Insulin pricing and other major diabetes-related concerns in the USA: a study of 46 407 tweets between 2017 and 2019

Ahne, Adrian; Orchard, Francisco; Tannier, Xavier; Perchoux, Camille; Balkau, Beverley; Pagoto, Sherry L.; Harding, Jessica L.; Czernichow, Thomas; Fagherazzi, Guy

doi:10.1136/bmjdrc-2020-001190

Cited by 16 publications

(24 citation statements)

References 26 publications

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“…Along with big data growth, machine learning and data mining techniques such as text mining and natural language processing are constantly evolving and are thus increasingly used in the field of public health research based on SM [71][72][73]. These techniques can be particularly interesting to analyze social media data and, for instance, to develop sentiment or topic analysis among a specific population [19,74] or to predict epidemics [75]. Twitter is mainly used for such work because Twitter developed a streaming application programming interface.…”

Section: Principal Findingsmentioning

confidence: 99%

The Use of Social Media for Health Research Purposes: Scoping Review

Bour¹,

Ahne²,

Schmitz³

et al. 2021

J Med Internet Res

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Background As social media are increasingly used worldwide, more and more scientists are relying on them for their health-related projects. However, social media features, methodologies, and ethical issues are unclear so far because, to our knowledge, there has been no overview of this relatively young field of research. Objective This scoping review aimed to provide an evidence map of the different uses of social media for health research purposes, their fields of application, and their analysis methods. Methods We followed the scoping review methodologies developed by Arksey and O’Malley and the Joanna Briggs Institute. After developing search strategies based on keywords (eg, social media, health research), comprehensive searches were conducted in the PubMed/MEDLINE and Web of Science databases. We limited the search strategies to documents written in English and published between January 1, 2005, and April 9, 2020. After removing duplicates, articles were screened at the title and abstract level and at the full text level by two independent reviewers. One reviewer extracted data, which were descriptively analyzed to map the available evidence. Results After screening 1237 titles and abstracts and 407 full texts, 268 unique papers were included, dating from 2009 to 2020 with an average annual growth rate of 32.71% for the 2009-2019 period. Studies mainly came from the Americas (173/268, 64.6%, including 151 from the United States). Articles used machine learning or data mining techniques (60/268) to analyze the data, discussed opportunities and limitations of the use of social media for research (59/268), assessed the feasibility of recruitment strategies (45/268), or discussed ethical issues (16/268). Communicable (eg, influenza, 40/268) and then chronic (eg, cancer, 24/268) diseases were the two main areas of interest. Conclusions Since their early days, social media have been recognized as resources with high potential for health research purposes, yet the field is still suffering from strong heterogeneity in the methodologies used, which prevents the research from being compared and generalized. For the field to be fully recognized as a valid, complementary approach to more traditional health research study designs, there is now a need for more guidance by types of applications of social media for health research, both from a methodological and an ethical perspective. International Registered Report Identifier (IRRID) RR2-10.1136/bmjopen-2020-040671

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Section: Principal Findingsmentioning

confidence: 99%

The Use of Social Media for Health Research Purposes: Scoping Review

Bour¹,

Ahne²,

Schmitz³

et al. 2021

J Med Internet Res

Self Cite

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show abstract

“…Of note, similar analyses of Twitter use by people with (all types of) diabetes were conducted recently by Ahne et al [ 22 ]. While our study is both conceptually and analytically similar to that of Ahne et al [ 22 ], we expand on the methodology and research questions in two ways: first, data collection was driven by the goal of including members of the type 1 DOC, rather than tweets covering a specific topic. This allows us to generate a more holistic view of these users’ lives and concerns.…”

Section: Introductionmentioning

confidence: 74%

“…Other machine learning–type methods have also been used to analyze web-based behavior. Relevant to this study, Ahne et al [ 22 ] identified tweets related to diabetes through the use of keywords and hashtags and summarized the topics therein using k -means clustering. They identified a set of 30 topics, several of which were variations on concerns regarding insulin pricing and availability.…”

Section: Introductionmentioning

confidence: 99%

Mapping Individual Differences on the Internet: Case Study of the Type 1 Diabetes Community

