TY  - JOUR
AU  - Wang, Karen
AU  - Grossetta Nardini, Holly
AU  - Post, Lori
AU  - Edwards, Todd
AU  - Nunez-Smith, Marcella
AU  - Brandt, Cynthia
PY  - 2020
DA  - 2020/7/20
TI  - Information Loss in Harmonizing Granular Race and Ethnicity Data: Descriptive Study of Standards
JO  - J Med Internet Res
SP  - e14591
VL  - 22
IS  - 7
KW  - continental population groups
KW  - multiracial populations
KW  - multiethnic groups
KW  - data standards
KW  - health status disparities
KW  - race factors
KW  - demography
AB  - 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. 
SN  - 1438-8871
UR  - http://www.jmir.org/2020/7/e14591/
UR  - https://doi.org/10.2196/14591
UR  - http://www.ncbi.nlm.nih.gov/pubmed/32706693
DO  - 10.2196/14591
ID  - info:doi/10.2196/14591
ER  -