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Abstract

Background Treatment burden represents the work patients undertake because of their healthcare, and the impact of that effort on the patient. Most research has focused on older adults (65+) with multiple long-term conditions (MLTC-M) but there are more younger adults (18-65) living with MLTC-M and they may experience treatment burden differently. Understanding experiences of treatment burden, and identifying those most at risk of high treatment burden, are important for designing primary care services to meet their needs. Aim To understand the treatment burden associated with MLTC-M, for people aged 18–65 years, and how primary care services affect this burden. Design & setting Mixed-methods study in 20–33 primary care practices in two UK regions. Method i. In-depth qualitative interviews with adults living with MLTC-M (approximately 40 participants) to understand their experiences of treatment burden and the impact of primary care; with a think-aloud aspect to explore face validity of a novel short treatment burden questionnaire for routine clinical use (STBQ) in the initial 15 interviews. ii. Cross-sectional patient survey (approximately 1,000 participants) with linked routine medical record data to examine the factors associated with treatment burden for people living with MLTC-M, and to test the validity of STBQ. Conclusion This study will generate in-depth understanding of the treatment burden experienced by people aged 18–65 years living with MLTC-M, and how primary care services affect this burden. This will inform further development and testing of interventions to reduce treatment burden, and potentially influence MLTC-M trajectories and improve health outcomes.
BJGP OPEN
Treatment burden in multiple long-term conditions: a
mixed-methods study protocol
Johnson, Rachel; Kovalenko, Anastasiia G; Blakeman, Thomas; Panagioti,
Maria; Lawton, Michael; Dawson, Shoba; Duncan, Polly; Fraser, Simon D;
Valderas, Jose; Chilcott, Simon; Goulding, Rebecca; Salisbury, Chris
DOI: https://doi.org/10.3399/BJGPO.2023.0097
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Received 02 June 2023
Accepted 05 June 2023
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Treatment burden in Multiple Long-Term Conditions: a mixed-methods study protocol
Authors:
Rachel Johnson, BM BCh PhD
https://orcid.org/0000-0266-3433
Bristol Medical School, University of Bristol, UK
Anastasiia G Kovalenko, BA, MSc, Doctoral Research Fellow*
https://orcid.org/0000-0002-4299-3587
Bristol Medical School, University of Bristol, UK
Tom Blakeman, PhD, MRCGP
https://orcid.org/0000-0003-3298-8423
Division of Population Health, Health Services Research and Primary Care, The University of
Manchester, UK
Maria Panagioti, BA, MSc, PhD
https://orcid.org/0000-0002-7153-5745
Division of Population Health, Health Services Research and Primary Care, The University of
Manchester, UK
Michael Lawton, PhD
https://orcid.org/0000-0002-3419-0354
Bristol Medical School, University of Bristol, UK
Shoba Dawson, BA, MSc, PhD
https://orcid.org/0000-0002-6700-6445
Bristol Medical School, University of Bristol, UK
Polly Duncan, MPH, MRCGP, NIHR Doctoral Research Fellow
https://orcid.org/0000-0002-2244-3254
Bristol Medical School, University of Bristol, UK
Simon DS Fraser, BM MSc DM FFPH MRCGP
http://orcid.org/0000-0002-4172-4406
School of Primary Care, Population Science and Medical Education, Faculty of Medicine, University of
Southampton, UK
Jose Valderas, MPH PhD
https://orcid.org/0000-0002-9299-1555
Centre for Research in Health Systems Performance (CRiHSP) and Division of Family Medicine,
Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore
Simon Chilcott, public contributor
Rebecca Goulding, PhD
https://orcid.org/0000-0003-0716-5126
Division of Population Health, Health Services Research and Primary Care; The University of
Manchester, UK
Chris Salisbury, MD, FRCGP
https://orcid.org/0000-0002-4378-3960
Bristol Medical School, University of Bristol, UK
* Corresponding author: Anastasiia G Kovalenko
Email: a.g.kovalenko@bristol.ac.uk
Funding: This study is funded by the NIHR School for Primary Care Research Grant Reference
Number 564
Ethical approval: This study has received favourable ethical opinion from Health Research Authority
and Health and Care Research Wales (IRAS 311163)
Competing interests: Prof Chris Salisbury and Dr Polly Duncan developed and validated the original
Multimorbidity Treatment Burden Questionnaire.
Dr Michael Lawton received fees for advising on a secondary analysis of an RCT sponsored by North
Bristol NHS trust.
Acknowledgements: We would like to acknowledge the PPI group for their input in designing the
study.
1
Abstract
Background: Treatment burden represents the work patients undertake because of their healthcare,
and the impact of that effort on the patient. Most research has focused on older adults (65+) with
multiple long-term conditions (MLTC-M) but there are more younger adults (18-65) living with
MLTC-M and they may experience treatment burden differently. Understanding experiences of
treatment burden, and identifying those most at risk of high treatment burden, are important for
designing primary care services to meet their needs.
