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A behavioral approach to personalizing public health

Published online by Cambridge University Press:  09 July 2020

KAI RUGGERI Open the ORCID record for KAI RUGGERI [Opens in a new window] ,
AMEL BENZERGA ,
SANNE VERRA Open the ORCID record for SANNE VERRA [Opens in a new window]  and
TOMAS FOLKE
KAI RUGGERI*
Affiliation:
Columbia University, Mailman School of Public Health, Department of Health Policy and Management, New York, NY, USA
AMEL BENZERGA
Affiliation:
Columbia University, Mailman School of Public Health, Department of Health Policy and Management, New York, NY, USA Sciences Po Paris, School of Public Affairs, Paris, France
SANNE VERRA
Affiliation:
Columbia University, Mailman School of Public Health, Department of Health Policy and Management, New York, NY, USA Department of Interdisciplinary Social Science, Utrecht University, Utrecht, The Netherlands
TOMAS FOLKE
Affiliation:
Columbia University, Mailman School of Public Health, Department of Health Policy and Management, New York, NY, USA
*
*Correspondence to: Columbia University, Mailman School of Public Health, Health Policy and Management Department,722 W 168th St., New York, NY 10032, USA. E-mail: [email protected]
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Abstract

Behavioral policies are increasingly popular in a number of health care contexts. However, evidence of their effectiveness, specifically in low-income and highly disadvantaged populations, is limited. Some positive effects have been found for adaptive interventions, which merge more personalized approaches with advances in data collection and modern analytical methods. These approaches have only recently become feasible, as their implementation requires a confluence of large-scale datasets, contemporary machine learning, and validated behavioral insights. Such methods have considerable potential to improve outcomes without requiring substantial increases in effort on the part of individuals. Using examples from health insurance choice, clinical attendance rates, and prescription of medicines, we present an argument for how adaptive approaches, especially those considering disadvantaged populations explicitly, offer an opportunity to generate equity in public health.

Type
New Voices
Information
Behavioural Public Policy , Volume 7 , Issue 2 , April 2023 , pp. 457 - 469
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

