Count regression model to predict spousal harms in Tamil Nadu - Elizabeth Varghese; Adhin Bhaskar; Chinnaiyan Ponnuraja (Wolters Kluwer Medknow Publications, 2022)
Introduction: Violence against women is becoming more prevalent over the world, particularly in India. Assessing the causes of violence in community will aid in planning supports for victims. This study aimed to compare the performance of various regression models for count data and focused on choosing appropriate count regression model to identify factors related with the number of domestic violence experienced by young married women. Methods: Data for this study were retrieved from "The Youth in India: Situation and Needs Study." The current study took the data of 1495 married women in Tamil Nadu. Factors associated with physical violence considered for the study were place of residence, age of husband and wife, education of husband and wife, dowry, miscarriage, abortion, and marriage type. Ordinary least square, Poisson regression, and negative binomial regression models were fitted for the data, and the best fitted model was identified using Akaike information criterion (AIC) and Bayesian information criterion (BIC). Results: Proportion of married women who have perpetrated any forms of physical violence was 30.8%. Among the fitted models, negative binomial regression model (AIC = 3020.621, BIC = 3079.030) was found to be the best model to predict violence. Significant factors identified were type of residence, marriage type, education of wife and spouse, miscarriage, and abortion. Conclusion: To tackle this public health issue, multisectoral approaches such as boosting literacy, raising awareness about legal assistance, and monitoring victims of violence at primary health facilities should be implemented. Comprehensive model testing is highly suggested for determining the best acceptable analytic model when dependent variable being studied comprises count data.