The aim of the study was to determine factors associated with changes in self-reported dentifrice consumption in an urban population group over 13 years. This study evaluated two surveys of 671 and 688 households sampled in the urban area of a city from Southern Brazil in 1996 and 2009, respectively. The mother of the family was asked to answer a structured questionnaire about demographics, socioeconomic and behavioral variables. The primary outcome was obtained by questioning "how long does a dentifrice tube last in your house?" The cut-off point of duration was less than 1 month. It was used to determine high consumption of dentifrice (HCD). Associations between HCD and independent variables were evaluated by multivariable Poisson regression. There was a significant decrease of 20% (81.2% to 61.2%) in the prevalence of HCD between 1996 and 2009, resulting in a crude annual decrease of 1.54%. Mother's age, family income, dental assistance, mother's brushing frequency and number of household members that use a toothbrush were significantly associated with HCD independent from the year of survey. The prevalence ratio (PR) of HCD for the year of survey was 0.75, indicating an overall decrease of 25% in the probability of HCD from 1996 to 2009. Probabilities of HCD also decreased over the 13 years among the strata of education, number of household members and reason for choice of dentifrice. It may be concluded that the factors associated with the observed decrease were higher educational levels, larger number of household members and reasons for choosing a dentifrice related to preventive/therapeutic effects.

This work solves an experiment design problem for a linear regression problem using a reduced order model. The quality of the model is assessed using a mean square error measure that depends linearly on the parameters. The designed input signal ensures a predefined quality of the model while minimizing the input energy.

The Prediction Error Method (PEM) is related to an optimization problem built on input/output data collected from the system to be identified. It is often hard to find the global solution of this optimization problem because the corresponding objective function presents local minima and/or the search space is constrained to a nonconvex set. The existence of local minima, and hence the difficulty in solving the optimization, depends mainly on the experimental conditions, more specifically on the spectrum of the input/output data collected from the system. It is therefore possible to avoid the existence of local minima by properly choosing the spectrum of the input; in this paper we show how to perform this choice. We present sufficient conditions for the convergence of PEM to the global minimum and from these conditions we derive two approaches to avoid the existence of nonglobal minima. We present the application of one of these two approaches to a case study where standard identification toolboxes tend to get trapped in nonglobal minima.

This cross-sectional study evaluated the consumption of fluoridated dentifrice and associated factors in Passo Fundo, Rio Grande do Sul State, Brazil. A total of 688 households were selected. A structured questionnaire was answered by the mother to obtain demographics, habits, and toothpaste consumption. Household toothpaste consumption was considered low when a tube lasted > 1 month and high when < 1 month. Logistic regression models were applied to evaluate the association between consumption and independent variables. 61.2% of households showed high toothpaste consumption. In the multivariate model, mother's age > 50 years (OR = 1.62; 95%CI: 1.02-2.61), mother's brushing frequency (OR = 2.53; 95%CI: 1.53-4.16), number of people brushing (OR = 5.69; 95%CI: 3.68-8.81), and cosmetic features in choice of the dentifrice (OR = 1.64; 95%CI: 1.03-2.61) showed the highest odds of high toothpaste consumption.