Kauh Tina J, Read Jen'nan Ghazal, Scheitler A J
Research-Evaluation-Learning Unit, Robert Wood Johnson Foundation, 50 College Road East, Princeton, NJ 08543 USA.
Department of Sociology, Global Health Institute, Duke University, Durham, NC USA.
Popul Res Policy Rev. 2021;40(1):1-7. doi: 10.1007/s11113-020-09631-6. Epub 2021 Jan 8.
Population-level health outcomes and measures of well-being are often described relative to broad racial/ethnic categories such as White or Caucasian; Black or African American; Latino or Hispanic; Asian American; Native Hawaiian and Pacific Islander; or American Indian and Alaska Native. However, the aggregation of data into these groups masks critical within-group differences and disparities, limiting the health and social services fields' abilities to target their resources where most needed. While researchers and policymakers have recognized the importance of disaggregating racial/ethnic data-and many organizations have advocated for it over the years-progress has been slow and disparate. The ongoing lack of racial/ethnic data disaggregation perpetuates existing inequities in access to much-needed resources that can ensure health and well-being. In its efforts to help build a Culture of Health and promote health equity, the Robert Wood Johnson Foundation has supported activities aimed to advance the meaningful disaggregation of racial/ethnic data-at the collection, analysis, and reporting phases. This special issue presents further evidence for the importance of disaggregation, the technical and policy challenges to creating change in practice, and the implications of improving the use of race and ethnicity data to identify and address gaps in health.
人群层面的健康结果和幸福指标通常是相对于宽泛的种族/族裔类别来描述的,比如白人或高加索人;黑人或非裔美国人;拉丁裔或西班牙裔;亚裔美国人;夏威夷原住民和太平洋岛民;或美国印第安人和阿拉斯加原住民。然而,将数据汇总到这些群体中掩盖了群体内部的关键差异和差距,限制了健康和社会服务领域将资源投向最需要之处的能力。虽然研究人员和政策制定者已经认识到对种族/族裔数据进行分类的重要性——多年来许多组织也一直在倡导——但进展缓慢且参差不齐。持续缺乏对种族/族裔数据的分类使得在获取确保健康和幸福所需的急需资源方面存在的现有不平等长期存在。为努力营造健康文化并促进健康公平,罗伯特·伍德·约翰逊基金会支持了旨在推动在数据收集、分析和报告阶段对种族/族裔数据进行有意义分类的活动。本期特刊提供了进一步的证据,证明了分类的重要性、在实践中实现变革所面临的技术和政策挑战,以及改善种族和族裔数据的使用以识别和解决健康差距的影响。