Pregnancy complications and caesarean section: a latent class analysis

Background: The incidence and overuse of caesarean section, an adverse pregnancy outcome closely associated with pregnancy complications, is markedly high globally. However, previous research has predominantly examined individual complications in isolation, leaving a need for a comprehensive evaluation of multimorbidity patterns. We aimed to explore the associations between caesarean sections and adverse pregnancy outcomes in a Chinese population. Methods: test to compare the differences between spontaneous labour and caesarean section. Then, we used latent class analysis (LCA) to cluster pregnancy complications and logistic regression to examine their association with modes of delivery. Results: We included 85 446 pregnant women, of whom 53 916 (63.1%) had undergone caesarean sections and 31 530 (36.9%) had experience spontaneous labour. There were significant differences in terms of pregnancy complications between pregnant women who underwent spontaneous labour and those who had caesarean sections. We then clustered pregnancy complication symptoms into six classes using LCA and fitted three models. After adjusting for potential confounders, the incidence of caesarean sections was significantly higher in pregnant women with diabetes and hypothyroidism (odds ratio (OR) = 1.177; 95% confidence interval (CI) = 1.105-1.253), hyperthyroidism and kidney disease (OR = 2.078; 95% CI = 1.391-3.106), with hypertension and hypothyroidism (OR = 3.613; 95% CI = 3.217-4.058), and hypertension, diabetes, and anaemia (OR = 3.365; 95% CI = 2.997-3.779) when compared to pregnant women with a lower incidence of pregnancy complications. Conclusions: Caesarean sections occur frequently among pregnant women in China and are significantly associated with specific pregnancy complication clusters, particularly those involving hypertension, diabetes, anaemia, and thyroid dysfunction. These findings suggest that women with multimorbidity profiles should receive enhanced antenatal surveillance and individualised delivery planning to optimise maternal outcomes.