It is by now well-established that both health and health disparities are profoundly social phenomena. However, the majority of consumer health informatics interventions to date have focused solely on individuals, with particular attention accorded to influencing individual-level psychosocial characteristics and health behaviors. For example, a 2018 systematic review of mobile health intervention (mHealth) research for racial/ethnic minorities and/or those of low-socioeconomic status found that most interventions targeted individuals and were most commonly informed by psychosocial theories.1 This is despite the fact that interventions which rely primarily on individual effort, behavior, and choice tend to be less effective for marginalized groups—groups that experience socially stratifying processes of marginalization or exclusion from mainstream social, economic, cultural, or political life2—than those that target the context in which behavior occurs.3,4
Perhaps related to this individual focus, the impact of consumer health informatics interventions on marginalized groups is often limited. For example, the aforementioned systematic review revealed few significant impacts of mHealth interventions on health outcomes for these groups.1 In addition, as we have argued elsewhere, health informatics interventions are at particular risk of reinforcing health disparities by disproportionately benefiting nonmarginalized groups that already possess health-related advantages.5 The result can be intervention-generated inequality, in part from differential effectiveness of prevailing intervention models for marginalized groups.5
We contend that health informatics, including its subfields of consumer, population, and clinical informatics, will be more effective for marginalized groups (and less likely to produce intervention-generated inequality) if we better apply our understandings of the societal origins of health and health disparities to intervention design. We argue for greater emphasis on “upstream” interventions that focus on the social, political, economic, and physical contexts in which health is (re-)produced.6–8 We define these interventions, also called “structural” and “environmental,” as meso-level and macro-level interventions. In addition to the potential effectiveness improvements mentioned previously, these interventions may have multiple efficiency-related advantages through targeting multiple pathways to multiple health outcomes,8 and through reaching large numbers of people due to their lack of dependence upon individual patient/consumer agency for uptake.8
Despite the potential advantages of upstream interventions described above, their broad nature means that there are conceptual difficulties in applying this approach in health informatics. Accordingly, there is a need to systematically identify potential targets for interventions, to think critically about technological capabilities, and to consider the ways in which prior noninformatics interventions might better incorporate information and communication technologies (ICTs). To these ends, we introduce a model of health disparities that identifies potential targets for intervention at different levels of social organization. Next, we discuss types of noninformatics interventions that have addressed these targets. Finally, we present a typology of ICT capabilities, mapping the potential roles that ICT can play in influencing these intervention targets. We conclude by outlining the implications of upstream interventions for downstream intervention effectiveness, and consider their possible synergies.
A MODEL OF HEALTH DISPARITIES: TARGETS FOR INTERVENTION
Health disparities are persistent differences in the incidence and prevalence of disease, morbidity, mortality, and survival rates in one group compared with the general population. By contrast, health equity, which is the goal of upstream interventions, occurs when everyone has an equal opportunity to be healthy.
