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- W2285909541 abstract "There is a high potential of information available in Behaviour Risk Factor Surveillance (BRFS) data, and especially for studying trends, as these data collect information in an ongoing and almost continuous manner for long periods of time. In order to account for the complex and dynamic relationships between the variables and avoid the aggregation of measures so as not to lose information in variability, the use of varying coefficient models with non-parametric techniques have been studied. These models allow the study of the trends and inter-relationships in the effects of the variables on the outcome of interest either over time or space, therefore providing valuable information for health policy interventions. A comparison of the possible estimation techniques, using the Italian surveillance data, has resulted in the selection of P-splines for estimation due to the flexibility in their use and the faster computation times. This estimation method was applied for a time varying coefficient model for a smoking status outcome variable using Italian surveillance data, and a time varying coefficient model for an obesity status outcome variable using U.S.A. surveillance data. The results of these models provide coefficient plots in which one can observe which subgroups of the population have an effect on the outcome which is changing over time. A spatial varying coefficient model was also studied for one point in time using smoothing spline estimation with tensor product smooths, and the maps produced from this model were able to show how the probabilities of the outcome variable (obesity) are changing across the counties of a U.S. state within each population subgroup. The strengths and limitations of these methods are discussed, as well as recommendations for further research such as the study of a spatial-temporal model using health surveillance data. Notwithstanding few limitations, the varying coefficient model represents an effective approach proving to produce interesting results (not accessible with the usual standard epidemiological approach) in this particular field of application and with BRFS data." @default.
- W2285909541 created "2016-06-24" @default.
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- W2285909541 date "2014-01-28" @default.
- W2285909541 modified "2023-09-27" @default.
- W2285909541 title "Behaviour Risk Factor Surveillance Data Analysis Using Varying Coefficient Models" @default.
- W2285909541 hasPublicationYear "2014" @default.
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