Investigating The Factors Influencing Mother-To-Child Transmission of Syphilis in Ebonyi North Senatorial District Using Logistic, Poisson, And Negative Binomial Regression Models

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@article{1711647,
      author = {Nwuzor Ozoemena, Okoro Chiemeka Nwankwor, Igwe Sunday Theophilus},
      title = {Investigating The Factors Influencing Mother-To-Child Transmission of Syphilis in Ebonyi North Senatorial District Using Logistic, Poisson, And Negative Binomial Regression Models},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {9},
      number = {5},
      pages = {6-13},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1711647.pdf},
      abstract = {This study investigated the factors influencing mother-to-child transmission (MTCT) of syphilis in Ebonyi North Senatorial District, Nigeria, using logistic, Poisson, and negative binomial regression models. Data spanning 2010?2024 were collected from four healthcare facilities and questionnaires were equally distributed to the pregnant women for their responses. The analysis assessed the effectiveness of existing prevention and treatment strategies, availability of essential drugs, counseling services, and the predictive capacity of different statistical models. Results from the logistic regression model demonstrated that variables such as pregnancy status, syphilis symptoms, presence of other sexually transmitted infections, syphilis screening, transmission counseling, and infant treatment were statistically significant predictors of MTCT (p < 0.05). The model achieved excellent predictive performance with a McFadden R? of 0.916, Nagelkerke R? of 0.959, and an area under the ROC curve (AUC) of 0.998, indicating near-perfect classification ability. Principal component analysis (PCA)-selected predictors yielded a slightly reduced but still robust performance (AUC = 0.964).Poisson and negative binomial regressions further highlighted maternal age (IRR = 0.86, p < 0.01) and syphilis knowledge (IRR ? 3.0, p < 0.001) as consistent predictors of MTCT risk. The negative binomial model provided a better fit for overdispersed count data compared to the Poisson model. The findings reveal gaps in drug availability, follow-up, and counseling services, which undermine current MTCT prevention efforts. Evidence indicates that strengthening syphilis screening, improving awareness, ensuring continuous availability of essential drugs, and enhancing counseling and follow-up services are critical to reducing MTCT in Ebonyi State. This study concludes that while logistic regression offered the strongest predictive performance, Poisson and negative binomial models provided complementary insights into the determinants of MTCT. The evidence generated provides a basis for evidence-driven policies to improve syphilis control programs and safeguard maternal and child health in Ebonyi State.},
      keywords = {Syphilis, Transmission, Influencing, Logistic, Poison and Negative Binomial Regression Models.},
      month = {November},
      doi = {https://doi.org/10.64388/IREV9I5-1711647-7444}
  }