Socioeconomic status, birth weight, maternal smoking during pregnancy and adiposity in early adult life: an analysis using structural equation modeling

Abstract

We describe here an example of structural equation modeling in epidemiology. The association between birth weight and adiposity in early adult life, adjusted for the number of cigarettes smoked during pregnancy and socioeconomic status at birth, was evaluated. Data involving 2,063 adults from the 1978/1979 Ribeirão Preto cohort study were used. Adiposity was measured by body mass index (BMI), waist circumference (WC) and the sum of triceps and subscapular skinfolds (STSS). Models were submitted to maximum likelihood estimation, separately for men and women. Birth weight had a small and significant effect on adiposity in men (standardized coefficient, SC = 0.08) and women (SC = 0.09). Smoking during pregnancy did not influence adiposity in men (SC = 0.004), but its effect was marginally significant in women (SC = 0.07; P = 0.056). Socioeconomic status at birth had a small and positive effect on adiposity in men (SC = 0.08) and a moderate and negative effect in women (SC = -0.16). In this young adult population, BMI, WC and STSS used alone or in combination were valid estimators of body adiposity.

Impact of oral health on physical and psychosocial dimensions: an analysis using structural equation modeling

“…It was the only variable in the model with missing responses (10.5% of observations). To mitigate this limitation, estimates of missing values were obtained by linear regression performed in SPSS Amos 19 , considered the best alternative for dealing with missing data 18,31 . Even so, the estimated per capita income coefficient in constructing socioeconomic status may have been underestimated.…”

“…Another limitation to the study is that in order to adjust the structural model, a maximum likelihood method was used, which requires univariate and multivariate normality, in addition to variables on a continuous measurement scale 18,19,30,31 . However, the only continuous variables were per capita income and household crowding.…”

“…This stage used the complex sample module implemented in SPSS 16.0 (SPSS Inc., Chicago, USA). SEM was used to assess severity of impact of oral disorders on adolescents' physical, psychological, and social well-being 17,18,19 . According to the proposed model, socioeconomic status exerts direct effects on habits and health care, use of dental services, normative conditions of oral health, and severity of impact.…”

“…Figure 1 shows the hypothetical test model. The observed variables are represented by rectangles, the latent variables by ellipses, and the associations by arrows or lines (from the independent to the dependent variable) 18,19 .…”

“…Structural equation analysis is based on a theoretical framework defined a priori by the researcher, constructing a model for relations between the variables and later testing whether the data confirm the hypothetical model 18 . SEM allows the researcher to simultaneously analyze a series of relations of dependency, i.e., when a dependent variable becomes independent in subsequent relations of dependency, and not in isolation as is done traditionally in statistical analysis with classical techniques 18,19 . Another advantage of this technique is the possibility of incorporating latent variables into the analysis, i.e., variables that are not directly observable, but whose effects or manifestations are "felt" through other variables 18,19 , like the impact of oral health, assessed by the OHIP-14 instrument.…”