generalized least squares method in general linear models setting to deal with the variance-covariance structure in our model. In order to estimate the variance-covariance matrix, we referred to the time-series models, especially the autoregressive and moving average models. Furthermore, we will compared results from general linear model with correlation structure to that from ordinary least squares method without taking into account the correlation structure to check how significantly the estimates change.^

Identification of genes associated with quantitative traits involved in cardiovascular disease and lipoprotein metabolism

Cardiovascular disease (CVD) is a threat to public health. It has been reported to be the leading cause of death in United States. The invention of next generation sequencing (NGS) technology has revolutionized the biomedical research. To investigate NGS data of CVD related quantitative traits would contribute to address the unknown etiology and disease mechanism of CVD. NHLBI's Exome Sequencing Project (ESP) contains CVD related phenotypes and their associated NGS exomes sequence data. Initially, a subset of next generation sequencing data consisting of 13 CVD-related quantitative traits was investigated. Only 6 traits, systolic blood pressure (SBP), diastolic blood pressure (DBP), height, platelet counts, waist circumference, and weight, were analyzed by functional linear model (FLM) and 7 currently existing methods. FLM outperformed all currently existing methods by identifying the highest number of significant genes and had identified 96, 139, 756, 1162, 1106, and 298 genes associated with SBP, DBP, Height, Platelet, Waist, and Weight respectively. ^

New methods for quantification and analysis of quantitative real-time polymerase chain reaction data

Quantitative real-time polymerase chain reaction (qPCR) is a sensitive gene quantitation method that has been widely used in the biological and biomedical fields. The currently used methods for PCR data analysis, including the threshold cycle (CT) method, linear and non-linear model fitting methods, all require subtracting background fluorescence. However, the removal of background fluorescence is usually inaccurate, and therefore can distort results. Here, we propose a new method, the taking-difference linear regression method, to overcome this limitation. Briefly, for each two consecutive PCR cycles, we subtracted the fluorescence in the former cycle from that in the later cycle, transforming the n cycle raw data into n-1 cycle data. Then linear regression was applied to the natural logarithm of the transformed data. Finally, amplification efficiencies and the initial DNA molecular numbers were calculated for each PCR run. To evaluate this new method, we compared it in terms of accuracy and precision with the original linear regression method with three background corrections, being the mean of cycles 1-3, the mean of cycles 3-7, and the minimum. Three criteria, including threshold identification, max R2, and max slope, were employed to search for target data points. Considering that PCR data are time series data, we also applied linear mixed models. Collectively, when the threshold identification criterion was applied and when the linear mixed model was adopted, the taking-difference linear regression method was superior as it gave an accurate estimation of initial DNA amount and a reasonable estimation of PCR amplification efficiencies. When the criteria of max R2 and max slope were used, the original linear regression method gave an accurate estimation of initial DNA amount. Overall, the taking-difference linear regression method avoids the error in subtracting an unknown background and thus it is theoretically more accurate and reliable. This method is easy to perform and the taking-difference strategy can be extended to all current methods for qPCR data analysis.^

Snow-pit measurements on Atka Bay landfast sea ice during ANT-Land 2012/2013 season

Up to now, snow cover on Antarctic sea ice and its impact on radar backscatter, particularly after the onset of freeze/thaw processes, are not well understood. Here we present a combined analysis of in situ observations of snow properties from the landfast sea ice in Atka Bay, Antarctica, and high-resolution TerraSAR-X backscatter data, for the transition from austral spring (November 2012) to summer (January 2013). The physical changes in the seasonal snow cover during that time are reflected in the evolution of TerraSAR-X backscatter. We are able to explain 76-93% of the spatio-temporal variability of the TerraSAR-X backscatter signal with up to four snowpack parameters with a root-mean-squared error of 0.87-1.62 dB, using a simple multiple linear model. Over the complete study, and especially after the onset of early-melt processes and freeze/thaw cycles, the majority of variability in the backscatter is influenced by changes in snow/ice interface temperature, snow depth and top-layer grain size. This suggests it may be possible to retrieve snow physical properties over Antarctic sea ice from X-band SAR backscatter.