Figure 1 shows an extended version of the World Health Organization (WHO) Committee on the Social Determinants of Health empirically derived conceptual framework. The framework maps pathways between health outcomes and social conditions, so as to highlight approaches to reduce health disparities and enhance health equity.9 As shown, the social determinants of health are found at the meso level and micro levels of social organization. However, the social determinants of health disparities are macro-level social, political, and economic mechanisms that generate socioeconomic hierarchies and a range of possible socioeconomic positions that individuals can occupy.9 These structural determinants then shape health through intermediary social determinants of health that are unevenly distributed, primarily due to macro-level factors.9
To increase the utility of the model for health informatics, we extend the original WHO model by mapping the role of ICTs at each level, as well as incorporating the contribution of “flexible resources” to health technology uptake in disparities.10 Furthermore, we incorporate insights from other models that have better detailed meso-level factors11–15 and environmental health-related determinants.16–18 In addition, because the WHO model primarily focused on socioeconomic status, we expanded this model to better represent health disparities rooted in social stratification based on sex,19–23 race/ethnicity,22–29 lesbian, gay, bisexual, and transgender identity,30–36 disability,30,37–41 and place of residence (including rural residence).25,42–44
Targets for Intervention in the Model
The WHO model identifies specific pathways by which social stratification and marginalization influence socioeconomic position.9 Our expanded model suggests 4 broad types of intervention strategies to confront disparities:
- Influencing social hierarchies, which may involve disrupting processes of stratification and marginalization;
- Reducing exposures, which may be environmental, psychosocial, biological, or combined, as in physical stress responses;
- Decreasing vulnerability, both vulnerability to health conditions and vulnerabilities related to lower resource availability;
- Preventing unequal consequences of ill health, which may be related to health or socioeconomic position.45
If we apply this framework, we see that many informatics interventions are designed to reduce vulnerability to disease (eg, enhancing the psychological resource of self-efficacy, increasing physical activity, encouraging screening for early disease detection) or reducing negative consequences once disease has developed (eg, developing disease management or coping strategies, monitoring biometrics such as blood glucose at home, text-based medication, or health management reminders). We also note that many prior interventions do not specifically address differences in vulnerability or consequences for marginalized groups, but rather focus on consumer/patient groups in general.
Targets of Upstream Interventions Outside of Health Informatics
Outside of informatics, meso-level approaches have focused on living and working conditions (typically by reducing exposures) and social and community networks (typically by reducing vulnerabilities, though sometimes by reducing exposures to behaviors such as bullying). Meso-level interventions have included strategies such as redesign of spaces; changing social norms; making resources more or less available in a given area (eg, condom distribution programs, zoning to reduce density of alcohol retailers); and collective mobilization around unequal negative exposures such as environmental hazards or stigma.6,8,46,47 Health care system-led strategies have also included quality improvement initiatives designed to benefit all patients of the health care organization, as well as service reorganization and/or integration.48
Macro-level approaches focus on larger-scale phenomena such as the economy, political system, legislation, and culture. Macro-level interventions typically focus on influencing social hierarchies or reducing exposures or vulnerabilities. Interventions have included policy change; pricing through mechanisms such as subsidies, incentives and taxes; product modification (eg, food safety standards, requiring seatbelts in cars); and resource redistribution (eg, municipal minimum wage laws).8 At the macro level, redesigning health insurance programs (Medicaid expansion and insurance subsidies)49 and subsidizing health care through tax-supported community health centers50 have also been designed to make persons with lower income less likely to experience negative consequences from illness and accidents.
Interventions which use the above approaches tend to organize efforts around: (1) specific settings (eg, schools, workplaces); (2) specific populations (eg, African Americans, low-income groups or neighborhoods); (3) specific behaviors/risks (eg, physical activity, smoking, motor vehicle accidents); or (4) enhancing resources and power for marginalized groups (eg, microfinancing programs to empower women economically, stable housing).8,46,51
Using the above noninformatics examples, we next identify potential roles for ICT below.
POTENTIAL ROLES FOR ICT
ICTs may have a negligible role to play in confronting some of the social determinants of health disparities. For example, ICT may not have much ability to ameliorate childhood poverty. Nevertheless, we hold that ICT can play a much greater role in upstream interventions than it has to date. To facilitate this role, we must systematically consider what ICT can do well. Therefore, as Table 1 shows, we outline key ICT capabilities, which include: (1) collection and display of information; (2) customization of information; (3) education; (4) mediation of communication; (5) support of workflows and activities; (6) framing and support of decisions; (7) social coordination; (8) optimization of resource allocation; (9) identification of patterns and anomalies; and (10) prediction of outcomes.
Table 1 presents a nonexhaustive list of potential ICT intervention types that can target health at macro and meso levels. These strategies address a mix of pathways, including influencing social hierarchies (eg, decision support for policy-makers, diversifying social networks beyond segregated schools and neighborhoods); decreasing exposures (eg, preventing injuries with smart vehicles and home appliances); reducing vulnerability (eg, enabling resource pooling); and preventing unequal consequences (eg, facilitating coordination and referrals between health systems and the nonprofit sector).
