Prediction of risk factors of periodontal disease by logistic regression: a study done in Karnataka, India - Javali, Shivalingappa Basavantappa; Sunkad, Mohan Anantarao; Wantamutte, Appasaheb Saheb (International Journal Of Community Medicine And Public Health, 2018)
Background: The purpose of the study was to analyze the dependence of oral health diseases i.e. periodontal disease by Community Periodontal Index of Treatment Needs (CPITN) by considering the number of risk factors through the applications of logistic regression model. Methods: This cross sectional study involves a systematic random sample of 600 permanent dentition aged between 18-40 years in Karnataka, India. The mean age was 34.26±7.28. The risk factors of periodontal disease were established by multiple logistic regression models using SPSS 21.0 statistical software. Results: The factors like frequency of brushing, timings of cleaning teeth and type of toothpastes are significant persistent predictors of periodontal disease. The log likelihood value of full model is –1085.7876 and AIC is 1.2577 followed by reduced regression model are -1019.8106 and 1.1748 respectively for periodontal disease. The area under receiver operating characteristic (ROC) curve for the periodontal disease is 0.6128 (full model) and 0.5821 (reduced model). Conclusions: The logistic regression model is useful in predicting risk factors like-frequency of brushing, timings of cleaning teeth and type of toothpastes for periodontal disease. The fitting performance of reduced logistic regression model is slightly a better fit as compared to full logistic regression model in identifying the these risk factors for both dichotomous periodontal disease.