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Information Loss in Harmonizing Granular Race and Ethnicity Data: Descriptive Study of Standards

机译:协调粒状和种族数据的信息损失:标准描述性研究

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Background Data standards for race and ethnicity have significant implications for health equity research. Objective We aim to describe a challenge encountered when working with a multiple–race and ethnicity assessment in the Eastern Caribbean Health Outcomes Research Network (ECHORN), a research collaborative of Barbados, Puerto Rico, Trinidad and Tobago, and the US Virgin Islands. Methods We examined the data standards guiding harmonization of race and ethnicity data for multiracial and multiethnic populations, using the Office of Management and Budget (OMB) Statistical Policy Directive No. 15. Results Of 1211 participants in the ECHORN cohort study, 901 (74.40%) selected 1 racial category. Of those that selected 1 category, 13.0% (117/901) selected Caribbean; 6.4% (58/901), Puerto Rican or Boricua; and 13.5% (122/901), the mixed or multiracial category. A total of 17.84% (216/1211) of participants selected 2 or more categories, with 15.19% (184/1211) selecting 2 categories and 2.64% (32/1211) selecting 3 or more categories. With aggregation of ECHORN data into OMB categories, 27.91% (338/1211) of the participants can be placed in the “more than one race” category. Conclusions This analysis exposes the fundamental informatics challenges that current race and ethnicity data standards present to meaningful collection, organization, and dissemination of granular data about subgroup populations in diverse and marginalized communities. Current standards should reflect the science of measuring race and ethnicity and the need for multidisciplinary teams to improve evolving standards throughout the data life cycle.
机译:种族和种族的背景数据标准对健康股权研究具有重大影响。目标我们的目标是描述在东加勒比健康成果研究网络(echorn),巴巴多斯,波多黎各,特立尼达和多巴哥和美国维尔京群岛的研究中遇到的竞争时遇到的挑战。方法审查了使用管理和预算办公室(OMB)统计政策指令第15号委员会第15号的统一和多种族群体协调竞争和种族数据的数据标准.15。梯队队列研究的1211名参与者的结果,901(74.40%) )选择了1个种族类别。选择1类别的人,13.0%(117/901)选择加勒比; 6.4%(58/901),波多黎各或富通; 13.5%(122/901),混合或多种族类别。共有17.84%(216/1211)的参与者选择了2个或更多类别,15.19%(184/1211)选择2个类别和2.64%(32/1211)选择3个或更多类别。随着ESHORN数据的汇总到OMB类别,参与者的27.91%(338/1211)可以放在“多个比赛”类别中。结论这一分析公布了基本信息学,目前对多种和边缘社区中的有意义的收集,组织和传播粒度数据的颗粒数据的基本信息学挑战。目前的标准应反映测量种族和种族的科学以及多学科团队在整个数据生命周期中提高不断变化的标准。

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