Skip to main content
BMC Psychiatry logoLink to BMC Psychiatry
. 2024 Oct 8;24:659. doi: 10.1186/s12888-024-06111-5

Assessment of beliefs and attitudes towards benzodiazepines using machine learning based on social media posts: an observational study

Laura de Anta 1,2, Miguel Ángel Alvarez-Mon 1,2,3, Victor Pereira-Sanchez 4, Carolina C Donat-Vargas 5,6,7,8, Francisco J Lara-Abelenda 2,9, María Arrieta 1, María Montero-Torres 10, Cielo García-Montero 2,3, Óscar Fraile-Martínez 2,3,, Fernando Mora 1,11, Miguel Ángel Ortega 2,3, Melchor Alvarez-Mon 2,3, Javier Quintero 1,11
PMCID: PMC11462674  PMID: 39379861

Abstract

Background

Benzodiazepines are frequently prescribed drugs; however, their prolonged use can lead to tolerance, dependence, and other adverse effects. Despite these risks, long-term use remains common, presenting a public health concern. This study aims to explore the beliefs and opinions held by the public regarding benzodiazepines, as understanding these perspectives may provide insights into their usage patterns.

Methods

We collected public tweets published in English between January 1, 2019, and October 31, 2020, that mentioned benzodiazepines. The content of each tweet and the characteristics of the users were analyzed using a mixed-method approach, including manual analysis and semi-supervised machine learning.

Results

Over half of the Twitter users highlighted the efficacy of benzodiazepines, with minimal discussion of their side effects. The most active participants in these conversations were patients and their families, with health professionals and institutions being notably absent. Additionally, the drugs most frequently mentioned corresponded with those most commonly prescribed by healthcare professionals.

Conclusions

Social media platforms offer valuable insights into users’ experiences and opinions regarding medications. Notably, the sentiment towards benzodiazepines is predominantly positive, with users viewing them as effective while rarely mentioning side effects. This analysis underscores the need to educate physicians, patients, and their families about the potential risks associated with benzodiazepine use and to promote clinical guidelines that support the proper management of these medications.

Clinical trial number

Not applicable.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12888-024-06111-5.

Keywords: Benzodiazepines; Psychopharmacology; Twitter, social media, public opinion

Significant outcomes

  • • Most Twitter users consider benzodiazepines effective, with only 5% mentioning side effects.

  • • A significant percentage of users reported combining benzodiazepines with other psychopharmacological drugs, or even with alcohol and other addictive substances.

  • • Our results indicate an alarming minimization of the risks associated with benzodiazepine use.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12888-024-06111-5.

Introduction

Benzodiazepines are among the most prescribed drugs [1]. Its efficacy has been demonstrated for a number of anxiety disorders and insomnia [24]. Despite this efficacy, antidepressants are the first-line treatment for anxiety due to the side effect profile of benzodiazepine [58]. Nonbenzodiazepine hypnotics, known as Z-drugs, are also widely used, sharing similar efficacy and side effect profiles [9].

According to guidelines from the World Health Organization, the European Medicines Agency, and other regulatory agencies, benzodiazepines should only be used in short-term regimens [1012]. However, long term uses, defined as two months or longer, is very common [1316]. This has become a worldwide problem, leading to substantial and longstanding controversy [17]. Indeed, these drugs are often publicly perceived as innocuous, although prolonged use can lead to serious side effects such as tolerance and dependence [18, 19]. Other notable side effects include potential cognitive and psychomotor impairment, which are related to adverse events such as falls, bone fractures, motor vehicle accidents, and other medical emergencies [2024]. The increasing mortality rate due to benzodiazepine overdose is also a significant concern [2527]. These risks have gained such relevance that in 2016, the Centers for Disease Control and Prevention and the Food and Drug Administration of the United States issued warnings to make prescribers and patients aware of them, although the problem persists [28, 29].

We believe it is crucial to gather more knowledge about the views held by people with lived experiences and the general public regarding these medications and their effects in order to address this global public health challenge. While some previous studies have investigated the attitudes of professionals regarding benzodiazepine prescription, use rates, and patterns, there is little research concerning broader social views on benzodiazepines [1, 30]. It is well known that public perspectives on pharmacological treatments influence attitudes towards them, which justifies studying such perspectives [31, 32].

The use of social media as a proxy to understand public perceptions is broadly supported by statistical data. In 2020, 3.80 billion of people were using social media, particularly Facebook, Youtube and WhatsApp [33]. Twitter (now rebranded as X), though not the largest platform, is renowned for its real-time information sharing, with more than 330 million monthly active users worldwide [34]. The most commonly discussed topics on this platform include festivals or religious events, followed by media events, politics, human interest, and sports [35]. In this context, past studies have suggested that Twitter may be better suited for capturing more sincere and spontaneous disclosures and opinions than traditional methods (e.g., surveys), as has been shown for some health conditions and treatments [36, 37]. Artificial intelligence (AI) facilitates the analysis of large datasets, with Machine Learning (ML) being a key focus within AI for deriving insights from data through computational models [38]. There are various ML models, including supervised learning (SL) and semi-supervised learning (SSL). The latter offers significant advantages over SL and other methods, such as achieving competitive results with minimal labeled data and providing a cost-effective alternative by reducing the need for extensive labeling efforts [39]. The use of ML techniques has led to significant applications in diverse areas related to drug research on different platforms like Twitter, including the detection of abuse patterns [40] and associated risks [41], as well as examining public perceptions and attitudes towards drugs [42]. Initial studies using AI and ML techniques on social media have helped understand social perceptions related to some benzodiazepines [43], and detect the emergence of illicit benzodiazepine sale [44]; however, a deeper characterization of and perspectives on different areas of interest related to benzodiazepines in social media are still needed. Therefore, we anticipated that by applying these methods, our study would provide novel insights into public views and attitudes regarding benzodiazepines and Z-drugs (for simplicity, we will generally refer to both as benzodiazepines).

The specific aims of this study were to: (1) identify social media content on Twitter related to benzodiazepines; (2) characterize the types of users and content involved in those posts; and (3) analyze public interest and sentiment regarding such content, as related to the types of users and topics covered.

