Impact of educational interventions on adverse drug events reporting

Spontaneous adverse drug events (ADE) reporting is the main source of data for assessing the risk/benefit of drugs available in the pharmaceutical market. However, its major limitation is underreporting, which hinders and delays the signal detection by Pharmacovigilance (PhV). To identify the techniques of educational intervention (EI) for promotion of PhV by health professionals and to assess their impact. A systematic review was performed in the PUBMED, PAHO, LILACS and EMBASE databases, from November/2011 to January/2012, updated in March/2013. The strategy search included the use of health descriptors and a manual search in the references cited by selected papers. 101 articles were identified, of which 16 met the inclusion criteria. Most of these studies (10) were conducted in European hospitals and physicians were the health professionals subjected to most EI (12), these studies lasted from one month to two years. EI with multifaceted techniques raised the absolute number, the rate of reporting related to adverse drug reactions (ADR), technical defects of health technologies, and also promoted an improvement in the quality of reports, since there was increased reporting of ADR classified as serious, unexpected, related to new drugs and with high degree of causality. Multifaceted educational interventions for multidisciplinary health teams working at all healthcare levels, with sufficient duration to reach all professionals who act in the institution, including issues related to medication errors and therapeutic ineffectiveness, must be validated, with the aim of standardizing the Good Practice of PhV and improve drug safety indicators.

Eletrodinâmica quântica generalizada a la teoria de perturbação causal

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Representações sociais como instrumento de informação para a saúde bucal coletiva

In addition to understanding the distribution of the populations’ health-disease process, epidemiology has sought to study the causality associated with this process, which humanity developed over time, and to interpret the narrative of this field of knowledge. A solid review of the literature was done to emphasize the importance of using popular knowledge as a qualitative health-related investigation strategy and to demystify the use of social representations in the field of dentistry. By initiating the design of a new paradigm for understanding the oral health-disease process, which favors the idea that it is also the result of a sociocultural production, knowledge of the circumstances and context in which it is inserted becomes critical for health assessment actions. Although scientific dentistry has advanced the understanding of oral diseases, communication with popular knowledge leaves much to be desired, since most professionals find themselves trapped in a fragmented model of care. Reconstruction of the logic by which the representations of oral health were produced and socialized over time can be considered a relevant and productive purpose of the representations in the dental area.

Notificações espontâneas de eventos adversos a medicamentos hospitalares: estudo piloto

Introduction: Post-marketing surveillance of drugs aims to detect problems related to safety, effectiveness and quality. The identification of adverse drug events (ADE) is made, mainly, by health professionals´ spontaneous reporting. This method allows risk communication in pharmacovigilance and contributes for market regulation. Objective: To estimate the prevalence of adverse drug reaction (ADR) and the suspicions of therapeutic failure (TF) reported by health professionals; to verify the active principle and type of drugs related to ADE, seriousness, causality, production mechanism and clinical manifestation of the events identified. METHODS: A cross-sectional study was performed in a teaching and public hospital which integrates the Sentinel Hospital Network, in 2008. ADR seriousness was classified according to intensity (mild, moderate, serious and lethal); drugs associated with ADE were categorized according to type (brand name drugs and non-brand name drugs); causality was imputed with Naranjo algorithm and the mechanism of occurrence was analyzed according to Rawlins e Thompson definitions (A or B). Results: There were 103 ADE reports in the period, of which 39 comprised TF and 64 ADR. Nurses reported the most ADE (53.4%). The majority of ADR were classified as type A (82.8%), mild (81.3%), possible (57.8%), according to causality assessment, and related to brand name drugs (20/35). Human immunoglobulin, docetaxel and paclitaxel were the drugs frequently associated with ADR. TF arising from no-brand name drugs (26/29), regarding, mainly, midazolam and ganciclovir. Conclusion: The results of the ADE report contribute for proposition of trigger tools for intensive monitoring of drug safety, as well as for the supplier qualification and for the improvement of quality products.

