Prevalence and Predictors of Domestic Violence in India: Complex Sample Analysis of a Nationally Representative Study Conducted Between 2019 and 2021.

Background:  Violence against women has been one of the dreaded social evils that humanity is facing. There have been concerted efforts to eliminate this evil, and sustainable development goals goal 5.2.1 gave it a timeline. The current study was carried out to estimate the burden of domestic violence (DV) against women and to investigate the sociodemographic correlates of DV victims in India.

Methods:  Data were drawn from the fifth National Family Health Survey round. According to Demographic Health Survey guidelines, DV is measured using a 13-item questionnaire in the women's survey. Complex sample analysis was done using a primary sampling unit, sample weight, and stratification variables to estimate the weighted prevalence. Chi-square and multivariate logistic regression determine the unadjusted and adjusted odds ratio. The analysis is carried out using SPSS version 26 (IBM Corp., Armonk, NY).

Results:  The weighted prevalence of DV against women in India in 2019-2021 was 31.2%. Approximately 28.5%, 13.1%, and 5.7% of women reported experiences of physical, emotional, and sexual violence, respectively. Karnataka was the worst affected state, with 47.3% of women facing DV. Individual factors like education and occupation, household factors like husband's education, occupation, drinking habit, wealth index, and community-level factors like caste, religion, and place of residence were significant predictors of DV. Lower levels of education and lower socioeconomic status were essential predictors of DV.

Conclusion:  The importance of education for both females and males has repeatedly been directly associated with DV, but the interventions have failed to improve the situation and warrant a new strategy. Awareness about the legal consequences of DV in lower socioeconomic classes also has the potential to cut down the numbers. Further research into the causality can improve the planning for better intervention modalities.