Abstract
Summary The study of temporal and spatial trends in large databases, such as behavioural risk factor surveillance data, can be a great challenge, especially when the intent is to study the time-related effects of multiple independent variables; this is an issue which is not usually addressed in trend analysis in epidemiological studies. This study demonstrates the use of varying coefficient models using non-parametric techniques, which can show how coefficients vary in time or space; it is a useful statistical tool that is applied for the first time to health surveillance data. Using the US ‘Behavioral risk factor surveillance system’, a varying coefficient model is constructed using obesity as an outcome measure. Odds ratio plots and probability maps illustrate the temporal or spatial changes in coefficients of the independent variables; these results can be used to identify changes in at-risk subgroups of the population for the odds of obesity.
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