Childhood adversities as risk factors for onset and persistence of suicidal behaviour

Background Suicide is a leading cause of death worldwide, but the precise effect of childhood adversities as risk factors for the onset and persistence of suicidal behaviour (suicide ideation, plans and attempts) are not well understood. Aims To examine the associations between childhood adversities as risk factors for the onset and persistence of suicidal behaviour across 21 countries worldwide. Method Respondents from nationally representative samples (n = 55 299) were interviewed regarding childhood adversities that occurred before the age of 18 years and lifetime suicidal behaviour. Results Childhood adversities were associated with an increased risk of suicide attempt and ideation in both bivariate and multivariate models (odds ratio range 1.2-5.7). The risk increased with the number of adversities experienced, but at a decreasing rate. Sexual and physical abuse were consistently the strongest risk factors for both the onset and persistence of suicidal behaviour, especially during adolescence. Associations remained similar after additional adjustment for respondents` lifetime mental disorder status. Conclusions Childhood adversities (especially intrusive or aggressive adversities) are powerful predictors of the onset and persistence of suicidal behaviours.

Comparison of HRT-3 glaucoma probability score and subjective stereophotograph assessment for prediction of progression in glaucoma

PURPOSE. To assess whether baseline Glaucoma Probability Score (GPS; HRT-3; Heidelberg Engineering, Dossenheim, Germany) results are predictive of progression in patients with suspected glaucoma. The GPS is a new feature of the confocal scanning laser ophthalmoscope that generates an operator-independent, three-dimensional model of the optic nerve head and gives a score for the probability that this model is consistent with glaucomatous damage. METHODS. The study included 223 patients with suspected glaucoma during an average follow-up of 63.3 months. Included subjects had a suspect optic disc appearance and/or elevated intraocular pressure, but normal visual fields. Conversion was defined as development of either repeatable abnormal visual fields or glaucomatous deterioration in the appearance of the optic disc during the study period. The association between baseline GPS and conversion was investigated by Cox regression models. RESULTS. Fifty-four (24.2%) eyes converted. In multivariate models, both higher values of GPS global and subjective stereophotograph assessment ( larger cup-disc ratio and glaucomatous grading) were predictive of conversion: adjusted hazard ratios (95% CI): 1.31 (1.15 - 1.50) per 0.1 higher global GPS, 1.34 (1.12 - 1.62) per 0.1 higher CDR, and 2.34 (1.22 - 4.47) for abnormal grading, respectively. No significant differences ( P > 0.05 for all comparisons) were found between the c-index values ( equivalent to area under ROC curve) for the multivariate models (0.732, 0.705, and 0.699, respectively). CONCLUSIONS. GPS values were predictive of conversion in our population of patients with suspected glaucoma. Further, they performed as well as subjective assessment of the optic disc. These results suggest that GPS could potentially replace stereophotograph as a tool for estimating the likelihood of conversion to glaucoma.

Ovarian reserve evaluation: state of the art

Purpose Revise role of hormonal basal and dynamic tests, as well as ultrasonographic measures as ovarian reserve markers, in order to provide better counseling to subfertile couples. Methods Review of publications on the topic, with an emphasis on recent well designed articles. Results Currently available ovarian reserve tests do not provide sufficient evidence to be solely considered ideal, even for premature ovarian senescence patients who do not present subfertility complaints. However, these markers occupy important place in initial approach to treatment of subfertile couples, predicting unsatisfactory results that could be improved by differentiated induction schemes and reducing excessive psychological and financial burdens, and adverse effects. Conclusions In order to remedy the limitations due to the scarcity of strong evidence about this topic, future studies should try to clarify predictive value of markers in groups of specific diseases-related subfertility and pay special attention to propaedeutic multivariate models including anti-Mullerian hormone and antral follicle count.

