EMBEDDING ADULT PROTECTIVE SERVICE WORKERS IN A HEALTHCARE SYSTEM TO IDENTIFY AND REPORT ELDER ABUSE

Abstract Background Elder abuse (EA) is common and has devastating health consequences yet is not systematically assessed or documented in most health systems, limiting efforts to target healthcare-based interventions. Our objective was to examine sociodemographic and medical characteristics associated with documented referrals for EA assessment or services in a national US healthcare system. Methods We conducted a national case-control study in US Veterans Health Administration facilities of primary care (PC)-engaged Veterans age ≥60 years who were evaluated by social work (SW) for EA-related concerns between 2010-18. Cases were matched 1:5 to controls with a PC visit within 60 days of the matched case SW encounter. We examined the association of patient sociodemographic and health factors with receipt of EA services in unadjusted and adjusted models. Results Of 5,567,664 Veterans meeting eligibility criteria during the study period, 15,752 (0.3%) received services for EA (cases). Cases were mean age 74, and 54% unmarried. In adjusted logistic regression models (aOR; 95%CI), age ≥85 (3.56 v. age 60-64; 3.24-3.91), female sex (1.96; 1.76-2.21), child as next-of-kin (1.70 v. spouse; 1.57-1.85), lower neighborhood socioeconomic status (1.18 per higher quartile; 1.15-1.21), dementia diagnosis (3.01; 2.77-3.28) and receiving a VA pension (1.34; 1.23-1.46) were associated with receiving EA services. Conclusion In the largest cohort of patients receiving EA-related healthcare services studied to date, this study identified novel factors associated with clinical suspicion of EA that can be used to inform improvements in healthcare-based EA surveillance and detection.

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USING DATA SCIENCE TO GENERATE PSYCHOSOCIAL PROFILES OF FINANCIAL EXPLOITATION IN SENIORS

Innovation in Aging ◽

10.1093/geroni/igz038.1780 ◽

2019 ◽

Vol 3 (Supplement_1) ◽

pp. S478-S478

Author(s):

Cassandra J Enzler ◽

Robert Suchting ◽

Charles Green ◽

Jason Burnett

Keyword(s):

Data Science ◽

Elder Abuse ◽

Public Health Problem ◽

Financial Loss ◽

Protective Services ◽

Protective Service ◽

Financial Exploitation ◽

Significant Public Health ◽

Using Data ◽

Psychosocial Profiles

Abstract Financial exploitation (FE) in older adults is a significant public health problem linked to outcomes including depression, financial ruin and early mortality. This study applied exploratory data science techniques to a multi-year statewide protective services dataset of over 8,000 elder abuse cases. The goal was to derive data-driven psychosocial profiles of abuse with an emphasis on determining which factors, commonly shared across abuse cases, were most important for determining when elder FE was occurring and whether it was occurring alone or in conjunction with other types of abuse. We found that pronounced psychological distress (i.e. verbalizing suicide, homicide, self-harm) was most important for indicating when abuse had occurred and predicted non-FE related abuse. Drug paraphernalia in the home and perpetrator drug/alcohol use were important predictors of FE-related abuse. When differentiating pure FE from hybrid FE, factors indicative of long-term FE occurrence and substantial financial loss were most important (i.e. facing foreclosure, lack of food, medications, and utilities). The findings parallel some existing work characterizing pure and hybrid FE, but also highlight new profile factors that may help determine when FE is occurring and when it is less likely. Applying data science approaches to other large protective service datasets and national datasets such as the National Adult Maltreatment Registry could help improve characterization of abuse types such as pure and hybrid FE resulting in better detection, response and prevention.

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