A key, deliberate, aspect of the proposed intervention types (eg, collective action) is that they do not necessarily require significant agency on behalf of an individual patient or person at risk of a health condition to have an effect. Rather, this agency can be located in policy-makers, community activists, health care providers, public health officials, or nonprofit organization staff. Alternatively, the interventions reduce the effort involved in the activity, as with the approach of reducing complexity for patients by redesigning workflows. Although there are some prior examples of the informatics interventions in Table 1 (see references in the table), most have not been widely implemented—and others have yet to be realized at all.
MULTI-LEVEL INTERVENTIONS: RECONCILING THE UPSTREAM AND THE DOWNSTREAM
Although we have argued for a greater focus on upstream interventions in this paper, we acknowledge the complimentary need for downstream interventions that support marginalized individuals who want to change their health behaviors, but face multi-level barriers to doing so. To support these individuals, we should recognize that the effectiveness of individual-level interventions is sensitive not only to psychosocial, behavioral, and biological factors, but also to contextual factors beyond individual control. For instance, a recent meta-analysis of human immunodeficiency virus prevention interventions for African Americans found that condom use effect sizes were moderated by local levels of racism and racial residential segregation.90
Accordingly, building on previous noninformatics research, we also argue for the potential value of multi-level interventions, which typically target both individual-level and meso-level or macro-level factors (eg, school-based interventions that focus on both food supply and nutritional education91). Any of the intervention types outlined in Table 1 could be implemented as part of a multi-level intervention, or on its own.
The model and typology provided in this paper offer conceptual resources for upstream interventions in informatics. Specifically, they can enable informatics researchers and practitioners in informatics to “level up” by more systematically focusing their efforts on health equity, using an expanded set of intervention targets and strategies. We also hope to inspire more comprehensive thinking about the disparity-related mechanisms by which informatics interventions may operate. Although the approaches outlined in this paper may require some refocusing on our parts, we argue that leveling up offers unparalleled opportunities to enhance health equity with informatics.
Some ideas for this paper were generated at the Computing Research Association (CRA) Computing Community Consortium (CCC) sponsored workshop on Sociotechnical Interventions for Health Disparity Reduction.92
1. Stowell E, Lyson MC, Saksono H, et al. Designing and evaluating mhealth interventions for vulnerable populations: a systematic review. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems; Montreal QC, Canada. 2018.
2. Cook KEGiven LM. Marginalized populations. The SAGE Encyclopedia of Qualitative Research Methods. Thousand Oaks, CA: SAGE Publications, Inc; 2008:496.
3. Boelsen-Robinson T, Peeters A, Beauchamp A, et al. A systematic review of the effectiveness of whole-of-community interventions by socioeconomic position. Obes Rev. 2015;16:806–816.
4. Hillier-Brown FC, Bambra CL, Cairns J-M, et al. A systematic review of the effectiveness of individual, community and societal level interventions at reducing socioeconomic inequalities in obesity amongst children. BMC Public Health. 2014;14:1483–1490.
5. Veinot TC, Mitchell H, Ancker JS. Good intentions are not enough: how informatics interventions can worsen inequality. J Am Med Inform Assoc. 2018;25:1080–1088.
6. Blankenship KM, Bray SJ, Merson MH. Structural interventions in public health. AIDS. 2000;14:S11–S21.
7. Sommer M, Parker RSommer M, Parker R. Introduction: Structural approaches for unintentional injury prevention. Structural Approaches in Public Health. London, UK: Taylor & Francis Group; 2013:1–14.
8. Lieberman L, Golden SD, Earp JA. Structural approaches to health promotion: what do we need to know about policy and environmental change? Health Educ Behav. 2013;40:520–525.
