A Review of Digital Surveillance Methods and Approaches to Combat Prescription Drug Abuse
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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.
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.
KeywordsNon-medical use of prescription drugs Prescription drug abuse Digital surveillance Twitter Social media Infoveillance
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.Voelker R. Opioid overdoses continue to climb. JAMA. 2016;315:550.Google Scholar
- 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.Meldrum ML. The ongoing opioid prescription epidemic: historical context. Am J Pub Health. American Public Health Association. 2016;106(8):1365–1366.Google Scholar
- 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
- 14.Untangling the web of opioid addictions in the USA. Lancet. 2017;389:2443.Google Scholar
- 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.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.A digital revolution in health care is speeding up [Internet]. economist.com. 2017 [cited 2017 Jul 7]. Available from: https://www.economist.com/news/business/21717990-telemedicine-predictive-diagnostics-wearable-sensors-and-host-new-apps-will-transform-how
- 23.Greenwood S, Perrin A, Duggan M. Social Media Update 2016 [Internet]. pewinternet.org. 2016 [cited 2017 Jul 7]. Available from: http://www.pewinternet.org/2016/11/11/social-media-update-2016/
- 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
- 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.•• 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
- 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.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.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.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.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.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.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.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.Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. NIPS'12 2012;1097–1105.Google Scholar
- 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
- 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.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