BackgroundThe World Mental Health Survey Initiative was designed to evaluate the prevalence, the correlates, the impact and the treatment patterns of mental disorders. This paper describes the rationale and the methodological details regarding the implementation of the survey in Portugal, a country that still lacks representative epidemiological data about psychiatric disorders.MethodsThe World Mental Health Survey is a cross-sectional study with a representative sample of the Portuguese population, aged 18 or older, based on official census information. The WMH-Composite International Diagnostic Interview, adapted to the Portuguese language by a group of bilingual experts, was used to evaluate the mental health status, disorder severity, impairment, use of services and treatment. Interviews were administered face-to-face at respondent’s dwellings, which were selected from a nationally representative multi-stage clustered area probability sample of households. The survey was administered using computer-assisted personal interview methods by trained lay interviewers. Data quality was strictly controlled in order to ensure the reliability and validity of the collected information.ResultsA total of 3,849 people completed the main survey, with 2,060 completing the long interview, with a response rate of 57.3%. Data cleaning was conducted in collaboration with the WMHSI Data Analysis Coordination Centre at the Department of Health Care Policy, Harvard Medical School. Collected information will provide lifetime and 12-month mental disorders diagnoses, according to the International Classification of Diseases and to the Diagnostic and Statistical Manual of Mental Disorders.ConclusionsThe findings of this study could have a major influence in mental health care policy planning efforts over the next years, specially in a country that still has a significant level of unmet needs regarding mental health services organization, delivery of care and epidemiological research.

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Conditionally specified Gaussian Markov random field (GMRF) models with adjacency-based neighbourhood weight matrix, commonly known as neighbourhood-based GMRF models, have been the mainstream approach to spatial smoothing in Bayesian disease mapping. In the present paper, we propose a conditionally specified Gaussian random field (GRF) model with a similarity-based non-spatial weight matrix to facilitate non-spatial smoothing in Bayesian disease mapping. The model, named similarity-based GRF, is motivated for modelling disease mapping data in situations where the underlying small area relative risks and the associated determinant factors do not vary systematically in space, and the similarity is defined by “similarity” with respect to the associated disease determinant factors. The neighbourhood-based GMRF and the similarity-based GRF are compared and accessed via a simulation study and by two case studies, using new data on alcohol abuse in Portugal collected by the World Mental Health Survey Initiative and the well-known lip cancer data in Scotland. In the presence of disease data with no evidence of positive spatial correlation, the simulation study showed a consistent gain in efficiency from the similarity-based GRF, compared with the adjacency-based GMRF with the determinant risk factors as covariate. This new approach broadens the scope of the existing conditional autocorrelation models.

Using a novel exploratory technique for time series analysis, Single Spectrum Analysis (SSA), it was possible to develop a comprehensive study of the Portuguese pharmaceutical market. The technique is described for the decomposition step, homogeneity structure testing and forecasting. A bibliography review was conducted on the technique. To the best of our knowledge, this was the first time that SSA was applied to any pharmaceutical market, so it was not possible to compare results with other published papers. An explanation on the Portuguese pharmaceutical market is provided in order to allow comprehensiveness of the work. The Portuguese pharmaceutical market is divided into 15 classes, which aggregates all drugs sold in the country. The technique was applied on the ‘Total Market’ time series, which is the sum of those 15 time series. Applying SSA, time series were decomposed in the respective components, which can be described as trend, cyclical movements and seasonality. The structure of time series was tested for homogeneity. With those steps concluded, a monthly forecast for the years 2008 and 2009 (with the respective confidence bounds) was produced. As a complex methodology, decisions need to be taken in several steps of the study. Even if not all possible choices are presented in this article, lengthy analyses were done to reach the best possible results. In fact, choosing between possible window lengths, Singular Value Decomposition approaches, and eigentriples to be grouped together is sometimes more an ‘art’ than a science; experience and previous knowledge of the actual phenomena can and should help. For confidentiality reasons the raw data are not provided in this article, but both forecast values and confidence bounds are presented.