Materials and methods

Tweet search and collection

This mixed-method quantitative and qualitative analysis focused on individual Twitter posts (tweets) related to benzodiazepines and Z-drugs. Our inclusion criteria were: (1) public tweets (i.e., not protected by users); (2) containing readable text in English; (3) using any of the keywords identifying the generic and commercial names of some of the most commonly used benzodiazepines internationally: alprazolam (Trankimazin, Xanax), clonazepam (Rivotril, Klonopin), clorazepate (Tranxilium, Tranxene), diazepam (Valium), lorazepam (Orfidal, Idalprem, Ativan), clobazam (Onfi), flurazepam, midazolam, oxazepam, lormetazepam (Noctamid), temazepam (Restoril), triazolam (Halcion), zolpidem (Stilnox, Ambien), zopiclone (Limovan), and zaleplon (Sonata); and (4) posted between January 1, 2019, and October 31, 2020.

We used Tweet Binder to find and extract tweets and their metadata, a tool we have extensively employed in past research, which can access 100% of public tweets within a given search query framed by keywords and publication date [45, 46]. In addition to tweet texts, this tool provides retweet and like counts for each tweet, as well as their publication time and date, permanent link, and user description. The search query led to the collection of a total of 63,098 tweets.

Content analysis: general procedure

Content analysis focused on the text of individual collected tweets and employed a semi-supervised machine learning approach with three phases: first, a filter was applied to remove tweets that did not match all inclusion criteria (42,831 were excluded); second, some of the remaining 20,267 tweets were subjected to manual, qualitative content classification (n = 1,800) by investigators; and third, an automated computerized classification of the entire, larger set of tweets (n = 20,267, including those manually classified to train the machine) was performed, based on the topical categories created in the first phase, with the addition of sentiment analysis software. During the machine learning analysis, 8,637 tweets were removed, resulting in a final number of 11,630 tweets classified into content categories and included in the subsequent statistical analysis, as described in Fig. 1.

Fig. 1.

Fig. 1

Tweet analysis flowchart

Content analysis: exploration of data and identification of topical categories

We used a mixed inductive-deductive approach to develop a codebook to classify tweet contents based on key topical categories (that is, codes). Deductively, we brought categories from previous Twitter research from our team [47, 48]. Inductively, we explored an initial subset of 300 tweets (from the small, manual classification subset) to identify potential new topics and refine the codebook. Two investigators (LAT and MA) separately coded these 300 tweets, discussed discrepancies, and reached a final consensus on coding with the mediation of the senior supervisor (MAAM). Once the final codebook was agreed upon, the two investigators coded the remaining 1,500 tweets from the first subset, adding the agreed-upon codes to the initial 300 tweets they had used for training.

The content analysis first distinguished between ‘classifiable’ and ‘unclassifiable’ tweets. Classifiable tweets were further analyzed. The main categorical distinction among these was between ‘medical’ and ‘nonmedical’ tweets. Among the medical tweets, we coded for the presence of areas of medical interest (‘cognitive complaints/anxiety,’ ‘sleep,’ ‘other’), the presence and content of information related to efficacy and side effects (or lack thereof), the type of diagnosis related to benzodiazepine use, if stated (‘no psychiatric diagnosis,’ ‘psychiatric diagnosis,’ ‘epileptic disorder diagnosis’), the presence and type of external references/sources provided through hyperlinks, if available, and whether the content aligned with current scientific evidence-based knowledge, if other psychopharmacological medications were used alongside benzodiazepines, and whether there was compliance with medical prescription instructions. On the other hand, ‘nonmedical’ tweets were classified into the following categories: ‘commercial activities & dissemination’ (related to educational and scientific events such as conferences, webinars, etc.), ‘request, offer, or thanksgiving,’ ‘trivializing’ (jokes, stigmatizing content, vulgarity, etc.), and ‘other.’ For each tweet, we also identified whether or not a ‘personal opinion’ was provided and analyzed the sentiment to distinguish ‘positive’ and ‘negative’ opinions. Topic categories were not mutually exclusive, and tweets could be multiply coded if they contained information related to more than one specific type of content.

In addition to the tweet content, tweets were also classified according to the types of users publishing them. Based on our previous Twitter research, we divided users into the following categories: ‘patients and relatives’ (including people with lived experience, family members, and close friends or acquaintances), ‘healthcare professionals’ (including healthcare personnel and institutions), and ‘other.’ Classification criteria and examples of tweets are shown in Supplementary Material.

Machine learning classifier

The dataset, composed of 20,267 tweets, was classified into codebook categories through semi-supervised machine learning to enable automated analysis of this large volume of data. First, we preprocessed the tweets by removing special characters such as emojis, hashtags, mentions, and long blank spaces. We then divided the 1,800 manually classified tweets into three partitions: training, validation, and test sets. The training set comprised 65% of the tweets, the validation set 15%, and the test set 20%.

Next, we fine-tuned a model for each category using the training set. For fine-tuning, we used the XLM-RoBERTa transformer, a multilingual version of RoBERTa pre-trained on 2.5 TB of filtered CommonCrawl data containing 100 languages [49]. For each fine-tuned model, we selected the optimal values for two hyperparameters: learning rate and batch size. We fine-tuned using various hyperparameter values and evaluated the models on the validation set. The tested learning rates were [1e-5, 1e-4, 1e-3, 1e-2], and the batch sizes were [20, 30, 50, 69]. Lastly, we used the test set to evaluate the performance of the models in each selected category. We measured performance using the F1 score [50]. The classifiers achieved F1 scores between 0.6 and 0.90 across all categories. The categories with the best results were Link to source/reference, Areas of medical interest, and Types of content, all scoring above 0.85. Conversely, Compliance with medical prescription instructions and Concomitant use of other psychopharmacological medications achieved the lowest performance, with an F1 score of 0.6. This methodology has been extensively used in the literature [51, 52].

Statistical analysis

We estimated the frequency distribution (percentages) of tweets according to different categories based on tweet characteristics. Statistical comparisons of the proportion of tweets between categories were carried out using Pearson’s chi-square test, from which the p-value of statistical significance is reported. We also used multivariable linear regression models to evaluate the associations between tweet characteristics and the interest generated, measured by likes and retweets. Individual beta coefficients were mutually adjusted for other tweet characteristics and reported with 95% confidence intervals. All p-values presented were two-tailed, with < 0.05 considered statistically significant. Analyses were performed using STATA version 16.0 (Stata Corp, College Station, TX).

Ethical considerations

This study was approved by the Research Ethics Committee of Universidad de Alcalá and is compliant with the ethical principles from the World Medical Association Declaration of Helsinki (7th revision, 2013).

