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Epidemiology/Health Services Research
Insulin pricing and other major diabetes-related concerns in the USA: a study of 46 407 tweets between 2017 and 2019
  1. Adrian Ahne1,2,
  2. Francisco Orchard2,
  3. Xavier Tannier3,
  4. Camille Perchoux4,
  5. Beverley Balkau1,
  6. Sherry Pagoto5,
  7. Jessica Lee Harding6,
  8. Thomas Czernichow2,
  9. Guy Fagherazzi7
  1. 1Center for Research in Epidemiology and Population Health (CESP), INSERM, University Paris Saclay, Villejuif (Paris), Île-de-France, France
  2. 2Epiconcept Company, Paris, France
  3. 3LIMICS, INSERM U1142, Sorbonne University, Paris, Île-de-France, France
  4. 4Luxembourg Institute of Socio-Economic Research, Esch-sur-Alzette, Luxembourg
  5. 5Department of Allied Health Sciences, UConn Center for mHealth & Social Media, University of Connecticut, Storrs, Connecticut, USA
  6. 6Division of Transplantation, Department of Surgery, Emory University School of Medicine, Emory University Hospital, Atlanta, Georgia, USA
  7. 7Digital Epidemiology Hub, Department of Population Health, Luxembourg Institute of Health, Strassen, Luxembourg
  1. Correspondence to Dr Guy Fagherazzi; Guy.Fagherazzi{at}lih.lu

Abstract

Introduction Little research has been done to systematically evaluate concerns of people living with diabetes through social media, which has been a powerful tool for social change and to better understand perceptions around health-related issues. This study aims to identify key diabetes-related concerns in the USA and primary emotions associated with those concerns using information shared on Twitter.

Research design and methods A total of 11.7 million diabetes-related tweets in English were collected between April 2017 and July 2019. Machine learning methods were used to filter tweets with personal content, to geolocate (to the USA) and to identify clusters of tweets with emotional elements. A sentiment analysis was then applied to each cluster.

Results We identified 46 407 tweets with emotional elements in the USA from which 30 clusters were identified; 5 clusters (18% of tweets) were related to insulin pricing with both positive emotions (joy, love) referring to advocacy for affordable insulin and sadness emotions related to the frustration of insulin prices, 5 clusters (12% of tweets) to solidarity and support with a majority of joy and love emotions expressed. The most negative topics (10% of tweets) were related to diabetes distress (24% sadness, 27% anger, 21% fear elements), to diabetic and insulin shock (45% anger, 46% fear) and comorbidities (40% sadness).

Conclusions Using social media data, we have been able to describe key diabetes-related concerns and their associated emotions. More specifically, we were able to highlight the real-world concerns of insulin pricing and its negative impact on mood. Using such data can be a useful addition to current measures that inform public decision making around topics of concern and burden among people with diabetes.

  • emotion
  • psychological stress
  • methodology
  • epidemiology
http://creativecommons.org/licenses/by-nc/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

https://doi.org/10.1136/bmjdrc-2020-001190

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    Footnotes

    • Contributors GF takes full responsibility for the work as a whole, and for the decision to submit and publish the manuscript. The authors’ contributions were as follows: GF designed the research; AA and GF conducted the research; FO and AA collected the data; AA, FO and GF analyzed data; AA, GF, FO, TC, XT interpreted the data; AA and GF drafted the article; GF, FO, TC, XT, BB, JLH, CP and SP revised the manuscript critically; GF had primary responsibility for the final content of the manuscript. All authors read and approved the final manuscript.

    • Funding This work has been supported by the MSDAvenir Foundation, the French Speaking Diabetes Society and the Luxembourg Institute of Health. These study sponsors had no role in the design or the interpretation of the results of the present study. AA, FO and TC are supported by Epiconcept Company. Epiconcept was involved in the data collection and writing of the report. No study sponsor influenced the decision to submit the paper for publication.

    • Map disclaimer The depiction of boundaries on the map(s) in this article do not imply the expression of any opinion whatsoever on the part of BMJ (or any member of its group) concerning the legal status of any country, territory, jurisdiction or area or of its authorities. The map(s) are provided without any warranty of any kind, either express or implied.

    • Competing interests None declared.

    • Patient consent for publication Not required.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Data availability statement Data are available on reasonable request. All code is publicly available in this github repository (https://github.com/WDDS/Tweet-Diabetes-Classification).