An Application of CATANOVA and Logistic Regression on the Most Prevalent Sexually Transmitted Infection (A Case Study of the University of Nigeria Teaching Hospital)

Year: 2022       Vol.: 71       No.: 1      

Authors: Nnaemeka Martin Eze, Oluchukwu Chukwuemeka Asogwa, Samson Offorma Ugwu, Chinonso Michael Eze, Felix Obi Ohanuba, Tobias Ejiofor Ugah

Abstract:

This research focused on the application of CATANOVA and logistic regression on the most prevalent Sexually Transmitted Infection (STI) reported in the University of Nigeria Teaching Hospital from 2010- 2020. A population of 20,704 patients was recorded to have contracted eight(8) selected STIs. Prevalence analysis was computed to determine the most prevalent STI. Two-way CATANOVA cross-classification was computed to ascertain the age group and gender that suffer more from the most prevalent STI. Three-way CATANOVA was computed to ascertain the association among drug prescription, age, and gender of the Gonorrhea patients. A logistic regression model was fitted to predict infertility as an effect of the most prevalent STI. The prevalence analysis showed Gonorrhea infection as the most prevalent STI at 33.08%. A population of 6,850 patients recorded to have contracted Gonorrhea infection from 2010-2020 was employed for the analysis. Two-way CATANOVA cross-classification showed that gender, age, and interaction effects were statistically significant at a 5% significance level. Male (3,752; 54.8%) suffers Gonorrhea infection more than female (3,098;45.2%) and aged 30-39 years (1,946; 28.4%) suffers it more than any other age interval. The interaction effect shows that the rate of contracting Gonorrhea infection by gender differs from one age interval to another. Three-way CATANOVA results showed that drugs prescribed for the treatment of Gonorrhea infection depend on gender and age. Logistic regression results showed that an increase in age, body mass index, blood pressure, blood sugar, bacteria quantity, and Gonorrhea history were associated with an increased likelihood of the Gonorrhea patient being infertile.

Keywords: Chi-square test, Prediction, Prevalence

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