Aim: To understand the treatment burden associated with MLTC-M, for people aged 18-65 years,
and how primary care services affect this burden.
Design & setting: Mixed-methods study in 20- 33 primary care practices in two UK regions.
Method: i. In-depth qualitative interviews with adults living with MLTC-M (approximately 40
participants) to understand their experiences of treatment burden and the impact of primary care;
with a think-aloud aspect to explore face validity of a novel short treatment burden questionnaire
for routine clinical use (STBQ) in the initial 15 interviews. ii. Cross-sectional patient survey
(approximately 1000 participants) with linked routine medical record data to examine the factors
associated with treatment burden for people living with MLTC-M, and to test the validity of STBQ.
Conclusion: This study will generate in-depth understanding of the treatment burden experienced by
people aged 18-65 years living with MLTC-M, and how primary care services affect this burden. This
will inform further development and testing of interventions to reduce treatment burden, and
potentially influence MLTC-M trajectories and improve health outcomes.
Introduction
Multiple long-term conditions (multimorbidity or MLTC-M, defined as the existence of two or more
long-term conditions) affects approximately 1 in 4 of the UK population, is associated with reduced
quality of life and increased hospital admissions [1, 2] and accounts for over half of the costs of
primary and secondary care [3]. MLTC-M is more prevalent, and occurs at a younger age, in more
deprived areas, contributing to health inequalities [3-5]. MLTC-M disproportionately affects those
living in areas of socio-economic deprivation and minority ethnic groups [6]. Most research on
MLTC-M has included patients aged over 65 years, however, almost a third of people with four or
more conditions are under this age [7].
Treatment burden represents the work that patients undertake because of their healthcare, and the
impact of that effort on patients [8, 9]. Younger populations may experience different challenges
that affect their treatment burden. Interventions focused on younger populations have the
potential, through addressing treatment burden at an earlier stage, to influence trajectories of
MLTC-M [10]
United Kingdom (UK) National Institute for Health and Care Excellence (NICE) MLTC-M guidance for
[11] recognises the need to reduce treatment burden [12]. However, no existing interventions have
shown convincing reduction of treatment burden for people living with MLTC-M [13-15].
Few qualitative studies have investigated the experience of treatment burden for people with MLTC-
M in primary care [16, 17]. A recent systematic review of the impact of interventions on patient-
2
reported burden of treatment included 11 studies, only one of which focussed on people living with
MLTC-M [14]. Available measures of treatment burden are too time consuming to be used in routine
clinical practice to identify patients at risk of being overburdened by the demands of their
healthcare. The multimorbidity treatment burden questionnaire (MTBQ) is a validated measure
developed to capture the effort required to manage MLTC-M [7]. It has been used to evaluate
treatment burden in two UK surveys, largely focused on people in older age groups, and more
affluent / minimally diverse populations [7, 18]. Each of these studies has evaluated the
performance of a different single-item measure alongside the MTBQ; these had limited sensitivity
and positive predictive value [18, 19]. Practical ways of measuring treatment burden in routine
primary care practice would be valuable, enabling identification of people who are more likely to be
over-burdened.
To our knowledge, no studies have explored the impact the organisation of primary care services has
on the treatment burden experienced by people with MLTC-M. This protocol describes a mixed-
methods study. The overarching aim is to understand the treatment burden associated with MLTC-M
for people aged 18-65 years, and how primary care services affect this burden, in order to inform
service design. We will:
1a) explore, in-depth, their experiences of treatment burden and its impact;
1b) explore the face validity of a short treatment burden screening questionnaire (STBQ).
2a) examine the factors associated with treatment burden for adults living with MLTC-M;
2b) test the validity of the STBQ for routine clinical use.
Method
Concurrent mixed-methods study including qualitative and quantitative components, and
stakeholder engagement (Figure 1).
[insert figure 1 here]
Theoretical Framework
The cumulative complexity model [20] is used as a theoretical framework for the study. It describes
the balance between the workload that an individual experiences because of their healthcare, and
the capacity they have to manage that workload.
Definition of multimorbidity
We will use the 20-condition Cambridge multimorbidity score to identify eligible participants [4, 21].
We will develop GP electronic record searches, based on the published code sets to identify people
with two or more of these 20 conditions.
Participants Adults (18-65 years) with two or more long-term conditions.
We will exclude people with dementia, those lacking capacity to consent, people receiving palliative
care, and nursing home or care home residents.
Inclusivity
People from ethnic minority groups are likely to report poorer health outcomes and experiences of
accessing health services than their white-British counterparts [6]. They are often under-represented
in research, limiting the relevance and generalisability of results.
3
We will seek to increase participation of people from ethnic minority groups and socio-economically
disadvantaged communities. All participant materials will be translated and back-translated into
commonly spoken languages in the study areas. Interpreters will be available for interviews.
Qualitative study
Design
Semi-structured qualitative interviews with adults living with MLTC-M exploring objectives 1a,b.
