Prevention Science

, Volume 18, Issue 5, pp 541–544 | Cite as

The Charlie Sheen Effect on Rapid In-home Human Immunodeficiency Virus Test Sales

  • Jon-Patrick Allem
  • Eric C. Leas
  • Theodore L. Caputi
  • Mark Dredze
  • Benjamin M. Althouse
  • Seth M. Noar
  • John W. Ayers
Article

Abstract

One in eight of the 1.2 million Americans living with human immunodeficiency virus (HIV) are unaware of their positive status, and untested individuals are responsible for most new infections. As a result, testing is the most cost-effective HIV prevention strategy and must be accelerated when opportunities are presented. Web searches for HIV spiked around actor Charlie Sheen’s HIV-positive disclosure. However, it is unknown whether Sheen’s disclosure impacted offline behaviors like HIV testing. The goal of this study was to determine if Sheen’s HIV disclosure was a record-setting HIV prevention event and determine if Web searches presage increases in testing allowing for rapid detection and reaction in the future. Sales of OraQuick rapid in-home HIV test kits in the USA were monitored weekly from April 12, 2014, to April 16, 2016, alongside Web searches including the terms “test,” “tests,” or “testing” and “HIV” as accessed from Google Trends. Changes in OraQuick sales around Sheen’s disclosure and prediction models using Web searches were assessed. OraQuick sales rose 95% (95% CI, 75–117; p < 0.001) of the week of Sheen’s disclosure and remained elevated for 4 more weeks (p < 0.05). In total, there were 8225 more sales than expected around Sheen’s disclosure, surpassing World AIDS Day by a factor of about 7. Moreover, Web searches mirrored OraQuick sales trends (r = 0.79), demonstrating their ability to presage increases in testing. The “Charlie Sheen effect” represents an important opportunity for a public health response, and in the future, Web searches can be used to detect and act on more opportunities to foster prevention behaviors.

Keywords

Human immunodeficiency virus HIV HIV prevention Surveillance Health informatics 

Notes

Acknowledgements

Dr. Ayers had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. We thank OraSure for freely sharing their testing data with our research team.

Compliance with Ethical Standards

Funding

None

Conflict of Interest

Dr. Ayers and Dr. Althouse share an equity stake in Directing Medicine LLC that advises clinician-scientists how to implement some of the methods embodied in this work. Dr. Dredze has received consulting fees from Directing Medicine LLC and Sickweather LLC, who use social media for public health surveillance. Bloomberg LP provided salary support for authors [MD], but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The company who provided testing data (OraSure) did not have any role in the study design, data analysis, decision to publish, or preparation of the manuscript. Neither the data nor the methods described in this article are proprietary. There are no other conflicts to be reported.

Ethical Approval

This study did not involve human subjects but relied upon secondary data analysis of publicly available data.

