Development and validation of a risk model for prediction of hazardous alcohol consumption in general practice attendees : the PredictAL study

Background: Little is known about the risk of progression to hazardous alcohol use in people currently drinking at safe limits. We aimed to develop a prediction model (predictAL) for the development of hazardous drinking in safe drinkers. Methods: A prospective cohort study of adult general practice attendees in six European countries and Chile followed up over 6 months. We recruited 10,045 attendees between April 2003 to February 2005. 6193 European and 2462 Chilean attendees recorded AUDIT scores below 8 in men and 5 in women at recruitment and were used in modelling risk. 38 risk factors were measured to construct a risk model for the development of hazardous drinking using stepwise logistic regression. The model was corrected for over fitting and tested in an external population. The main outcome was hazardous drinking defined by an AUDIT score >= 8 in men and >= 5 in women. Results: 69.0% of attendees were recruited, of whom 89.5% participated again after six months. The risk factors in the final predictAL model were sex, age, country, baseline AUDIT score, panic syndrome and lifetime alcohol problem. The predictAL model's average c-index across all six European countries was 0.839 (95% CI 0.805, 0.873). The Hedge's g effect size for the difference in log odds of predicted probability between safe drinkers in Europe who subsequently developed hazardous alcohol use and those who did not was 1.38 (95% CI 1.25, 1.51). External validation of the algorithm in Chilean safe drinkers resulted in a c-index of 0.781 (95% CI 0.717, 0.846) and Hedge's g of 0.68 (95% CI 0.57, 0.78). Conclusions: The predictAL risk model for development of hazardous consumption in safe drinkers compares favourably with risk algorithms for disorders in other medical settings and can be a useful first step in prevention of alcohol misuse.

The use of analytical hierarchy process in spatial decision support system for land use management

Geographic information systems give us the possibility to analyze, produce, and edit geographic information. Furthermore, these systems fall short on the analysis and support of complex spatial problems. Therefore, when a spatial problem, like land use management, requires a multi-criteria perspective, multi-criteria decision analysis is placed into spatial decision support systems. The analytic hierarchy process is one of many multi-criteria decision analysis methods that can be used to support these complex problems. Using its capabilities we try to develop a spatial decision support system, to help land use management. Land use management can undertake a broad spectrum of spatial decision problems. The developed decision support system had to accept as input, various formats and types of data, raster or vector format, and the vector could be polygon line or point type. The support system was designed to perform its analysis for the Zambezi river Valley in Mozambique, the study area. The possible solutions for the emerging problems had to cover the entire region. This required the system to process large sets of data, and constantly adjust to new problems’ needs. The developed decision support system, is able to process thousands of alternatives using the analytical hierarchy process, and produce an output suitability map for the problems faced.

Exploring the experience of stigma in severe mental illness : a Portuguese contribution to the validation of a psychometric instrument

RESUMO: Fizemos uma análise da evolução do conceito de estigma, das suas correlações e das suas consequências e analisámos os instrumentos psicométricos utilizados para estudar experiências pessoais de estigma. Revimos os principais estudos de investigação sobre estigma em Portugal. Revimos, igualmente, os estudos relevantes utilizando o “Consumer Experiences of Stigma Questionnaire” (CESQ) e as propriedades psicométricas já documentadas. O nosso estudo teve como objetivos: explorar as experiências de estigma numa amostra portuguesa de pessoas com perturbação mental grave e contribuir para a documentação das propriedades psicométricas do “Consumer Experiences of Stigma Questionnaire” e para a validação da sua versão portuguesa. Fizemos um estudo transversal, descritivo e analítico, recolhemos dados sociodemográficos e clínicos e medimos as experiências de estigma e o funcionamento global. A frequência das respostas da secção de estigma foi semelhante à dos restantes estudos utilizando a CESQ. A frequência das respostas na secção de discriminação foi ligeiramente inferior à reportada noutros estudos. Verificámos a existência de uma associação entre a pontuação da subescala de discriminação, o sexo masculino e o facto de se viver na comunidade. A pontuação da subescala de discriminação está também correlacionada de forma positiva com o funcionamento global. Os alfas de Cronbach para a CESQ e para as suas subescalas foram considerados bons. Os coeficientes de correlação intraclasse foram igualmente considerados igualmente bons. Utilizando técnicas de análise fatorial, verificámos que a maior parte dos itens da CESQ se enquadrava em dois fatores, correspondendo sensivelmente às subescalas definidas previamente. Concluímos que o presente estudo explorou com sucesso a questão do estigma em Portugal, contribuindo em simultâneo para a validação do “Consumer Experiences of Stigma Questionnaire.--------------ABSTRACT: We reviewed the evolution of concept of stigma, its correlates and consequences, and analysed psychometric instruments that were used to study personal experiences of stigma. We provided an insight over research of stigma in Portugal. We reviewed relevant studies that use Consumer Experiences of Stigma Questionnaire and documented psychometric properties of this instrument. Our study aimed both to explore experiences of stigma in a Portuguese sample of people with severe mental illness and to contribute to the assessment of the psychometric properties of Consumer Experiences of Stigma Questionnaire and to the validation of its Portuguese translation. We performed a cross sectional descriptive and analytic study, collected socio-demographic data and measured experiences of stigma and global functioning. Frequency of responses regarding stigma section of CESQ matched previous studies using that scale. Frequency of responses in discrimination section was slightly lower than previously reported studies. We found an association between the discrimination score of CESQ and both male gender and living in the community. The discrimination score also positively correlated with global functioning. Cronbach alphas for CESQ and its subscales were good. Intraclass correlation coefficients for CESQ and stigma subscale were also good. Using factor analysis we found most of the items in CESQ would fit 2 factors, grossly corresponding to the previously defined subscales. We conclude that this study successfully explored stigma in Portugal, contributing in simultaneous to the validation of Consumer Experiences Questionnaire.

Mapping the Pasture Steppe in Bayankhongor, Mongolia: comparison of classification methods, using Landsat-8 and geophysical data

Grasslands in semi-arid regions, like Mongolian steppes, are facing desertification and degradation processes, due to climate change. Mongolia’s main economic activity consists on an extensive livestock production and, therefore, it is a concerning matter for the decision makers. Remote sensing and Geographic Information Systems provide the tools for advanced ecosystem management and have been widely used for monitoring and management of pasture resources. This study investigates which is the higher thematic detail that is possible to achieve through remote sensing, to map the steppe vegetation, using medium resolution earth observation imagery in three districts (soums) of Mongolia: Dzag, Buutsagaan and Khureemaral. After considering different thematic levels of detail for classifying the steppe vegetation, the existent pasture types within the steppe were chosen to be mapped. In order to investigate which combination of data sets yields the best results and which classification algorithm is more suitable for incorporating these data sets, a comparison between different classification methods were tested for the study area. Sixteen classifications were performed using different combinations of estimators, Landsat-8 (spectral bands and Landsat-8 NDVI-derived) and geophysical data (elevation, mean annual precipitation and mean annual temperature) using two classification algorithms, maximum likelihood and decision tree. Results showed that the best performing model was the one that incorporated Landsat-8 bands with mean annual precipitation and mean annual temperature (Model 13), using the decision tree. For maximum likelihood, the model that incorporated Landsat-8 bands with mean annual precipitation (Model 5) and the one that incorporated Landsat-8 bands with mean annual precipitation and mean annual temperature (Model 13), achieved the higher accuracies for this algorithm. The decision tree models consistently outperformed the maximum likelihood ones.