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Patterns of Healthcare Resource Utilisation of Critical Care Survivors between 2006 and 2017 in Wales: A Population-Based Study

Mohammad Al Sallakh Orcid Logo, Laura Tan Orcid Logo, Richard Pugh Orcid Logo, Ashley Akbari Orcid Logo, Rowena Bailey, Rowena Griffiths, Ronan Lyons Orcid Logo, Tamas Szakmany Orcid Logo

Journal of Clinical Medicine, Volume: 12, Issue: 3, Start page: 872

Swansea University Authors: Mohammad Al Sallakh Orcid Logo, Ashley Akbari Orcid Logo, Rowena Bailey, Rowena Griffiths, Ronan Lyons Orcid Logo

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DOI (Published version): 10.3390/jcm12030872

Abstract

In this retrospective cohort study, we used the Secure Anonymised Information Linkage (SAIL) Databank to characterise and identify predictors of the one-year post-discharge healthcare resource utilisation (HRU) of adults who were admitted to critical care units in Wales between 1 April 2006 and 31 D...

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Published in: Journal of Clinical Medicine
ISSN: 2077-0383
Published: MDPI AG 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa62437
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We modelled one-year post-critical-care HRU using negative binomial models and used linear models for the difference from one-year pre-critical-care HRU. We estimated the association between critical illness and post-hospitalisation HRU using multilevel negative binomial models among people hospitalised in 2015. We studied 55,151 patients. Post-critical-care HRU was 11&#x2013;87% greater than pre-critical-care levels, whereas emergency department (ED) attendances decreased by 30%. Age &#x2265;50 years was generally associated with greater post-critical-care HRU; those over 80 had three times longer hospital readmissions than those younger than 50 (incidence rate ratio (IRR): 2.96, 95% CI: 2.84, 3.09). However, ED attendances were higher in those younger than 50. High comorbidity was associated with 22&#x2013;62% greater post-critical-care HRU than no or low comorbidity. The most socioeconomically deprived quintile was associated with 24% more ED attendances (IRR: 1.24 [1.16, 1.32]) and 13% longer hospital stays (IRR: 1.13 [1.09, 1.17]) than the least deprived quintile. Critical care survivors had greater 1-year post-discharge HRU than non-critical inpatients, including 68% longer hospital stays (IRR: 1.68 [1.63, 1.74]). Critical care survivors, particularly those with older ages, high comorbidity, and socioeconomic deprivation, used significantly more primary and secondary care resources after discharge compared with their baseline and non-critical inpatients. Interventions are needed to ensure that key subgroups are identified and adequately supported.</abstract><type>Journal Article</type><journal>Journal of Clinical Medicine</journal><volume>12</volume><journalNumber>3</journalNumber><paginationStart>872</paginationStart><paginationEnd/><publisher>MDPI AG</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint/><issnElectronic>2077-0383</issnElectronic><keywords>critical care survivorship; healthcare resource utilisation; Wales</keywords><publishedDay>21</publishedDay><publishedMonth>1</publishedMonth><publishedYear>2023</publishedYear><publishedDate>2023-01-21</publishedDate><doi>10.3390/jcm12030872</doi><url/><notes>Data Availability Statement:The anonymised person-level data supporting the conclusions of this article are held by the SAIL Databank (https://saildatabank.com/ [accessed on 20 January 2023]) and are restricted and not publicly available but can be accessed upon reasonable request, with permission from SAIL. All proposals to use SAIL are carefully reviewed by an independent information governance review panel (IGRP) that includes members of the public to ensure the proper and appropriate use of data (https://www.saildatabank.com/application-process [accessed on 20 January 2023]). When approved, access is then provided through the SAIL Gateway, a privacy-protecting safe haven and a secure remote access system.</notes><college>COLLEGE NANME</college><department>Health Data Science</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>HDAT</DepartmentCode><institution>Swansea University</institution><apcterm/><funders>This study was enabled by a Pathway to Portfolio Development scheme fund from Health and Care Research Wales and supported by Health Data Research UK, which receives its funding from HDR UK Ltd. (HDR-9006), which is funded by the UK Medical Research Council, the Engineering and Physical Sciences Research Council, the Economic and Social Research Council, the Department of Health and Social Care (England), the Chief Scientist Office of the Scottish Government Health and Social Care Directorates, the Health and Social Care Research and Development Division (Welsh Government), the Public Health Agency (Northern Ireland), the British Heart Foundation, and the Wellcome Trust.