Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Pediatr Res. 2021 Dec;90(6):1132-1138. doi: 10.1038/s41390-021-01386-w. Epub 2021 Feb 18.
Given the diversity of sex, gender identity, race, ethnicity, and socioeconomic position (SEP) in children across the United States, it is incumbent upon pediatric and epidemiologic researchers to conduct their work in ways that promote inclusivity, understanding and reduction in inequities. Current child health research often utilizes an approach of "convenience" in how data related to these constructs are collected, categorized, and included in models; the field needs to be more systematic and thoughtful in its approach to understand how sociodemographics affect child health. We offer suggestions for improving the discourse around sex, gender identity, race, ethnicity, and SEP in child health research. We explain how analytic models should be driven by a conceptual framework grounding the choices of variables that are included in analyses, without the automatic "adjusting for" all sociodemographic constructs. We propose to leverage newly available data from large multi-cohort consortia as unique opportunities to improve the current standards for analyzing and reporting core sociodemographic constructs. Improving the characterization and interpretation of child health studies with regards to core sociodemographic constructs is critical for optimizing child health and reducing inequities in the health and well-being of all children across the United States. IMPACT: Current child health research often utilizes an approach of "convenience" in how data related to sex, race/ethnicity, and SEP are collected, categorized, and included in models. We offer suggestions for how scholars can improve the discourse around sex, gender identity, race, ethnicity, and SEP in child health research. We explain how analytic models should be driven by a conceptual framework grounding the choices of variables that are included in analyses. We propose to leverage newly available large cohort consortia of child health studies as opportunities to improve the current standards for analyzing and reporting core sociodemographic constructs.
鉴于美国儿童的性别、性别认同、种族、民族和社会经济地位（SEP）存在多样性，儿科和流行病学研究人员有责任以促进包容性、理解和减少不平等为目标开展工作。目前，儿童健康研究通常在收集、分类和纳入模型中与这些结构相关的数据时采用“方便”的方法；该领域需要更加系统和深思熟虑地研究其方法，以了解社会人口统计学如何影响儿童健康。我们提出了改进儿童健康研究中性别、性别认同、种族、民族和 SEP 论述的建议。我们解释了分析模型应如何由一个概念框架驱动，该框架为纳入分析的变量选择提供基础，而不是自动“调整”所有社会人口统计学结构。我们建议利用来自大型多队列联盟的新可用数据作为改善当前分析和报告核心社会人口统计学结构标准的独特机会。提高核心社会人口统计学结构在儿童健康研究中的描述和解释对于优化儿童健康以及减少美国所有儿童健康和福祉方面的不平等至关重要。影响：目前的儿童健康研究通常在收集、分类和纳入模型中与性别、种族/民族和 SEP 相关的数据时采用“方便”的方法。我们提出了一些建议，供学者在儿童健康研究中改进性别、性别认同、种族、民族和 SEP 论述。我们解释了分析模型应如何由一个概念框架驱动，该框架为纳入分析的变量选择提供基础。我们建议利用新出现的大型儿童健康研究队列联盟作为改善当前分析和报告核心社会人口统计学结构标准的机会。