9. Solar O, Irwin A. A Conceptual Framework for Action on the Social Determinants of Health Social Determinants of Health Discussion Paper 2 (Policy and Practice). Geneva, Switzerland: World Health Organization; 2018. Available at: http://www.who.int/sdhconference/resources/ConceptualframeworkforactiononSDH_eng.pdf
. Accessed July 19, 2018.
10. Phelan JC, Link BG, Tehranifar P. Social conditions as fundamental causes of health inequalities: theory, evidence, and policy implications. J Health Soc Behav. 2010;51(suppl):S28–S40.
11. Dahlgren G, Whitehead M. Policies and Strategies to Produce Social Equity in Health. Stockholm: Institute for Futures Studies; 1991.
12. Sallis JF, Owen N, Fisher E. Ecological Models of Health Behavior Health Behavior: Theory, Research, and Practice, 5th ed. San Francisco, CA: Jossey-Bass; 2015:43–64.
13. Swinburn B, Egger G, Raza F. Dissecting obesogenic environments: the development and application of a framework for identifying and prioritizing environmental interventions for obesity. Prev Med. 1999;29:563–570.
14. Veinot TC, Caldwell E, Loveluck J, et al. HIV testing behavior and social network characteristics and functions among young men who have sex with men (YMSM) in metropolitan detroit. AIDS Behav. 2016;20:2739–2761.
15. National Institute on Minority Health and Health Disparities. NIMHD Minority Health and Health Disparity Research Framework. Available at: www.nimhd.nih.gov/images/research-framework-slide.pdf
. Accessed July 19, 2018.
16. Schulz A, Northridge ME. Social determinants of health: implications for environmental health promotion. Health Educ Behav. 2004;31:455–471.
17. Knol AB, Briggs DJ, Lebret E. Assessment of complex environmental health problems: framing the structures and structuring the frameworks. Sci Tot Environ. 2010;408:2785–2794.
18. Barton H, Grant M. A health map for the local human habitat. J R Soc Promot Health. 2006;126:252–253.
19. Einspahr J. Structural domination and structural freedom: a feminist perspective. Feminist Review. 2010;94:1–19.
20. Gupta GR. How men’s power over women fuels the HIV epidemic. It limits women’s ability to control sexual interactions. BMJ. 2002;324:183–184.
21. Andreassen L, Di Tommaso ML. Estimating capabilities with random scale models: women’s freedom of movement. Social Choice Welfare. 2018;50:625–661.
22. Nurius PS, Green S, Logan-Greene P, et al. Stress pathways to health inequalities: embedding ACEs within social and behavioral contexts. Int Public Health J. 2016;8:241–256.
23. Centers for Disease Control and Prevention. Adverse childhood experiences reported by adults—five states, 2009. MMWR Morb Mortal Wkly Rep. 2010;59:1609–1613.
24. Williams DR, Collins C. Racial residential segregation: a fundamental cause of racial disparities in health. Public Health Rep. 2001;116:404–416.
25. Taylor MM. Rural Health Disparities: The Political Economy Application of the Political Economy to Rural Health Disparities. Cham: Springer International Publishing; 2018:1–8.
26. Phelan JC, Link BG. Is racism a fundamental cause of inequalities in health? Annu Rev Sociol. 2015;41:311–330.
27. Mersky JP, Janczewski CE. Racial and ethnic differences in the prevalence of adverse childhood experiences: findings from a low-income sample of U.S. women. Child Abuse Negl. 2018;76:480–487.
28. Moon H, Roh S, Lee YS, et al. Disparities in health, health care access, and life experience between American Indian and White adults in South Dakota. J Racial Ethn Health Disparities. 2016;3:301–308.
29. Umberson D, Olson JS, Crosnoe R, et al. Death of family members as an overlooked source of racial disadvantage in the United States. Proc Natl Acad Sci. 2017;114:915–920.
30. Hatzenbuehler ML, Phelan JC, Link BG. Stigma as a fundamental cause of population health inequalities. Am J Public Health. 2013;103:813–821.
