The association between multimorbidity and needs-based quality of life in primary care: a cross-sectional questionnaire study

Abstract

Introduction

A negative association between the number of chronic conditions and quality of life (QoL) is well known, but the complexity of this association is not fully understood. This study aimed to 1) examine the association between the number of diagnosis groups, as a measure of multimorbidity, and needs-based QoL, and 2) explore how this association varies across sociodemographic subgroups.

Methods

This cross-sectional study included adults with chronic conditions managed at a general practitioner (GP) who participated in a cluster-randomized trial. The exposure was the number of self-reported diagnosis groups, using an organ-specific definition, and the outcome was needs-based QoL measured using the Multi Morbidity Questionnaire 1 (MMQ1). Multivariable linear regression models were used, and a Minimal Important Difference (MID) for each domain were calculated to assess the clinical relevance.

Results

The study included 31,753 patients. Significant, linear, associations were found between the number of diagnosis groups and needs-based QoL. Age, education, occupation, and living alone were identified as effect modifiers. The strongest associations were observed among participants aged 40–59 years, those with lower educational levels, the unemployed, and those living alone.

Conclusion

A cumulative burden of multimorbidity was identified as increasing number of diagnoses was associated with lower needs-based QoL. Socioeconomically and socially vulnerable groups may experience greater impacts on their QoL and may benefit from additional support or more personalized care approaches. These findings highlight the importance of a bio-psycho-social approach when caring for patients with chronic disease and multimorbidity in general practice.

Introduction

As the population age increases and medical interventions improve, the prevalence of chronic diseases continues to rise [1]. Multimorbidity, often defined as the coexistence of two or more chronic conditions within an individual [2], is associated with increased mortality, polypharmacy, higher health-care utilization, and lower quality of life (QoL) [3–6]. Thus, management of patients with multimorbidity poses a complex and increasingly prevalent challenge in primary care [7].

From a primary care perspective, a better understanding of how multimorbidity affects different aspects of QoL is needed to assist development of intervention strategies, addressing not only medical management but also psychosocial and environmental factors [4].

Numerous studies have investigated the association between multimorbidity and QoL, often demonstrating a negative correlation between the number of chronic conditions and QoL [4,8]. However, studies are difficult to compare and apply to the general practice population due to three main reasons: Firstly, great heterogeneity exists in how multimorbidity is defined, including differences in types and quantity of conditions considered, as well as the threshold for classifying individuals with multimorbidity [9,10]. Secondly, there is a lack of uniformity when measuring QoL, with many assessment tools being generic and few tailored specifically for individuals with multimorbidity [4,11]. Thirdly, variations in study populations and diversity in data sources and collection methods utilized, further deteriorate study comparability, and add to the complexity when discussing multimorbidity [4,12,13].

To characterize the association between multimorbidity and QoL, the definition of multimorbidity must relate to the possible mechanisms by which multimorbidity may affect the QoL in patients. In addition to individual and clinical factors, organizational aspects such as fragmented care pathways, lack of care coordination, and siloed service delivery can further complicate the management of multimorbidity and contribute to poorer QoL. In quantitative studies, patients often refer to an increased treatment burden due to the fragmentation of healthcare [14,15].

Willadsen et al. [16], defines multimorbidity as having a diagnosis from two or more of ten diagnosis groups (lung, musculoskeletal, endocrine, mental, cancer, neurological, gastrointestinal, cardiovascular, kidney, and sensory organs). This definition reflects healthcare organization in silos and acknowledges the complexity of managing multiple conditions. In this study, we adopt the same overall definition of multimorbidity but apply a slightly modified version of Willadsen’s classifications of diagnostic groups to better align with our data structure.

Generic QoL measures like EQ-5D and PROMIS primarily assess health status or functional limitations, which may not fully capture the lived experience of patients with multimorbidity [17,18]. In contrast, needs-based tools such as Multimorbidity Questionnaire 1 (MMQ1) [19] focus on whether essential life needs are being met despite living with chronic illness. This approach is particularly suited to people with multimorbidity, whose QoL is shaped not only by symptoms and functioning, but also by their ability to maintain autonomy, social relationships, and financial stability.

