RADx-UP Program Addresses Data Gaps in Underrepresented Communities

Guest post by Richard J. Hodes, MD, Director, National Institute on Aging, and Eliseo Pérez-Stable, MD, Director, National Institute on Minority Health and Health Disparities, NIH.

A few months into the COVID-19 pandemic, we shared how NIH was working to speed innovation in the development, commercialization, and implementation of technologies for COVID-19 through NIH’s Rapid Acceleration of Diagnostics (RADx) initiative.

Two years later, one of the RADx programs—RADx Underserved Populations (RADx-UP)—reflects on lessons learned that have broken the mold of standard research paradigms to address health disparities.

Use of Common Data Elements

RADx-UP has presented unique challenges in terms of data collection, privacy concerns, measurement standardization, principles of data-sharing, and the opportunity to reexamine community-engaged research. Establishment of Common Data Elements (CDEs)—standardized, precisely defined questions paired with a set of allowable responses used systematically across different sites, studies, or clinical trials to ensure that the whole is greater than the sum of its parts—are not commonly used in community-engaged research. Use of CDEs enables data harmonization, aggregation, and analysis of related data across study sites as well as the ability to investigate relationships among data in unrelated data sets. CDEs can also lend statistical power to analyses of data for small subpopulations typically underrepresented in research.

RADx-UP is a community-engaged research program that builds on years of developing partnerships between communities and scientists. RADx-UP has funded 127 research projects with sites in every state and six U.S. territories as well as a RADx-UP Coordination and Data Collection Center (CDCC). RADx-UP assesses the needs and barriers related to COVID-19 testing and increase access to COVID-19 testing in underserved and vulnerable populations experiencing the highest rates of disparities in morbidity and mortality.

The COVID-19 pandemic necessitated establishing RADx-UP and its associated CDEs with unprecedented speed relying heavily on data elements derived from those already defined in the NIH-based PhenX Toolkit and Disaster Research Response (DR2) resources. The short time frame for this process did not allow for as extensive collaboration and input from RADx-UP investigators and community partners that would have been ideal. Additionally, many researchers, especially community partners engaged in RADx-UP projects, were not familiar with CDE data collection practices. As a result, CDE questionnaires had to be modified as studies progressed to better suit the needs of the consortium and investigators new to CDE collection had to be familiarized with these processes quickly. NIH program officers, NIH RADx-UP and CDCC leadership and engagement impact teams (EITs)—staff liaisons provided by the CDCC that link RADx-UP research teams to testing, data, and community-engagement resources—helped research teams implement and adjust CDE collection, ensured alignment across consortium research teams, and assisted with other data-related issues that arose.

All RADx programs are required to collect a standardized set of CDEs, including sociodemographic, medical history, and health status elements with the intent to provide researchers rapid access to data for secondary research analyses in the RADx Data Hub, the central repository for RADx data. However, implementation of CDEs in the context of underserved communities in the rapidly evolving COVID-19 pandemic presented complex issues for consideration.

Some of these issues included data privacy, the risk of re-identification of underserved and undocumented populations, and data collection burden on participants as well as researchers. The privacy of health data is protected under federal law. The RADx-UP program instituted measures to ensure program participants’ data remain protected and de-identified using a token-based hashing algorithm methodology that allows researchers to share individual-level participant data without exposing personally identifiable information. To address data collection and respondent burden concerns, projects modified questions to allow some flexibility in expanding response options more appropriate to some underserved communities. The CDCC also developed COLECTIV, a digital interface for projects to directly enter data into the data repository and included gateway questions to relieve respondent burden.

Respect for Tribal Data Sovereignty

RADx-UP leadership and investigators recognized that additional considerations for tribal sovereignty, practices, and policies needed to be addressed for projects that include American Indian and Alaska Native (AI/AN) participants. Through consultations with the NIH Tribal Advisory Committee and the broader AI/AN community and meetings with an informal RADx-UP AI/AN project working group established by the CDCC, NIH realized that deposition of tribal data into the RADx Data Hub would not meet the cultural, governance, or sovereignty needs of AI/AN RADx research data. In response, NIH hopes to establish a RADx Tribal Data Repository (TDR) responsible for the collection, protection, and sharing of data collected in AI/AN communities with respect for the practices and policies of Tribal data sovereignty. Applications for the repository have been solicited and NIH hopes to make an award for the TDR sometime in FY23.

Rapid Data Sharing

One of the largest hurdles the RADx-UP program has faced is implementing rapid sharing of research data for secondary analyses and to inform decision-making and public health practices related to the COVID-19 pandemic. RADx-UP research teams are expected to share their data on a timely cadence before data collection ends. This is a far more stringent practice relative to the current standard NIH data-sharing policy that requires data to be shared at the time of acceptance for publication of the main findings from the final data set. NIH and CDCC staff have worked together with the RADx research community to highlight the importance of and compliance with rapid data-sharing. Within the first six months, a total of 69 Phase 1 projects began transmitting CDE data to the RADx-UP CDCC. The COVID-19 pandemic posed a tremendous challenge, and NIH responded by collaborating with vulnerable and underserved communities. This collaboration has opened an unprecedented opportunity to build on a now established foundation for future research to address gaps in understanding the broader social, cultural, and structural factors that influence disparities in morbidity and mortality from COVID-19 and other diseases. Data collection and sharing efforts of the RADx-UP initiative comprise a significant contribution. Collaboration among the NIH, research investigators, and communities impacted by COVID-19 has been the catalyst. To learn more about RADx-UP, please visit a recent journal article available on PubMed.


