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Effects of the COVID-19 pandemic on self-reported 12-month pneumococcal vaccination series completion rates in Canada.

Katherine M AtkinsonBlaise NtacyabukuraSteven HawkenLucie LaflammeKumanan Wilson
Published in: Human vaccines & immunotherapeutics (2022)
Routine childhood vaccination improves health and prevents morbidity and mortality from vaccine-preventable diseases. There are indications that the COVID-19 pandemic has negatively impacted immunization rates globally, but systematic studies on this are still lacking in Canada. This study aims to add knowledge on the pandemic's effect on children's immunization rates with pneumococcal vaccine using self-reported immunization data from CANImmunize. An interrupted time series analysis was conducted on aggregated monthly enrollment of children on the platform (2016-2021) and their pneumococcal immunization series completion rates (2016-2020). Predicted trends before and after the onset of the COVID19-related restriction (March 1, 2020) were compared by means of an Autoregressive Integrated Moving Average (ARIMA). The highest monthly enrollment was 3,474 new infant records observed in January 2020, and the lowest was 100 records in December 2021. The highest Self-reported pneumococcal immunization series completion rate was 78.89%, observed in February 2017, and the lowest was 6.94% in December 2021. Enrollment decreased by 1177.52 records (95% CI: -1865.47, -489.57), with a continued decrease of 80.84 records each month. Completion rates had an immediate increase of 14.57% (95% CI 4.64, 24.51), followed by a decrease of 3.54% each month. The onset of the COVID-19 related restrictions impacted the enrollment of children in the CANImmunize digital immunization platform and an overall decrease in self-reported pneumococcal immunization series completion rates. Our findings support efforts to increase catch-up immunization campaigns so that children who could not get scheduled immunization during the pandemic are not missed.
Keyphrases
  • coronavirus disease
  • sars cov
  • young adults
  • healthcare
  • public health
  • mental health
  • electronic health record
  • mouse model
  • deep learning
  • machine learning
  • clinical practice
  • climate change
  • social media
  • adverse drug