Anticholinergic Plant Misuse and Schizophrenia: Regarding a Case Report

Anticholinergic medication abuse is common in patients with schizophrenia. The recreational use of anticholinergic plants for their euphoria inducing and hallucinogenic properties is a rising concern in America and Eastern Europe, but rare in Portugal. Anticholinergic misuse poses a challenge to Psychiatrists treating patients with dual pathology, for its addictive potential. In managing antipsychotic medication and its side effects in this population one must have in mind the potential for abuse of anticholinergics. We present a case report of a patient with schizophrenia and abuse of anticholinergic plants, after receiving biperiden to treat extrapyramidal symptoms. Later we discuss anticholinergic effects and potential for addiction and explore ways to prevent and treat drug misuse in this context.

Is This All There Is?

The angry and dissatisfied patient is a reality that every clinician will face eventually. In many cases, this is related to unrealistic treatment goals and expectations. Some patients are perpetually angry, for reasons that are often unclear. Their anger, and manner in which it is expressed, can contribute to the intensity and impact of the pain. Their emotional displays can be somewhat histrionic and disruptive. Some are included tor act out via medication abuse or threats of litigation. Early assessment and the ruling out of a personality disorder should be considered. Boundaries need to be set and appropriate consequences enforced. The clinician must maintain a professional attitude. The difficult patient often finds creating chaos reinforcing.

Zolpidem: a masked hero. A reply to ZORRO study

The use of zolpidem has been driven by the still-widespread false belief among doctors that, since zolpidem is chemically not a benzodiazepine, it cannot lead to addiction and tolerance. We would like to contribute to better highlight certain characteristics of zolpidem and its potential as a substance of abuse due to the fact that our operating unit, which is entirely dedicated to medication abuse, has described among the most numerous cases of addiction to high doses of benzodiazepines and related hypnotics. - Zolpidem was in fourth place among the 29 molecules present on the Italian market; - We believe it’s now time to drop the term “Z-drugs”: zolpidem, zopiclone e zaneplon all have different chemical structures, they bind to different receptors and have completely different abuse potentials3. In our case history, both zopiclon and zaneplon were virtually absent, albeit being commonly used in Italy; - Istvan & colleagues highlight the fact that addiciton and abuse are prevalent in samples suffering from mental illness. In our case history this hasn’t been confirmed: about half of our patients had no history of psychiatric illnesses, nor a history of addiction to illicit substances or alcohol; - Lastly, regarding zolpidem’s hazardousness, we would like to report the fact that the drug was significantly preferred by addicts with a positive ADHD test result. In conclusion, the 2000s saw solid confirmation of the effectiveness of partial agonists in the treatment of some common addictions, such as buprenorphine, varenicline, cytisine. This didn’t happen for BZs

Educational Intervention for Bupropion Abuse Prevention

BackgroundBupropion is being abused due to effects that are comparable with methamphetamine and cocaine. Current research indicates several interventions that can prevent prescription medication abuse.ObjectivesA research study was performed at two healthcare organizations to evaluate whether education on prevention-based interventions increased self-efficacy of healthcare staff in addressing potential and ongoing bupropion abuse and whether the education reduced the rate of bupropion prescribing.MethodsThe study sample consisted of 43 staff members who completed a paper-based preeducation survey, attended a 1-hour educational session, and completed a paper-based posteducation survey.ResultsThere was a 42% increase in total staff self-efficacy scores along with significant differences between pre-/postsurvey scores (p ≤ .001). After education prescribers answered they plan to change prescribing practices and the number of bupropion prescriptions filled decreased.Implications for NursingFuture practice recommendations should include education on bupropion abuse and implementation of prevention interventions to reduce the occurrence of the abuse of bupropion.ConclusionsThe research findings suggested that education on interventions for bupropion abuse prevention improved healthcare staff self-efficacy in the management of potential and ongoing bupropion abuse, influenced prescribing practices of prescribers, and decreased the number of bupropion prescriptions. This research can be used to continue providing education to help prevent further cases of bupropion abuse.

PHARMACEUTICAL ATTENTION IN THE CELIAC DISEASE: SAMPLINGS IN THE CITY OF FOZ DO IGUAÇU/PR

Objective: To analyze the difficulty found in the diagnosis of celiac disease; the identification of patients’ knowledge about the pathology; Self-medication to treat the symptoms before the disease is detected; the significance of the pharmaceutical care to the celiac patients, among others related problems. Methods: Biographical survey and gathering of data encompassing the sampling of individuals of Foz do Iguaçu, members of the local celiac society. Due to the COVID-19 pandemic, the interviews occurred by electronic contact, trough “Google Forms”. This work had its project submitted to the Research Ethics Committee of UNIOESTE, approved on August 7, 2020 in all of its terms and proposals, with opinion n. 4.198.688. Results: It was found that, of the 180 interviewed, 56.7% of all the carriers made used self-medication. And 41, 1% of all patients reported that the necessary time for the disease diagnosis was 1 to 5 years. Among the most common symptoms was abdominal swelling with 94,4% incidence, cramps with 60,6% and flatulence with 76,1%. Another important factor to be considered is that 87,8% reported to have the pathology under study, due to genetic predisposition. Conclusion: Such responses prove the importance of the pharmaceutical professional to identify the pathology and guide about which conduct must be followed by the patient, as well how to guide them on the possible presence of gluten in some medications, aiming to avoid self-medication, abuse of drugs that can aggravate the symptoms and a greater incentive to clinical knowledge in order to obtain an early diagnosis.

