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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

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...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7925867/
https://www.ncbi.nlm.nih.gov/pubmed/33550264
http://dx.doi.org/10.1136/bmjopen-2020-044768
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author 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
author_facet 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
author_sort Rosen, Tony
collection PubMed
description 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.
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spelling pubmed-79258672021-03-19 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 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 BMJ Open Geriatric Medicine 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. BMJ Publishing Group 2021-02-05 /pmc/articles/PMC7925867/ /pubmed/33550264 http://dx.doi.org/10.1136/bmjopen-2020-044768 Text en © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. http://creativecommons.org/licenses/by-nc/4.0/ http://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.
spellingShingle Geriatric Medicine
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
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
title 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
title_full 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
title_fullStr 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
title_full_unstemmed 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
title_short 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
title_sort 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
topic Geriatric Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7925867/
https://www.ncbi.nlm.nih.gov/pubmed/33550264
http://dx.doi.org/10.1136/bmjopen-2020-044768
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