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Treatment burden in multimorbidity: an integrative review

BMC Primary Care volume 25, Article number: 352 (2024) Cite this article

Abstract

Background

People living with multimorbidity experience increased treatment burden, which can result in poor health outcomes. Despite previous efforts to grasp the concept of treatment burden, the treatment burden of people living with multimorbidity has not been thoroughly explored, which may limit our understanding of treatment burden in this population. This study aimed to identify the components, contributing factors, and health outcomes of treatment burden in people with multiple diseases to develop an integrated map of treatment burden experienced by people living with multimorbidity. The second aim of this study is to identify the treatment burden instruments used to evaluate people living with multimorbidity and assess the comprehensiveness of the instruments.

Methods

This integrative review was conducted using the electronic databases MEDLINE, EMBASE, CINAHL, and reference lists of articles through May 2023. All empirical studies published in English were included if they explored treatment burden among adult people living with multimorbidity. Data extraction using a predetermined template was performed.

Results

Thirty studies were included in this review. Treatment burden consisted of four healthcare tasks and the social, emotional, and financial impacts that these tasks imposed on people living with multimorbidity. The context of multimorbidity, individual’s circumstances, and how available internal and external resources affected treatment burden. We explored that an increase in treatment burden resulted in non-adherence to treatment, disease progression, poor health status and quality of life, and caregiver burden. Three instruments were used to measure treatment burden in living with multimorbidity. The levels of comprehensiveness of the instruments regarding healthcare tasks and impacts varied. However, none of the items addressed the healthcare task of ongoing prioritization of the tasks.

Conclusions

We developed an integrated map illustrating the relationships between treatment burden, the context of multimorbidity, people’s resources, and the health outcomes. None of the existing measures included an item asking about the ongoing process of setting priorities among the various healthcare tasks, which highlights the need for improved measures. Our findings provide a deeper understanding of treatment burden in multimorbidity, but more research for refinement is needed. Future studies are also needed to develop strategies to comprehensively capture both the healthcare tasks and impacts for people living with multimorbidity and to decrease treatment burden using a holistic approach to improve relevant outcomes.

Trial registration

DOI: https://doi.org/10.17605/OSF.IO/UF46V

Peer Review reports

Background

Multimorbidity, the co-existence of two or more chronic diseases, is a major global health issue affecting over one-third of the population [1, 2]. People living with multimorbidity encounter unique challenges of simultaneously managing multiple conditions, such as managing polypharmacy, and conflicting treatment regimens, while also coping with altered physical and mental function [3,4,5,6]. An ineffective and fragmented healthcare system that focuses on a single disease can add challenges to understanding and navigating healthcare tasks, which, in turn, can exacerbate people’s treatment burden [7,8,9]. Recognizing that the healthcare system contributes to people’s treatment burden, May and colleagues proposed the concept of minimally disruptive medicine [10]. This approach emphasizes coordinated and patient-centered collaborative care services designed to reduce people’s treatment burden. Minimally disruptive medicine helps to streamline the care process, making treatment of health conditions less burdensome and more manageable for people’s daily lives [10, 11].

Treatment burden refers to patients’ workload in treating and managing chronic health conditions and the combined impact on their well-being [12]. Treatment burden is recognized as an important patient-reported outcome in people living with multimorbidity [13]. Considerable research has focused on understanding the attributes and characteristics of treatment burden in multimorbidity [14,15,16,17]. Two groups of investigators developed the conceptual framework or taxonomy of treatment burden in multimorbidity [18, 19]. Despite the substantial scholarly progress in understanding the treatment burden in multimorbidity, a significant knowledge gap remains for three reasons. First, existing studies have identified the contributing factors and components of treatment burden but have not addressed health outcomes resulting from treatment burden. For example, the two research groups included factors exacerbating treatment burden, elements of work or tasks people living with multimorbidity must perform, and the impacts of the tasks on patients’ well-being (e.g., emotional impact, social activity limitations) [18, 19]. Second, the elements of treatment burden have been identified from a limited number of empirical studies and they have not specifically examined people living with multimorbidity. For example, Tran and colleagues recruited a large number of participants from three Western countries [19]. However, their suggested taxonomy was developed based on a single quantitative study in which the sample was not limited to people living with multimorbidity. Third, while review studies have synthesized treatment burden [17, 20, 21], they have primarily focused on people with chronic conditions [20, 21] and have included only qualitative [20] or quantitative studies [17]. Thus, the unique aspects of the treatment burden experienced by people living with multimorbidity have not been fully elucidated in existing conceptual framework and taxonomy. Due to this knowledge gap, the current measures for treatment burden may not capture the distinct aspects of treatment burden experienced by people living with multimorbidity [22]. Therefore, it is important to also evaluate the contents of the instruments that have been used to measure treatment burden in people living with multimorbidity.

The purpose of this integrative review is to gain a comprehensive understanding of the treatment burden experienced by people living with multimorbidity by synthesizing the empirical literature on the treatment burden of people living with multimorbidity, and evaluate the treatment burden measures. The specific aims are 1) to identify the components of treatment burden, contributing factors, and health outcomes of treatment burden as revealed in the literature and 2) to evaluate the comprehensiveness of the instruments that have been used to assess treatment burden in people living with multiple conditions.

Methods

This review was registered in the Open Science Framework on September 5, 2022 (https://doi.org/https://doi.org/10.17605/OSF.IO/UF46V) [23]. To provide a more comprehensive understanding of treatment burden in people living with multiple conditions, we made two key modifications to our original protocol. First, we extended the literature search to include all available years rather than limiting it to the last 10 years. Second, we changed our review methodology from a scoping review to an integrative review, which allows for the inclusion of diverse research methodologies such as quantitative and qualitative studies. We followed the steps outlined by Whittemore and Knafl for the integrative review process: problem identification, literature search and selection, data evaluation, data analysis and presentation [24].

Search strategy

A systematic search was conducted using three electronic databases: MEDLINE, EMBASE, and CINAHL. The search strategy involved the use of MeSH terms, EMTREE, and/or free text keywords such as "multimorbidity," "comorbidity," "burden," "workload," and other relevant keywords related to treatment burden and specific domains suggested from a previous study describing treatment burden in chronic conditions [25] such as "time," "travel," "financial," and "healthcare." After selecting the included articles, the references were manually searched for additional relevant studies. The search was limited to articles published in English and the year of publication up to May 2023. We consulted with a medical librarian on the search process. Supplementary file 1 lists the MEDLINE search queries.

Study selection

The studies were selected based on the following inclusion criteria: 1) targeting adults over 19 years with at least two chronic conditions; 2) studies describing any aspects of treatment burden and/or related factors (contributing factors or health outcomes) from perspectives of people living with multimorbidity, and 3) published in English up to May 2023. In our review, chronic disease was defined as a long-term, incurable condition requiring ongoing care [26], and treatment burden as the healthcare workload and its impact on patient well-being [14]. Studies were excluded based on the following criteria: 1) studies measuring the treatment burden of specific conditions (e.g., Diabetic Treatment Burden Questionnaire) [27], 2) studies describing treatment burden from the perspectives of samples other than people living with multimorbidity (e.g., caregivers, healthcare professionals), or 3) non-empirical studies such as review articles. Among the eligible studies, an additional inclusion criterion was applied to analyze the comprehensiveness of the contents of the treatment burden instruments such as studies reporting on the psychometric properties of the measures.

