Assessing Adverse Events in Madeira Primary

In last three decades, several epidemiological studies have been developed in order to assess the magnitude, nature and type of adverse events (AEs). Most of these studies focus on hospital settings, where the activities are more standardised, but imultaneously more complex and involving higher risks. However, in the last years, there is a growing movement and strong evidence that point out the importance of studying other healthcare contexts, such as primary care and long-term care. In Portugal, studies on primary care setting are scarce and still in the early stages. In this article, the authors describe the AEs assessment in Portuguese Primary Health Care (PHC) units in Madeira Island/Portugal. This study was quantitative, cross-sectional, observational and analytical, with probability sampling. We quantify and analyse the AEs registered by healthcare providers using the APEAS-PT formulary. A link to the APEAS–PT form was sent to 520 healthcare professionals (111 specialist in Family Medicine, 27 medical students, 382 nurses) who worked in 32 PHC centres. These professionals identified and analysed 85 AEs and 42 incidents, which corresponds to a prevalence of 3.9 AEs per 10,000 visits,with a 95% confidence interval (CI) between 3.7 and 4 AE. Most of the AEs were preventable (96%). The most frequent causal factors of AEs were associated with medication (69%), health care provided to users (54%), communication (41%) and diagnosis (22%). This analysis of AEs in Madeira island PHC contributed to reinforce patient safety culture and to better understand quaternary prevention.info:eu-repo/semantics/publishedVersio

Cnidocyte discharge is regulated by light and opsin-mediated phototransduction

Cnidocyte discharge is regulated by light and opsin-mediated phototransduction Plachetzki et al. Plachetzki et al. BMC Biology 2012, 10:1

FRAX™ and the assessment of fracture probability in men and women from the UK

SUMMARY: A fracture risk assessment tool (FRAX) is developed based on the use of clinical risk factors with or without bone mineral density tests applied to the UK. INTRODUCTION: The aim of this study was to apply an assessment tool for the prediction of fracture in men and women with the use of clinical risk factors (CRFs) for fracture with and without the use of femoral neck bone mineral density (BMD). The clinical risk factors, identified from previous meta-analyses, comprised body mass index (BMI, as a continuous variable), a prior history of fracture, a parental history of hip fracture, use of oral glucocorticoids, rheumatoid arthritis and other secondary causes of osteoporosis, current smoking, and alcohol intake 3 or more units daily. METHODS: Four models were constructed to compute fracture probabilities based on the epidemiology of fracture in the UK. The models comprised the ten-year probability of hip fracture, with and without femoral neck BMD, and the ten-year probability of a major osteoporotic fracture, with and without BMD. For each model fracture and death hazards were computed as continuous functions. RESULTS: Each clinical risk factor contributed to fracture probability. In the absence of BMD, hip fracture probability in women with a fixed BMI (25 kg/m(2)) ranged from 0.2% at the age of 50 years for women without CRF's to 22% at the age of 80 years with a parental history of hip fracture (approximately 100-fold range). In men, the probabilities were lower, as was the range (0.1 to 11% in the examples above). For a major osteoporotic fracture the probabilities ranged from 3.5% to 31% in women, and from 2.8% to 15% in men in the example above. The presence of one or more risk factors increased probabilities in an incremental manner. The differences in probabilities between men and women were comparable at any given T-score and age, except in the elderly where probabilities were higher in women than in men due to the higher mortality of the latter. CONCLUSION: The models provide a framework which enhances the assessment of fracture risk in both men and women by the integration of clinical risk factors alone and/or in combination with BMD

