Best statistic to compare disease appearance in two distinct groups I have two distinct groups made of a different numbers of subjects (111 Millions and 126 Millions).
My goal is to evaluate how many subjects in the two groups encounter 10 different diseases.
For this purpose, I build a table (reporting here only 4 out of 10 diseases) as follows:




Disease
Group A
Group B




A
23 M
19 M


B
45 M
18 M


C
19 M
18 M


D
21 M
20 M




In this case there are no means involved: I'm simply counting the number of occurrences (frequency) within each group and for each disease.
Is there a way to check whether the the difference is statistically significant for each disease between the two groups?
I would proceed with a Chi-squared test isolating each disease, building a contingency table as follows and then run the test.




Disease A
Group A
Group B
Sum




Infected
23 M
19 M
42 M


Not-Infected
88 M
107 M
195 M


Sum
111 M
126 M





Is, in this case the Chi-squared test, the most appropriate test?
 A: With such large samples, do you think results of a statistical test adds much value?
Formally, you could used prop.test in R (or essentially equivalently, a chi-squared test on a 2-by-2 table).
prop.test(c(23*10^6,19*10^6), c(111*10^6,126*10^6), cor=F)

        2-sample test for equality of proportions 
        without continuity correction

data:  c(23 * 10^6, 19 * 10^6) out of c(111 * 10^6, 126 * 10^6)
X-squared = 1288000, df = 1, p-value < 2.2e-16
alternative hypothesis: two.sided
95 percent confidence interval:
 0.05631563 0.05651148
sample estimates:
   prop 1    prop 2 
0.2072072 0.1507937 

With millions of subjects is there any doubt that proportions $0.2091$ and $0.1508$ differ?
Or does a P-value (predictably) extremely near $0$ somehow seem impressive--or tick someone's
supposedly mandatory box?
Note: Output for chisq.test in R follows:
TBL = 10^6*matrix(c(23,19,88,107), byrow=T, nrow=2)
TBL
        [,1]     [,2]
[1,] 2.3e+07 1.90e+07
[2,] 8.8e+07 1.07e+08

chisq.test(TBL, cor=F)

         Pearson's Chi-squared test

data:  TBL
X-squared = 1288000, df = 1, p-value < 2.2e-16