Data abstraction

All records were collected into a single EndNote library file to delete duplicates, and the remaining records then were exported to an Excel sheet with essential information for screening. Two authors independently screened the titles and abstracts, and then read the full texts of studies based on the eligibility criteria. Any discrepancies were discussed, and a third author resolved disagreements between the authors.

Quality assessment

Study quality was assessed using the Mixed Methods Appraisal Tool (MMAT) [28, 29]. MMAT is a versatile tool that can be applied across a variety of study designs, including quantitative, qualitative, and mixed methods studies. Each study was evaluated as “yes,” “no,” or “can’t tell” based on five criteria. “Can’t tell” means that appropriate information was not reported or the information was unclear. The ratings for the criteria were presented without calculating the overall score as recommended [30]. Two authors independently evaluated one study and discussed discrepancies to reach a consensus. The evaluation scores for each study are presented in Supplementary file 2.

Data synthesis

We conducted data analysis following the four steps suggested by Whittemore and Knafl. In the first stage of data reduction, we abstracted the data from the primary sources by organizing studies into groups based on different methodologies (quantitative and qualitative) and predetermined factors (i.e., treatment burden, contributing factors, and health outcomes). The five authors independently extracted data from the full text of each article using a predetermined data extraction template (see Supplementary file 3). The development of the initial template was guided by the aims of our review and then the template was refined through several rounds of discussion among the five authors. We also conducted pilot testing to ensure that we captured all of the necessary information. The extracted data were cross-checked independently by two authors. Any unclear information in the original paper was clarified by contacting the original author(s) of the paper.

In the second stage, displaying the data, we presented the extracted data through matrices and charts. The third step, data comparison, involved an iterative examination of the data displays to identify patterns, themes, or relationships from both quantitative and qualitative data. The key outcomes of the quantitative studies were summarized in a table format, which included inferential statistics (e.g., standardized and unstandardized coefficients with a 95% confidence interval). Results from the multivariate regression analyses were included unless univariate analysis results were only available. We determined the significance by considering a p-value threshold of 0.05 and a 95% confidence intervals.

Qualitative data were analyzed by extracting the segments of results that were related to our review aims. These extracted segments were grouped into categories identified during the quantitative data synthesis. The results from both the quantitative and qualitative data were integrated using matrices (Tables 3 and 4), to help identify common patterns and relationships across both types of data. Similarly, items from the instruments measuring treatment burden and the segments of the qualitative results that were relevant to the attributes to treatment burden were displayed side-by-side to compare the data (Table 2). Finally, in the fourth step, conclusion drawing, we developed an integrated map of the treatment burden of multimorbidity based on the previous step. This map provides a comprehensive visual representation of how different factors and outcomes related to treatment burden are interconnected (Fig. 2).

Results

Search results

The initial database search resulted in 9118 articles, of which 6069 remained after duplicates were eliminated (Fig. 1). An additional 95 articles were included from the reference lists for screening. After screening the titles and abstracts, 137 full text articles were assessed for eligibility. As a result, 30 studies were included in this integrative review. Of the 30 studies, nine were qualitative studies and 21 were quantitative studies.

Fig. 1
figure 1

PRISMA flowchart illustrating the systematic reviews

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Characteristics of included studies

Of the 30 studies in our sample, 23 targeted people living with multimorbidity. The overall average across the 11 studies reporting the mean number of multimorbidity yielded a mean of 5.38 (SD 2.25). Among the ten studies that reported the median number of diseases, the median ranged from three [12, 31] to five [32,33,34,35,36,37] (Table 1). The most common inclusion criterion of multimorbidity was having at least two chronic conditions, whereas some studies included people with at least three or four conditions with or without additional criteria (e.g., the number of medications) [38,39,40,41,42,43,44]. The remaining seven studies targeted people with index chronic conditions and co-morbidity [5, 6, 9, 45,46,47,48]. The most prevalent index conditions in these seven studies were hypertension and/or type 2 diabetes (n = 3). Researchers have collected a list of chronic conditions based on medical record reviews (n = 20) or self-report (n = 5) [37, 38, 49,50,51] or both (n = 3) [6, 40, 41] although two studies did not indicate how they collected this information [5, 9] (Table 1).

Table 1 Characteristics of included study (N = 30)
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Treatment burden was measured based on three instruments and their variations: the Patient Experience with Treatment burden and Self-management (PETS) and its variations (n = 8) [12, 31,32,33,34,35,36, 48]; the Treatment Burden Questionnaire (TBQ) (n = 5) [42, 43, 45, 50, 54]; and the Multimorbidity Treatment Burden Questionnaire (MTBQ) (n = 4) [44, 51, 55, 56], and the MTBQ with a single-item (n = 2) [40, 41] and a four-item measure (n = 1) [37].

Methodological quality

Nine qualitative studies met all five quality assessment criteria. In the 19 quantitative descriptive studies, one study (5.3%) met only one criterion [54], two studies (10.5%) met two criteria [48, 51], and the rest (84.2%) met three to four criteria. The most unmet criterion in quantitative descriptive studies (68.4%) was related to the representativeness of the samples. One quantitative randomized controlled trial did not meet one criterion related to adherence to intervention, as more than 20% of participants did not receive a medication review intervention [44]. One quantitative non-randomized study met four criteria, but the authors did not state whether participants were exposed to the intervention as planned [43].

Components of treatment burden

The results of the studies included in this review indicated that treatment burden consisted of several healthcare tasks that people living with multimorbidity are asked to perform to manage their health conditions and the impacts of those healthcare tasks on their lives (Fig. 2). Healthcare tasks was interconnected with impacts [4, 5, 8, 9, 38, 39, 52, 53], and two studies indicated that impacts affected healthcare tasks [36, 38].

Fig. 2
figure 2

Integrated map of treatment burden in multimorbidity. The dotted line refers to a small number of studies indicating the relationship, implying the scarcity of evidence

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Healthcare tasks

As shown in Table 2, people living with multimorbidity invest time, money, and efforts to engage in four categories of healthcare tasks: self-care activities, knowledge acquisition, paperwork, and ongoing prioritization. The self-care activities category was the most frequently reported across the studies that explicitly mentioned these activities [4,5,6, 8, 9, 39, 41, 42, 46, 53]. This category included organizing and remembering the medication schedule, and taking multiple medications as directed [4,5,6, 8, 9, 39, 41, 42, 46, 53]. In addition, people with multimorbidity reported challenges organizing and attending multiple medical appointments including the inconvenience of making transportation arrangements and traveling to multiple clinics on different dates at different locations [4, 5, 8, 9, 38, 39, 52, 53].