Results

‘Patients and relatives’ were the types of users contributing most on Twitter contents regarding benzodiazepines and ‘Medical’ were the most frequent types of contents

We firstly examined the types of users authoring the tweets. More than half of total tweets (56.72%) were posted by ‘patients and relatives’, whereas ‘healthcare professionals’ had authored less than 1% of tweets, and the remaining 42.56% were published by ‘other’ users. Another specific type of users that we expected to find, namely the ‘media’, were actually absent from these Twitter conversations (Table 1).

Table 1.

General tweet distribution among users and contents. Percentages are relative to the total sample of tweets

Freq. Percent
Types of users
Patients and relatives 6597 56.72%
Healthcare professionals 83 0.71%
Other 4950 42.56%
Types of content
Medical 9586 82.42%
Nonmedical 2044 17.58%
Efficacy
Positive 5524 47.50%
Negative/unstated 6105 52.50%
Side effects
Present 675 5.80%
Absent/unstated 10,955 94.20%
Areas of medical interest
Cognitive complaints/anxiety 985 8.47%
Sleep 1617 13.90%
Other 9028 77.63%
Compliance with medical prescription instructions
Good compliance 3171 27.27%
Not using the prescribed dose 809 6.96%
Taking medication alongside toxic substances 2689 23.12%
Noncompliance due to other reasons/unstated 4961 42.66%
Mentions psychiatric diagnosis
No psychiatric diagnosis 9592 82.48%
Psychiatric diagnosis 928 7.98%
Epileptic disorder diagnosis 1110 9.54%
Contents follow scientific evidence
Does not follow scientific evidence 8481 72.92%
Follows scientific evidence 3149 27.08%
Link to source/reference
Scientific reference 230 1.98%
Non-scientific reference 473 4.07%
No links provided 10,927 93.96%
Concomitant use of other psychopharmacological medications
Present 1251 10.76%
Absent 4463 38.37%
Unknown 5916 50.87%
Personal opinion
Positive 1734 14.91%
Negative 146 1.26%
Not a personal opinion 9750 83.83%
Nonmedical content
Commercial activities & divulgation 924 7.94%
Request, offer, or thanksgiving 949 8.16%
Trivialization 3540 30.44%

Among the total set of tweets, we found that the vast majority (n = 9586, 82,4%) had ‘medical’ content, while 2044 tweets (17.6%) had exclusive ‘nonmedical’ content (Table 1). ‘Patients and relatives’ had authored 62.5% of ‘medical’ tweets, while ‘other’ users had published more than 30%. Among ‘nonmedical’ tweets, ‘other’ users were the most frequent authors, while ‘patients and relatives’ published the remaining 29.6%, and ‘healthcare professionals’ were absent among those tweets.

Almost half of ‘medical’ tweets referred to efficacy, while just 5.8% indicated side effects, and nearly one quarter indicated concomitant use of toxic substances (Table 1)

Further content analysis of ‘medical’ tweets revealed that almost half (47,50%) of total tweets of those reported good efficacy, while only 5.8% referred to side effects of benzodiazepines. The area of medical interest most covered was ‘sleep’ (13.9% of total tweets). Regarding compliance with prescription, 7% total tweets disclosed lack of compliance with prescription due to not using the prescribed dose (including higher or lower doses, or higher or lower frequency of medication intake), and 23.12% reported lack of compliance with prescription due to taking medications alongside toxic substances.

Analysis of ‘nonmedical’ contents revealed a predominance of ‘trivializing’ tweets (30.44% of total tweets), while ‘commercial activities & divulgation’, and ‘request, offer, or thanksgiving’ had similar, lower percentages (around 8%).

Finally, a ‘personal opinion’ about benzodiazepines was present in 16.17% of total tweets, and the vast majority of those were positive (14.91% of total tweets, versus 1.26% of negative opinions).

Negative contents related to benzodiazepines receive the greatest number of retweets

We investigated public interest in contents as measured by retweets and likes. Regarding types of contents, tweets related to prescription compliance had higher chances of being liked, while tweets with positive ‘personal opinion’ and the ‘medical’ areas of interest of ‘sleep’ and ‘cognitive complaints/anxiety’ had less chances. On the other hand, contents of negative ‘personal opinion’ and prescription noncompliance related to the use alongside toxic substances had higher chances of receiving retweets (Fig. 2).

Fig. 2.

Fig. 2

Association between the different characteristics of the tweet and public interest measured in likes and retweets. The β coefficient and the confidence intervals at 95% are presented, obtained from a multivariate linear regression. Each coefficient is adjusted for all other variables

In relation to specific drugs, we first observed that, among all the benzodiazepines and Z-drugs included in our study, only seven were mentioned in at least 5% (n = 581) of the total tweets. Their prevalence is graphically displayed in Fig. 3, and the drugs were clonazepam (20.79%), lorazepam (18.48%), zolpidem (18.01%), diazepam (13.89%), alprazolam (11.24%), clobazam (11.06%), and midazolam (7.11%). None of those was associated to relatively higher or lower retweet and likes counts (Fig. 4). Flurazepam y Oxacepam did not generate any tweet.

Fig. 3.

Fig. 3

Benzodiazepines and Z-drugs most frequently found in our sample of tweets

Fig. 4.

Fig. 4

Association between specific drugs and the public interest measured in likes and retweets. The β coefficient and the confidence intervals at 95% are presented, obtained from a multivariate linear regression

Discussion

Main findings

Contents related to medical aspects were the most frequently found in our study. Almost half of the tweets positively mentioned the efficacy of these drugs, while only 5% mentioned side effects. A significant portion of the content discussed real-life patterns of benzodiazepine use, including the simultaneous use of other psychopharmacological and illegal drugs. Over half of the users were identified as people with lived experience, their relatives, or acquaintances, while healthcare professionals and institutions were largely absent from these conversations.

The majority of comments on Twitter focused on the medical aspects of these drugs, contrasting with findings from previous studies on other mental health treatments, where non-medical content often predominated, including a high percentage of negative and stigmatizing content [48, 53]. In our study, almost half of the ‘medical’ content about benzodiazepines referred to them as effective. This social perception aligns with medical literature on the efficacy of these drugs [2, 3, 54]. However, this contrasts with findings from other research on drug discussions on Twitter, where efficacy is rarely mentioned. Instead, users often focus on specific symptoms or side effects [37, 53, 55]. This suggests that Twitter users discussing benzodiazepines are particularly interested in expressing their perceived effectiveness of these drugs.

