Most previous studies have assessed the mediating role of socioeconomic and behavioral factors in adulthood in the association between CSES and mental health, general health, and well-being in adulthood (Peck, 1994; Gilman, 2002; Galobardes et al., 2004, 2008; Pudrovska and Anikputa, 2012; Morgan et al., 2014; Sheikh et al., 2014). However, when assessing mediation, one assumes that there are no intermediate confounders (Cole and Hernán, 2002; Robins, 2003; Loeys et al., 2014; Sheikh et al., 2014; Tchetgen Tchetgen, 2014). Put simply, it is assumed that there are no variables (measured or unmeasured) that affect both the mediator(s) and the outcome, or that are affected by the exposure itself (see Figure 1). CSES may affect health in adolescence and early adulthood, which may in turn affect both socioeconomic status and health in adulthood. A wealthy upbringing and ample parental resources may expose children to experiences and styles of interacting that are useful for getting ahead in society. Similarly, behavioral factors in adulthood, such as alcohol and tobacco consumption are likely to be influenced by parental behavioral patterns and social environment in childhood (Sheikh et al., 2016a) and early adulthood. Due to the long time period between CSES, and socioeconomic status and behavioral factors in adulthood, there can be many potential intermediate confounders (Sheikh et al., 2014). Therefore, the “no intermediate confounding” assumption is difficult to satisfy. Such is the case when assessing mediators from adulthood in the association between CSES and health and well-being in adulthood.

Furthermore, the assumption of no “intermediate confounding” has rarely been explained graphically. The simplest (and broadest) definition of a confounder was given by Hernán (2008, p. 357) as “any variable that is necessary to eliminate the bias in the analysis” (see also Hernán et al., 2002). In the psychology literature, a confounder is referred to as the third (extraneous) variable (although, there is no limit on the number of potential confounders). Figure 1 presents the hypothesized association between the exposure (independent variable of interest), the mediator, potential confounders, intermediate confounders, and the outcome (dependent variable of interest). The intermediate confounder is a mediator-outcome confounder, as it influences both the mediator and the outcome (represented by paths e and f). Therefore under the “sequential ignorability” assumption (also referred to as “no unmeasured confounding,” “conditional exchangeability,” “selection on variables,” or “exogeneity” assumption), the intermediate confounder should be included in regression models as a confounder. However, the intermediate confounder is also influenced by the exposure (represented by path d) and acts as a mediator in the association between exposure and outcome (represented by path d^*f). Therefore, if the intermediate confounder is included as a confounder to estimate the total effect (without controlling for the mediator), then the influence of exposure through the intermediate confounder is also blocked (path d^*f). Similarly, the direct effect is supposed to represent the influence of an exposure on an outcome not mediated by the meditator. However, by adjusting for the intermediate confounder, part of the direct effect is blocked (path d^*f), and hence the direct effect will be underestimated, as it represents the influence (represented by path c) of the exposure on the outcome not mediated by the mediator and the intermediate confounder. Moreover, the indirect effect (the difference between the total and direct effect) no longer represents the influence of the exposure on the outcome via the mediator, as the path d^*e^*b (note that the intermediate confounder acts as a mediator between exposure and mediator) is also blocked while estimating the direct effect. Therefore, the indirect effect will also be underestimated. Since the intermediate confounder is also an intermediary (synonymous with mediator) in the association between the exposure and the mediator (path d^*e) and between the exposure and the outcome (path d^*f), it creates problems when trying to break down the total effect. In the present study, we assess the role of psychological abuse and physical abuse as mediators in the association between CSES (exposure) and mental health, general health, and well-being outcomes in adulthood. Assuming that CSES is likely to precede psychological abuse and physical abuse in childhood, the relatively short time period between CSES and psychological and physical abuse in childhood allows us to avoid intermediate confounding.