Table 2 The components of treatment burden identified in empirical studies and instrument of treatment burden
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Another challenge was that people living with multimorbidity spent time and efforts to understand their health conditions, including seeking information from various sources (e.g., websites) and assessing their personal experience [4, 9]. Some people described difficulties obtaining comprehensive information across their multiple diseases [8, 9]. Paperwork was an additional task people performed to reimburse medical costs and maintain their medical records for efficient communication with clinicians [5, 53].

People living with multimorbidity described that they spent a substantial amount of time and efforts evaluating the significance of healthcare tasks in their current situations compared to their other life demands or values (e.g., work and family life) and contemplating the potential impacts of their choices [5, 38, 39, 52, 53]. People also described their efforts to decide what action to take when faced with treatment regimens that seemed incompatible [5, 6]. This prioritization was not static but constant as their situations and values changed over time [5, 38, 39]. For example, one participant reported that she usually placed a high priority on her health condition over her life demands. However, she sometimes chose her social life over her health conditions, although she anticipated negative consequences on her health as a result [38].

Impact

Healthcare tasks impacted various aspects of people’s lives, particularly their social, emotional, and financial aspects (Table 2 and Fig. 2) [5, 8, 38, 39, 53]. Asking for help from others, particularly financial support for treatment, made people living with multimorbidity dependent on others, which affected their sense of autonomy [5, 8, 9, 38, 52, 53]. People also expressed negative feelings such as anger, frustration, and a sense of worthlessness when they felt that they did not have control over managing their health conditions. This sense of loss of control was exacerbated by overwhelming demands of healthcare tasks, which posed threats to their well-being (e.g., insecurity maintaining jobs, losing time for leisure) [8, 9, 38, 39, 53]. However, the emotional impact of healthcare tasks was not entirely negative. For instance, in the study by Duguay and colleagues where people living with at least four chronic conditions were recruited in family medicine clinics, people who faithfully adhered to prescribed tasks such as medication and exercise experienced a sense of being healthy [39]. Medical costs to manage health (e.g., purchasing healthy foods and medications and transportation costs) impacted people's financial status. Many people had to rely on their savings or financial support from their families to cover these costs [9, 49].

Contributing factors that affect treatment burden

The included studies (n = 24) indicated that when people had multiple chronic conditions (i.e., the context of multimorbidity), their circumstances and available resources (i.e., internal and external resources) affected their treatment burden (Fig. 2).

Context of multimorbidity

Findings from the included studies indicated that healthcare tasks and the impacts on the well-being of people with multimorbidity were complicated due to the management and nature of multimorbidity including the accumulating quantity and difficulty of healthcare tasks and the evolving and fluctuating health status from multiple conditions (Fig. 2). The studies found that having multiple chronic conditions tended to increase treatment burden [5, 32, 33, 40,41,42, 51, 55, 57], possibly due to the increased number of healthcare tasks, which could also contribute to an increase in the complexity of the healthcare tasks [6, 8, 38, 39, 49, 52, 53]. For example, participants mentioned that taking multiple medications as directed for their various conditions was significant work. It also increased their vigilance to potential interactions between chronic conditions and/or between therapeutic regimens across chronic conditions (e.g., side effects due to medication interactions) and increased their dependency on their family [38, 49]. When people living with multimorbidity perceived that their healthcare tasks were interdependent or incompatible, the difficulty of undertaking these healthcare tasks was amplified [5, 6, 38, 39, 49]. The addition of a new diagnosis or a change in their health status also forced them to integrate their additional healthcare tasks into their existing routines. Duguay and colleagues described this burden as "a wheel that turns" due to the evolving and fluctuating nature of multiple conditions [39]. The dynamic nature of the multiple conditions also contributed to the emotional status of people with multimorbidity, such as feeling that their health trajectory was unpredictable [5, 39, 49].

Circumstance-related factors of people with multimorbidity

In 16 studies, a variety of circumstance-related factors were investigated or described in relation to treatment burden (Table 3). Frequently mentioned circumstance-related factors included socio-demographic factors such as place of residence, employment status, identity, and the value of life of people with multimorbidity.

Table 3 Contributing factors that affect treatment burden: Circumstance-related factors of people with multimorbidity
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Although sociodemographic factors such as age, sex, and marital status were frequently addressed in the 11 studies [5, 9, 32, 36, 37, 40,41,42, 45, 46, 51], most studies indicated the lack of a statistically significant association between these factors and treatment burden (p-values > 0.05 in the inferential statistics) [36, 37, 40, 41, 45, 46]. The relationships between education level and treatment burden were also inconsistent across the studies including a longitudinal study [5, 32, 36, 42, 45, 48, 52]. However, several studies consistently indicated that living in rural, suburb, or unsafe areas increased treatment burden because traveling to the clinic required more time and financial resources [9, 39, 42, 49] or posed a risk of assault or robbery [9]. Although having a job allowed people with multimorbidity to manage the financial demands of their health (e.g., medical expenses), it also posed a challenge of arranging clinic appointments with their work schedule [9, 38, 52]. Two qualitative studies described how participants’ identity and value affected their treatment burden [5, 38]. Specifically, people who desired to be independent and valued work over treatment reported higher levels of treatment burden.

Resources

Internal resources

Several studies indicated that decreased physical capacity [4, 6, 39], negative emotions (e.g., depressive symptoms) [4], and cognitive dysfunction [5] affected people's treatment burden (Table 4). These findings align with a quantitative study conducted in outpatient clinics, which revealed that half of participants experienced a high degree of treatment burden, demonstrating an association between perceived health status and treatment burden [51]. However, in Eton and colleagues’ study, where 42% and 29% of participants were diagnosed with depression and anxiety, respectively, factors such as a mental health diagnosis and the number of unhealthy physical or mental health days in the past 30 days did not consistently predict long-term trajectories of the burden of healthcare tasks [36].

Table 4 Contributing factors that affect treatment burden: Resources
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Several qualitative studies highlighted that people living with multimorbidity often faced financial difficulties in performing healthcare tasks [4, 5, 8, 9, 52]. This finding is aligned with the finding that paying for healthcare costs was associated with an increase in treatment burden [41]. However, household income levels did not predict the trajectory of healthcare tasks and impact over 24 months in Eton and colleagues’ study where 55% of the participants had a household income below the country’s median [36].

Several qualitative studies found that people with multimorbidity who were knowledgeable about their health conditions and had adequate health literacy were likely to actively communicate with their healthcare providers and clearly comprehend their illness, which reduced the burden of managing their health conditions [5, 6, 8, 9, 38, 39, 49]. One study also found that people’s health literacy was associated with the burden from the trajectory of healthcare tasks, but not the burden from the impact [36]. However, their study measured health literacy with only one item, asking about their perceived difficulty understanding the provided medical information.

