Obstetrics and Gynecology International infants is 1.16 (95% CI 0.92–1.45) and in the female infant 0.83 (95% CI 0.70–0.99). These two estimates diﬀer: z = 2.27, P = 0.03. This means that the drug (or the condition which is the indication for drug use) reduces the risk for hip subluxation in girls but not (or at least less) in boys. There exist arguments that this condition has diﬀerent etiology in the two sexes. This procedure is an example of stratiﬁcation in the analysis in order to see if an eﬀect diﬀers in subgroups of the material, in this case between boys and girls. When one makes such subgroup analyses it is important to remember that the chances for random “signiﬁcances” increases with the number of subgroups studied and if diﬀerences are found one must verify that they are not due to chance (which thus seems not to be the case in example above). One way to explore such a possibility is to add, in a logistic regression analysis, an interaction term between exposure and infant sex to see if the latter factor modiﬁes the eﬀect of the exposure. If this interaction term is statistically signiﬁcant, it means that both sex and exposure aﬀect the outcome risk and that the eﬀect of exposure diﬀers according to infant sex. An extreme situation is when outcome like hypospadias is only present in one sex. Intuitively, one would then com- pare exposures between infants with hypospadias and male infants without hypospadias. If exposure is independent of infant sex (which is true for most drugs), the result of only comparing with male control infants is to reduce the material which is used for estimating the background exposure rate and therefore slightly widen the conﬁdence interval. 3.3. Adjusting for Intermediary Factors in the Pathway of the Eﬀect of the Exposure on the Outcome. To adjust for a non- confounder usually does not change the eﬀect of the exposure—sometimes confounders are simply deﬁned as factors which change the risk estimate with, for instance, 10% or more. There is another common situation, where adjustment may give results which can easily be misunder- stood. If the exposure acts via an intermediary phenomenon which aﬀects outcome, adjustment for the phenomenon may remove the eﬀect more or less completely (Figure 1(b)). We can exemplify this phenomenon from studies of neonatal conditions where the eﬀect is more easily seen than in studies of birth defects. If maternal use of a drug increases the risk for preterm birth, any neonatal condition which occurs at an increased rate among preterm newborns will probably be increased after the exposure. If this is the only way in which the drug aﬀects the neonatal condition, the eﬀect will disappear after adjustment for gestational duration or a paradoxical eﬀect can even be obtained. A classical example is that maternal smoking increases perinatal mor- tality but after adjustment for gestational duration or birth weight, the increased risk changes to a seemingly protective eﬀect [3]. One will then compare infants born of smoking women (which are born preterm or with low birth weight because of the smoking but may be otherwise healthy) with infants born of nonsmoking women. Some of the latter infants are born preterm or with low birth weight because of fetal pathology and are therefore at an increased risk for perinatal death. 3 In such situations, the proper way of analysis is to look at the eﬀect of exposure without considering the intermediary factor. If one wants to see, if the eﬀect of the exposure is solely or partly due to an intermediary factor, the eﬀect will disappear if adjustment is made for it. This may be of interest, for instance, if one wants to ﬁnd out if an eﬀect of a speciﬁc drug occurs by a direct pharmacological action on the infant or if it an eﬀect of an intermediary, for example, an increased risk for preterm birth. We can exemplify this with the eﬀect of late pregnancy use of certain CNS active drugs on neonatal morbidity (Table 1). Such neonatal morbidity is markedly more fre- quent in preterm than in term infants and also occurs more often in infants born by mothers using CNS-active drugs during late pregnancy than in other infants. If this had merely been an eﬀect of preterm birth, a risk among term birth should not be seen but it is actually slightly higher than for all births. In studies of birth defects, which originate early in the pregnancy, such intermediary eﬀects are more diﬃ- cult to detect. In this situation, another relationship may appear. Infants with some congenital malformations are born preterm more often than expected and use of a drug may increase the risk for preterm birth. Figure 1(c) illustrates this situation. As a rule, preterm birth cannot cause the malformation, so gestational duration is no intermediary and no confounder and generally no adjustment should be made for gestational duration. The drug could, however, aﬀect for instance the placenta, which could both increase the risk for preterm birth and for the occurrence of a congenital malformation. That such a mechanism can exist may be suggested by the observation that malformed fetuses already in mid-gestation may have an increased risk of being small for date, leading to misdating from 2nd trimester sonography [4]. The placenta eﬀect would then be an intermediary in both causative pathways and the situation is the same as in the situation of an intermediary as discussed in the former paragraph. 3.4. Confounding by Maternal Characteristics 3.4.1. Maternal Age. This is a classical confounder in many studies of teratogenic eﬀects of drugs. The eﬀect of maternal age (5-year classes) on the risk for any relatively severe congenital malformation is seen in Table 2. Note that each studied variable in this Table is adjusted for all other variables in the table in order to identify speciﬁc eﬀects of each variable. There is a not very strong J-shaped relationship between maternal age and the risk for any relatively severe malformation. Malformations vary with respect to maternal age dependency, however. In a few, a strong increase in infants born of young women is seen. The most well-known example is gastroschisis, for which the odds ratio at age <20 in the present material is 4.51 (95% CI 2.63–7.71) and at age 20–24 it is 2.41 (95% CI 1.67–3.48), using age 25– 29 as a reference. For many other malformations, the risk increases with maternal age, and for some, like trisomies, the risk increases sharply at high age. For Down syndrome,