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Coverage and determinants of childhood vaccination during the COVID-19 pandemic in Fortaleza, Northeastern Brazil: a longitudinal analysis.

David Augusto Batista Sá AraújoLuciano Lima CorreiaPedro Lucas Grangeiro de Sá Barreto LimaSophia Costa VasconcelosSimone Farias-AntuñezYuri Valentim Carneiro GomesDenise Lima NogueiraMarcia Caldas de CastroMarcia Maria Tavares Machado
Published in: Cadernos de saude publica (2024)
Brazil has seen a decrease in vaccination coverage since 2016. This study analyzes the immunization status of children born during the COVID-19 pandemic in Fortaleza, Northeastern Brazil. This is a longitudinal analysis that included vaccination data of 313 children aged 12 and 18 months. Vaccination cards were checked for dose application considering the schedule of immunization recommended by the Brazilian Ministry of Health. Factors associated with no retention of vaccination cards and incomplete immunization by 18 months were identified by Tobit regression analysis. About 73% of mothers presented their child's vaccination card. Non-availability of vaccination cards was associated with maternal age < 25 years and mothers with paid jobs. Only 33% and 45% of the children aged 12 and 18 months had all vaccines up to date, respectively. For 3-dose vaccines, the delay rate was around 10% for the first dose application, but 40% for the third dose. Despite delays, most children with available vaccine cards had coverage above 90% by 18 months of age. Adjusted factors associated with incomplete vaccination included living in a household with more than one child (p = 0.010) and monthly income of less than one minimum wage (p = 0.006). Therefore, delays in child vaccine application were high during the COVID-19 pandemic but a considerable uptake by 18 months of age was found. Poorer families with more than one child were particularly at risk of not fully immunizing their children and should be the target of public policies.
Keyphrases
  • mental health
  • healthcare
  • young adults
  • public health
  • risk assessment
  • pregnant women
  • social media
  • health information
  • machine learning
  • artificial intelligence
  • weight loss
  • low birth weight
  • human health