This study is based on data collected through the MM600 trial [20] and uses MMQ1 to assess needs-based QoL. The aim is to evaluate the association between multimorbidity, based on the number of diagnosis groups, and needs-based QoL, while exploring how this association varies across sociodemographic subgroups. We hypothesize that having a condition from an increasing number of diagnostic groups is associated with lower needs-based QoL, and that this association is stronger among individuals with social or economic vulnerability.

This study adds to the literature by using a needs-based, patient-centered measure of QoL to evaluate how multimorbidity affects individuals in a large primary care population, offering new insights into the social and clinical complexity of living with multiple chronic conditions.

Materials and methods

This study is reported in accordance with the STROBE statement [21].

Study design, setting and participants

The study design is cross-sectional and is based on a questionnaire survey completed in relation to a cluster-randomized controlled trial run in Danish general practices [20]. In Denmark, every citizen is allocated a general practitioner (GP). The recruitment of GPs is described in detail in the MM600 trial protocol [20].

The cohort was identified through the Danish Health Data Authority using civil registration (CPR) number, enabling linkage between public registers and additional data sources [22]. Eligible participants were aged ≥18 years, had attended at least one annual chronic disease consultation in general practice in 2022 (or listed as a chronic-fee patient), and were listed with a participating GP as of January 1^st, 2023. In March 2023, they received an electronic invitation to complete a questionnaire. Only those who responded to the initial invitation and reported having at least one chronic condition were included in this study. The study uses baseline data collected prior to randomization in the MM600 trial; therefore, no participants had been allocated to intervention or control groups at the time of data collection used in this analysis.

Data sources and variables

Information on age and sex was obtained from the CPR number. Demographic data (education level, living alone/with others, and work status) were assessed through a questionnaire, specifically developed for the MM600 trial, which comprises the MMQ1, the Multi Morbidity Questionnaire 2 (MMQ2) and a basic questionnaire concerning health, including chronic conditions and medicine use [20]. The MMQ2 measures Self-perceived inequity and is not relevant for the analysis of this study.

Exposure

The exposure was the number of self-reported diagnosis groups from the MM600 trial questionnaire. Multimorbidity was defined as having chronic conditions in two or more diagnostic groups, based on the framework by Willadsen et al. which categorizes conditions by shared clinical and organizational characteristics rather than specific diagnoses. This approach allows for the inclusion of less common conditions and reflects how patients perceive and manage their illnesses. It also aligns with the needs-based focus of this study by facilitating the exploration of multimorbidity’s broader impact on QoL, particularly in relation to complexity in care delivery and lived experience. Although multimorbidity was defined as having diagnoses from two or more groups, patients with only one diagnostic group were included to enable a continuous analysis of the association between diagnostic burden and needs-based QoL. This study uses a slightly adapted version of Willadsen et al.’s definition of multimorbidity, which originally classified chronic conditions into ten diagnostic groups based on shared clinical and organizational characteristics [14]. A full description of Willadsen’s original classification is provided in Appendix A. In this survey, participants reported diagnoses across 13 distinct diagnostic groups. Most diagnostic groups were merged to reflect Willadsen’s classification; however, ‘Allergy’ was retained as a distinct group in the analysis, whereas it was originally merged with lung diseases in Willadsen’s framework.

In the questionnaire, respondents were asked whether they had experienced conditions within each of the 13 groups: Cancer, Allergy, Diabetes, Metabolic Disease, Neurological diagnoses, Airways, Cardiovascular, Gastrointestinal, Genitourinary, Musculoskeletal, Psychiatric, Skin and Ear/Eye. For each diagnostic group, participants were asked: ‘Have you had a disease from this group, for example Lung diseases?’ with response options: ‘No, I have never had it’, ‘Yes, and I still have it’, ‘Yes, but I don’t have it anymore’, and ‘I don’t know’.

If participants answered ‘Yes, but I don’t have it anymore’, they were further asked: ‘Are you still affected by this disease?’ and ‘Do you still take medication because of this disease?’ with response options being ‘Yes’, ‘No’, and ‘I don’t know’. In this study, a chronic condition was considered present if the participant either answered ‘Yes, and I still have it’, or answered ‘Yes, but I don’t have it anymore’ combined with a ‘Yes’ to at least one of the two follow-up questions. For some conditions, such as headache or back pain, this clarification was included: ‘which has led to contact with a doctor’, to help distinguish diagnosed conditions from general symptom experiences. For other conditions - such as diabetes or hypertension - this distinction was not made, as these are typically recognized as formal diagnoses. As few patients reported more than eight diagnosis groups, these were coded as 9+ in the analyses.