Dr. Hodes has served as NIA director since 1993, overseeing studies of the biological, clinical, behavioral, and social aspects of aging. He has devoted his tenure to the development of a strong, diverse, and balanced research program focused on the genetics and biology of aging, basic and clinical studies aimed at reducing disease and disability, and investigation of the behavioral and social aspects of aging. Ultimately, these efforts have one goal — improving the health and quality of life for older people and their families. As a leading researcher in the field of immunology, Dr. Hodes has published more than 250 peer-reviewed papers.

Dr. Pérez-Stable practiced primary care internal medicine for 37 years at the University of California, San Francisco before becoming the Director of NIMHD in 2015. His research interests have centered on improving the health of individuals from racial and ethnic minority communities through effective prevention interventions, understanding underlying causes of health disparities, and advancing patient-centered care for underserved populations. Recognized as a leader in Latino health care and disparities research, he spent 32 years leading research on smoking cessation and tobacco control in Latino populations in the United States and Latin America. Dr. Pérez-Stable has published more than 300 peer-reviewed papers.

Is Age Really Just a Number?

Last week I turned 69! Can you believe that??? This is so amazing to me—how could I be THAT OLD?? Two years ago (when I was just 67!), I shared that…

In midlife, I think I’m where I’m supposed to be, because I feel like I’m 39, think I look like I’m 49, believe I have a career worthy of someone who’s 59, and am approaching the wisdom of someone who’s 69.

So now that I am 69, I still believe all those things are true—particularly the wisdom part. I am wiser about the speed of change, the value of tempering my vision with a dose of realism, and the importance of understanding people clearly. I still feel youthful, look pretty good for a woman my age, and remain proud of my career.

But suppose I want to pick the number that really represents my age. Age is a very important descriptor of patients and research participants. Across all types of clinical research, one of the most common variables collected is a participant’s age. Age is an important indicator of many things human, from physical capabilities that determine their likely response to a treatment, to potential behavioral or mental health challenges. Knowing participants’ ages helps guide the interpretation of research results, allowing scientists and clinicians to determine the relevance of those results to specific groups of people or to better understand the clinical manifestation of a disease. And knowing the age of a participant provides evidence that our NIH studies appropriately engage people across their lifespan.

You might be surprised to know that there are many ways to represent age. For most of us, age is estimated by counting the number of years since our birth. However, for babies, it may be more important to know the number of days, weeks, or months since birth. Some studies compute age as the difference between the date of birth and the date that the data are collected. In fact, in the PhenX Toolkit, a web-based catalogue of expert-provided recommended measurement protocols, there are almost 200 different ways to measure age in a research study. Sometimes information about age is acquired through self-report of the participant, and other times the information is obtained from some existing document like a patient’s clinical record. The PhenX Toolkit is an enumeration of a wide range of measurement approaches and allows for broad coverage in a way that lets a researcher pick the measure that best represents the phenomena of interest to their study.

Over the past decade, NLM has supported the creation, identification, and distribution of Common Data Elements (CDEs). CDEs are specialized ways to measure concepts common to two or more research projects in a manner that is consistent across studies. Using a similar approach to measures similar concepts sounds like a no-brainer, right? It improves the rigor and reproducibility of research and allows data collected in different studies to be grouped together, adding power to the interpretation of research efforts. The COVID-19 pandemic illustrated the value of the common approaches to measuring research concepts by allowing us to track this deadly virus and its manifestations across time and people.

NLM established the NIH CDE Repository to serve as a one-stop location for research programs and for NIH Institutes and Centers to house CDEs and make them available to other researchers. Each record includes the definition of the variable as an indicator of the concept, a way to measure the variable (usually a question-and-answer pair with acceptable responses), and machine-readable codes where possible. Recently, the NIH CDE Repository began supporting an NIH governance process that indicates which of the proposed CDEs that have been received are described with sufficient rigor to be designated as NIH-endorsed. This endorsement helps potential users who are seeking good ways to measure complex concepts. NIH-endorsed CDEs support FAIR (findable, accessible, interoperable, and reusable) data sharing. Adherence to FAIR principles provides high-quality, “computation-ready” data with standardized vocabularies and readable metadata retrievable by identifiers that modernize the NIH data ecosystem. When data are collected consistently across studies using CDEs, it’s possible to integrate data from multiple studies, which can make it easier to get meaningful results. CDEs can also make it easier to reuse data for future research by improving the data quality.

So if I wanted to be “counted” according to the years-alive mode of assessing age, I guess I am 69. But if you really want to know something else, like how happy I am in my career or how I’m feeling, don’t be surprised if I give a different number!

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