Change in treatment burden among people with multimorbidity: Protocol of a follow up survey and development of efficient measurement tools for primary care.
Journal
PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081
Informations de publication
Date de publication:
2021
2021
Historique:
received:
14
06
2021
accepted:
04
11
2021
entrez:
29
11
2021
pubmed:
30
11
2021
medline:
8
1
2022
Statut:
epublish
Résumé
Treatment burden is the effort required of patients to look after their health and the impact this has on their functioning and wellbeing. It is likely treatment burden changes over time as circumstances change for patients and health services. However, there are a lack of population-level studies of treatment burden change and factors associated with this change over time. Furthermore, there are currently no practical screening tools for treatment burden in time-pressured clinical settings or at population level. This is a three-year follow-up of a cross-sectional survey of 723 people with multimorbidity (defined as three or more long-term conditions; LTCs) registered at GP practices in in Dorset, England. The survey will repeat collection of information on treatment burden (using the 10-item Multimorbidity Treatment Burden Questionnaire (MTBQ) and a novel single-item screening tool), sociodemographics, medications, LTCs, health literacy and financial resource, as at baseline. Descriptive statistics will be used to compare change in treatment burden since the baseline survey in 2019 and associations of treatment burden change will be assessed using regression methods. Diagnostic test accuracy metrics will be used to evaluate the single-item treatment burden screening tool using the MTBQ as the gold-standard. Routine primary care data (including demographics, medications, LTCs, and healthcare usage data) will be extracted from medical records for consenting participants. A forward-stepwise, likelihood-ratio logistic regression model building approach will be employed in order to assess the utility of routine data metrics in quantifying treatment burden in comparison to self-reported treatment burden using the MTBQ. To the authors' knowledge, this will be the first study investigating longitudinal aspects of treatment burden. Findings will improve understanding of the extent to which treatment burden changes over time for people with multimorbidity and factors contributing to this change, as well as allowing better identification of people at risk of high treatment burden.
Sections du résumé
BACKGROUND
Treatment burden is the effort required of patients to look after their health and the impact this has on their functioning and wellbeing. It is likely treatment burden changes over time as circumstances change for patients and health services. However, there are a lack of population-level studies of treatment burden change and factors associated with this change over time. Furthermore, there are currently no practical screening tools for treatment burden in time-pressured clinical settings or at population level.
METHODS AND ANALYSIS
This is a three-year follow-up of a cross-sectional survey of 723 people with multimorbidity (defined as three or more long-term conditions; LTCs) registered at GP practices in in Dorset, England. The survey will repeat collection of information on treatment burden (using the 10-item Multimorbidity Treatment Burden Questionnaire (MTBQ) and a novel single-item screening tool), sociodemographics, medications, LTCs, health literacy and financial resource, as at baseline. Descriptive statistics will be used to compare change in treatment burden since the baseline survey in 2019 and associations of treatment burden change will be assessed using regression methods. Diagnostic test accuracy metrics will be used to evaluate the single-item treatment burden screening tool using the MTBQ as the gold-standard. Routine primary care data (including demographics, medications, LTCs, and healthcare usage data) will be extracted from medical records for consenting participants. A forward-stepwise, likelihood-ratio logistic regression model building approach will be employed in order to assess the utility of routine data metrics in quantifying treatment burden in comparison to self-reported treatment burden using the MTBQ.
IMPACT
To the authors' knowledge, this will be the first study investigating longitudinal aspects of treatment burden. Findings will improve understanding of the extent to which treatment burden changes over time for people with multimorbidity and factors contributing to this change, as well as allowing better identification of people at risk of high treatment burden.
Identifiants
pubmed: 34843541
doi: 10.1371/journal.pone.0260228
pii: PONE-D-21-19089
pmc: PMC8629211
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e0260228Subventions
Organisme : Department of Health
Pays : United Kingdom
Déclaration de conflit d'intérêts
The authors have declared that no competing interests exist.
Références
BMJ. 2009 Aug 11;339:b2803
pubmed: 19671932
BMC Health Serv Res. 2014 Jun 26;14:281
pubmed: 24969758
Med Care. 2014 Mar;52 Suppl 3:S118-25
pubmed: 24561750
Age Ageing. 2013 Jan;42(1):62-9
pubmed: 22910303
BMC Med Res Methodol. 2017 Dec 6;17(1):162
pubmed: 29207961
Qual Life Res. 2017 Feb;26(2):489-503
pubmed: 27566732
BMJ Open. 2018 Apr 12;8(4):e019413
pubmed: 29654011
Eur Respir J. 2010 Apr;35(4):787-94
pubmed: 19797134
JAMA. 2007 Jan 10;297(2):177-86
pubmed: 17213401
PLoS One. 2015 May 29;10(5):e0125457
pubmed: 26024379
J Gen Intern Med. 2005 May;20(5):479-82
pubmed: 15963177
PLoS One. 2013 Dec 20;8(12):e81852
pubmed: 24376504
Patient Relat Outcome Meas. 2013 Jun 05;4:7-20
pubmed: 23833553
BMJ Open. 2014 Jul 11;4(7):e004694
pubmed: 25015470
PLoS One. 2014 Mar 27;9(3):e93288
pubmed: 24676421
Patient Relat Outcome Meas. 2012;3:39-49
pubmed: 23185121
J Clin Epidemiol. 2008 Nov;61(11):1104-12
pubmed: 18538993
Patient Relat Outcome Meas. 2015 Mar 27;6:117-26
pubmed: 25848328
Lancet. 2012 Jul 7;380(9836):37-43
pubmed: 22579043
BMJ Open. 2020 Aug 13;10(8):e038423
pubmed: 32792448
J R Coll Physicians Edinb. 2015;45(2):114-7
pubmed: 26181525
Patient Relat Outcome Meas. 2017 Nov 09;8:143-156
pubmed: 29184456
J R Coll Physicians Edinb. 2015;45(2):118-22
pubmed: 26181526
Br J Gen Pract. 2021 Apr 29;71(706):e381-e390
pubmed: 33875419
BMJ. 2014 Nov 10;349:g6680
pubmed: 25385748
BMC Med. 2012 Jul 04;10:68
pubmed: 22762722
J Clin Epidemiol. 2012 Oct;65(10):1041-51
pubmed: 22910536
Arch Intern Med. 2006 Sep 25;166(17):1836-41
pubmed: 17000939
Health Expect. 2015 Jun;18(3):312-24
pubmed: 23363080