Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Letter
  • Published:

Letters designed with behavioural science increase influenza vaccination in Medicare beneficiaries

Nature Human Behaviour volume 2, pages 743–749 (2018)Cite this article

Subjects

Abstract

The influenza (‘flu’) vaccination is low cost1 and effective, typically reducing the likelihood of infection by 50–60%2. It is recommended for nearly everyone older than 6 months of age3; yet, only 40% of Americans are immunized each year. Vaccination rates are higher among at-risk groups, such as those ≥65 years of age, but still only 6 in 10 receive it4. There have been numerous attempts to improve vaccination rates using strategies such as school-based programmes, financial incentives and reminders, but these have generally had limited success5,6,7. Of the attempts that are successful, most are expensive—limiting scalability—and have not been evaluated in the elderly8. Conversely, lower-cost interventions, such as mailed information, hold promise for a scalable solution, but their limited effectiveness may result from how they have been designed. We randomly assigned 228,000 individuals ≥66 years of age to one of five versions of letters intended to motivate vaccination, including versions with an implementation intention prompt and an enhanced active choice implementation prompt. We found that a single mailed letter significantly increased influenza vaccination rates compared with no letter. However, there was no difference in vaccination rates across the four different letters tailored with behavioural science techniques.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Study participants and randomization scheme.
Fig. 2: Enhanced active choice implementation intention prompt included in the arm 5 letter.

Similar content being viewed by others

Data availability

Restrictions apply to the availability of the raw data, which were used under data use agreements for the current study and therefore cannot be shared publicly. However, data may be available upon reasonable request and permission of the vendor.

References

  1. CDC Vaccine Price List (CDC, 2018); https://www.cdc.gov/vaccines/programs/vfc/awardees/vaccine-management/price-list/index.html

  2. Vaccine Effectiveness—How Well Does the Flu Vaccine Work? (CDC, 2017); https://www.cdc.gov/flu/about/qa/vaccineeffect.html

  3. Advisory Committee on Immunization Practices (ACIP) Recommendations and Immunization Schedules (CDC, 2012); http://www.cdc.gov/vaccines/acip/recs/index.html

  4. Flu Vaccination Coverage, United States, 2015–16 Influenza Season (CDC, 2016); https://www.cdc.gov/flu/fluvaxview/coverage-1516estimates.html

  5. Briss, P. A. et al. Reviews of evidence regarding interventions to improve vaccination coverage in children, adolescents, and adults. The task force on community preventive services. Am. J. Prev. Med. 18, 97–140 (2000).

    Article  CAS  Google Scholar 

  6. Jacob, V. et al. Increasing coverage of appropriate vaccinations: a community guide systematic economic review. Am. J. Prev. Med. 50, 797–808 (2016).

    Article  Google Scholar 

  7. Stinchfield, P. K. Practice-proven interventions to increase vaccination rates and broaden the immunization season. Am. J. Med. 121, S11–S21 (2008).

    Article  Google Scholar 

  8. Arthur, A. J. et al. Improving uptake of influenza vaccination among older people: a randomised controlled trial. Br. J. Gen. Pract. 52, 717–722 (2002).

    PubMed  PubMed Central  Google Scholar 

  9. Social and Behavioral Sciences Team Social and Behavioral Sciences Team Annual Report (Office of Science and Technology Policy, 2015); https://sbst.gov/download/2015%20SBST%20Annual%20Report.pdf

  10. Milkman, K. L., Beshears, J., Choi, J. J., Laibson, D. & Madrian, B. C. Using implementation intentions prompts to enhance influenza vaccination rates. Proc. Natl Acad. Sci. USA 108, 10415–10420 (2011).

    Article  CAS  Google Scholar 

  11. Gollwitzer, P. Implementation intentions: strong effects of simple plans. Am. Psychol. 54, 493–503 (1999).

    Article  Google Scholar 

  12. Keller, P., Harlam, B., Loewenstein, G. & Volpp, K. Enhanced active choice: a new method to motivate behavior change. J. Consum. Psychol. 21, 376–383 (2011).

    Article  Google Scholar 

  13. O’Keefe, D. J. in The International Encyclopedia of Communication (ed. Donsbach, W.) https://doi.org/10.1002/9781405186407.wbiece011.pub2 (John Wiley and Sons, 2013).

  14. Bleich, S. N., Gudzune, K. A., Bennett, W. L., Jarlenski, M. P. & Cooper, L. A. How does physician BMI impact patient trust and perceived stigma? Prev. Med. 57, 120–124 (2013).

    Article  Google Scholar 

  15. Herrett, E. et al. Text messaging reminders for influenza vaccine in primary care: a cluster randomised controlled trial (TXT4FLUJAB). BMJ Open 6, e010069 (2016).

    Article  Google Scholar 

  16. Regan, A. K., Bloomfield, L., Peters, I. & Effler, P. V. Randomized controlled trial of text message reminders for increasing influenza vaccination. Ann. Fam. Med. 15, 507–514 (2017).

    Article  Google Scholar 

  17. Cutrona, S. L. et al. Improving rates of outpatient influenza vaccination through ehr portal messages and interactive automated calls: a randomized controlled trial. J. Gen. Intern. Med. 33, 659–667 (2018).

