Hostname: page-component-586b7cd67f-l7hp2 Total loading time: 0 Render date: 2024-11-22T05:45:43.292Z Has data issue: false hasContentIssue false
  • English
  • Français

Ethical and Equitable Digital Health Research: Ensuring Self-Determination in Data Governance for Racialized Communities

Published online by Cambridge University Press:  03 April 2024

Mozharul Islam ,
Arafaat A. Valiani ,
Ranjan Datta ,
Mohammad Chowdhury  and
Tanvir C. Turin Open the ORCID record for Tanvir C. Turin [Opens in a new window]
Mozharul Islam
Affiliation:
Department of Family Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada Department of Sociology, Istanbul Sabahattin Zaim University, İstanbul, Türkiye
Arafaat A. Valiani
Affiliation:
Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada Department of History, Department of Indigenous, Race and Ethnic Studies, Global Health Program, University of Oregon, Eugene, OR, USA
Ranjan Datta
Affiliation:
Canada Research Chair in Community Disaster Research at the Indigenous Studies, Department of Humanities, Mount Royal University, Calgary, Canada
Mohammad Chowdhury
Affiliation:
Department of Family Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
Tanvir C. Turin*
Affiliation:
Department of Family Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
*
Corresponding author: Tanvir C. Turin; Email: [email protected]
Rights & Permissions [Opens in a new window]

Abstract

Recent studies highlight the need for ethical and equitable digital health research that protects the rights and interests of racialized communities. We argue for practices in digital health that promote data self-determination for these communities, especially in data collection and management. We suggest that researchers partner with racialized communities to curate data that reflects their wellness understandings and health priorities, and respects their consent over data use for policy and other outcomes. These data governance approach honors and builds on Indigenous Data Sovereignty (IDS) decolonial scholarship by Indigenous and non-indigenous researchers and its adaptations to health research involving racialized communities from former European colonies in the global South. We discuss strategies to practice equity, diversity, inclusion, accessibility and decolonization (EDIAD) principles in digital health. We draw upon and adapt the concept of Precision Health Equity (PHE) to emphasize models of data sharing that are co-defined by racialized communities and researchers, and stress their shared governance and stewardship of data that is generated from digital health research. This paper contributes to an emerging research on equity issues in digital health and reducing health, institutional, and technological disparities. It also promotes the self-determination of racialized peoples through ethical data management.

Keywords

data governancedecolonizationdigital healthdigital technologyhealth equityprecision health equityraceself-determination
Type
Commentary
Information
Cambridge Quarterly of Healthcare Ethics , First View , pp. 1 - 11
Copyright
© The Author(s), 2024. Published by Cambridge University Press

Introduction

The adoption of digital technologies in the field of health has accelerated at an incredible pace in the past two decades because of innovations introduced in the fields of data management, bioinformatics, and human genomics (among other fields pertinent to the health and life sciences). As evidenced by the coronavirus disease-2019 (COVID-19) pandemic (and continuing at the time of writing), some—but certainly not all—populations around the globe reaped the benefits of the digitalization in health that, for example, provided access to health facilities remotely.Footnote 1 Advances in digital technologies are largely viewed to be a productive resource in the health sciences, and thus their incorporation into its respective fields is expected to continue to increase. Nevertheless, digitalization of health may also create disparities between non-racialized and racialized populations in the global north, while adversely affecting populations of the global South as well.Footnote 2, Footnote 3 Current research inquiring into the problem of inequality in digital health suggests that it is not technology itself but the individuals who, through their research and practices, may inadvertently contribute to unequal access to digital health services experienced by racialized population groups. The World Health Organization (WHO) has begun to address this problem in that it frames digital health to be an integral part of a society’s health priorities that ought to be maintained in an ethical, equitable, and sustainable manner.Footnote 4 Despite this, the outcomes of using information technology in the field of health remain differentiated based on unequal socio-economic conditions of global populations and poor technological infrastructure within racialized communities.Footnote 5, Footnote 6 Among several cited reasons that sustain such forms of inequality, racialized people typically do not have equal access to digital health services.Footnote 7 Therefore, multi-dimensional transdisciplinary research attentive to issues of equity, diversity, inclusion, accessibility and decolonization (EDIAD) seems to be urgently needed to ensure that racialized communities can equitably access digital health resources, practices, and commitments.Footnote 8

Recent studies examining the problem of introducing social justice principles to digital health emphasize the need for ethical and equitable digital health research that deliberately enacts safeguards of the rights and interests of racialized communities.Footnote 9, Footnote 10, Footnote 11, Footnote 12 One facet of such forms of enactment concerns data collection, ownership, and its application; this literature stresses the need for data collection and interpretation to be organized around the goal of benefitting racialized peoples that such forms of evidence enumerate and quantify.Footnote 13, Footnote 14, Footnote 15, Footnote 16, Footnote 17, Footnote 18, Footnote 19, Footnote 20, Footnote 21, Footnote 22, Footnote 23

