Binary logistic regression to assess the factors affecting the infection of toxoplasmosis

Logistic regression method (LR) is one of the most widely used modeling techniques in various fields of science, especially in clinical medicine where variables are often dichotomous. The factors that cause toxoplasmosis (T. gondii) are well explained by most clinical investigators. Therefore, the main objective of this article is to identify the most significant factors leading to toxoplasmosis infection. The binary logistic regression method has been used to interpret the study's findings. A clustered sampling technique with an informative questionnaire was used in the survey to collect a relevant sample of 508 individuals from the most affected areas, specifically the northern part of Saudi Arabia. SPSS as well as AMOS are the typical statistical analysis tools used to investigate the results. The binary logistic techniques showed that the factors (stillbirth, women’s direct contact with soil, and keeping indoor cats) were the most significant factors influencing infection with toxoplasmosis, without neglecting some other invisible factors. Only 18.9% of the variation in the dependent variable (Toxoplasmosis infection) is attributed to the independent variables (which is moderate, with Nagelkerke’s R square = 0.189). Early medical follow-up and health awareness campaigns should be adopted, especially in remote rural communities.