Community-Level Interventions to Collect Race/Ethnicity and Language Data to Reduce Disparities
Published Online: September 22, 2012
Romana Hasnain-Wynia, PhD; Deidre M. Weber, BA; Julie C. Yonek, MPH; Javiera Pumarino, BA; and Jessica N. Mittler, PhD
The systematic collection and use of race/ethnicity and language (REL) data by healthcare organizations has long been recognized as a critical step in identifying and reducing healthcare disparities locally and nationally.1-3 The American Recovery and Reinvestment Act of 2009 urges the “use of electronic systems to ensure the comprehensive collection of patient demographic data, including, at a minimum, REL and gender information.”4 The 2010 Affordable Care Act has explicit expectations regarding the standardized collection and reporting of self-reported REL data. Private accreditation organizations such as the Joint Commission and the National Committee for Quality Assurance have developed standards regarding the collection of REL data by hospitals and health plans.5 Despite widespread consensus that these data are necessary for identifying, monitoring, and reducing disparities in care,6 few healthcare organizations systematically collect this information.
Kilbourne et al recommended a framework for addressing disparities in the healthcare system, which included 3 broad phases: “The first requires detection; the second, understanding; and the third involves the development, implementation, and evaluation of interventions that reduce or eliminate disparities.”7 Collecting REL data is largely about detection. In this regard, evidence of pervasive healthcare disparities is readily available through national surveys, reports, and peer-reviewed literature.1,3,8 However, among healthcare providers and leaders, including even those familiar with the national data pointing to disparities in care, there is a tendency to believe that disparities exist “somewhere out there” but not within their own organizations.9,10 One reason such views can persist is the frequent lack of data to demonstrate or disprove the existence of disparities at the local, organizational level. Organizations need to collect data to detect potential disparities within their communities and be accountable for addressing this pervasive problem.
A substantial body of work has examined the barriers and facilitators to collecting REL data in individual healthcare organizations such as hospitals, physician practices, and health plans.2,3,11-13 This paper examines a set of community-level interventions for collecting REL data. The Aligning Forces for Quality (AF4Q) initiative, a national program of the Robert Wood Johnson Foundation (RWJF), is designed to help targeted communities improve the overall quality of healthcare including reductions in racial and ethnic health disparities.14 It provides an unprecedented opportunity to examine a range of community-level strategies to institute standardized REL data collection to detect disparities in local health system performance. The multi-stakeholder AF4Q alliances (the generic term used for the multi-stakeholder partnership in each community) are focused on propelling communitywide efforts to build the local infrastructure to collect patient demographic data for disparities detection and reduction activities. As federal and state mandates and accreditation standards begin to require the collection of REL data, the experiences of these AF4Q alliances might provide important lessons.
Even under ideal circumstances, meeting the multiple AF4Q programmatic goals would be daunting. Each of the AF4Q alliances comprises volunteer partner organizations and individuals, bringing distinct perspectives and sometimes competing interests to the table,11 which can present opportunities and challenges in accomplishing goals. Given resource limitations and competing demands within these voluntary alliances, the challenges and opportunities they have faced in collecting REL data might presage experiences soon to arise in other communities nationwide. This article describes why collecting these data has remained difficult despite the fact that many alliance leaders understand that this information forms the foundation for detecting and reducing disparities in care.
This paper examines the experience of 14 AF4Q alliances (data from additional alliances that joined the AF4Q initiative in 2009-2010 are not included in this analysis). This study’s target population included AF4Q alliance leaders, project directors, and disparities/equity staff leads; we selected these individuals based upon their overall roles in their alliance, and because they had broad and specific knowledge about challenges and strategies to reduce disparities in their communities. The study protocol was approved by the Office of Research Protections at Pennsylvania State University.
Two senior researchers from the AF4Q evaluation team conducted 1-hour, face-to-face, semi-structured interviews with key informants during 2-day visits to each of the 14 AF4Q communities in 2010. Protocol questions covered a wide range of topics, including questions about disparities-related activities and REL data collection. All informants were asked a set of key questions. The interview protocol was also tailored to ensure that we captured the most relevant information from the best sources.
Alliance visit data were supplemented with data collected from the AF4Q initiative project directors in the summer and winter of 2011 about the alliances’ progress on disparities-related activities; senior researchers collected these data through regular, 6-month, 90-minute, semi-structured telephone interviews to provide ongoing perspective on the alliances’ activities. All interviews were digitally recorded after consent was obtained.
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