Models were fitted to the grouped data with Poisson
regression analysis. Logarithms of observed and expected
cases were linked with a set covariate values in a linear
model that dealt with Poisson overdispersion. To build
flexible models, penalized regression splines (based on 25
knots) were used [20]. Since the dioxin concentration dis-tribution was right-skewed, a square root transformation
was applied to force the exposure data to follow a normal
distribution. Log-transformed industry × years, log-trans-formed population density, socio-economic level, and
NO2 concentration were entered as continuous variables
in the models. Urbanisation (yes/no, by collapsing the 3
urban categories), and administrative departments
(department of Isère as reference category) were treated as
categorical variables.