A produção de conhecimento em Psicologia: a análise do comportamento

O artigo discute a proposta behaviorista radical de constituição da Psicologia como ciência do comportamento, destacando três conjuntos de questões: a) a noção de conhecimento com a qual opera, especialmente do ponto de vista da rejeição de princípios do positivismo lógico e adoção de uma concepção instrumental e relacional; b) uma interpretação da Psicologia como campo de saber que articula conteúdos filosóficos, científicos e aplicados e c) o programa de investigação dos fenômenos psicológicos orientado por um recorte externalista e por uma concepção selecionista de causalidade. A elaboração behaviorista radical é contrastada com concepções modernas acerca do homem, salientando-se seu alcance e seu caráter crítico e inovador na Psicologia e na cultura em geral.

Environmental and Farm Management Factors Associated with Tuberculosis on Cattle Farms in Northeastern Michigan

Objective—To identify major environmental and farm management factors associated with the occurrence of tuberculosis (TB) on cattle farms in northeastern Michigan. Design—Case-control study. Sample Population—17 cattle farms with infected cattle and 51 control farms. Procedure—Each case farm (laboratory confirmed diagnosis of Mycobacterium bovis infection) was matched with 2 to 4 control farms (negative whole-herd test results within previous 12 months) on the basis of type of farm (dairy or beef) and location. Cattle farm data were collected from in-person interviews and mailed questionnaires. Wildlife TB data were gathered through state wildlife surveillance. Environmental data were gathered from a satellite image-based geographic information system. Multivariable conditional logistic regression for matched analysis was performed. Results—Major factors associated with increased farm risk of TB were higher TB prevalence among wild deer and cattle farms in the area, herd size, and ponds or creeks in cattle housing areas. Factors associated with reduced farm risk of TB were greater amounts of natural open lands in the surrounding area and reducing deer access to cattle housing areas by housing cattle in barns, barnyards, or feedlots and use of electrified wire or barbed wire for livestock fencing. Conclusions and Clinical Relevance—Results suggest that certain environmental and management factors may be associated with risk of TB on cattle farms.

Effects of a CPT-even and Lorentz-violating nonminimal coupling on electron-positron scattering

We propose a new CPT-even and Lorentz-violating nonminimal coupling between fermions and Abelian gauge fields involving the CPT-even tensor (K-F)(mu nu alpha beta) of the standard model extension. We thus investigate its effects on the cross section of the electron-positron scattering by analyzing the process e(+) + e(-) -> mu(+) + mu(-). Such a study was performed for the parity-odd and parity-even nonbirefringent components of the Lorentz-violating (K-F)(mu nu alpha beta) tensor. Finally, by using experimental data available in the literature, we have imposed upper bounds as tight as 10(-12) (eV)(-1) on the magnitude of the CPT-even and Lorentz-violating parameters while nonminimally coupled. DOI: 10.1103/PhysRevD.86.125033

Incidence and Predictors of Adverse Drug Reactions Caused by Drug-Drug Interactions in Elderly Outpatients: A Prospective Cohort Study