In contrast to the high number of tweets about efficacy, very few tweets discussed benzodiazepine side effects in our research. This is concerning given the significant and growing risks associated with prolonged benzodiazepine use. Long-term use can lead to decreased efficacy in treating anxiety and the emergence of severe side effects [56]. After a few weeks of use, there is an increased risk of side effects such as daytime sleepiness, impaired cognitive functioning, memory problems, reduced mobility, increased risk of falls and fractures in older patients, and reduced driving skills, which increases the risk of traffic accidents and fatalities [23, 24, 5760]. Other side effects are related to dependence [18, 6165]. The importance of the above risks cannot be overlooked, since they represent a public health concern to be addressed [66].

The predominance of positive mentions about benzodiazepines’ efficacy on Twitter could be due to several factors. Users may share personal experiences that highlight immediate relief from anxiety and insomnia, which are the primary benefits of these medications [67]. This focus on short-term effectiveness may occur because individuals seek validation and support for their experiences or advice on similar issues, thus emphasizing positive outcomes. Moreover, social media discussions tend to be brief, often omitting detailed medical information, including side effects. This could be due to a lack of awareness or a preference for prioritizing immediate benefits over potential long-term risks. Another possible explanation is that the subset of users discussing psychiatric drugs on Twitter may differ from the broader population of psychiatric medication users. It is possible that individuals experiencing negative side effects from benzodiazepines did not see the benefit of sharing their negative experiences on Twitter. Additionally, previous studies suggest that Twitter is a platform where individuals may feel more comfortable sharing experiences publicly without fear of judgment or stigma, finding social networks conducive to such discussions [36, 68]. As a result, the social nature of Twitter, driven by peer influence and the desire to conform to positive narratives, further amplifies this trend, overshadowing more balanced or critical perspectives on benzodiazepines.

The withdrawal syndrome that appears with drug dose reduction or cessation may be particularly important when considering the efficacy data and side effects found in our study [69]. The emergence of these symptoms upon reducing or discontinuing benzodiazepines, along with their disappearance upon resuming treatment, may lead to the misconception that benzodiazepines are not associated with long-term withdrawal symptoms [70]. This misunderstanding could be reflected in the Twitter content we analyzed.

The hypothesis that Twitter users are not identifying certain symptoms as side effects of benzodiazepines might extend to other complications of prolonged use, which are difficult to correlate directly with benzodiazepine intake, such as the risk of traffic accidents or falls [71, 72]. Notably, the increasing emergence of other adverse effects of benzodiazepine use, such as overdoses, is not reflected in Twitter content. From 2019 to 2020, visits to the emergency department for benzodiazepine overdoses, as well as deaths from such overdoses, increased [73, 74]. Another study published in 2018 showed that deaths attributed to benzodiazepine overdose had multiplied sevenfold over the last two decades [75].

Despite this, numerous studies report that benzodiazepine use can extend over months, years, or even decades [13]. Additionally, it has been reported that up to 15% of benzodiazepine prescriptions do not comply with regulations regarding the duration of treatment [19]. Moreover, the prolonged use of benzodiazepines occurs under medical prescription, as shown by studies conducted in various countries [15, 7678]. In Australia, 15–42% of older adults use benzodiazepines chronically [79, 80]. The low presence of healthcare professionals on social media platforms (less than 1% in our study) suggests that this group may not be fully aware of the major problem we face [75]. Given the results of clinical trials where deprescribing in elderly patients has been evaluated using patient education, progressive dose reduction, and shared decision-making, it seems crucial to provide patients with evidence-based information [81]. Accurate and adequate information could promote deprescribing by addressing patients’ concerns and fears [82, 83]. Therefore, increasing the presence of healthcare professionals and institutions on social media platforms is essential.

Another significant clinical and public health issue in our findings is the apparent minimization of the risks associated with benzodiazepine use. Users reported the concurrent use of benzodiazepines with other psychotropic drugs in 10.76% of tweets, a percentage well below findings from previous studies using more objective methods [84, 85]. Up to 64% of oxycodone users are also dependent on benzodiazepines [86]. As for the combined use with illegal substances, our research found that 23.12% of tweets acknowledged this type of consumption, which is lower than findings elsewhere, including a study where 64% of heroin users also abused benzodiazepines [87]. Although benzodiazepines received less public health attention than opioids until recently, it is known that combining both is dangerous because benzodiazepines potentiate the depressant effects of opioids [29]. This concurrent use is associated with a higher risk of requiring emergency services, being treated for complications related to these substances, or even a higher risk of death due to overdose [25, 88]. This is also the case with illicit opioid use [89]. The Centers for Disease Control and Prevention (CDC) reported that in 2020, 16% of opioid-related overdose deaths also involved benzodiazepines [90]. Earlier studies showed even higher percentages of fatal opioid overdoses involving benzodiazepines, likely because they focused on particularly vulnerable populations, such as veterans [91, 92].

Zolpidem, lorazepam, and clonazepam were the most frequently mentioned medications in our sample of tweets, which aligns with their being the most commonly prescribed by healthcare professionals. This supports the view that much of this Twitter content reflects personal or close proxy experiences with these drugs. The United States National Institute on Drug Abuse reported that diazepam, alprazolam, and clonazepam [90] were the most used benzodiazepines in November 2022, while the Spanish Agency of Medicines and Medical Devices reported that zolpidem was the most frequently prescribed hypnotic and lorazepam was the most frequently prescribed benzodiazepine [93]. Indeed, in the United States, clonazepam and lorazepam are among the ten most frequently used psychotropic medications [75].

Limitations

Our study has several important limitations that should be considered. For instance, Twitter users may not reflect the general population, which could limit the generalizability of our results. Including studies analyzing benzodiazepines on other platforms like Facebook or TikTok would help create a more precise understanding of public perceptions of these drugs on social media and in the general population. Additionally, the qualitative analysis of tweets has an inherent degree of subjectivity, making absolute objectivity unattainable.

Moreover, some methodological limitations should be highlighted. For example, the omission of emojis during data processing may have significantly influenced our results, as emojis provide crucial emotional context and nuance that text alone often lacks, impacting the accuracy of sentiment analysis. Another important limitation could be the search terms used, which may not include all benzodiazepines on the market. Additionally, we did not use generic terms such as “benzodiazepines,” so it is possible that we missed potential tweets. Although we included the main commercial names and active ingredients in Europe and the USA, some additional market names of benzodiazepines may have been omitted from our analysis, limiting our results. Furthermore, we were unable to explore differences across countries and regions using techniques such as geolocation. Future studies should analyze global variations in the perception of benzodiazepines.