Outcome

Th outcome measure was needs-based QoL. It was assessed using the Multi Morbidity Questionnaire 1 (MMQ1), a condition-specific PROM, tested and validated for patients living with MM [19]. It consists of six domains with unidimensional scales: worries (6 items), limitations in everyday life (10 items), my social life (6 items), self-image (6 items), personal finances (3 items), and physical ability (6 items). The domains represent different aspects of how a patient’s needs-based QoL can be affected when living with multimorbidity Each item is a statement related and relevant to the domain, and respondents could answer ‘No, not at all ‘, (0) Yes, a little bit ‘, (1) ‘Yes, quite a lot’, (2) and ‘Yes, a lot’ (3). The items are structured so that a higher score indicates lower needs-based QoL, for example’, My illnesses prevent me from being as active as I would like to be’. Each MMQ1 domain has different item scores, thus mean scores for each domain were calculated (ranging from 0 to 3) and used in the analyses.

Covariates

Potential confounders were selected a priori based on clinical relevance and existing evidence linking sociodemographic factors to both multimorbidity and quality of life outcomes.

Participants were grouped into three educational levels: ‘Basic schooling’, ‘High school or additional’, and ‘College or University’. The last group included all higher education. Grouping was also done depending on employment status: ‘No work and no study’, ‘Work or study’, and ‘Retired’. All participants above the age of 67 were considered retired since we did not have data on retirement. Cohabitation status was assessed based on participants’ self-reported living arrangements and categorized as either living alone or living with others.

Statistical methods

Baseline characteristics were presented using descriptive statistics. In the unadjusted analyses, no missing data were present, as respondents with missing exposure or outcome variables were excluded. In the adjusted analyses, respondents with missing data on a specific covariate were excluded from the analyses in which that covariate was included. The association between the number of self-reported diagnosis groups and MMQ1 score was investigated first by visualizing the unadjusted association in a bar chart, and secondly with three multivariable linear regression models adjusting for an increasing number of covariates. Model 1 was adjusted for potentially confounding covariates: age, sex, and educational level. Model 2 was adjusted for the covariates in Model 1 and variables that were considered potential mediators of the association between self-reported diagnosis groups and MMQ1 score: living alone and occupation. Model 3 was adjusted for the covariates in Model 2 and medicine use, a potential mediator. In the unadjusted analysis and Model 1–3, the number of diagnoses was treated as a categorical variable. The Minimal Important Difference (MID) for each domain was calculated to assess the clinical relevance of changes in MMQ1 scores. This was derived from the average difference in domain scores between the global item responses ‘Acceptable’ and ‘Poor’, based on the discrimination ability analyses of the MMQ1 scales as described by Bissenbakker et al. (2023) [17]. The MID was then scaled to a 0–3 range. The number of diagnostic groups required to achieve MMQ1 scores exceeding the MID threshold in each domain was calculated by dividing the MID for each domain by the increase in MMQ1 score per additional diagnostic group, determined from the linear regression trendline equation of Model 2.

The association was expected to differ across subgroups. Thus, all covariates in Model 2 were tested for effect modification. The interaction terms of significant effect modifiers were introduced to Model 2, separately, to assess the change in MMQ1 score across domains per additional diagnosis group for every subgroup. Based on a visual interpretation of the bar chart presenting the unadjusted analyses, the association was close to linear in most of the domains. In these analyses, the number of diagnosis groups was treated as continuous variables. p < 0.05 was set as the threshold for what was considered significant. All statistical analyses were performed using the Statistical Analysis System vs. 9.4 (SAS).

Ethics statement

This study is based on questionnaire data collected as part of a cluster-randomized controlled trial (cRCT). The study was presented to the Capital Region’s Ethical Committee, and in accordance with Danish legislation, ethical approval was not required (Ref: H-22041229). Approval to obtain patient contact information from the Danish Health Data Authority was granted (FSEID-00006324).

