Exploring the Influence of Social Class and Sex on Self-Reported Health: Insights from a Representative Population-Based Study.

This study investigates the intricate interplay between social class, sex, and self-reported health (SRH) using data from the European Health Survey of Spain 2020 (EESE2020). Employing a cross-sectional design and a representative sample of 22,072 individuals, the analysis explores the persistence of disparities after adjusting for covariates, focusing on health-related variables. The study employs logistic regression models and directed acyclic graphs (DAGs) to delineate the direct effects of social class and sex on SRH, identifying a minimum adjustment set to control for confounding variables. Results reveal a gradient effect of social class on SRH, emphasizing the enduring impact of socioeconomic factors. Sex-based disparities in SRH diminish after considering additional health-related variables, highlighting the importance of a holistic approach. DAGs serve as transparent tools in disentangling complex relationships, guiding the identification of essential covariates. The study concludes that addressing health inequalities requires comprehensive strategies considering both individual health behaviours and socio-economic contexts. While recognizing limitations, such as the cross-sectional design, the findings contribute to a nuanced understanding of health disparities, informing evidence-based interventions and policies for a more equitable healthcare system.

Checking adjustment sets and finding common adjustment sets based on a causal graph model / 四川精神卫生

The purpose of this paper was to introduce the method of checking adjustment sets based on a causal graph model， finding common adjustment sets and implementing the statistical calculation with SAS software. Firstly， the basic concepts related to the causal graph model were introduced.Secondly， the primary contents of the causal graph theory were given， including the composition and terminology of the causality diagram. Finally， for the two instances and with the help of the CAUSALGRAPH procedure in SAS/STAT， the following two tasks were completed： the first task was to examine the adjustment set and enumerate paths； the second task was to find the adjustment set common to the multiple causal graph models.

Constructing and searching adjustment sets based on a causal graph model / 四川精神卫生

The purpose of this paper was to introduce the basic knowledge of the causal graph model， the contents of the CAUSALGRAPH procedure and the method of constructing and searching adjustment sets based on the CAUSALGRAPH procedure in SAS/STAT. The causal graph model was the product of the combination of graph theory and probability theory. It could find all possible adjustment sets including the minimum adjustment set based on the action relationship between the variables set by the user. The contents of the CAUSALGRAPH procedure mainly included three identification criteria， two operating modes and one verification checking method. This paper analyzed the causal effect of two instances based on the CAUSALGRAPH procedure in SAS， and explained the output results.