Self-efficacy and self-care skills including coping skills were valuable assets for lowering treatment burden [4,5,6, 8, 38, 42, 53, 55]. People who accepted their health tasks and maintained hope through faith and spirituality experienced lower treatment burden [5, 8, 9, 38, 39, 53].

External resources

People with multimorbidity who received support from family members and others reported experiencing reduced burden from healthcare tasks and the negative impacts [5, 8, 9, 38, 39, 52, 54, 55]. They noted the integral role of caregivers who could share responsibility for some of the patients’ self-care activities and life demands (e.g., household chores and financial support). Eton and colleagues found that distress from negative relations with members of the patients’ social networks (e.g., interpersonal challenges) was associated with both the trajectory of burden from healthcare tasks and the impact, while social support, in general, was unrelated to either burden from healthcare tasks or impact [36].

Many participants in six qualitative studies expressed frustration with unsupportive healthcare providers [5, 8, 9, 38, 49, 53]. Tinetti and colleagues' interventional study demonstrated that the implementation of care aligned with the priority of the people with multimorbidity via shared decision-making was effective in reducing treatment burden [43]. These findings have been further supported by other studies indicating the importance of healthcare providers' empathic attitude and provision of comprehensive information with appropriate communication skills [5, 8, 9, 31, 38, 39, 49, 53].

Positive experiences of people with multimorbidity in a primary care setting along with government support (e.g., old age pension and supportive policy) were associated with a decrease in treatment burden by reducing the financial impact [8, 9, 45, 49, 53]. In contrast, factors that frequently increase treatment burden included poor access to the healthcare system, dissatisfaction with the quality of care, and discontent and challenges with the fragmented healthcare system [5, 8, 9, 38,39,40, 49, 52]. One participant with multimorbidity described the struggles: "It’s not the disease that I’m fighting; it’s the healthcare system” [39].

Health outcomes of treatment burden

The health outcomes of treatment burden were described in 11 studies (five quantitative and six qualitative studies) [5, 6, 8, 9, 12, 32, 38, 47, 50, 53, 55] (Table 5). The most commonly described health outcomes across the studies was non-adherence to self-care activities, with the main activity being medication non-adherence [5, 6, 9, 12, 32, 33, 38, 53, 55]. Non-adherence was an intentional action (e.g., ignore or modify required guidance) [5, 6, 9, 38, 53] or a non-intentional action [9], but most studies found that intentional non-adherence was prevalent. For example, Corbett and colleagues found that several participants strategically chose to deviate from or ignore recommended therapeutic regimens in order to "live their life as they wanted" [5].

Table 5 Health outcomes related to an increase in treatment burden
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The disease progression and deterioration of health status was another health outcome described in the studies [8, 9, 38, 47, 50]. Eton and colleagues showed that higher levels of treatment burden were associated with mental and physical health status six months after the baseline [47]. A relationship between treatment burden and quality of life was also found in three studies [12, 50, 55]. Caregiver burden due to healthcare tasks of people living with multimorbidity and their impacts on caregivers’ daily lives was also described in Ortenblad and colleagues’ study [38]. In their study, people living with multimorbidity reported that their family members faced the challenge of not being able to enjoy their own personal and social activities as they prioritized the health of their family member with multimorbidity.

Instruments measuring treatment burden in multimorbidity

To evaluate the comprehensiveness of the instruments, we analyzed seven quantitative studies that reported the psychometric properties of the instruments. Three instruments and their variations were found: PETS and its variations (i.e., the brief version of PETS and PETS version 2.0) [12, 32,33,34]; the Chinese version of the TBQ [50], and the Chinese and German version of the MTBQ [55, 56] (see Supplementary file 4). The number of items in each instrument varied: 60 items in PETS version 2.0 [33], 15 items in the TBQ [50], and 13 items in the MTBQ [58].

Among the three versions of the PETS included in the review, the latest version of PETS version 2.0 was used to examine the contents because this latest version was more comprehensive compared to the original PETS [12, 33]. In addition, there were deleted items in the final translated versions of the MTBQ [55, 56]. In the process of cultural adaptation, translated versions of the MTBQ often excluded items that were irrelevant to local healthcare systems. For instance, in the German version of the MTBQ, the item, "Getting help from community services" was removed due to no similar service structures in Germany [55]. Therefore, to ensure a comprehensive evaluation of item content, we opted to use the original version of the MTBQ [58].

Comprehensiveness of the contents

Items in PETS version 2.0, the TBQ, and the MTBQ addressed both components of treatment burden (i.e., healthcare tasks and the impacts) (Table 2) [33, 50, 55, 56, 58]. However, some items in the three instruments asked about resources that exacerbated treatment burden (e.g., “problems with different healthcare providers not communicating with each other about my medical care” in PETS version 2.0) [33].

Three groups of healthcare tasks that people with multimorbidity performed were included in the three instruments: self-care activities, knowledge acquisition, and paperwork. All three instruments addressed self-care activities (e.g., medication management and health status and symptom monitoring) [33, 50, 55, 56, 58]. However, the detailed contents of the items in each instrument varied slightly. For instance, items in the TBQ and the MTBQ only addressed the burden of exercising and changing one’s diet for self-care activities [50, 55, 56, 58]. However, items in PETS version 2.0 also asked about difficulties related to using medical equipment [33].

Items asking about knowledge acquisition were found in PETS version 2.0 and the MTBQ [33, 55, 56, 58]. However, items in PETS version 2.0 asked about learning various information (e.g., healthy food, medications, and treatment plans), while the item in the MTBQ asked about obtaining information that was understandable and up-to-date. Items about paperwork were addressed in PETS version 2.0 and the TBQ [33, 50]. However, no instrument included items asking about the burden of constant prioritization between healthcare tasks and people’s personal lives or among the various healthcare tasks [5, 8, 38, 39, 52, 53].

Three types of impact from healthcare tasks on people’s lives were identified in our review: social, emotional, and financial impacts (Table 2). PETS version 2.0 and the TBQ addressed all three types of impact [33, 50], while the MTBQ included only social and financial impact [55, 56, 58]. Among the items related to social impact, being dependent on others was included in all three instruments [33, 50, 55, 56, 58]. Role/social activity limitations were only addressed in PETS version 2.0 [33]. Emotional impact that was included in PETS version 2.0 and the TBQ [33, 50] were slightly different. PETS version 2.0 asked about mental exhaustion such as anger, frustration and depression due to self-management [33], while the TBQ included one item related to how they felt about being sick (“The need for medical health care on a regular basis reminds me of my health problems”) [50]. Financial impacts were addressed in all three instruments [33, 50, 55, 56, 58], but the level of exhaustiveness and details varied slightly among the three instruments. Items in PETS version 2.0 asked about the burden of paying for medications, healthy foods, and medical expenses as well as the impact of medical costs on future plans [33].