Sampling
We will recruit up to eight primary care practices across two geographical areas. Participants will be
purposively sampled to achieve maximal variation in practice-level deprivation and rurality; patient
age, gender, ethnicity, employment status, being a carer, and type of MLTC. Invitations will be sent
to eligible patients identified by electronic record searches in participating practices. Interested
people will contact the study team to arrange an interview in-person, by telephone or videocall.
Fully informed consent will be taken at the time of the interview (written or audio-recorded).
Data collection
Topic guides have been developed and piloted with input from the PPI (patient and public
involvement) group. In-depth interviews will focus on patients’ experiences of MLTC-M burden and
their capacity to manage the workload. We will explore how different health conditions interact how
the experience of burden changes with time and circumstances; how patients navigate primary care
services, and the impact of health services on MLTC-M burden and capacity. Interviews will be
audio-recorded, professionally transcribed, anonymised, and managed in NVivo 12.
Up to 15 initial interviews will explore participants’ thoughts about the STBQ. Participants will be
asked to think aloud [22] as they complete the measure, including commenting on the layout and
wording, and discussing the reasoning behind their questionnaire responses. These interviews will
be carried out in blocks of 3-5. At the end of each block, the data will be reviewed and the
questionnaire modified.
Participating patients will be offered a £25 shopping voucher.
Analysis
Analysis will involve two stages.
Objective 1a,b. Data analysis will be thematic [23], conducted by the interviewers, members of the
research team and up to two public contributors from the PPI group. Analysis will begin with line-by-
line coding, followed by discussion to agree the coding frame. Transcripts will be coded by one
researcher, and a randomly chosen sample will be reviewed independently by a second researcher.
The researchers will initially identify themes, which will then be discussed with other members of
the research team. Analysis will continue alongside data collection, allowing the topic guides to be
modified to respond to findings. Up to two members of the PPI group will be invited to contribute to
the analysis by (i) being involved in a facilitated discussion in which codes are developed and
researcher interpretations of the data checked; (ii) using selected extracts from transcripts to sense-
check, refine and expand themes.
4
Objective 1b. Framework analysis [24] will be conducted to analyse the think-aloud interviews. The
researchers will summarise the data within a framework matrix, based on the different aspects of
the questionnaire. The final version of the STBQ will be used in the survey.
Sample size and participant recruitment will be determined based on the concept of information
power [25]. Our analysis is informed by established theory, interviews will be focused on the
research questions, and we anticipate participants will have rich experiences relevant to the
research question. These factors will increase the information power of our sample. Sufficient
information power will be achieved when the sample is deemed to have addressed the study’s
research questions (we estimate 30-40 interviews).
Quantitative study
Design
Cross-sectional patient survey and analysis of linked routinely collected GP record data, addressing
objectives 2a, b.
Sampling
We will sample up to 25 primary care practices across two geographical areas in England, aiming to
recruit 50% of practices from Index of Multiple Deprivation (IMD) deciles 1-5 [with one being most
deprived and 10 least deprived] and at least 6 practices in deciles 1-3. Practices will run electronic
searches to identify eligible patients, and will invite a random sample of up to 500. Sample size
calculation is presented in Table 1.
[insert table 1 here]
Data collection
i. Survey
The measures included in the survey are described in Table 2.
[insert table 2 here]
ii. Medical records
Survey respondents will be asked to consent to access to their medical records. For consenting
participants, the following data will be collected and linked to the survey data for analysis: age, sex,
individual-level deprivation, number and type of long-term health conditions, number of prescribed
medications, and number and type of consultations in general practice. In addition, we will collect
anonymised data on the age, sex, deprivation level and number of long-term conditions of all
patients invited to complete the survey, facilitating comparison with the respondent sample.
Data management and analysis.
Data will be managed in a REDCap database and analysed using Stata 17. Unclear questionnaire data
will be treated as missing. Descriptive analyses will report MTBQ, PROMIS 10 and PCPCM by the
other variables of interest.
Regression analyses: We will investigate the association of MTBQ scores with the variables of
interest. We will explore three types of regression models: logistic regression where MTBQ scores
are dichotomised into those with and without high burden; ordinal logistic regression where the
MTBQ score is categorised into different levels of burden, and linear regression with the global
5
MTBQ scores as a continuous measure. Initially we will assess each of the variables of interest in
univariate models and will then build multivariable models using stepwise methods. Multicollinearity
will be assessed using variance inflation factors and we will consider non-linear associations for
numeric variables. Depending on missing data we will carry out complete case analyses and also
imputed analyses. The results from the linear regression will be presented as primary results with
the rest as a sensitivity analyses dependent on testing the assumptions in the regression models.
More details are available in Supplementary Material 1.
STBQ validation: Different versions of the STBQ will be explored through inter-item correlations,
internal consistency (Cronbach’s alpha), and comparison of the association between the STBQ with
high treatment burden as measured by the MTBQ using the receiver operator characteristic curve
and diagnostic parameters: sensitivity, specificity, positive and negative predictive values. We aim to
achieve the shortest possible questionnaire that has a high sensitivity.