Purpose. The primary objective of this study was to investigate the incidence of drug-drug interactions (DDIs) related to adverse drug reactions (ADRs) in elderly outpatients who attended public primary healthcare units in a southeastern region of Brazil. The secondary objective was to investigate the possible predictors of DDI-related ADRs. Methods. A prospective cohort study was conducted between November 1, 2010, and November 31, 2011, in the primary public healthcare system in the Ourinhos micro-region in Brazil. Patients who were at least 60 years old, with at least one potential DDI, were eligible for inclusion in the study. Eligible patients were assessed by clinical pharmacists for DDI-related ADRs for 4 months. The causality of DDI-related ADRs was assessed independently by four clinicians using three decisional algorithms. The incidence of DDI-related ADRs during the study period was calculated. Logistic regression analysis was used to study DDI-related ADR predictors. Results. A total of 433 patients completed the study. The incidence of DDI-related ADRs was 6.5%. A multivariate analysis indicated that the adjusted odds ratios (ORs) rose from 0.91 (95% confidence interval [CI] = 0.75-1.12, p = 0.06) in patients aged 65-69 years to 4.40 (95% CI = 3.00-6.12, p < 0.01) in patients aged 80 years or older. Patients who presented two to three diagnosed diseases presented lower adjusted ORs (OR = 0.93 [95% CI = 0.68-1.18, p = 0.08]) than patients who presented six or more diseases (OR = 1.12 [95% CI = 1.02-2.01, p < 0.01]). Elderly patients who took five or more drugs had a significantly higher risk of DDI-related ADRs (OR = 2.72 [95% CI = 1.92-3.12, p < 0.01]) than patients who took three to four drugs (OR = 0.93 [95% CI = 0.74-1.11, p = 0.06]). No significant difference was found with regard to sex (OR = 1.08 [95% CI 0.48-2.02, p = 0.44]). Conclusion. The incidence of DDI-related ADRs in elderly outpatients was significant, and most of the events presented important clinical consequences. Because clinicians still have difficulty managing this problem, highlighting the factors that increase the risk of DDI-related ADRs is essential. Polypharmacy was found to be a significant predictor of DDI-related ADRs in our sample.

Adverse drug reactions caused by drug-drug interactions in elderly outpatients: a prospective cohort study

Although the prevalence of drug-drug interactions (DDIs) in elderly outpatients is high, many potential DDIs do not have any actual clinical effect, and data on the occurrence of DDI-related adverse drug reactions (ADRs) in elderly outpatients are scarce. This study aimed to determine the incidence and characteristics of DDI-related ADRs among elderly outpatients as well as the factors associated with these reactions. A prospective cohort study was conducted between 1 November 2010 and 31 November 2011 in the primary public health system of the Ourinhos micro-region, Brazil. Patients aged a parts per thousand yen60 years with at least one potential DDI were eligible for inclusion. Causality, severity, and preventability of the DDI-related ADRs were assessed independently by four clinicians using validated methods; data were analysed using descriptive analysis and multiple logistic regression. A total of 433 patients completed the study. The incidence of DDI-related ADRs was 6 % (n = 30). Warfarin was the most commonly involved drug (37 % cases), followed by acetylsalicylic acid (17 %), digoxin (17 %), and spironolactone (17 %). Gastrointestinal bleeding occurred in 37 % of the DDI-related ADR cases, followed by hyperkalemia (17 %) and myopathy (13 %). The multiple logistic regression showed that age a parts per thousand yen80 years [odds ratio (OR) 4.4; 95 % confidence interval (CI) 3.0-6.1, p < 0.01], a Charlson comorbidity index a parts per thousand yen4 (OR 1.3; 95 % CI 1.1-1.8, p < 0.01), consumption of five or more drugs (OR 2.7; 95 % CI 1.9-3.1, p < 0.01), and the use of warfarin (OR 1.7; 95 % CI1.1-1.9, p < 0.01) were associated with the occurrence of DDI-related ADRs. With regard to severity, approximately 37 % of the DDI-related ADRs detected in our cohort necessitated hospital admission. All DDI-related ADRs could have been avoided (87 % were ameliorable and 13 % were preventable). The incidence of ADRs not related to DDIs was 10 % (n = 44). The incidence of DDI-related ADRs in elderly outpatients is high; most events presented important clinical consequences and were preventable or ameliorable.