Discussion

We found that treatment burden consisted of burden from four healthcare tasks (i.e., self-care activities, knowledge acquisition, paperwork, ongoing prioritization) and their impacts on social, emotional, and financial lives of people with multimorbidity. In the context of multimorbidity, individual’s circumstances and available resources affected their treatment burden. We also found that items included in the existing instruments measuring treatment burden in this population did not address all the details of the components of treatment burden identified in our review.

Our review showed that people with multimorbidity felt the burden of treatment on their lives from various healthcare tasks and the impacts of the tasks. This finding is consistent with previous studies describing the conceptual framework and taxonomy of treatment burden for people with chronic conditions [18, 19]. However, our integrated map revealed two additional unique aspects of treatment burden of multimorbidity along with contributing factors and health outcomes of treatment burden. First, we identified ongoing prioritization as a healthcare task that has not been explicitly addressed in previous conceptual models or taxonomy [18, 19] or in instruments measuring treatment burden in people with multimorbidity [12, 32,33,34, 50, 55, 56, 58]. Although PETS version 2.0 included items asking about the role (e.g., roles in workplace and family) and social activity limitations due to healthcare tasks, ongoing prioritization was not considered a healthcare task [33]. We also found that ongoing prioritization included not only prioritizing between their healthcare tasks and people’s daily lives but also prioritizing among people’s various healthcare tasks.

Our review showed that people living with multiple chronic conditions frequently faced the additional challenge of setting day-to-day priorities and decision-making [5, 8, 38, 39, 52, 53]. This finding has also been well described in previous review papers [59, 60]. Some investigators have mentioned prioritization as a strategy to alleviate treatment burden of people living with multimorbidity [15, 61, 62]. However, in our study, we specifically identified ongoing prioritization as a distinct healthcare task based on the iterative nature of managing chronic conditions [63, 64]. Paterson and colleagues reported that people with a single disease made an average of 21 decisions related to self-care per day [65], underscoring the continuous nature of this task. For people living with multimorbidity, the act of setting priorities is an ongoing task because they frequently experience changes in disease status, which could prompt them to consider how to manage their health given their available resources and circumstances [5, 39, 49]. Yin and colleagues noted that this type of healthcare task is not always visible to others and is often unappreciated, so people living with multimorbidity may receive little assistance from others [66].

Second, our integrated map explicitly describes the role of multimorbidity in understanding the treatment burden of people living with multimorbidity. Most identified healthcare tasks performed by patients with multimorbidity in our review and other studies are comparable to those performed by patients with a single chronic condition [67, 68]. For example, people with heart failure should adhere to multiple medications for heart failure, a low sodium diet, and symptom monitoring, and they should keep their appointments with cardiologists [67]. However, when people with heart failure are diagnosed with new chronic conditions, the quantity and complexity of the tasks can significantly increase, such as difficulty interpreting changes in symptoms [69, 70]. Thus, the treatment burden of people living with multimorbidity was distinct compared to people with a single chronic condition because of the context of multimorbidity.

Our review revealed three types of impact on healthcare tasks: social, emotional, and financial, which have been consistently addressed in previous conceptual models and taxonomy of multimorbidity treatment burden [18, 19] and instruments measuring treatment burden [12, 32,33,34, 50, 55, 56, 58]. However, unlike previous models and taxonomy, the reciprocal relationship between healthcare tasks and the impacts is reflected in our integrated map. Given that only two studies in our review showed this interrelated relationship [36, 38], further investigation is needed to support the association between healthcare tasks and impacts for people with multimorbidity.

Studies have frequently investigated resources and included them in previous conceptual frameworks or taxonomy of the treatment burden of people with multimorbidity [14, 18, 19, 21, 71]. Knowledge about health conditions and health literacy were identified in several studies as internal resources, while support from the healthcare system (e.g., accessibility to care, multidisciplinary and coordinated care, improvement of care quality) was been frequently mentioned as external resources in our review and previous studies [15, 16, 71]. Knowing what resources are accessible to people living with multimorbidity is critical. Shippee’s Cumulative Complexity Model suggests that the treatment burden arises from an imbalance between patients’ workload and capacity, which refers to their preparedness to meet various demands [15]. Thus, to successfully decrease the treatment burden, healthcare providers should have a holistic view when helping people living with multimorbidity and comprehensively assess the burden so they do not miss any key information about people’s internal and external resources and their circumstances. In particular, improving the continuity of care can be valuable to reduce their treatment burden. Continuity of care was the most frequently reported factor for reducing treatment burden in our review.

As a health outcome of treatment burden, non-adherence to treatment emerged as the most described outcome, and intentional non-adherence was the most common. This finding highlights the importance of developing interventions to decrease treatment burden in this population. For instance, shared decision-making could serve as an effective strategy to mitigate the treatment burden associated with multimorbidity. Tinetti and colleagues conducted an intervention study on people with multimorbidity and found that discussing self-care activities and medical procedures with this group based on their life priorities was effective to decrease in treatment burden [43]. They found that the intervention led to increased medication discontinuation, decreased orders for diagnostic/laboratory tests, and fewer additional self-care activity recommendations. Our review also revealed that treatment burden amplified caregivers’ burden, which could ultimately lead to depleted social resources. However, although several studies have indicated that caregiver burden is an important factor affecting health outcomes of people with chronic illness [25, 72, 73], only one study in our review reported this relationship [38].

Our review found that three measures of treatment burden adequately addressed the majority of the specific components of treatment burden. However, none of the three measures included items about ongoing prioritization. Regarding impacts, the TBQ included one item for each of the three categories of impacts (i.e., social, emotional, and financial) [74] and the MTBQ included no item about emotional impact [58]. Although items in PETS version 2.0 addressed treatment burden in great detail, the measure is lengthy with 60 items, and some of the items assessed components other than treatment burden (e.g., resources) [33]. Both the TBQ and the MTBQ contained items indicating resources, which is not a component of treatment burden based on the definition of treatment burden (i.e., the burden from performing healthcare tasks and the impact of those tasks on the well-being of people living with multimorbidity) [58, 74]. Thus, the measures of multimorbidity treatment burden need further improvement by considering the contents and applicability in clinical settings.

Limitations

There are limitations to be noted in our review. The participants of the included studies were mostly from Western countries and were older people, which limits the generalizability of our findings to the population with multiple chronic conditions. Excluding non-English articles also limited the comprehensiveness of our findings. Most of the studies included in the review used a medical records review method to collect data on chronic conditions. Although a medical records review is considered the gold standard, self-reported chronic conditions may be more realistic. People with multimorbidity may feel burdened by healthcare tasks from the chronic conditions that they believe they have, rather than those they actually have. Thus, it is possible that studies included in our review understated the relationship between the context of multimorbidity and treatment burden.

Conclusion

We developed an integrated map of treatment burden illustrating the dynamic relationships among treatment burden, the multimorbidity context, individual’s circumstances and available resources, and health outcomes. Our findings can help scholars and medical professionals comprehensively understand the treatment burden experienced by people living with multimorbidity and the unique features of their treatment burden. The findings can also help professionals develop person-centered interventions considering individuals’ available resources given their circumstances and the context of multimorbidity. However, more research is needed to support and refine our integrated map. We also found that existing instruments measuring multimorbidity treatment burden often overlooked certain aspects, such as ongoing prioritization, which is particularly relevant for people living with multimorbidity. Further work is also needed to develop instruments that overcome the weaknesses of the current instruments.