Phycoerythrin concentration during POLARSTERN cruises ANT-XXIV/4, ANT-XXV/1, and ANT-XXVI/4

Phycobiliproteins are a family of water-soluble pigment proteins that play an important role as accessory or antenna pigments and absorb in the green part of the light spectrum poorly used by chlorophyll a. The phycoerythrins (PEs) are one of four types of phycobiliproteins that are generally distinguished based on their absorption properties. As PEs are water soluble, they are generally not captured with conventional pigment analysis. Here we present a statistical model based on in situ measurements of three transatlantic cruises which allows us to derive relative PE concentration from standardized hyperspectral underwater radiance measurements (Lu). The model relies on Empirical Orthogonal Function (EOF) analysis of Lu spectra and, subsequently, a Generalized Linear Model with measured PE concentrations as the response variable and EOF loadings as predictor variables. The method is used to predict relative PE concentrations throughout the water column and to calculate integrated PE estimates based on those profiles.

Responses of the tropical gorgonian coral Eunicea fusca to ocean acidification conditions

Ocean acidification can have negative repercussions from the organism to ecosystem levels. Octocorals deposit high-magnesium calcite in their skeletons, and according to different models, they could be more susceptible to the depletion of carbonate ions than either calcite or aragonite-depositing organisms. This study investigated the response of the gorgonian coral Eunicea fusca to a range of CO2 concentrations from 285 to 4,568 ppm (pH range 8.1-7.1) over a 4-week period. Gorgonian growth and calcification were measured at each level of CO2 as linear extension rate and percent change in buoyant weight and calcein incorporation in individual sclerites, respectively. There was a significant negative relationship for calcification and CO2 concentration that was well explained by a linear model regression analysis for both buoyant weight and calcein staining. In general, growth and calcification did not stop in any of the concentrations of pCO2; however, some of the octocoral fragments experienced negative calcification at undersaturated levels of calcium carbonate (>4,500 ppm) suggesting possible dissolution effects. These results highlight the susceptibility of the gorgonian coral E. fusca to elevated levels of carbon dioxide but suggest that E. fusca could still survive well in mid-term ocean acidification conditions expected by the end of this century, which provides important information on the effects of ocean acidification on the dynamics of coral reef communities. Gorgonian corals can be expected to diversify and thrive in the Atlantic-Eastern Pacific; as scleractinian corals decline, it is likely to expect a shift in these reef communities from scleractinian coral dominated to octocoral/soft coral dominated under a "business as usual" scenario of CO2 emissions.

Agglomeration and firm-level productivity : a Bayesian spatial approach

This paper estimates the impact of industrial agglomeration on firm-level productivity in Chinese manufacturing sectors. To account for spatial autocorrelation across regions, we formulate a hierarchical spatial model at the firm level and develop a Bayesian estimation algorithm. A Bayesian instrumental-variables approach is used to address endogeneity bias of agglomeration. Robust to these potential biases, we find that agglomeration of the same industry (i.e. localization) has a productivity-boosting effect, but agglomeration of urban population (i.e. urbanization) has no such effects. Additionally, the localization effects increase with educational levels of employees and the share of intermediate inputs in gross output. These results may suggest that agglomeration externalities occur through knowledge spillovers and input sharing among firms producing similar manufactures.

Tendencias actuales en la construcción sismorresistente para edificios en hormigón armado

Este estudio aborda la recopilación de nuevas tendencias del diseño sismorresistente, enfocándose en la técnica del aislamiento de base, por ser la más efectiva, difundida y utilizada; y el análisis de las ventajas que puede tener una edificación que aplica dicha técnica, desde el punto de vista estructural y económico. Se elige la tipología más frecuente o común de edificios de hormigón armado propensos a ser aislados, que en este caso es un hospital, cuyo modelo empotrado se somete a varias normas sismorresistentes comparando principalmente fuerzas de cortante basal, y considerando la interacción suelo-estructura; para asistir a este cálculo se desarrolla un programa de elementos viga de 6 gdl por nodo en código Matlab. El modelo aislado incluye el análisis de tres combinaciones de tipos de aisladores HDR, LPR y FPS, alternando modelos lineales simplificados de 1 y 3 gdl por piso, evaluando diferencias de respuestas de la estructura, y procediendo a la elección de la combinación que de resultados más convenientes; para la modelación no lineal de cada sistema de aislamiento se utiliza el método explícito de diferencias centrales. Finalmente, se realiza un análisis comparativo de daños esperados en el caso de la ocurrencia del sismo de diseño, utilizando el método rápido y tomando como referencia el desplazamiento espectral del último piso; llegando a dar conclusiones y recomendaciones para el uso de sistemas de aislamiento. This study addresses the collection of new seismic design trends, focusing on base isolation technique, as the most effective and widely used, and the analysis of the advantages in buildings that apply this technique, from the structurally and economically point of view. Choosing the most common types of concrete buildings likely to be isolated, which in this case is a hospital, the fix model is subjected to various seismic codes mainly comparing base shear forces, and considering the soil-structure interaction; for this calculation attend a program of bars 6 dof per node is made in Matlab code. The isolated model includes analysis of three types of isolators combinations HDR, LPR and FPS, alternating simplified linear model of 1 and 3 dof per floor, evaluating differences in the response of the structure, and proceeding to the choice of the combination of results more convenient; for modeling nonlinear each insulation system, the explicit central difference method is used. Finally, a comparative analysis of expected damage in the case of the design earthquake, using a fast combined method and by reference to the spectral displacement of the top floor; reaching conclusions and give recommendations for the use of insulation systems.

