Related Experiment Video
Updated: May 9, 2025
10:46
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
Published on: December 9, 2015
10.6K
Time series analysis for forecasting neonatal intensive care unit census and neonatal mortality
Hosein Dalili1, Mamak Shariat2, Leyla Sahebi3
1Maternal-Fetal and Neonatal Research Center, Family Health Research Institute, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran.
BMC Pediatrics
|April 29, 2025
Summary
This study analyzed Neonatal Intensive Care Unit (NICU) data, finding that longer hospital stays correlate with increased mortality. Advanced forecasting models like ARIMA and SARIMA show promise for optimizing NICU resource planning and improving infant outcomes.
Area of Science:
- Neonatalogy
- Biostatistics
- Healthcare Management
Background:
- Analyzed seven years of retrospective Neonatal Intensive Care Unit (NICU) data (March 2016 - December 2022).
- Included daily census, hospitalization days, and mortality rates for 7,227 infants.
- Addressed the need for predictive modeling in NICU resource allocation.
Purpose of the Study:
- To forecast NICU census numbers, lengths of hospital stays, and mortality proportions.
- To explore relationships between NICU census, hospitalization duration, and mortality.
- To assess the predictive capabilities of time series models for NICU management.
Main Methods:
- Utilized Auto Regressive Integrated Moving Average (ARIMA) and Seasonal ARIMA (SARIMA) models for time series forecasting.
- Applied regression analysis to identify correlations between key NICU variables.
- Analyzed seven years of daily NICU data.
Main Results:
- Mortality proportion peaked at 9.94% in 2017, with an overall decreasing trend observed.
- Average hospitalization duration was 12.42 days, varying significantly between deceased and surviving neonates.
- Longer hospital stays showed a positive correlation with increased mortality (B=0.339, P=0.018) and an inverse relationship with hospitalizations (-2.58 days, P<0.001).
- NICU census numbers exhibited seasonal patterns, peaking in winter.
Conclusions:
- Seasonal variations impact NICU admissions and hospital stay durations, with winter months showing highest admissions and shortest stays.
- Extended hospitalizations are linked to higher mortality rates.
- ARIMA and SARIMA models demonstrated strong predictive power, emphasizing their utility in NICU resource planning for improved patient outcomes.
Related Concept Videos
01:18Introduction To Survival Analysis
114
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
The primary goal of survival analysis is to estimate survival time—the time...
114
01:18Steps in Outbreak Investigation
90
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
90
01:22Applications of Life Tables
30
Life tables are versatile across various fields, providing a quantitative basis for analyzing mortality and survival rates. Whether used by demographers, actuaries, epidemiologists, or sociologists, life tables offer valuable insights into the dynamics of life and death, facilitating informed decisions in public health, insurance, conservation, and beyond. Their broad applicability highlights the interconnectedness of demographic data with practical outcomes in everyday life and strategic...
30
01:20Actuarial Approach
39
The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
39
01:09Censoring Survival Data
40
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
40
01:25Statistical Methods for Analyzing Epidemiological Data
236
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
236