NIH Collaboration Seeks to Help Understand U.S. Burden of Health Disparities: Why Your County Matters
Since the early 1990s, federal support of research has increased to understand minority health and identify and address health disparities. Research in these areas has evolved from a starting point of developing a basic descriptive understanding of health disparities and who is most affected. Now, it is discovering the underlying complexity of factors involved in health outcomes to inform interventions and reduce these disparities.
One of these many factors is where we live, learn, work, and play and how that affects different people. A group of NIH scientists and their colleagues recently published a study in the journal The Lancet that they hope is a step toward better understanding geographic disparities and their role in health equity .
As Director of NIH’s National Institute on Minority Health and Health Disparities (NIMHD), I worked with NIMHD’s Scientific Director, Anna María Nápoles, to conceive the study and establish the Global Burden of Disease (GBD) U.S. Health Disparities Collaborators at NIH with five NIH Institutes and two Offices. Through this collaboration, NIH funded the Institute for Health Metrics and Evaluation (IHME), University of Washington to conduct the analysis. The IHME has worked for 30 years on the GBD project in over 200 countries.
The Lancet paper offered the first comprehensive U.S. county-level life expectancy estimates to highlight the significant gaps that persist among racial and ethnic populations across the nation. The analysis revealed that despite overall life expectancy gains of 2.3 years from 2000–2019, Black populations experienced shorter life expectancy than White populations.
In addition, American Indian and Alaska Native populations’ life expectancy did not improve and, in fact, decreased in most counties. We found national-level life expectancy advantages for Hispanic/Latino and Asian populations ranging from three to seven years, respectively, compared to White populations. But there were notable exceptions for Hispanic/Latino populations in selected counties in the Southwest.
Certainly the most-alarming trend identified in the paper was that during the study’s last 10 years (2010–2019), life expectancy growth was stagnant across all races and ethnicities. Moreover, 60 percent of U.S. counties experienced a decrease in life expectancy.
While these findings provide an important frame for how disparities exist along many dimensions—by race, ethnicity, and geographic region—they also highlight these differences within our local communities. This level of detail offers an unprecedented opportunity for researchers and public health leaders to focus on where these differences are the most prominent, and possibly give us a clearer picture on what can be done about it.
These data raise many important questions, too. What can we learn from places that are doing well in caring for their most disadvantaged populations? How can these factors be sustained, replicated, and transferred to other places? Are there current policies and/or community services that contribute to or inhibit gaining access to appropriate clinical care, healthy and affordable food, good schools, and/or economic opportunities?
To help answer these questions, the GBD U.S. Health Disparities Collaborators at NIH, in partnership with IHME, have developed a comprehensive database and interactive data visualization tool that provides life expectancy and all-cause mortality by race and ethnicity for 3,110 U.S. counties from 2000-2019. Efforts are underway to expand the database to include causes of death and risk factors by race/ethnicity and education, as well as to disaggregate some of the major racial-ethnic groups.
Using IHME’s established model of comprehensive and replicable data collection, the joint effort aims to improve access to health data resources, bolster analytic approaches, and deliver user-friendly estimates to the wider research and health policy community. The collection’s standardized, comprehensive, historical, and real-time data can be the cornerstone for efforts to address disparities and advance health equity.
It is important to note that the Lancet study only included data from before the COVID-19 pandemic. The pandemic’s disproportionate effect on overall mortality and life expectancy has exacerbated existing health disparities. Disaggregated data are essential in helping to understand the underlying mechanisms of health disparities and guiding the development and implementation of interventions that address local needs.
As a clinician scientist, I have made a personal commitment at NIMHD to foster and encourage data collection with standardized measures, harmonization, and efficient data sharing to help us explore the nuances within all populations and their communities. Without these guiding principles for managing data, inequities remain unseen and unaddressed. Scientists, clinicians, and policymakers can all potentially benefit from this work if we use the data to inform our actions. It is an opportunity to implement real change in our NIH-wide combined efforts to reduce health disparities and improve quality of life and longevity for all populations.
