Health disparities arise when there are differences in health outcomes or health determinants between populations.1,2 These are measured in differences in incidence, prevalence, mortality, burden of disease, or other adverse conditions.3 For example, although all-cause mortality between African Americans and whites decreased between 1999 and 2015, African Americans continued to have higher mortality rates than whites younger than age 65 years as well as higher levels of self-reported risk factors for certain chronic diseases.4
Racial/ethnic disparities in health care access and quality are differences between racial/ethnic groups that are attributable to socioeconomic factors and health insurance but not to clinical need and treatment preferences.5,6 A recent study noted that racial/ethnic disparities in access to mental health care increased for African Americans and Latinos compared to whites, despite similar prevalence rates of mental disorders between groups.7 An important contributor to these disparities in under-resourced communities of color is the persistent distrust engendered by historical abuses in biomedical research. Exemplified by the Tuskegee Syphilis Study, recent observational studies of lead paint exposure of children in Baltimore, as well as the misappropriation of science to justify discrimination, has led many people in those communities to distrust researchers and scientific findings.8–10
Social Determinants of Mental Health
In addition to this trust gap, marked differences in social determinants of health (ie, “the conditions in which people are born, grow, live, work, and age”)11 are an important factor in health disparities. This includes low socioeconomic status and lack of access to and variable quality of care that contribute to poor mental health outcomes.6 Social determinants of health are community-level contextual factors (eg, poor housing or health care access) and individual-level behavioral factors (eg, substance abuse, perceived discrimination) contributing to health and mental health. Markers of low socioeconomic status that are also associated specifically with mental health disparities include unemployment, homelessness, exposure to violence, inadequate education structures, and poor access to health care.
The World Health Organization summarized interventions with evidence of effectiveness to address social determinants of mental health such as (1) increasing health care access (eg, integrated primary care and mental health service); (2) addressing vulnerability for mental illness (eg, adolescent depression prevention; older adult peer support); (3) reducing exposure to risk factors (eg, violence prevention, disaster stress support, housing improvement, employment skills training); and (4) addressing socioeconomic context through policy (eg, mental health, alcohol, welfare, labor, and education policy).11
Potential Risks of Digital Technologies
The development of digital technologies holds tremendous promise for helping address the critical lack of access to mental health care services in under-resourced communities. And yet, there remains a significant risk of worsening or creating new fissures in trust between the users of technologies and those creating them and using the data they produce. As seen in recent examples from social media companies like Facebook, people may not always realize the various ways their data may be used, which may diverge significantly from their expectations.12 In prior examples, such as the Tuskegee experiments,10 the implications and risks were known to the researchers but not communicated to the participants. We now have Institutional Review Boards and informed consent policies to reduce the risk of this type of violation in trust in the research context. However, digital health approaches often aim to develop novel interventions and analyses that may lead to unexpected results and abilities to predict outcomes. Therefore, it can be challenging to adequately inform a person about the potential risks and benefits of a study when they are not clear to the researchers or the impact may evolve over time (for example when new, unexpected findings are introduced).
There is also a risk of research studies not including enough participants from different racial/ethnic groups due to combinations of mistrust of communities, limited access, and recruitment of convenience samples from academic medical centers that may not adequately reflect different racial/ethnic groups. This issue has renewed importance in the context of technologies, especially analytic approaches such as machine learning that are trained on and learn from individual data. If there are less data from people of certain socioeconomic groups, the algorithms may be less effective for them. If the algorithms are beneficial, (eg, reducing mortality from depression due to targeted treatments) then those people may have worse outcomes than those where the algorithms were better trained and therefore more effective. For example, in the predication of cardiovascular risk for nonwhite populations from the Framingham Heart Study, a sample representing mostly a white population, there was both overestimation and underestimation of risk when applied to nonwhite populations.15
An important driver of disparities is the unequal distribution of assets. The World Economic Forum suggested to consider personal data as a new asset class, similar to traditional assets such as land and currency (personal communication, 2011); for example, data created through the use of mobile apps and activity sensors. The report describes the transfer of personal data to companies that created the technology as a transfer of assets from the individual to the company, which is especially relevant to groups where there are existing disparities, potentially increasing the degree of disparities through this process.
Participatory Approaches to Address Disparities
Lessons learned from decades of community engagement work in mental health disparities and in under-represented communities may serve as an effective guide for how best to approach new challenges and opportunities presented through digital technologies. Community engagement has been recommended as a key approach to enhance trust in and to increase the relevance of translational science with racial/ethnic minority communities that are lower income. Community engagement has been recommended by the National Institutes of Health (NIH) and the Institute of Medicine as an approach to eliminate racial and ethnic disparities in health care and outcomes.16 The NIH defines community engagement as “the process of working collaboratively with and through groups of people affiliated by geographic proximity, special interest, or similar situations to address issues affecting the well-being of those people.”16 Participatory research models emphasize community engagement principles to assure the transparent, equitable inclusion of individual (community members, patients, families) and institutional partners especially from communities of color that are lower income and under-resourced to overcome the significant distrust within those communities.17 Community Partnered Participatory Research (CPPR) is an outreach program that focuses special attention on fostering equal partnerships and power sharing.18 Through these partnerships, stakeholders work together to co-create the research priorities and direction. CPPR aims to improve trust, respect, power sharing, alignment of stakeholder priorities with the project goals, and ultimately improved outcomes and sustainability of the project.