Building on these insights, we argue for the adoption of practices in digital health that promote self-determination for racialized communities specifically as it concerns the data collection and management processes. We suggest that such an approach might enable institutional-based researchers to work in partnership with members of racialized communities to curate data in a manner that is aligned with the latter’s cultural-historical understandings of wellness and their health priorities while also affording them the right to define and revise the terms of consent over the use of their data for policy formulation, among other outcomes of such forms of equitable data governance. Such an approach to data governance honors, recognizes, and stives to build on the insights from research on Indigenous Data Sovereignty (IDS), which is an innovative and important body of decolonial scholarship contributed by Indigenous and non-indigenous researchers. It also extends recent adaptations of IDS to the context of health research involving racialized communities, who hail from former European colonies primarily in the global South, regarding strategies by which to practice EDIAD principals in digital health.Footnote 24, Footnote 25, Footnote 26, Footnote 27, Footnote 28, Footnote 29, Footnote 30 Though discussed in the context of genetics and precision medicine/health, which we bracket here, Valiani et al.’s (2023) concept of Precision Health Equity (PHE) is germane to our argument about aligning the goals of racialized communities, that emphasize self-determination, with the collection and storage of digital data. Models of digital data collection aligned with the decolonial commitments of PHE foreground a model of data sharing that is co-defined by members of a participating racialized community and researchers which stresses their shared governance and stewardship of data generated from a study.Footnote 31

This paper is part of a body of emerging research that focuses on issues of equity in digital health and reduces health, institutional, and technological disparities, and—vitally—promotes the self-determination of racialized peoples through the ethical management of data. Below, we highlight the voices and needs of historically underprivileged racialized communities as it concerns knowledge production in digital health. In adopting such an approach to digital health, it is our aim to also blend together concerns about intersectionality and post- and de-coloniality elaborated in the social and behavioral sciences, which we are convinced provide a productive lens by which to assess if and how technological innovations in data science promote EDIAD.Footnote 32, Footnote 33

In describing what practical forms equity and self-determination in data governance might take below (also referred to as EDIAD), we argue that such decolonial approaches might promote digital health literacy and engagement as preliminary studies of digital health during the height of the COVID public health crisis demonstrate.Footnote 34 In addition, we also delineate different challenges faced by the racialized communities in their access to digital health research and data and then suggest possible directions in future research about digital health in manners that promote full, inclusive, and equitable participation of racialized people.

Self-determination, equitable data governance, and its meanings for racialized communities

Studies focusing on digital literacy reveal the possible diversity of contexts in which digital health tools might be employed, thus revealing the conditions under which digital health technologies can be effectively introduced.Footnote 35, Footnote 36 Opportunities for training and knowledge exchange in digital health which inheres a decolonial approach must enable the self-determination of racialized communities. In this respect, the promotion of digital health literacy is paramount because, as recent insights suggest, it can democratize access to the forms of knowledge that constitute health care systems.Footnote 37, Footnote 38, Footnote 39 According to current findings, digital health literacy has the potential to promote equity for racialized people by providing them with the tools to navigate digital interfaces, electronic records, and diagnostic results, etc.Footnote 40, Footnote 41, Footnote 42 For example, a randomized clinical trial run in 2022 indicated that Black women who sought treatment for insomnia were able to receive cognitive behavioral therapy when they were trained in how to engage with, and thus mediate, therapies that they received digitally.Footnote 43

Adopting approaches that embrace EDIAD in data collection, and in the management and application of data are vital to the promotion of self-determination of racialized communities in the practice of digital health. Because the EDIAD approaches adopted in this essay strive to both honor and contribute to existing bodies of relevant and allied knowledge, we turn to the findings regarding IDS advanced by social and health scientists which are particularly relevant as it concerns the rights, interests and ethical obligations of collecting data about peoples colonized by European imperial powers. Tahu Kukutai and John Taylor’s (2016) seminal study identifies data control, data quality, and comprehensiveness of racialized data as being central to data governance that promotes the self-determination of racialized peoples and sovereignty in the context of Aboriginal and Indigenous peoples. As postcolonial historians comprehensively document, the classification, collection, dissemination, ownership, and impact of demographic data collected by European colonial states (and their postcolonial incarnations also) have created several contradictions historically, particularly related to the aims of creating such forms of quantitative knowledge.Footnote 44, Footnote 45, Footnote 46, Footnote 47, Footnote 48 The COVID-19 pandemic revealed the problematic nature of such data, and the institutional-based methods on which it depends, underlining how health data about Black, Indigenous, Asian, South Asian, Caribbean, and other racialized groups has typically been collected and analyzed with reference to academic and broader public health concerns which, in many cases, have been privileged over consistently providing benefits and access to treatments to racialized and Indigenous communities.Footnote 49

The findings of C. Matthew Snipp (2016) suggest that data collection, curation, and access, approached in a manner that is aligned with EDIAD, can afford members of racialized communities with the power to determine who should be counted among them, what their interests and priorities data collection and curation serves, and how data libraries might be accessed, by whom and over what kind of time horizon. Historically, institutional data collections have been monopolized by state institutions, often colonial ones, which thus has historically constrained demographic data collection and it remains a legacy that constrains the prospect for truly digital health that is aligned with EDIAD.Footnote 50 As a concept, IDS powerfully redefines the locus of authority over the collection, use, and access of data, relocating it to Indigenous nations, their territories, and in alignment with their ways of life. IDS does not presume to construct a template, or ‘one size fits all’, prescription. Indeed, the meaning of IDS rightfully differs between communities varying as it might on, for example, how a respective community and its polity defines the aims, rights, and responsibilities of community-based data and information.Footnote 51, Footnote 52 In all, IDS proposes a decolonial approach to data collection, storage, and access which seeks to provincialize the dominance of the data-dependent nation-state system privileging instead, the priorities, benefits, and knowledge cultures of more local racialized communities.

