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Self-reported dental treatment needs during the COVID-19 outbreak in Brazil: an infodemiological study.

Leandro Machado OliveiraFabrício Batistin Zanatta
Published in: Brazilian oral research (2020)
The aim of the present infodemiological study was to evaluate whether the COVID-19 outbreak has influenced the volume of content related to the dental treatment needs of Brazilian Twitter users to summarize the trends, and to identify the perceptions of the treatment needed. We collected tweets related to dental care needs of individuals exposed to the COVID-19 outbreak scenario between March 23 to May 4, 2020 and of those not exposed to the COVID-19 pandemic (unexposed group) on the same reported days of 2019 using the terms "dentista (dentist), dente (tooth), siso (third molar), and aparelho (orthodontic appliance)." Descriptive analysis was performed to provide summary statistics of the frequencies of tweets related to different dental treatment needs and also the differences in volume content between the years 2019 and 2020. Moreover, the data were analyzed by qualitative analysis using an inductive approach. A total of 1,763 tweets from 2020 and 1,339 tweets from 2019 were screened. Those tweets posted by non-Brazilian users, duplicates, and those unrelated to dental treatment needs were removed and, therefore 1,197 tweets from 2020 and 719 tweets from 2019 were selected. Content volume related to dental treatment needs greatly increased during the COVID-19 outbreak. Findings from the word cloud and content analysis suggest that dental pain, related or not to the third molar, and problems with orthodontic appliances were the topics most commonly related to dental treatment needs discussed during the COVID-19 outbreak, mainly conveying anxiety and distress. The volume of tweets related to dental treatment needs posted by Brazilian users increased during the COVID-19 outbreak and self-reported pain and urgencies were the most popular topics.
Keyphrases
  • oral health
  • healthcare
  • systematic review
  • spinal cord injury
  • machine learning
  • physical activity
  • palliative care
  • neuropathic pain
  • electronic health record
  • drug induced