Conclusion

Our study of public views on social media platforms shows that the low number of mentions of adverse effects and negative consequences of benzodiazepine use may reflect a minimization or lack of understanding of the risks associated with benzodiazepines. In fact, a majority of users view these drugs as effective, with little mention of their potentially serious side effects. However, these results could also be attributed to privacy concerns, possible stigma, the personal nature of these experiences, or the lack of perceived benefit in sharing such experiences publicly. Healthcare professionals and institutions are largely absent from these social media conversations. We call on them to be more active on these platforms to better understand views from patients, families, and the public regarding benzodiazepines and other medical treatments and conditions, and to disseminate and promote scientifically accurate information.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1 (26.2KB, docx)

Author contributions

Conceptualization, L.A., M.A.A.-M., V.P-S, C.G-M, O.F-M, M.A.O. and M.A.-M.; methodology, L.A. C.D.-V., F.J.L-A; software, C.D.-V and FJLA; formal analysis, L.A., M.A., M.M-T.; data curation, L.A.; writing—original draft preparation, L.A., M.A.A.-M., V.P-S, F.M., J.Q.; writing—review and editing, CGM, OFM, MAO.; supervision, FM, JQ.; funding acquisition, M.A.A.-M. and M.A.-M. All authors have read and agreed to the published version of the manuscript.

Funding

This study (FIS-PI22/00653) was supported by the Instituto de Salud Carlos III (grant no. Estatal de I + D + I 2020–2027) and co-financed by the European Development Regional Fund “A way to achieve Europe” and P2022/BMD-7321 (Comunidad de Madrid), and ProA Capital, Halekulani S.L. and MJR.

