Visualize links and context, using directed acyclic graphs

Even if the concepts of sex and gender have been well defined, differentiated and measured, with all the limitations mentioned, this is still not sufficient to isolate and analyze the effects of sex and gender on a health outcome, denoted Y. Mainly because individual gender, defined as the result of gender pressure on an individual, is strongly associated with sex: if a newborn baby is defined as male, he will be socialized as a boy, whereas if defined as female, she will be socialized as a girl. The gendered characteristics that each child will have, even if modulated by other social and individual factors, thus strongly depend on their sex, which is a “parent” (direct cause) of these characteristics, in a causal-framework sense. Therefore, an association between an individual score of gender and an outcome Y cannot be interpreted as proof that gender pressure explains part of Y, because sex is a confounder in this association. The reverse interpretation would be equally flawed: we cannot conclude from an association between birth-sex and an outcome Y that biological mechanisms only explain this association and not the gender pressure, because the effect of sex on Y can be mediated by (= can pass through) gender.

It is therefore insufficient to simply avoid confusing sex with gender concepts and the variables that measure them, we also have to avoid confusing the mechanisms that relate one to the other. To grasp these issues, we propose to use causal-approach tools to clearly identify the mechanisms of interest and, on this basis, define our analysis strategy: directed acyclic graphs (DAG) and counterfactual notations [26, 27]. The principle is to visually represent all the variables of interest (the “nodes”), measured or not, and all the possible causal links between these variables (the “arrows”). This tool allows us (1) to be transparent about the a priori hypotheses regarding the underlying causal structure; (2) to precisely define the effect to be estimated in order to meet the objectives (which can be expressed using counterfactual notations); (3) to build the appropriate model to identify and estimate this effect, taking into account the context and thus avoid the main methodological biases like not adjusting on a confounder or adjusting on a mediator, etc. [27].

Figure 1 is an example of DAG, allowing us to visualize the sequence of causes and therefore the whole causal structure. This graph represents a very general scenario of a sequence of two exposures X1 and X2 that cause an outcome Y, each node representing a variable or set of variables. Fundamental and independent determinants of X1, X2 and Y are innate factors, including sex, and environmental factors.

Fig. 1
figure 1

General graph of causal links

What are “sex effects” and “gender effects”?

Strictly speaking, the “effect of sex” on Y corresponds to all directed paths that begin at the Sex node and end at Y (double arrows in Fig. 2a). However, it is sometimes implicitly suggested that when we talk about the “effect of sex”, we are only talking about biological mechanisms and that we are therefore only referring to paths that would not pass through social factors. In fact, this “biological effect of sex” would be the direct effect (double continuous arrow in Fig. 2b) and the indirect effects which pass through exposures not linked to the environment (double dashed arrows, with the hypothesis of independence between Env and X1), and assuming that no mediators with social–environmental determinant have been omitted.

Fig. 2
figure 2

Effects of sex and gender: Total effect of Sex (a), Biologic effect of Sex (b), Effect of a gendered variable (c) and Effect of a gender variable (d)

When we observe a result where there is an association between Sex and an outcome Y, this finding corresponds to the total effect of Sex on Y (Fig. 2a). Again, we cannot know if this total effect is explained by biological or social mechanisms, even if we have well defined the Sex variable as a biological phenomenon. It is therefore important to determine if this is really the effect of interest. By using these graphic representations, we can also highlight the complexity of isolating the biological effect of sex, which would require us to first make the strong assumption of an independence between the environment and all the intermediate factors (as for X1 in our example), and second to “block” all other paths to identify the direct effect.

We can also focus on gendered exposure(s). For example, if the probability of playing football X is different according to birth-sex and to the place of residence, we will say that this activity is socially determined and gendered. We would want to identify the risk factors for a rupture of the anterior cruciate ligament Y, assuming that there is no direct effect of sex on the probability of this pathology occurring but an effect of playing football X (see Fig. 2c). Since playing football is a risk factor for the disease and a gendered activity, we would find here a “sex effect”, i.e., statistical association and even a causal pathway (mediated by football X) between the variable Sex and Y. In this example, X is a gendered activity, but we could have used another gendered dimension, gender identity, a set of gendered variables or a gender diagnosis, as described above and as represented in Fig. 2d. This figure allows us to visualize the potential confounding effect of sex and environment when we look at the effect of this kind of gender marker on Y. If we wanted to identify and measure the specific effect of individual gender, we would have to make sure that we could control all these confounders. These examples demonstrate that it is necessary to ensure that the assumptions regarding mechanisms and pathways of interest are clearly defined a priori.

Exploring mechanisms with mediation analysis

When we want to understand the health effects of sex and gender, i.e., describe them, distinguish them and explore their mechanisms, different questions can be addressed that do not involve the same analytical strategies. If the question is: “Are the differences in health observed between men and women explained, at least partially, by social mechanisms?”, then our focus will be on the pathways operating through social dimensions of a Sex → Y effect, i.e., in the socially mediated indirect effect of sex. If the question is “Does a gendered dimension(s), like a gender diagnosis, have an effect on health?”, then our focus will be on the total effect of a gendered exposure, as described in Fig. 2c. It is therefore important to distinguish, name, and define the multiple pathways that link sex and gender to the outcome.

Based on the causal framework, mediation analysis strategies [28, 29] have been defined in order to estimate mediated effects. These strategies could allow us to answer questions such as “how much of the sex-difference on that outcome is explained by gendered behaviors?” for example. Based on these methods and on the counterfactual formulation (“if the situation had not been as it is”), we propose a typology of several effects of interest in Table 1, with corresponding examples of counterfactual formulation. We will denote YS=s or YS=s, E=e the potential outcome had a subject been exposed, respectively, to the counterfactual interventions S = s or {S = s and E = e}. In this table, the gender variable is described as a binary variable G = {f;m} in order to simplify the presentation rather than for a conceptual reason.

Table 1 Typology of sex S and gender G effects on a health outcome Y

Ideally in this typology, G should represent “being / acting / living / etc. as a man” (or “as a woman”), i.e., everything that socially makes a man (or a woman) in a given time, place and population. In this case, the direct effect, RES (what does not pass through G), would correspond to the non-socially mediated or the biological effect of sex in these time, place and population. But, as we said before, gender is so diffuse that it is impossible to think that we can capture all its dimensions in one or a few variables. With this analytical strategy, at most we can: (1) verify the hypothesis that social pathways (SMIES) explain, at least in part, a sex effect (TES), and (2) have an order of magnitude of the biological effect of sex on a phenomenon Y depending on the conceptual extensivity of G. But a RES can never be said to be the pure biological effect of sex, even if we have considered many gendered dimensions in G.

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