Prospective cohort study reports that season of conception influences maternal health metrics, suggesting implications for pregnancy management.
Background Global maternal health progress stagnated during the Sustainable Development Goal era, while the impact of meteorological conditions on maternal-infant outcomes remains contentious. We aimed to investigate the relationship between the season of conception and adverse perinatal outcomes. Methods We conducted a prospective cohort study, collecting basic demographic characteristics of pregnant women and estimating the season of conception based on the date of the last menstrual period. We did a follow-up until delivery to monitor pregnancy health issues, such as gestational diabetes mellitus (GDM), gestational hypertension (GH), premature rupture of membranes (PROM), and postpartum haemorrhage (PPH), as well as neonatal health indicators such as birth weight and other relevant outcomes. Results We included data from 26 341 pregnant women in our analysis. The average age of pregnant women was 30.3 years (standard deviation (SD) = 4.0), and 60.9% were primiparas. Compared to conception in spring (reference group), conception in summer was associated with a 15% reduction in GDM risk (odds ratio (OR) = 0.85; 95% confidence interval (CI) = 0.77–0.94) and a 10% lower PROM risk (OR = 0.90; 95% CI = 0.82–0.99), but a 46% increased PPH risk (OR = 1.46; 95% CI = 1.07–1.99). Conceptions in autumn and winter demonstrated even more pronounced protective effects, with the former showing a GDM risk reduction of 23% (OR = 0.77; 95% CI = 0.70–0.85) and 26% lower GH risk (OR = 0.74; 95% CI = 0.61–0.90), and the latter a 14% GDM risk reduction (OR = 0.86; 95% CI = 0.79–0.94) and 20% lower GH risk (OR = 0.80; 95% CI = 0.68–0.96). Conclusions We found that conception in spring was associated with an increased risk of GDM, GH, and PROM, while conception in summer was linked to a higher risk of PPH. However, the preliminary nature of our findings suggests that further research is needed to confirm causality and assess the feasibility of any potential interventions.
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Li et al. (2025) studied this question.
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