Data availability

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Declarations

Ethics approval and consent to participate

This study was approved by the Research Ethics Committee of Universidad de Alcalá and is compliant with the ethical principles from the World Medical Association Declaration of Helsinki (7th revision, 2013). The data used was of public domain and in any case we have ensured confidentiality of the users.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  • 1.Olfson M, King M, Schoenbaum M. Benzodiazepine Use in the United States. JAMA Psychiatry. 2015;72:136–42. [DOI] [PubMed] [Google Scholar]
  • 2.Martin JLR, Sainz-Pardo M, Furukawa TA, Martin-Sanchez E, Seoane T, Galan C. Benzodiazepines in generalized anxiety disorder: heterogeneity of outcomes based on a systematic review and meta-analysis of clinical trials. J Psychopharmacol. 2007;21:774–82. [DOI] [PubMed] [Google Scholar]
  • 3.Van Balkom AJLM, Barker A, Spinhoven P, Blaauw BMJW, Smeenk S, Ruesink B. A meta-analysis of the treatment of panic disorder with or without agoraphobia: a comparison of psychopharmacological, cognitive-behavioral, and combination treatments. J Nerv Ment Dis. 1997;185:510–6. [DOI] [PubMed] [Google Scholar]
  • 4.Quagliato LA, Freire RC, Nardi AE. Risks and benefits of medications for panic disorder: a comparison of SSRIs and benzodiazepines. 10.1080/1474033820181429403. 2018;17:315–24. [DOI] [PubMed]
  • 5.Baldwin DS, Anderson IM, Nutt DJ, Bandelow B, Bond A, Davidson JRT, et al. Evidence-based guidelines for the pharmacological treatment of anxiety disorders: recommendations from the British Association for Psychopharmacology. J Psychopharmacol. 2005;19:567–96. [DOI] [PubMed] [Google Scholar]
  • 6.Edinoff AN, Nix CA, Hollier J, Sagrera CE, Delacroix BM, Abubakar T, et al. Benzodiazepines: uses, dangers, and clinical considerations. Neurol Int. 2021;13:594–607. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Brett J, Murnion B. Management of benzodiazepine misuse and dependence. Aust Prescr. 2015;38:152–5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Tyrer P. Risks of dependence on benzodiazepine drugs: the importance of patient selection. BMJ. 1989;298. [DOI] [PMC free article] [PubMed]
  • 9.benzo.org.uk. 4.1 Hypnotics and anxiolytics : British National Formulary. https://benzo.org.uk/BNF.htm. Accessed 19 Jan 2023.
  • 10.benzo.org.uk. - The Effects of Tranquillization: Health Canada 1982 : Overview. https://www.benzo.org.uk/hcb/hcb2.htm. Accessed 19 Jan 2023.
  • 11.Scopus preview - Scopus. - Document details - Current problems in Pharmacovigilance. https://www.scopus.com/record/display.uri?eid=2-s2.0-85075528131&origin=inward&txGid=f23ef6d5478bc210d637ac6923d39799. Accessed 19 Jan 2023.
  • 12.Plan d’actions de l’ANSM visant à réduire le mésusage des. benzodiazépines - Point d’information - ANSM: Agence nationale de sécurité du médicament et des produits de santé. https://archiveansm.integra.fr/S-informer/Points-d-information-Points-d-information/Plan-d-actions-de-l-ANSM-visant-a-reduire-le-mesusage-des-benzodiazepines-Point-d-information. Accessed 19 Jan 2023.
  • 13.Ashton H. Guidelines for the rational use of benzodiazepines. Drugs 1994. 2012;48:1. [DOI] [PubMed] [Google Scholar]
  • 14.Lagnaoui R, Depont F, Fourrier A, Abouelfath A, Bégaud B, Verdoux H, et al. Patterns and correlates of benzodiazepine use in the French general population. Eur J Clin Pharmacol. 2004;60:523–9. [DOI] [PubMed] [Google Scholar]
  • 15.Davies J, Rae TC, Montagu L. Long-term benzodiazepine and Z-drugs use in England: a survey of general practice. Br J Gen Pract. 2017;67:e609–13. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.van Hulten R, Isacson D, Bakker A, Leufkens HG. Comparing patterns of long-term benzodiazepine use between a Dutch and a Swedish community. Pharmacoepidemiol Drug Saf. 2003;12:49–53. [DOI] [PubMed] [Google Scholar]
  • 17.benzo.org.uk: Benzodiazepines: How They Work & How to Withdraw, Prof CH, Ashton DM. FRCP, 2002. https://www.benzo.org.uk/manual/index.htm. Accessed 1 Dec 2022.
  • 18.Fenton MC, Keyes KM, Martins SS, Hasin DS. The role of a prescription in anxiety medication use, abuse, and dependence. https://doi.org/101176/appi.ajp201009081132. 2010;167:1247–53. [DOI] [PMC free article] [PubMed]
  • 19.Bonnay M, Soeiro T, Megard R, Micallef J, Rol-Land B, Chappuy M. Cahier de développement professionnel continu Usages et bon usage des benzodiazépines anxiolytiques et hypnotiques Uses and proper use of anxiolytic and hypnotic benzodiazepines. 2021.
  • 20.The TEDS, Report. Admissions Reporting Benzodiazepine and Narcotic Pain Reliever Abuse at Treatment Entry. https://www.samhsa.gov/data/sites/default/files/BenzodiazepineAndNarcoticPainRelieverAbuse/BenzodiazepineAndNarcoticPainRelieverAbuse/BenzodiazepineAndNarcoticPainRelieverAbuse.htm. Accessed 19 Jan 2023.
  • 21.French DD, Campbell R, Spehar A, Angaran DM. Benzodiazepines and injury: a risk adjusted model. Pharmacoepidemiol Drug Saf. 2005;14:17–24. [DOI] [PubMed] [Google Scholar]
  • 22.Barbone F, McMahon AD, Davey PG, Morris AD, Reid IC, McDevitt DG, et al. Association of road-traffic accidents with benzodiazepine use. Lancet. 1998;352:1331–6. [DOI] [PubMed] [Google Scholar]
  • 23.Smink BE, Egberts ACG, Lusthof KJ, Uges DRA, De Gier JJ. The relationship between benzodiazepine use and traffic accidents: a systematic literature review. CNS Drugs. 2010;24:639–53. [DOI] [PubMed] [Google Scholar]
  • 24.Wagner AK, Zhang F, Soumerai SB, Walker AM, Gurwitz JH, Glynn RJ, et al. Benzodiazepine Use and Hip fractures in the Elderly: who is at Greatest Risk? Arch Intern Med. 2004;164:1567–72. [DOI] [PubMed] [Google Scholar]
  • 25.Bachhuber MA, Hennessy S, Cunningham CO, Starrels JL. Increasing Benzodiazepine prescriptions and Overdose Mortality in the United States, 1996–2013. Am J Public Health. 2016;106:686–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Overdose Death Rates | National Institute on Drug Abuse (NIDA). https://nida.nih.gov/research-topics/trends-statistics/overdose-death-rates. Accessed 19 Jan 2023.
  • 27.Multiple Cause of Death. 1999–2020. https://wonder.cdc.gov/wonder/help/mcd.html. Accessed 19 Jan 2023.
  • 28.FDA Drug Safety Communication. FDA warns about serious risks and death when combining opioid pain or cough medicines with benzodiazepines; requires its strongest warning | FDA. https://www.fda.gov/drugs/drug-safety-and-availability/fda-drug-safety-communication-fda-warns-about-serious-risks-and-death-when-combining-opioid-pain-or. Accessed 19 Jan 2023.
  • 29.Dowell D, Haegerich TM, Chou R. CDC Guideline for Prescribing opioids for Chronic Pain—United States, 2016. JAMA. 2016;315:1624–45. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Neves IT, Sara J, Oliveira S, Catarina M, Fernandes C, Santos OR et al. Physicians’ beliefs and attitudes about benzodiazepines: a cross-sectional study. 10.1186/s12875-019-0965-0 [DOI] [PMC free article] [PubMed]