These participatory concepts are especially relevant to technology development.19 There are several design frameworks that involve direct engagement of people to help shape the direction of technologies including through user-centered design20 and participatory design approaches.21 In relation to issues of disparities and technologies, The World Economic Forum identified three useful areas to explore: (1) alignment of key stakeholders, similar to recommendations from CPPR; (2) viewing “data as money,” shifting the mindset that an individual's data has value and should be treated similarly to other assets and the expected ability to control and meaningfully use those assets; (3) end-user centricity: valuing end-users in the digital ecosystems and engaging them as co-creators in the technology development process.22
These concepts of participatory engagement and social determinants of health also extend to recommendations regarding informed consent in medical research and health care. Recently, it has been recommended that informed consent should be understood in the context of an individual's social, ethnic, and political backgrounds.23 Similarly, a recent report describing the potential for bias in machine learning analysis of electronic health record (EHR) data included recommendations to (1) use interdisciplinary teams to inform analytic approaches; (2) ensure that key racial/ethnic and socioeconomic variables are captured within the dataset; and (3) create a process for continued human feedback to monitor for bias and clinical importance.24
Opportunities Moving Forward
Being mindful of the potential risks, and participatory and community-engaged approaches to mitigate these risks, there are several ways that technologies may offer new opportunities to improve care and health disparities. First, telehealth and remote monitoring applications are helping to reduce geographic barriers to access to care.25 There have been extensive implementations of telemental health in rural settings where access to psychiatric services has been limited.26 However, there continues to be a lack of providers, especially subspecialist providers, in under-resourced urban areas, which predominantly affects African American and Hispanic populations.27 Using telehealth, it is now possible to engage providers across geographic regions as well as subspecialty areas to dynamically respond to needs in different areas.28
Digital technologies such as mobile apps may also help reduce stigma and increase psychoeducation rates related to mental health issues by making content more accessible (on mobile phones that can be conveniently accessed). People with stigmatized illnesses have been shown to access informational resources and interventions online more.29 Co-creating apps with relevant populations may help ensure the content is culturally relevant and tailored to their specific needs.30 Participatory approaches may also increase the interest and capacity to be involved in technology development as one community stakeholder noted in a recent participatory technology development study: “I have no background in technology, and [this project] made me feel like our partnership could do anything. . .it was a whole new level of feeling like we could step up to the plate and use even modern technology in a way that was respectful of the community.”30
With increasing availability of “big data”, there are more approaches to deriving insights from large datasets such as through machine learning. While often used for the purpose of predicting health outcomes, these approaches could also be applied to address disparities and social determinants of health. For example, in addition to EHR data generated by health systems, there also exist datasets generated by social service agencies. This includes public health departments, law enforcement/probation entities, service agencies, and parks systems. If data from these systems can be linked, then analysis could be conducted across social determinant and health domains to better understand mediators and moderators of mental health outcomes, identify needs, and tailor interventions and services to address them.31 Machine learning models could be integrated within mobile apps and EHRs that build insights across social determinant and other domains contributing to disparities to inform clinical decision-making.
Although algorithms may introduce new biases, it has been suggested that they could also be used to compensate for areas of known bias in human decision-making.32 For example, in the legal setting, a group used algorithmic models to show the potential for making more equitable decisions than judges and the possibility of reducing incarceration without an increase in crime rates.33
There is increasing utilization of peer networks and community health workers to augment the limited supply of psychiatric providers, especially in under-resourced communities. Mobile technologies may help enable “upskilling” of people by supporting their workflow and facilitating data flow between their activities and more traditional health service entities so they can function more effectively. This would effectively extend the workforce in terms of the number of people able to participate and the skill level at which they participate.34
- US Department of Health and Human Services. HHS action plan to reduce racial and ethnic health disparities: a nation free of disparities in health and health care. https://minorityhealth.hhs.gov/npa/files/plans/hhs/hhs_plan_complete.pdf. Accessed April 16, 2019.
- Centers for Disease Control and Prevention. Conclusion and future directions: CDC Health Disparities and Inequalities Report—United States, 2013. CDC health disparities and inequalities report—United States 2013. https://www.cdc.gov/mmwr/pdf/other/su6203.pdf. Accessed April 16, 2019.
- National Heart, Lung, and Blood Institute. Health disparities. https://www.nhlbi.nih.gov/health/educational/healthdisp/. Accessed April 16, 2019.
- Cunningham TJ, Croft JB, Liu Y, Lu H, Eke PI, Giles WH. Vital signs: racial disparities in age-specific mortality among blacks or African Americans—United States, 1999–2015. MMWR Morb Mortal Wkly Rep. 2017;66(17):444–456. doi:. doi:10.15585/mmwr.mm6617e1 [CrossRef]
- Nelson A. Unequal treatment: confronting racial and ethnic disparities in health care. J Natl Med Assoc. 2002;94(8):666–668.
