Demographic and Predictive Health indicators for Gestational Diabetes Mellitus: A Logistic Regression Analysis

Pregnant women are at high risk for Gestational Diabetes Mellitus (GDM), which is linked to a number of demographic and health variables. For management and preventative tactics to be effective, it is vital to comprehend these variables. This study aimed to investigate the demographic and predictive factors associated with Gestational Diabetes Mellitus (GDM) using logistic regression analysis. In order to determine whether certain demographic factors and health indicators among pregnant women (n=140) were associated with GDM, a cross-sectional study was conducted. Data were gathered via self-structured questionnaires and checklists. ROC curve analyses, correlation matrices, logistic regression, and descriptive statistics were all used in the data analysis process. It was discovered that 15% of the study population had GDM. A family history of diabetes (OR=4.1, 95% CI: 1.8-9.2), higher BMI (OR=3.0, 95% CI: 1.4-6.5), and advanced maternal age (OR=2.5, 95% CI: 1.2-5.1) were all significant predictors of GDM, according to logistic regression analysis. Although they were not statistically significant after controlling for covariates, multiparity (OR=1.7, 95% CI: 0.9-3.2) and increased glucose levels in early pregnancy (OR=3.5, 95% CI: 1.6-7.7) also shown a strong connection. Age of the mother, a family history of diabetes, and PCOD were found to be important indicators of GDM. These results highlight the need of focused interventions and individualized healthcare approaches by highlighting the need of targeted screening and intervention techniques to reduce GDM risks among pregnant women. To address the complications and challenges offered by gestational diabetes mellitus, more research and a comprehensive approach to prenatal care are necessary.