  • 31.Nanna MG, Navar AM, Zakroysky P, Xiang Q, Goldberg AC, Robinson J, et al. Association of Patient Perceptions of Cardiovascular Risk and beliefs on statin drugs with racial differences in statin use: insights from the patient and Provider Assessment of lipid Management Registry. JAMA Cardiol. 2018;3:739–48. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Bradley CK, Wang TY, Li S, Robinson JG, Roger VL, Goldberg AC et al. Patient-reported reasons for declining or discontinuing statin therapy: insights from the PALM registry. J Am Heart Assoc. 2019;8. [DOI] [PMC free article] [PubMed]
  • 33.Kanchan S, Gaidhane A. Social Media Role and its impact on Public Health: a narrative review. Cureus. 2023;15. [DOI] [PMC free article] [PubMed]
  • 34.Kusudo M, Terada M, Kureyama N, Wanifuchi-Endo Y, Fujita T, Asano T et al. Characterizing user demographics in posts related to breast, lung and colon cancer on Japanese twitter (X). Scientific Reports 2024 14:1. 2024;14:1–8. [DOI] [PMC free article] [PubMed]
  • 35.Wilkinson D, Thelwall M. Trending Twitter topics in English: an international comparison. J Am Soc Inform Sci Technol. 2012;63:1631–46. [Google Scholar]
  • 36.Sinnenberg L, Buttenheim AM, Padrez K, Mancheno C, Ungar L, Merchant RM. Twitter as a Tool for Health Research: a systematic review. Am J Public Health. 2017;107:e1–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.de Anta L, Alvarez-Mon MA, Ortega MA, Salazar C, Donat-Vargas C, Santoma-Vilaclara J et al. Areas of Interest and Social Consideration of Antidepressants on English tweets: a Natural Language Processing classification study. J Pers Med. 2022;12. [DOI] [PMC free article] [PubMed]
  • 38.Helm JM, Swiergosz AM, Haeberle HS, Karnuta JM, Schaffer JL, Krebs VE, et al. Machine learning and Artificial Intelligence: definitions, applications, and future directions. Curr Rev Musculoskelet Med. 2020;13:69–76. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Al-Azzam N, Shatnawi I. Comparing supervised and semi-supervised machine learning models on diagnosing breast Cancer. Annals Med Surg. 2021;62:53. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Hu H, Phan NH, Geller J, Iezzi S, Vo H, Dou D, et al. An Ensemble Deep Learning Model for drug abuse detection in Sparse Twitter-Sphere. Stud Health Technol Inf. 2019;264:163–7. [DOI] [PubMed] [Google Scholar]
  • 41.Castillo-Toledo C, Fraile-Martínez O, Donat-Vargas C, Lara-Abelenda FJ, Ortega MA, Garcia-Montero C, et al. Insights from the Twittersphere: a cross-sectional study of public perceptions, usage patterns, and geographical differences of tweets discussing cocaine. Front Psychiatry. 2024;15:1282026. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Carabot F, Fraile-Martínez O, Donat-Vargas C, Santoma J, Garcia-Montero C, da Costa MP et al. Understanding public perceptions and discussions on Opioids through Twitter: cross-sectional infodemiology study. J Med Internet Res. 2023;25. [DOI] [PMC free article] [PubMed]
  • 43.Mullin A, Scott M, Vaccaro G, Floresta G, Arillotta D, Catalani V, et al. Benzodiazepine Boom: Tracking Etizolam, Pyrazolam, and Flubromazepam from Pre-UK Psychoactive Act 2016 to Present using Analytical and Social listening techniques. Pharm (Basel). 2024;12:13. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Sarker A, Al-Garadi MA, Ge Y, Nataraj N, McGlone L, Jones CM, et al. Evidence of the emergence of illicit benzodiazepines from online drug forums. Eur J Public Health. 2022;32:939. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Alvarez-Mon MA, del Barco AA, Lahera G, Quintero J, Ferre F, Pereira-Sanchez V et al. Increasing Interest of Mass Communication Media and the General Public in the distribution of Tweets about Mental disorders: Observational Study. J Med Internet Res. 2018;20. [DOI] [PMC free article] [PubMed]
  • 46.Viguria I, Alvarez-Mon MA, Llavero-Valero M, Asunsolo del Barco A, Ortuño F, Alvarez-Mon M. Eating disorder awareness campaigns: thematic and quantitative analysis using Twitter. J Med Internet Res. 2020;22:e17626. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Pereira-Sanchez V, Alvarez-Mon MA, Del Barco AA, Alvarez-Mon M, Teo A. Exploring the extent of the Hikikomori Phenomenon on Twitter: mixed methods study of Western Language tweets. J Med Internet Res. 2019;21. [DOI] [PMC free article] [PubMed]
  • 48.de Anta L, Alvarez-Mon MA, Donat-Vargas C, Lara-Abelanda FJ, Pereira-Sanchez V, Rodriguez CG, et al. Assessment of beliefs and attitudes about electroconvulsive therapy posted on Twitter: an observational study. Eur Psychiatry. 2023;66:e11. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Conneau A, Khandelwal K, Goyal N, Chaudhary V, Wenzek G, Guzmán F et al. Unsupervised Cross-lingual Representation Learn Scale. 2020;:8440–51.
  • 50.Fujino A, Isozaki H, Suzuki J. Multi-label text categorization with Model Combination based on F1-score maximization. 2008.
  • 51.Correia Lopes F, Pinto da Costa M, Fernandez-Lazaro CI, Lara-Abelenda FJ, Pereira-Sanchez V, Teo AR, et al. Analysis of the hikikomori phenomenon – an international infodemiology study of Twitter data in Portuguese. BMC Public Health. 2024;24:1–12. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Baker W, Colditz JB, Dobbs PD, Mai H, Visweswaran S, Zhan J et al. Classification of Twitter Vaping Discourse using BERTweet: comparative deep learning study. JMIR Med Inf. 2022;10. [DOI] [PMC free article] [PubMed]
  • 53.Alvarez-Mon MA, Donat-Vargas C, Santoma-Vilaclara J, de Anta L, Goena J, Sanchez-Bayona R et al. Assessment of Antipsychotic Medications on Social Media: machine learning study. Front Psychiatry. 2021;12. [DOI] [PMC free article] [PubMed]
  • 54.Buscemi N, Vandermeer B, Friesen C, Bialy L, Tubman M, Ospina M, et al. The efficacy and safety of drug treatments for chronic insomnia in adults: a meta-analysis of RCTs. J Gen Intern Med. 2007;22:1335–50. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Golder S, O’Connor K, Hennessy S, Gross R, Gonzalez-Hernandez G. Assessment of beliefs and attitudes about statins posted on Twitter. JAMA Netw Open. 2020;3:e208953. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Gomez AF, Barthel AL, Hofmann SG. Comparing the efficacy of benzodiazepines and serotonergic anti-depressants for adults with generalized anxiety disorder: a meta-analytic review. 10.1080/1465656620181472767. 2018;19:883–94. [DOI] [PMC free article] [PubMed]
  • 57.Billioti S, Bégaud B, Bazin F, Verdoux H, Dartigues JF, Pérès K et al. Benzodiazepine use and risk of dementia: prospective population based study. BMJ. 2012;345. [DOI] [PMC free article] [PubMed]
  • 58.Huang AR, Mallet L, Rochefort CM, Eguale T, Buckeridge DL, Tamblyn R. Medication-related falls in the elderly: causative factors and preventive strategies. Drugs Aging. 2012;29:359–76. [DOI] [PubMed] [Google Scholar]
  • 59.Madhusoodanan S, Bogunovic OJ. Safety of benzodiazepines in the geriatric population. Expert Opin Drug Saf. 2004;3:485–93. [DOI] [PubMed] [Google Scholar]
  • 60.Berghaus G, Sticht G, Grellner W, Lenz D, Naumann T, Wiesenmüller S. D 1.1.2b META-ANALYSIS DRUID 6TH FRAMEWORK PROGRAMME PAGE 2 6th Framework Programme Deliverable D 1.1.2b Meta-analysis of empirical studies concerning the effects of medicines and illegal drugs including pharmacokinetics on safe driving Status. Public; 2010.
