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Variations in cesarean and repeated cesarean section rates in Brazil according to gestational age at birth and type of hospital.

Barbara Almeida Soares DiasMaria do Carmo LealAna Paula Esteves PereiraMarcos Nakamura-Pereira
Published in: Cadernos de saude publica (2022)
This study aimed to describe cesarean and repeated cesarean section rates in Brazil according to gestational age (GA) at birth and type of hospital. This is an ecologic study using data from the Brazilian Information System on Live Births and the 2017 National Registry of Health Facilities. Overall and repeated cesarean section rates were calculated and analyzed according to GA, region of residence, and type of hospital. Spearman correlations were performed between cesarean and repeated cesarean section rates by GA subgroups at birth (≤ 33, 34-36, 37-38, 39-41, and ≥ 42 weeks) and analyzed according to the type of hospital. Overall and repeated cesarean section rates were 55.1% and 85.3%, respectively. More than 60% of newborns between 37-38 weeks were delivered via cesarean section. Private hospitals in all regions showed the highest cesarean section rates, especially those in the Central-West Region, with more than 80% at all GAs. The overall cesarean section rate was highly correlated with all cesarean section rates of GA subgroups (r > 0.7, p < 0.01). Regarding repeated cesarean sections, the overall rate was strongly correlated with the rates of 37-38 and 39-41 weeks in public/mixed hospitals, differing from private hospitals, which showed moderate correlations. This finding indicates the decision for cesarean section is not based on clinical factors, which can cause unnecessary damage to the health of both the mother and the baby. Then, changes in the delivery care model, strengthening public policies, and encouragement of vaginal delivery after a cesarean section in subsequent pregnancies are important strategies to reduce cesarean section rates in Brazil.
Keyphrases
  • gestational age
  • healthcare
  • birth weight
  • preterm birth
  • pet ct
  • public health
  • mental health
  • pregnant women
  • health information
  • acute care
  • electronic health record
  • climate change
  • social media
  • big data
  • data analysis