Availability of data and materials

The datasets supporting the conclusions of this article are included within the article and its Supplementary files.

Abbreviations

MMAT:

The Mixed Methods Appraisal Tool

MTBQ:

Multimorbidity Treatment Burden Questionnaire

PETS:

Patient Experience with Treatment burden and Self-management

TBQ:

Treatment Burden Questionnaire

References

  1. Fortin M, Stewart M, Poitras M-E, Almirall J, Maddocks H. A systematic review of prevalence studies on multimorbidity: toward a more uniform methodology. The Annals of Family Medicine. 2012;10(2):142–51.

    Article  PubMed  Google Scholar 

  2. WHO. Multimorbidity. 2016.

  3. Tran V-T, Montori VM, Ravaud P, editors. Is my patient Overwhelmed?: determining thresholds for acceptable burden of treatment using data from the compare e-Cohort. Mayo Clinic Proceedings; 2020: Elsevier.

  4. Hardman R, StephenSpelten, Evelien. Multimorbidity and its effect on perceived burden, capacity and the ability to self-manage in a low-income rural primary care population: A qualitative study. PloS one. 2021;16(8):e0255802.

  5. Corbett T, Lee K, Cummings A, Calman L, Farrington N, Lewis L, et al. Self-management by older people living with cancer and multi-morbidity: a qualitative study. Support Care Cancer. 2022;30(6):4823–33.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Fix GM, Cohn ES, Solomon JL, Cortes DE, Mueller N, Kressin NR, et al. The role of comorbidities in patients’ hypertension self-management. Chronic Illn. 2014;10(2):81–92.

    Article  PubMed  Google Scholar 

  7. Moffat K, Mercer SW. Challenges of managing people with multimorbidity in today’s healthcare systems. BMC Fam Pract. 2015;16(1):129.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Matima R, Murphy K, Levitt NS, BeLue R, Oni T. A qualitative study on the experiences and perspectives of public sector patients in Cape Town in managing the workload of demands of HIV and type 2 diabetes multimorbidity. PLoS ONE. 2018;13(3):e0194191.

    Article  PubMed  PubMed Central  Google Scholar 

  9. van Pinxteren M, Mbokazi N, Murphy K, Mair FS, May C, Levitt N. The impact of persistent precarity on patients’ capacity to manage their treatment burden: A comparative qualitative study between urban and rural patients with multimorbidity in South Africa. Front Med (Lausanne). 2023;10:1061190.

    Article  PubMed  Google Scholar 

  10. May C, Montori VM, Mair FS. We need minimally disruptive medicine. Bmj. 2009;339.

  11. Leppin AL, Montori VM, Gionfriddo MR. Minimally Disruptive Medicine: A Pragmatically Comprehensive Model for Delivering Care to Patients with Multiple Chronic Conditions. Healthcare (Basel). 2015;3(1):50–63.

    Article  PubMed  Google Scholar 

  12. Eton DT, Yost KJ, Lai JS, Ridgeway JL, Egginton JS, Rosedahl JK, et al. Development and validation of the Patient Experience with Treatment and Self-management (PETS): a patient-reported measure of treatment burden. Qual Life Res. 2017;26(2):489–503.

    Article  PubMed  Google Scholar 

  13. Smith SM, Bayliss EA, Mercer SW, Gunn J, Vestergaard M, Wyke S, et al. How to design and evaluate interventions to improve outcomes for patients with multimorbidity. J Comorb. 2013;3(1):10–7.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Eton DT, Ramalho de Oliveira D, Egginton JS, Ridgeway JL, Odell L, May CR, et al. Building a measurement framework of burden of treatment in complex patients with chronic conditions: a qualitative study. Patient Related Outcome Measures. 2012;3:39–49.

  15. Shippee ND, Shah ND, May CR, Mair FS, Montori VM. Cumulative complexity: a functional, patient-centered model of patient complexity can improve research and practice. J Clin Epidemiol. 2012;65(10):1041–51.

    Article  PubMed  Google Scholar 

  16. Rosbach M, Andersen JS. Patient-experienced burden of treatment in patients with multimorbidity – A systematic review of qualitative data. PLoS ONE. 2017;12(6):e0179916.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Matthews KS, Rennoldson SC, Fraser SD. Influence of health-system change on treatment burden: a systematic review. Br J Gen Pract. 2023;73(726):e59–66.

    Article  PubMed  Google Scholar 

  18. Eton DT, Ridgeway JL, Egginton JS, Tiedje K, Linzer M, Boehm DH, et al. Finalizing a measurement framework for the burden of treatment in complex patients with chronic conditions. Patient Relat Outcome Meas. 2015;6:117–26.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Tran V-T, Barnes C, Montori VM, Falissard B, Ravaud P. Taxonomy of the burden of treatment: a multi-country web-based qualitative study of patients with chronic conditions. BMC Med. 2015;13(1):115.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Demain S, Gonçalves AC, Areia C, Oliveira R, Marcos AJ, Marques A, et al. Living with, managing and minimising treatment burden in long term conditions: a systematic review of qualitative research. PLoS ONE. 2015;10(5):e0125457.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Sav A, Salehi A, Mair FS, McMillan SS. Measuring the burden of treatment for chronic disease: implications of a scoping review of the literature. BMC Med Res Methodol. 2017;17:1–14.

    Article  Google Scholar 

  22. Mendoza-Quispe D, Perez-Leon S, Alarcon-Ruiz CA, Gaspar A, Cuba-Fuentes MS, Zunt JR, et al. Scoping review of measures of treatment burden in patients with multimorbidity: advancements and current gaps. J Clin Epidemiol. 2023;159:92–105.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Lee KS, Lee J. Scoping review of treatment burden in multimorbidity: definition to interventions. OSF. 2023.

  24. Whittemore R, Knafl K. The integrative review: updated methodology. J Adv Nurs. 2005;52(5):546–53.

    Article  PubMed  Google Scholar 

  25. Sav A, King MA, Whitty JA, Kendall E, McMillan SS, Kelly F, et al. Burden of treatment for chronic illness: a concept analysis and review of the literature. Health Expect. 2015;18(3):312–24.

    Article  PubMed  Google Scholar 

  26. Bernell S, Howard SW. Use Your Words Carefully: What Is a Chronic Disease? Front Public Health. 2016;4:159.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Ishii H, Shin K, Tosaki T, Haga T, Nakajima Y, Shiraiwa T, et al. Reproducibility and Validity of a Questionnaire Measuring Treatment Burden on Patients with Type 2 Diabetes: Diabetic Treatment Burden Questionnaire (DTBQ). Diabetes Ther. 2018;9(3):1001–19.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Pluye P, Gagnon MP, Griffiths F, Johnson-Lafleur J. A scoring system for appraising mixed methods research, and concomitantly appraising qualitative, quantitative and mixed methods primary studies in Mixed Studies Reviews. Int J Nurs Stud. 2009;46(4):529–46.

