Assessment of Beliefs and Attitudes About Statins Posted on Twitter

Golder, Su; O’Connor, Karen; Hennessy, Sean; Gross, Robert; Gonzalez-Hernandez, Graciela

doi:10.1001/jamanetworkopen.2020.8953

Cited by 66 publications

(64 citation statements)

References 54 publications

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“…There has been interest in using Twitter as a source for collecting anecdotal accounts of adverse drug reactions [ 13 ]. In our analysis of statins [ 33 ], we identified 6.8% (353/5201) of the tweets to be about adverse reactions compared to just 1.3% (21/1664) in this study. This was unexpected, given that dose-related serious adverse effects with drugs to treat diabetes are considered to be among the adverse drug effects with the highest public health impact [ 52 ], while statins have a much higher degree of safety.…”

Section: Discussionmentioning

confidence: 99%

“…Within our health-related tweets, it was possible to identify whether the person tweeting was discussing their own diabetes in 24.9% of the cases (415/1664), someone known to them with diabetes in 17.9% of the cases (298/1664), or if they were in a health care profession (45/1664, 2.7%). Interestingly, with those tweeting on statins [ 33 ], it was possible to identify whether the person tweeting was taking statins in 32.8% of the cases (1707/5201), someone they know taking statins in 6.6% of the cases (346/5201), or whether the person was a health care professional (325/5201, 6.2%). The much higher proportion of people discussing someone known to them with diabetes may be because of the large scale concern for people with diabetes not being able to afford their insulin.…”

Section: Discussionmentioning

confidence: 99%

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Public Perspectives on Anti-Diabetic Drugs: Exploratory Analysis of Twitter Posts

Golder¹,

Bach²,

O’Connor³

et al. 2021

JMIR Diabetes

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Background Diabetes mellitus is a major global public health issue where self-management is critical to reducing disease burden. Social media has been a powerful tool to understand public perceptions. Public perception of the drugs used for the treatment of diabetes may be useful for orienting interventions to increase adherence. Objective The aim of this study was to explore the public perceptions of anti-diabetic drugs through the analysis of health-related tweets mentioning such medications. Methods This study uses an infoveillance social listening approach to monitor public discourse using Twitter data. We coded 4000 tweets from January 1, 2019 to October 1, 2019 containing key terms related to anti-diabetic drugs by using qualitative content analysis. Tweets were coded for whether they were truly about an anti-diabetic drug and whether they were health-related. Health-related tweets were further coded based on who was tweeting, which anti-diabetic drug was being tweeted about, and the content discussed in the tweet. The main outcome of the analysis was the themes identified by analyzing the content of health-related tweets on anti-diabetic drugs. Results We identified 1664 health-related tweets on 33 anti-diabetic drugs. A quarter (415/1664) of the tweets were confirmed to have been from people with diabetes, 17.9% (298/1664) from people posting about someone else, and 2.7% (45/1664) from health care professionals. However, the role of the tweeter was unidentifiable in two-thirds of the tweets. We identified 13 themes, with the health consequences of the cost of anti-diabetic drugs being the most extensively discussed, followed by the efficacy and availability. We also identified issues that patients may conceal from health care professionals, such as purchasing medications from unofficial sources. Conclusions This study uses an infoveillance approach using Twitter data to explore public perceptions related to anti-diabetic drugs. This analysis gives an insight into the real-life issues that an individual faces when taking anti-diabetic drugs, and such findings may be incorporated into health policies to improve compliance and efficacy. This study suggests that there is a fear of not having access to anti-diabetic drugs due to cost or physical availability and highlights the impact of the sacrifices made to access anti-diabetic drugs. Along with screening for diabetes-related health issues, health care professionals should also ask their patients about any non–health-related concerns regarding their anti-diabetic drugs. The positive tweets about dietary changes indicate that people with type 2 diabetes may be more open to self-management than what the health care professionals believe.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Public Perspectives on Anti-Diabetic Drugs: Exploratory Analysis of Twitter Posts