Sample size calculation for microarray experiments with blocked one-way design

<p>Abstract</p> <p>Background</p> <p>One of the main objectives of microarray analysis is to identify differentially expressed genes for different types of cells or treatments. Many statistical methods have been proposed to assess the treatment effects in microarray experiments.</p> <p>Results</p> <p>In this paper, we consider discovery of the genes that are differentially expressed among <it>K </it>(> 2) treatments when each set of <it>K </it>arrays consists of a block. In this case, the array data among <it>K </it>treatments tend to be correlated because of block effect. We propose to use the blocked one-way ANOVA <it>F</it>-statistic to test if each gene is differentially expressed among <it>K </it>treatments. The marginal p-values are calculated using a permutation method accounting for the block effect, adjusting for the multiplicity of the testing procedure by controlling the false discovery rate (FDR). We propose a sample size calculation method for microarray experiments with a blocked one-way design. With FDR level and effect sizes of genes specified, our formula provides a sample size for a given number of true discoveries.</p> <p>Conclusion</p> <p>The calculated sample size is shown via simulations to provide an accurate number of true discoveries while controlling the FDR at the desired level.</p

The cost of large numbers of hypothesis tests on power, effect size and sample size

Advances in high-throughput biology and computer science are driving an exponential increase in the number of hypothesis tests in genomics and other scientific disciplines. Studies using current genotyping platforms frequently include a million or more tests. In addition to the monetary cost, this increase imposes a statistical cost owing to the multiple testing corrections needed to avoid large numbers of false-positive results. To safeguard against the resulting loss of power, some have suggested sample sizes on the order of tens of thousands that can be impractical for many diseases or may lower the quality of phenotypic measurements. This study examines the relationship between the number of tests on the one hand and power, detectable effect size or required sample size on the other. We show that once the number of tests is large, power can be maintained at a constant level, with comparatively small increases in the effect size or sample size. For example at the 0.05 significance level, a 13% increase in sample size is needed to maintain 80% power for ten million tests compared with one million tests, whereas a 70% increase in sample size is needed for 10 tests compared with a single test. Relative costs are less when measured by increases in the detectable effect size. We provide an interactive Excel calculator to compute power, effect size or sample size when comparing study designs or genome platforms involving different numbers of hypothesis tests. The results are reassuring in an era of extreme multiple testing

Liquid-gas phase transition in nuclear multifragmentation

The equation of state of nuclear matter suggests that at suitable beam energies the disassembling hot system formed in heavy ion collisions will pass through a liquid-gas coexistence region. Searching for the signatures of the phase transition has been a very important focal point of experimental endeavours in heavy ion collisions, in the last fifteen years. Simultaneously theoretical models have been developed to provide information about the equation of state and reaction mechanisms consistent with the experimental observables. This article is a review of this endeavour.Comment: 63 pages, 27 figures, submitted to Adv. Nucl. Phys. Some typos corrected, minor text change

Non-compartment model to compartment model pharmacokinetics transformation meta-analysis – a multivariate nonlinear mixed model

Background To fulfill the model based drug development, the very first step is usually a model establishment from published literatures. Pharmacokinetics model is the central piece of model based drug development. This paper proposed an important approach to transform published non-compartment model pharmacokinetics (PK) parameters into compartment model PK parameters. This meta-analysis was performed with a multivariate nonlinear mixed model. A conditional first-order linearization approach was developed for statistical estimation and inference. Results Using MDZ as an example, we showed that this approach successfully transformed 6 non-compartment model PK parameters from 10 publications into 5 compartment model PK parameters. In simulation studies, we showed that this multivariate nonlinear mixed model had little relative bias (<1%) in estimating compartment model PK parameters if all non-compartment PK parameters were reported in every study. If there missing non-compartment PK parameters existed in some published literatures, the relative bias of compartment model PK parameter was still small (<3%). The 95% coverage probabilities of these PK parameter estimates were above 85%. Conclusions This non-compartment model PK parameter transformation into compartment model meta-analysis approach possesses valid statistical inference. It can be routinely used for model based drug development

