Strengths and limitations
The major strengths of our study include the population based data,
large sample size, detailed and standardised data on CA, and inclusion
of TOPFA, which constitute a large proportion of some
CAs.55 Additionally, exposures were mainly
prospectively ascertained, and our case-malformed control design limits
recall and information bias, especially for the small proportion of
retrospectively ascertained exposures. Our study was hypothesis driven,
and the use of two control and exposure comparison groups together with
sensitivity analysis, allowed us to evaluate the robustness of any
associations.
Our study also had some limitations. Exposure to antibiotics (2.36%)
was low, compared with the expected 3 - 14% rate of first trimester
antibiotic exposure in the European
population.10,19,56 This suggests antibiotic exposures
were under-ascertained in our data, as was shown also in a study
comparing the registry data to linked prescription
data.57 The reporting of antibiotic exposures would
not, however, have been different between cases and controls and thus
should not lead to biased odds ratios. We had very little data about the
indication for prescribing, or about untreated infection, so we could
not examine confounding by indication in this way. Our sensitivity
analysis comparing macrolide use with penicillins obtained similar
results to that with the primary exposure comparison. Since macrolides
and penicillins are commonly used for the same indication, this suggests
that the excess risk we found relates to macrolides rather than the
underlying infection. Other studies have used this same approach as a
proxy to account for residual confounding by
indication.12,19,25 We examined evidence of teratogen
non-specificity bias and found our results to be robust; they were
similar after excluding two control subgroups which were associated with
antibiotics, and similar for the secondary comparison with genetic
syndromes which cannot be associated with first trimester medication
exposure. Finally, we conducted many tests with some significant
findings possible by chance, and, therefore, specified our hypotheses in
advance, and recommend confirmation of new (exploratory) findings in
independent datasets.