All participants received written information about the study, including their rights to withdraw and the assurance that participation would not affect their treatment. Data protection followed the General Data Protection Regulation (GDPR), with personal data securely stored and managed in accordance with the University of Copenhagen’s data policies. The study is registered in the university’s research registry (514-0754/22-3000)

Results

A total of 159,619 had a secure electronic mail and 35,522 completed the survey (22%). 31,753 were included in the analyses (Figure 1).

Figure 1.

Flowchart: total study population.

*DHDA = Danish Health Data Authority

Baseline characteristics of the study population are presented in Table 1. Women and men had similar response rates, while older age groups were more likely to respond to the survey than younger age groups. People aged 70 years or older constituted 38% of the total digital population and 43.3% of the respondents while people younger than 40 years constituted 7.9% of the total digital population and 4.4% of the respondents. Multimorbidity was prevalent among most of the participants, with 83% reporting diagnoses from two or more diagnostic groups. Educational levels were generally high, with 93% of participants having attained at least a high school diploma. Approximately 54% of the cohort were retired. Regarding medication use, 65% of the participants reported using between one and four prescription drugs, while only 4% reported no use of medications.

Table 1.

Baseline characteristics.

Variable	Overall	Male	Female
	n = 31 753	n = 15 038	n = 16 715
Age (grouped), n (%) *
18–39 years	1295 (4.4%)	381 (2.5%)	914 (5.5%)
40–49 years	1972 (6.2%)	654 (4.3%)	1318 (7.9%)
50–59 years	5325 (17%)	2117 (14%)	3208 (19%)
60–69 years	9379 (30%)	4437 (30%)	4942 (30%)
70+ years	13 782 (43.3%)	7449 (50%)	6333 (38%)
Diagnosis groups, n (%) *
1	5453 (17%)	2964 (20%)	2489 (15%)
2	7666 (24%)	3933 (26%)	3733 (22%)
3	7250 (23%)	3481 (23%)	3769 (23%)
4	5529 (17%)	2424 (16%)	3105 (19%)
5	3135 (9.9%)	1262 (8.4%)	1873 (11%)
6	1660 (5.2%)	617 (4.1%)	1043 (6.2%)
7	706 (2.2%)	250 (1.7%)	456 (2.7%)
8	277 (0.9%)	81 (0.5%)	196 (1.2%)
9+	77 (0.2%)	26 (0.2%)	51 (0.3%)
Education, n (%) *
Basic schooling	2138 (6.9%)	886 (6.0%)	1252 (7.7%)
High school or other education	14 223 (46%)	7102 (48%)	7121 (44%)
College or University	14 730 (47%)	6779 (46%)	7951 (49%)
Working or studying, n (%) *
Not working or not studying	4847 (15%)	1777 (12%)	3070 (19%)
Working or studying	9466 (30%)	4077 (27%)	5389 (33%)
Retired	17 052 (54%)	9042 (61%)	8010 (49%)
Medications, n (%) *
None	1270 (4.0%)	528 (3.5%)	742 (4.4%)
1–4	20 581 (65%)	9141 (61%)	11 440 (68%)
5–8	8071 (25%)	4343 (29%)	3728 (22%)
>8	1831 (5.8%)	1026 (6.8%)	805 (4.8%)

^*Response missing = 0.

The unadjusted association between the number of diagnosis groups and the mean MMQ1 score across the six domains is illustrated in Figure 2. Across all six MMQ1 domains, higher MMQ1 scores correlated with increasing numbers of diagnosis groups.

Figure 2.

The unadjusted association between the number of self-reported diagnosis groups and mean MMQ1-score across domains.

See Appendix B for the unadjusted associations, including confidence intervals, between the number of self-reported diagnosis groups and mean MMQ1-score. The higher MMQ-1 score, the lower the needs-based quality of life. MMQ1 = Multi Morbidity Questionnaire 1.

The adjusted analyses are shown in Figure 3. All three models showed statistically significant (p < 0.0001) associations between all numbers of self-reported diagnosis groups and MMQ1-scores, across all six domains. An increase in MMQ1-score was observed for every additional reported diagnosis group in the domains. However, a gradual reduction in the strength of the association was observed through Model 1–3 across all six domains, with the greatest reduction seen in Model 3. These results indicate that as the number of diagnosis groups increases, patients experience greater impairments in multiple aspects of their needs-based QoL. Furthermore, part of the association between multimorbidity and lower needs-based QoL is explained by factors, such as education, occupation, living situation and medicine use.

