Comparing data across studies to look at trends can be thwarted by different aggregation schemes. For example, one study may examine prevalence by gender, race and age, while another looks at prevalence by age and race; making it difficult to interpret results. In addition, studies often use different definitions for variables. For example, researchers use different “cut offs” for age (<65 or >65...or 50–60, 60–70, etc.).
Researchers are only beginning to develop quality measures intended for disparities research. Weissman et. al. (2011) released a report outlining recommendations for the development of quality measures to monitor potential healthcare disparities from the National Quality Forum’s (NQF) 700 available quality measures. The report recommended a three-step process for identifying disparities-sensitive quality measures: 1) Assess the NQF’s quality measures using disparities-sensitive principles, 2) Apply new criteria for disparities sensitivity for quality measures that do not stratify data by race/ethnicity, or other disparities variables, and 3) develop new disparities specific measures (pg. 7).
There are challenges in obtaining state and local data to for intervention research at the local level, as well.