While Kukutai and Taylor’s original interventions focused largely on demographic data, we foresee future research in digital health acknowledging and contributing to explorations of IDS by extending its spirit, which is grounded in notions of sovereignty for communities historically subjected to colonial rule, to the self-determination of racialized communities.Footnote 53, Footnote 54, Footnote 55, Footnote 56 In making such a suggestion, we are convinced that such an adaptation would be productive, and aligned with EDIAD commitments, particularly as it concerns the ownership and handling of biomedical data for the purpose of promoting health equity. In this regard, personal health-related data of racialized communities have a purpose for both the health outcomes for racialized individuals (and families) and broader public health initiatives and thus are at the forefront of research about EDID and digital health.Footnote 57 Such debates are diverse, ranging in a number of relevant topics falling under the coupled rubric of sovereignty and self-determination ranging from definitions of data sovereignty and how it differs from other visions of sovereignty, such as cyber sovereignty, internet sovereignty, digital sovereignty, national sovereignty, and socio-political sovereignty.Footnote 58

Recent studies about data self-determination and sovereignty interrogate the institutionalized practice in which data governance has typically been the sole jurisdiction of national states.Footnote 59, Footnote 60 These studies offer a reconsideration of both the authority of the governments over data as well as the place of IDS within such an established system of ‘data power’.Footnote 61, Footnote 62 Floridi’s (2020) findings recommend the necessity to systematically research data sovereignty to nurture scholarly discussions among researchers working on data self-determination and sovereignty while also including policymakers to ground commitments, protocols, and practices of EDIAD-informed data governance in established institutions, authorities and, ultimately, the state. In this regard, Winandy (2011)Footnote 63 used an example of the German Electronic Health Card (eHC) system, which ensures authentication, authorization, and audit mechanisms for local communities to achieve data autonomy over their health-related data; the eHC has made strides in placing control over access to health data in the hands of local communities. In our view, the case of the eHC is a powerful one that exemplifies the purchase of approaches that share affinities with EDIAD commitments that privilege principals of community-centered autonomy, control, power, and privacy of health-related digital data.Footnote 64 Similarly, such forms of data governance also enable local communities to control and verify the geolocation of their data.Footnote 65, Footnote 66, Footnote 67

Current scenarios and challenges of digital data

Previous research on race and information technology suggests that unequal access, defined broadly, to digital devices reproduces social forms of inequality because access to computers and the internet is, in fact, a conduit for racialized communities to access information.Footnote 68 These studies also indicate that as long as marginalized groups are not empowered to deploy digital technology (which is part of the definition of digital technology access), existing inequalities are likely to be reproduced—or even exacerbated—because these communities remain unable to learn about technological innovations.Footnote 69 Ruha Benjamin also stresses that technological advances impact almost every dimension of a modern individual’s economic, political, social, and cultural life; we extend this observation to an individual’s family and community also. Racialized people have historically participated in health research or data collection processes often without becoming fully informed of the risks to their health, privacy, or potential outcomes of the research data.Footnote 70, Footnote 71, Footnote 72 While one study seemed to suggest that African Americans and Latinx peoples are considerably more active in using the internet than white people,Footnote 73 a series of studies counter such a claim indicating that there exists a digital divide in personal technology consisting of a racialized boundary that separates low and high-tech access.Footnote 74, Footnote 75, Footnote 76 Similarly, Nakamura (2002) interrogates the dual presumptions that assert that Asian Americans have a natural proclivity towards digital culture, and Black Americans comparatively ‘do not’; their findings indicate that such conclusions reify ‘race theories’ in which Asians are viewed as a ‘model minority’, capable of modern practices (like technology use) and placed higher in a hierarchy of races, at the bottom of which are typically—and reprehensively—located Black Americans. Although the findings on race and information technology support a utopian ideology of a race-free culture of digital technology, that will reduce the social differences between the racialized communities, through universal access to digital information,Footnote 77, Footnote 78 Nelson (2002) posits that racism in technology is a structural barrier and it is considered a liability and a significant source of exclusion in digital life.

Building on these findings in the study of race and information technology, we emphasize that engagement with digital technology is a central challenge for racial and ethnic minority groups striving to attain equal benefits in the context of digital health. For example, a study of online smoking termination programs found that racial and ethnic minority groups were less likely than non-racialized individuals to create an account in such programs; Black Americans in particular were less likely than non-racialized individuals to log into such programs that was underwritten (and therefore offered free) by National Cancer Institute.Footnote 79, Footnote 80 Therefore, we suggest that future research on equitable access to digital health develop a deeper understanding of the manners in which racialized communities engage with digital health technologies; the findings of such inquiries might explain how members of these communities might be better included and thus be afforded the tools and opportunities to engage with digital health.Footnote 81, Footnote 82, Footnote 83

Ethics and equity in research: Optimal practical guidelines and implementation process

As we have argued, a lack of knowledge about digital technology is one important barrier to access to digital research and design experienced by racialized communities and thus an impediment to benefitting from innovations in digital health.Footnote 84 Existing insights suggest that the employment of community-based participatory research (CBPR) design can ensure that interventions of digital health, and its associated tools, address the needs of the racialized people particularly because when community participants engage in the design of research, they are empowered to determine the research outputs that are most meaningful to them and their community members.Footnote 85 Collaborative research design also has the potential to equally engage both the researchers and community members in such a way that the research process becomes engaging and a learning opportunity for all participants, and thus far more effective.Footnote 86, Footnote 87, Footnote 88, Footnote 89 In this regard, research focusing on knowledge about and engagement with digital health tools ought to explore how community health workers, providers, receivers, and other health-related organizations can utilize their strategies best and build communication techniques and digital tools so that they can ensure equal benefits of digital health services for all the community people.Footnote 90 The effectiveness of participatory research models has been affirmed in the University of Pittsburg, where Equity Design Thinking has been pioneered particularly for its facility.Footnote 91, Footnote 92, Footnote 93