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References

Aggarwal, A., Davies, J. and Sullivan, R. (2016), ‘“Nudge” and the epidemic of missed appointments: Can behavioural policies provide a solution for missed appointments in the health service?’ J Health Organ Manag, 30(4): 558–564.CrossRefGoogle Scholar
Allmark, P. and Tod, A. M. (2014), ‘Can a nudge keep you warm? Using nudges to reduce excess winter deaths: insight from the Keeping Warm in Later Life Project (KWILLT)’, Journal of Public Health, 36(1): 111–116.CrossRefGoogle ScholarPubMed
Alyass, A., Turcotte, M. and Meyre, D. (2015), ‘From big data to personalized medicine for all: challenges and opportunities’, BMC Med Genomics, 8: 33.CrossRefGoogle ScholarPubMed
Baicker, K., Congdon, W. J. and Mullainathan, S. (2012), ‘Health insurance coverage and take-up: Lessons from behavioral economics’, The Milbank Quarterly, 90(1): 107–134.CrossRefGoogle ScholarPubMed
Bellucci, E., Dharmasena, L., Nguyen, L. and Calache, H. (2017), ‘The effectiveness of SMS Reminders and the impact of patient characteristics on missed appointments in a public dental outpatient clinic’, Australasian Journal of Information Systems 21: 1–21,CrossRefGoogle Scholar
Chaiyachati, K. H. et al. (2018), ‘Association of rideshare-based transportation services and missed primary care appointments: a clinical trial’, JAMA Int Med, 178(3), 383–389.CrossRefGoogle ScholarPubMed
Cook, C. A. L., Selig, K. L., Wedge, B. J. and Gohn-Baube, E. A. (1999), ‘Access Barriers and the Use of Prenatal Care by Low-Income, Inner-City Women’, Social Work, 44(2): 129–129.CrossRefGoogle ScholarPubMed
Cruz, H. E., Gawrys, J., Thompson, D., Mejia, J., Rosul, L. and Lazar, D. (2018), ‘A Multipronged Initiative to Improve Productivity and Patient Access in a Federally Qualified Health Center Network’, Journal of Ambulatory Care Management, 41(3): 225–37.CrossRefGoogle Scholar
Dantas, L. F., Fleck, J. L., Cyrino Oliveira, F. L. and Hamacher, S. (2018), ‘No-shows in appointment scheduling - a systematic literature review’, Health Policy, 122(4): 412–421.CrossRefGoogle ScholarPubMed
DuMontier, C., Rindfleisch, K., Pruszynski, J. and Frey, J. (2013), ‘A multi-method intervention to reduce no-shows in an urban residency clinic’, Fam Med, 45(9): 634–641.Google Scholar
Ericson, K. M. M., Kingsdale, J., Layton, T. and Sacarny, A. (2017), ‘Nudging Leads Consumers in Colorado To Shop But Not Switch ACA Marketplace Plans’, Health Affairs, 36(2).Google Scholar
Franklin, M., Folke, T. and Ruggeri, K. (2019), ‘Optimising nudges and boosts for financial decisions under uncertainty’, Palgrave Communications, 5(113).CrossRefGoogle Scholar
Handel, B. R. (2013), ‘Adverse selection and inertia in health insurance markets: When nudging hurts’, American Economic Review, 103(7): 2643–82.CrossRefGoogle ScholarPubMed
Hasvold, P. E., and Wootton, R. (2011), ‘Use of telephone and SMS reminders to improve attendance at hospital appointments: a systematic review’, Journal of telemedicine and telecare, 17(7): 358–364.CrossRefGoogle ScholarPubMed
Hertwig, R., and Grüne-Yanoff, T. (2017), ‘Nudging and Boosting: Steering or Empowering Good Decisions’, Perspectives on Psychological Science, 12(6): 973–986.CrossRefGoogle ScholarPubMed
Hollands, G. J., Bignardi, G., Johnston, M., Kelly, M. P., Ogilvie, D., Petticrew, M., Prestwich, A., Shemilt, I., Sutton, S. and Marteau, T. M. (2017), ‘The TIPPME intervention typology for changing environments to change behaviour’, Nature Human Behaviour, 1(8): 1–9.CrossRefGoogle Scholar
Johnson, E. J., Shu, S. B., Dellaert, B. G., Fox, C., Goldstein, D. G., Häubl, G., Larrick, R. P., Payne, J. W., Peters, E., Schkade, D. and Wansink, B. (2012), ‘Beyond nudges: Tools of a choice architecture’, Marketing Letters, 23(2): 487–504.CrossRefGoogle Scholar
Kangovi, S., Barg, F. K., Carter, T., Long, J. A., Shannon, R. and Grande, D. (2013), ‘Understanding Why Patients of Low Socioeconomic Status Prefer Hospitals Over Ambulatory Care’, Health Affairs, 32(7): 1196–1203.CrossRefGoogle ScholarPubMed
Kaplan-Lewis, E. and Perac-Lima, S. (2013), ‘No-Show to Primary Care Appointments: Why Patients Do Not Come’, Journal of Primary Care & Community Health, 4(4): 251–255.CrossRefGoogle ScholarPubMed
King, D., Greaves, F., Vlaev, I. and Darzi, I. (2013), ‘Approaches based on behavioral economics could help nudge patients and providers toward lower health spending growth’, Health Affairs, 32(4): 661–8.CrossRefGoogle ScholarPubMed
