Recent studies from the US and several

Recent studies from the US and several Nordic countries have shown that an educational gradient in morbidity and mortality exists between siblings (e.g., Krieger, Chen, Coull & Selby, 2005;Madsen, Andersen, Christensen, Andersen & Osler, 2010;Naess, Hoff, Lawlor & Mortensen, 2012;Sondergaard et al., 2012). In addition, some of these studies also show that the relative educational gradient in mortality is less steep in within-sibling comparisons than in the population as a whole, which has been interpreted as evidence of familial confounding of the association between education and mortality. However, the education-mortality association remained unchanged in a Swedish twin study, which suggests that the association is not driven by the environmental and/or genetic factors that are common to the twin pair (Lundborg, Lyttkens & Nystedt, 2016). To examine the genetic input, comparing the health outcomes for dizygotic and monozygotic twins provides a strong research design. A concern with twin studies may however be whether the results are generalizable to the overall population (Strully & Mishra, 2009).
Data The basis for including individuals in this study was the Census of 1990, which covered the entire Swedish population in November of that year. The Multi-generation Register with information on parents was linked to the individuals in the Census to identify full biological siblings. The Multi-generation Register contains parental information for individuals who were born after 1932; however, the parent-child abk is more complete for individuals who were born in 1934 or later. We therefore include birth cohorts from 1934 and onwards, with the upper limit set to 1959 so that most of the individuals had completed their education before the start of the follow-up. All full siblings were possible to identify if the parents were born in Sweden in the year 1915 or later. The Multi-Generation Register and the validation procedures that weer used have been described in detail elsewhere (Ekbom, 2011).
Variables
Statistical analyses In the survival analyses, we used age in months as the time. Individuals were counted as being under the risk of dying from the age that they had in January 1991 until the age of emigration, age of death, or age at the end of follow-up, which was December 2012. All of the analyses were adjusted for sex. The sex-specific estimates of the association between education and mortality were obtained by including an interaction term between sex and education. We conducted analyses for all-cause mortality and cause-specific mortality. We further stratified the analyses by age-group and by social class background. We conducted two sets of Cox proportional hazards regression models: with and without a family fixed effect. We will refer to these as population-based analyses and family-based, respectively. In the analyses without a family fixed effect (i.e., the population-based analyses) the model is of the form λij(t)=λ0(t) exp(β1xij1+…+ βmxijm), where λij(t) is the hazard for the ith sibling within the jth family, λ0(t) is the baseline hazard, βm is the mth element of the vector of regression coefficients, and xijm is the mth element of the vector of covariates for the ith sibling in the jth family (i.e., dummy variables pertaining to sex and education). In these analyses, we took interdependence of siblings into account by using a robust variance estimator. In the analyses with a family fixed effect, the model is of the form λij(t)=λ0j(t) exp(β1xij1+…+ βmxijm). Note that the model now contains a separate baseline hazard λ0j(t) for every jth family. The family-specific baseline hazard ensures that comparisons are only made within families. Unlike shared frailty models, this model makes no assumption of independence between the shared component (i.e., the baseline) and covariates. To quantify the importance of the family of origin, we compare the Hazard Ratios between the analyses with and without sibling fixed effects.