Figure 3.

The Association between the number of self-reported diagnosis groups and MMQ1 domain scores: Comparison of adjusted models.

The figure shows the increase in MMQ1 domain score compared to having conditions from 1 diagnosis group. Estimate from Model 1, 2, and 3.

Model 1: Adjusted for age, sex, and educational level, Model 2: Adjusted for living alone and occupation + model 1, Model 3: adjusted for medicine consumption + model 2. See Appendix C for estimates and confidence intervals for all three models. The higher MMQ1 score, the lower the needs-based quality of life. Confidence intervals are omitted in this figure for clarity but can be found in Appendix C.

The MID threshold is used to determine the number of diagnostic groups needed to achieve MMQ scores exceeding MID, compared to having conditions from one diagnostic group.

MID = Minimal Important Difference, MMQ1 = Multi Morbidity Questionnaire 1.

The Minimal Important Difference (MID), represented as a vertical red line in Figure 3, reflects the smallest change in MMQ1 score considered meaningful from a patient perspective. Across all domains, MMQ1 scores exceeded the MID as the number of diagnosis groups increased in all three models, indicating clinically relevant declines in needs-based QoL. The number of additional diagnostic groups needed to exceed MID varied across domains: three (worries), four (my social life), four (Self-image), four (Limitations in Everyday Life), seven (Personal Finances), and two (Physical Ability).

Subgroup analyses of covariates from Model 2 are shown in Table 2. Each additional diagnosis group was associated with an average increase in MMQ 1 score of 0.20 in the domain of worries, 0.11 in social life, 0.13 in self-image, 0.17 in limitations in everyday life, 0.10 in personal finances, and 0.18 in physical ability.

Table 2.

Change in MMQ1-score for every additional reported diagnosis group across subgroups.

Domain		Worries	Social life	Self-image	Limitations in everyday life	Personal finances	Physical ability
Average increase in MMQ1-score per additional diagnosis group		0.20(0.19; 0.20)	0.11(0.11; 0.12)	0.13(013; 0.14)	0.17(0.17; 0.18)	0.10(0.10; 0.12)	0.18(0.18; 0.19)
Subgroups:
Age		(p  =  .50)	(p < .0001)	(p < .0001)	(p < .001)	(p < .0001)	(p = .29)
	< 40	0.18(0.15; 0.20)	0.12(0.09; 0.13)	0.13(0.11; 0.15)	0.15(0.13; 0.17)	0.16(0.13; 0.18)	0.19(0.16; 0.21)
	40–49	0.01(0.18; 0.22)	0.15(0.13 ;0.16)	0.17(0.15; 0.18)	0.18(0.16; 0.20)	0.20(0.19; 0.22)	0.18(0.16; 0.20)
	50–59	0.20(0.19; 0.20)	0.14(0.13; 0.14)	0.15(0.14; 0.16)	0.17(0.16; 0.18)	0.16(0.15; 0.17)	0.18(0.16; 0.20)
	60–69	0.20(0.19; 0.20)	0.11(0.10; 0.11)	0.13(0.12; 0.14)	0.16(0.15; 0.17)	0.11(0.10; 0.12)	0.18(0.17; 0.19)
	70 +	0.20(0.19; 0.21)	0.11(0.10; 0.11)	0.12(0.11; 0.12)	0.18(0.18; 0.19)	0.06(0.05; 0.07)	0.19(0.18; 0.20)
Sex		(p = .04)	(p = .60)	(p = .38)	(p = .66)	(p = .13)	(p = .38)
	Male	0.20(0.20; 0.21)	0.11(0.11; 0.12)	0.13(0.12; 0.14)	0.17(0.17; 0.18)	0.10(0.10; 0.12)	0.19(0.18; 0.19)
	Female	0.19(0.19; 0.20)	0.11(0.11; 0.12)	0.13(0.13; 0.14)	0.17(0.16; 0.18)	0.11(0.10; 0.11)	0.18(0.17; 0.19)
Education		(p = .15)	(p = .005)	(p < .0001)	(p = .04)	(p = .24)	(p = .06)
	Basic schooling	0.19(0.17; 0.21)	0.13(0.12; 0.14)	0.15(0.14; 0.17)	0.18(0.16; 0.20)	0.11(0.09; 0.12)	0.19(0.17; 0.21)
	High school or other additional	0.20(0.20; 0.21)	0.12(0.11; 0.12)	0.14(0.13; 0.14)	0.18(0.17; 0.18)	0.11(0.10; 0.11)	0.19(0.18; 0.20)
	College or university	0.19(0.19; 0.20)	0.11(0.10; 0.11)	0.12(0.11; 0.13)	0.17(0.16; 0.17)	0.10(0.10; 0.12)	0.18(0.17; 0.18)
Occupation		(p = .20)	(p < .0001)	(p < .0001)	(p < .0001)	(p < .0001)	(p = .01)
	Not working or studying	0.19(0.18; 0.20)	0.15(0.14; 0.15)	0.15(0.14; 0.16)	0.18(0.17; 0.19)	0.15(0.14; 0.16)	0.19(0.18; 0.20)
	Working or studying	0.20(0.19; 0.21)	0.11(0.10; 0.11)	0.14(0.13; 0.15)	0.15(0.14; 0.16)	0.15(0.14; 0.16)	0.17(0.16; 0.18)
	Retired	0.20(0.19; 0.21)	0.11(0.10; 0.11)	0.12(0.11; 0.13)	0.18(0.17; 0.19)	0.06(0.06; 0.07)	0.19(0.18; 0.19)
Living alone		(p = .10)	(p < .0001)	(p = .12)	(p < .001)	(p = .003)	(p = .89)
	No	0.20(0.19; 0.21)	0.10(0.10; 0.12)	0.13(0.12; 0.13)	0.17(0.16; 0.17)	0.10(0.10; 0.12)	0.18(0.18; 0.19)
	Yes	0.20(0.18; 0.20)	0.14(0.13; 0.14)	0.14(0.13; 0.15)	0.18(0.18; 0.19)	0.11(0.11; 0.12)	0.18(0.17; 0.19)

