Dynamic systems modeling for quality improvement in reducing neonatal mortality: evidence from Kerman and Bam, Iran.

Purpose: Neonatal mortality is a significant global health issue, particularly in low- and middle-income countries. This study aims to identify and understand the factors contributing to high neonatal mortality rates in the cities of Kerman and Bam, Iran, to develop effective strategies for improvement.

Design/methodology/approach: We employed systems dynamics to develop Causal Loop Diagrams that capture qualitative interactions among determinants of neonatal mortality. These CLDs were transformed into stock and flow diagrams for quantitative analysis. Using least squares regression techniques in MATLAB, we analyzed 60 months (2017-2021) of historical data from the Iranian Maternal and Neonatal database, the Integrated Health System (SIB), and Iran's Statistical Yearbooks. Expert interviews and hospital informatics were also utilized to enhance the model's robustness.

Findings: The developed model demonstrated a validation accuracy of approximately 94% based on Mean Absolute Percentage Error (MAPE). Key determinants were categorized into health factors (e.g. preterm birth, eclampsia), socio-demographic factors (e.g. maternal education, substance abuse), and healthcare system factors (e.g. NICU capacity, specialist staff). Simulation scenarios revealed that a 20% increase in NICU capacity could reduce neonatal mortality by 35% in Bam and 7% in Kerman. Additionally, hiring 15% more specialist staff reduced mortality by 10% in Bam and 7% in Kerman.

Research limitations/implications: While this study is based on data from two specific cities, which may limit its applicability to other regions with different healthcare infrastructures and socio-economic conditions, the integrated qualitative and quantitative methodology employed can be effectively applied to other areas and societies. Future research should expand to additional regions and incorporate more factors, such as genetic predispositions and environmental influences, to enhance the model's generalizability and accuracy. The findings provide clear guidance for healthcare policymakers on effective resource allocation, such as expanding NICU capacity and training more healthcare professionals, to reduce neonatal mortality rates.

Practical implications: The model offers a robust framework for simulating intervention scenarios, enabling data-driven decision-making for optimizing healthcare strategies. By reducing neonatal mortality, this research contributes to the overall health and well-being of communities, fostering healthier families and populations, and leading to long-term societal benefits, including enhanced quality of life and economic productivity.

Originality/value: This study is among the first in Iran to utilize a comprehensive systems dynamics approach to analyze factors affecting neonatal mortality. It presents a highly accurate dynamic model that integrates qualitative and quantitative data, offering a replicable methodology adaptable to other regions facing similar health challenges. The innovative application of dynamic systems modeling in neonatal health provides significant contributions to healthcare management and public health, supporting global efforts to reduce neonatal mortality.