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Understanding Elder Abuse in India: Contributing Factors and Policy Suggestions

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Abstract

Elder abuse is a multifaceted public health issue. The aim of this study is to provide a concise overview of elder abuse among adults age 60 and above, at the national and state levels in India. The main objective of this research is to examine the prevalence and determinants of elder abuse in light of the latest available data, with an emphasis on working status of older adults. Further, we explore the relative importance of distinctive factors explaining the gendered differential in elder abuse. This study also suggests some strategies to address the problem of elder abuse. Data from the 2020 Longitudinal Ageing Study in India indicates that although the overall prevalence of elder abuse is relatively low in India (5.22%), wide state-level variations prevail. Women, working older adults (especially working women), those under the age of 70, those with greater household assets, those not in a marital union, those staying in rural areas, and those in poor health have significantly higher chances of abuse than their counterparts. Both wealth and education must reach a critical level to curb abuse. Differences in economic factors explain only 10% of the gender gap in elder abuse prevalence. Sociodemographic factors alone account for around 29%, and health-related factors contribute to 28% of the gender differential in elder abuse. We argue for widespread protective policies and targeted program interventions to address elder abuse in India.

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Data Availability

The data used in this study is freely available and easily accessible for researchers through IIPS LASI Study (https://www.iipsindia.ac.in/lasi/).

Notes

  1. Social learning theory posits that violence is a learned behavior that may be passed down intergenerationally. Social exchange theory explains that elder abuse may occur because of the victim’s dependence on the abuse and vice versa. Feminist theory argues that elder abuse is the product of patriarchal family. structure. Examining the psychopathology of the caregiver theory would include looking at how an abuser’s behavioral characteristics contribute to elder abuse. For instance, caregivers who consume alcohol and experience depression and anxiety are more likely to use physical and verbal abuse against an elder. Symbolic interactionism assumes that people view and react to elements or situations according to the subjective meanings they attach to those elements. Everyone attaches their own meaning to every other object, and this meaning is created or modified through social interactions involving symbolic communication with other people. Hence, the perception and interpretation of an object is not always the same for all people.

  2. Tier I cities have a population of 100,000 or more, and Tier II cities have a population from 50,000 to 99,999.

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Acknowledgements

The authors are grateful to the International Institute for Population Sciences (IIPS), Harvard T. H. Chan School of Public Health (HSPH), and the University of Southern California (USC) for providing access to the data for this research. The data themselves are available for public access through IIPS LASI Study (https://www.iipsindia.ac.in/lasi/). The authors also acknowledge Ministry of Health and Family Welfare (MoHFW), National Institute on Aging (NIA/NIH), USA, and United Nations Population Fund, India, for financially supporting the survey. We would also like to thank Todd Manza for extensive copyediting support. We are grateful to IIPS, Mumbai for providing access to the published research papers. Further, we show our gratitude to the Editorial board of the journal and the reviewers as their comments improved the paper significantly.

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Correspondence to Palak Sharma.

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Appendix

Appendix

Tables

Table 5 Characteristics of the study population (n = 30,427)

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Table 6 Multivariate Nonlinear Decomposition for Gender Gap in the Prevalence of Abuse Among Indian Older Adults

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Maurya, P., Chattopadhyay, A., Rao, S. et al. Understanding Elder Abuse in India: Contributing Factors and Policy Suggestions. Population Ageing 17, 5–32 (2024). https://doi.org/10.1007/s12062-022-09399-x

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  • DOI: https://doi.org/10.1007/s12062-022-09399-x

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