The numbers represent the increase in MMQ1-score for each additional diagnosis group with confidence intervals, adjusted for age, sex, education, occupation and living alone (Model 2).

Changes significant for the domains are highlighted in blue. MMQ1 = Multi Morbidity Questionnaire 1.

Age, sex, educational level, occupation, and living alone were all significant effect modifiers for one or more MMQ1 domain (Changes considered significant (p < 0.05) for the domains are highlighted in blue in Table 2). Age significantly modified the association in the domains of social life, self-image, limitations in everyday life, and personal finances, with a U-shaped pattern observed. The strongest associations were seen among individuals aged 40–59 years, compared to both younger and older participants. Sex was a significant modifier only for the domain of worries, with men reporting a slightly greater increase in worry scores per additional diagnosis group compared to women. No significant sex differences were observed for the other domains. Educational level modified the associations in the domains of social life, self-image, and limitations in everyday life, with participants with basic schooling showing the strongest deterioration in needs-based quality of life. Education did not significantly modify the association for personal finances. Occupational status significantly modified the associations in all domains except worries. Participants who were not working or studying showed a stronger association between the number of diagnosis groups and lower quality of life across social life, self-image, limitations in everyday life, personal finances, and physical ability. The domain of physical ability was influenced only by occupational status. Living arrangement was a significant modifier for the domains of social life, limitations in everyday life, and personal finances. Participants living alone showed stronger associations between an increasing number of diagnosis groups and worsening quality of life in these domains. No significant modification by living arrangement was found for the domain of self-image.

Discussion

Main findings

This study investigated the association between multimorbidity, measured by the number of self-reported diagnosis groups, and needs-based QoL, among Danish primary care patients. A consistent positive and close-to-linear association was observed across all six domains of the MMQ1 questionnaire. However, the clinical relevance of this association varied by domain. The association remained significant after adjusting for various demographic and socioeconomic factors. Age, sex, educational level, occupation, and living alone were identified as significant effect modifiers for one or more MMQ1 domains. Individuals aged 40–59 years, those with lower levels of education, individuals not working or studying, and those living alone experienced the steepest decrease in needs-based QoL for each additional diagnosis group.

