Some risk factors for preterm birth in Gerash County, Fars Province using the method of Geographic Weighted Regression (GWR)

Document Type : Original Article

Authors

1 Head of the Department of Population Youth, Family Health and Schools of Gerash Faculty of Medical Sciences, Gerash, Fars, Iran.

2 PHD of Geomorphological Hazards, Expert of Monitoring the Environmental Protection Department, Ewaz County, Fars, Iran.

3 Infectious Diseases Expert, Vice-Chancellor of Health, Gerash Faculty of Medical Sciences, Gerash, Fars, Iran.

4 Maternal Health Program Expert, Vice-Chancellor of Health, Gerash Faculty of Medical Sciences, Fars, Iran.

5 Head of the Department of Diseases, Executive Vice President of Health, Gerash Faculty of Medical Sciences, Gerash, Fars, Iran.

10.22038/ijogi.2025.83501.6239

Abstract

Introduction: Preterm birth is defined as birth before 37 weeks of gestation and is the leading cause of infant mortality. Therefore, researchers worldwide have identified various risk factors associated with preterm birth. The aim of this study was that using the Geographically Weighted Regression (GWR) method identifies the areas most affected by preterm birth in Gerash County, considering maternal age groups, gestational weeks, and socioeconomic conditions.
Methods: In this study, the GWR model was employed to examine the relationship between preterm birth and 26 risk factors in Gerash County. These 26 risk factors included various age groups, different gestational weeks, income level, history of cesarean section, placenta previa, inadequate maternal weight gain, history of urinary and genital tract infections, first-trimester bleeding, minor thalassemia, multiple pregnancies, thyroid disorders, diabetes, kidney diseases, history of hypertension, COVID-19, and blood-related complications as independent variables, and preterm birth as dependent variable.
Results: Among the 26 risk factors, 13 cases or half were significantly associated with preterm birth. Among these factors, some showed varying degrees of correlation in different parts of the county, making it easier to implement effective preventive and management measures in areas with high correlation. However, some factors such as thalassemia minor, inadequate weight gain, and gestational weeks 30-33 and 34-36 showed a very high correlation in all parts of the county, requiring significant attention for prevention.
Conclusion: The practical results of the GWR model and its high power for spatial modeling can help managers and planners to identify sensitive areas of diseases and various issues, such as preterm birth, and manage them more effectively.

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Main Subjects

References

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The Iranian Journal of Obstetrics, Gynecology and Infertility
Volume 27, Issue 12
February 2025
Pages 43-60

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