Temporomandibular Disorders Are Differentially Associated With Headache Diagnoses A Controlled Study

Objectives: Temporomandibular disorders (TMDs) are considered to be comorbid with headaches. Earlier population studies have suggested that TMD may also be a risk factor for migraine progression. If that is true, TMD should be associated with specific headache syndromes (eg, migraine and chronic migraine), but not with headaches overall. Accordingly, our aim was to explore the relationship between TMD subtypes and severity with primary headaches in a controlled clinical study. Methods: The sample consisted of 300 individuals. TMDs were assessed using the Research Diagnostic Criteria for TMD, and primary headache was classified according to International Classification for Headache Disorders-2. Univariate and multivariate models assessed headache diagnoses and frequency as a function of the parameters of TMD. Results: Relative to those without TMD, individuals with myofascial TMD were significantly more likely to have chronic daily headaches (CDHs) [ relative risk (RR) = 7.8; 95% confidence interval (CI), 3.1-19.6], migraine (RR = 4.4; 95% CI, 1.7-11.7), and episodic tension-type headache (RR = 4.4; 95% CI, 1.5-12.6). Grade of TMD pain was associated with increased odds of CDH (P < 0.0001), migraine (P < 0.0001), and episodic tension-type headache (P < 0.05). TMD severity was also associated with headache frequency. In multivariate analyses, TMD was associated with migraine and CDH (P = 0.001). Painful TMD (P = 0.0034) and grade of TMD pain (P < 0.001) were associated with headache frequency. Discussion: TMD, TMD subtypes, and TMD severity are independently associated with specific headache syndromes and with headache frequency. This differential association suggests that the presence of central facilitation of nociceptive inputs may be of importance, as positive association was observed only when muscular TMD pain was involved.

Prevalence of equine Piroplasmosis and its association with tick infestation in the State of Sao Paulo, Brazil

Serum samples were collected from 582 horses from 40 stud farms in the State of Sao Paulo and tick (Acari: Ixodidae) infestations were evaluated on them. Serum samples were subjected to the complement fixation test (CFT) and a competitive inhibition ELISA (cELISA) for Babesia caballi and Theileria equi. Logistic regression analyses were performed to construct multivariate models that could explain the dependent variable (horses positive for B. caballi or T equi) as a function of the independent variables (presence or abundance of each one of the rick species found on the farms). A higher overall prevalence of B. caballi (54.1%) than of T equi (21.6%) was found by the two tests. The ticks Dermacentor nitens Neumann, 1897, Amblyomma cajennense (Fabricius, 1787) and Rbipicephalus (Boopbilus) microplus (Canestrini, 1887) were present on horses on 38 (95%), 20 (50%), and 4 (10%) farms, respectively. Infestations by D. nitens were statistically associated with B. caballi-positive horses on the farms by either the CFT or cELISA. Infestations by A. cajennense were statistically associated with T equi-positive horses on the farms by either CFT or cELISA.

Inequalities in dental services utilization among Brazilian low-income children: the role of individual determinants

Objectives: To assess the role of the individual determinants on the inequalities of dental services utilization among low-income children living in the working area of Brazilian`s federal Primary Health Care program, which is called Family Health Program (FHP), in a big city in Southern Brazil. Methods: A cross-sectional population-based study was performed. The sample included 350 children, ages 0 to 14 years, whose parents answered a questionnaire about their socioeconomic conditions, perceived needs, oral hygiene habits, and access to dental services. The data analysis was performed according to a conceptual framework based on Andersen`s behavioral model of health services use. Multivariate models of logistic regression analysis instructed the hypothesis on covariates for never having had a dental visit. Results: Thirty one percent of the surveyed children had never had a dental visit. In the bivariate analysis, higher proportion of children who had never had a dental visit was found among the very young, those with inadequate oral hygiene habits, those without perceived need of dental care, and those whose family homes were under absent ownership. The mechanisms of social support showed to be important enabling factors: children attending schools/kindergartens and being regularly monitored by the FHP teams had higher odds of having gone to the dentist, even after adjusting for socioeconomic, demographic, and need variables. Conclusions: The conceptual framework has confirmed the presence of social and psychosocial inequalities on the utilization pattern of dental services for low-income children. The individual determinants seem to be important predictors of access.

