Multivariate logistic regression was used to further explore associations between the exposures and outcome. We first modelled the association between multi-channel exposure and the outcome, adjusted for potential confounders (age, marital status, socioeconomic status (SES) and education level). Separate models were then used to explore the independent effect of each of the single-channel exposures on the outcome, adjusted for the other channels of exposure and potential confounders. Variables for inclusion in the model were decided upon a priori based on conceptual considerations, however models were also checked for collinearity problems. As with the unadjusted analysis, 95% confidence intervals were calculated to estimate the precision of the adjusted odds ratio (aOR), and the overall p-value for each exposure generated using the LRT to test for model fit.