How to get the p value for RERI in additive interaction model (logistic regression)?

Question

I'm doing an biological interaction analysis from logistic regression, and apply it to additive model, as suggested by Hosmer and Lemeshow (1992). But I learnt the code from the epi.interaction function | R Documentation. It tells only the calculation of confidence interval of RERI, AP and S, but how to have a p value?

Take this example:

### Construct the data
Cohort <- function(a,b,c,d){
  n <- a+b+c+d
  Pre <- sample(c(rep(1,a),rep(2,b),rep(3,c),rep(4,d)),n)
  ID <- c(1:n) 
  DF <- data.frame(ID=ID,Pre=Pre)

  DF$A <- rep(NA,n)
  DF$A[DF$Pre==1 | DF$Pre==3] <- 0
  DF$A[DF$Pre==2 | DF$Pre==4] <- 1

  DF$B <- rep(NA,n)
  DF$B[DF$Pre==1 | DF$Pre==2] <- 0
  DF$B[DF$Pre==3 | DF$Pre==4] <- 1

  DF <- data.frame(ID=DF$ID,A=DF$A,B=DF$B)

  return(DF)
}
Com <- function(D,C){
  Disease <- c(rep(1,nrow(D)),rep(0,nrow(C)))
  ID <- c(1:(nrow(D)+nrow(C)))
  Pooled <- rbind(D,C)
  Pooled <- cbind(ID,Disease,Pooled[,2:3])
  return(Pooled)
}

DataCase <- Cohort(1180,340,540,120)
DataControl <- Cohort(2240,230,460,30)
Data <- as.data.frame(Com(DataCase,DataControl))

## Set the dummy variable
Data$Dum <- NULL
Data$Dum[Data$A==0 & Data$B==0] <- 0
Data$Dum[Data$A==1 & Data$B==0] <- 1
Data$Dum[Data$A==0 & Data$B==1] <- 2
Data$Dum[Data$A==1 & Data$B==1] <- 3
Data$Dum <- factor(Data$Dum)

## Fit the Regression Model
Data.glm <- glm(Disease ~ Dum, family = binomial, data = Data)
summary(Data.glm)

## Calculate RERI, AP and S along with confidence interval
epi.interaction(model = Data.glm, coeff = c(2,3,4), type = "RERI", conf.level = 0.95)
epi.interaction(model = Data.glm, coeff = c(2,3,4), type = "APAB", conf.level = 0.95)
epi.interaction(model = Data.glm, coeff = c(2,3,4), type = "S", conf.level = 0.95)

So, how to calculate the p value of this model with R? And how about the OR or RR?