Application of Binary Logistic Regression in Assessing Risk Factors Affecting the Prevalence of Toxoplasmosis

Abstract

Toxoplasmosis is a parasitic disease caused by the protozoan parasite Toxoplasma Gondii (T.gondii). The parasite infects warm-blooded animals among them humans especially those whose immunity has been compromised. The transmission mode of the parasite vary from living in unhygienic conditions, contact with cat faeces to contact with raw meat or the practice of raw meat eating, such as commonly practiced in Ethiopia. Binary logistic regression was used to determine the risk factors affecting the prevalence of toxoplasmosis in HIV/AIDS patients. Significant risk factors were detected using the Wald and the likelihood ratio tests. The model selected was then subjected to diagnostic checks to assess its fitness using the Hosmer and Lemeshow test as well as the Pearson, and deviance goodness of fit tests. The results showed that patients living under unhygienic conditions, aged patients, illiterate and less educated patients were mostly affected by toxoplasmosis. There was more prevalence in urban areas than in rural areas possibly due to the high density of people in urban areas.