Temporal Trends, Seasonality, and Regional Inequalities in Perinatal Mortality in Hungary, 2000–2023: A Population-Based Ecological Study

Background/Objectives: Perinatal mortality reflects maternal health, quality of care, and broader social and environmental conditions. Despite long-term improvements across Europe, disparities persist. We assessed trends in perinatal, late fetal, and early neonatal mortality in Hungary during 2000–2023 and examined variation by sex, region, and maternal characteristics. Methods: This nationwide ecological study used aggregated Hungarian Central Statistical Office data on live births, late fetal deaths (≥24 gestational weeks), and early neonatal deaths (within six days). Quasi-Poisson regression models estimated incidence rate ratios (IRRs) with 95% confidence intervals. Temporal, seasonal, sex-specific, and NUTS 2 regional patterns were analyzed; maternal age and marital status were explored. Results: Among 2,235,078 live births, 10,693 late fetal and 5343 early neonatal deaths occurred. Perinatal mortality declined significantly (IRR 0.976, 95% CI 0.972–0.980), with a steeper decline in early neonatal than in late fetal mortality. Mortality during 2020–2023 exceeded counterfactual estimates based on the modeled continuation of pre-pandemic trends. Mortality was higher among males and in Northern Hungary than in Budapest. Late fetal and overall perinatal mortality peaked in May, while early neonatal mortality showed spring and autumn peaks. Mortality rates were higher among births in the youngest and oldest maternal-age groups and among those registered to mothers classified as not married. Recorded mortality attributed to intrauterine hypoxia, birth asphyxia, and fetal growth restriction increased. Conclusions: Perinatal mortality in Hungary declined substantially during 2000–2023, but sex-related and regional differences persisted. The observed seasonal, demographic, and cause-specific patterns warrant continued surveillance and further individual-level and multilevel research to investigate their underlying determinants.