In both Canada and the United States significant breaches in research ethics protocols in internal review board-approved studies (IRB) involving Indigenous communities have resulted in the creation of significantly higher research standards for studies involving Indigenous and Aboriginal, and/or individuals and/or communities.Footnote 94, Footnote 95, Footnote 96, Footnote 97 While we applaud these important reforms and acknowledge the research community’s broad adoption of such protocols, Indigenous and racialized peoples remain unevenly included in such forms of research; and when they are, established IRB protocols are often insufficient in providing access to the benefits of such research particularly as it concerns new treatments or diagnostics.Footnote 98, Footnote 99, Footnote 100, Footnote 101

Again, the findings from researchers in and around the IDS space are a good starting place to explore strategies by which to achieve both inclusion and equity in digital health. In the late 1990s, researchers at the First Nations Information Governance Centre (FNIGC) conducted a health survey and, through reflections on how the data that would be produced and used, created OCAP® which is now a trademark held by the FNIGC and protocol to maintain Ownership, Control, Access and Possession of data pertaining to Indigenous peoples.Footnote 102 As an alternative to the growing open-data environment that is being endorsed in some scientific circles, though partially constrained by existing patient privacy constraints of course, CARE principals (Collective Benefit, Authority to Control, Responsibility, and Ethics) seek to safeguard the interests of Indigenous communities when it concerns data collection. These principles seek to complement FAIR principles which emphasize Findable, Accessible, Interoperable, and Reusable data curation and design.Footnote 103

Though early in their explorations, scholars are investigating parallel problems of inclusion and equity in human genomics, precision medicine (PM) and precision health (PH) research that involves racialized communities suggest a productive pathway to equitable data governance.Footnote 104, Footnote 105, Footnote 106 The findings of these scholars acknowledge important strides in the field of human genomics and precision medicine, which depends in significant ways on digitally coded and organized genomic data, to include racialized communities in studies in this increasingly important field. Importantly, these researchers identify a unique problem in which inclusion, while necessary, is insufficient in also providing equity to racialized groups that might be included in human genomics research. Specifically, they underscore the issue of data governance, emphasizing its co-sharing and stewardship, in contrast to conventional data ownership models that typically place control in the hands of university-based researchers (or the institutions with which they are affiliated).Footnote 107, Footnote 108 We identify a comparable problem in initiatives that strive to include racialized communities in digital health and thus adapt the following prescription concerning equitable inclusion:

“the co-creation of governance protocols, structures, and timelines through a partnership between racialized community members and [digital health] researchers, perhaps also including policy makers and other entities funding such research”Footnote 109

In our view, such an approach might genuinely enable the self-determination of racialized communities and thus make digital health both inclusive and equitable.

Conclusion and recommendation

The equitable governance of health data pertaining to racialized communities is consequential for the design of EDIAD-committed digital health because it includes them in the research enterprise, inviting them to engage with, manage, control, and own their data, ultimately to improve health outcomes and health knowledge and training within these communities. We are convinced that self-determination in digital health research involving racialized communities can be achieved. We draw inspiration from, and seek to deepen partnerships with, the First Nations Information Governance Center(s) that lent Indigenous communities in Turtle Island significant control over information pertaining to them.Footnote 110 Future research in digital health can explore institutional solutions to achieve similar forms of jurisdiction over data about racialized peoples and its attendant knowledge practices. Digital health research committed to EDIAD principals must develop a path for the racialized communities to ensure their self-determination in the collection of their health data. We believe that it comprises awareness, participation, control, and utilization of their health data. In order to achieve this, we suggest the following principles principles be undertaken in the context of digital health research involving racialized communities particularly:

  1. 1. Recognition that full participation in research projects is essential to maintain equal rights of access to health services must be broadly recognized and institutionalized.

  2. 2. CBPR approaches must be substantively incorporated into research rendering racialized communities’ partners in the enterprise. Equitable inclusion in research therefore requires that data-sharing and stewardship characterize governance models of the knowledge and data produced from research.

  3. 3. Research results must be published in a language understandable to the racialized people and returned to them on a schedule that is co-defined by researchers and community members/representatives.

  4. 4. As part of CBPR, it is optimal the aims of digital health research are aligned with the cultural-historical views of racialized community members regarding the body, disease, and wellness.

  5. 5. Research team members must respect other sensitive issues related to racialized communities’ historical, geographical, and demographic factors.

Competing Interest

The authors has no competing interest to declare.

Footnotes

Mozharul Islam and Arafaat A. Valiani contributed equally.

References

Notes

1. Jaworski, BK, et al. Advancing digital health equity: Directions for behavioral and social science research. Translational Behavioral Medicine 2022;13:132–9. doi:10.1093/tbm/ibac088 CrossRefGoogle Scholar.

2. Richardson, S, Lawrence, K, Schoenthaler, AM, Mann, D. A framework for digital health equity. NPJ Digital Medicine 2022;18;5(1):119. doi:10.1038/s41746-022-00663-0 CrossRefGoogle ScholarPubMed.

3. Rodriguez, JA, Shachar, C, Bates, DW. Digital inclusion as health care—Supporting health care equity with digital-infrastructure initiatives. The New England Journal of Medicine 2022;386:1101–03. doi:10.1056/NEJMp2115646 CrossRefGoogle ScholarPubMed.