Stakeholder engagement
We will hold three workshops in the two study sites. An early workshop will engage a diverse group
of people living with MLTC-M in discussions about the content of the interviews and survey. In two
late workshops we will engage with stakeholders representing patients, primary healthcare services,
commissioners and policy makers to identify the implications of our research findings.
Patient and Public Involvement
Our PPI group of eight members with lived experience of MLTC-M contributed to the development
of the research questions, the study protocol, study documentation, and the design of the survey
and qualitative topic guide. They will be involved in the analysis of the qualitative data,
interpretation and dissemination of the study findings.
Discussion
This study uses qualitative interviews with patients, and a cross-sectional patient survey linked to
routine data to understand treatment burden experienced by people 18-65 years living with MLTC-
M, and the ways in which the organisation of primary care services affects? this burden. Through the
qualitative research we will use the cumulative complexity model to understand how and why
treatment burden affects younger people and how workload and capacity interact [20]. Through the
cross-sectional survey with linked routine data we will identify the factors associated with treatment
burden, and associations between treatment burden and quality of life. Finally, we will seek to
validate a short treatment burden measure for use in routine clinical care. We will produce practical
recommendations for how primary care services can reduce treatment burden experienced by
people 18-65 years living with MLTC-M. This will lead to further development and testing of
interventions to reduce MLTC-M burden, with the potential to influence MLTC-M trajectories and
improve health outcomes.
6
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Table 1. Power for total sample size of 1000, baseline risk of high burden 20% and risk of high
burden 30% in group with characteristic of interest.
Prevalence of patient characteristic of interest a
0.2
0.25
0.3
0.35
0.4
0.45
0.5
POWER
84.4%
89.2%
92.1%
93.8%
94.9%
95.4%
95.6%
aFor example, patient living in a deprived area
Table 2. Survey measures
Concept
Description
Socio-
demographic
data
Participants are asked to describe:
age
gender
ethnicity (selecting from the list provided here:
https://www.ethnicity-facts-
figures.service.gov.uk/style-guide/ethnic-
groups)
employment status
Health status
a. A validated 10-item person-centered measure of
health and functioning for people with long-term
conditions. The questions are a better fit for our
purposes than those included in, for example, the SF-12.
b. One question asks the participant to list the
conditions they believe they have, that have or will last
longer than 6 months
Treatment
burden
a. The MTBQ is a concise, simply-worded set of
questions to measure treatment burden in people with
MLTC-M. In this study, we will use the 13-item
questionnaire.
b. Building on previous work to develop a single
question screening measure for treatment burden [18]
we have developed, with PPI input, the STBQ. It includes
two questions: one - to screen for high treatment
burden, and one – to understand what they find difficult
from a range of options. The STBQ has been developed
for use in clinical practice, rather than as a research tool.
It may be revised in response to feedback from initial
qualitative interviews.
Primary care
experience
The PCPCM focuses on the patient’s access to care,
relationship with the doctor / practice, and ability to
reach health outcome goals. It comprises 11 items that
form an evaluation of access, continuity,
comprehensiveness, coordination, advocacy, family and
community context, and goal-oriented care.
Health
literacy
A validated single-item screening instrument, designed
to identify patients with limited reading ability who need
help reading health-related materials.
Healthcare
use
We will include five questions, adapted from Salisbury
and colleagues [30], asking whether participants have
recently stayed at an NHS hospital, visited A&E, and
taken time off work to attend hospital and GP
appointments.
Other
We may additionally include a small number of
questions from other validated questionnaires if it is
apparent from stakeholder work or initial qualitative
work that any issues create treatment burden for
patients which are not already included in the other
data sources proposed.
Figure 1. Study flowchart.
Qualitative patient interview study(months 1-25)
Cross-sectional patient survey (months 1-28)
Recruitment of primary care practices (n= up to 8)
Practices search electronic records to identify eligible participants, and
send invitations by post and SMS.
Practices search electronic records to identify eligible participants;
send invitations by post and SMS (first and reminder)
Survey completion (n = 1000)
Additional patient interviews (n = up to 25)
Data input and analysis
Mixed methods analysis
Recruitment of primary care practices (n = up to 25)
Workshop 1 (months 0-8)
Face-to-face or virtual workshops with patient stakeholders
To inform content of qualitative interviews / review survey materials
Extraction of routinely collected medical record data
Workshops 2 and 3 (months 22-24)
Face to face or virtual workshops with patient stakeholders to discuss the implications of the study findings
Writing up study reports
Data transcription and Interview analysis (n= approximately 40 interviews)
Initial patient interviews (n = up to 15) and rapid analysis to inform the
survey
... Further work is underway to develop and validate an adapted version of the MTBQ, known as the 'Short Treatment Burden Questionnaire', for use in clinical settings. 29 X Polly Duncan @polly_duncan and Chris Salisbury @prof_tweet ...
