model {

             for (i in 1 : N) {
          # Likelihood
                O[i] ~ dpois(mu[i])
                log(mu[i]) <- log(E[i]) + alpha + beta * depriv[i] + b[i] + h[i]
                # Area-specific relative risk (for maps)
                RR[i] <- exp(alpha + beta * depriv[i] + b[i] + h[i]) 

          # Exchangeable prior on unstructured random effects
                h[i] ~ dnorm(0, tau.h) 
             }

          # CAR prior distribution for spatial random effects: 
             b[1 : N] ~ car.normal(adj[], weights[], num[], tau.b)
             for(k in 1:sumNumNeigh) {
                weights[k] <- 1
             }

          # Other priors:
             alpha ~ dflat() 
             beta ~ dnorm(0.0, 1.0E-5)
             tau.b ~ dgamma(0.5, 0.0005) 
             sigma.b <- sqrt(1 / tau.b) 
             tau.h ~ dgamma(0.5, 0.0005) 
             sigma.h <- sqrt(1 / tau.h) 

         }