These findings suggest that as patients accumulate chronic conditions from different diagnostic groups, they experience a progressively greater negative impact on multiple aspects of their daily lives. These results underline the importance of considering the cumulative burden of multimorbidity when addressing patients’ QoL in primary care.

Strengths and limitations

The strengths of this study include; firstly the use of a validated instrument to measure needs-based QoL allowing for a more nuanced understanding of the impact of multimorbidity on needs-based QoL, secondly the use of groups of chronic conditions rather than individual conditions in the definition of multimorbidity, adding an organizational aspect, and finally, the study is based on a large cross-sectional cohort consisting of a adults in the general population, which enhances the generalizability of the findings to the population in general practice.

However, the study also has limitations, including the fact that we cannot yet evaluate the representativeness of the sample. We know, older persons were more likely to respond to the online questionnaire, but a number of groups are likely to be underrepresented in this cohort. Moreover, while the focus was on the cumulative burden of diagnosis groups, the study did not distinguish between concordant and discordant condition combinations, which may influence QoL differently. In particular, mental health conditions may have a disproportionately negative effect, not only due to their psychological burden but also because they often complicate the management of coexisting physical conditions and are less likely to be addressed in a coordinated care framework. Since the online reminder and the postal questionnaire were sent only to randomly selected subgroups of patients - and would require weighting using registry data to improve representativeness - only respondents to the initial online questionnaire were included in the present analysis. While the intention behind this uniform method of data collection was to minimize response bias and support internal validity by applying consistent procedures across the cohort, it may have introduced selection bias if certain patient characteristics (e.g. higher education, greater digital literacy) were associated with a higher likelihood of response. This could affect the generalizability of our findings to the broader primary care population. In terms of external validity, the sample may not fully reflect the broader population of individuals with multimorbidity in Danish general practice. Furthermore, multimorbidity was based on self-reported diagnoses, and although self-reports may better reflect patients’ perceived health burden relevant for QoL assessments, it may introduce recall bias and underreporting [23,24]. Voluntary participation by general practices could also introduce selection bias [25] although prior research, suggests that GP non-participation introduces only minor bias at the patient level [26]. However, representativeness with respect to the covariates adjusted for is not strictly necessary to estimate the association of interest in this study. The key technical assumption is that non-response is adequately explained by multimorbidity and the included covariates. If this assumption holds, the estimated association between multimorbidity and MMQ1 scores remains unbiased despite limited representativeness of the sample overall.

The large sample size may increase the likelihood of finding statistically significant associations that may not be clinically meaningful, and multiple testing could have increased the risk of Type I errors despite cautious interpretation.

If the survey were to be repeated, translated versions could help improve inclusion of non-Danish speaking respondents. Additionally, offering an option for the survey to be read aloud—either through audio support or assisted completion—could improve accessibility for individuals with visual impairments or dyslexia. As the survey was population-based, some degree of non-response may reflect lack of time or motivation, which must be respected in voluntary participation contexts.

Interpretation

Most of the findings from this study are in accordance with previous literature.

The clinical relevance of the association between multimorbidity and needs-based QoL varied across domains. Physical ability was the most sensitive to the burden of multimorbidity, while financial concerns required a higher number of diagnosis groups to reach a clinically meaningful decline. This variation highlights that different aspects of daily life are differentially impacted by the accumulation of chronic conditions.

In a study investigating the role of potential contributing factors besides socioeconomics [27], they suggest that accounting for polypharmacy is important when exploring the association between multimorbidity and QoL because it is recognized as an indicator of treatment burden [28] and can therefore interfere with the association. However, medicine use was closely linked to the reported number of diagnosis groups and thereby a mediator of the association. Indeed, when adjusting for medicine use in Model 3, a large reduction in the association between the number of diagnosis groups and needs-based QoL was observed. Hence, we view Model 2, which does not include medicine use, as addressing best the association between multimorbidity and QoL, where Model 3 artificially hides this association.