Bedford-Petersen¹,

Weston²

2021

JMIR Diabetes

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Background Social media platforms, such as Twitter, are increasingly popular among communities of people with chronic conditions, including those with type 1 diabetes (T1D). There is some evidence that social media confers emotional and health-related benefits to people with T1D, including emotional support and practical information regarding health maintenance. Research on social media has primarily relied on self-reports of web-based behavior and qualitative assessment of web-based content, which can be expensive and time-consuming. Meanwhile, recent advances in natural language processing have allowed for large-scale assessment of social media behavior. Objective This study attempts to document the major themes of Twitter posts using a natural language processing method to identify topics of interest in the T1D web-based community. We also seek to map social relations on Twitter as they relate to these topics of interest, to determine whether Twitter users in the T1D community post in “echo chambers,” which reflect their own topics back to them, or whether users typically see a mix of topics on the internet. Methods Through Twitter scraping, we gathered a data set of 691,691 tweets from 8557 accounts, spanning a date range from 2008 to 2020, which includes people with T1D, their caregivers, health practitioners, and advocates. Tweet content was analyzed for sentiment and topic, using Latent Dirichlet Allocation. We used social network analysis to examine the degree to which identified topics are siloed within specific groups or disseminated through the broader T1D web-based community. Results Tweets were, on average, positive in sentiment. Through topic modeling, we identified 6 broad-bandwidth topics, ranging from clinical to advocacy to daily management to emotional health, which can inform researchers and practitioners interested in the needs of people with T1D. These analyses also replicate prior work using machine learning methods to map social behavior on the internet. We extend these results through social network analysis, indicating that users are likely to see a mix of these topics discussed by the accounts they follow. Conclusions Twitter communities are sources of information for people with T1D and members related to that community. Topics identified reveal key concerns of the T1D community and may be useful to practitioners and researchers alike. The methods used are efficient (low cost) while providing researchers with enormous amounts of data. We provide code to facilitate the use of these methods with other populations.

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“…People with DM2 express concern due to insulin prices, therefore, it generates a negative impact on their state of mind [11], since personal care and living with complications related to DM2, causes negativity at a psychosocial level affecting health in it.…”

Section: Discussionmentioning

confidence: 99%

“…Although, when you have DM2 and other cardiovascular diseases that occur at a younger age, they tend to develop ASTESJ ISSN: 2415-6698 aggressively, compromising the health of the diabetic person [11]. It can also lead to low life expectancy and this as a consequence causes a decline in quality of life [12].…”

Section: Introductionmentioning

confidence: 99%

Quality of Life in People with Type 2 Diabetes Residing in a Vulnerable Area in the Los Olivos district – Lima

Perez-Siguas¹,

Matta-Solis²,

Matta-Solís³

2021

Adv. sci. technol. eng. syst. j.

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Non-communicable chronic diseases are more frequent in developing countries, having a significant impact on morbidity, mortality, health care costs and productivity. A study indicates that physical exercise, glucose control, complications, hypertension, duration of diabetes, diet and depression are associated with the quality of life in patients with type 2 diabetes. The purpose of the study is to determine the quality of life in people with Type 2 Diabetes who reside in a vulnerable area of Los Olivos. The focus of this study is quantitative, its design is descriptive because it allows the study population to be described at a given moment. The population was 173 people with type 2 diabetes, from the Juan Pablo II Confraternity Maternal and Child Center in Los Olivos. The diabetes 39 questionnaire (D-39) was applied, made up of 39 items grouped into five dimensions, which measures the quality of life in diabetes. The results showed a half quality of life (51.4%), followed by high (26%) and low (22.5%). These results support the importance of promoting and educating about healthy habits in the diabetic patient, so that they can maintain an adequate quality of life. Type 2 diabetes is a silent and progressive disease in its initial stage, public health systems must increase efforts to make timely diagnoses, where the disease can be controlled avoiding the presence of complications and sequelae that can be fatal for the patient.

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Insulin pricing and other major diabetes-related concerns in the USA: a study of 46 407 tweets between 2017 and 2019

Cited by 16 publications

References 26 publications

The Use of Social Media for Health Research Purposes: Scoping Review

The Use of Social Media for Health Research Purposes: Scoping Review

Mapping Individual Differences on the Internet: Case Study of the Type 1 Diabetes Community

Quality of Life in People with Type 2 Diabetes Residing in a Vulnerable Area in the Los Olivos district – Lima

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