31. Branstrom R, Hatzenbuehler ML, Pachankis JE, et al. Sexual orientation disparities in preventable disease: a fundamental cause perspective. Am J Public Health. 2016;106:1109–1115.
32. Williams SL, Mann AK. Sexual and gender minority health disparities as a social issue: how stigma and intergroup relations can explain and reduce health disparities. J Soc Issues. 2017;73:450–461.
33. Andersen JP, Blosnich J. Disparities in adverse childhood experiences among sexual minority and heterosexual adults: results from a multi-state probability-based sample. PLoS One. 2013;8:e54691.
34. Eliason MJ, Fogel SC. An ecological framework for sexual minority women’s health: factors associated with greater body mass. J Homosex. 2015;62:845–882.
35. Chaudoir SR, Wang K, Pachankis JE. What reduces sexual minority stress? A review of the intervention “toolkit”. J Soc Issues. 2017;73:586–617.
36. Valentine SE, Shipherd JC. A systematic review of social stress and mental health among transgender and gender non-conforming people in the United States. Clin Psychol Rev. 2018;66:24–38.
37. Brock ME. Trends in the educational placement of students with intellectual disability in the United States over the past 40 years. Am J Intellect Dev Disabil. 2018;123:305–314.
38. Oliver M, Barnes C. The new politics of disablement. Macmillan International Higher Education. 2012.
39. Iezzoni LI. Eliminating health and health care disparities among the growing population of people with disabilities. Health Aff. 2011;30:1947–1954.
40. Drum CE, Krahn GL, Peterson JJ, et alDrum CE, Krahn GL, Bersani HA. Health of people with disabilities: determinants and disparities. Disability and Public Health. Washington, DC: American Public Health Association; 2009:125–144.
41. Austin A, Herrick H, Proescholdbell S, et al. Disability and exposure to high levels of adverse childhood experiences: effect on health and risk behavior. N C Med J. 2016;77:30–36.
42. Lichter DT, Brown DL. Rural America in an urban society: changing spatial and social boundaries. Annu Rev Sociol. 2011;37:565–592.
43. Burton LM, Lichter DT, Baker RS, et al. Inequality, family processes, and health in the “New” rural America. Am Behav Scientist. 2013;57:1128–1151.
44. Radcliff E, Crouch E, Strompolis M. Rural-urban differences in exposure to adverse childhood experiences among South Carolina adults. Rural Remote Health. 2018;18:4434.
45. Diderichsen F, Evans T, Whitehead MEvans T. The social basis of disparities in health. Challenging Inequities in Health. New York, NY: Oxford University Press; 2001:13–23.
46. Freudenberg N, Franzosa E, Chisholm J, et al. New approaches for moving upstream:how state and local health departments can transform practice to reduce health inequalities. Health Educ Behav. 2015;42 (suppl):46S–56S.
47. Hollands GJ, Bignardi G, Johnston M, et al. The TIPPME intervention typology for changing environments to change behaviour. Nat Hum Behav. 2017;1:140.
48. Ellner A, Pace C, Lee S, et alSommer M, Parker R. Embracing complexity: towards platforms for integrated health and social service delivery. Structural Approaches in Public Health. London, UK: Taylor & Francis Group; 2013:47–64.
49. Office of the Legislative CounselRepresentatives UHo. Patient Protection and Affordable Care Act. Public Law 111–148
Vol 111. Washington, DC: Government Publishing Office; 2010:759–762.
50. United States Health Resources and Services Administration. What is a health center? 2018. Available at: https://bphc.hrsa.gov/about/what-is-a-health-center/index.html
. Accessed July 30, 2018.
51. Auerbach J. Transforming social structures and environments to help in HIV prevention. Health Aff. 2009;28:1655–1665.
52. Lathrop D, Ruma L. Open Government: Collaboration, Transparency, and Participation in Practice. Cambridge, UK: O’Reilly Media Inc; 2010.
