We are committed to ensuring accountability and transparency of NIH research spending. To this end, we are sharing how we review and adapt our methodologies to provide accurate reports of NIH expenditures for NIH Research, Condition, and Disease Categorization (RCDC) categories. This year, the categories of Minority Health, Health Disparities, and Biodefense are being replaced with several new categories to improve their accuracy and transparency.
Today, we are focusing on changes made to the minority health and health disparities RCDC categories for fiscal year 2023; these changes reflect switching from manual to automated coding. The manual minority health and health disparities categories are now five new automated categories:
- Health Disparities Research
- Racial and Ethnic Minority Health Research
- Health Disparities and Racial or Ethnic Minority Health Research (combination of the two individual categories but eliminates double counting of projects that are in both of the first two categories)
- Workforce Diversity and Outreach
- Building Research Capacity at Institutions with Limited NIH Funding
Before going further into these new categories, we want to briefly explain why the shifts are made, and how we do it.
Our goal is to transition remaining manual coded RCDC categories to the automated system. This is easier said than done. Each manual category requires its own automated feasibility analysis to consider its complexity and unique characteristics. However, the effort is well worthwhile given the improved reporting accuracy and time saved from future years of repetitive manual coding.
The minority health and health disparities categories were, until recently, among the few remaining manually coded categories. The methodologies for these two categories previously included funding for research as well as for activities related to workforce diversity and capacity building. Additionally, imprecise accounting or proration of projects with a significant percentage of human subjects belonging to racial/ethnic minority groups resulted in inconsistent reporting.
Our commitment to health disparities and minority health research was the underlying factor prompting us to better understand and improve their reporting. Improving the accuracy and transparency of racial and ethnic equity data was also a primary focus of the NIH UNITE initiative. The UNITE-N committee, one of five UNITE committees, helped lead the charge to provide the most accurate and transparent data. Over two years, experts from across NIH thoroughly and carefully reviewed the methodologies and project listings for these categories in more detail and developed new replacement automated categories. The five new categories that emerged have the following benefits:
- The same classification methodology is used across all projects, eliminating inconsistencies inherent with individual interpretation of coding standards
- Separating workforce diversity and capacity building from research and assigning them to their own publicly available categories increases transparency
- Automatic coding increases the accuracy of and reduces the time and effort to produce the category data
- NIH’s ability to track the progress of the NIH Minority Health and Health Disparities Strategic Plan goals has been enhanced by categories that are closely aligned with those goals
Users may notice differences in the data for these research areas. Importantly, actual funding numbers have not changed; only the reporting methodology has changed. There is no change to NIH’s commitment to supporting Minority Health and Health Disparities research.
There remain a few RCDC categories that are still coded manually due to their unique reporting requirements. Footnotes on the RCDC categorical spending page explain which categories remain manually coded and the reasons why. Going forward, it will remain our goal to continue assessing each RCDC category to ensure the data we report is specific, precise, transparent, and consistent across NIH.
Before closing, we wanted to also share some additional important information about RCDC, what it is and what it is not.
NIH created the RCDC reporting system in 2009 to provide expenditures and estimated total NIH funding for more than 215 select categories of diseases, conditions, or research areas. Since 2009, NIH has added more than 100 new categories. These categories are not mutually exclusive, meaning the same research project may be counted in multiple different categories, wherever relevant.
Financial data for separate RCDC categories should not be combined because some topics overlap and individual projects can be reported in multiple categories, leading to the resulting dollars being overinflated. Thus, one should be cautious when comparing RCDC funding or funding priorities across diseases and conditions. More info on the RCDC process is available on this blog.
Most RCDC categories use the automated indexing approach. The process to create and maintain each category uses subject matter experts to derive the parameters of the category and validate results. Automation increases the rigor, accuracy, and transparency of the categorical data because the same (standardized) classification methodology is applied across all projects. These benefits typically lead automated categorization to outperform manual coding in accuracy and efficiency.
*** Since manual and automated figures are not comparable, the historical funding numbers for the previous manually collected categories of Minority Health, Health Disparities, and Biodefense will no longer be posted on the RCDC categorical spending page. These historical funding numbers are available upon request by contacting RCDC@mail.nih.gov.
See also:
- NIMHD Director’s Message: NIH Improves MH and HD Research Categories Reporting
- UNITE Co-Chairs Corner Message: Enhancing Transparency and Accountability for NIH Minority Health and Health Disparities Research
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