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.

Seroprevalence and potential risk factors of T. gondii infection in pregnant women attending antenatal care at Bonga Hospital, Southwestern Ethiopia

“…In contrast previous studies showed high seropositivity of T.gondii infection in urban pregnant women. 49 Although the odds ratio of having T.gondii infection was more in pregnant women who have contact with contaminated soil, no significant association was observed between contact with soil and T.gondii seropositivity. This result agrees with different findings 50 and oppose with previous studies of Kudakwashe and Yesuf.…”

“…This result agrees with different findings 50 and oppose with previous studies of Kudakwashe and Yesuf. 49…”

The determinants of employability of people living with HIV/AIDS in Turkey

“…Binary response models were used to directly describe the response probabilities of the dependent variable that takes the values 0 and 1. Logit and probit binary response models are widely used to analyze the risk factors for various diseases in the literature . Logistic regression, also called a logit model, can be used to explain the effects of explanatory variables on the binary response .…”

A predictive model for prediction of heart surgery procedure

“…Binary logistic regression model was used in order to model procedure (CABG vs DES) based on associated risk factors (lifestyle profile, co-morbid condition and vessel disease). From the findings, it clearly justified that binary logistic regression model was appropriate to find the risks factors of disease [24][25][26], particularly in this study that associated with the selection of either CABG or DES. With the reference to lifestyle, smoking and obese patients were more likely to have DES.…”