Harmonizing Race and Ethnicity Data to Facilitate Data Analysis of Pooled Environmental Health Studies

Abstract

BACKGROUND AND AIM: Race and ethnicity are social constructs that function as necessary tools in environmental health research, helping to identify underserved populations. Despite much discussion of more inclusive ways for individuals to self-identify, there is insufficient consideration for organizing these diverse subgroups for use as data across multiple studies, facilitating comparisons. The HHEAR (Human Health Exposure Analysis Resource) Data Repository houses deidentified data of environmental and exposome studies. Data are harmonized to a common vocabulary to facilitate data pooling. Most HHEAR studies use variations of the current Office of Management and Budget (OMB) standards to place groups of people into categories. These different categorizations can hinder harmonization, as some studies separate race and ethnicity using a two-question format while others construct a combined format. Few incorporate country or region of origin. Additionally, OMB standards are extended to studies that take place outside the United States, which may not have originally used these categorizations. METHODS: We conducted a review of published literature, standard ontologies used by research collaborations, and national reviews from countries outside the United States to review the harmonization of race and ethnicity and identify methods to facilitate data pooling in the HHEAR repository. RESULTS:As there was no clear consistency in mappings across literature, we created our own broad data standards to accommodate the diverse definitions incorporating race and ethnicity on both national and international levels. We encourage more standardized reporting in future studies in order to facilitate harmonization. CONCLUSIONS:Large, diverse datasets allow for investigations with larger sample sizes and greater exposure variability. By improving and standardizing methodology for data harmonization, environmental health studies can promote social justice by informing policy to allocate time and resources to at-risk communities and, in turn, reduce health disparities. KEYWORDS: Dictionary Mapping, Knowledge Modeling, Data Standards, Harmonization, Race, Ethnicity