4. World Health Organization (WHO). Global strategy on digital health 2020–2025; 2021; available at https://apps.who.int/iris/bitstream/handle/10665/344249/9789240020924-eng.pdf (last accessed 14 June 2023).

5. See note 1, Jaworski et al. 2022, at 132–9.

6. Karimi M, Lee EC, Couture SJ, Gonzales A, Grigorescu V, Smith SR, De Lew N, Sommers BD. National Survey Trends in Telehealth Use in 2021: disparities in utilization and audio versus video services. U.S. Department of Health & Human Services; 2021; available at https://aspe.hhs.gov/sites/default/files/documents/4e1853c0b4885112b2994680a58af9ed/telehealth-hps-ib.pdf (accessed 17 May 2023).

7. Davies, AR, Honeyman, M, Gann, B. Addressing the digital inverse care law in the time of COVID-19: potential for digital technology to exacerbate or mitigate health inequalities. Journal of Medical Internet research 2021;23(4):e21726 CrossRefGoogle ScholarPubMed.

8. See note 1, Jaworski et al. 2022, at 132–9.

9. Brall, C, Schröder-Bäck, P, Maeckelberghe, E. Ethical aspects of digital health from a justice point of view. European Journal of Public Health 2019;29(3):18–22. doi:10.1093/eurpub/ckz167 CrossRefGoogle ScholarPubMed.

10. Eaton, AA, Grzanka, PR, Schlehofer, MM, Silka, L. Public psychology: Introduction to the special issue. The American Psychologist 2021;76(8):1209–16. doi:10.1037/amp0000933 CrossRefGoogle ScholarPubMed.

11. Fine, M, Barreras, R. To be of use. Analyses of Social Issues and Public Policy 2004;1:175–82. doi:10.1111/1530-2415.00012 CrossRefGoogle Scholar.

12. See note 1, Jaworski et al. 2022, at 132–9.

13. See note 9, Brall et al. 2019, at 18–22.

14. Crawford, A, Serhal, E. Digital health equity and COVID-19: The innovation curve cannot reinforce the social gradient of health. Journal of Medical Internet Research 2020;22(6):e19361 CrossRefGoogle ScholarPubMed.

15. See note 7, Davies et al. 2021, at e21726.

16. See note 10, Eaton et al. 2021, at 1209–16.

17. See note 11, Fine, Barreras 2004, at 175–82.

18. Gooch K. More than 70 healthcare organizations commit to digital health equity. Becker’s Hospital Review 2021; available at https://www.beckershospitalreview.com/digital-health/more-than-70-healthcare-organizations-commit-to-digital-health-equity.html (last accessed 30 May 2023).

19. Kaihlanen, AM, Virtanen, L, Buchert, U, et al. Towards digital health equity—A qualitative study of the challenges experienced by vulnerable groups in using digital health services in the COVID-19 era. BMC Health Services Research 2022;22(188). doi:10.1186/s12913-022-07584-4 CrossRefGoogle Scholar.

20. Kukutai, T, Taylor, J. Indigenous Data Sovereignty. Toward an Agenda. Canberra: Australian National University Press; 2016 CrossRefGoogle Scholar.

21. Lyles, CR, Wachter, RM, Sarkar, U. Focusing on Digital Health Equity. JAMA 2021;326(18):1795–96. doi:10.1001/jama.2021.18459 CrossRefGoogle ScholarPubMed.

22. See note 3, Rodrigues et al. 2022, at 1101–03.

23. Simmons D, Kaganoff S, Drasser K. Building toward equity: A working model for digital health. Rock Health; 2021; available at https://rockhealth.com/insights/building-toward-equity-a-working-model-for-digital-health/ (last accessed 21 July 2023).

24. Arbour, L, Cook, D. DNA on Loan: Issues to Consider when Carrying Out Genetic Research with Aboriginal Families and Communities. Community Genetics 2006;9(3):153–60Google ScholarPubMed.

25. Caron, NR, Chongo, M, Hudson, M, Arbour, L, Wasserman, WW, Robertson, S, Correard, S, Wilcox, P. Indigenous genomic databases: Pragmatic considerations and cultural contexts. Frontiers in Public Health 2020;8:111 CrossRefGoogle ScholarPubMed.

26. See note 20, Kukutai, Taylor 2016.

27. Mweemba, O, Musuku, J, Mayosi, BM, Parker, M, Rutakumwa, R, Seeley, J, Tindana, P, De Vries, J. Use of broad consent and related procedures in genomics research: Perspectives from research participants in the Genetics of Rheumatic Heart Disease (RHDGen) study in a University Teaching Hospital in Zambia. Problemi di bioetica 2020;31(1):184–99Google Scholar.

28. Tsosie, KS, Yracheta, JM, Dickenson, D. Overvaluing individual consent ignores risks to tribal participants. Nature Reviews Genetics 2019;20(9):497–8CrossRefGoogle ScholarPubMed.

29. Valiani, AA. Frontiers of bio-decolonization: Indigenous data sovereignty as a possible model for community-based participatory genomic health research for racialized peoples in postgenomic Canada. Genealogy 2022;6(3):68. doi:10.3390/genealogy6030068 CrossRefGoogle Scholar.

30. Valiani, AA, Anderson, D, Gonzales, A, Gray, M, Hardcastle, L, Turin, TC. Precision health equity for racialized communities. International Journal for Equity in Health 2023;22:259. doi:10.1186/s12939-023-02049-4 CrossRefGoogle ScholarPubMed.