The view of teachers on bullying and implications for nursing

Objetivo: Compreender o bullying escolar, na perspectiva dos professores, e refletir sobre as possíveis ações da área da saúde em seu enfrentamento. Para tanto, tomaram- se por base as diretrizes do Programa Saúde na Escola, dos Ministérios da Saúde e da Educação. Método: Estudo de caso qualitativo, realizado com professores de uma escola pública de Minas Gerais. Foram utilizados grupos focais na coleta de dados e o material empírico foi decodificado a partir de técnica de análise temática de conteúdo, resultando em uma categoria analítica: concepções e experiências de professores diante do bullying. Resultados: Foram identificadas percepções pontuais sobre o fenômeno e utilização de recursos de intervenção pouco eficazes. No plano interpretativo, problematizaram-se as contribuições da saúde e da enfermagem no redimensionamento das intervenções e no processo de formação continuada dos professores. Conclusão: Os resultados apontam para a construção de práticas intersetoriais para o enfrentamento do bullying.To understand school bullying from the perspective of teachers and reflect about the possible actions of the health area when coping with it. The guidelines of the School Health Program of the Ministries of Health and Education were used to reach that purpose. Method: A qualitative study carried out with teachers of a public school in Minas Gerais. Focus groups were used to collect data and the empirical material was decoded from thematic analysis of content, resulting in an analytical category: conceptions and experiences of teachers on bullying. Results: Specific perceptions about the phenomenon and the use of ineffective intervention resources were identified. In the interpretive plan were problematized the health and nursing contributions with resizing the interventions and the continuing training process of teachers. Conclusion: The results point to the construction of intersectoral practices forcoping with bullying.Objetivo: Comprender el bullying escolar desde la perspectiva de los profesores, y reflexionar sobre las posibles acciones del área de salud en su enfrentamiento. Tomando como base los lineamientos del Programa de Salud Escolar, de los Ministerios de Salud y de Educación. Método: Estudio de caso cualitativo realizado con los profesores de una escuela pública en Minas Gerais. Para la recolección de datos se utilizaron grupos focales y el material empírico fue decodificado a partir de la técnica de análisis temático de contenido, dando lugar a una categoría analítica: concepciones y experiencias de los profesores sobre el acoso escolar. Resultados: Se identificaron percepciones específicas sobre el fenómeno y la utilización de recursos ineficaces de intervención. En el plano interpretativo, se problematizaron las contribuciones de la salud y de la enfermería en el redimensionamiento de las intervenciones y en el proceso de formación continua de los profesores. Conclusión: Los resultados apuntan a la construcción de prácticas intersectoriales para el enfrentamiento del bullying.CIEC - Centro de Investigação em Estudos da Criança, UM (UI 317 da FCT

Bayesian versus frequentist statistical inference for investigating a one-off cancer cluster reported to a health department

Background. The problem of silent multiple comparisons is one of the most difficult statistical problems faced by scientists. It is a particular problem for investigating a one-off cancer cluster reported to a health department because any one of hundreds, or possibly thousands, of neighbourhoods, schools, or workplaces could have reported a cluster, which could have been for any one of several types of cancer or any one of several time periods. Methods. This paper contrasts the frequentist approach with a Bayesian approach for dealing with silent multiple comparisons in the context of a one-off cluster reported to a health department. Two published cluster investigations were re-analysed using the Dunn-Sidak method to adjust frequentist p-values and confidence intervals for silent multiple comparisons. Bayesian methods were based on the Gamma distribution. Results. Bayesian analysis with non-informative priors produced results similar to the frequentist analysis, and suggested that both clusters represented a statistical excess. In the frequentist framework, the statistical significance of both clusters was extremely sensitive to the number of silent multiple comparisons, which can only ever be a subjective "guesstimate". The Bayesian approach is also subjective: whether there is an apparent statistical excess depends on the specified prior. Conclusion. In cluster investigations, the frequentist approach is just as subjective as the Bayesian approach, but the Bayesian approach is less ambitious in that it treats the analysis as a synthesis of data and personal judgements (possibly poor ones), rather than objective reality. Bayesian analysis is (arguably) a useful tool to support complicated decision-making, because it makes the uncertainty associated with silent multiple comparisons explicit