Anesthesia-related and perioperative cardiac arrest in low- and high-income countries: a systematic review with meta-regression and proportional meta-analysis

The anesthesia-related cardiac arrest (CA) rate is a quality indicator to improve patient safety in the perioperative period. A systematic review with meta-analysis of the worldwide literature related to anesthesia-related CA rate has not yet been performed.This study aimed to analyze global data on anesthesia-related and perioperative CA rates according to country's Human Development Index (HDI) and by time. In addition, we compared the anesthesia-related and perioperative CA rates in low- and high-income countries in 2 time periods.A systematic review was performed using electronic databases to identify studies in which patients underwent anesthesia with anesthesia-related and/or perioperative CA rates. Meta-regression and proportional meta-analysis were performed with 95% confidence intervals (CIs) to evaluate global data on anesthesia-related and perioperative CA rates according to country's HDI and by time, and to compare the anesthesia-related and perioperative CA rates by country's HDI status (low HDI vs high HDI) and by time period (pre-1990s vs 1990s-2010s), respectively.Fifty-three studies from 21 countries assessing 11.9 million anesthetic administrations were included. Meta-regression showed that anesthesia-related (slope: -3.5729; 95% CI: -6.6306 to -0.5152; P = 0.024) and perioperative (slope: -2.4071; 95% CI: -4.0482 to -0.7659; P = 0.005) CA rates decreased with increasing HDI, but not with time. Meta-analysis showed per 10,000 anesthetics that anesthesia-related and perioperative CA rates declined in high HDI (2.3 [95% CI: 1.2-3.7] before the 1990s to 0.7 [95% CI: 0.5-1.0] in the 1990s-2010s, P < 0.001; and 8.1 [95% CI: 5.1-11.9] before the 1990s to 6.2 [95% CI: 5.1-7.4] in the 1990s-2010s, P < 0.001, respectively). In low-HDI countries, anesthesia-related CA rates did not alter significantly (9.2 [95% CI: 2.0-21.7] before the 1990s to 4.5 [95% CI: 2.4-7.2] in the 1990s-2010s, P = 0.14), whereas perioperative CA rates increased significantly (16.4 [95% CI: 1.5-47.1] before the 1990s to 19.9 [95% CI: 10.9-31.7] in the 1990s-2010s, P = 0.03).Both anesthesia-related and perioperative CA rates decrease with increasing HDI but not with time. There is a clear and consistent reduction in anesthesia-related and perioperative CA rates in high-HDI countries, but an increase in perioperative CA rates without significant alteration in the anesthesia-related CA rates in low-HDI countries comparing the 2 time periods.

High blood pressure and sedentary behavior in adolescents are associated even after controlling for confounding factors

The aim of this study was to determine whether high blood pressure (HBP) is associated with sedentary behavior in young people even after controlling for potential confounders (gender, age, socioeconomic level, tobacco, alcohol, obesity and physical activity). In this epidemiological study, 1231 adolescents were evaluated. Blood pressure was measured with an oscillometric device and waist circumference with an inextensible tape. Sedentary behavior (watching television, computer use and playing video games) and physical activity were assessed by a questionnaire. We used mean and standard deviation to describe the statistical analysis, and the association between HBP and sedentary behavior was assessed by the chi-squared test. Binary logistic regression was used to observe the magnitude of association and cluster analyses (sedentary behavior and abdominal obesity; sedentary behavior and physical inactivity). HBP was associated with sedentary behaviors [odds ratio (OR) = 2.21, 95% confidence interval (CI) = 1.41-3.96], even after controlling for various confounders (OR = 1.68, CI = 1.03-2.75). In cluster analysis the combination of sedentary behavior and elevated abdominal obesity contributed significantly to an increased likelihood of having HBP (OR = 13.51, CI 7.21-23.97). Sedentary behavior was associated with HBP, and excess fat in the abdominal region contributed to the modulation of this association.