    Article  PubMed  Google Scholar 

  29. Pace R, Pluye P, Bartlett G, Macaulay AC, Salsberg J, Jagosh J, et al. Testing the reliability and efficiency of the pilot Mixed Methods Appraisal Tool (MMAT) for systematic mixed studies review. Int J Nurs Stud. 2012;49(1):47–53.

    Article  PubMed  Google Scholar 

  30. Hong QN, Fàbregues S, Bartlett G, Boardman F, Cargo M, Dagenais P, et al. The Mixed Methods Appraisal Tool (MMAT) version 2018 for information professionals and researchers. Educ Inf. 2018;34(4):285–91.

    Google Scholar 

  31. Eton DT, Ridgeway JL, Linzer M, Boehm DH, Rogers EA, Yost KJ, et al. Healthcare provider relational quality is associated with better self-management and less treatment burden in people with multiple chronic conditions. Patient Prefer Adherence. 2017;11:1635–46.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Eton DT, Linzer M, Boehm DH, Vanderboom CE, Rogers EA, Frost MH, et al. Deriving and validating a brief measure of treatment burden to assess person-centered healthcare quality in primary care: a multi-method study. BMC Fam Pract. 2020;21(1):221.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Eton DT, Lee MK, St Sauver JL, Anderson RT. Known-groups validity and responsiveness to change of the Patient Experience with Treatment and Self-management (PETS vs. 2.0): a patient-reported measure of treatment burden. Qual Life Res. 2020;29(11):3143–54.

  34. Lee MK, St Sauver JL, Anderson RT, Linzer M, Eton DT. Confirmatory Factor Analyses and Differential Item Functioning of the Patient Experience with Treatment and Self-Management (PETS vs. 2.0): A Measure of Treatment Burden. Patient Relat Outcome Meas. 2020;11:249–63.

  35. Eton DT, Anderson RT, Cohn WF, Kennedy EM, St. Sauver JL, Bucknell BJ, et al. Risk factors for poor health-related quality of life in cancer survivors with multiple chronic conditions: exploring the role of treatment burden as a mediator. Patient Related Outcome Measures. 2019;10:89–99.

  36. Eton DT, Anderson RT, St Sauver JL, Rogers EA, Linzer M, Lee MK. Longitudinal trajectories of treatment burden: A prospective survey study of adults living with multiple chronic conditions in the midwestern United States. J Multimorb Comorb. 2022;12:26335565221081292.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Siddiqui A, Ornstein KA, Ankuda CK. Prevalence of Treatment Burden in the Last Three Years of Life. J Palliat Med. 2020;24(6):879–86.

    Article  PubMed  Google Scholar 

  38. Ortenblad L, Meillier L, Jonsson AR. Multi-morbidity: A patient perspective on navigating the health care system and everyday life. Chronic Illn. 2018;14(4):271–82.

    Article  PubMed  Google Scholar 

  39. Duguay C, Gallagher F, Fortin M. The experience of adults with multimorbidity: a qualitative study. J Comorb. 2014;4(1):11–21.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Hounkpatin HO, Roderick P, Harris S, Morris JE, Smith D, Walsh B, et al. Change in treatment burden among people with multimorbidity: a follow-up survey. Br J Gen Pract. 2022;72(724):e816–24.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Morris JE, Roderick PJ, Harris S, Yao G, Crowe S, Phillips D, et al. Treatment burden for patients with multimorbidity: cross-sectional study with exploration of a single-item measure. Br J Gen Pract. 2021;71(706):e381–90.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Herzig L, Zeller A, Pasquier J, Streit S, Neuner-Jehle S, Excoffier S, et al. Factors associated with patients’ and GPs’ assessment of the burden of treatment in multimorbid patients: a cross-sectional study in primary care. BMC Fam Pract. 2019;20(1):88.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Tinetti ME, Naik AD, Dindo L, Costello DM, Esterson J, Geda M, et al. Association of patient priorities-aligned decision-making with patient outcomes and ambulatory health care burden among older adults with multiple chronic conditions: a nonrandomized clinical trial. JAMA Intern Med. 2019;179(12):1688–97.

    Article  PubMed  PubMed Central  Google Scholar 

  44. McCarthy C, Clyne B, Boland F, Moriarty F, Flood M, Wallace E, et al. GP-delivered medication review of polypharmacy, deprescribing, and patient priorities in older people with multimorbidity in Irish primary care (SPPiRE Study): A cluster randomised controlled trial. PLoS Med. 2022;19(1):e1003862.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Hu X-J, Harry H. X.Li, Yu-TingWu, Xiao-YaWang, YiChen, Jia-HengWang, Jia-JiWong, Samuel Y. S.Mercer, Stewart W. Healthcare needs, experiences and treatment burden in primary care patients with multimorbidity: An evaluation of process of care from patients' perspectives. Health expectations : an international journal of public participation in health care and health policy. 2022;25(1):203–13.

  46. Aschmann HE, Puhan MA, Robbins CW, Bayliss EA, Chan WV, Mularski RA, et al. Outcome preferences of older people with multiple chronic conditions and hypertension: a cross-sectional survey using best-worst scaling. Health Qual Life Outcomes. 2019;17(1):186.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Eton DT, Anderson RT, Cohn WF, Kennedy EM, St Sauver JL, Bucknell BJ, et al. Risk factors for poor health-related quality of life in cancer survivors with multiple chronic conditions: exploring the role of treatment burden as a mediator. Patient Relat Outcome Meas. 2019;10:89–99.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Song M-K, Paul S, Plantinga L, Henry C, Turberville-Trujillo L. social networks of self-care and perceived treatment burden among patients on in-center hemodialysis. Kidney Medicine. 2019;1(3):97–103.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Hardman R, Begg S, Spelten E. Multimorbidity and its effect on perceived burden, capacity and the ability to self-manage in a low-income rural primary care population: A qualitative study. PLoS ONE. 2021;16(8):e0255802.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Chin WY, Wong CKH, Ng CCW, Choi EPH, Lam CLK. Cultural adaptation and psychometric properties of the Chinese Burden of Treatment Questionnaire (C-TBQ) in primary care patients with multi-morbidity. Fam Pract. 2019;36(5):657–65.

    Article  PubMed  Google Scholar 

  51. El-Nagar SA, Nady SE, Elzyen ES, El-Saidy TMK. Relationship between Health Literacy and Treatment Burden among Patients with Multi-Morbidity. 2021.

  52. Morgan SA, Eyles C, Roderick PJ, Adongo PB, Hill AG. Women living with multi-morbidity in the Greater Accra Region of Ghana: a qualitative study guided by the Cumulative Complexity Model. J Biosoc Sci. 2019;51(4):562–77.