It is well established that measurement error (or misclassification bias) in the retrospective measurement of an exposure is a concern in observational studies. A distinction must be drawn between inaccurate recall (non-differential) and recall which is biased (differential). If the error in CSES is independent of the health and well-being outcomes, conditional on confounding variables, than it is considered non-differential, i.e., it may be systematic (concern for validity), but not with regard to the health/well-being status of respondents. Measurement error in the exposure (CSES) due to health/well-being in adulthood is considered “differential measurement error bias” (commonly referred to as recall bias), if the error in CSES depends on the subject's health/well-being at the time they participated, conditional on the confounding variables. As Mausner et al. (1985) noted, “people may be more likely to search for explanations for the disease in the cases and, therefore, may assign more significance to past events.” (Mausner et al., 1985, p. 165). On the other hand, healthy respondents may be less likely to recall a true exposure. Whether the unhealthy respondents recall their childhood socioeconomic conditions as worse than they were, or the healthy respondents recall their childhood socioeconomic status to be better, the aggregate effect of CSES on mental health, general health, and well-being will be biased upwards (overestimated). Furthermore, experts in mediation analysis propose that longitudinal data are suitable for the assessment of mechanisms or processes, as temporality is assumed between the exposure, the mediators, and the outcome (Hayes, 2013; Preacher, 2015). Much less attention is given to the question of how differential recall bias may bias estimates of total, direct, and indirect effects. No previous study has shown if there are any differences in estimates between analyses based on data from the same wave (cross-sectional data with exposure, mediator, and outcome measured at the same time point), and longitudinal data (exposure measured at an earlier time point). Self-reported information on childhood adversities, such as CSES may be influenced by the present mental health, general health, and well-being. For instance, if CSES is measured retrospectively at the same time point as health, unhealthy respondents may recall their CSES to be worse than it was (and vice versa), resulting in an overestimation of total and direct effects. However, no previous study has assessed the direction of differential recall bias for a subjective measure of childhood adversity, such as CSES and its influence on total, direct, and proportion of mediated effects. In this study we use the same exposure (CSES) measured at two time points, and compare the total, direct, and proportion of indirect effect estimates.

Using a representative sample from a large cohort study in Norway, we assessed: (i) the gender differences in self-reported CSES, psychological abuse, and physical abuse in childhood; (ii) the influence of CSES on psychological abuse and physical abuse in childhood; (iii) the influence of CSES, psychological abuse, and physical abuse on mental health (Hopkins Symptoms Check List-10, SCL-10), general health (EQ-5D), and subjective well-being (satisfaction with life scale, SWLS); (iv) the mediating role of psychological abuse and physical abuse in the association between CSES and mental health, general health, and well-being, and; (v) the role of differential recall bias in the estimation of total, direct, and proportion of mediated effects.

The Tromsø Study is a prospective cohort study which started in 1974 and contains data from 40,051 respondents. Tromsø Study participants are considered representative of the adult population residing in the municipality of Tromsø, Norway (Jacobsen et al., 2012). Between 1974 and 2007–2008, six waves of the Tromsø Study were conducted (referred to as Tromsø I–VI) (Jacobsen et al., 2012). The present study is based on 10,325 respondents participating in both Tromsø IV and Tromsø VI (aged 25–74 in Tromsø IV, and 38–87 in Tromsø VI).

Mental health, general health, and well-being was measured in the Tromsø VI questionnaire. We transformed all outcomes on the same scale [0–10], so that meaningful comparison could be made between estimates.

Mental health status (anxiety and depression) was measured by the SCL-10, which is widely used in epidemiological studies. Respondents rated each of the 10 items in the SCL-10 on a four-point scale ranging from not at all (1) to extremely (4). We found an acceptable degree of internal consistency for the four-point scale in this sample (Cronbach's alpha 0.86, mean inter-item correlation: 0.41, McDonald's omega coefficient for composite reliability: 0.87). A composite variable was constructed as the sum of the 10 items. The total sum of scores were linearly transformed to lie on the scale [0–10], where 10 represented the worst mental health and 0 represented perfect mental health [mean: 0.89, standard deviation (SD): 1.20].

General health was assessed with the EQ-5D generic measure of health-related quality of life. Each health dimension has three levels: (1) no problems, (2) some problems, and (3) unable or extreme problems. Utility weights for EQ-5D_utility health states was based on the widely used UK value set (Dolan, 1997). These weights lie on a scale on which perfect health and being dead have a score of 1 and 0, respectively. The scores was linearly transformed to lie on the scale [0–10]; where 10 represented the lowest general health, and 0 represented perfect general health (mean: 1.36, SD: 1.61).

Well-being was measured by the response to the first three items on the SWLS (Diener et al., 1985; Sheikh et al., 2014, 2016a). These were “In most ways my life is close to my ideal,” “The conditions of my life are excellent,” and “I am satisfied with my life.” Respondents rated these statements using a 7-point scale ranging from completely disagree (1) to completely agree (7). A composite variable was constructed as the sum of the three items. The total sum of scores were linearly transformed to lie on the scale [0–10]; where 10 represented the lowest well-being, and 0 represented the highest well-being (mean: 2.68, SD: 1.98).