Modelling the spatio-temporal pattern of primary dispersal in stone pine (Pinus pinea L.) stands in the Northern Plateau (Spain)

Natural regeneration in stone pine (Pinus pinea L.) managed forests in the Spanish Northern Plateau is not achieved successfully under current silviculture practices, constituting a main concern for forest managers. We modelled spatio-temporal features of primary dispersal to test whether (a) present low stand densities constrain natural regeneration success and (b) seed release is a climate-controlled process. The present study is based on data collected from a 6 years seed trap experiment considering different regeneration felling intensities. From a spatial perspective, we attempted alternate established kernels under different data distribution assumptions to fit a spatial model able to predict P. pinea seed rain. Due to P. pinea umbrella-like crown, models were adapted to account for crown effect through correction of distances between potential seed arrival locations and seed sources. In addition, individual tree fecundity was assessed independently from existing models, improving parameter estimation stability. Seed rain simulation enabled to calculate seed dispersal indexes for diverse silvicultural regeneration treatments. The selected spatial model of best fit (Weibull, Poisson assumption) predicted a highly clumped dispersal pattern that resulted in a proportion of gaps where no seed arrival is expected (dispersal limitation) between 0.25 and 0.30 for intermediate intensity regeneration fellings and over 0.50 for intense fellings. To describe the temporal pattern, the proportion of seeds released during monthly intervals was modelled as a function of climate variables – rainfall events – through a linear model that considered temporal autocorrelation, whereas cone opening took place over a temperature threshold. Our findings suggest the application of less intensive regeneration fellings, to be carried out after years of successful seedling establishment and, seasonally, subsequent to the main rainfall period (late fall). This schedule would avoid dispersal limitation and would allow for a complete seed release. These modifications in present silviculture practices would produce a more efficient seed shadow in managed stands.

Estimation of daily global solar radiation from measured temperatures at Cañada de Luque, Córdoba, Argentina

Solar radiation is the most important source of renewable energy in the planet; it's important to solar engineers, designers and architects, and it's also fundamental for efficiently determining irrigation water needs and potential yield of crops, among others. Complete and accurate solar radiation data at a specific region are indispensable. For locations where measured values are not available, several models have been developed to estimate solar radiation. The objective of this paper was to calibrate, validate and compare five representative models to predict global solar radiation, adjusting the empirical coefficients to increase the local applicability and to develop a linear model. All models were based on easily available meteorological variables, without sunshine hours as input, and were used to estimate the daily solar radiation at Cañada de Luque (Córdoba, Argentina). As validation, measured and estimated solar radiation data were analyzed using several statistic coefficients. The results showed that all the analyzed models were robust and accurate (R2 and RMSE values between 0.87 to 0.89 and 2.05 to 2.14, respectively), so global radiation can be estimated properly with easily available meteorological variables when only temperature data are available. Hargreaves-Samani, Allen and Bristow-Campbell models could be used with typical values to estimate solar radiation while Samani and Almorox models should be applied with calibrated coefficients. Although a new linear model presented the smallest R2 value (R2 = 0.87), it could be considered useful for its easy application. The daily global solar radiation values produced for these models can be used to estimate missing daily values, when only temperature data are available, and in hydrologic or agricultural applications.