Identification of Associated Factors and Prediction for the Level of Intimate Partner Violence against Women in Sri Lanka

  • Niroshan Withanage University of Sri Jayewardenepura, SRI LANKA
  • Sithara Wijekoon University of Kelaniya, SRI LANKA
Keywords: Feedforward neural network (FNN), Intimate partner violence (IPV), Multiple Correspondence Analysis, Violence Index, Women

Abstract

Intimate partner violence (IPV) can be defined as a serious social problem rapidly increasing in Sri Lanka as same as in other countries in the world. The Sri Lanka Demographic and Health Survey (SLDHS) 2016 revealed that 17% of married women age 15-49 in Sri Lanka have become victims of IPV. The objectives of this study were to determine the factors associated with IPV against women in Sri Lanka and to develop an appropriate regression model and feedforward neural network (FNN) to predict the Violence Index which describes the level of IPV against women in the country. The data records of 2494 ever-married women that have experienced IPV were considered from Sri Lanka Demographic and Health Survey 2016. The Violence Index was estimated using Multiple Correspondence Analysis. Gamma regression analysis revealed that religion, education level of the woman, husband’s occupation, woman’s married time, the age difference between husband and wife, Empowerment Indicator, enough money for daily household expenses, and household alcohol consumption were significantly associated with IPV against women. The optimum FNN consists of one hidden layer with 3 neurons provided a better prediction on the Violence Index with the minimum mean squared error for the testing set. Based on the prediction accuracy, the FNN was found to be better than the gamma regression model. The findings of this study would support an effort to develop the current policies and implement prevention programs against IPV in Sri Lanka.

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References

Abramsky, T., Watts, C. H., Garcia-Moreno, C., Devries, K., Kiss, L., Ellsberg, M., … Heise, L. (2011). What factors are associated with recent intimate partner violence? findings from the WHO Multi-country Study on women’s Health and Domestic Violence. BMC Public Health, 11.

https://doi.org/10.1186/1471-2458-11-109

Agumasie Semahegn, Tefera Belachew, & Misra Abdulahi. (2013). 19. BMC domestic violence_2013 MPH 1st. Reproductive Health, 10(63), 1–9.

https://doi.org/10.1186/1742-4755-10-63

Amusa, L. B., Bengesai, A. V., & Khan, H. T. A. (2020). Predicting the Vulnerability of Women to Intimate Partner Violence in South Africa: Evidence from Tree-based Machine Learning Techniques. Journal of Interpersonal Violence, (September).

https://doi.org/10.1177/0886260520960110

Asselin, L. M., & Anh, V. T. (2008). Multidimensional poverty and multiple correspondence analysis. Quantitative Approaches to Multidimensional Poverty Measurement, 80–103.

https://doi.org/10.1057/9780230582354

Babcock, J. C., & Cooper, J. (2019). Testing the utility of the neural network model to predict history of arrest among intimate partner violent men. Safety, 5(1).

https://doi.org/10.3390/safety5010002

Canuel, M., Abdous, B., Bélanger, D., & Gosselin, P. (2014). Development of composite indices to measure the adoption of pro-environmental behaviours across Canadian provinces. PLoS ONE, 9(7).

https://doi.org/10.1371/journal.pone.0101569

de Mel, N., Peiris, P., & Gomez, S. (2013). Broadening Gender: Why Masculinities Matter: Attitudes, practices and gender-based violence in four districts in Sri Lanka. 1–35.

Ezzrari, A., & Verme, P. (2013). A multiple correspondence analysis approach to the measurement of multidimensional poverty in Morocco 2001–2007. Economic Studies in Inequality, Social Exclusion and Well-Being, 9(June), 181–209.

https://doi.org/10.1007/978-1-4614-5263-8_7

Greenacre, M. (2017). Correspondence analysis in practice, third edition. In Correspondence Analysis in Practice, Third Edition.

https://doi.org/10.1201/9781315369983

Guruge, S., Jayasuriya-Illesinghe, V., Gunawardena, N., & Perera, J. (2015). Intimate partner violence in Sri Lanka: a scoping review. The Ceylon Medical Journal, 60(4), 133–138.

https://doi.org/10.4038/cmj.v60i4.8100

Hall, M., Chappell, L. C., Parnell, B. L., Seed, P. T., & Bewley, S. (2014). Associations between Intimate Partner Violence and Termination of Pregnancy: A Systematic Review and Meta-Analysis. PLoS Medicine, 11(1).