Self-rated childhood financial condition was used as the indicator of CSES, and was measured in both the Tromsø IV and Tromsø VI questionnaires by the question, “How was your family's financial situation during childhood?” on a 4-point scale (1 = very good, 2 = good, 3 = difficult, 4 = very difficult). The test-retest reliability of CSES was good (Kappa_weighted: 0.63, 99% CI: 0.61–0.65) in this sample (data not shown).

Psychological abuse and physical abuse in childhood were used as mediators. Self-reported information on psychological abuse and physical abuse in childhood was collected in the Tromsø VI questionnaire by the question: “Have you over a long period experienced any of the following? (as a child).” The possible responses included: (i) “Being tormented, or threatened with violence” and; (ii) “Being beaten, kicked, or the victim of other types of violence.” Respondents could tick one or both responses. Those who ticked the first response were classified as having experienced psychological abuse in childhood, while those who ticked the second response were classified as having experienced physical abuse in childhood.

To assess whether there is an additive effect of childhood abuse on mental health, general health, and well-being, we constructed a separate variable of Abuse frequency: 0 = not exposed to psychological or physical abuse (reference), 1 = exposed to either psychological or physical abuse, 2 = exposed to both psychological and physical abuse.

Data on confounders were taken from the Tromsø IV questionnaire. The potential confounders age, gender, exposure to passive smoke in childhood (yes, no), living in Norway at age 1 (yes, no), mother's history of psychological problems, and father's history of psychological problems, were chosen based on a priori knowledge of the association between the exposures, mediators, and outcomes under study (Sheikh et al., 2014, 2016a). Mother's/father's history of psychological problems was measured as: “Does your mother/father have/has your mother/father ever had psychological problems?” (yes, no).

All analyses were conducted using Stata version 13. Assuming that data was missing at random (MAR), imputed values were generated with multiple imputation (with chained equations), to avoid any bias in the association of interest introduced by excluding individuals with missing data. 100 multiple datasets are generated to help account for the uncertainty in the imputation procedure. In order to increase the predictive power of the imputation procedure, we included all indicators of mental health, general health, and well-being in the imputation models. A comparison between the complete-case (excluding missing) and the imputed dataset is presented with proportions (%), and mean (standard error, SE). All statistical analyses were performed on imputed dataset.

All confounding variables were tested for pairwise multiplicative interaction with CSES, psychological abuse, and physical abuse with logistic, Poisson, and linear regression (Ordinary least square, OLS) models. Statistically significant (p < 0.05) multiplicative interactions were observed with gender. Lower CSES among women was associated with a significantly lower mental health, general health, and well-being, as compared to men (data not shown). Similarly, psychologically or physically abused women had significantly lower mental health, general health, and well-being, as compared to men (data not shown). Therefore, associations between CSES, psychological abuse, physical abuse, mental health, general health, and well-being are presented separately for men and women. Robust standard errors were derived with bias-corrected accelerated bootstrapping (Carpenter and Bithell, 2000) and 99% confidence intervals (CIs) are reported. Both unadjusted (crude) and adjusted estimates are presented.

We assessed whether there was a significant difference between men and women for the exposures, mediators, and outcomes under study (Table 2). Linear regression was used for continuous and ordinal variables (CSES, mental health, general health, and well-being), while Poisson regression (relative risk, RR) was used for childhood abuse (psychological abuse, physical abuse, and abuse frequency).

Poisson regression analysis (RR and 99% CIs) with robust error variance (Barros and Hirakata, 2003; Zou, 2004) was used to assess the association between CSES and psychological abuse, physical abuse, and abuse frequency in childhood (Table 3). To assess the linear association, abuse frequency was modeled as a continuous variable. In order to assess whether differential recall bias influences the association between CSES and psychological and physical abuse, the % differences between estimates of CSES in Tromso IV and Tromsø VI were also calculated, and 99% CIs are presented (Table 3). The differences between estimates of CSES in Tromsø IV and Tromsø VI were considered as a crude proxy for differential recall bias due to present mental health, general health, and well-being).

