Date of Completion
Brenda Shipley, Jane A. Ungemack
Field of Study
Master of Public Health
Public health researchers, practitioners and policy makers are increasingly trying to uncover, quantify and address health disparities, which are differences in health outcomes among population subgroups (Nepaul et al., 2007). Health disparities are understood to be the consequences of differences in care, the health services infrastructure, and information systems available to persons by virtue of their gender, race, ethnicity, education level, etc. (U.S. Department of Health and Human Services, 2011; Institute of Medicine, 2009). In the United States, we commonly study health disparities occurring across racial and ethnic groups, requiring the collection of race, ethnicity and language data in order to determine the existence and/or the extent to which health disparities are occurring (Carter-Pokras et al., 2002; Thorlby et al., 2011). However, this information is not always easily collected, if collected at all. In order for public health agencies to address health disparities in their communities, they must first know whether disparities exist and whom they affect. Therefore, accurate reporting of race, ethnicity and primary language (REL) data is necessary to properly identify, describe and investigate potential health disparities in the community of concern.
This study explored perceptions of race, ethnicity, and primary language data collection in healthcare settings, and more specifically the attitudes and beliefs that people have when they are asked to self-report this information. The study was conducted in collaboration with the University of Connecticut’s Health Disparities Institute. The goal of this study was to develop recommendations to encourage individuals in the state of Connecticut to self-report this information, and increase their level of comfort when requested to do so in a healthcare setting.
Henry, Roberto A., "Attitudes and Beliefs Regarding the Collection of Race, Ethnicity and Primary Language Information in Healthcare Settings" (2014). Master's Theses. 578.
David I. Gregorio