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J Gerontol B Psychol Sci Soc Sci. 2018 Jun 14;73(5):e49-e58. doi: 10.1093/geronb/gbx023.

Using Latent Class Analysis to Identify Profiles of Elder Abuse Perpetrators.

Author information

1
Stanford Center on Longevity, Stanford University, California.
2
Leonard Davis School of Gerontology, University of Southern California, Los Angeles.
3
Professor Emeritus of Health Policy and Administration School of Public Health, University of Illinois at Chicago.

Abstract

Objectives:

Research suggests that abuser risk factors differ across elder mistreatment types, but abuse interventions are not individualized. To move away from assumptions of perpetrator homogeneity and to inform intervention approaches, this study classifies abusers into subtypes according to their behavior profiles.

Method:

Data are from the Older Adult Mistreatment Assessment administered to victims by Adult Protective Service (APS) in Illinois. Latent class analysis was used to categorize abusers (N = 336) using victim and caseworker reports on abusers' harmful and supportive behaviors and characteristics. Multinomial logistic regression was then used to determine which abuser profiles are associated with 4 types of mistreatment-neglect, physical, emotional, and financial-and other sociodemographic characteristics.

Results:

Abusers fall into 4 profiles descriptively labeled "Caregiver," "Temperamental," "Dependent Caregiver," and "Dangerous." Dangerous abusers have the highest levels of aggression, financial dependency, substance abuse, and irresponsibility. Caregivers are lowest in harmful characteristics and highest in providing emotional and instrumental support to victims. The 4 profiles significantly differ in the average age and gender of the abuser, the relationship to victims, and types of mistreatment committed.

Discussion:

This is the first quantitative study to identify and characterize abuser subtypes. Tailored interventions are needed to reduce problem behaviors and enhance strengths specific to each abuser profile.

PMID:
28329841
PMCID:
PMC6283306
[Available on 2019-06-14]
DOI:
10.1093/geronb/gbx023

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