Global diabetes burden: analysis of regional differences to improve diabetes care

Charline Bour, Adrian Ahne, Gloria Aguayo, Aurélie Fischer, David Marcic, Philippe Kayser, Guy Fagherazzi*

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

Abstract

INTRODUCTION: The current evaluation processes of the burden of diabetes are incomplete and subject to bias. This study aimed to identify regional differences in the diabetes burden on a universal level from the perspective of people with diabetes. RESEARCH DESIGN AND METHODS: We developed a worldwide online diabetes observatory based on 34 million diabetes-related tweets from 172 countries covering 41 languages, spanning from 2017 to 2021. After translating all tweets to English, we used machine learning algorithms to remove institutional tweets and jokes, geolocate users, identify topics of interest and quantify associated sentiments and emotions across the seven World Bank regions. RESULTS: We identified four topics of interest for people with diabetes (PWD) in the Middle East and North Africa and another 18 topics in North America. Topics related to glycemic control and food are shared among six regions of the world. These topics were mainly associated with sadness (35% and 39% on average compared with levels of sadness in other topics). We also revealed several region-specific concerns (eg, insulin pricing in North America or the burden of daily diabetes management in Europe and Central Asia). CONCLUSIONS: The needs and concerns of PWD vary significantly worldwide, and the burden of diabetes is perceived differently. Our results will support better integration of these regional differences into diabetes programs to improve patient-centric diabetes research and care, focused on the most relevant concerns to enhance personalized medicine and self-management of PWD.

Original languageEnglish
JournalBMJ Open Diabetes Research and Care
Volume10
Issue number5
DOIs
Publication statusPublished - 1 Oct 2022

Keywords

  • algorithms
  • patient reported outcome measures
  • patient-centered care
  • population health

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