Time-series regression models to study the short-term effects of environmental factors on health

Time series regression models are especially suitable in epidemiology for evaluating short-term effects of time-varying exposures on health. The problem is that potential for confounding in time series regression is very high. Thus, it is important that trend and seasonality are properly accounted for. Our paper reviews the statistical models commonly used in time-series regression methods, specially allowing for serial correlation, make them potentially useful for selected epidemiological purposes. In particular, we discuss the use of time-series regression for counts using a wide range Generalised Linear Models as well as Generalised Additive Models. In addition, recently critical points in using statistical software for GAM were stressed, and reanalyses of time series data on air pollution and health were performed in order to update already published. Applications are offered through an example on the relationship between asthma emergency admissions and photochemical air pollutants

Values and their influence on the life satisfaction of adolescents aged 12 to 16: a study of some correlates

In this research we explore several aspects of quality of life in young people, working with factors such as self-esteem, locus of control, perceived social support, values, and so on. We examine the correlations among factors that influence the values and life satisfaction of adolescents aged 12-16. Furthermore, we analyze the data obtained from the children, on the one hand, and their parents, on the other, we explore the relationships between the factors and we consider the agreements and discrepancies between the responses of parents and their offspring