Analysing Behavioural Risk Factor Surveillance Data by Using Spatially and Temporally Varying Coefficient Models

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.