    Article  PubMed  Google Scholar 

  53. van Merode T, van de Ven K, van den Akker M. Patients with multimorbidity and their treatment burden in different daily life domains: a qualitative study in primary care in the Netherlands and Belgium. J Comorb. 2018;8(1):9–15.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Schreiner N, Daly B. A Pilot Study Exploring Treatment Burden in a Skilled Nursing Population. Rehabilitation Nursing Journal. 2020;45(3).

  55. Schulze J, Breckner A, Duncan P, Scherer M, Pohontsch NJ, Luhmann D. Adaptation and validation of a German version of the Multimorbidity Treatment Burden Questionnaire. Health Qual Life Outcomes. 2022;20(1):90.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Dou L, Huang J, Duncan P, Guo L. Translation, cultural adaptation and validation of the Chinese Multimorbidity Treatment Burden Questionnaire(C-MTBQ): a study of older hospital patients. Health Qual Life Outcomes. 2020;18(1):194.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Schreiner N, DiGennaro S, Harwell C, Burant C, Daly B, Douglas S. Treatment burden as a predictor of self-management adherence within the primary care population. Appl Nurs Res. 2020;54:151301.

    Article  PubMed  Google Scholar 

  58. Duncan P, Murphy M, Man MS, Chaplin K, Gaunt D, Salisbury C. Development and validation of the Multimorbidity Treatment Burden Questionnaire (MTBQ). BMJ Open. 2018;8(4):e019413.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Bratzke LC, Muehrer RJ, Kehl KA, Lee KS, Ward EC, Kwekkeboom KL. Self-management priority setting and decision-making in adults with multimorbidity: a narrative review of literature. Int J Nurs Stud. 2015;52(3):744–55.

    Article  PubMed  Google Scholar 

  60. Gobeil-Lavoie AP, Chouinard MC, Danish A, Hudon C. Characteristics of self-management among patients with complex health needs: a thematic analysis review. BMJ Open. 2019;9(5):e028344.

    Article  PubMed  PubMed Central  Google Scholar 

  61. Rosbach M, Andersen JS. Patient-experienced burden of treatment in patients with multimorbidity - A systematic review of qualitative data. PLoS ONE. 2017;12(6):e0179916.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Bunn F, Goodman C, Russell B, Wilson P, Manthorpe J, Rait G, et al. Supporting shared decision making for older people with multiple health and social care needs: a realist synthesis. BMC Geriatr. 2018;18(1):165.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Novak M, Costantini L, Schneider S, Beanlands H, editors. Approaches to self‐management in chronic illness. Seminars in dialysis; 2013: Wiley Online Library.

  64. Thorne S, Paterson B, Russell C. The structure of everyday self-care decision making in chronic illness. Qual Health Res. 2003;13(10):1337–52.

    Article  PubMed  Google Scholar 

  65. Paterson B, Thorne S, Russell C. Disease-specific influences on meaning and significance in self-care decision-making in chronic illness. Canadian Journal of Nursing Research Archive. 2002.

  66. Yin K, Jung J, Coiera E, Laranjo L, Blandford A, Khoja A, et al. Patient Work and Their Contexts: Scoping Review. J Med Internet Res. 2020;22(6):e16656.

    Article  PubMed  PubMed Central  Google Scholar 

  67. Heidenreich Paul A, Bozkurt B, Aguilar D, Allen Larry A, Byun Joni J, Colvin Monica M, et al. 2022 AHA/ACC/HFSA Guideline for the Management of Heart Failure. J Am Coll Cardiol. 2022;79(17):e263–421.

    Article  CAS  PubMed  Google Scholar 

  68. Educators AAoD. An effective model of diabetes care and education: revising the AADE7 Self-Care Behaviors®. The Diabetes Educator. 2020;46(2):139–60.

  69. Lee KS, Oh S. An Integrative Review of the Symptom Perception Process in Heart Failure. J Cardiovasc Nurs. 2022;37(2):122–33.

    Article  PubMed  Google Scholar 

  70. Lee KS, Moser DK, Dracup K. The association between comorbidities and self-care of heart failure: a cross-sectional study. BMC Cardiovasc Disord. 2023;23(1):157.

    Article  PubMed  PubMed Central  Google Scholar 

  71. Ridgeway JL, Egginton JS, Tiedje K, Linzer M, Boehm D, Poplau S, et al. Factors that lessen the burden of treatment in complex patients with chronic conditions: a qualitative study. Patient preference and adherence. 2014:339–51.

  72. Adelman RD, Tmanova LL, Delgado D, Dion S, Lachs MS. Caregiver burden: a clinical review. JAMA. 2014;311(10):1052–60.

    Article  CAS  PubMed  Google Scholar 

  73. Suksatan W, Tankumpuan T, Davidson PM. Heart failure caregiver burden and outcomes: a systematic review. J Prim Care Community Health. 2022;13:21501319221112584.

    Article  PubMed  PubMed Central  Google Scholar 

  74. Tran VT, Montori VM, Eton DT, Baruch D, Falissard B, Ravaud P. Development and description of measurement properties of an instrument to assess treatment burden among patients with multiple chronic conditions. BMC Med. 2012;10:68.

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The authors would like to express their gratitude to Minji Kim and Yo Rim Park for their assistance in reviewing the topic and refining the data. This research was supported by the BK21 four project (Center for World-leading Human-care Nurse Leaders for the Future) funded by the Ministry of Education (MOE, Korea) and National Research Foundation of Korea (NRF).

Funding

This study was supported by the National Research Foundation of Korea, which provides funding from the Korean government (MEST: 2021R1C1C1008498) and the BK21 four project (Center for World-leading Human-care Nurse Leaders for the Future) funded by the Ministry of Education (MOE, Korea).

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  1. College of Nursing, Seoul National University, 103 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea

    Ji Eun Lee, Jihyang Lee, Rooheui Shin & Kyoung Suk Lee

  2. Center for World-leading Human-care Nurse Leaders for the Future by Brain Korea 21 (BK 21) four project, College of Nursing, Seoul National University, Seoul, South Korea

    Jihyang Lee & Kyoung Suk Lee

  3. School of Nursing, University of Pennsylvania, Philadelphia, PA, USA

    Oonjee Oh

  4. Research Institute of Nursing Science, College of Nursing, Seoul National University, Seoul, South Korea

    Kyoung Suk Lee

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KS conceptualized the review and devised the methodologies. JE, JH, RH, OJ, and KS collaboratively proceeded to the selection, extraction and analysis of the data. JE was responsible for visualizing the data. JE, JH, RH, and KS wrote the manuscript. KS oversaw the review process, edited the manuscript, and provided supervision. KS secured the funding for the project.

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Lee, J.E., Lee, J., Shin, R. et al. Treatment burden in multimorbidity: an integrative review. BMC Prim. Care 25, 352 (2024). https://doi.org/10.1186/s12875-024-02586-z

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