How to compare two Crosstabs for significance? I have two crosstabs and need to determine whether their differences are significant. Both tables have the same independent variable but different populations. E.g., 

The only test I know to do is to convert these two 2x3 tables into 1x6 tables and then perform a 2x6 chi square. Is this a reasonable way to proceed?
EDIT: Changed the titles of the columns
 A: This looks like a stratified contingency table.  I'm assuming here that Meds is an ordinal variable and that the column names are the outcome (e.g. Died or Lived).  
You haven't told us what difference you want to evaluate.  Between population differences?  Between medication type differences? Depending on the complexity of the hypothesis you are evaluating, you can perform a logistic regression.  Here is some sample R code:
library(tidyverse)

#Population 1
x = as.table(matrix(c(42,58,44,59,50,47), nrow = 3))
rownames(x) = c(0,1,2)
colnames(x) = c(0,1)
dimnames(x) = list(Meds = c(0,1,2) ,Outcome = c(0,1))

x = as.data.frame.table(x) %>% mutate(Population = 1)

#Population 2
y = as.table(matrix(c(40,64,52,61,44,39), nrow = 3))
rownames(y) = c(0,1,2)
colnames(y) = c(0,1)
dimnames(y) = list(Meds = c(0,1,2) ,Outcome = c(0,1))

y = as.data.frame.table(y) %>% mutate(Population = 2)


data = x %>% 
      bind_rows(y) %>% 
      as.tibble() %>% 
      mutate(Population = factor(Population))



mod = glm(Outcome ~ Meds + Population, data = data, family = binomial(), weights = Freq)

summary(mod)

The variables in the model explain some of the variability observed in the data (this is evaluated by a deviance goodness of fit test), but a lot of residual variability remains. Again, the appropriateness of this approach depends on what hypothesis you are evaluating
