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探索社会阶层和性别对自我报告健康状况的影响:基于代表性人群研究的见解

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

作者信息

Prieto Luis

机构信息

Distance Learning, Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK.

出版信息

Life (Basel). 2024 Jan 26;14(2):184. doi: 10.3390/life14020184.

Abstract

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.

摘要

本研究利用西班牙2020年欧洲健康调查(EESE2020)的数据,调查社会阶层、性别与自我报告健康状况(SRH)之间的复杂相互作用。采用横断面设计和22,072名个体的代表性样本,该分析在调整协变量后探讨差异的持续性,重点关注与健康相关的变量。该研究采用逻辑回归模型和有向无环图(DAGs)来描述社会阶层和性别对SRH的直接影响,确定一个最小调整集以控制混杂变量。结果显示社会阶层对SRH有梯度效应,强调社会经济因素的持久影响。在考虑其他与健康相关的变量后,SRH中基于性别的差异有所减少,凸显了整体方法的重要性。有向无环图是解开复杂关系的透明工具,有助于确定关键协变量。该研究得出结论,解决健康不平等问题需要综合策略,既要考虑个人健康行为,也要考虑社会经济背景。虽然认识到研究存在横断面设计等局限性,但研究结果有助于对健康差异有更细致入微的理解,为基于证据的干预措施和政策提供信息,以建立更公平的医疗体系。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a1b/10890034/0b0077710033/life-14-00184-g001.jpg

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