- Institute of Medicine. Committee on Crossing the Quality Chasm: Adaptation to Mental Health Addictive Disorders. Improving the quality of health care for mental and substance-use conditions. https://www.ncbi.nlm.nih.gov/books/NBK19830/pdf/Bookshelf_NBK19830.pdf. Accessed April 16, 2019.
- Cook BL, Trinh N-H, Li Z, Hou SS-Y, Progovac AM. Trends in racial-ethnic disparities in access to mental health care, 2004–2012. Psychiatric Serv. 2016;68(1):9–16. doi:. doi:10.1176/appi.ps.201500453 [CrossRef]
- Committee on Veterans' Affairs House of Representatives. Suspension of medical research at West Los Angeles and Sepulveda VA medical facilities and informed consent and patient safety in VA medical research. https://ia801903.us.archive.org/30/items/gov.gpo.fdsys.CHRG-106hhrg58700/CHRG-106hhrg58700_text.pdf. Accessed April 16, 2019.
- Corbie-Smith GM. Minority recruitment and participation in health research. N C Med J. 2004;65(6):385–387.
- Gamble VN. Under the shadow of Tuskegee: African Americans and health care. Am J Public Health. 1997;87(11):1773–1778. doi:10.2105/AJPH.87.11.1773 [CrossRef]
- World Health Organization. Social determinants of mental health. https://www.who.int/mental_health/publications/gulbenkian_paper_social_determinants_of_mental_health/en/. Accessed April 16, 2019.
- Granville K. Facebook and Cambridge Analytica: what you need to know as fallout widens. The New York Times. https://www.nytimes.com/2018/03/19/technology/facebook-cambridge-analytica-explained.html. Accessed April 16, 2019.
- Sunyaev A, Dehling T, Taylor PL, Mandl KD. Availability and quality of mobile health app privacy policies. J Am Med Inform Assoc. 2015;22(e1):e28–e33. doi:10.1136/amiajnl-2013-002605 [CrossRef].
- Walsh TM, Volsko TA. Readability assessment of internet-based consumer health information. Respir Care. 2008;53(10):1310–1315.
- Gijsberts CM, Groenewegen KA, Hoefer IE, et al. Race/ethnic differences in the associations of the Framingham risk factors with carotid IMT and cardiovascular events. PLoS One. 2015;10(7):e0132321. doi:. doi:10.1371/journal.pone.0132321 [CrossRef]
- Institute of MedicineBoard on Health Sciences PolicyCommittee to Review the Clinical and Translational Science Awards Program at the National Center for Advancing Translational Sciences. The CTSA program at NIH: opportunities for advancing clinical and translational research. https://www.nap.edu/catalog/18323/the-ctsa-program-at-nih-opportunities-for-advancing-clinical-and. Accessed April 16, 2019.
- Israel BA, Schulz AJ, Parker EA, Becker AB. Review of community-based research: assessing partnership approaches to improve public health. Annu Rev Public Health. 1998;19(1):173–202. doi:. doi:10.1146/annurev.publhealth.19.1.173 [CrossRef]
- Jones L, Wells K. Strategies for academic and clinician engagement in community-participatory partnered research. JAMA. 2007;297(4):407–410. doi:. doi:10.1001/jama.297.4.407 [CrossRef]
- Unertl KM, Schaefbauer CL, Campbell TR, et al. Integrating community-based participatory research and informatics approaches to improve the engagement and health of underserved populations. J Am Med Inform Assoc. 2016;23(1):60–73. doi:. doi:10.1093/jamia/ocv094 [CrossRef]
- Ritter FE, Baxter GD, Churchill EF. Foundations for Designing User-Centered Systems. London, England: Springer-Verlag; 2014. doi:10.1007/978-1-4471-5134-0 [CrossRef]
- Simonsen J, Robertson T. Routledge International Handbook of Participatory Design. Abingdon, United Kingdom: Routledge; 2012. doi:10.1145/2348144 [CrossRef]
- Schwab K, Marcus A, Oyola JR, Hoffman W. Personal data: the emergence of a new asset class. An initiative of the World Economic Forum. January 2011. . http://www3.weforum.org/docs/WEF_ITTC_PersonalDataNewAsset_Report_2011.pdf. Accessed April 16, 2019.
- Manson NC, O'Neill O. Rethinking Informed Consent in Bioethics. Cambridge, United Kingdom: Cambridge University Press; 2007. doi:10.1017/CBO9780511814600 [CrossRef]
- Gianfrancesco MA, Tamang S, Yazdany J, Schmajuk G. Potential biases in machine learning algorithms using electronic health record data. JAMA Intern Med. 2018; 178(11):1544–1547. doi:. doi:10.1001/jamainternmed.2018.3763 [CrossRef]
- West J. Advancing innovation to eliminate health disparities. https://www.healthaffairs.org/do/10.1377/hblog20140904.040973/full/. Accessed April 16, 2019.
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- Miller AP. Want less-biased decisions? Use algorithms. https://hbr.org/2018/07/want-less-biased-decisions-use-algorithms. Accessed April 16, 2019.
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