Identifying patterns of health care utilisation among physical elder abuse victims using Medicare data and legally adjudicated cases: protocol for case–control study using data linkage and machine learning. Issue 2 (5th February 2021)

• Record Type: Journal Article
• Authors: Rosen, Tony; Bao, Yuhua; Zhang, Yiye; Clark, Sunday; Wen, Katherine; Elman, Alyssa; Jeng, Philip; Bloemen, Elizabeth; Lindberg, Daniel; Krugman, Richard; Campbell, Jacquelyn; Bachman, Ronet; Fulmer, Terry; Pillemer, Karl; Lachs, Mark
• Is Part Of: BMJ open. Volume 11:Issue 2(2021)
• Publication Date: 2021-02-05
• Subjects: geriatric medicine -- health economics -- protocols & guidelines; Medicine -- Research -- Periodicals; 610.72
• DOI: 10.1136/bmjopen-2020-044768
• Languages: English
• ISSNs: 2044-6055

Abstract

Abstract : Introduction: Physical elder abuse is common and has serious health consequences but is under-recognised and under-reported. As assessment by healthcare providers may represent the only contact outside family for many older adults, clinicians have a unique opportunity to identify suspected abuse and initiate intervention. Preliminary research suggests elder abuse victims may have different patterns of healthcare utilisation than other older adults, with increased rates of emergency department use, hospitalisation and nursing home placement. Little is known, however, about the patterns of this increased utilisation and associated costs. To help fill this gap, we describe here the protocol for a study exploring patterns of healthcare utilisation and associated costs for known physical elder abuse victims compared with non-victims. Methods and analysis: We hypothesise that various aspects of healthcare utilisation are differentially affected by physical elder abuse victimisation, increasing ED/hospital utilisation and reducing outpatient/primary care utilisation. We will obtain Medicare claims data for a series of well-characterised, legally adjudicated cases of physical elder abuse to examine victims' healthcare utilisation before and after the date of abuse detection. We will also compare these physical elder abuse victims to a matched comparison group of non-victimised older adults using Medicare claims. We will use machine learning approaches to extend our ability to identify patterns suggestive of potential physical elder abuse exposure. Describing unique patterns and associated costs of healthcare utilisation among elder abuse victims may improve the ability of healthcare providers to identify and, ultimately, intervene and prevent victimisation. Ethics and dissemination: This project has been reviewed and approved by the Weill Cornell Medicine Institutional Review Board, protocol #1807019417, with initial approval on 1 August 2018. We aim to disseminate our results in peer-reviewed journals at national and international conferences and among interested patient groups and the public.