mittees (DCCs), and other stakeholder organizations. The secretary of DHHS may propose changes to the standards that the DSMOs, DCCs, and other organizations may then consider. The panel suggests further exploration of this avenue for the federal mandating of racial and ethnic data collection.
Whatever standards are chosen should use the OMB standards for their base, supplemented with further detail as needed. DHHS should also work with hospital and health plan-related groups to determine which SEP data are feasible to collect on enrollment or admissions forms. Collection of these data will necessarily be limited as the collection of detailed wealth and income information may impose a burden on providers and on the individuals providing the data. However, an individual’s education level may be the easiest and least sensitive item to collect.
In developing standards for data collection, it is critically important to provide clear information about how the data will be used so that individuals providing the data are fully informed. DHHS should work with industry agents and legal experts to develop a list of these uses for hospitals, health plans, and medical groups to give to individuals from whom the data are collected.
RECOMMENDATION 6-2: DHHS should provide leadership in developing standards for collecting data on race, ethnicity, socioeconomic position, and acculturation and language use by health insurers, hospitals, and private medical groups.
Implementation of this report’s recommendations would greatly enhance the data infrastructure available for understanding and eliminating disparities. However, if these recommendations cannot be implemented such that high-quality data are produced, linking aggregate-level data on race, ethnicity, SEP, and acculturation and language use may be needed to bridge the gaps. These data aggregated at the level of census geographical units (Zip Code tabulation areas, tracts, or block groups) could be used to proxy individual-level data by linking them to the individual level data that are available.
Suitable confidentiality protections are critical for the use of such linked geocoded data. The precise combination of values of the sociodemographic variables might identify a subject’s geographical area and thus pose a risk of disclosure of confidential information about health plan members. Methods have been developed for masking such data by rounding or adding random noise. Such masked data sets can then be analyzed with appropriate corrections for the effects of masking. But development of the specific procedures and parameters required to implement data masking requires particular statistical expertise that is not likely to be found within health insurers.