 Life expectancy by county, race, and ethnicity in the USA, 2000-19: a systematic analysis of health disparities. GBD US Health Disparities Collaborators. Lancet. 2022 Jul 2;400(10345):25-38.
Understand Health Disparities Series (National Institute on Minority Health and Health Disparities/NIH)
HD Pulse (NIMHD)
Institute for Health Metrics (University of Washington, Seattle)
NIH Support: The members of the GBD U.S. Health Disparities Collaborators at NIH include: National Heart, Lung, and Blood Institute; National Cancer Institute; National Institute on Aging; National Institute of Arthritis and Musculoskeletal and Skin Diseases; NIH Office of Disease Prevention; NIH Office of Behavioral and Social Science Research
Note: Dr. Lawrence Tabak, who performs the duties of the NIH Director, has asked the heads of NIH’s Institutes and Centers (ICs) to contribute occasional guest posts to the blog to highlight some of the interesting science that they support and conduct. This is the 17th in the series of NIH IC guest posts that will run until a new permanent NIH director is in place.
Nearly four years ago, NIH opened national enrollment for the All of Us Research Program. This historic program is building a vital research community within the United States of at least 1 million participant partners from all backgrounds. Its unifying goal is to advance precision medicine, an emerging form of health care tailored specifically to the individual, not the average patient as is now often the case. As part of this historic effort, many participants have offered DNA samples for whole genome sequencing, which provides information about almost all of an individual’s genetic makeup.
Earlier this month, the All of Us Research Program hit an important milestone. We released the first set of nearly 100,000 whole genome sequences from our participant partners. The sequences are stored in the All of Us Researcher Workbench, a powerful, cloud-based analytics platform that makes these data broadly accessible to registered researchers.
The All of Us Research Program and its many participant partners are leading the way toward more equitable representation in medical research. About half of this new genomic information comes from people who self-identify with a racial or ethnic minority group. That’s extremely important because, until now, over 90 percent of participants in large genomic studies were of European descent. This lack of diversity has had huge impacts—deepening health disparities and hindering scientific discovery from fully benefiting everyone.
The Researcher Workbench also contains information from many of the participants’ electronic health records, Fitbit devices, and survey responses. Another neat feature is that the platform links to data from the U.S. Census Bureau’s American Community Survey to provide more details about the communities where participants live.
This unique and comprehensive combination of data will be key in transforming our understanding of health and disease. For example, given the vast amount of data and diversity in the Researcher Workbench, new diseases are undoubtedly waiting to be uncovered and defined. Many new genetic variants are also waiting to be identified that may better predict disease risk and response to treatment.
To speed up the discovery process, these data are being made available, both widely and wisely. To protect participants’ privacy, the program has removed all direct identifiers from the data and upholds strict requirements for researchers seeking access. Already, more than 1,500 scientists across the United States have gained access to the Researcher Workbench through their institutions after completing training and agreeing to the program’s strict rules for responsible use. Some of these researchers are already making discoveries that promote precision medicine, such as finding ways to predict how to best to prevent vision loss in patients with glaucoma.
Beyond making genomic data available for research, All of Us participants have the opportunity to receive their personal DNA results, at no cost to them. So far, the program has offered genetic ancestry and trait results to more than 100,000 participants. Plans are underway to begin sharing health-related DNA results on hereditary disease risk and medication-gene interactions later this year.
This first release of genomic data is a huge milestone for the program and for health research more broadly, but it’s also just the start. The program’s genome centers continue to generate the genomic data and process about 5,000 additional participant DNA samples every week.
The ultimate goal is to gather health data from at least 1 million or more people living in the United States, and there’s plenty of time to join the effort. Whether you would like to contribute your own DNA and health information, engage in research, or support the All of Us Research Program as a partner, it’s easy to get involved. By taking part in this historic program, you can help to build a better and more equitable future for health research and precision medicine.
Note: Joshua Denny, M.D., M.S., is the Chief Executive Officer of NIH’s All of Us Research Program.
