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The Associations of Tonsillectomy with Adenoidectomy with Pneumonia and Appendicitis Based on National Sample Cohort Data from the Korean National Health Insurance Service.

Junhui JeongJung Kyu ChoiHyun Seung ChoiChang Eui HongHyang Ae ShinJung Hyun Chang
Published in: International archives of otorhinolaryngology (2021)
Introduction  The association between tonsillectomy with adenoidectomy (T&A) with appendicitis is controversial, and the association of T&A with pneumonia has not been investigated. Objective  To investigate the associations of T&A with pneumonia and appendicitis using data from the Korean National Health Insurance Service National Sample Cohort. Methods  We selected patients between the ages of 3 and 10 years who had undergone T&A in 2005 and were monitored since the performance of the T&A until 2013. The control group was established to have similar propensities for demographic characteristics compared to the T&A group. For eight years after the T&A, the number of patients with a diagnosis of pneumonia, patients who were admitted due to pneumonia, and those who underwent appendectomy were analyzed. The risk factors for pneumonia and appendectomy were analyzed. Results  The number of pneumonia diagnoses was significantly higher in the T&A group than in the control group ( p  = 0.023), but there were no significant differences in the number of admissions due to pneumonia between the 2 groups ( p  = 0.155). Younger age and T&A were significant risk factors for the development of pneumonia. There were no significant differences in the number of appendectomies between the T&A and the control groups ( p  = 0.425), neither were there significant risk factors for appendectomy. Conclusion  Tonsillectomy with adenoidectomy was associated with an increase in pneumonia diagnoses, but it was not associated with the number of appendectomies. The associations of T&A with pneumonia and appendicitis were analyzed in this population-based study.
Keyphrases
  • health insurance
  • respiratory failure
  • community acquired pneumonia
  • healthcare
  • quality improvement
  • intensive care unit
  • deep learning
  • artificial intelligence
  • mechanical ventilation
  • single molecule
  • data analysis