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Objectives To undertake further psychometric testing of the Multimorbidity Treatment Burden Questionnaire (MTBQ) and examine whether reversing the scale reduced floor effects. Design Survey. Setting UK primary care. Participants Adults (≥18 years) with three or more long-term conditions randomly selected from four general practices and invited by post. Measures Baseline survey: sociodemographics, MTBQ (original or version with scale reversed), Treatment Burden Questionnaire (TBQ), four questions (from QQ-10) on ease of completing the questionnaires. Follow-up survey (1–4 weeks after baseline): MTBQ, TBQ and QQ-10. Anonymous data collected from electronic GP records: consultations (preceding 12 months) and long-term conditions. The proportion of missing data and distribution of responses were examined for the original and reversed versions of the MTBQ and the TBQ. Intraclass correlation coefficient (ICC) and Spearman’s rank correlation (Rs) assessed test–retest reliability and construct validity, respectively. Ease of completing the MTBQ and TBQ was compared. Interpretability was assessed by grouping global MTBQ scores into 0 and tertiles (>0). Results 244 adults completed the baseline survey (consent rate 31%, mean age 70 years) and 225 completed the follow-up survey. Reversing the scale did not reduce floor effects or data skewness. The global MTBQ scores had good test–retest reliability (ICC for agreement at baseline and follow-up 0.765, 95% CI 0.702 to 0.816). Global MTBQ score was correlated with global TBQ score (Rs 0.77, p<0.001), weakly correlated with number of consultations (Rs 0.17, p=0.010), and number of different general practitioners consulted (Rs 0.23, p<0.001), but not correlated with number of long-term conditions (Rs −0.063, p=0.330). Most participants agreed that both the MTBQ and TBQ were easy to complete and included aspects they were concerned about. Conclusion This study demonstrates test–retest reliability and ease of completion of the MTBQ and builds on a previous study demonstrating good content validity, construct validity and internal consistency reliability of the questionnaire.
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Background Treatment burden is the effort required of patients to look after their health and the impact this has on their functioning and wellbeing. Little is known about change in treatment burden over time for people with multimorbidity. Aim To quantify change in treatment burden, determine factors associated with this change, and evaluate a revised single-item measure for high treatment burden in older adults with multimorbidity. Design and setting A 2.5-year follow-up of a cross-sectional postal survey via six general practices in Dorset, England. Method GP practices identified participants of the baseline survey. Data on treatment burden (measured using the Multimorbidity Treatment Burden Questionnaire; MTBQ), sociodemographics, clinical variables, health literacy, and financial resource were collected. Change in treatment burden was described, and associations assessed using regression models. Diagnostic test performance metrics evaluated the revised single-item measure relative to the MTBQ. Results In total, 300 participants were recruited (77.3% response rate). Overall, there was a mean increase of 2.6 (standard deviation 11.2) points in treatment burden global score. Ninety-eight (32.7%) and 53 (17.7%) participants experienced an increase and decrease, respectively, in treatment burden category. An increase in treatment burden was associated with having >5 long-term conditions (adjusted β 8.26, 95% confidence interval [CI] = 4.20 to 12.32) and living >10 minutes (versus ≤10 minutes) from the GP (adjusted β 3.88, 95% CI = 1.32 to 6.43), particularly for participants with limited health literacy (mean difference: adjusted β 9.59, 95% CI = 2.17 to 17.00). The single-item measure performed moderately (sensitivity 55.7%; specificity 92.4%. Conclusion Treatment burden changes over time. Improving access to primary care, particularly for those living further away from services, and enhancing health literacy may mitigate increases in burden.
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Background Multimorbidity, defined as the co-existence of two or more chronic conditions, presents significant challenges to patients, healthcare providers and health systems. Despite this, there is ongoing uncertainty about the most effective ways to manage patients with multimorbidity. This review updated and narrowed the focus of a previous Cochrane review and aimed to determine the effectiveness of interventions designed to improve outcomes in people with multimorbidity in primary care and community settings, compared to usual care. Methods We searched eight databases and two trials registers up to 9 September 2019. Two review authors independently screened potentially eligible titles and selected studies, extracted data, evaluated study quality and judged the certainty of the evidence (GRADE). Interventions were grouped by their predominant focus into care-coordination/self-management support, self-management support and medicines management. Main outcomes were health-related quality of life (HRQoL) and mental health. Meta-analyses were conducted, where possible, but the synthesis was predominantly narrative. Results We included 16 RCTs with 4753 participants, the majority being older adults with at least three conditions. There were eight care-coordination/self-management support studies, four self-management support studies and four medicines management studies. There was little or no evidence of an effect on primary outcomes of HRQoL (MD 0.03, 95% CI −0.01 to 0.07, I ² = 39%) and mental health or on secondary outcomes with a small number of studies reporting that care coordination may improve patient experience of care and self-management support may improve patient health behaviours. Overall, the certainty of the evidence was graded as low due to significant variation in study participants and interventions. Conclusions There are remaining uncertainties about the effectiveness of interventions for people with multimorbidity, despite the growing number of RCTs conducted in this area. Our findings suggest that future research should consider patient experience of care, optimising medicines management and targeted patient health behaviours such as exercise.