Higher screen time is associated with overweight, poor dietary habits and physical inactivity in Brazilian adolescents, mainly among girls

To analyse the associations between high screen time and overweight, poor dietary habits and physical activity in adolescents according to sex. The study comprised 515 boys and 716 girls aged 14-17 years from Londrina, Brazil. Nutritional status (normal weight or overweight/obese) was assessed by calculating the body mass index. Eating habits and time spent in physical activity were reported using a questionnaire. The measurement of screen time considered the time spent watching television, using a computer and playing video games during a normal week. Associations between high screen time and dependent variables (nutritional status, eating habits and physical activity levels) were assessed by binary logistic regression, adjusted for sociodemographic and lifestyle variables. Most adolescents (93.8% of boys and 87.2% of girls) spent more than 2 hours per day in screen-time activities. After adjustments, an increasing trend in the prevalence of overweight and physical inactivity with increasing time spent on screen activities was observed for both sexes. Screen times of >4 hours/day compared with <2 hours/day were associated with physical inactivity, low consumption of vegetables and high consumption of sweets only in girls and the consumption of soft drinks in both sexes. The frequency of overweight and physical inactivity increased with increasing screen time in a trending manner and independently of the main confounders. The relationship between high screen time and poor eating habits was particularly relevant for adolescent girls.

GIS-based analytical tools for transport planning: spatial regression models for transportation demand forecast

Considering the importance of spatial issues in transport planning, the main objective of this study was to analyze the results obtained from different approaches of spatial regression models. In the case of spatial autocorrelation, spatial dependence patterns should be incorporated in the models, since that dependence may affect the predictive power of these models. The results obtained with the spatial regression models were also compared with the results of a multiple linear regression model that is typically used in trips generation estimations. The findings support the hypothesis that the inclusion of spatial effects in regression models is important, since the best results were obtained with alternative models (spatial regression models or the ones with spatial variables included). This was observed in a case study carried out in the city of Porto Alegre, in the state of Rio Grande do Sul, Brazil, in the stages of specification and calibration of the models, with two distinct datasets.

Diffusion of oxygen in some binary Ti-Based alloys used as biomaterials

Ti and its alloys are widely used as biomaterials. Their main properties are excellent corrosion resistance, relatively low elastic modulus, high specific strength, and good biocompatibility. The development of new Ti alloys with properties favorable for use in the human body is desired. To this end, Ti alloys with Mo, Nb, Zr, and Ta are being developed, because these elements do not cause cytotoxicity. The presence of interstitial elements (such as oxygen and nitrogen) induces strong changes in the elastic properties of the material, which leads to hardening or softening of the alloy. By means of anelastic spectroscopy, we are able to obtain information on the diffusion of these interstitial elements present in the crystalline lattice. In this paper, the effect of oxygen on the anelastic properties of some binary Ti-based alloys was analyzed with anelastic spectroscopy. The diffusion coefficients, pre-exponential factors, and activation energies were calculated for oxygen and nitrogen in these alloys.

Fatores de risco relacionados à perda de produtividade laboral e aumento dos custos ambulatoriais de pacientes atendidos por unidades básicas de saúde

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Space–time zero-inflated count models of Harbor seals

Environmental data are spatial, temporal, and often come with many zeros. In this paper, we included space–time random effects in zero-inflated Poisson (ZIP) and ‘hurdle’ models to investigate haulout patterns of harbor seals on glacial ice. The data consisted of counts, for 18 dates on a lattice grid of samples, of harbor seals hauled out on glacial ice in Disenchantment Bay, near Yakutat, Alaska. A hurdle model is similar to a ZIP model except it does not mix zeros from the binary and count processes. Both models can be used for zero-inflated data, and we compared space–time ZIP and hurdle models in a Bayesian hierarchical model. Space–time ZIP and hurdle models were constructed by using spatial conditional autoregressive (CAR) models and temporal first-order autoregressive (AR(1)) models as random effects in ZIP and hurdle regression models. We created maps of smoothed predictions for harbor seal counts based on ice density, other covariates, and spatio-temporal random effects. For both models predictions around the edges appeared to be positively biased. The linex loss function is an asymmetric loss function that penalizes overprediction more than underprediction, and we used it to correct for prediction bias to get the best map for space–time ZIP and hurdle models.