Multivariate measurement error models based on scale mixtures of the skew-normal distribution

Scale mixtures of the skew-normal (SMSN) distribution is a class of asymmetric thick-tailed distributions that includes the skew-normal (SN) distribution as a special case. The main advantage of these classes of distributions is that they are easy to simulate and have a nice hierarchical representation facilitating easy implementation of the expectation-maximization algorithm for the maximum-likelihood estimation. In this paper, we assume an SMSN distribution for the unobserved value of the covariates and a symmetric scale mixtures of the normal distribution for the error term of the model. This provides a robust alternative to parameter estimation in multivariate measurement error models. Specific distributions examined include univariate and multivariate versions of the SN, skew-t, skew-slash and skew-contaminated normal distributions. The results and methods are applied to a real data set.

The full Bayesian significance test for mixture models: results in gene expression clustering

Gene clustering is a useful exploratory technique to group together genes with similar expression levels under distinct cell cycle phases or distinct conditions. It helps the biologist to identify potentially meaningful relationships between genes. In this study, we propose a clustering method based on multivariate normal mixture models, where the number of clusters is predicted via sequential hypothesis tests: at each step, the method considers a mixture model of m components (m = 2 in the first step) and tests if in fact it should be m - 1. If the hypothesis is rejected, m is increased and a new test is carried out. The method continues (increasing m) until the hypothesis is accepted. The theoretical core of the method is the full Bayesian significance test, an intuitive Bayesian approach, which needs no model complexity penalization nor positive probabilities for sharp hypotheses. Numerical experiments were based on a cDNA microarray dataset consisting of expression levels of 205 genes belonging to four functional categories, for 10 distinct strains of Saccharomyces cerevisiae. To analyze the method's sensitivity to data dimension, we performed principal components analysis on the original dataset and predicted the number of classes using 2 to 10 principal components. Compared to Mclust (model-based clustering), our method shows more consistent results.

Comparison of univariate and multivariate calibration for the determination of micronutrients in pellets of plant materials by laser induced breakdown spectrometry

The application of laser induced breakdown spectrometry (LIBS) aiming the direct analysis of plant materials is a great challenge that still needs efforts for its development and validation. In this way, a series of experimental approaches has been carried out in order to show that LIBS can be used as an alternative method to wet acid digestions based methods for analysis of agricultural and environmental samples. The large amount of information provided by LIBS spectra for these complex samples increases the difficulties for selecting the most appropriated wavelengths for each analyte. Some applications have suggested that improvements in both accuracy and precision can be achieved by the application of multivariate calibration in LIBS data when compared to the univariate regression developed with line emission intensities. In the present work, the performance of univariate and multivariate calibration, based on partial least squares regression (PLSR), was compared for analysis of pellets of plant materials made from an appropriate mixture of cryogenically ground samples with cellulose as the binding agent. The development of a specific PLSR model for each analyte and the selection of spectral regions containing only lines of the analyte of interest were the best conditions for the analysis. In this particular application, these models showed a similar performance. but PLSR seemed to be more robust due to a lower occurrence of outliers in comparison to the univariate method. Data suggests that efforts dealing with sample presentation and fitness of standards for LIBS analysis must be done in order to fulfill the boundary conditions for matrix independent development and validation. (C) 2009 Elsevier B.V. All rights reserved.

Estimation of hexenuronic acids and kappa number in kraft pulps of Eucalyptus globulus by Fourier transform near infrared spectroscopy and multivariate analysis

Fourier transform near infrared (FT-NIR) spectroscopy was evaluated as an analytical too[ for monitoring residual Lignin, kappa number and hexenuronic acids (HexA) content in kraft pulps of Eucalyptus globulus. Sets of pulp samples were prepared under different cooking conditions to obtain a wide range of compound concentrations that were characterised by conventional wet chemistry analytical methods. The sample group was also analysed using FT-NIR spectroscopy in order to establish prediction models for the pulp characteristics. Several models were applied to correlate chemical composition in samples with the NIR spectral data by means of PCR or PLS algorithms. Calibration curves were built by using all the spectral data or selected regions. Best calibration models for the quantification of lignin, kappa and HexA were proposed presenting R-2 values of 0.99. Calibration models were used to predict pulp titers of 20 external samples in a validation set. The lignin concentration and kappa number in the range of 1.4-18% and 8-62, respectively, were predicted fairly accurately (standard error of prediction, SEP 1.1% for lignin and 2.9 for kappa). The HexA concentration (range of 5-71 mmol kg(-1) pulp) was more difficult to predict and the SEP was 7.0 mmol kg(-1) pulp in a model of HexA quantified by an ultraviolet (UV) technique and 6.1 mmol kg(-1) pulp in a model of HexA quantified by anion-exchange chromatography (AEC). Even in wet chemical procedures used for HexA determination, there is no good agreement between methods as demonstrated by the UV and AEC methods described in the present work. NIR spectroscopy did provide a rapid estimate of HexA content in kraft pulps prepared in routine cooking experiments.

