H&L is popular because there are not a lot of alternatives. It is also conservative so its more robust than other test to large sample size. Still a hundred thousand cases will generate tremendous statistical power and thus might lead the test to be signficant when it should not be. The problem is that it is hard to show that the test is wrong because there are no easy alternatives to use (and those that exist such as the Pearson chi square test probably will be even more influenced by sample size).

One thing that occurs to me - but I don't know if this is statistically valid, would be to run the same test with a random subsample of your data, say a thousand cases, and see if the H&L test is still signficant. If it is then sample size is not the issue (although some like Paul Alison are critical of the test generally).