  • 61.Voshaar RCO, Couvée JE, Van Balkom AJLM, Mulder PGH, Zitman FG. Strategies for discontinuing long-term benzodiazepine use: meta-analysis. Br J Psychiatry. 2006;189 SEP.:213–20. [DOI] [PubMed]
  • 62.Rickels K, Schweizer E, Case WG, Greenblatt DJ. Long-term therapeutic use of benzodiazepines. I. effects of abrupt discontinuation. Arch Gen Psychiatry. 1990;47:899–907. [DOI] [PubMed] [Google Scholar]
  • 63.Lader M, Llb O, Sci M. Benzodiazepine harm: how can it be reduced? Br J Clin Pharmacol. 2014;77:295–301. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 64.Lader M. Benzodiazepines revisited—will we ever learn? Addiction. 2011;106:2086–109. [DOI] [PubMed]
  • 65.Darker CD, Sweeney BP, Barry JM, Farrell MF, Donnelly-Swift E. Psychosocial interventions for benzodiazepine harmful use, abuse or dependence. Cochrane Database Syst Reviews. 2015;2015. [DOI] [PMC free article] [PubMed]
  • 66.Weich S, Pearce HL, Croft P, Singh S, Crome I, Bashford J et al. Effect of anxiolytic and hypnotic drug prescriptions on mortality hazards: retrospective cohort study. BMJ. 2014;348. [DOI] [PMC free article] [PubMed]
  • 67.Brandt J, Leong C, Benzodiazepines. An updated review of major adverse outcomes reported on in Epidemiologic Research. Drugs R D. 2017;17:493. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 68.Edo-Osagie O, De La Iglesia B, Lake I, Edeghere O. A scoping review of the use of Twitter for public health research. Comput Biol Med. 2020;122:103770. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 69.ASHTON H. Benzodiazepine Withdrawal: outcome in 50 patients. Br J Addict. 1987;82:665–71. [DOI] [PubMed] [Google Scholar]
  • 70.Vorspan F, Barré T, Pariente A, Montastruc F, Tournier M. Faut-Il limiter la durée des traitements par benzodiazépines ? Presse Med. 2018;47:892–8. [DOI] [PubMed] [Google Scholar]
  • 71.Neutel CI. Risk of traffic accident injury after a prescription for a benzodiazepine☆. Ann Epidemiol. 1995;5:239–44. [DOI] [PubMed] [Google Scholar]
  • 72.Blanco C, Han B, Jones CM, Johnson K, Compton WM. Prevalence and correlates of Benzodiazepine Use, Misuse, and Use disorders among adults in the United States. J Clin Psychiatry. 2018;79:1865. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 73.Liu S, O’Donnell J, Gladden RM, McGlone L, Chowdhury F. Trends in Nonfatal and Fatal overdoses Involving benzodiazepines — 38 States and the District of Columbia, 2019–2020. MMWR Morb Mortal Wkly Rep. 2021;70:1136–41. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 74.Bachhuber MA, Maughan BC, Mitra N, Feingold J, Starrels JL. Prescription monitoring programs and emergency department visits involving benzodiazepine misuse: early evidence from 11 United States metropolitan areas. Int J Drug Policy. 2016;28:120–3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 75.Lembke A, Papac J, Humphreys K. Our other prescription drug Problem. N Engl J Med. 2018;378:693–5. [DOI] [PubMed] [Google Scholar]
  • 76.Scopus preview - Scopus. - Document details - Patterns and correlates of benzodiazepine use in the French general population. https://www.scopus.com/record/display.uri?eid=2-s2.0-5344261760&origin=inward&txGid=e0ae5db19ebc479a6848e7063e2b1434. Accessed 18 Jan 2023.
  • 77.Cunningham CM, Hanley GE, Morgan S. Patterns in the use of benzodiazepines in British Columbia: examining the impact of increasing research and guideline cautions against long-term use. Health Policy (New York). 2010;97:122–9. [DOI] [PubMed] [Google Scholar]
  • 78.Scopus preview - Scopus. - Document details - Comparing patterns of long-term benzodiazepine use between a Dutch and a Swedish community. https://www.scopus.com/record/display.uri?eid=2-s2.0-12244305528&origin=inward&txGid=121849639123737363ca503274d36d51. Accessed 18 Jan 2023.
  • 79.Westbury JL, Jackson S, Peterson GM. Psycholeptic use in aged care homes in Tasmania, Australia. J Clin Pharm Ther. 2010;35:189–93. [DOI] [PubMed] [Google Scholar]
  • 80.Windle A, Elliot E, Duszynski K, Moore V. Benzodiazepine prescribing in elderly Australian general practice patients. Aust N Z J Public Health. 2007;31:379–81. [DOI] [PubMed] [Google Scholar]
  • 81.Reeve E, Ong M, Wu A, Jansen J, Petrovic M, Gnjidic D. A systematic review of interventions to deprescribe benzodiazepines and other hypnotics among older people. Eur J Clin Pharmacol. 2017;73:927–35. [DOI] [PubMed] [Google Scholar]
  • 82.Reeve E, To J, Hendrix I, Shakib S, Roberts MS, Wiese MD. Patient barriers to and enablers of deprescribing: a systematic review. Drugs Aging. 2013;30:793–807. [DOI] [PubMed] [Google Scholar]
  • 83.Tannenbaum C, Martin P, Tamblyn R, Benedetti A, Ahmed S. Reduction of inappropriate benzodiazepine prescriptions among older adults through direct patient education: the EMPOWER Cluster randomized trial. JAMA Intern Med. 2014;174:890–8. [DOI] [PubMed] [Google Scholar]
  • 84.[Benzodiazepine use. in a sample of patients on a treatment program with opiate derivatives (PTDO)] - PubMed. https://pubmed.ncbi.nlm.nih.gov/19578731/. Accessed 18 Jan 2023.
  • 85.EJ D, MH T, SR W, AA N, BN G. Health-related quality of life in depression: a STAR*D report. Ann Clin Psychiatry. 2010;22:43–55. [PubMed] [Google Scholar]
  • 86.Rooney S, Kelly G, Bamford L, Sloan D, O’connor JJ. Co-abuse of opiates and benzodiazepines. Ir J Med Sci. 1999;168:36–41. [DOI] [PubMed] [Google Scholar]
  • 87.Ross J, Darke S. The nature of benzodiazepine dependence among heroin users in Sydney, Australia. Addiction. 2000;95:1785–93. [DOI] [PubMed] [Google Scholar]
  • 88.Sun EC, Dixit A, Humphreys K, Darnall BD, Baker LC, Mackey S. Association between concurrent use of prescription opioids and benzodiazepines and overdose: retrospective analysis. BMJ. 2017;356:760. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 89.Laing MK, Ti L, Marmel A, Tobias S, Shapiro AM, Laing R, et al. An outbreak of novel psychoactive substance benzodiazepines in the unregulated drug supply: preliminary results from a community drug checking program using point-of-care and confirmatory methods. Int J Drug Policy. 2021;93:103169. [DOI] [PubMed] [Google Scholar]
  • 90.Benzodiazepines and Opioids | National Institute on Drug Abuse (NIDA). https://nida.nih.gov/research-topics/opioids/benzodiazepines-opioids. Accessed 22 Dec 2022.
  • 91.Morasco BJ, Duckart JP, Carr TP, Deyo RA, Dobscha SK. Clinical characteristics of veterans prescribed high doses of opioid medications for chronic non-cancer pain. Pain. 2010;151:625–32. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 92.Saunders KW, Von Korff M, Campbell CI, Banta-Green CJ, Sullivan MD, Merrill JO, et al. Concurrent use of alcohol and sedatives among persons prescribed chronic opioid therapy: prevalence and risk factors. J Pain. 2012;13:266–75. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 93.Utilización de medicamentos. ansiolíticos e hipnóticos en España | Agencia Española de Medicamentos y Productos Sanitarios. https://www.aemps.gob.es/medicamentos-de-uso-humano/observatorio-de-uso-de-medicamentos/informes-ansioliticos-hipnoticos/. Accessed 18 Jan 2023.

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1 (26.2KB, docx)

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.


Articles from BMC Psychiatry are provided here courtesy of BMC

RESOURCES