Linear regression models were used to assess the influence of psychological abuse, physical abuse, and abuse frequency in childhood on mental health, general health, and well-being in adulthood (Table 4). In order to assess whether differential recall bias in reporting psychological or physical abuse is present, the models were also adjusted for CSES in Tromsø VI. Five estimates are presented: unadjusted (model 1); adjusted for confounding variables and CSES in Tromsø IV (model 2); adjusted for confounding variables, CSES in Tromsø IV and psychological or physical abuse (model 3); adjusted for confounding variables, CSES in Tromsø IV, psychological or physical abuse, and CSES in Tromsø VI (model 4); and a difference between estimates from model 4 and 5 (% difference and 99% CIs) (Table 4).

To assess if there is an additive effect of psychological and physical abuse, we assessed the association between abuse frequency and mental health, general health, and well-being with linear regression models. In order to assess whether differential recall bias influenced the association between abuse frequency, and mental health, general health, and well-being, the differences between the estimates of abuse frequency adjusted for confounding varibles, CSES in Tromsø IV and adjusted for confounding variables, CSES in Tromsø IV and CSES in Tromsø VI were also calculated (Table 5). Four estimates are presented: unadjusted (model 1); adjusted for confounding variables and CSES in Tromsø IV (model 2); adjusted for confounding variables, CSES in Tromsø IV and CSES in Tromsø VI (model 3); and a difference between estimates from model 2 and 3 (% difference and 99% CIs) (Table 5).

The estimation strategy for assessing mediation was based on the chains of risk model and the Causal Steps method (Judd and Kenny, 1981; Baron and Kenny, 1986). We used the “difference method” approach (Wright, 1934; Judd and Kenny, 1981; Clogg et al., 1992) to assess mediation. An important assumption of assessing mediation is that there is no multiplicative exposure-mediator interaction (Clogg et al., 1992; Robins and Greenland, 1992; Have et al., 2004; Kaufman et al., 2004; Martinussen, 2009; Sheikh et al., 2014, 2016a). A multiplicative exposure-mediator interaction suggests that CSES interacts with psychological or physical abuse in its impact on mental health, general health, and well-being. Another assumption for assessing mediation with multiple mediators is that there is no multiplicative interaction between mediators (Lange et al., 2014; Sheikh et al., 2016a). No statistically significant (p > 0.1) multiplicative interaction between the exposure and mediators, or between the mediators was observed (data not shown). Psychological abuse and physical abuse were included in the models to assess the indirect effect and the proportion of mediated effect (%) (Table 6). Exposure (CSES) was measured at two time points (Tromsø IV and Tromsø VI). Mediation was assessed in both cross-sectional and longitudinal data. Four estimates are presented: total effects (adjusted for confounding variables), direct effects (adjusted for confounding variables and mediators), indirect effects, and proportion mediated (%) (Table 6). Indirect effect was calculated as: (1)βIndirect effect=βTotal effect-βDirecteffect Proportion mediated (%) was calculated as: (2)PM (%) = βTotal effect - βDirect effect βDirect effect + βIndirect effect*100 We calculated 99% CIs for all estimates using a bias-corrected accelerated bootstrap method (Carpenter and Bithell, 2000) with 500 re-samplings. Total effects, direct effects, indirect effects and proportion mediated were assessed with both CSES in Tromsø IV and CSES in Tromsø VI (Table 6).

Furthermore, the % difference in total effects and direct effects was calculated between estimates of CSES in Tromsø IV and CSES in Tromsø VI (Table 7). The % difference (H₀: β_TE.T4 = β_TE.T6) in total effects was calculated as: (3)% difference TE = (βDE.T6 + βIE.T6)-(βDE.T4 + βIE.T4)βDE.T6 + βIE.T6*100 Where β_DE._T6 is the direct effect of CSES (Tromsø VI), β_IE._T6 the indirect effect of CSES (Tromsø VI), β_DE._T4 the direct effect of CSES (Tromsø IV), and β_IE._T4 the indirect effect of CSES (Tromsø IV).

The % difference (H₀: β_DE.T4 = β_DE.T6) in direct effects was calculated as: (4)% difference DE = (βTE.T6- βIE.T6)-(βTE.T4- βIE.T4)βTE.T6- βIE.T6*100 Where β_TE._T6 is the total effect of CSES (Tromsø VI), β_IE._T6 the indirect effect of CSES (Tromsø VI), β_TE._T4 the total effect of CSES (Tromsø IV), and β_IE._T4 the indirect effect of CSES (Tromsø IV).

Standard errors were derived with bias-corrected bootstrapping for hypothesis testing, and 99% CIs are presented.