Kuhnen, C. M. (2015), ‘Asymmetric learning from financial information’, The Journal of Finance, 70(5): 2029–2062.CrossRefGoogle Scholar
Marteau, T. M., Hollands, G. J. and Fletcher, P. C. (2012), ‘Changing human behavior to prevent disease: the importance of targeting automatic processes’, Science, 337(6101): 1492–1495.CrossRefGoogle ScholarPubMed
Medicare Rights Center (2006), Part D 2007: Addressing Access Problems for Low Income People with Medicare. Retrieved from: https://www.medicarerights.org/pdf/Part_D_2007.pdf (accessed: 18 November 2019).Google Scholar
Moffitt, T. E., Arseneault, L., Belsky, D., Dickson, N., Hancox, R. J., Harrington, H., Houts, R., Poulton, R., Roberts, B. W., Ross, S. and Sears, M. R. (2011), ‘A gradient of childhood self-control predicts health, wealth, and public safety’, Proceedings of the National Academy of Sciences, 108(7): 2693–2698.CrossRefGoogle ScholarPubMed
Mullainathan, S. and Shafir, E. (2013), Scarcity: Why having too little means so much, Macmillan.Google Scholar
Münscher, R., Vetter, M. and Scheuerle, T. (2016), ‘A review and taxonomy of choice architecture techniques’, Journal of Behavioral Decision Making, 29(5): 511–524.CrossRefGoogle Scholar
Patel, M. S., Volpp, K. G. and Asch, D. A. (2018), ‘Nudge Units to Improve the Delivery of Health Care’, N Engl J Med, 378(3): 214.CrossRefGoogle ScholarPubMed
Pence, B. W. et al. (2018), ‘Association of Increased Chronicity of Depression With HIV Appointment Attendance, Treatment Failure, and Mortality Among HIV-Infected Adults in the United States’, JAMA Psych, 75(4): 379–385.CrossRefGoogle ScholarPubMed
Roberto, C. A. et al. (2019), ‘Association of a beverage tax on sugar-sweetened and artificially sweetened beverages with changes in beverage prices and sales at chain retailers in a large urban setting’, JAMA, 321(18): 1799–1810.CrossRefGoogle Scholar
Rose, G. (2001), ‘Sick individuals and populations (reiteration)’, International Journal of Epidemiology, 30(3): 427–432.CrossRefGoogle Scholar
Ruggeri, K., Folke, T., Benzerga, A., Verra, S., Büttner, C., Steinbeck, V., Yee, S. and Chaiyachati, K. (2020a), ‘Nudging New York: Adaptive models and the limits of behavioral interventions to reduce no-show and health inequalities’, BMC Health Services Research, 20: 363.CrossRefGoogle ScholarPubMed
Ruggeri, K., Garcia-Garzon, E., Maguire, A., Matz, S. C. and Huppert, F. A. (2020b), ‘Well-being is more than happiness and life satisfaction: A multidimensional analysis of 21 countries.’ Health & Quality of Life Outcomes.CrossRefGoogle Scholar
Sacarny, A., Barnett, M. L., Le Pharm, J., Tetkoski, F., Yokum, D. and Agrawal, S. (2018), ‘Effect of Peer Comparison Letters for High Volume Primary Care Prescribers of Quetiapine in Older and Disabled Adults. A Randomized Clinical Trial’, JAMA Psychiatry, 75(10): 1003–1011.CrossRefGoogle ScholarPubMed
Sacarny, A., Yokum, D., Finkelstein, A. and Agrawal, S. (2016), ‘Medicare Letters To Curb Overprescribing Of Controlled Substances Had No Detectable Effect On Providers’, Health Affairs, 35(3).CrossRefGoogle ScholarPubMed
Starbird, L. E., DiMaina, C., Sun, C. and Han, H. (2019), ‘A Systematic Review of Interventions to Minimize Transportation Barriers Among People with Chronic Diseases’, Journal of Community Health, 44(2): 400–411.CrossRefGoogle ScholarPubMed
Sunstein, C. R. (2017), ‘Nudges that fail’, Behavioural Public Policy, 1(1): 4–25.CrossRefGoogle Scholar
Sunstein, C. R. (2019), ‘Algorithms, correcting biases’, Social Research: An International Quarterly, 86(2): 499–511.CrossRefGoogle Scholar
Taylor, R. L. (2019), ‘Bag leakage: The effect of disposable carryout bag regulations on unregulated bags’, Journal of Environmental Economics and Management, 93: 254–271.CrossRefGoogle Scholar
West, J. C. et al. (2007), ‘Medication access and continuity: The experiences of dual-eligible psychiatric patients during the first 4 months of the Medicare prescription drug benefit’, American Journal of Psychiatry, 164(5): 789–796.CrossRefGoogle ScholarPubMed
Zafari, Z., Jiao, B., Will, B., Li, S. and Muenning, P. A. (2018), ‘The trade-off between optimizing flight patterns and human health: a case study on aircraft noise in Queens, NY, USA’, Int. J. Res. Public Health, 15: 1753.CrossRefGoogle Scholar