Why Do People Buy Prescription Medicines Online? A Qualitative Interview Study

Introduction: Many people in the United Kingdom (UK) are turning to the internet to obtain prescription medicines. This introduces a significant public health risk and patient safety concerns, for example because the internet is a source of fake medicines. According to an estimate by the UK government, 1 in 10 people in the UK bought a fake medical product online in 2021 (1). To help address this problem, it is important to understand why people buy prescription medicines online in the first place. Aim(s): This qualitative study aimed to identify why people in the UK purchase medicines online, including their perceptions of risks posed by the availability of fake medicines online. The focus was on prescription-only medicines (POMs). Method(s): Semi-structured interviews were conducted with adults based in the UK who had previously purchased medicines online. Purposive sampling was adopted to achieve diversity regarding participants' experiences and demography. The interviews were conducted online using Microsoft Teams. The recruitment process started in April- 2021 and ended in May-2022. The recruitment was continued until no new codes were identified (data saturation was reached). Interview transcripts were analysed using thematic analysis with the Theory of Planned Behaviour (TPB) acting as a framework to develop the coding of themes (2). Result(s): Twenty participants (12 female, 8 male) were interviewed. Participant age groups were 18-29 (n = 4), 30-39 (n = 4), 40-49 (n = 5), 50-59 (n=4), and >=70 (n = 3). Participants had bought various types of POMs (e.g., antibiotics, and high-risk controlled medicines). Participants demonstrated awareness of the presence of fake medicines online, and they understood risks associated with them. The factors that influenced participants to buy medicines online were grouped into themes including advantages (Avoiding long waiting times;Bypassing gatekeepers;Medicines availability;Lower costs;Convenient process;Privacy) and disadvantages (Medicines safety concerns;Medicines quality concerns;Higher costs;Online payment risks;Lack of accountability;Engage in an illegal behaviour) of purchasing medicines online, the social influencing factors (Interactions with healthcare providers;Other consumers' reviews and experiences;Words of mouth by friends;Influencers' endorsement), the barriers (General barriers;Website specific barriers) and facilitators (Facilitators offered by the illegal sellers of medicines;Facilitators offered by various internet platforms;COVID-19 outbreak as facilitating condition;Consumer personability) of the purchase as well as factors that lead consumers to trust (Website features;Product appearance;Positive previous purchase experience) the online sellers of medicines. Conclusion(s): The in-depth insight into what could drive people in the UK to buy medicines online could enable the development of effective and evidence-based public awareness campaigns that warn consumers about the risks of buying fake medicines from online sources. The findings could also help researchers to design other interventions to prevent people from buying POMs online. A limitation of this study is that although the interviews were in-depth and data saturation was reached, the findings may not be generalisable as this was a qualitative study. However, the TPB which informed the analysis has well-established guidelines to develop a questionnaire, for a future quantitative study.

Controlled Substance Prescription History among Individuals Who Died of an Accidental Opioid-Involved Drug Overdose in Rhode Island.

Background: Accidental opioid-involved overdose deaths are increasing nationally in the wake of the COVID-19 pandemic, but it is unclear if this reflects a change in populations most at risk. Objective: To determine whether the demographic characteristics and controlled substance prescription history of accidental opioid-involved drug overdose decedents in 2020 differed from prior years. Methods: We identified accidental opioid-involved overdose decedents using Rhode Island (RI) State Medical Examiner's Office data. Decedents were linked to the RI Prescription Drug Monitoring Program database. We compared demographic characteristics and prescription history by year of death. Results: From 2018 to 2020, 763 RI residents died from accidental opioid-involved overdose in RI. From 2018 to 2019, deaths decreased by 7%, but then increased by 31% from 2019 to 2020. Demographic characteristics were similar by year of death (all p > 0.05). The percentage of decedents with a prior opioid prescription and a prior benzodiazepine prescription declined from 2018 to 2020 (p < 0.01 and p = 0.03). Conclusions: We found that opioid-involved overdose deaths in RI are increasing overall, but without significant changes in demographics. While prior exposure to some controlled substances did decline over time, it is not clear if these changes reflect more responsible prescribing practices, or a more concerning pattern such as patient abandonment or decreased healthcare access. More studies are needed to better describe the current trend of increasing opioid-involved deaths while also pursuing current evidence-based interventions.