Infrastructure Support for Controlled Experimentation with Software Testing and Regression Testing Techniques

Where the creation, understanding, and assessment of software testing and regression testing techniques are concerned, controlled experimentation is an indispensable research methodology. Obtaining the infrastructure necessary to support such experimentation, however, is difficult and expensive. As a result, progress in experimentation with testing techniques has been slow, and empirical data on the costs and effectiveness of techniques remains relatively scarce. To help address this problem, we have been designing and constructing infrastructure to support controlled experimentation with testing and regression testing techniques. This paper reports on the challenges faced by researchers experimenting with testing techniques, including those that inform the design of our infrastructure. The paper then describes the infrastructure that we are creating in response to these challenges, and that we are now making available to other researchers, and discusses the impact that this infrastructure has and can be expected to have.

Improved Regression Estimation of a Multivariate Relationship with Population Data on the Bivariate Relationship

Regression coefficients specify the partial effect of a regressor on the dependent variable. Sometimes the bivariate or limited multivariate relationship of that regressor variable with the dependent variable is known from population-level data. We show here that such population- level data can be used to reduce variance and bias about estimates of those regression coefficients from sample survey data. The method of constrained MLE is used to achieve these improvements. Its statistical properties are first described. The method constrains the weighted sum of all the covariate-specific associations (partial effects) of the regressors on the dependent variable to equal the overall association of one or more regressors, where the latter is known exactly from the population data. We refer to those regressors whose bivariate or limited multivariate relationships with the dependent variable are constrained by population data as being ‘‘directly constrained.’’ Our study investigates the improvements in the estimation of directly constrained variables as well as the improvements in the estimation of other regressor variables that may be correlated with the directly constrained variables, and thus ‘‘indirectly constrained’’ by the population data. The example application is to the marital fertility of black versus white women. The difference between white and black women’s rates of marital fertility, available from population-level data, gives the overall association of race with fertility. We show that the constrained MLE technique both provides a far more powerful statistical test of the partial effect of being black and purges the test of a bias that would otherwise distort the estimated magnitude of this effect. We find only trivial reductions, however, in the standard errors of the parameters for indirectly constrained regressors.

A cyclic bipolar wind in the interacting binary V 393 Scorpii

V393 Scorpii is a double periodic variable characterized by a relatively stable non-orbital photometric cycle of 253 d. Mennickent et al. argue for the presence of a massive optically thick disc around the more massive B-type component and describe the evolutionary stage of the system. In this paper, we analyse the behaviour of the main spectroscopic optical lines during the long non-orbital photometric cycle. We study the radial velocity of the donor determining its orbital elements and find a small but significant orbital eccentricity (e = 0.04). The donor spectral features are modelled and removed from the spectrum at every observing epoch using the light-curve model given by Mennickent et al. We find that the line emission is larger during eclipses and mostly comes from a bipolar wind. We also find that the long cycle is explained in terms of a modulation of the wind strength; the wind has a larger line and continuum emissivity at the high state. We report the discovery of highly variable chromospheric emission in the donor, as revealed by the Doppler maps of the emission lines Mg II 4481 and C I 6588. We discuss notable and some novel spectroscopic features like discrete absorption components, especially visible at blue depressed O I 7773 absorption wings during the second half-cycle, Balmer double emission with V/R curves showing 'Z-type' and 'S-type' excursions around secondary and main eclipses, respectively, and H beta emission wings extending up to +/- 2000 km s(-1). We also discuss possible causes for these phenomena and for their modulations with the long cycle.