Informed Consent

Did not apply

References

  1. Anderson, C. A. (1983). Abstract and concrete data in the perseverance of social theories: When weak data lead to unshakeable beliefs. Journal of Experimental Social Psychology, 19, 93–108.CrossRefGoogle Scholar
  2. Ayers, J. W., Althouse, B. M., Noar, S. M., & Cohen, J. E. (2014a). Do celebrity cancer diagnoses promote primary cancer prevention? Preventive Medicine, 58, 81–84.CrossRefPubMedGoogle Scholar
  3. Ayers, J. W., Althouse, B. M., & Dredze, M. (2014b). Could behavioral medicine lead the web data revolution? JAMA, 311, 1399–1400.CrossRefPubMedPubMedCentralGoogle Scholar
  4. Ayers, J. W., Althouse, B. M., Dredze, M., Leas, E. C., & Noar, S. M. (2016a). News and internet searches about human immunodeficiency virus after Charlie Sheen’s disclosure. JAMA Internal Medicine, 176, 552–554.CrossRefPubMedGoogle Scholar
  5. Ayers, J. W., Westmaas, J. L., Leas, E. C., Benton, A., Chen, Y., Dredze, M., & Althouse, B. M. (2016b). Leveraging big data to improve health awareness campaigns: A novel evaluation of the Great American Smokeout. JMIR public health and surveillance, 2.Google Scholar
  6. Cameron, C. D., & Payne, B. K. (2011). Escaping affect: How motivated emotion regulation creates insensitivity to mass suffering. Journal of Personality and Social Psychology, 100, 1–15.CrossRefPubMedGoogle Scholar
  7. Centers for Disease Control and Prevention. (2016). Trends in US HIV Diagnoses, 2005–2014. Fact Sheet: http://www.cdc.gov/nchhstp/newsroom/docs/factsheets/hiv-data-trends-fact-sheet-508.pdf.
  8. Frieden, T. R., Foti, K. E., & Mermin, J. (2015). Applying public health principles to the HIV epidemic—How are we doing? New England Journal of Medicine, 373, 2281–2287.CrossRefPubMedGoogle Scholar
  9. Hoffman, S. J., & Tan, C. (2013). Following celebrities’ medical advice: Meta-narrative analysis. BMJ, 73, 3.Google Scholar
  10. Hyndman, R.J., & Khandakar, Y. (2007). Automatic time series forecasting: The forecast package for R 7. 2008. URL: https://www.jstatsoft.org/article/view/v027i03 .
  11. Krosnick, J. A., & Petty, R. E. (1995). Attitude strength: An overview. In R. E. Petty & J. A. Krosnick (Eds.), Attitude strength: Antecedents and consequences (pp. 1–24). Mahwah: Erlbaum.Google Scholar
  12. Leas, E. C., Althouse, B. M., Dredze, M., Obradovich, N., Fowler, J. H., Noar, S. M., & Ayers, J. W. (2016). Big data sensors of organic advocacy: The case of Leonardo DiCaprio and climate change. PloS One, 11, e0159885.CrossRefPubMedPubMedCentralGoogle Scholar
  13. LELO HEX. (2016). Charlie Sheen talks condoms for LELO HEX | Youth is wasted on the young. URL https://youtu.be/0iOOPY0pQf4.
  14. Lin, F., Farnham, P. G., Shrestha, R. K., Mermin, J., & Sansom, S. L. (2016). Cost effectiveness of HIV prevention interventions in the US. American Journal of Preventive Medicine, 50, 699–708.CrossRefPubMedGoogle Scholar
  15. Noar, S. M., Ribisl, K. M., Althouse, B. M., Willoughby, J. F., & Ayers, J. W. (2013). Using digital surveillance to examine the impact of public figure pancreatic cancer announcements on media and search query outcomes. Journal of the National Cancer Institute Monographs, 2013, 188–194.CrossRefPubMedGoogle Scholar
  16. Sunguya, B. F., Munisamy, M., Pongpanich, S., Yasuoka, J., & Jimba, M. (2016). Ability of HIV advocacy to modify behavioral norms and treatment impact: A systematic review. American Journal of Public Health, 106, e1–e8.CrossRefPubMedGoogle Scholar
  17. Young, S. D., Yu, W., & Wang, W. (2017). Toward automating HIV identification: Machine learning for rapid identification of HIV-related social media data. JAIDS Journal of Acquired Immune Deficiency Syndromes, 74, S128–S131.CrossRefPubMedGoogle Scholar

Copyright information

© Society for Prevention Research 2017

Authors and Affiliations

  • Jon-Patrick Allem
    • 1
  • Eric C. Leas
    • 2
    • 3
  • Theodore L. Caputi
    • 4
    • 5
  • Mark Dredze
    • 6
    • 7
  • Benjamin M. Althouse
    • 8
    • 9
    • 10
  • Seth M. Noar
    • 11
  • John W. Ayers
    • 3
  1. 1.Keck School of MedicineUniversity of Southern CaliforniaLos AngelesUSA
  2. 2.University of California San Diego School of MedicineLa JollaUSA
  3. 3.Graduate School of Public HealthSan Diego State UniversitySan DiegoUSA
  4. 4.The Wharton SchoolUniversity of PennsylvaniaPhiladelphiaUSA
  5. 5.Drug Policy Institute, College of MedicineUniversity of FloridaGainesvilleUSA
  6. 6.Human Language Technology Center of ExcellenceJohns Hopkins UniversityBaltimoreUSA
  7. 7.Bloomberg L.PNew YorkUSA
  8. 8.Institute for Disease ModelingBellevueUSA
  9. 9.Santa Fe InstituteSanta FeUSA
  10. 10.New Mexico State UniversityLas CrucesUSA
  11. 11.School of Media and JournalismUniversity of North CarolinaChapel HillUSA

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