An increase in cesarean section rate during the first wave of COVID-19 pandemic in Iran

BMC Public Health. 2023 May 24;23(1):936. doi: 10.1186/s12889-023-15907-1.

Abstract

Background: The COVID-19 pandemic and its impact on healthcare services is likely to affect birth outcomes including the delivery mode. However, recent evidence has been conflicting in this regard. The study aimed to assess changes to C-section rate during the COVID-19 pandemic in Iran.

Methods: This is a retrospective analysis of electronic medical records of women delivered in the maternity department of hospitals in all provinces of Iran before the COVID-19 pandemic (February-August 30, 2019) and during the pandemic (February-August 30, 2020). Data were collected through the Iranian Maternal and Neonatal Network (IMAN), a country-wide electronic health record database management system for maternal and neonatal information. A total of 1,208,671 medical records were analyzed using the SPSS software version 22. The differences in C-section rates according to the studied variables were tested using the χ2 test. A logistic regression analysis was conducted to determine the factors associated with C-section.

Results: A significant rise was observed in the rates of C-section during the pandemic compared to the pre-pandemic (52.9% vs 50.8%; p = .001). The rates for preeclampsia (3.0% vs 1.3%), gestational diabetes (6.1% vs 3.0%), preterm birth (11.6% vs 6.9%), IUGR (1.2% vs 0.4%), LBW (11.2% vs 7.8%), and low Apgar score at first minute (4.2% vs 3.2%) were higher in women who delivered by C-section compared to those with normal delivery (P = .001).

Conclusions: The overall C-section rate during the first wave of COVID-19 pandemic was significantly higher than the pre-pandemic period. C-section was associated with adverse maternal and neonatal outcomes. Thus, preventing the overuse of C-section especially during pandemic becomes an urgent need for maternal and neonatal health in Iran.

Keywords: COVID-19; Cesarean section; Maternal health.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • COVID-19* / epidemiology
  • Cesarean Section
  • Electronic Health Records
  • Female
  • Humans
  • Infant, Newborn
  • Iran / epidemiology
  • Pandemics
  • Pregnancy
  • Premature Birth*
  • Retrospective Studies