Men reported a lower CSES (Tromsø IV) than women (β = −0.08, p < 0.001). The risk of psychological abuse in childhood among men was 20% (1/0.83 = 1.20) higher than that among women (p < 0.001), while the risk of physical abuse in childhood was 54% (1/0.65 = 1.54) higher among men than women (p < 0.001). The abuse frequency was linearly associated (p < 0.001) with lower mental health, general health, and well-being in adulthood. Women reported lower mental health (β = 0.29, p < 0.001) and general health (β = 0.28, p < 0.001), but higher well-being (β = −0.15, p < 0.001), as compared to men (Table 2).

Table 3 presents the association between CSES in Tromsø IV, CSES in Tromsø VI, and psychological abuse, physical abuse, and abuse frequency. Lower CSES (Tromsø IV) was significantly (p < 0.001) associated with psychological abuse (RR = 1.85 among men and RR = 1.90 among women), physical abuse (RR = 1.60 among men and RR = 1.82 among women), and abuse frequency (RR = 1.74 among men and RR = 1.87 among women) in childhood (Table 3). The association between CSES and psychological abuse was stronger than that between CSES and physical abuse for both men and women (Table 3). Furthermore, the associations were stronger for women than men (Table 3).

Confounding variables must not be a consequence of CSES, and lie on the causal path between CSES and psychological abuse and physical abuse in childhood. If confounding variables were influenced by CSES, the adjusted estimates would be weaker than the unadjusted estimates. The adjusted estimates were higher than the unadjusted estimates, indicating that none of the confounding variables act as mediator in the association between CSES and psychological abuse and physical abuse in childhood. This also suggests that confounding variables are not intermediate confounders in CSES→abuse→MH/GH/WB association. Generally, the association between CSES measured at the same time point (Tromsø VI) as the psychological or physical abuse, was stronger than the association with CSES measured at an earlier point in time (Tromsø IV) (Table 3). Among women, the % differences between estimates of CSES in Tromsø IV and Tromsø VI indicate that the association between CSES in Tromsø VI and psychological abuse was 14.45% (99% CI: 6.20–24.91) stronger. Similarly, the association between CSES in Tromsø VI and physical abuse was 27.47% (99% CI: 5.95–39.49) stronger among men, while it was 17.83% (99% CI: 5.25–32.29) stronger among women. Moreover, there was a pattern between men and women. The % differences indicate that psychologically abused women tended to report lower CSES in Tromsø VI than in Tromsø IV, while physically abused men tended to report lower CSES in Tromsø VI than in Tromsø IV (and vice versa). Furthermore, relative to the association between CSES in Tromsø IV and abuse frequency, the association between CSES in Tromsø VI and abuse frequency was 13.53% (99% CI: 2.08–25.47) stronger among men, while it was 15.73% (99% CI: 5.85–27.41) stronger among women (Table 3).

Table 4 presents the association between psychological abuse, physical abuse, and mental health, general health, and well-being. Adjusted for confounding variables (Model 3), psychological abuse in childhood was associated with lower mental health (β = 0.38, p <0.001 among men vs. β = 0.54, p < 0.001 among women), general health (β = 0.18, p < 0.1 among men vs. β = 0.50, p < 0.001 among women), and well-being (β = 0.32, p < 0.01 among men vs. β = 0.67, p < 0.001 among women) in adulthood (Table 4). Similarly, physical abuse in childhood was associated with lower mental health (β = 0.21, p < 0.05 among men vs. β = 0.33, p < 0.01 among women), general health (β = 0.34, p < 0.01 among men vs. β = 0.37, p < 0.05 among women), and well-being (β = 0.21, p < 0.1 among men vs. β = 0.35, p < 0.05 among women) in adulthood (Model 3). Generally, psychological abuse in childhood had a greater influence than physical abuse on mental health and well-being in adulthood. However, among men, physical abuse in childhood led to lower general health in adulthood than psychological abuse (Model 3).