    Article  Google Scholar 

  18. Jacobson Vann, J. C., Jacobson, R. M., Coyne-Beasley, T., Asafu-Adjei, J. K. & Szilagyi, P. G. Patient reminder and recall interventions to improve immunization rates. Cochrane Database Syst. Rev. 1, CD003941 (2018).

    PubMed  Google Scholar 

  19. Michaelidis, C. I., Zimmerman, R. K., Nowalk, M. P. & Smith, K. J. Cost-effectiveness of programs to eliminate disparities in elderly vaccination rates in the United States. BMC Public Health 14, 718 (2014).

    Article  Google Scholar 

  20. Reminder Systems and Strategies for Increasing Childhood Vaccination Rates (CDC, 2017); https://www.cdc.gov/vaccines/hcp/admin/reminder-sys.html

  21. Kim, M. & Yoo, B. K. Cost-effectiveness analysis of a television campaign to promote seasonal influenza vaccination among the elderly. Value Health 18, 622–630 (2015).

    Article  Google Scholar 

  22. Shoup, J. A. et al. Effectiveness and cost of influenza vaccine reminders for adults with asthma or chronic obstructive pulmonary disease. Am. J. Manag. Care 21, e405–e413 (2015).

    PubMed  Google Scholar 

  23. Anderson, L. J. et al. The cost of interventions to increase influenza vaccination: a systematic review. Am. J. Prev. Med. 54, 299–315 (2018).

    Article  Google Scholar 

  24. VIII. Privacy—Telephone Consumer Protection Act. FDIC Compliance Examination Manual (Federal Deposit Insurance Corporation, 2016); https://www.fdic.gov/regulations/compliance/manual/index.html

  25. Kuerbis, A., van Stolk-Cooke, K. & Muench, F. An exploratory study of mobile messaging preferences by age: middle-aged and older adults compared to younger adults. J. Rehabil. Assist. Technol. Eng. 4, 1–10 (2017).

    Article  Google Scholar 

  26. Mobile Fact Sheet (Pew Research Center, 2018); http://www.pewinternet.org/fact-sheet/mobile/

  27. Lochner, K. A., Wynne, M. A., Wheatcroft, G. H., Worrall, C. M. & Kelman, J. A. Medicare claims versus beneficiary self-report for influenza vaccination surveillance. Am. J. Prev. Med. 48, 384–391 (2015).

    Article  Google Scholar 

  28. Alemayehu, B. & Warner, K. E. The lifetime distribution of health care costs. Health Serv. Res. 39, 627–642 (2004).

    Article  Google Scholar 

  29. Kahan, B. C., Jairath, V., Doré, C. J. & Morris, T. P. The risks and rewards of covariate adjustment in randomized trials: an assessment of 12 outcomes from 8 studies. Trials 15, 139 (2014).

    Article  Google Scholar 

  30. Lee, P. H. Covariate adjustments in randomized controlled trials increased study power and reduced biasedness of effect size estimation. J. Clin. Epidemiol. 76, 137–146 (2016).

    Article  Google Scholar 

  31. Rothman, K. J. No adjustments are needed for multiple comparisons. Epidemiology 1, 43–46 (1990).

    Article  CAS  Google Scholar 

  32. Wason, J. M. S., Stecher, L. & Mander, A. P. Correcting for multiple-testing in multi-arm trials: is it necessary and is it done? Trials 15, 364 (2014).

    Article  Google Scholar 

  33. McAlister, F. A. The “number needed to treat” turns 20—and continues to be used and misused. CMAJ 179, 549–553 (2008).

    Article  Google Scholar 

Download references

Acknowledgements

This project would not have been possible without the collaboration of the White House’s Social and Behavioral Sciences Team (SBST), the General Service Administration’s Office of Evaluation Sciences, the National Vaccine Program Office and the Centers for Medicare and Medicaid Services (CMS) at the US Department of Health and Human Services. We especially thank G. Brill, M. Donneyong, B. Gellin, T. A. Johnson, B. Luca and B. Sivak. We also thank The Laura and John Arnold Foundation for generous financial support. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. This work was supported by an unrestricted grant from the Laura and John Arnold Foundation.

Author information

Authors and Affiliations

  1. The Lab @ DC, Washington DC, USA

    David Yokum

  2. Center for Healthcare Delivery Sciences (C4HDS) and Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

    Julie C. Lauffenburger, Roya Ghazinouri & Niteesh K. Choudhry

Authors
  1. David Yokum
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Julie C. Lauffenburger
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Roya Ghazinouri
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Niteesh K. Choudhry
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

D.Y. and N.K.C. contributed to the study conception and design and interpretation of the results. J.C.L. prepared and analysed the data. D.Y. and J.C.L. contributed to manuscript drafting. N.K.C. and R.G. provided interpretation of the results and critical manuscript revisions.

Corresponding author

Correspondence to Niteesh K. Choudhry.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Supplementary Information

Supplementary Figure 1, Supplementary Tables 1–5

Reporting Summary

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yokum, D., Lauffenburger, J.C., Ghazinouri, R. et al. Letters designed with behavioural science increase influenza vaccination in Medicare beneficiaries. Nat Hum Behav 2, 743–749 (2018). https://doi.org/10.1038/s41562-018-0432-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41562-018-0432-2

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing