Time Series Analysis for Forecasting Neonatal Intensive Care Unit Census and Neonatal Mortality

Time Series Analysis for Forecasting Neonatal Intensive Care Unit Census and Neonatal Mortality Publisher Pubmed

Dalili H¹ ; Shariat M² ; Sahebi L³

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1. Maternal-Fetal and Neonatal Research Center, Family Health Research Institute, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran
2. Vali-E-Asr Reproductive Health Research Center, Family Health Research Institute, Tehran University of Medical Sciences, Tehran, Iran
3. Maternal-Fetal and Neonatal Research Center, Family Health Research Institute, Tehran University of Medical Science, Tehran, 1419733141, Iran

Source: BMC Pediatrics Published:2025

Abstract

Background: This study analyzes time series data related to NICU (Neonatal Intensive Care Unit) census numbers, hospitalization days, and mortality rates. Methods: We utilized seven years of retrospective daily NICU census data for model development, covering the period from March 2016 to December 2022, encompassing a total of 7,227 infants. We applied the best-fitting models of ARIMA (Auto Regressive Integrated Moving Average) and SARIMA (Seasonal ARIMA) to forecast census numbers, lengths of hospital stays, and mortality proportions. Additionally, we conducted regression time series analysis to explore the relationships among these variables. Results: The mortality proportion peaked in 2017 at 9.94%. The average duration of hospitalization was 12.42 days, with significant variability observed between deceased and surviving neonates. Multiple regression analysis indicated an inverse relationship between the number of hospitalizations and the duration of hospital stays, with a coefficient of -2.58 days (P-value < 0.001). There was also a notable correlation between longer hospital stays and increased mortality, with a regression coefficient (B) of 0.339 (P-value = 0.018). Time series analysis revealed a decreasing trend in mortality proportion in the NICU, alongside seasonal patterns in census numbers, which peaked during the winter months. Conclusion: Seasonal variations were observed, with the highest admissions occurring in the winter months and the shortest hospital stays during this period. Additionally, longer hospital stays were associated with higher mortality. Forecasting using ARIMA and SARIMA models demonstrated strong predictive capabilities, highlighting the importance of effective resource planning to optimize outcomes in the NICU. Clinical trial number: Not applicable. © The Author(s) 2025.

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Mohamadreza Zarkesh (1)

Style	Citing Format
MLA	Dalili H, Shariat M, Sahebi L. "Time Series Analysis for Forecasting Neonatal Intensive Care Unit Census and Neonatal Mortality." BMC Pediatrics, vol. 25, no. 1, 2025, pp. -.
APA	Dalili H, Shariat M, Sahebi L (2025). Time Series Analysis for Forecasting Neonatal Intensive Care Unit Census and Neonatal Mortality. BMC Pediatrics, 25(1), -.
Chicago	Dalili H, Shariat M, Sahebi L. "Time Series Analysis for Forecasting Neonatal Intensive Care Unit Census and Neonatal Mortality." BMC Pediatrics 25, no. 1 (2025): -.
Harvard	Dalili H, Shariat M, Sahebi L (2025) 'Time Series Analysis for Forecasting Neonatal Intensive Care Unit Census and Neonatal Mortality', BMC Pediatrics, 25(1), pp. -.
Vancouver	Dalili H, Shariat M, Sahebi L. Time Series Analysis for Forecasting Neonatal Intensive Care Unit Census and Neonatal Mortality. BMC Pediatrics. 2025;25(1):-.
BibTex	@article{ author = {Dalili H and Shariat M and Sahebi L}, title = {Time Series Analysis for Forecasting Neonatal Intensive Care Unit Census and Neonatal Mortality}, journal = {BMC Pediatrics}, volume = {25}, number = {1}, pages = {-}, year = {2025} }
RIS	TY - JOUR AU - Dalili H AU - Shariat M AU - Sahebi L TI - Time Series Analysis for Forecasting Neonatal Intensive Care Unit Census and Neonatal Mortality JO - BMC Pediatrics VL - 25 IS - 1 SP - EP - PY - 2025 ER -

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