Join All of Us (NIH)
Cataloging and characterizing the thousands of genomic variants—differences in DNA sequences among individuals—across human populations is a foundational component of genomics. Scientists from various disciplinary fields compare the variation that occurs within and between the genomes of individuals and groups. Such efforts include attributing descriptors to population groups, which have historically included the use of social constructs such as race, ethnicity, ancestry, and political geographic location. Like any descriptors, these words do not fully account for the scope and diversity of the human species.
The use of race, ethnicity, and ancestry as descriptors of population groups in biomedical and genomics research has been a topic of consistent and rigorous debate within the scientific community. Human health, disease, and ancestry are all tied to how we define and explain human diversity. For centuries, scientists have incorrectly inferred that people of different races reflect discrete biological groups, which has led to deep-rooted health inequities and reinforced scientific racism.
In recent decades, genomics research has revealed the complexity of human genomic variation and the limitations of these socially derived population descriptors. The scientific community has long worked to move beyond the use of the social construct of race as a population descriptor and provide guidance about agreed-upon descriptors of human populations. Such a need has escalated with the growing numbers of large population-scale genomics studies being launched around the world, including in the United States.
To answer this call, NIH is sponsoring a National Academies of Sciences, Engineering, and Medicine (NASEM) study that aims to develop best practices in the use of race, ethnicity, and genetic ancestry in genomics research. The NASEM study is sponsored by 14 NIH institutes, centers, offices, and programs, and the resulting report will be released in February 2023.
Experts from various fields—including genomics, medicine, and social sciences—are conducting the study. Much of the effort will revolve around reviewing and assessing existing methodologies, benefits, and challenges in the use of race and ethnicity and other population descriptors in genomics research. The ad hoc committee will host three public meetings to obtain input. Look for more information regarding the committee’s next public session planned for April 2022 on the NASEM “Race, Ethnicity, and Ancestry as Population Descriptors in Genomics Research” website.
To further underscore the need for the NASEM study, an NIH study published in December 2021 revealed that the descriptors for human populations used in the genetics literature have evolved over the last 70 years . For example, the use of the word “race” has substantially decreased, while the uses of “ancestry” and “ethnicity” have increased. The study provided additional evidence that population descriptors often reflect fluid, social constructs whose intention is to describe groups with common genetic ancestry. These findings reinforce the timeliness of the NASEM study, with the clear need for experts to provide guidance for establishing more stable and meaningful population descriptors for use in future genomics studies.
The full promise of genomics, including its application to medicine, depends on improving how we explain human genomic variation. The words that we use to describe participants in research studies and populations must be transparent, thoughtful, and consistent—in addition to avoiding the perpetuation of structural racism. The best and most fruitful genomics research demands a better approach.
 Evolving use of ancestry, ethnicity, and race in genetics research—A survey spanning seven decades. Byeon YJJ, Islamaj R, Yeganova L, Wilbur WJ, Lu Z, Brody LC, Bonham VL. Am J Hum Genet. 2021 Dec 2;108(12):2215-2223.
Use of Race, Ethnicity, and Ancestry as Population Descriptors in Genomics Research (National Academies of Sciences, Engineering, and Medicine)
“Language used by researchers to describe human populations has evolved over the last 70 years.” (National Human Genome Research Institute/NIH)
Genomic Variation Program (NHGRI)
[Note: Acting NIH Director Lawrence Tabak has asked the heads of NIH’s institutes and centers to contribute occasional guest posts to the blog as a way to highlight some of the cool science that they support and conduct. This is the third in the series of NIH institute and center guest posts that will run until a new permanent NIH director is in place.]
Posted on by Dr. Francis Collins
Many factors influence our risk of illness from SARS-CoV-2, the coronavirus responsible for COVID-19. That includes being careful to limit our possible exposures to the virus, as well as whether we have acquired immunity from a vaccine or an earlier infection. But once a person is infected, a host of other biological factors, including age and pre-existing medical conditions, will influence one’s risk of becoming severely ill.
