Importance of Public and Private Partnership Supporting Data Disaggregation to Measure Racial, Sexual Orientation, and Gender Identity Disparities in COVID-19

Abstract

The COVID-19 pandemic has exposed shortcomings in the US public health data system infrastructure, including incomplete or disparate processes related to data collection, management, sharing, and analysis. Public health data modernization is critical to ensure health equity is at the core of preparedness and response efforts and policies that prioritize equitable responses to health emergencies. To address the inequitable uptake and distribution of COVID-19 vaccinations in communities most disproportionately impacted by the pandemic, the CDC Foundation's Response Crisis and Preparedness Unit began partnering with community-based organizations in March 2021 to provide education and outreach and facilitate access to vaccines. These organizations engaged with partners and communities to address vaccine-related concerns, develop innovative and culturally appropriate communication strategies, and promote timely vaccination. Two grantees, Out Boulder County in Colorado and the Coalition of Asian American Leaders in Minnesota, experienced issues related to public health data collection standards and practices for COVID-19. Data collection tools often lack the appropriate or necessary demographic variables or level of disaggregation needed to be able to assess prioritization and disparities within racial and ethnic groups and across sexual orientation and gender identity categories. In this case study, both grantee organizations document their experiences, challenges, and strategies to overcome barriers to implementing their projects resulting from a lack of meaningful data. These examples identify inequities and systems-level changes related to data collection and surveillance, and they provide recommendations and lessons learned to improve data surveillance for more equitable public health responses.

Keywords: COVID-19; Community-based organizations; Data modernization; Disease surveillance; Public health preparedness/response.