Transformative Use of an Improved All-Payer Hospital Discharge Data Infrastructure for Community-Based Participatory Research: A Sustainability Pathway

Through the 2009 American Recovery and Reinvestment Act (ARRA), the U.S. Congress appropriated $1.1 billion for comparative effectiveness research (CER), which was designed to promote informed decision making through the acquisition, utilization, and synthesis of data (Dreyer et al. 2010). According to the Institute of Medicine, effectiveness and sustainability of a national CER program would be best achieved through the development of “large-scale, clinical and administrative data networks to facilitate better use of data and more efficient ways to collect new data to inform CER” (Institute of Medicine 2009). To that effect, in 2010, the Agency for Healthcare Research and Quality (AHRQ) awarded eight, 3-year state data infrastructure development grants to improve clinical and race/ethnicity data in all-payer statewide hospital discharge data systems (Agency for Healthcare Research and Quality 2012). The enhanced data repositories have permitted states to generate evaluative measures of clinical outcomes for use in public reporting and value-based purchasing, and to assess health care system performance (clinical data grants). Additionally, the data have been instrumental in improving the ability to measure population health disparities and inform decisions regarding health programs and priorities (race/ethnicity grants). However, a key marker of the success of any data infrastructure initiative is the degree to which the new data systems are useable, sustainable, and capable of continued growth beyond the life of the original grant. Given the limited time frame of development awards, grantees were encouraged to devise creative approaches to engage stakeholders and identify funding mechanisms to support ongoing use of the data. This paper will describe how the University of South Florida’s (USF) CER data infrastructure award resulted in the creation of a clinically enhanced and validated maternal and child health (MCH) database covering over 2.3 million Florida births (mother–infant dyads) spanning a 12-year period. Specifically, we will discuss how USF proposed to integrate the enhanced database into research involving community–academic partnerships, which resulted in the university being awarded a community-based participatory research (CBPR) grant from the National Institutes of Health (R24 MD008056-02). Then, we will describe how the database was used during the CBPR grant to produce detailed epidemiological and economic data on MCH disparity issues for a disproportionately disadvantaged community in the Tampa Bay area. In the last few decades, community-engaged research strategies including CBPR have emerged as some of the most promising approaches to bridge the gap between science and practice through stakeholder engagement and targeted local action to address health disparities (Wallerstein and Duran 2010). CBPR is highly compatible with translational science because it purposely incorporates community engagement into health services research and health care decision making, which increases the chances that interventions remain not only cost-effective but also culturally relevant (Fagnan et al. 2010; Tapp and Dulin 2010; Mullins, Abdulhalim, and Lavallee 2012). To promote wider community representation, CBPR researchers often keep methods simple and intuitive for community members, resulting in an almost exclusive adoption of qualitative techniques (e.g., focus groups [FG] and key informant interviews) and, less often, quantitative community surveys aimed at gathering new behavioral data. However, for CBPR initiatives to generate a more complete evidence base on the determinants of health and health disparities, multiple and diverse data sources are needed. We will discuss how integration of large, statewide administrative and clinical databases into data-gathering efforts for CBPR can help to empower underserved communities and simultaneously encourage sustainability and use of newly developed data systems.