While earlier studies have tied COVID-19 severity to genetic variations in a person’s antiviral defenses and blood type, we still have a lot to learn about how a person’s genetic makeup influences COVID-19 susceptibility and severity. So, I was pleased to see the recent findings of an impressive global effort to map the genetic underpinnings of SARS-CoV-2 infection and COVID-19 severity, which involved analyzing the genomes of many thousands of people with COVID-19 around the globe.
This comprehensive search led to the identification of 13 regions of the human genome that appear to play a role in COVID-19 infection or severity. Though more research is needed to sort out these leads, they represent potentially high-quality clues to the pathways that this virus uses to cause illness, and help to explain why some people are more likely to become infected with SARS-CoV-2 or to develop severe disease.
The international effort, known as The COVID-19 Host Genetics Initiative, is led by Andrea Ganna, Institute for Molecular Medicine Finland, Helsinki, and colleagues in the United States and around the world. Teasing out those important genetic influences is no easy task. It requires vast amounts of data, so Ganna reached out to the scientific community via Twitter to announce a new COVID-19 gene-hunting effort and ask for help. Thousands of researchers around the world answered his call. The new study, published in the journal Nature, includes data collected through the initiative as of January 2021, and represents nearly 50,000 COVID-19 patients and another 2 million uninfected controls .
In search of common gene variants that may influence who becomes infected with SARS-CoV-2 and how sick they will become, Ganna’s international team turned to genome-wide association studies (GWAS). As part of this, the team analyzed patient genome data for millions of so-called single-nucleotide polymorphisms, or SNPs. While these single “letter” nucleotide substitutions found all across the genome are generally of no health significance, they can point the way to the locations of gene variants that turn up more often in association with particular traits or conditions—in this case, COVID-19 susceptibility or severity. To find them, the researchers compared SNPs in people with COVID-19 to those in about 2 million healthy blood donors from the same population groups. They also looked for variants that turned up significantly more often in people who became severely ill.
Their analyses uncovered a number of gene variants associated with SARS-CoV-2 infection or severe COVID-19 in 13 regions of the human genome, six of which were new. Four of the 13 affect a person’s risk for becoming infected with SARS-CoV-2. The other nine influence a person’s risk for developing severe illness following the infection.
Interestingly, some of these gene variants already were known to have associations with other types of lung or autoimmune diseases. The new findings also help to confirm previous studies suggesting that the gene that determines a person’s blood type may influence a person’s susceptibility to SARS-CoV-2 infection, along with other genes that play a role in immunity. For example, the findings show overlap with variants within a gene called TYK2, which was earlier shown to protect against autoimmune-related diseases. Some of the variants also point to the need for further work to study previously unexplored biological processes that may play potentially important roles in COVID-19.
Two of the new variants associated with disease severity were discovered only by including individuals with East Asian ancestry, highlighting the value of diversity in such analyses to gain a more comprehensive understanding of the biology. One of these newfound variants is close to a gene known as FOXP4, which is especially intriguing because this gene is known to play a role in the airways of the lung.
The researchers continue to look for more underlying clues into the biology of COVID-19. In fact, their latest unpublished analysis has increased the number of COVID-19 patients from about 50,000 to 125,000, making it possible to add another 10 gene variants to the list.
 Mapping the human genetic architecture of COVID-19. COVID-19 Host Genetics Initiative. Nature. 2021 Jul 8.
COVID-19 Research (NIH)
Posted on by Dr. Francis Collins
Medical research hasn’t always fully represented our nation’s rich diversity. As the video above shows, NIH’s All of Us Research Program is committed to doing things differently by enrolling individuals of many different races, ethnicities, and walks of life. The more we know about what makes each person unique, the more customized health care can become.
Want to be part of this pioneering effort? Go to the All of Us website, click the “Join Now” button, and follow the three easy steps. First, create an account. It’s free and takes just a minute or two. Next, complete the enrollment and consent forms. That usually takes 30 minutes or less. Then, complete some baseline surveys and find out what to do next. Thank you!