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Background Patients with multimorbidities have the greatest healthcare needs and generate the highest expenditure in the health system. There is an increasing focus on identifying specific disease combinations for addressing poor outcomes. Existing research has identified a small number of prevalent “clusters” in the general population, but the limited number examined might oversimplify the problem and these may not be the ones associated with important outcomes. Combinations with the highest (potentially preventable) secondary care costs may reveal priority targets for intervention or prevention. We aimed to examine the potential of defining multimorbidity clusters for impacting secondary care costs. Methods and findings We used national, Hospital Episode Statistics, data from all hospital admissions in England from 2017/2018 (cohort of over 8 million patients) and defined multimorbidity based on ICD-10 codes for 28 chronic conditions (we backfilled conditions from 2009/2010 to address potential undercoding). We identified the combinations of multimorbidity which contributed to the highest total current and previous 5-year costs of secondary care and costs of potentially preventable emergency hospital admissions in aggregate and per patient. We examined the distribution of costs across unique disease combinations to test the potential of the cluster approach for targeting interventions at high costs. We then estimated the overlap between the unique combinations to test potential of the cluster approach for targeting prevention of accumulated disease. We examined variability in the ranks and distributions across age (over/under 65) and deprivation (area level, deciles) subgroups and sensitivity to considering a smaller number of diseases. There were 8,440,133 unique patients in our sample, over 4 million (53.1%) were female, and over 3 million (37.7%) were aged over 65 years. No clear “high cost” combinations of multimorbidity emerged as possible targets for intervention. Over 2 million (31.6%) patients had 63,124 unique combinations of multimorbidity, each contributing a small fraction (maximum 3.2%) to current-year or 5-year secondary care costs. Highest total cost combinations tended to have fewer conditions (dyads/triads, most including hypertension) affecting a relatively large population. This contrasted with the combinations that generated the highest cost for individual patients, which were complex sets of many (6+) conditions affecting fewer persons. However, all combinations containing chronic kidney disease and hypertension, or diabetes and hypertension, made up a significant proportion of total secondary care costs, and all combinations containing chronic heart failure, chronic kidney disease, and hypertension had the highest proportion of preventable emergency admission costs, which might offer priority targets for prevention of disease accumulation. The results varied little between age and deprivation subgroups and sensitivity analyses. Key limitations include availability of data only from hospitals and reliance on hospital coding of health conditions. Conclusions Our findings indicate that there are no clear multimorbidity combinations for a cluster-targeted intervention approach to reduce secondary care costs. The role of risk-stratification and focus on individual high-cost patients with interventions is particularly questionable for this aim. However, if aetiology is favourable for preventing further disease, the cluster approach might be useful for targeting disease prevention efforts with potential for cost-savings in secondary care.
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Objectives To describe and analyse studies aiming at quantitatively assessing the impact of interventions on patient-reported burden of treatment as an outcome (primary or secondary). Methods The aim of the search strategy was to identify all publications describing a medical intervention intended to reduce patient-reported burden of treatment in adult patients with long-term conditions, from January 1, 2008 to July 15, 2019. Four databases (Medline, PsycINFO, the “Trials” section of the Cochrane-Library, and OpenGrey) were searched in English, French, Spanish, Italian and Portuguese. Each identified article was reviewed and the risk of bias was assessed using a tool adapted from the Cochrane Collaboration recommendations. Results Of 641 articles retrieved, 11 were included in this review. There were nine randomized controlled trials, one non-randomized controlled trial, and one before-and-after study. The sample sizes ranged from 55 to 1,546 patients. Eight out of the eleven studies reported significant positive outcomes of the studied interventions. Reducing dosing frequency, improving background therapy, offering home care or providing easier-to-use medical devices were associated with positive outcomes. Conclusions Only a few studies have specifically focused on decreasing the subjective burden of treatment. Small trials conducted in patients with a single specific disorder have reported positive outcomes. However, a large, high-quality study assessing the impact of a change in care process in patients with multiple morbidities did not show such results. Further studies are needed to implement this aspect of patient-centred care.