31. See note 30, Valiani et al. 2023.

32. Figueroa, CA, Luo, T, Aguilera, A, Lyles, CR. The need for feminist intersectionality in digital health. Lancet Digital Health 2021;3(8):e526–e533. doi:10.1016/S2589-7500(21)00118-7 CrossRefGoogle ScholarPubMed.

33. Noble SA. A future for intersectional black feminist technology studies. The Scholar and Feminist Online 2016; available at https://sfonline.barnard.edu/traversing-technologies/safiya-umoja-noble-a-future-for-intersectional-black-feminist-technology-studies/ (last accessed 14 Oct 2023).

34. See note 7, Davies et al. 2021, at e21726.

35. Creswell JW, Klassen AC, Clark VLP, Smith KC. Best practices for mixed methods research in the health sciences. Office of Behavioral and Social Sciences Research, National Institutes of Health 2011; available at https://obssr.od.nih.gov/sites/obssr/fles/Best_Practices_for_Mixed_Methods_Researc.pdf (last accessed 09 May 2023).

36. See note 1, Jaworski et al. 2022, at 132–9.

37. Metzl, JM, Maybank, A, De Maio, F. Responding to the COVID-19 pandemic: The need for a structurally competent health care system. JAMA 2020;324(3):231–2CrossRefGoogle Scholar.

38. Sørensen, K, Levin-Zamir, D, Duong, TV, Okan, O, Brasil, VV, Nutbeam, D. Building health literacy system capacity: A framework for health literate systems. Health Promotion International 2021;36(Supplement_1):i13–i23. doi:10.1093/heapro/daab153 CrossRefGoogle ScholarPubMed.

39. van Kessel, R, Wong, B, Clemens, T, Brand, H. Digital health literacy as a super determinant of health: more than simply the sum of its parts. Internet Interventions 2022;27:100500. doi:10.1016/j.invent.2022.100500 CrossRefGoogle ScholarPubMed.

40. Hoffman, L, Wisniewski, H, Hays, R, et al. Digital opportunities for outcomes in recovery services (DOORS): A pragmatic hands-on group approach toward increasing digital health and smartphone competencies, autonomy, relatedness, and alliance for those with serious mental illness. Journal of Psychiatric Practice 2020;26(2):80–8. doi:10.1097/PRA.0000000000000450 CrossRefGoogle ScholarPubMed.

41. Torous J, Camacho E, Myrick K. Equitable and informed digital mental health: Skills, evaluation, and integration of apps into care. Technology, Mind, & Society 2021 Conference Proceedings; 2021. doi:10.1037/tms0000058.

42. Torous, J, Jän, MK, Rauseo-Ricupero, N, Firth, J. Digital mental health and COVID-19: Using technology today to accelerate the curve on access and quality tomorrow. JMIR Mental Health 2020;7(3):e18848. doi:10.2196/18848 CrossRefGoogle ScholarPubMed.

43. Zhou, ES, Ritterband, LM, Bethea, TN, Robles, YP, Heeren, TC, Rosenberg, L. Effect of culturally tailored, internet-delivered cognitive behavioral therapy for insomnia in Black women: A randomized clinical trial. JAMA Psychiatry 2022;79(6):538–49. doi:10.1001/jamapsychiatry.2022.0653 CrossRefGoogle ScholarPubMed.

44. Cohn, BS. An Anthropologist Among the Historians and Other Essays. Oxford: Oxford University Press; 1987 Google Scholar.

45. Curtis, B. The politics of population: State formation, statistics, and the census of Canada, 1840–1875. Toronto; Buffalo: University of Toronto Press; 2001 CrossRefGoogle Scholar.

46. Dirks, NB. Castes of Mind: Colonialism and the Making of Modern India. Chicago: Chicago University Press; 2001 Google Scholar.

47. Thompson, D. The Schematic State. Cambridge: Cambridge University Press; 2016 CrossRefGoogle Scholar.

48. Black Health Equity Group, (2021). Engagement, Governance, Access, and Protection (EGAP): A Data Governance Framework for Health Data Collected from Black Communities. Retrieved from https://blackhealthequity.ca.

49. Ruha Benjamin underscores the risks associated with race-based data generation for racialized communities. Specifically, she suggests that race-based data collection can involve the exercise of a form of social control because, historically, such forms of knowledge creation have been tied to surveillance by colonial powers, thus abrogating or denying various legally enshrined rights. Our assertions regarding self-determination in data governance stresses her point that concomitant reforms to the manners in which state institutions generate and use data are crucial to genuinely practicing self-determination in digital health data governance. At a minimum, racialized communities participating digital health research ought to co-define and agree with researchers which state agencies and professional groups might access their health data and the terms of such access. See Benjamin R. Race After Technology: Abolitionist Tools for the New Jim Code. Polity; 2019.

50. Snipp, CM. What does data sovereignty imply: What does it look like? In: Kukutai, T, Taylor, J, eds. Indigenous Data Sovereignty. Toward an Agenda. Canberra: Australian National University Press; 2016:39–55 Google Scholar.

51. See note 20, Kukutai, Taylor 2016.

52. See note 49, Snipp 2016, at 39–55.

53. See note 29, Valiani 2022.

54. See note 30, Valiani et al. 2023.

55. Fatumo, S, Chikowore, T, Choudhury, A, Ayub, M, Martin, AR, Kuchenbaecker, K. A roadmap to increase diversity in genomic studies. Nature Medicine 2022;28(2):243–50CrossRefGoogle ScholarPubMed.