Promoting Reproducible Research for Characterizing Nonmedical Use of Medications Through Data Annotation: Description of a Twitter Corpus and Guidelines

Background Social media data are being increasingly used for population-level health research because it provides near real-time access to large volumes of consumer-generated data. Recently, a number of studies have explored the possibility of using social media data, such as from Twitter, for monitoring prescription medication abuse. However, there is a paucity of annotated data or guidelines for data characterization that discuss how information related to abuse-prone medications is presented on Twitter. Objective This study discusses the creation of an annotated corpus suitable for training supervised classification algorithms for the automatic classification of medication abuse–related chatter. The annotation strategies used for improving interannotator agreement (IAA), a detailed annotation guideline, and machine learning experiments that illustrate the utility of the annotated corpus are also described. Methods We employed an iterative annotation strategy, with interannotator discussions held and updates made to the annotation guidelines at each iteration to improve IAA for the manual annotation task. Using the grounded theory approach, we first characterized tweets into fine-grained categories and then grouped them into 4 broad classes—abuse or misuse, personal consumption, mention, and unrelated. After the completion of manual annotations, we experimented with several machine learning algorithms to illustrate the utility of the corpus and generate baseline performance metrics for automatic classification on these data. Results Our final annotated set consisted of 16,443 tweets mentioning at least 20 abuse-prone medications including opioids, benzodiazepines, atypical antipsychotics, central nervous system stimulants, and gamma-aminobutyric acid analogs. Our final overall IAA was 0.86 (Cohen kappa), which represents high agreement. The manual annotation process revealed the variety of ways in which prescription medication misuse or abuse is discussed on Twitter, including expressions indicating coingestion, nonmedical use, nonstandard route of intake, and consumption above the prescribed doses. Among machine learning classifiers, support vector machines obtained the highest automatic classification accuracy of 73.00% (95% CI 71.4-74.5) over the test set (n=3271). Conclusions Our manual analysis and annotations of a large number of tweets have revealed types of information posted on Twitter about a set of abuse-prone prescription medications and their distributions. In the interests of reproducible and community-driven research, we have made our detailed annotation guidelines and the training data for the classification experiments publicly available, and the test data will be used in future shared tasks.

Promoting Reproducible Research for Characterizing Nonmedical Use of Medications Through Data Annotation: Description of a Twitter Corpus and Guidelines (Preprint)

BACKGROUND Social media data are being increasingly used for population-level health research because it provides near real-time access to large volumes of consumer-generated data. Recently, a number of studies have explored the possibility of using social media data, such as from Twitter, for monitoring prescription medication abuse. However, there is a paucity of annotated data or guidelines for data characterization that discuss how information related to abuse-prone medications is presented on Twitter. OBJECTIVE This study discusses the creation of an annotated corpus suitable for training supervised classification algorithms for the automatic classification of medication abuse–related chatter. The annotation strategies used for improving interannotator agreement (IAA), a detailed annotation guideline, and machine learning experiments that illustrate the utility of the annotated corpus are also described. METHODS We employed an iterative annotation strategy, with interannotator discussions held and updates made to the annotation guidelines at each iteration to improve IAA for the manual annotation task. Using the grounded theory approach, we first characterized tweets into fine-grained categories and then grouped them into 4 broad classes—<i>abuse or misuse, personal consumption, mention,</i> and <i>unrelated</i>. After the completion of manual annotations, we experimented with several machine learning algorithms to illustrate the utility of the corpus and generate baseline performance metrics for automatic classification on these data. RESULTS Our final annotated set consisted of 16,443 tweets mentioning at least 20 abuse-prone medications including opioids, benzodiazepines, atypical antipsychotics, central nervous system stimulants, and gamma-aminobutyric acid analogs. Our final overall IAA was 0.86 (Cohen kappa), which represents high agreement. The manual annotation process revealed the variety of ways in which prescription medication misuse or abuse is discussed on Twitter, including expressions indicating coingestion, nonmedical use, nonstandard route of intake, and consumption above the prescribed doses. Among machine learning classifiers, support vector machines obtained the highest automatic classification accuracy of 73.00% (95% CI 71.4-74.5) over the test set (n=3271). CONCLUSIONS Our manual analysis and annotations of a large number of tweets have revealed types of information posted on Twitter about a set of abuse-prone prescription medications and their distributions. In the interests of reproducible and community-driven research, we have made our detailed annotation guidelines and the training data for the classification experiments publicly available, and the test data will be used in future shared tasks.