https://doi.org/10.1371/journal.pmed.1001581

Han Almis, B., Koyuncu Kutuk, E., Gumustas, F., & Celik, M. (2017). Risk Factors for Domestic Violence in Women and Predictors of Development of Mental Disorders in These Women. Noro Psikiyatri Arsivi, 67–72.

https://doi.org/10.5152/npa.2017.19355

Jayasuriya, V., Wijewardena, K., & Axemo, P. (2011). Intimate partner violence against women in the capital province of Sri Lanka: Prevalence, risk factors, and help seeking. Violence Against Women, 17(8), 1086–1102. https://doi.org/10.1177/1077801211417151

Kargar Jahromi, M., Jamali, S., Rahmanian Koshkaki, A., & Javadpour, S. (2015). Prevalence and Risk Factors of Domestic Violence Against Women by Their Husbands in Iran. Global Journal of Health Science, 8(5), 175–183.

https://doi.org/10.5539/gjhs.v8n5p175

Kisaka Ngimbi, J. J. (2019). Psychosocial Factors Associated with Domestic Violence Inflicted on Women by their Husbands at Kenge , Kwango Province, DRC. Biomedical Journal of Scientific & Technical Research, 18(2).

https://doi.org/10.26717/bjstr.2019.18.003130

Kohn, J. L. (2012). What is health? A multiple correspondence health index. Eastern Economic Journal, 38(2), 223–250.

https://doi.org/10.1057/eej.2011.5

Kohombange, C. (2012). Intimate partner violence: the silent burden in Sri Lankan women. Injury Prevention, 18(Suppl 1), A183.2-A183.

https://doi.org/10.1136/injuryprev-2012-040590q.18

Kuruppuarachchi, K., Wijeratne, L., Weerasinghe, G., Peris, M., & Williams, S. (2010). A study of intimate partner violence among females attending a Teaching Hospital out-patient department. Sri Lanka Journal of Psychiatry, 1(2), 60.

https://doi.org/10.4038/sljpsyc.v1i2.2577

Laeheem, K. (2016). Factors affecting domestic violence risk behaviors among Thai Muslim married couples in Satun province. Kasetsart Journal of Social Sciences, 37(3), 182–189.

https://doi.org/10.1016/j.kjss.2016.08.008

Mohamadian, F., Hashemian, A., Bagheri, M., & Direkvand-Moghadam, A. (2016). Prevalence and risk factors of domestic violence against Iranian women: A cross-sectional study. Korean Journal of Family Medicine, 37(4), 253–258.

https://doi.org/10.4082/kjfm.2016.37.4.253

Muzrif, M. M., Perera, D., Wijewardena, K., Schei, B., & Swahnberg, K. (2018). Domestic violence: A cross-sectional study among pregnant women in different regions of Sri Lanka. BMJ Open, 8(2), 1–8.

https://doi.org/10.1136/bmjopen-2017-017745

Rahayu, A., Purhadi, Sutikno, & Prastyo, D. D. (2020). Multivariate gamma regression: Parameter estimation, hypothesis testing, and its application. Symmetry, 12(5).

https://doi.org/10.3390/SYM12050813

Subramaniam, P., & Sivayogan, S. (2001). The prevalence and pattern of wife beating in the Trincomalee district in eastern Sri Lanka. Southeast Asian Journal of Tropical Medicine and Public Health, 32(1), 186–195.

United Nations. (2007). Indicators to measure violence against women: report of the Expert Group Meeting. (October).

Vadysinghe, A. N., Rathnayake, R. M. I. S. D., Premaratne, B. G., & Wickramasinghe, W. M. M. H. P. (2017). A preliminary study of domestic violence in a rural community in Central province, Sri Lanka. Sri Lanka Journal of Forensic Medicine, Science & Law, 7(1), 13.

https://doi.org/10.4038/sljfmsl.v7i1.7770

Williford, E., Haley, V., McNutt, L. A., & Lazariu, V. (2020). Dealing with highly skewed hospital length of stay distributions: The use of Gamma mixture models to study delivery hospitalizations. PLoS ONE, 15(4), 1–12. https://doi.org/10.1371/journal.pone.0231825

Published
2021-09-08
How to Cite
Withanage, N., & Wijekoon, S. (2021). Identification of Associated Factors and Prediction for the Level of Intimate Partner Violence against Women in Sri Lanka. International Journal for Research in Applied Sciences and Biotechnology, 8(5), 8-16. https://doi.org/10.31033/ijrasb.8.5.2