Current Addiction Reports

, Volume 4, Issue 4, pp 397–409 | Cite as

A Review of Digital Surveillance Methods and Approaches to Combat Prescription Drug Abuse

  • Janani Kalyanam
  • Tim K. MackeyEmail author
Adolescent / Young Adult Addiction (T Chung, Section Editor)
Part of the following topical collections:
  1. Topical Collection on Adolescent / Young Adult Addiction


Purpose of Review

The use of social media to conduct digital surveillance to address different health challenges is growing. This multidisciplinary review assesses the current state of methods and applied research used to conduct digital surveillance for prescription drug abuse.

Recent Findings

Fifteen studies met our inclusion criteria from the databases reviewed (PubMed, IEEE Xplore, and ACM Digital Library). The articles were characterized based on their overarching goals and aims, data collection and dataset attributes, and analysis approaches. Overall, reviewed studies grouped into two overarching categories as either being method-focused (advancing novel methodologies using social media data), applied-focused (generating new information on prescription drug abuse behavior), or having both elements. The social media platform most predominantly used was Twitter, with wide variation in sample size and duration of data collection. Several data analysis strategies were employed, including machine learning, temporal analysis, rule-based approaches, and statistical analysis.


Our review indicates that the field of prescription drug abuse digital surveillance is still maturing. Though many studies captured large volumes of data, the majority did not analyze data to characterize user behavior, a critical step needed in order to better explain the underlying risk environment for prescription drug abuse. Future studies need to better translate method-based approaches into applied research, use data generated from social media platforms other than Twitter, and take advantage of emerging data analysis strategies, including deep learning and multimodal approaches.


Non-medical use of prescription drugs Prescription drug abuse Digital surveillance Twitter Social media Infoveillance 


Author Contributions

JK and TM jointly collected the data, designed the study, conducted the data analyses, and wrote the manuscript. All authors contributed to the formulation, drafting, completion, and approval of the final manuscript. There was no involvement of anyone other than the authors in the conception, design, collection, planning, conduct, analysis, interpretation, writing, and discussion to submit this work. Authors report no other financial relationships with any organizations that might have an interest in the submitted work.

Compliance with Ethical Standards

Conflict of Interest

Janani Kalyanam declares that she has no conflict of interest.

Tim K. Mackey is a non-compensated member of the academic advisory panel of the Alliance for Safe Online Pharmacies (ASOP), a 501(c)(4) social welfare organization engaged on the issue of illicit online pharmacies. He received funding for a separate ASOP pilot research grant exploring prescription drug abuse risks online not related to this study.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.


Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. 1.
    Voelker R. Opioid overdoses continue to climb. JAMA. 2016;315:550.Google Scholar
  2. 2.
    Rudd RA, Aleshire N, Zibbell JE, Gladden RM. Increases in drug and opioid overdose deaths—United States, 2000–2014. Morb Mortal Wkly Rep. 2016;64:1378–82.CrossRefGoogle Scholar
  3. 3.
    Schuchat A, Houry D, Guy GP. New data on opioid use and prescribing in the United States. JAMA. American Medical Association. 2017.Google Scholar
  4. 4.
    Meldrum ML. The ongoing opioid prescription epidemic: historical context. Am J Pub Health. American Public Health Association. 2016;106(8):1365–1366.Google Scholar
  5. 5.
    Nelson LS, Juurlink DN, Perrone J. Addressing the opioid epidemic. JAMA. American Medical Association. 2015;314:1453–4.CrossRefGoogle Scholar
  6. 6.
    Becker WC, Fiellin DA. Abuse-deterrent opioid formulations—putting the potential benefits into perspective. N Engl J Med. 2017;376:2103–5.CrossRefPubMedGoogle Scholar
  7. 7.
    Califf RM, Woodcock J, Ostroff S. A proactive response to prescription opioid abuse. N Engl J Med. 2016;374:1480–5.CrossRefPubMedGoogle Scholar
  8. 8.
    Centers for Disease Control and Prevention (CDC). CDC grand rounds: prescription drug overdoses—a U.S. epidemic. Morb Mortal Wkly Rep. 2012;61:10–3.Google Scholar
  9. 9.
    Frank RG, Pollack HA. Addressing the fentanyl threat to public health. N Engl J Med. 2017;376:605–7.CrossRefPubMedGoogle Scholar
  10. 10.
    Strathdee SA, Beyrer C. Threading the needle—how to stop the HIV outbreak in rural Indiana. N Engl J Med. 2015;373:397–9.CrossRefPubMedGoogle Scholar
  11. 11.
    McHugh RK, Nielsen S, Weiss RD. Prescription drug abuse: from epidemiology to public policy. J Subst Abus Treat. 2015;48:1–7.CrossRefGoogle Scholar
  12. 12.
    Longo DL, Compton WM, Jones CM, Baldwin GT. Relationship between nonmedical prescription-opioid use and heroin use. N Engl J Med. 2016;374:154–63.CrossRefGoogle Scholar
  13. 13.
    Florence CS, Zhou C, Luo F, Xu L. The economic burden of prescription opioid overdose, abuse, and dependence in the United States, 2013. Medical Care. 2016;54:901–6.CrossRefPubMedGoogle Scholar
  14. 14.
    Untangling the web of opioid addictions in the USA. Lancet. 2017;389:2443.Google Scholar
  15. 15.
    Kalyanam J, Katsuki T, Lanckriet GR, Mackey TK. Exploring trends of nonmedical use of prescription drugs and polydrug abuse in the Twittersphere using unsupervised machine learning. Addict Behav. 2017;65:289–95.CrossRefPubMedGoogle Scholar
  16. 16.
    Han B, Compton WM, Jones CM, Cai R. Nonmedical prescription opioid use and use disorders among adults aged 18 through 64 years in the United States, 2003–2013. JAMA. 2015;314:1468–78.CrossRefPubMedGoogle Scholar
  17. 17.
    McCabe SE, West BT. Medical and nonmedical use of prescription stimulants: results from a national multicohort study. Journal of the American Academy of Child & Adolescent Psychiatry. 2013;52:1272–80.CrossRefGoogle Scholar
  18. 18.
    Harrison L, Hughes A. Introduction—the validity of self-reported drug use: improving the accuracy of survey estimates. NIDA Res Monogr. 1997;167:1–16.PubMedGoogle Scholar
  19. 19.
    Hay SI, George DB, Moyes CL, Brownstein JS. Big data opportunities for global infectious disease surveillance. PLoS Med. 2013;10:e1001413.Google Scholar
  20. 20.
    Salathé M, Bengtsson L, Bodnar TJ, Brewer DD, Brownstein JS, Buckee C, et al. Digital epidemiology. Bourne PE, editor. PLoS Comp Biol. 2012;8:e1002616.Google Scholar
  21. 21.
    A digital revolution in health care is speeding up [Internet]. 2017 [cited 2017 Jul 7]. Available from:
  22. 22.
    Villanti AC, Johnson AL, Ilakkuvan V, Jacobs MA, Graham AL, Rath JM. Social media use and access to digital technology in US young adults in 2016. J Med Internet Res. 2017;19:e196.CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Greenwood S, Perrin A, Duggan M. Social Media Update 2016 [Internet]. 2016 [cited 2017 Jul 7]. Available from:
  24. 24.
    Scott KR, Nelson L, Meisel Z, Perrone J. Opportunities for exploring and reducing prescription drug abuse through social media. J Addict Dis. 2015;34:178–84.CrossRefPubMedGoogle Scholar
  25. 25.
    Mackey TK, Liang BA, Strathdee SA. Digital social media, youth, and nonmedical use of prescription drugs: the need for reform. J Med Internet Res. 2013;15:e143.CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Golder S, Ahmed S, Norman G, Booth A. Attitudes toward the ethics of research using social media: a systematic review. J Med Internet Res. 2017;19:e195.CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Shaw JM, Mitchell CA, Welch AJ, Williamson MJ. Social media used as a health intervention in adolescent health: a systematic review of the literature. Digital Health. 2015;1:205520761558839.CrossRefGoogle Scholar
  28. 28.