53. Bazemore AW, Cottrell EK, Gold R, et al. “Community vital signs”: incorporating geocoded social determinants into electronic records to promote patient and population health. J Am Med Inform Assoc. 2016;23:407–412.
54. Parker AG. Reflection-through-performance: personal implications of documenting health behaviors for the collective. Person Ubiq Comput. 2014;18:1737–1752.
55. Hannaford A, Lipshie-Williams M, Starrels JL, et al. The use of online posts to identify barriers to and facilitators of HIV pre-exposure prophylaxis (PrEP) among men who have sex with men: a comparison to a systematic review of the peer-reviewed literature. AIDS Behav. 2018;22:1080–1095.
56. Gottlieb LM, Tirozzi KJ, Manchanda R, et al. Moving electronic medical records upstream: incorporating social determinants of health. Am J Prev Med. 2015;48:215–218.
57. Kind AJ, Jencks S, Brock J, et al. Neighborhood socioeconomic disadvantage and 30-day rehospitalization: a retrospective cohort study. Ann Intern Med. 2014;161:765–774.
58. Bao J, He T, Ruan S, et al. Planning bike lanes based on sharing-bikes’ trajectoies. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; Halifax, NS, Canada. 2017.
59. Grimes A, Bednar M, Bolter JD, et al. Eat well: sharing nutrition-related memories in a low-income community. Proceedings of the 2008 ACM conference on Computer supported cooperative work; San Diego, CA. 2008.
60. Kaziunas E, Ackerman MS, Veinot TCE. Localizing chronic disease management: information work and health translations. Proceedings of the 76th ASIS&T Annual Meeting: Beyond the Cloud: Rethinking Information Boundaries; Montreal, Quebec, Canada. 2013.
61. Gottlieb LM, Hessler D, Long D, et al. Effects of social needs screening and in-person service navigation on child health: a randomized clinical trial. JAMA Pediatr. 2016;170:e162521.
62. Berkowitz SA, Hulberg AC, Standish S, et al. Addressing unmet basic resource needs as part of chronic cardiometabolic disease management. JAMA Intern Med. 2017;177:244–252.
63. Goodspeed R, Pelzer P, Pettit CSanchez TW. Planning knowledge and research. Planning Knowledge and Research. New York, NY: Routledge; 2017:210–225.
64. Johnson E. At your service: enhanced property Web sites offering tenant and resident amenities improve customer service. J Property Manage. 2006;71:24–29.
65. White FA, Harvey LJ, Abu-Rayya HM. Improving intergroup relations in the Internet age: a critical review. Rev Gen Psychol. 2015;19:129–139.
66. Tobin SJ, Vanman EJ, Verreynne M, et al. Threats to belonging on Facebook: lurking and ostracism. Soc Influ. 2015;10:31–42.
67. Legrand S, Muessig KE, Pike EC, et al. If you build it will they come? Addressing social isolation within a technology-based HIV intervention for young black men who have sex with men. AIDS Care. 2014;26:1194–1200.
68. Speyer R, Denman D, Wilkes-Gillan S, et al. Effects of telehealth by allied health professionals and nurses in rural and remote areas: a systematic review and meta-analysis. J Rehab Med. 2018;50:225–235.
69. Marsh S, Foley LS, Wilks DC, et al. Family-based interventions for reducing sedentary time in youth: a systematic review of randomized controlled trials. Obes Rev. 2014;15:117–133.
70. Valdez RS, Holden RJ, Novak LL, et al. Transforming consumer health informatics through a patient work framework: connecting patients to context. J Am Med Inform Assoc. 2015;22:2–10.
71. Schaefbauer CL, Khan DU, Le A, et al. Snack buddy: supporting healthy snacking in low socioeconomic status families. Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing; Vancouver, BC, Canada. 2015.
72. Ancker JS, Mauer E, Hauser D, et al. Expanding access to high-quality plain-language patient education information through context-specific hyperlinks. AMIA Annu Symp Proc. 2016;2016:277–284.
