Guest post by Dr. Fred Wood, Outreach and Evaluation Scientist in the Office of Health Information Programs Development.
Socially disadvantaged populations have fewer opportunities to achieve optimal health. They also experience preventable differences when facing disease or injury. These inequities, known collectively as health disparities, significantly impact personal and public health.
Despite decades of research on health disparities, researchers, clinicians, and public health specialists have not seen the changes we were hoping for. Instead many health disparities are proving difficult to reduce or eliminate.
With that in mind the National Institutes of Health (NIH) National Institute on Minority Health and Health Disparities (NIMHD) launched a Science Visioning Process in 2015 with the goal of producing a scientific research plan that would spark major breakthroughs in addressing disparities in health and health care. NIMHD defines health disparities populations as including racial or ethnic minorities, gender or sexual minorities, those with low socioeconomic status, and underserved rural populations.
Through a mix of staff research and trans-NIH work groups—of which the National Library of Medicine is a part—NIMHD is gathering input on the current state of the science on minority health and health disparities.
Prompted in part by the NIH All of Us precision medicine initiative, one key visioning area—methods and measures for studying health disparities—includes big data.
We expect big data to bring significant benefits and changes to health care, but can it also play a part in reducing health disparities?
Last month the journal Ethnicity & Disease published a special issue focused on big data and its applications to health disparities research (Vol. 27, No. 2).
The issue includes a paper co-authored by the current NIMHD director, several NIH researchers (including me), and several academic partners. Titled “Big Data Science: Opportunities and Challenges to Address Minority Health and Health Disparities in the 21st Century,” (PDF | 436 KB) the paper identified three major opportunities for big data to reduce health disparities:
- Incorporate social determinants of health disparities information—such as race/ethnicity, socioeconomic status, and genomics—in electronic health records (EHRs) to facilitate research into the underlying causes of health disparities.
- Include in public health surveillance systems environmental, economic, health services, and geographic data on targeted populations to help focus public health interventions.
- Expand data-driven research to include genetic, exposure, health history, and other information, to better understand the etiology of health disparities and guide effective interventions.
But using big data for health disparities research has its challenges, including ethics and privacy issues, inadequate data, data access, and a skilled, diverse workforce.
The paper offered eight recommendations to counteract those challenges:
- Incorporate standardized collection and input of race/ethnicity, socioeconomic status, and other social determinants of health measures in all systems that collect health data.
- Enhance public health surveillance by incorporating geographic variables and social determinants of health for geographically defined populations.
- Advance simulation modeling and systems science using big data to understand the etiology of health disparities and guide intervention development.
- Build trust to avoid historical concerns and current fears of privacy loss and “big brother surveillance” through sustainable long-term community relationships.
- Invest in data collection on area-relevant small sample populations to address incompleteness of big data.
- Encourage data sharing to benefit under-resourced minority-serving institutions and underrepresented minority researchers in research intensive institutions.
- Promote data science in training programs for underrepresented minority scientists.
- Assure active efforts are made up front during both the planning and implementing stages of new big data resources to address disparities reduction.
Big data, it seems, is the classic double-edged sword. It offers tremendous opportunities to understand and reduce health disparities, but without deliberate and concerted action to address its inherent challenges and without the active engagement of minority communities in that process, those disparities could widen, keeping the benefits of precision medicine—including improved diagnosis, treatment, and prevention—from millions of those who need them.
How do you think big data will inform health disparities research? And what else might we do to ensure the disparities gap continues to close?
