Incentives Versus Defaults: Cost-Effectiveness of Behavioral Approaches for HIV Screening

  • Zachary WagnerEmail author
  • Juan Carlos C. Montoy
  • Emmanuel F. Drabo
  • William H. Dow
Original Paper


Many HIV positive individuals are still undiagnosed, which has led health systems to try many approaches to expand HIV testing. In a randomized controlled trial, we found that behavioral economics interventions (opt-out testing and financial incentives) each improved HIV testing rates and these approaches are being implemented by several hospital systems. However, it is unclear if these strategies are cost-effective. We quantified the cost-effectiveness of different behavioral approaches to HIV screening—opt-out testing, financial incentives, and their combination—in terms of cost per new HIV diagnosis and infections averted. We estimated the incremental number of new HIV diagnoses and program costs using a mathematical screening model, and infections averted using and HIV transmission model. We used a 1-year time horizon and a hospital perspective. Switching from opt-into opt-out results in 39 additional diagnoses (56% increase) after 1-year at a cost of $3807 per new diagnosis. Switching from no incentive to a $1, $5, or $10 incentive adds 14, 13, and 28 new diagnoses (20, 19, and 41% increases) at a cost of $11,050, $17,984, and $15,298 per new diagnosis, respectively. Layering on financial incentives to opt-out testing enhances program effectiveness, though at a greater marginal cost per diagnosis. We found a similar pattern for infections averted. This is one of the first cost-effectiveness analyses of behavioral economics interventions in public health. Changing the choice architecture from opt-into opt-out and giving financial incentives for testing are both cost-effective in terms of detecting HIV and reducing transmission. For hospitals interested in increasing HIV screening rates, changing the choice architecture is an efficient strategy and more efficient than incentives.


Behavioral economics Cost-effectiveness HIV testing Incentives Defaults 


Compliance with Ethical Standards

Conflict of interest

All authors have no conflict of interest to declare.

Ethical Approval

The study received institutional review board approval from the University of California, San Francisco.

Supplementary material

10461_2019_2425_MOESM1_ESM.pdf (703 kb)
Supplementary material 1 (PDF 702 kb)