Hybrid Pain Management Strategy Attenuated Acute Pain and Reduced Left-Over Opioid Doses

Problem: Clinicians treating postprocedure acute pain after third molar removal face a twofold challenge: attenuating pain levels while simultaneously limiting leftover opioid doses. Strategies for achieving the dual goals range from “letting patients decide,” which can lead to leftover doses and misuse, or “letting clinicians decide,” only prescribing opioids for those predicted to experience severe discomfort, which risks under-managing acute pain. A hybrid strategy relies on joint decision-making between the patient and clinician. The hypothesis for this IRB-approved prospective study was that a hybrid-strategy would be successful in moderating acute pain and reducing leftover opioid doses. Methods and Materials: This study included patients who met the American Society of Anesthesiologists, risk classification I or II, ages 18 to 35 years, with at least 2 mandibular third molars removed. Patients being treated for opioid addiction/abuse were excluded. All enrolled subject patients were consented and treated with a multimodal analgesic protocol consisting of intraoperative IV preventive antibiotics, dexamethasone, ketorolac, ondansetron, local anesthetics including liposomal bupivacaine and postoperative cold therapy, and scheduled ibuprofen. Patients were given 2 prescriptions (Rx), each for 4 doses of Hydrocodone/APAP 5/325, to be taken as needed for pain;1 Rx could be filled on the day of surgery, the second on any subsequent day. Opioid Rx data were retrieved from patient records and North Carolina Controlled Substances Reporting System. Pain scores and opioid-use data for each postsurgery day (PSD) were derived from a 14-day diary recorded by subjects. For the patients in this series, the goal was median pain levels ranked 1 or 2 on a 7-point scale, meaning no pain and minimal pain by postoperative day (POD) 3. Descriptive statistics were used for analyses. Results: Data were analyzed from 96 eligible patients treated consecutively from 2018 to 22, with a 15-month hiatus from COVID-19. Fifty-two patients (54%) did not fill an opioid prescription. Twenty-seven patients (28%) filled 1 opioid prescription and 17 patients (18%) filled 2 of the prescriptions. The patients who filled 1 prescription had 72 leftover doses (67% of possible doses), and the patients who filled 2 prescriptions had 50 leftover doses (74% of possible doses). Median worst pain levels reached 1 to 2 out of 7 on POD 4;median average pain on POD 3. Conclusions: The hybrid strategy reduced the number of opioid doses in circulation without compromising the patient's postoperative pain level. Decreasing the number of leftover opioid doses is an important step toward addressing opioid addiction and overdose. References: 1 Magraw CBL, Pham M, Neal T, Kendell B, Reside G, Phillips C, White RP Jr: A multimodal analgesic protocol may reduce opioid use after third molar surgery: A pilot study. Oral Surg Oral Med Oral Path Oral Radiol 126:214, 2018. 2 Pham M, Magraw C, Neal T, Kendell B, Reside G, Phillips C, White R: A Multi-modal Analgesic Protocol reduced opioid use/misuse after 3rd Molar Surgery: An Exploratory Study. Submitted Oral Surg Oral Med Oral Path Oral Radiol March 2019 3 Pham M, Magraw C, Neal T, Kendell B, Reside G, Phillips C, White R: A Multimodal Analgesic Protocol reduced acute pain levels after 3rd molar surgery. In preparation JOMS 4 White RP Jr, Shugars DA, Shafer DM, Laskin DM, Buckley MJ, Phillips C: Recovery after third molar surgery: clinical and health-related quality of life outcomes. J Oral and Maxillofacial Surgery 61:535, 2003. 5 American Society of Anesthesiologists Task Force on Acute Pain Management. Anesthesiology 116:248, 2012 6-Savarese JJ, Tabler NG Jr: Multimodal analgesia as an alternative to the risks of opioid monotherapy in surgical pain management. J Health Care Risk Manag 37:24, 2017