    •• Buntain C, Golbeck J. This is your Twitter on drugs: any questions? WWW '15 Companion. New York: ACM; 2015. p. 777–82. Study that uses crucial techniques for temporal analysis like window-smoothing and linear regression to determine trends in drug abuse.Google Scholar
  29. 29.
    Cameron D, Smith GA, Daniulaityte R, Sheth AP, Dave D, Chen L, et al. PREDOSE: a semantic web platform for drug abuse epidemiology using social media. J Biomed Inform. 2013;46:985–97.CrossRefPubMedGoogle Scholar
  30. 30.
    • Ding T, Roy A, Chen Z, Zhu Q, Pan S. Analyzing and retrieving illicit drug-related posts from social media. 2016 I.E. International Conference on Bioinformatics and Biomedicine (BIBM). IEEE; 2016. pp. 1555–60. First and only study to utilize the power of neural networks and deep learning in study design. Google Scholar
  31. 31.
    •• Hanson CL, Burton SH, Giraud-Carrier C, West JH, Barnes MD, Hansen B. Tweaking and tweeting: exploring Twitter for nonmedical use of a psychostimulant drug (Adderall) among college students. J Med Internet Res. 2013;15:e62. Study that utilizes multiple approaches to data analysis including temporal and geospatial analysis to identify trends in Adderall drug-abuse behavior CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Hanson CL, Cannon B, Burton S, Giraud-Carrier C. An exploration of social circles and prescription drug abuse through Twitter. J Med Internet Res. 2013;15:e189.CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Jenhani F, Gouider MS, Said LB. A hybrid approach for drug abuse events extraction from Twitter. Procedia Computer Science. 2016;96:1032–40.CrossRefGoogle Scholar
  34. 34.
    • Katsuki T, Mackey TK, Cuomo R. Establishing a link between prescription drug abuse and illicit online pharmacies: analysis of Twitter data. J. Med Internet Res. 2015;17:e280. First study to establish empirical link between twitter content and illegal prescription drug sales by online pharmacies CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Phan N, Chun SA, Bhole M, Geller J. Enabling real-time drug abuse detection in tweets. 2017 I.E. 33rd International Conference on Data Engineering (ICDE). IEEE pp. 1510–4 2017.Google Scholar
  36. 36.
    Raja BS, Ali A, Ahmed M, Khan A, Malik AP. Semantics enabled role based sentiment analysis for drug abuse on social media: a framework. 2016 I.E. Symposium on Computer Applications & Industrial Electronics (ISCAIE). IEEE; pp. 206–11 2016.Google Scholar
  37. 37.
    Sarker A, O'Connor K, Ginn R, Scotch M, Smith K, Malone D, et al. Social media mining for toxicovigilance: automatic monitoring of prescription medication abuse from Twitter. Drug Saf. 2016;39:231–240.Google Scholar
  38. 38.
    Seaman I, Giraud-Carrier C. Prevalence and attitudes about illicit and prescription drugs on Twitter. 2016 I.E. International Conference on Healthcare Informatics (ICHI). IEEE. pp. 14–7 2016.Google Scholar
  39. 39.
    Yang X, Luo J. Tracking illicit drug dealing and abuse on Instagram using multimodal analysis. ACM Transactions on Intelligent Systems and Technology (TIST). ACM; 2017;8:58–15.Google Scholar
  40. 40.
    Zhou Y, Sani N, Luo J. Fine-grained mining of illicit drug use patterns using social multimedia data from Instagram. 2016 I.E. International Conference on Big Data (Big Data). IEEE. pp. 1921–30 2016.Google Scholar
  41. 41.
    Kalyanam J, Mantrach A, Saez-Trumper D, Vahabi H, Lanckriet G. Leveraging social context for modeling topic evolution. KDD '15. New York: ACM; 2015. p. 517–26.Google Scholar
  42. 42.
    Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. NIPS'12 2012;1097–1105.Google Scholar
  43. 43.
    Bach FR, Lanckriet GR, Jordan MI. Multiple kernel learning, conic duality, and the SMO algorithm. ICML '04. New York: ACM Press; 2004. p. 6.Google Scholar
  44. 44.
    Baumgartner P, Peiper N. Utilizing big data and Twitter to discover emergent online communities of cannabis users. Subst Abuse. 2017;11:1178221817711425.PubMedPubMedCentralGoogle Scholar
  45. 45.
    Mackey TK, Liang BA. Global reach of direct-to-consumer advertising using social media for illicit online drug sales. J Med Internet Res. 2013;15:e105.Google Scholar
  46. 46.
    Mackey TK, Nayyar G. Digital danger: a review of the global public health, patient safety and cybersecurity threats posed by illicit online pharmacies. Br Med Bull. 2016;118:110–26.Google Scholar
  47. 47.
    Forman RF. Availability of opioids on the Internet. JAMA. 2003;290:889.CrossRefPubMedGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringUniversity of California, San DiegoSan DiegoUSA
  2. 2.Global Health Policy InstituteSan DiegoUSA
  3. 3.Department of AnesthesiologyUniversity of California, San Diego School of MedicineSan DiegoUSA
  4. 4.Department of Medicine, Division of Global Public HealthUniversity of California, San Diego School of MedicineSan DiegoUSA

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