A Bayesian Analysis in the Presence of Covariates for Multivariate Survival Data: An example of Application

In this paper, we introduce a Bayesian analysis for survival multivariate data in the presence of a covariate vector and censored observations. Different ""frailties"" or latent variables are considered to capture the correlation among the survival times for the same individual. We assume Weibull or generalized Gamma distributions considering right censored lifetime data. We develop the Bayesian analysis using Markov Chain Monte Carlo (MCMC) methods.

Use of Bayesian Methods for Multivariate Bioequivalence Measures

In this paper, we introduce a Bayesian analysis for bioequivalence data assuming multivariate pharmacokinetic measures. With the introduction of correlation parameters between the pharmacokinetic measures or between the random effects in the bioequivalence models, we observe a good improvement in the bioequivalence results. These results are of great practical interest since they can yield higher accuracy and reliability for the bioequivalence tests, usually assumed by regulatory offices. An example is introduced to illustrate the proposed methodology by comparing the usual univariate bioequivalence methods with multivariate bioequivalence. We also consider some usual existing discrimination Bayesian methods to choose the best model to be used in bioequivalence studies.

Bayesian analysis of skew-t multivariate null intercept measurement error model

The multivariate skew-t distribution (J Multivar Anal 79:93-113, 2001; J R Stat Soc, Ser B 65:367-389, 2003; Statistics 37:359-363, 2003) includes the Student t, skew-Cauchy and Cauchy distributions as special cases and the normal and skew-normal ones as limiting cases. In this paper, we explore the use of Markov Chain Monte Carlo (MCMC) methods to develop a Bayesian analysis of repeated measures, pretest/post-test data, under multivariate null intercept measurement error model (J Biopharm Stat 13(4):763-771, 2003) where the random errors and the unobserved value of the covariate (latent variable) follows a Student t and skew-t distribution, respectively. The results and methods are numerically illustrated with an example in the field of dentistry.

Hypotheses Testing on a Multivariate Null Intercept Errors-in-Variables Model

Considering the Wald, score, and likelihood ratio asymptotic test statistics, we analyze a multivariate null intercept errors-in-variables regression model, where the explanatory and the response variables are subject to measurement errors, and a possible structure of dependency between the measurements taken within the same individual are incorporated, representing a longitudinal structure. This model was proposed by Aoki et al. (2003b) and analyzed under the bayesian approach. In this article, considering the classical approach, we analyze asymptotic test statistics and present a simulation study to compare the behavior of the three test statistics for different sample sizes, parameter values and nominal levels of the test. Also, closed form expressions for the score function and the Fisher information matrix are presented. We consider two real numerical illustrations, the odontological data set from Hadgu and Koch (1999), and a quality control data set.

Bayesian analysis for a skew extension of the multivariate null intercept measurement error model

Skew-normal distribution is a class of distributions that includes the normal distributions as a special case. In this paper, we explore the use of Markov Chain Monte Carlo (MCMC) methods to develop a Bayesian analysis in a multivariate, null intercept, measurement error model [R. Aoki, H. Bolfarine, J.A. Achcar, and D. Leao Pinto Jr, Bayesian analysis of a multivariate null intercept error-in -variables regression model, J. Biopharm. Stat. 13(4) (2003b), pp. 763-771] where the unobserved value of the covariate (latent variable) follows a skew-normal distribution. The results and methods are applied to a real dental clinical trial presented in [A. Hadgu and G. Koch, Application of generalized estimating equations to a dental randomized clinical trial, J. Biopharm. Stat. 9 (1999), pp. 161-178].