56. Wonkam, A, Munung, NS, Dandara, C, Esoh, KK, Hanchard, NA, Landoure, G. Five priorities of African genomics research: The next frontier. Annual Review of Genomics and Human Genetics 2022;23(1):499–521 CrossRefGoogle ScholarPubMed.

57. Hummel, P, Braun, M, Tretter, M, Dabrock, P. Data sovereignty: A review. Big Data & Society 2021;8(1):1–17. doi:10.1177/2053951720982012 CrossRefGoogle Scholar.

58. Floridi, L. The fight for digital sovereignty: What it is, and why it matters, especially for the EU. Philosophy & Technology 2020;33:369–78CrossRefGoogle ScholarPubMed.

59. Baezner M, Robin P. Cyber sovereignty and data sovereignty. CSS Cyber Defense Project; 2018.

60. Couture, S, Toupin, S. What does the notion of “sovereignty” mean when referring to the digital? New Media & Society 2019;21(10):2305–22CrossRefGoogle Scholar.

61. Irion, K. Government cloud computing and national data sovereignty. Policy & Internet 2012;4(3–4):40–71 CrossRefGoogle Scholar.

62. See note 20, Kukutai, Taylor 2016.

63. Winandy, M. A note on the security in the card management system of the german e-health card. In: Szomszor M, Kostkova P, eds. Electronic Healthcare. eHealth 2010, Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Berlin, Heidelberg: Springer; 2011:196–203. doi:10.1007/978-3-642-23635-8_25.CrossRefGoogle Scholar

64. Rainie, SC, Schultz, JL, Briggs, E, Riggs, P, Palmanteer-Holder, NL. Data as a strategic resource: Self-determination, Governance, and the data challenge for indigenous nations in the United States. International Indigenous Policy Journal 2017;8(2):1–29 CrossRefGoogle Scholar.

65. Similar to IDS, self-determination of racialized communities in the context of digital health becomes a space in which future research seeks to explore specific problems. Although there are differences between state sovereignty and data sovereignty, self-determination as it concerns data collection, curation, and access is considered a precondition for institutional/state sovereignty (see note 61, Irion 2012). Following Walker et al., we argue that self-determination in the field of digital health, like IDS, is a fillip to the self-determination of the racialized communities through the collection, organization and employment of data about themselves. See Walker, J, Healy, B, Healy, C, et al. Perspectives on linkage involving indigenous data. International Journal of Population Data Science 2018;3(4):1–2 CrossRefGoogle Scholar.

66. Alboaie S, Cosovan D. Private data system enabling self-sovereign storage managed by executable choreographies. In: Chen LY, Reiser HP, eds. Distributed Applications and Interoperable Systems, Lecture Notes in Computer Science, LNCS 2017, Vol. 10320. Cham: Springer; 2017:83–98.

67. Esposito, C, et al. On data sovereignty in cloud-based computation offloading for smart cities applications. IEEE Internet of Things Journal 2019;6(3):4521–35CrossRefGoogle Scholar.

68. van Dijk, JAGM. Digital divide research, achievements and shortcomings. Poetics 2006;34 (4–5):221–35CrossRefGoogle Scholar.

69. See note 49, Benjamin 2019.

70. Duster, T. Backdoor to Eugenics, 2nd edn. New York: Routledge; 2003 Google Scholar.

71. Centre FNIG: Exploration of the Impact of Canada’s Information Management Regime on First Nations Data Sovereignty. In. Edited by Centre FNIG: First Nations Information Governance Centre; 2022.

72. See note 65, Walker et al. 2018.

73. Daniels, J. Race and racism in Internet studies: A review and critique. New Media & Society 2013;15:695–719 CrossRefGoogle Scholar.

74. Everett, A. The revolution will be digitized: Afrocentricity and the digital public sphere. Social Text 2022;20:125–46CrossRefGoogle Scholar.

75. UCSB Interdisciplinary Humanities Center. AfroGEEKS: From Technophobia to Technophilia. Center for Black Studies, UCSB 2004; available at http://international.ucla.edu/africa/event/1761 (last accessed 25 June 2023).

76. Nakamura, L. Cybertypes: Race, Ethnicity, and Identity on the Internet. London: Routledge; 2002 CrossRefGoogle Scholar.

77. Nelson, A. Introduction: Future texts. Social Text 2002;20(2):1–15 CrossRefGoogle Scholar.

78. Nakamura, L. Digitizing Race: Visual Cultures of the Internet. Minneapolis: University of Minnesota Press; 2008 Google Scholar.

79. Robinson, CD, Wiseman, KP, Webb, HM, et al. Engagement and short-term abstinence outcomes among blacks and whites in the National Cancer Institute’s Smoke free TXT program. Nicotine & Tobacco Research 2020;22(9):1622–1626. doi:10.1093/ntr/ntz178 CrossRefGoogle Scholar.

80. Webb, HM, Carpenter, KM, Salmon, EE. Web-based tobacco cessation interventions and digital inequality across US racial/ethnic groups. Ethnicity & Disease 2019;29(3):495–504. doi:10.18865/ed.29.3.495 CrossRefGoogle Scholar.

81. Innovations and insights from one context can be productively considered and adapted to others. For example, the United States developed the US Indigenous Data Sovereignty Network to pursue similar goals to Canada and focused on four areas such as data for sovereignty, data collection and access, data storage and security, and data as intellectual property. See note 20, Kukutai, Taylor, 2016, and United States Indigenous Data Sovereignty Network (USIDSN). Strengthening Indigenous Governance. Native Nations Institute, Tuscon 2016; available at http://nni.arizona.edu/news/articles/us-indigenous-data-sovereignty-network (last accessed 29 May 2023).