Real-World Dispensing of Buprenorphine and Methadone from MAR 2019 to MAR 2021 in California

Objectives: The US is amid a national opioid crisis before and during the COVID-19 pandemic. The Food and Drug Administration has approved methadone, buprenorphine, and naltrexone as medications for opioid use disorder (MOUD). This study examined the real-world dispensing of MOUD. Methods: All dispensing pharmacies, clinics, or other dispensers of Schedule II-V controlled substances in California report to the Controlled Substance Utilization Review and Evaluation System (CURES) on the day of prescriptions refills. Leveraging the data of buprenorphine (schedule III) and methadone (Schedule II) prescriptions from Mar 2019-Mar 2021 employing California’s deidentified CURES database, this study examined real-world dispensing of methadone and buprenorphine before (03/19/2019-03/18/2020) and during the pandemic (03/19/2020-03/18/2021). We did not review naltrexone dispensing, which is not a controlled substance. Results: In Mar 2019-Mar 2021, 182,367 patients≥18 in California obtained 875,051 buprenorphine and methadone prescriptions: Before the pandemic, there were 482,965 MOUD prescriptions dispensed to 116,644 patients;since the pandemic, 97,887 patients received 392,086 prescriptions, of which 32,164 patients(as “non-naïve” patients) started their MOUD before Mar 2020. On average, patients refilled their prescriptions 4.1 times/year before the pandemic and 4.0 times/year since the pandemic. The MOUD non-naïve patients (n=32,164) received 8.1 prescriptions/year before Mar 2020 and 7.4 refills/year afterward. The MOUD medications most widely prescribed in Mar 2019-Mar 2021 were buprenorphine (473,206 (98.0%) and 383,297 (97.8%), respectively, before and after the pandemic), which included 802,936 counts of buprenorphine alone and 53,567 combination medications of buprenorphine and naloxone. The number of methadone prescriptions declined from 9,759 before Mar 2020 to 8,789 during the pandemic. Conclusions: Buprenorphine is the leading MOUD prescribed for patients in California. The decline in MOUD dispensing for non-naïve patients may indicate restricted access to medication-assisted treatment under the pandemic. Policymakers should maintain or modify the policy strategies to help support medication access.

Detecting drug diversion in health-system data using machine learning and advanced analytics.

PURPOSE: The theft of drugs from healthcare facilities, also known as drug diversion, occurs frequently but is often undetected. This paper describes a research study to develop and test novel drug diversion detection methods. Improved diversion detection and reduction in diversion improves patient safety, limits harm to the person diverting, reduces the public health impact of substance use disorder, and mitigates significant liability risk to pharmacists and their organizations. METHODS: Ten acute care inpatient hospitals across 4 independent health systems extracted 2 datasets from various health information technology systems. Both datasets were consolidated, normalized, classified, and sampled to provide a harmonious dataset for analysis. Supervised machine learning methods were iteratively used on the initial sample dataset to train algorithms to classify medication movement transactions as involving a low or high risk of diversion. Thereafter, the resulting machine learning model classified the risk of diversion in a historical dataset capturing 8 to 24 months of history that included 27.9 million medication movement transactions by 19,037 nursing, 1,047 pharmacy, and 712 anesthesia clinicians and that included 22 known, blinded diversion cases to measure when the model would have detected the diversion compared to when the diversion was actually detected by existing methods. RESULTS: The machine learning model had 96.3% accuracy, 95.9% specificity, and 96.6% sensitivity in detecting transactions involving a high risk of diversion using the initial sample dataset. In subsequent testing using the much larger historical dataset, the analytics detected known diversion cases (n = 22) in blinded data faster than existing detection methods (a mean of 160 days and a median of 74 days faster; range, 7-579 days faster). CONCLUSION: The study showed that (1) consolidated datasets and (2) supervised machine learning can detect known diversion cases faster than existing detection methods. Users of the technology also noted improved investigation efficiency.