</funders><projectreference/><lastEdited>2023-02-13T10:03:50.7110473</lastEdited><Created>2023-01-24T20:27:25.2585673</Created><path><level id="1">Faculty of Medicine, Health and Life Sciences</level><level id="2">Swansea University Medical School - Health Data Science</level></path><authors><author><firstname>Mohammad</firstname><surname>Al Sallakh</surname><orcid>0000-0002-8333-7279</orcid><order>1</order></author><author><firstname>Laura</firstname><surname>Tan</surname><orcid>0000-0003-1921-3260</orcid><order>2</order></author><author><firstname>Richard</firstname><surname>Pugh</surname><orcid>0000-0002-2848-4444</orcid><order>3</order></author><author><firstname>Ashley</firstname><surname>Akbari</surname><orcid>0000-0003-0814-0801</orcid><order>4</order></author><author><firstname>Rowena</firstname><surname>Bailey</surname><order>5</order></author><author><firstname>Rowena</firstname><surname>Griffiths</surname><order>6</order></author><author><firstname>Ronan</firstname><surname>Lyons</surname><orcid>0000-0001-5225-000X</orcid><order>7</order></author><author><firstname>Tamas</firstname><surname>Szakmany</surname><orcid>0000-0003-3632-8844</orcid><order>8</order></author></authors><documents><document><filename>62437__26571__85d9adc2baf949579bd52b86bc740624.pdf</filename><originalFilename>62437_VoR.pdf</originalFilename><uploaded>2023-02-13T10:02:47.9167352</uploaded><type>Output</type><contentLength>619371</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>&#xA9; 2023 by the authors. 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spelling 2023-02-13T10:03:50.7110473 v2 62437 2023-01-24 Patterns of Healthcare Resource Utilisation of Critical Care Survivors between 2006 and 2017 in Wales: A Population-Based Study 6efc53139ba1416418a6c6e584a25f2d 0000-0002-8333-7279 Mohammad Al Sallakh Mohammad Al Sallakh true false aa1b025ec0243f708bb5eb0a93d6fb52 0000-0003-0814-0801 Ashley Akbari Ashley Akbari true false 455e2c1e6193448f6269b9e72acaf865 Rowena Bailey Rowena Bailey true false 381464f639f98bd388c29326ca7f862c Rowena Griffiths Rowena Griffiths true false 83efcf2a9dfcf8b55586999d3d152ac6 0000-0001-5225-000X Ronan Lyons Ronan Lyons true false 2023-01-24 HDAT In this retrospective cohort study, we used the Secure Anonymised Information Linkage (SAIL) Databank to characterise and identify predictors of the one-year post-discharge healthcare resource utilisation (HRU) of adults who were admitted to critical care units in Wales between 1 April 2006 and 31 December 2017. We modelled one-year post-critical-care HRU using negative binomial models and used linear models for the difference from one-year pre-critical-care HRU. We estimated the association between critical illness and post-hospitalisation HRU using multilevel negative binomial models among people hospitalised in 2015. We studied 55,151 patients. Post-critical-care HRU was 11–87% greater than pre-critical-care levels, whereas emergency department (ED) attendances decreased by 30%. Age ≥50 years was generally associated with greater post-critical-care HRU; those over 80 had three times longer hospital readmissions than those younger than 50 (incidence rate ratio (IRR): 2.96, 95% CI: 2.84, 3.09). However, ED attendances were higher in those younger than 50. High comorbidity was associated with 22–62% greater post-critical-care HRU than no or low comorbidity. The most socioeconomically deprived quintile was associated with 24% more ED attendances (IRR: 1.24 [1.16, 1.32]) and 13% longer hospital stays (IRR: 1.13 [1.09, 1.17]) than the least deprived quintile. Critical care survivors had greater 1-year post-discharge HRU than non-critical inpatients, including 68% longer hospital stays (IRR: 1.68 [1.63, 1.74]). Critical care survivors, particularly those with older ages, high comorbidity, and socioeconomic deprivation, used significantly more primary and secondary care resources after discharge compared with their baseline and non-critical inpatients. Interventions are needed to ensure that key subgroups are identified and adequately supported. Journal Article Journal of Clinical Medicine 12 3 872 MDPI AG 2077-0383 critical care survivorship; healthcare resource utilisation; Wales 21 1 2023 2023-01-21 10.3390/jcm12030872 Data Availability Statement:The anonymised person-level data supporting the conclusions of this article are held by the SAIL Databank (https://saildatabank.com/ [accessed on 20 January 2023]) and are restricted and not publicly available but can be accessed upon reasonable request, with permission from SAIL. All proposals to use SAIL are carefully reviewed by an independent information governance review panel (IGRP) that includes members of the public to ensure the proper and appropriate use of data (https://www.saildatabank.com/application-process [accessed on 20 January 2023]). When approved, access is then provided through the SAIL Gateway, a privacy-protecting safe haven and a secure remote access system. COLLEGE NANME Health Data Science COLLEGE CODE HDAT Swansea University This study was enabled by a Pathway to Portfolio Development scheme fund from Health and Care Research Wales and supported by Health Data Research UK, which receives its funding from HDR UK Ltd. (HDR-9006), which is funded by the UK Medical Research Council, the Engineering and Physical Sciences Research Council, the Economic and Social Research Council, the Department of Health and Social Care (England), the Chief Scientist Office of the Scottish Government Health and Social Care Directorates, the Health and Social Care Research and Development Division (Welsh Government), the Public Health Agency (Northern Ireland), the British Heart Foundation, and the Wellcome Trust. 