In order to assess whether retrospective assessment of psychological abuse and physical abuse in childhood is influenced by recall bias, the estimates were further adjusted for CSES in Tromsø VI (Model 4), which was used as a crude proxy for differential recall bias (Table 4). The % differences in Model 3 and 4 indicate that some of the association between psychological abuse and mental health, general health, and well-being is driven by differential recall bias (2.66–8.28%, p < 0.05 among men vs. 6.44–9.75%, p < 0.001 among women). Similarly, after adjusting for CSES in Tromsø VI, the estimates for physical abuse in Model 3 were further attenuated (decreased) by 10.19–23.69%, p < 0.01 among men, and 6.17–9.19%, p < 0.01 among women. This indicates that even if CSES in Tromsø IV is included in the models, additional variables (such as CSES in Tromsø VI) should be included to account for differential recall bias (assuming that CSES reported in Tromsø IV does not influence reporting the CSES in Tromsø VI). Moreover, there was a pattern between men and women. The % differences indicate that women with lower mental health, general health, and well-being over-reported psychological abuse in childhood (and vice versa), while men with lower mental health, general health, and well-being over-reported physical abuse in childhood (and vice versa).

Table 5 presents the additive effect of psychological and physical abuse in childhood on mental health, general health, and well-being. Abuse frequency (Model 2) was associated (additively) with lower mental health and general health (p < 0.05). However, among men the association between psychological or physical abuse, and well-being was stronger (β = 0.44, p < 0.001) than with both psychological and physical abuse (β = 0.40, p < 0.001) (Model 2).

In order to assess whether abuse frequency is influenced by differential recall bias, the estimates were further adjusted for CSES in Tromsø VI (Model 3). The % differences between Models 2 and 3 indicate that some of the association between abuse frequency and mental health, general health, and well-being is driven by the differential recall bias (4.22–15.81%, p < 0.001 among men vs. 5.65–9.60%, p < 0.001 among women).

Table 6 presents the total, direct and indirect effect of CSES measured in Tromsø IV and Tromsø VI. After adjusting for confounding variables, lower CSES (Tromsø IV) was significantly (p < 0.001) associated with lower mental health, general health, and well-being in adulthood for both men and women. Lower CSES (Tromsø IV) led to lower mental health and well-being among women, as compared to men. However, lower CSES (Tromsø IV) led to a greater negative impact on general health among men compared to women. Decomposition of total effects showed that both the direct effects and indirect effects were statistically significant (p < 0.001). Psychological abuse and physical abuse in childhood significantly mediated some of the association between CSES (Tromsø IV) and mental health, general health, and well-being in adulthood (6.46–16.43% among men and 7.38–14.07% among women, p < 0.001) (Table 6).

Assessing mediation through the use of CSES from the same wave (Tromsø VI) as the mediators and outcomes showed some differences. The direction of effects remained the same; however, the total and direct effects showed a stronger association, while the proportion of mediated (%) effects were weaker in magnitude (Table 6). This implies that assessing mediation with cross-sectional data biased the total and direct effects upwards, while the % mediated effects were biased downwards.

Table 7 presents the % difference in total effects and direct effects estimated with the CSES in Tromsø VI and the CSES in Tromsø IV (from Table 6). The total effect of CSES in Tromsø VI on mental health, general health, and well-being was stronger as compared to the total effect of CSES in Tromsø IV (27.89–32.14%, p < 0.001 among men vs. 18.99–44.42%, p < 0.001 among women). This indicates that differential recall bias has a substantial influence on the cross-sectional association between CSES and mental health, general health, and well-being. Moreover, the direct effect of CSES in Tromsø VI on mental health, general health, and well-being was stronger as compared to the direct effect of CSES in Tromsø IV (29.20–35.75%, p < 0.001 among men vs. 19.48–47.66%, p < 0.001 among women).

Statistically, the change in the estimate for exposure due to a confounder or a mediator may be assessed in the same manner (with either the “difference method” or statistical methods based on counterfactuals/potential outcomes). Therefore, the estimates for direct effects can be interpreted with an alternative hypothesis of whether CSES affects mental health, general health, and well-being in adulthood, independent of psychological abuse, and physical abuse in childhood.

The estimates from model 4 in Table 4, and the direct effects from Table 6 (CSES from Tromsø IV), are presented in Figure 2 for comparison between the three childhood adversities. The relative impact of CSES, psychological abuse, and physical abuse in childhood to mental health was similar among men and women, i.e., psychological abuse had the strongest effect on mental health, followed by physical abuse, and CSES. However, for general health, physical abuse was most important among men, followed by CSES, and psychological abuse. For women, psychological abuse was most important, followed by physical abuse, and CSES. The pattern was different between men and women for well-being as well. For men, CSES had the strongest effect on well-being, followed by psychological abuse and physical abuse, while for women, psychological abuse was most important, followed by CSES and physical abuse (Figure 2).

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.