“Moving Toward Equitable Data Collection for the Hispanic/Latino Population in the Census and Health Surveillance Systems”
Author: Ishmael Mendoza
Department: UIC Institute for Policy & Civic Engagement
Advisor: Dr. Joseph K. Hoereth, IPCE
Abstract: This project systematically reviews data-inequity for the Hispanic/Latino population, with a primary focus on the attribute of ethnicity. This review highlights the discussion of disaggregating data, reviewing the findings of research that has addressed health in Hispanic/Latino subgroups rather than the monolithic standard set by the Office of Management and Budget’s 1997 Statistical Policy Directive No.15: “Standards for Maintaining, Collecting, and Presenting Federal Data on Race and Ethnicity.” The research supports the use of ethnically disaggregated data for Hispanic/Latinos in making data more equitable, illustrating health disparities otherwise unaddressed. In addition, mistrust presents a unique barrier in creating more equitable characterization of the Hispanic/Latino population in data. The mistrust of data collecting institutions in the Hispanic/Latino is addressed by viewing the sentiments surrounding the 2020 Census, accompanied by a document analysis of public comments focused on the Hispanic/Latino racial/ethnic identifier in the Census. Policy recommendations derived from this review include: (a) supporting the adoption of disaggregated race/ethnicity standards in the Census through the open commentary; (b) supporting outreach to rebuild trust with Hispanic/Latino communities; (c) and training systems and researchers to collect disaggregated data ethically, maintaining the safety of participants. These recommendations work towards better understanding health disparities, allowing for the creation of strategies with more cultural approaches. This will also allow more effective resource distribution at Federal and State levels with increased participation from these historically hard-to-count populations.
Keywords: Health equity, Hispanic/Latino, Health disparities, Data disaggregation