Multinomial Logistic Regression Model for the Inferential Risk Age Groups for Infection Caused by Vibrio cholerae in Kolkata, India

Abstract

Multinomial Logistic Regression (MLR) modeling is an effective approach for categorical outcomes, as compared with discriminant function analysis and log-linear models for profiling individual category of dependent variable. To explore the yearly change of inferential age groups of acute diarrhoeal patients infected with Vibrio cholerae during 1996-2000 by MLR, systematic sampling data were generated from an active surveillance study. Among 1330 V.cholerae infected cases, the predominant age category was up to 5 years accounting for 478 (30.5%) cases. The independent variables V.cholerae O1 (p<0.001) and non-O1 and non-O139 (p < 0.001) were significantly associated with children under 5 years age group. V.cholerae O139 inferential age group was > 40 years. The infection mediated by V.cholerae O1 had significantly decreasing trend Exp(B) year wise from 1996 to 2000 (p < 0.001, p < 0.001, p < 0.001, p < 0.001 and p < 0.001, respectively). MLR model showed that up to 5 year’s age children are more vulnerable to infection caused by V.cholerae O1.