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.
Rajendran, Krishnan; Ramamurthy, Thandavarayan; and Sur, Dipika
"Multinomial Logistic Regression Model for the Inferential Risk Age Groups for Infection Caused by Vibrio cholerae in Kolkata, India,"
Journal of Modern Applied Statistical Methods:
1, Article 30.
Available at: http://digitalcommons.wayne.edu/jmasm/vol6/iss1/30