ArticleFrontiers in reproductive health2026
Monthly trends, determinants, and forecasting of perinatal mortality in Ghana: a comparison of ARIMA, BPNN, DLNN, and GRNN models.
Article in Frontiers in reproductive health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Perinatal mortality is a critical indicator of the quality of maternal and newborn care across sub-Saharan Africa. A recent systematic review and meta-analysis estimated Ghana's pooled perinatal mortality rate at 44.8 per 1,000 births, highlighting ongoing barriers to meeting Sustainable Development Goal targets for neonatal survival. Methods: We conducted a retrospective, hospital-based time series analysis of 192 monthly observations from January 2010 through December 2025. Perinatal mortality rate (PMR) was defined as the sum of stillbirths and early neonatal deaths per 1,000 births. Stationarity was evaluated using the Augmented Dickey Fuller (ADF) test. Forecasting performance was compared across four models-ARIMA, backpropagation neural network (BPNN), deep learning neural network (DLNN), and generalized regression neural network (GRNN)-with model validation performed on a 2025 temporal holdout. Results: The hospital recorded 46,108 live births and 1,152 perinatal deaths, giving an overall PMR of 24.98 per 1,000 births. The undifferenced monthly PMR series was borderline non-stationary (ADF statistic -2.695; Conclusion: Perinatal mortality declined over the long term but remained unstable. Among the evaluated models, ARIMA showed the best out-of-sample accuracy, while GRNN was the strongest neural-network comparator. Forecasts should be interpreted as operational projections rather than causal predictions.What is already known on this topic?Perinatal mortality remains high in many low- and middle-income countries, and stillbirths plus early neonatal deaths continue to contribute substantially to under-5 mortality in Ghana ( 1- 6).What this study addsThis study provides a 16-year monthly hospital time series, compares classical time-series forecasting with three neural-network approaches, and demonstrates that better ANC coverage and lower hypertension burden track with lower monthly PMR in this setting.How this study might affect research, practice, or policyMonthly PMR surveillance may help maternity hospitals monitor service quality, and ARIMA-based operational forecasting may assist planning for high-risk periods while service-improvement efforts focus on ANC utilization and maternal complication control.
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