A BAYESIAN APPROACH TO MODELLING PREGNANCY RISKS USING LATENT GAUSSIAN MODEL
DOI:
https://doi.org/10.60787/tnamp.v25.742Keywords:
latent Gaussian model, high-risk pregnancy, Bayesian, INLA, logistic, maternal-fetal, public healthAbstract
A latent Gaussian model (LGM) that follows a Gaussian Markov random field was developed for pregnancy risk data. Inference was done through INLA. Sensitivity analysis was done to adjust for underweight bias in BMI. Positive maternal Hepatitis B status (HBsAg) was the strongest risk factor, increasing the odds of high-risk pregnancy more than 300-fold (AOR = 313.69, 95% CI: 43.72–39,858.39). Elevated systolic blood pressure independently increased the odds by 4.3% per unit increase (AOR = 1.043, 95% CI: 1.025–1.061). In contrast, a negative blood or urine sugar test reduced the odds by 54.6% (AOR = 0.454, 95% CI: 0.285–0.723), while each unit increase in BMI within the normal-to-overweight range reduced the odds by 31% (AOR = 0.690, 95% CI: 0.634–0.743). LGM was comparable to classical logistic model (AUC ≈ 0.734; accuracy 70.54% vs. 70.44%). Prior sensitivity analysis demonstrated the robustness of LGM to prior specifications.
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