2023-02-13T10:03:50.7110473 2023-01-24T20:27:25.2585673 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Health Data Science Mohammad Al Sallakh 0000-0002-8333-7279 1 Laura Tan 0000-0003-1921-3260 2 Richard Pugh 0000-0002-2848-4444 3 Ashley Akbari 0000-0003-0814-0801 4 Rowena Bailey 5 Rowena Griffiths 6 Ronan Lyons 0000-0001-5225-000X 7 Tamas Szakmany 0000-0003-3632-8844 8 62437__26571__85d9adc2baf949579bd52b86bc740624.pdf 62437_VoR.pdf 2023-02-13T10:02:47.9167352 Output 619371 application/pdf Version of Record true © 2023 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license true eng https://creativecommons.org/licenses/by/4.0/
title Patterns of Healthcare Resource Utilisation of Critical Care Survivors between 2006 and 2017 in Wales: A Population-Based Study
spellingShingle Patterns of Healthcare Resource Utilisation of Critical Care Survivors between 2006 and 2017 in Wales: A Population-Based Study
Mohammad Al Sallakh
Ashley Akbari
Rowena Bailey
Rowena Griffiths
Ronan Lyons
title_short Patterns of Healthcare Resource Utilisation of Critical Care Survivors between 2006 and 2017 in Wales: A Population-Based Study
title_full Patterns of Healthcare Resource Utilisation of Critical Care Survivors between 2006 and 2017 in Wales: A Population-Based Study
title_fullStr Patterns of Healthcare Resource Utilisation of Critical Care Survivors between 2006 and 2017 in Wales: A Population-Based Study
title_full_unstemmed Patterns of Healthcare Resource Utilisation of Critical Care Survivors between 2006 and 2017 in Wales: A Population-Based Study
title_sort Patterns of Healthcare Resource Utilisation of Critical Care Survivors between 2006 and 2017 in Wales: A Population-Based Study
author_id_str_mv 6efc53139ba1416418a6c6e584a25f2d
aa1b025ec0243f708bb5eb0a93d6fb52
455e2c1e6193448f6269b9e72acaf865
381464f639f98bd388c29326ca7f862c
83efcf2a9dfcf8b55586999d3d152ac6
author_id_fullname_str_mv 6efc53139ba1416418a6c6e584a25f2d_***_Mohammad Al Sallakh
aa1b025ec0243f708bb5eb0a93d6fb52_***_Ashley Akbari
455e2c1e6193448f6269b9e72acaf865_***_Rowena Bailey
381464f639f98bd388c29326ca7f862c_***_Rowena Griffiths
83efcf2a9dfcf8b55586999d3d152ac6_***_Ronan Lyons
author Mohammad Al Sallakh
Ashley Akbari
Rowena Bailey
Rowena Griffiths
Ronan Lyons
author2 Mohammad Al Sallakh
Laura Tan
Richard Pugh
Ashley Akbari
Rowena Bailey
Rowena Griffiths
Ronan Lyons
Tamas Szakmany
format Journal article
container_title Journal of Clinical Medicine
container_volume 12
container_issue 3
container_start_page 872
publishDate 2023
institution Swansea University
issn 2077-0383
doi_str_mv 10.3390/jcm12030872
publisher MDPI AG
college_str Faculty of Medicine, Health and Life Sciences
hierarchytype
hierarchy_top_id facultyofmedicinehealthandlifesciences
hierarchy_top_title Faculty of Medicine, Health and Life Sciences
hierarchy_parent_id facultyofmedicinehealthandlifesciences
hierarchy_parent_title Faculty of Medicine, Health and Life Sciences
department_str Swansea University Medical School - Health Data Science{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Health Data Science
document_store_str 1
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description In this retrospective cohort study, we used the Secure Anonymised Information Linkage (SAIL) Databank to characterise and identify predictors of the one-year post-discharge healthcare resource utilisation (HRU) of adults who were admitted to critical care units in Wales between 1 April 2006 and 31 December 2017. We modelled one-year post-critical-care HRU using negative binomial models and used linear models for the difference from one-year pre-critical-care HRU. We estimated the association between critical illness and post-hospitalisation HRU using multilevel negative binomial models among people hospitalised in 2015. We studied 55,151 patients. Post-critical-care HRU was 11–87% greater than pre-critical-care levels, whereas emergency department (ED) attendances decreased by 30%. Age ≥50 years was generally associated with greater post-critical-care HRU; those over 80 had three times longer hospital readmissions than those younger than 50 (incidence rate ratio (IRR): 2.96, 95% CI: 2.84, 3.09). However, ED attendances were higher in those younger than 50. High comorbidity was associated with 22–62% greater post-critical-care HRU than no or low comorbidity. The most socioeconomically deprived quintile was associated with 24% more ED attendances (IRR: 1.24 [1.16, 1.32]) and 13% longer hospital stays (IRR: 1.13 [1.09, 1.17]) than the least deprived quintile. Critical care survivors had greater 1-year post-discharge HRU than non-critical inpatients, including 68% longer hospital stays (IRR: 1.68 [1.63, 1.74]). Critical care survivors, particularly those with older ages, high comorbidity, and socioeconomic deprivation, used significantly more primary and secondary care resources after discharge compared with their baseline and non-critical inpatients. Interventions are needed to ensure that key subgroups are identified and adequately supported.
published_date 2023-01-21T04:22:03Z
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