Golder¹,

Bach²,

O’Connor³

et al. 2021

JMIR Diabetes

Self Cite

View full text Add to dashboard Cite

show abstract

“…We collected 9,830 tweets to train and evaluate the classifiers used for this study. We combined tweets mentioning drugs from two existing corpora: (1) the corpus released during the first track of the SMM4H'18 shared-task (Weissenbacher, et al, 2018), a Natural Language Processing competition to identify drug mentions, and (2) a subset of 1,367 tweets, randomly selected from the corpus collected by Golder et al (Golder, et al, 2020). Two annotators labeled all tweets with "1" if the tweets mention a change in the medication treatment, "0" otherwise.…”

Section: Corporamentioning

confidence: 99%

Active Neural Networks to Detect Mentions of Changes to Medication Treatment in Social Media

Weissenbacher

Klein

et al. 2020

Preprint

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ObjectiveWe address a first step towards using social media data to supplement current efforts in monitoring population-level medication non-adherence: detecting changes to medication treatment. Medication treatment changes, like changes to dosage or to frequency of intake, that are not overseen by a physician are, by that, non-adherence to medication. Despite the consequences, including worsening health conditions or death, 50% of patients are estimated to not take medications as indicated. Current methods to identify non-adherence have major limitations. Direct observation may be intrusive or expensive, and indirect observation through patient surveys relies heavily on patients’ memory and candor. Using social media data in these studies may address these limitations.MethodsWe annotated 9,835 tweets mentioning medications and trained a convolutional neural network (CNN) to find mentions of medication treatment changes, regardless of whether the change was recommended by a physician. We used active and transfer learning from 12,972 reviews we annotated from WebMD to address the class imbalance of our Twitter corpus. To validate our CNN and explore future directions, we annotated 1,956 positive tweets as to whether they reflect non-adherence and categorized the reasons given.ResultsOur CNN achieved state-of-the-art performance with 0.50 F1-score. The manual analysis of positive tweets revealed that non-adherence is evident in a subset with nine categories of reasons for non-adherence.ConclusionWe showed that social media users publicly discuss medication treatment changes and may explain their reasons including when it constitutes non-adherence. This approach may be useful to supplement current efforts in adherence monitoring.

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“… Community (and public) health research has its own methodological framework, which needs to be addressed in building “keep-on-competence”, and it may have very different sources of information compared to what makes up “kick-off-competence”, which need to be validated in their role to inform “keep-on-competence [e.g. 78 ]. Selection of Faculty in CME targeting community/public health should ideally include all stakeholders, including regulators, politicians, etc.…”

mentioning

confidence: 99%

“…it may have very different sources of information compared to what makes up “kick-off-competence”, which need to be validated in their role to inform “keep-on-competence [e.g. 78 ].…”

mentioning

confidence: 99%

Outcomes in CME/CPD - Special Collection: How to make the “pyramid” a perpetuum mobile

Griebenow¹,

Mills²,

Stein³

et al. 2020

Journal of European CME

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Continuing medical education (CME) should not be an end in itself, but as expressed in Moore's pyramid, help to improve both individual patient and ultimately community, health. However, there are numerous barriers to translation of physician competence into improvements in community health. To enhance the effect CME may achieve in improving community health the authors suggest a kick-off/keep-on continuum of medical competence, and integration of aspects of public health at all levels from planning to delivery and outcomes measurement in CME.

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Assessment of Beliefs and Attitudes About Statins Posted on Twitter

Abstract: IMPORTANCE Statins are prescribed to help lower cholesterol levels but have poor adherence rates. Attitudes or beliefs toward medications are important to ascertain because they may be associated with patient behavior.

Cited by 66 publications

References 54 publications

Public Perspectives on Anti-Diabetic Drugs: Exploratory Analysis of Twitter Posts

Public Perspectives on Anti-Diabetic Drugs: Exploratory Analysis of Twitter Posts

Active Neural Networks to Detect Mentions of Changes to Medication Treatment in Social Media

Outcomes in CME/CPD - Special Collection: How to make the “pyramid” a perpetuum mobile

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