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Completeness of yellow fever notification forms in the state of Espírito Santo, Brazil, 2017.

Priscila Carminati SiqueiraEthel Leonor Noia MacielRafael de Castro CatãoAna Paula BrioschiTheresa Cristina Cardoso da SilvaThiago Nascimento do Prado
Published in: Epidemiologia e servicos de saude : revista do Sistema Unico de Saude do Brasil (2020)
Objective to describe the completeness of data on yellow fever notification forms in the municipalities of the state of Espírito Santo, Brazil, in 2017. Methods this is a descriptive ecological study with data from the Notifiable Health Conditions Information System (SINAN); form completeness was categorized as poor (<70.0%), regular (70-89.9%) or excellent (≥90.0%); thematic maps were prepared. Results 53.1% of the municipalities had poor or regular classification for many notification form variables, especially case Final Classification (57.1%), Confirmation/Dismissal Criterion (63.2%) and Closure Date (26.5%), which are required fields. Conclusion completeness was poor or regular for several variables, pointing to the need for a systematic assessment of information on yellow fever held on SINAN.
Keyphrases
  • machine learning
  • health information
  • deep learning
  • electronic health record
  • big data
  • healthcare
  • public health
  • mental health
  • climate change
  • social media
  • data analysis