73. Jean-Jacques M, Persell SD, Thompson JA, et al. Changes in disparities following the implementation of a health information technology-supported quality improvement initiative. J Gen Intern Med. 2012;27:71–77.
74. Goold SD, Biddle AK, Klipp G, et al. Choosing healthplans all together: a deliberative exercise for allocating limited health care resources. J Health Polit Policy Law. 2005;30:563–602.
75. Dimond JP, Dye M, Larose D, et al. Hollaback!: the role of storytelling online in a social movement organization. Proceedings of the 2013 conference on Computer supported cooperative work; San Antonio, TX. 2013.
76. Manning N. How Ushahidi helped thousands of peoples’ votes count in the 2012 USA election. Usahidi.com. Vol July 30, 2018. Nairobi, Kenya. 2016. Available at: www.ushahidi.com/blog/2016/05/06/how-ushahidi-helped-thousands-of-peoples-votes-count-in-the-2012-usa-election
. Accessed July 30, 2018.
77. Parker AG, Grinter RE. Collectivistic health promotion tools: Accounting for the relationship between culture, food and nutrition. Int J Hum Comput Stud. 2014;72:185–206.
78. Bates DW. Health information technology and care coordination: the next big opportunity for informatics? Yearbook Med Inform. 2015;10:11–14.
79. Dillahunt TR, Veinot TC. Getting there: strategies for addressing transportation needs in underserved communities. ACM Trans Comput Human Interact (TOCHI). 2018;25:29. Doi: 10.1145/3233985.
80. Schuurman N, Randall E, Berube M. A spatial decision support tool for estimating population catchments to aid rural and remote health service allocation planning. Health Inform J. 2011;17:277–293.
81. Angwin J, Larson J, Mattu S, et al. Machine bias: there’s software used across the country to predict future criminals. And it’s biased against blacks. 2016. Available at: www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
. Accessed July 30, 2018.
82. Garvie C, Frankle J. Facial-Recognition Software might have a racial bias problem. Atlantic. 2016.
83. Sweeney L. Discrimination in online ad delivery. Queue. 2013;11:10–29.
84. Nieuwenhuijsen MJ, Donaire-Gonzalez D, Rivas I, et al. Variability in and agreement between modeled and personal continuously measured black carbon levels using novel smartphone and sensor technologies. Environ Sci Technol. 2015;49:2977–2982.
85. Hardy JK, Veinot TC, Yan X, et al. User acceptance of location-tracking technologies in health research: implications for study design and data quality. J Biomed Inform. 2018;79:7–19.
86. Gomez-Lopez IN, Clarke P, Hill AB, et al. Using social media to identify sources of healthy food in urban neighborhoods. J Urban Health. 2017;94:429–436.
87. Choudhury MD, Sharma S, Kiciman E. Characterizing dietary choices, nutrition, and language in food deserts via social media. Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing; San Francisco, CA. 2016.
88. Joseph AJ, Tandon N, Yang LH, et al. Schizophrenia: use and misuse on Twitter. Schizophr Res. 2015;165:111–115.
89. Dalton JE, Perzynski AT, Zidar DA, et al. Accuracy of cardiovascular risk prediction varies by neighborhood socioeconomic position: a retrospective cohort study. Ann Intern Med. 2017;167:456–464.
90. Reid AE, Dovidio JF, Ballester E, et al. HIV prevention interventions to reduce sexual risk for African Americans: the influence of community-level stigma and psychological processes. Soc Sci Med. 2014;103:118–125.
91. Hoelscher DM, Kirk S, Ritchie L, et al. Position of the Academy of Nutrition and Dietetics: interventions for the prevention and treatment of pediatric overweight and obesity. J Acad Nutr Diet. 2013;113:1375–1394.
92. Computing Community Consortium. Sociotechnical interventions for health disparity reduction: a research agenda. 2018. Available at: https://cra.org/ccc/events/sociotechnical-interventions-health-disparity-reduction/
. Accessed July 30, 2018.