Prediction of enzymatic infarct size in ST-segment elevation myocardial infarction

Objectives Predictors of adverse outcomes following myocardial infarction (MI) are well established; however, little is known about what predicts enzymatically estimated infarct size in patients with acute ST-elevation MI. The Complement And Reduction of INfarct size after Angioplasty or Lytics trials of pexelizumab used creatine kinase (CK)-MB area under the curve to determine infarct size in patients treated with primary percutaneous coronary intervention (PCI) or fibrinolysis. Methods Prediction of infarct size was carried out by measuring CK-MB area under the curve in patients with ST-segment elevation MI treated with reperfusion therapy from January 2000 to April 2002. Infarct size was calculated in 1622 patients (PCI=817; fibrinolysis=805). Logistic regression was used to examine the relationship between baseline demographics, total ST-segment elevation, index angiographic findings (PCI group), and binary outcome of CK-MB area under the curve greater than 3000 ng/ml. Results Large infarcts occurred in 63% (515) of the PCI group and 69% (554) of the fibrinolysis group. Independent predictors of large infarcts differed depending on mode of reperfusion. In PCI, male sex, no prior coronary revascularization and diabetes, decreased systolic blood pressure, sum of ST-segment elevation, total (angiographic) occlusion, and nonright coronary artery culprit artery were independent predictors of larger infarcts (C index=0.73). In fibrinolysis, younger age, decreased heart rate, white race, no history of arrhythmia, increased time to fibrinolytic therapy in patients treated up to 2 h after symptom onset, and sum of ST-segment elevation were independently associated with a larger infarct size (C index=0.68). Conclusion Clinical and patient data can be used to predict larger infarcts on the basis of CK-MB quantification. These models may be helpful in designing future trials and in guiding the use of novel pharmacotherapies aimed at limiting infarct size in clinical practice. Coron Artery Dis 23:118-125 (C) 2012 Wolters Kluwer Health | Lippincott Williams & Wilkins.

Modeling gene expression regulatory networks with the sparse vector autoregressive model

Abstract Background To understand the molecular mechanisms underlying important biological processes, a detailed description of the gene products networks involved is required. In order to define and understand such molecular networks, some statistical methods are proposed in the literature to estimate gene regulatory networks from time-series microarray data. However, several problems still need to be overcome. Firstly, information flow need to be inferred, in addition to the correlation between genes. Secondly, we usually try to identify large networks from a large number of genes (parameters) originating from a smaller number of microarray experiments (samples). Due to this situation, which is rather frequent in Bioinformatics, it is difficult to perform statistical tests using methods that model large gene-gene networks. In addition, most of the models are based on dimension reduction using clustering techniques, therefore, the resulting network is not a gene-gene network but a module-module network. Here, we present the Sparse Vector Autoregressive model as a solution to these problems. Results We have applied the Sparse Vector Autoregressive model to estimate gene regulatory networks based on gene expression profiles obtained from time-series microarray experiments. Through extensive simulations, by applying the SVAR method to artificial regulatory networks, we show that SVAR can infer true positive edges even under conditions in which the number of samples is smaller than the number of genes. Moreover, it is possible to control for false positives, a significant advantage when compared to other methods described in the literature, which are based on ranks or score functions. By applying SVAR to actual HeLa cell cycle gene expression data, we were able to identify well known transcription factor targets. Conclusion The proposed SVAR method is able to model gene regulatory networks in frequent situations in which the number of samples is lower than the number of genes, making it possible to naturally infer partial Granger causalities without any a priori information. In addition, we present a statistical test to control the false discovery rate, which was not previously possible using other gene regulatory network models.

Author stance in doctoral dissertations of native and non-native speakers of English: a corpus-based study on epistemic adverbs

[EN]Our study concentrates on the epistemic adverbs used in conveying author stance in academic English. The Contrastive Interlanguage Analysis (Granger, 1996) was run to three sets of corpora comprising doctoral dissertations written by native and non-native academic authors of English. Epistemic adverbs occurring in the dissertations were identified through a computer programme and their frequencies were separately computed for each corpus. Lastly, a log-likelihood test was administered to see whether there is a statistically significant difference across the groups in concern concerning the use of these adverbs.

