Decomposing early and adult life social position effects on oral health and chronic diseases in a cross‐sectional study of Southern Brazil

Abstract

SESC had indirect effects on OHIP and number of teeth via SESA, supporting the chain of effects theory, but not for chronic diseases.

Social inequality in tooth loss, the mediating role of smoking and alcohol consumption

“…However, the findings did not suggest an indirect effect of smoking in the association of social position and tooth loss. Vendrame et al showed a direct effect of childhood socioeconomic position (SES) and adulthood SES on tooth loss; however, they did not find an indirect effect of smoking in the pathway of childhood SES or adulthood SES.…”

“…However, methodological developments such as structural equation models (SEM) and counterfactual models are available in the field of social epidemiology, allowing for decomposing total effects into direct and indirect effects of socioeconomic and intermediate variables. Vettore et al found that lower social position in 1999 directly predicted smoking and tooth loss prevalence in 2012 and that smoking was directly linked to tooth loss, using SEM.…”

Childhood Socioeconomic Status and Type 2 Diabetes Mellitus among Mid-late Chinese: A Structural Equation Modeling Analysis

“…Second, this study did not incorporate childhood adverse experiences such as child abuse and specify the duration and frequency of disadvantaged CSES, thereby the socioeconomic trajectories of mid-late Chinese was not tested effectively, which have been reported to be associated with negative health outcomes in later adulthood (69). The findings might not be generalized to other countries for the special experiences of mid-late Chinese, although which was similar to some previous literature on the effects of disadvantaged CSES (21). Further longitudinal researches especially natural experiments or intervention studies are needed to understand the life couse mechanisms and narrow the socioeconomic gaps in the incidence of T2DM.…”

“…Minimizing collinearity with confounding indictors, methodological challenges exist in decomposing the direct and indirect effects of CSES and most studies only concentrate on the direct relationship between a single outcome with several predictors employing common regression models (21). It is appropriate to apply structural equation modeling (SEM) incorporating multiple factors in this study to identify underlying pathways and disentangle the direct and indirect effects between CSES and T2DM within the LCHD conceptual framework.…”

“…It is appropriate to apply structural equation modeling (SEM) incorporating multiple factors in this study to identify underlying pathways and disentangle the direct and indirect effects between CSES and T2DM within the LCHD conceptual framework. Compared to common regression models which are limited in disentangling indirect effects between CSES and T2DM, SEM enables to estimate such effects with multiple pathways and helps to study mechanisms intensively to propose optimal interventions in the critical and sensitive period of life (21). Furthermore, previous studies have evidenced that SEM is useful to interpret and understand the underlying mechanisms of life course, which is helpful to construct social policies to improve later risks of T2DM through alleviation of disadvantaged CSES 5 (21).…”