Section 4302 of the Affordable Care Act mandated the creation of uniform data collection standards for use in the federal population health surveys which utilize self-reported data, such as the National Health Interview Survey (NHIS) and the National Health and Nutrition Examination Survey (NHANES). The final standards, which were published on October 31st, 2011, address the collection of race, ethnicity, gender, language, and disability items. The Affordable Care Act also instructed HHS that its data standards comply with any data collection standards published by the Office of Management and Budget (OMB). The data standards go into effect at the time of major revisions for each national population health survey (Office of Minority Health, 2013). The Office of Minority Health is working closely with ASPE, AHRQ and CMS to implement ACA data collection standards in NHIS, NHANES, and other population health surveys.
In addition to the changes required by the ACA, Cunningham et. al. recommend additional measures to improve the data:
- HHS should draft a consensus statement defining race, ethnicity, and ancestry.
- HHS should disseminate best practices for asking respondents for race and ethnicity data, including guidance on how to address respondents’ concerns about the uses of the data. Additionally, it would be helpful for HHS to encourage organizations to provide formal training to individuals who collect these data, including researchers, funeral directors, and clinical staff who register patients.
- HHS may consider issuing guidance to researchers and organizations about common resources and methods to determine appropriate granular ethnicity categories for their settings. Alternatively, HHS may consider disseminating a standard list of granular ethnicity categories.
- HHS should provide guidance on how multiracial data should be tabulated and analyzed.
- A question for “socially assigned race” should be further developed and tested.
- The Center for Medicare and Medicaid Services should verify the accuracy of current Medicare enrollees’ race and ethnicity data, which may have been imported from the Social Security Administration prior to the implementation of improved standards for data collection.
- HHS should develop guidance indicating appropriate circumstances under which indirect means, such as surname and geocoding, can be used for ascertaining race and ethnicity of populations when directly collected data are not available.
- HHS should require that electronic health technology software packages include fields for race, Hispanic/Latino origin, and granular ethnicity to obtain certification.
- As these standards are extended into health care delivery, HHS should consider the risks and benefits of collecting and sharing race and ethnicity data, as race and ethnicity data are not covered by the Health Insurance Portability and Accountability Act (HIPAA).
- As these data standards are extended into health delivery settings, HHS should require the analysis of health care quality metrics by race and ethnicity, and consider creating pay for performance incentives aimed at reducing racial and ethnic disparities.
Over the years Medicare has implemented a number of strategies to correct miscoded and address missing race/ethnicity information; such as the 1997 postcard survey of 2 million beneficiaries with Hispanic surnames or who were born in Latino countries and whose race/ethnicity data was either missing or “other”. The survey resulted in changes for approximately 885,000 beneficiaries (Eicheldinger 2008.)
AHRQ has published strategies that organizations can use to improve race/ethnicity information and by improving data collection procedures, enhancing legacy health IT systems, and implementing staff training (AHRQ, 2010).
The National Health Plan Collaborative (NHPC) to Reduce Disparities and Improve Quality is a nine health system partnership (public and private) that aims to address racial/ethnic disparities in care through improved data collection, data sharing, intervention implementation and shared learning (Lurie et al., 2008).