The evaluation of infectious and noninfectious disease management can be done through the use of a time series analysis. In this study, we expect to measure the results and prevent intervention effects on the disease. Clinical studies have benefited from the use of these techniques, particularly for the wide applicability of the ARIMA model. This study briefly presents the process of using the ARIMA model. This analytical tool offers a great contribution for researchers and healthcare managers in the evaluation of healthcare interventions in specific populations.

Markov Chains provide support for problems involving decision on uncertainties through a continuous period of time. The greater availability and access to processing power through computers allow that these models can be used more often to represent clinical structures. Markov models consider the patients in a discrete state of health, and the events represent the transition from one state to another. The possibility of modeling repetitive events and time dependence of probabilities and utilities associated permits a more accurate representation of the evaluated clinical structure. These templates can be used for economic evaluation in health care taking into account the evaluation of costs and clinical outcomes, especially for evaluation of chronic diseases. This article provides a review of the use of modeling within the clinical context and the advantages of the possibility of including time for this type of study.

Objective: To identify and evaluate latent variables (variables that are not directly observed) for adopting and using nuclear technologies in diagnosis and treatment of chronic diseases. The measurement and management of these latent factors are important for healthcare due to complexities of the sector. Methods: An exploratory factor analysis study was conducted among 52 physicians practicing in the areas of Cardiology, Neurology and Oncology in the State of Sao Paulo who agreed to participate in the study between 2009 and 2010. Data were collected using an attitude measurement questionnaire, and analyzed according to the principal component method with Varimax rotation. Results: The component matrix after factor rotation showed three elucidative groups arranged according to demand for nuclear technology: clinical factors, structural factors, and technological factors. Clinical factors included questionnaire answers referring to medical history, previous interventions, complexity and chronicity of the disease. Structural factors included patient age, physician's practice area, and payment ability. Technological factors included prospective growth in the use of nuclear technology and availability of services. Conclusions: The clinical factors group dimension identified in the study included patient history, prior interventions, and complexity and chronicity of the disease. This dimension is the main motivator for adopting nuclear technology in diagnosis and treatment of chronic diseases.Keywords: Chronic disease; Diagnostic imaging; Therapeutics; Biomedical technology; Nuclear medicine; Radioisotopes; Health services administration RESUMO Objetivo: Identificar e avaliar as variáveis latentes (que não podem ser observadas diretamente) no processo de adoção e uso de tecnologias nucleares no diagnóstico e tratamento de doenças crônicas. A mensuração e a gestão dos fatores latentes são importantes dentro da área da Saúde devido às complexidades inerentes do setor. Métodos: Foi realizado um estudo do tipo fatorial exploratório com 52 médicos das especialidades de Cardiologia, Neurologia e Oncologia no Estado de São Paulo que participaram do estudo entre 2009 e 2010. Os dados foram coletados por meio de questionário de mensuração de atitudes e analisados pelo método dos componentes principais, com rotacionamento do tipo Varimax. Resultados: A matriz de componentes após a rotação dos fatores apresentou três agrupamentos explicativos ordenados para a demanda de uso das tecnologias nucleares: fatores clínicos, fatores estruturais e fatores tecnológicos. O fator clínico é formado por respostas referentes a histórico clínico, intervenção anterior, complexidade e cronicidade. O fator estrutural é composto por idade do paciente, área de atuação do médico e capacidade de pagamento; o fator tecnológico diz respeito às perspectivas de aumento do uso da tecnologia nuclearquantidade de serviços. Conclusões: A dimensão de fatores clínicos é composta por histórico clínico, intervenção anterior, complexidade e cronicidade d...

Decision-making is fundamental when making diagnosis or choosing treatment. The broad dissemination of computed systems and databases allows systematization of part of decisions through artificial intelligence. In this text, we present basic use of probabilistic graphic models as tools to analyze causality in health conditions. This method has been used to make diagnosis of Alzheimer´s disease, sleep apnea and heart diseases.

In this paper we compared the results between stock portfolios of North American and European airlines. The model accesses the market risk using Value-at-Risk approach in both portfolios over one month period. The analysis was performed through the use of GARCH-EVT methods and Student’s-t Copula with a Monte Carlo Simulation. The assets in the financial market usually present heavy tails in their probability distributions, so, a process capable to deal with this issue is crucial to measure the risk of loss. We analyzed the period from mid-2007 to mid-2012 to compose comparison between these two portfolios. The financial crisis of 2008 had a great impact in the North America market in relative to the European market. The central role of transport in the economy makes studies dealing with investment risk measure in this sector crucial for the industrial development. The volatility of risk in the airline market happens by internal and external motives and the methodological development of financial tools can offer an important contribution due the investment flux dependency