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Background Treatment burden is the effort required of patients to look after their health, and the impact this has on their wellbeing. Quantitative data on treatment burden for patients with multimorbidity are sparse, and no single-item treatment burden measure exists. Aim To determine the extent of, and associations with, high treatment burden among older adults with multimorbidity, and to explore the performance of a novel single-item treatment burden measure. Design and setting Cross-sectional postal survey via general practices in Dorset, UK. Method Patients ≥55 years, living at home, with three or more long-term conditions (LTCs) were identified by practices. Treatment burden was measured using the Multimorbidity Treatment Burden Questionnaire. Data collected were sociodemographics, LTCs, medications, and characteristics including health literacy and financial resource. Associations with high treatment burden were investigated via logistic regression. Performance of a novel single-item measure of treatment burden was also evaluated. Results A total of 835 responses were received (response rate 42%) across eight practices. Patients’ mean age was 75 years, 55% were female ( n = 453), and 99% were white ( n = 822). Notably, 39% of patients self-reported fewer than three LTCs ( n = 325). Almost one-fifth (18%) of responders reported high treatment burden ( n = 150); making lifestyle changes and arranging appointments were particular sources of difficulty. After adjustment, limited health literacy and financial difficulty displayed strong associations with high treatment burden; more LTCs and more prescribed regular medications were also independently associated. The single-item measure discriminated moderately between high and non-high burden with a sensitivity of 89%, but a specificity of 58%. Conclusion High treatment burden was relatively common, underlining the importance of minimising avoidable burden. More vulnerable patients, with less capacity to manage, are at greater risk of being overburdened. Further development of a single-item treatment burden measure is required.
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Background: Health services have failed to respond to the pressures of multimorbidity. Improved measures of multimorbidity are needed for conducting research, planning services and allocating resources. Methods: We modelled the association between 37 morbidities and 3 key outcomes (primary care consultations, unplanned hospital admission, death) at 1 and 5 years. We extracted development (n = 300 000) and validation (n = 150 000) samples from the UK Clinical Practice Research Datalink. We constructed a general-outcome multimorbidity score by averaging the standardized weights of the separate outcome scores. We compared performance with the Charlson Comorbidity Index. Results: Models that included all 37 conditions were acceptable predictors of general practitioner consultations (C-index 0.732, 95% confidence interval [CI] 0.731-0.734), unplanned hospital admission (C-index 0.742, 95% CI 0.737-0.747) and death at 1 year (C-index 0.912, 95% CI 0.905-0.918). Models reduced to the 20 conditions with the greatest combined prevalence/weight showed similar predictive ability (C-indices 0.727, 95% CI 0.725-0.728; 0.738, 95% CI 0.732-0.743; and 0.910, 95% CI 0.904-0.917, respectively). They also predicted 5-year outcomes similarly for consultations and death (C-indices 0.735, 95% CI 0.734-0.736, and 0.889, 95% CI 0.885-0.892, respectively) but performed less well for admissions (C-index 0.708, 95% CI 0.705-0.712). The performance of the general-outcome score was similar to that of the outcome-specific models. These models performed significantly better than those based on the Charlson Comorbidity Index for consultations (C-index 0.691, 95% CI 0.690-0.693) and admissions (C-index 0.703, 95% CI 0.697-0.709) and similarly for mortality (C-index 0.907, 95% CI 0.900-0.914). Interpretation: The Cambridge Multimorbidity Score is robust and can be either tailored or not tailored to specific health outcomes. It will be valuable to those planning clinical services, policymakers allocating resources and researchers seeking to account for the effect of multimorbidity.
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Background: The burden of multimorbidity is likely higher in ethnic minority populations, as most individual diseases are more prevalent in minority groups. However, information is scarce. We examined ethnic inequalities in multimorbidity, and investigated to what extent they reflect differences in socioeconomic status (SES). Methods: We included Healthy Life in an Urban Setting study participants of Dutch (N = 4582), South-Asian Surinamese (N = 3258), African Surinamese (N = 4267), Ghanaian (N = 2282), Turkish (N = 3879) and Moroccan (N = 4094) origin (aged 18-70 years). Educational level, employment status, income situation and multimorbidity were defined based on questionnaires. We described the prevalence and examined age-adjusted ethnic inequalities in multimorbidity with logistic regression analyses. To assess the contribution of SES, we added SES indicators to the age-adjusted model. Results: The prevalence of multimorbidity ranged from 27.1 to 53.4% in men and from 38.5 to 69.6% in women. The prevalence of multimorbidity in most ethnic minority groups was comparable to the prevalence among Dutch participants who were 1-3 decades older. After adjustment for SES, the odds of multimorbidity remained significantly higher in ethnic minority groups. For instance, age-adjusted OR for multimorbidity for the Turkish compared to the Dutch changed from 4.43 (3.84-5.13) to 2.34 (1.99-2.75) in men and from 5.35 (4.69-6.10) to 2.94 (2.54-3.41) in women after simultaneous adjustment for all SES indicators. Conclusions: We found a significantly higher prevalence of multimorbidity in ethnic minority men and women compared to Dutch, and results pointed to an earlier onset of multimorbidity in ethnic minority groups. These inequalities in multimorbidity were not fully accounted for by differences in SES.