  1. 1.
    Centers for Disease Control and Prevention. HIV in the United States: At A Glance.Google Scholar
  2. 2.
    Hall HI, An Q, Tang T, et al. Prevalence of diagnosed and undiagnosed HIV infection—United States, 2008–2012. MMWR Morb Mortal Wkly Rep. 2015;64(24):657–62.Google Scholar
  3. 3.
    Center for Disease Control and Prevention. Diagnoses of HIV Infection in the United States and Dependent Areas, 2017, 2017.Google Scholar
  4. 4.
    Branson BM, Handsfield HH, Lampe MA, et al. Revised recommendations for HIV testing of adults, adolescents, and pregnant women in health-care settings. Morb Mortal Wkly Rep. 2006;55(14):1-CE.Google Scholar
  5. 5.
    Organization WH. Scaling up HIV testing and counselling in the WHO European Region as an essential component of efforts to achieve universal access to HIV prevention, treatment, care and support: policy framework. Scaling up HIV testing and counselling in the WHO European Region as an essential component of efforts to achieve universal access to HIV prevention, treatment, care and support: policy framework; 2010.Google Scholar
  6. 6.
    European Centre for Disease Prevention and Control. HIV testing: increasing uptake and effectiveness in the European Union. ECDC; 2010.Google Scholar
  7. 7.
    Qaseem A, Snow V, Shekelle P, Hopkins R, Owens DK. Screening for HIV in health care settings: a guidance statement from the American College of Physicians and HIV Medicine Association. Ann Intern Med. 2009;150(2):125–31.CrossRefGoogle Scholar
  8. 8.
    Stringer EM, Stringer JS, Cliver SP, Goldenberg RL, Goepfert AR. Evaluation of a new testing policy for human immunodeficiency virus to improve screening rates. Obstet Gynecol. 2001;98(6):1104–8.Google Scholar
  9. 9.
    Farnham PG, Sansom SL, Hutchinson AB. How much should we pay for a new HIV diagnosis? A mathematical model of HIV screening in US clinical settings. Med Decis Mak. 2012;32(3):459–69.CrossRefGoogle Scholar
  10. 10.
    Long EF, Brandeau ML, Owens DK. The cost-effectiveness and population outcomes of expanded HIV screening and antiretroviral treatment in the United States. Ann Intern Med. 2010;153(12):778–89.CrossRefGoogle Scholar
  11. 11.
    Sanders GD, Bayoumi AM, Sundaram V, et al. Cost-effectiveness of screening for HIV in the era of highly active antiretroviral therapy. N Engl J Med. 2005;352(6):570–85.CrossRefGoogle Scholar
  12. 12.
    UNAIDS. Getting to zero: 2011–2015 strategy Joint United Nations Programme on HIV/AIDS (UNAIDS); 2010.Google Scholar
  13. 13.
    Burns DN, DeGruttola V, Pilcher CD, et al. Toward an endgame: finding and engaging people unaware of their HIV-1 infection in treatment and prevention. AIDS Res Hum Retrovir. 2014;30(3):217–24.CrossRefGoogle Scholar
  14. 14.
    Sood N, Wagner Z, Jaycocks A, Drabo E, Vardavas R. Test-and-treat in Los Angeles: a mathematical model of the effects of test-and-treat for the population of men who have sex with men in Los Angeles County. Clin Infect Dis. 2013;56(12):1789–96.CrossRefGoogle Scholar
  15. 15.
    Zetola NM, Kaplan B, Dowling T, et al. Prevalence and correlates of unknown HIV infection among patients seeking care in a public hospital emergency department. Public Health Rep. 2008;123(3_suppl):41–50.CrossRefGoogle Scholar
  16. 16.
    Khullar D. How Behavioral Economics Can Produce Better Health Care: New York Times (The Upshot); 2017.Google Scholar
  17. 17.
    Benartzi S, Beshears J, Milkman KL, et al. Should governments invest more in nudging? Psychol Sci. 2017;28(8):1041–55.CrossRefGoogle Scholar
  18. 18.
    Gong CL, Zangwill KM, Hay JW, Meeker D, Doctor JN. Behavioral economics interventions to improve outpatient antibiotic prescribing for acute respiratory infections: a cost-effectiveness analysis. J Gen Intern Med. 2018. Scholar
  19. 19.
    Montoy JCC, Dow WH, Kaplan BC. Cash incentives versus defaults for HIV testing: a randomized clinical trial. PLoS ONE. 2018;13(7):e0199833.CrossRefGoogle Scholar
  20. 20.
    Montoy JCC, Dow WH, Kaplan BC. Patient choice in opt-in, active choice, and opt-out HIV screening: randomized clinical trial. BMJ. 2016;352:h6895.CrossRefGoogle Scholar
  21. 21.
    United States Census Bureau. QuickFacts, San Francisco County, California. 2017. Accessed 12 June 2017.
  22. 22.
    Haukoos JS, Lyons MS, Lindsell CJ, et al. Derivation and validation of the Denver Human Immunodeficiency Virus (HIV) risk score for targeted HIV screening. Am J Epidemiol. 2012;175(8):838–46.CrossRefGoogle Scholar
  23. 23.
    Haukoos JS, Hopkins E, Bucossi MM, et al. Brief report: validation of a quantitative HIV risk prediction tool using a National HIV testing cohort. J Acquir Immune Defic Syndr. 2015;68(5):599–603.CrossRefGoogle Scholar
  24. 24.
    Bath R, Ahmad K, Orkin C. Routine HIV testing within the emergency department of a major trauma centre: a pilot study. HIV Med. 2015;16(5):326–8.CrossRefGoogle Scholar
  25. 25.
    Schackman BR, Eggman AA, Leff JA, et al. Costs of expanded rapid HIV testing in four emergency departments. Public Health Rep. 2016;131(1_suppl):71–81.CrossRefGoogle Scholar
  26. 26.
    Zucker J, Cennimo D, Sugalski G, Swaminathan S. Identifying areas for improvement in the HIV screening process of a high-prevalence emergency department. AIDS Patient Care STDs. 2016;30(6):247–53.CrossRefGoogle Scholar
  27. 27.
    Centers for Medicare & Medicaid Services. Clinical Laboratory Fee Schedule; 2016.Google Scholar
  28. 28.
    San Francisco Department of Public Health. The HIV Epidemiology Annual Report 2013. San Francisco, CA, 2014.Google Scholar
  29. 29.
    RC Team. R: a language and environment for statistical computing. Vienna: RC Team; 2013.Google Scholar
  30. 30.
    Marks G, Crepaz N, Senterfitt JW, Janssen RS. Meta-analysis of high-risk sexual behavior in persons aware and unaware they are infected with HIV in the United States: implications for HIV prevention programs. J Acquir Immune Defic Syndr. 2005;39(4):446–53.CrossRefGoogle Scholar
  31. 31.
    Insight Start Study Group. Initiation of antiretroviral therapy in early asymptomatic HIV infection. N Engl J Med. 2015;2015(373):795–807.CrossRefGoogle Scholar
  32. 32.
    Goldman DP, Juday T, Seekins D, Linthicum MT, Romley JA. Early HIV treatment in the United States prevented nearly 13,500 infections per year during 1996–2009. Health Aff. 2014;33(3):362–9.CrossRefGoogle Scholar
  33. 33.
    Romley JA, Juday T, Solomon MD, Seekins D, Brookmeyer R, Goldman DP. Early HIV treatment led to life expectancy gains valued at $80 billion for people infected in 1996–2009. Health Aff. 2014;33(3):370–7.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Zachary Wagner
    • 1
    Email author
  • Juan Carlos C. Montoy
    • 2
  • Emmanuel F. Drabo
    • 3
  • William H. Dow
    • 4
  1. 1.RAND CorporationSanta MonicaUSA
  2. 2.Department Emergency MedicineUniversity of California San FranciscoSan FranciscoUSA
  3. 3.Department of Health Policy and ManagementJohns Hopkins UniversityBaltimoreUSA
  4. 4.School of Public HealthUniversity of California BerkeleyBerkeleyUSA

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