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Medical and Social Factors Associated With Referral for Elder Abuse Services in a National Health Care System.

Makaroun LK, Thorpe CT, Mor MK, Zhang H, Lovelace E, Rosen T, Dichter ME, Rosland AM. Medical and Social Factors Associated With Referral for Elder Abuse Services in a National Health Care System. The journals of gerontology. Series A, Biological sciences and medical sciences. 2022 Aug 12; 77(8):1706-1714.

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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 health care-based interventions. Our objective was to examine sociodemographic and medical characteristics associated with documented referrals for EA assessment or services in a national U.S. health care system. METHODS: We conducted a national case-control study in U.S. 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 and 2018. 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 (adjusted odds ratio; 95% confidence interval), age 85 (3.56 vs age 60-64; 3.24-3.91), female sex (1.96; 1.76-2.21), child as next-of-kin (1.70 vs 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 health care services studied to date, this study identified novel factors associated with clinical suspicion of EA that can be used to inform improvements in health care-based EA surveillance and detection.

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