Both used content analysis in general, achieving an accuracy of Our approach can potentially be applied to more general products than just drugs. As we propose a new methodology, we face many limitations. First, because of the limited source of the available ground truth of the status of online pharmacies and the data related to traffic analysis, we have a relatively small sample size for some of the traffic analysis.
We expect that a larger sample size when available will improve the accuracy of the results and allow more detailed analysis. When using Google search results, we also face many websites whose true status is unknown; hence, evaluation of our methods using the Google search results presented in our paper is limited.
Second, the current website information sources SEMRush and Similarweb do not provide reliable or any information for small websites lacking sufficient data for traffic overview. Accordingly, the findings of this research are mostly applicable to larger legitimate and illicit web-based players.
Third, our proposed method relies on referral website data. Our current referral database from the data we collected seemingly works well. However, obviously, the bigger the referral link database, the better. Although outdated links do not hurt the performance they will not be used , updating these links as more ground truth data becomes available would be desired.
Finally, we focus on proposing a novel structure-based prediction framework and developing simple models to help resolve an important and practical problem. More advanced models, such as a hybrid of structure based and contexture based, can be developed in the future to further improve performance. Previous research shows that illicit online pharmacies are present and widely accessed, posting dramatic risks to the drug supply chain integrity and patient health. In this study, we aimed to fill this gap by conducting a traffic analysis to increase our understanding of IOPs and proposed a new idea to predict the OP status based on referral data and developed 2 specific prediction models RRPM and RKNN using this idea.
Testing these models on a data set with online pharmacies showed that both models performed well, with an accuracy of When implementing both models on the Google search results for 3 drugs, we only incurred an error rate of 6.
Although further testing with a larger data set is being pursued the difficulty is the limited ground truth data , we believe our traffic analysis and the approach to use web analytics of referral websites to predict the status of OPs is among the first in the drug field and proposes a viable and evidently effective way to monitor OPs.
For example, they can be implemented by search engines, social media, web-based markets eg, Amazon , and payment companies eg, Visa and Master cards to filter IOPs or take the status of the online pharmacies into consideration when ranking search results, deciding advertising allocations, making payments, disqualifying vendors, or at least warning consumers of potential IOPs.
They can also be used with search engines and social media to develop a warning system to help consumers make informed decisions. The timeliness of this work could help address the current opioid crisis. Policy makers, government agencies, patient advocacy groups, and drug manufacturers may also use such a system to identify, monitor, curb IOPs, and educate consumers.
On a larger scale, we hope to inspire more research in other aspects to fight IOPs. Our framework and prediction models can be applied to other products, and we hope to inspire research in this general area as well.
Conflicts of Interest: None declared. National Center for Biotechnology Information , U. J Med Internet Res. Published online Aug Reviewed by Andras Fittler and Myra Spiliopoulou. Author information Article notes Copyright and License information Disclaimer. Corresponding author. Corresponding Author: Hui Zhao ude. This article has been cited by other articles in PMC. Objective This study aims to increase our understanding of IOPs through web data traffic analysis and propose a novel framework using referral links to predict and identify IOPs, the first step in fighting IOPs.
Conclusions Our prediction models use what we know referral links to tackle the many unknown aspects of IOPs. Keywords: online pharmacy, web analytics, classification, illicit online pharmacies, online traffic analysis. Table 1 Data sets and sample size. Data set names Legitimate pharmacies, n Illicit pharmacies, n Total samples, n Data collection period Traffic sources data 30 Average over 4 months October February Engagement data 30 Average over 4 months October February Country data 30 Average over 4 months October February Social media data 24 41 65 Average over 4 months October February Search data 30 60 90 Average over 4 months October February Referral data 50 September Open in a separate window.
OP Status Prediction Model One of the difficulties in predicting the status of an OP is that the proposed method and the data it uses need to be something that cannot be easily manipulated by IOPs to affect future prediction results.
Figure 1. Table 2 Demonstration of our data set for the prediction model. Pharmacy site, i Referral site, j 1 2 Figure 2. Results Traffic and Use Analysis of the Online Pharmacies Traffic sources of all websites are classified as direct, search, referral, social media, display, and email.
Table 3 Mean percentages of different traffic sources to online pharmacies. Table 4 Traffic from social media websites to online pharmacies. Table 5 Traffic from different countries to online pharmacies. Table 7 Performance of the classification models. Discussion Comparison With Previous Work In this study, we conducted a web traffic and engagement analysis of IOPs and LOPs, developed simple prediction models of the status of the OPs based on referral links, and tested the prediction models with data for online pharmacies.
Limitations As we propose a new methodology, we face many limitations. Applications and Conclusions Previous research shows that illicit online pharmacies are present and widely accessed, posting dramatic risks to the drug supply chain integrity and patient health.
Appendix Multimedia Appendix 1 More details of the engagement and traffic source data. Click here to view. Additional Information. Home Blog The dangers of Xanax misuse and addiction. Get in Touch Today. Free Addiction Assessment. Contact Us. Call our Enquiry Line Prescription Drug Addiction Information. Can't find what you're looking for? The government needs to research its use and gather clear data, raise public awareness and put support in place for those who have developed a dependency.
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Dosage should be individualized for maximum beneficial effect. In such cases, dosage should be increased cautiously to avoid adverse effects. Treatment for patients with anxiety should be initiated with a dose of 0.
The dose may be increased to achieve a maximum therapeutic effect, at intervals of 3 to 4 days, to a maximum daily dose of 4 mg, given in divided doses.
The lowest possible effective dose should be employed and the need for continued treatment reassessed frequently. The risk of dependence may increase with dose and duration of treatment. In all patients, dosage should be reduced gradually when discontinuing therapy or when decreasing the daily dosage.
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