82. Budhwani, S. Challenges and strategies for promoting health equity in virtual care: Findings and policy directions from a scoping review of reviews. Journal of the American Medical Informatics Association 2022;29(5):990–9. doi:10.1093/jamia/ocac022 CrossRefGoogle ScholarPubMed.

83. Yardley, L, Spring, BJ, Riper, H, et al. Understanding and promoting effective engagement with digital behavior change interventions. American Journal of Preventive Medicine 2016;51(5):833–42. doi:10.1016/j.amepre.2016.06.015 CrossRefGoogle ScholarPubMed.

84. See note 1, Jaworski et al. 2022, at 132–9.

85. See note 1, Jaworski et al. 2022, at 132–9.

86. Bird, M, et al. A generative co-design framework for healthcare innovation: development and application of an end-user engagement framework. Research Involvement and Engagement 2021;7(1):1–12. doi:10.1186/s40900-021-00252-7 CrossRefGoogle ScholarPubMed.

87. Crowe, B, et al. To improve quality, leverage design. BMJ Quality & Safety 2022;31:70–4CrossRefGoogle ScholarPubMed.

88. Fylan, B, Tomlinson, J, Raynor, DK, Silcock, J. Using experience-based co-design with patients, careers and healthcare professionals to develop theory-based interventions for safer medicines use. Research in Social & Administrative Pharmacy 2021;17(12):2127–35. doi:10.1016/j.sapharm.2021.06.004 CrossRefGoogle Scholar.

89. Latulippe, K, Hamel, C, Giroux, D. Co-design to support the development of inclusive eHealth tools for caregivers of functionally dependent older persons: Social justice design. Journal of Medical Internet Research 2020;22(11):e18399 CrossRefGoogle ScholarPubMed.

90. Leininger, LJ, Albrecht, SS, Buttenheim, A, et al. Fight like a nerdy girl: The Dear Pandemic playbook for combating health misinformation. American Journal of Health Promotion 2022;36(3):563–7. doi:10.1177/08901171211070956 CrossRefGoogle Scholar.

91. Equity Design Thinking. The equity design thinking educational series; 2022; available at https://pittequitydesignthinking.org/ (last accessed 18 June 2023).

92. See note 20, Kukutai, Taylor 2016.

93. See note 81, United States Indigenous Data Sovereignty Network (USIDSN) 2016.

94. See note 69, Centre FNIG 2022.

95. Mello, MM, Wolf, LE. The Havasupai Indian tribe case-lessons for research involving stored biologic samples. New England journal of medicine 2010;363(3):204–7CrossRefGoogle ScholarPubMed.

96. The Tri-Council Policy Statement. Ethical Conduct for Research Involving Humans In. Canada: Secretariat on Responsible Conduct of Research; 2018.

97. Taniguchi, NK, Taualii, M, Maddock, J. A comparative analysis of indigenous research guidelines to inform genomic research in indigenous communities. International Indigenous Policy Journal 2012;3(1):6CrossRefGoogle Scholar.

98. See note 30, Valiani et al. 2023.

99. Ahmed, S, Shommu, NS, Rumana, N, Barron, GRS, Wicklum, S, Turin, TC. Barriers to access of primary healthcare by immigrant populations in Canada: A literature review. Journal of Immigrant and Minority Health 2016;18(6):1522–40CrossRefGoogle ScholarPubMed.

100. Turin, TC, Chowdhury, N, Rumana, N, Lasker, MAA, Qasqas, M. Partnering with organisations beyond academia through strategic collaboration for research and mobilisation in immigrant/ethnic-minority communities. BMJ Global Health 2022;7(3):e008201 CrossRefGoogle ScholarPubMed.

101. Tsosie, KS, Yracheta, JM, Kolopenuk, JA, Geary, J. We have “Gifted” enough: Indigenous genomic data sovereignty in precision medicine. American Journal of Bioethics 2021;21(4):72–5CrossRefGoogle Scholar.

102. See note 69, Centre FNIG 2022.

103. Carroll, SR, et al. The CARE principles for indigenous data governance. Data Science Journal 2020;19(1):43 CrossRefGoogle Scholar.

104. See note 55, Wonkam et al. 2022, at 499–521.

105. de Vries, J Munung, SN, Matimba, A, McCurdy, S, Ouwe Missi Oukem-Boyer, O, Staunton, C, Yakubu, A, Tindana, P, HAC, . Regulation of genomic and biobanking research in Africa: A content analysis of ethics guidelines, policies and procedures from 22 African countries. BMC Medical Ethics 2017;18(1):8CrossRefGoogle Scholar.

106. Tindana, P, Depuur, C, de Vries, J, Seeley, J, Parker, M. Informed consent in genomic research and biobanking: Taking feedback of findings seriously. Problemi di bioetica 2020;31(1):200–15Google ScholarPubMed.

107. See note 29, Valiani 2022.

108. See note 98, Tsosie et al. 2021, at 72–5.

109. See note 29, Valiani 2022.

110. First Nations Information Governance Centre (FNIGC). Code of research ethics, First Nations Information Governance Centre, Ottawa; 2016; available at https://www.fnigc.ca/sites/default/files/ENpdf/RHS_General/rhs-code-ofresearchethics-2007.pdf (last accessed 12 June 2023).