MHD numerical simulations of colliding winds in massive binary systems - I. Thermal versus non-thermal radio emission

In the past few decades detailed observations of radio and X-ray emission from massive binary systems revealed a whole new physics present in such systems. Both thermal and non-thermal components of this emission indicate that most of the radiation at these bands originates in shocks. O and B-type stars and WolfRayet (WR) stars present supersonic and massive winds that, when colliding, emit largely due to the freefree radiation. The non-thermal radio and X-ray emissions are due to synchrotron and inverse Compton processes, respectively. In this case, magnetic fields are expected to play an important role in the emission distribution. In the past few years the modelling of the freefree and synchrotron emissions from massive binary systems have been based on purely hydrodynamical simulations, and ad hoc assumptions regarding the distribution of magnetic energy and the field geometry. In this work we provide the first full magnetohydrodynamic numerical simulations of windwind collision in massive binary systems. We study the freefree emission characterizing its dependence on the stellar and orbital parameters. We also study self-consistently the evolution of the magnetic field at the shock region, obtaining also the synchrotron energy distribution integrated along different lines of sight. We show that the magnetic field in the shocks is larger than that obtained when the proportionality between B and the plasma density is assumed. Also, we show that the role of the synchrotron emission relative to the total radio emission has been underestimated.

The role of clouds in improving the regression model for hourly values of diffuse solar radiation

The study introduces a new regression model developed to estimate the hourly values of diffuse solar radiation at the surface. The model is based on the clearness index and diffuse fraction relationship, and includes the effects of cloud (cloudiness and cloud type), traditional meteorological variables (air temperature, relative humidity and atmospheric pressure observed at the surface) and air pollution (concentration of particulate matter observed at the surface). The new model is capable of predicting hourly values of diffuse solar radiation better than the previously developed ones (R-2 = 0.93 and RMSE = 0.085). A simple version with a large applicability is proposed that takes into consideration cloud effects only (cloudiness and cloud height) and shows a R-2 = 0.92. (C) 2011 Elsevier Ltd. All rights reserved.

Estimation and diagnostics for heteroscedastic nonlinear regression models based on scale mixtures of skew-normal distributions

An extension of some standard likelihood based procedures to heteroscedastic nonlinear regression models under scale mixtures of skew-normal (SMSN) distributions is developed. This novel class of models provides a useful generalization of the heteroscedastic symmetrical nonlinear regression models (Cysneiros et al., 2010), since the random term distributions cover both symmetric as well as asymmetric and heavy-tailed distributions such as skew-t, skew-slash, skew-contaminated normal, among others. A simple EM-type algorithm for iteratively computing maximum likelihood estimates of the parameters is presented and the observed information matrix is derived analytically. In order to examine the performance of the proposed methods, some simulation studies are presented to show the robust aspect of this flexible class against outlying and influential observations and that the maximum likelihood estimates based on the EM-type algorithm do provide good asymptotic properties. Furthermore, local influence measures and the one-step approximations of the estimates in the case-deletion model are obtained. Finally, an illustration of the methodology is given considering a data set previously analyzed under the homoscedastic skew-t nonlinear regression model. (C) 2012 Elsevier B.V. All rights reserved.

The geometric Birnbaum-Saunders regression model with cure rate

In this paper, we propose a cure rate survival model by assuming the number of competing causes of the event of interest follows the Geometric distribution and the time to event follow a Birnbaum Saunders distribution. We consider a frequentist analysis for parameter estimation of a Geometric Birnbaum Saunders model with cure rate. Finally, to analyze a data set from the medical area. (C) 2011 Elsevier B.V. All rights reserved.

THE LOG-BURR XII REGRESSION MODEL FOR GROUPED SURVIVAL DATA

The log-Burr XII regression model for grouped survival data is evaluated in the presence of many ties. The methodology for grouped survival data is based on life tables, where the times are grouped in k intervals, and we fit discrete lifetime regression models to the data. The model parameters are estimated by maximum likelihood and jackknife methods. To detect influential observations in the proposed model, diagnostic measures based on case deletion, so-called global influence, and influence measures based on small perturbations in the data or in the model, referred to as local influence, are used. In addition to these measures, the total local influence and influential estimates are also used. We conduct Monte Carlo simulation studies to assess the finite sample behavior of the maximum likelihood estimators of the proposed model for grouped survival. A real data set is analyzed using a regression model for grouped data.