Log-linear modeling between risk factors and interactions for human papillomaviruses infection and papanicolaou smear abnormalities

Abstract

Modeling qualitative variables and their interactions often require multidimensional analysis through Log-linear models. Furthermore, these models are useful as alternatives in fields where probabilistic classification is required, such as speech recognition or pattern classification. This work uses log-linear modeling as a methodological approach to the analysis of 1114 valid cases of women participating in a human papillomavirus infection and cervical cancer screening program, thus relating a public health problem to biophysical knowledge. The objective of the study was to evaluate the main effects and interactions between the variables compared to the independence model. A backward stepwise selection with a 5% probability of elimination was performed to arrive at the best hierarchical model starting on the covariates that were significant in a previous bivariate analysis. This allows us to understand how biophysical process modeling can identify biomarkers and propose prevention methods for human papillomavirus infection and Papanicolaou smear abnormalities.