Hierarchical Structural Equation Model for Pathways of Socio-epidemiological Correlates Leading to Obesity-related Indicators

Background In spite of numerous theories, including physical self-efficacy theory, planned behavior theory, transtheoretical model, theory of reasoned action and so on, developed for the guidance of ameliorate obesity through intervention program either based on randomized controlled design or observational studies, whether the results of these intervention program can be generalized or applied to other settings, ethnics, countries, and areas is highly dependent on the underlying epidemiological causes leading to obesity in each of place. Although a body of evidence on the relationship of each correlate to obesity has been documented, elucidating the pathway of how these epidemiological and population-based characteristics are connected and affect obesity-related markers has been barely addressed. Most previous studies examined the relationship using the traditional two-state regression model, which cannot take intermediate process between variables into consideration. From the statistical viewpoint, traditional approach might have troubles in multiple comparisons and also over-parametrization given limited sample size. More importantly, only observed variables instead of latent contextual variables were implicated. Aim Our study aim was to apply likelihood-based and Bayesian-oriented structural equation model (SEM) to clarify the relationship interwoven with these socio-epidemiological characteristics leading to obesity-related phenotypes based on the community-based integrated screening data that offered various screening modalities for manifold cancers and chronic diseases. Bayesian hierarchical SEM was applied to assessing the influences among individual, district-level data (multilevel/ hierarchical), and interactions between latent constructs at different levels simultaneously. Materials and Methods The participants of Keelung Community-based Integrated Screening (KCIS) program during 2002 to 2010 were recruited for this study. Residents living in Keelung and aged 20 years or older have been invited to participate this program for mainly screening five neoplastic diseases and three non-neoplastic chronic diseases. Anthropometric measurements, including body weight, height, waist circumference and hip circumference, were measured by trained staff in each visitation. Information on demographic characteristics, dietary behaviors and intake diversity in frequency and quantity, health and unhealthy behaviors, and life styles were collected through face-to-face interview using a structured questionnaire conducted by trained interviewers. The biomarkers were also simultaneously collected and examined by central laboratory using the 8-hour fasting blood serum. The traditional multivariate regression method was generally employed in the studies to examine the relationship between observed variables and obesity-related factors. The exploratory factor analysis was conducted to cluster the indicators for each constructs and the path analysis was employed to constitute the candidate structural equation model (SEM). Both criteria of Akaike’s information criterion (AIC) and Bayesian information criterion (BIC) were used for parsimonious model selection. We also conducted hierarchical Bayesian SEM to examine the main effect and interaction between district-level and individual information. Results The overall 75077 subjects aged 20 years and older were recruited as study population, including 30042(age 49.54±16.06 and 45035(age 46.58±14.4) for male and female respectively. Based on the personal characteristics, obesity traits, metabolic biomarkers, life style of eating patterns, and habits, 7 clusters with significant factor loadings were generated by standardized approach, including socioeconomic status (SES), health motivation (HM), eating patterns (EP), regular diet (RD), diverse intakes (DI), unhealthy habits (UH), and obesity-related biomarkers (OB), which was nominated as best SEM after taking the significant associations among SES, unhealthy habits, health motivation, and eating patterns into account. There were two main pathways identified from constituted the SEM with 13 significant pathways. For the first layer, the path coefficients from SES were -0.055, 0.219, and -0.078, from unhealthy habits were 0.295, 0.350, and 0.253, from eating patterns (EP) were -0.080, 0.149, and 0.074, to regular diet (RD), diverse intake (DI), and obesity-related factors, respectively. The path coefficients from health motivation were -0.656 and -0.311 on diverse intake (DI) and obesity-related biomarkers. For the second layer, including diverse intakes and regular diet, both path coefficients on obesity-related biomarkers were -0.359 and -0.024, respectively. The similar findings were noted while Bayesian hierarchical SEM was used but , most importantly, there was a strong random intercept (baseline influence) effect of the variation of obesity-related phenotypes among districts and also significant interaction between district SES and other latent constructs (such as health motivation and so on). Conclusion Our study developed the seven latent constructs to clarify the relationship interwoven with these socio-epidemiological characteristics leading to obesity-related phenotypes, including socio-economic status (SES), health motivation (HM), unhealthy habit (UH), regular diet (RD), eating pattern (EP), diverse intakes (DI), and obesity-related phenotype (OB) and effect of these seven latent constructs on obesity-related phenotype was modified by the contextual factor of district SES. The development of Bayesian hierarchical SEM and its application to socio-epidemiological data here provide a new insight into a new paradigm on the pathway interplay with the underlying latent variables in multilevel, which has a significant implication for prevention of obesity-related phenotypes.