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Background: Many people with chronic disease have more than one chronic condition, which is referred to as multimorbidity. The term comorbidity is also used but this is now taken to mean that there is a defined index condition with other linked conditions, for example diabetes and cardiovascular disease. It is also used when there are combinations of defined conditions that commonly co-exist, for example diabetes and depression. While this is not a new phenomenon, there is greater recognition of its impact and the importance of improving outcomes for individuals affected. Research in the area to date has focused mainly on descriptive epidemiology and impact assessment. There has been limited exploration of the effectiveness of interventions to improve outcomes for people with multimorbidity. Objectives: To determine the effectiveness of health-service or patient-oriented interventions designed to improve outcomes in people with multimorbidity in primary care and community settings. Multimorbidity was defined as two or more chronic conditions in the same individual. Search methods: We searched MEDLINE, EMBASE, CINAHL and seven other databases to 28 September 2015. We also searched grey literature and consulted experts in the field for completed or ongoing studies. Selection criteria: Two review authors independently screened and selected studies for inclusion. We considered randomised controlled trials (RCTs), non-randomised clinical trials (NRCTs), controlled before-after studies (CBAs), and interrupted time series analyses (ITS) evaluating interventions to improve outcomes for people with multimorbidity in primary care and community settings. Multimorbidity was defined as two or more chronic conditions in the same individual. This includes studies where participants can have combinations of any condition or have combinations of pre-specified common conditions (comorbidity), for example, hypertension and cardiovascular disease. The comparison was usual care as delivered in that setting. Data collection and analysis: Two review authors independently extracted data from the included studies, evaluated study quality, and judged the certainty of the evidence using the GRADE approach. We conducted a meta-analysis of the results where possible and carried out a narrative synthesis for the remainder of the results. We present the results in a 'Summary of findings' table and tabular format to show effect sizes across all outcome types. Main results: We identified 17 RCTs examining a range of complex interventions for people with multimorbidity. Nine studies focused on defined comorbid conditions with an emphasis on depression, diabetes and cardiovascular disease. The remaining studies focused on multimorbidity, generally in older people. In 11 studies, the predominant intervention element was a change to the organisation of care delivery, usually through case management or enhanced multidisciplinary team work. In six studies, the interventions were predominantly patient-oriented, for example, educational or self-management support-type interventions delivered directly to participants. Overall our confidence in the results regarding the effectiveness of interventions ranged from low to high certainty. There was little or no difference in clinical outcomes (based on moderate certainty evidence). Mental health outcomes improved (based on high certainty evidence) and there were modest reductions in mean depression scores for the comorbidity studies that targeted participants with depression (standardized mean difference (SMD) -0.41, 95% confidence interval (CI) -0.63 to -0.2). There was probably a small improvement in patient-reported outcomes (moderate certainty evidence). The intervention may make little or no difference to health service use (low certainty evidence), may slightly improve medication adherence (low certainty evidence), probably slightly improves patient-related health behaviours (moderate certainty evidence), and probably improves provider behaviour in terms of prescribing behaviour and quality of care (moderate certainty evidence). Cost data were limited. Authors' conclusions: This review identifies the emerging evidence to support policy for the management of people with multimorbidity and common comorbidities in primary care and community settings. There are remaining uncertainties about the effectiveness of interventions for people with multimorbidity in general due to the relatively small number of RCTs conducted in this area to date, with mixed findings overall. It is possible that the findings may change with the inclusion of large ongoing well-organised trials in future updates. The results suggest an improvement in health outcomes if interventions can be targeted at risk factors such as depression in people with co-morbidity.
Article
PURPOSE To develop and evaluate a concise measure of primary care that is grounded in the experience of patients, clinicians, and health care payers. METHODS We asked crowd-sourced samples of 412 patients, 525 primary care clinicians, and 85 health care payers to describe what provides value in primary care, then asked 70 primary care and health services experts in a 21/2 day international conference to provide additional insights. A multidisciplinary team conducted a qualitative analysis of the combined data to develop a parsimonious set of patient-reported items. We evaluated items using factor analysis, Rasch modeling, and association analyses among 2 online samples and 4 clinical samples from diverse patient populations. RESULTS The resulting person-centered primary care measure parsimoniously represents the broad scope of primary care, with 11 domains each represented by a single item: accessibility, advocacy, community context, comprehensiveness, continuity, coordination, family context, goal-oriented care, health promotion, integration, and relationship. Principal axes factor analysis identified a single factor. Factor loadings and corrected item-total correlations were >0.6 in online samples (n = 2,229) and >0.5 in clinical samples (n = 323). Factor scores were fairly normally distributed in online patient samples, and skewed toward higher ratings in point-of-care patient samples. Rasch models showed a broad spread of person and item scores, acceptable item-fit statistics, and little item redundancy. Preliminary concurrent validity analyses supported hypothesized associations. CONCLUSIONS The person-centered primary care measure reliably, comprehensively, and parsimoniously assesses the aspects of care thought to represent high-value primary care by patients, clinicians, and payers. The measure is ready for further validation and outcome analyses, and for use in focusing attention on what matters about primary care, while reducing measurement burden.