Table 2.

Median posterior of the variance hyperparameter of the Gaussian field (${\sigma }^2$) for the unadjusted and adjusted model, median posterior of variation explained ($V\left(Z\left(s\right)\right)$) and median posterior of grid specific relative risk based on residence at diagnosis

	LGCPs
	All cancers	Leukaemia	Lymphoma	CNS tumours
${\sigma }^2$ unadjusteda (median, 95% CI)	0.01 (0, 0.02)	0.00 (0, 0.03)	0.01 (0, 0.04)	0.02 (0.01, 0.06)
${\sigma }^2$ adjustedb (median, 95% CI)	0.01 (0, 0.03)	0.00 (0, 0.01)	0.00 (0, 0.03)	0.02 (0, 0.06)
Variation explainedc (median; 95% CI)	0.72 (0.43, 0.89)	0.81 (0.58, 0.94)	0.82 (0.60, 0.94)	0.64 (0.31, 0.84)
RR unadjusteda (median; ranged)	0.99 (0.83, 1.13)	1.00 (0.96, 1.09)	0.99 (0.9, 1.13)	1.01 (0.82, 1.23)
RR adjustedb (median; ranged)	1.02 (0.86, 1.08)	1.00 (0.97, 1.04)	1.00 (0.96, 1.07)	1.00 (0.87, 1.25)

CI credibility intervals, RR grid specific relative risk compared to Switzerland as a whole, LGCP log-Gaussian Cox process, CNS Central and Nervous System

^aThe unadjusted model refers to the models without any covariates

^bAdjusted for NO₂, background radiation, years of general cancer registration, linguistic region and degree of urbanicity

^cVariation explained by the covariates from the fully adjusted model, defined as $R^2=\frac{V\left(\mathit{X}\left(s\right)\mathit{\beta }\right)}{V\left(\mathit{X}\left(s\right)\mathit{\beta }\right)+V\left(Z\left(s\right)\right)}$ where $V\left(·\right)$ denotes the variance over the $K$ spatial units, $\mathit{\beta }$ is the vector of intercept and covariates, $\mathit{X}$ the design matrix and $Z\left(s\right)$ the Gaussian field. The variation here refers to the fully adjusted model

^dRange is defined as [min, max]