A partial dependence factorial analysis to deal with selection bias in observational studis

This thesis presents a creative and practical approach to dealing with the problem of selection bias. Selection bias may be the most important vexing problem in program evaluation or in any line of research that attempts to assert causality. Some of the greatest minds in economics and statistics have scrutinized the problem of selection bias, with the resulting approaches – Rubin’s Potential Outcome Approach(Rosenbaum and Rubin,1983; Rubin, 1991,2001,2004) or Heckman’s Selection model (Heckman, 1979) – being widely accepted and used as the best fixes. These solutions to the bias that arises in particular from self selection are imperfect, and many researchers, when feasible, reserve their strongest causal inference for data from experimental rather than observational studies. The innovative aspect of this thesis is to propose a data transformation that allows measuring and testing in an automatic and multivariate way the presence of selection bias. The approach involves the construction of a multi-dimensional conditional space of the X matrix in which the bias associated with the treatment assignment has been eliminated. Specifically, we propose the use of a partial dependence analysis of the X-space as a tool for investigating the dependence relationship between a set of observable pre-treatment categorical covariates X and a treatment indicator variable T, in order to obtain a measure of bias according to their dependence structure. The measure of selection bias is then expressed in terms of inertia due to the dependence between X and T that has been eliminated. Given the measure of selection bias, we propose a multivariate test of imbalance in order to check if the detected bias is significant, by using the asymptotical distribution of inertia due to T (Estadella et al. 2005) , and by preserving the multivariate nature of data. Further, we propose the use of a clustering procedure as a tool to find groups of comparable units on which estimate local causal effects, and the use of the multivariate test of imbalance as a stopping rule in choosing the best cluster solution set. The method is non parametric, it does not call for modeling the data, based on some underlying theory or assumption about the selection process, but instead it calls for using the existing variability within the data and letting the data to speak. The idea of proposing this multivariate approach to measure selection bias and test balance comes from the consideration that in applied research all aspects of multivariate balance, not represented in the univariate variable- by-variable summaries, are ignored. The first part contains an introduction to evaluation methods as part of public and private decision process and a review of the literature of evaluation methods. The attention is focused on Rubin Potential Outcome Approach, matching methods, and briefly on Heckman’s Selection Model. The second part focuses on some resulting limitations of conventional methods, with particular attention to the problem of how testing in the correct way balancing. The third part contains the original contribution proposed , a simulation study that allows to check the performance of the method for a given dependence setting and an application to a real data set. Finally, we discuss, conclude and explain our future perspectives.

Structure learning of graphical models for task-oriented robot grasping

In the collective imaginaries a robot is a human like machine as any androids in science fiction. However the type of robots that you will encounter most frequently are machinery that do work that is too dangerous, boring or onerous. Most of the robots in the world are of this type. They can be found in auto, medical, manufacturing and space industries. Therefore a robot is a system that contains sensors, control systems, manipulators, power supplies and software all working together to perform a task. The development and use of such a system is an active area of research and one of the main problems is the development of interaction skills with the surrounding environment, which include the ability to grasp objects. To perform this task the robot needs to sense the environment and acquire the object informations, physical attributes that may influence a grasp. Humans can solve this grasping problem easily due to their past experiences, that is why many researchers are approaching it from a machine learning perspective finding grasp of an object using information of already known objects. But humans can select the best grasp amongst a vast repertoire not only considering the physical attributes of the object to grasp but even to obtain a certain effect. This is why in our case the study in the area of robot manipulation is focused on grasping and integrating symbolic tasks with data gained through sensors. The learning model is based on Bayesian Network to encode the statistical dependencies between the data collected by the sensors and the symbolic task. This data representation has several advantages. It allows to take into account the uncertainty of the real world, allowing to deal with sensor noise, encodes notion of causality and provides an unified network for learning. Since the network is actually implemented and based on the human expert knowledge, it is very interesting to implement an automated method to learn the structure as in the future more tasks and object features can be introduced and a complex network design based only on human expert knowledge can become unreliable. Since structure learning algorithms presents some weaknesses, the goal of this thesis is to analyze real data used in the network modeled by the human expert, implement a feasible structure learning approach and compare the results with the network designed by the expert in order to possibly enhance it.