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Model 3 was selected as the main confounder model.

Results of multivariable analyses adjusting for age and gender (model 1) and for the main confounders (model 2) are summarized in Table  2.

For each of these, we selected risk estimates from the main, confounder-adjusted models presented in each study, not those of sensitivity analyses.

Similar results were obtained after additional adjustment for the main confounders (table 3, model 2): 1.11 (0.84 to 1.46; P for trend 0.60).

Including water (the main predictor) in bivariate models reduced the differences to 0 65%, with only diabetes showing a change > 30%, suggesting water was the main confounder (data not shown).

We built a confounder model without including air pollutants.

We considered the confounder models to be the main models.

Data were subjected to descriptive analysis and multiple logistic regression models with adjustment for the main confounders.

The three models were adjusted for the main confounders measured in 2003, including sociodemographic variables, HRQoL, lifestyles other than LTPA and LTSB (e.g., alcohol and tobacco consumption), chronic diseases and BMI.

A binary logistic regression was also performed to adjust for the main confounders.

A series of different confounder models were fitted: included using year and month terms (model A) or 3, 9 or 12 pairs of Fourier terms (models B, C and D, respectively), in order to determine how sensitive the main results were to how underlying seasonality was accounted for.