Subgroup analyses revealed that individuals aged 40–59 years exhibited stronger associations between multimorbidity and lower needs-based QoL across various domains, compared to both younger and older age groups. Previous literature has described multimorbidity as having a greater impact on QoL in younger populations [27,29–31]. Consistent with the findings of this study, a previous study identified people aged 45–55 years, to report lower QoL, compared to both younger and older age groups [32]. The observed variation between age groups might be due to several reasons: first, elderly may adapt and accept that having multiple conditions is a part of the normal aging process [30]. Secondly, the burden of living with multimorbidity can be more pronounced for working-age populations [33]. Thirdly, mental disorders, which are most prevalent among younger individuals [34], have been described to have a greater impact on QoL in primary care patients, compared to other medical disorders [35], explaining lower reported QoL in younger population groups. It is unclear why weaker correlations between multimorbidity and QoL among participants below the age of 40 years are found in this study, but it could be due to selection bias caused by the lower number of younger respondents in this study.

Individuals under the age of 40 years reported the lowest association when examining the association between the number of diagnosis groups and QoL in the domain limitations in everyday life. The cumulative effects of multimorbidity, coupled with age-related changes and social factors, contribute to greater limitations on the everyday life of the elderly compared to younger individuals. A study identified an association between multimorbidity, functional limitations, and QoL, however, only people aged 50 years and above were included [36]. Although more than half of those living with multimorbidity are under the age of 65 years [37], the younger population is generally underrepresented in multimorbidity research.

Regarding economy, multimorbidity had a low impact on the MMQ1-score for retirees and individuals over the age of 70 years, indicating that, although multimorbidity generally affects QoL, older populations, particularly those who are retired, may experience less financial stress.

Stronger associations between multimorbidity and MMQ1-scores were found in the social domain among individuals not involved in employment or education and those living alone – two subgroups likely to be more vulnerable to feeling loneliness. A previous study has found a significant association between perceived social support and HRQoL [38] while another study identified loneliness as an important factor [27], suggesting that perceived social support and loneliness should be considered when examining the association between multimorbidity and QoL. This highlights the importance of a holistic approach when treating people with multimorbidity, as it can help facilitate a positive adaptation to multimorbidity.

The OECD’s PaRIS conceptual framework emphasizes that evaluations of primary care for people with chronic conditions should include both PROMs and PREMs (Patient Reported Experience Measures), covering broader domains such as patient experiences, health literacy, and shared decision-making [39]. While our study captures needs-based QoL using the MMQ1, it does not address PREM domains, limiting insight into how healthcare experiences might mediate the relationship between multimorbidity and QoL. Future research could integrate the PaRIS framework to better understand both health outcomes and patient experiences.

The Minimal Important Differences (MIDs) used in this study were derived using an anchor-based method from the MMQ1 validation study, providing direct clinical relevance but potentially subject to subjective bias. Triangulating different approaches, such as distribution-based or alternative clinical anchor methods, could strengthen MID estimation in future studies [40].

Conclusion

This study highlights a strong association between multimorbidity, based on the number of self-reported diagnosis groups, and needs-based QoL among Danish primary care patients. Greater multimorbidity was linked to consistently lower QoL. The impact of multimorbidity on QoL varies based on sociodemographic factors. Individuals who are unemployed, aged in their 40’s and 50’s, have lower educational levels, and live alone are more vulnerable to experiencing lower QoL. Thus, indicating that the observed associations may be driven by underlying factors related to loss of financial independence and reduced social support, which can exacerbate the challenges of managing multiple chronic conditions. While our health system is focused on the management of individual chronic conditions, our results indicate that patients with four to five chronic conditions have a much more impaired QoL. Thus, managing patients with multimorbidity requires a broader approach that extends beyond biomedical care to include support for factors like financial concerns and social participation, in order to better meet the complex needs identified across different domains of QoL.

Implication for practice

The findings of this study suggest that GPs should prioritize the management of multimorbidity patients who are vulnerable to lower QoL, including working-age individuals, those with lower educational levels, those not employed and not studying, and those living alone. General practitioners are in an optimal position to apply a holistic, biopsychosocial approach rather than a single-disease approach in the management of these patients, to optimize their ability to maintain autonomy and avoid isolation. Preventative measures targeting these population groups are needed to address their specific needs and challenges. Policymakers should prioritize the development of tailored support programs, particularly for MM patients who are vulnerable to lower QoL, by incorporating these recommendations.

Future research

Longitudinal studies are needed to explore how the age at which chronic conditions begin and their progression over a person’s lifetime may influence different trajectories of QoL. Additionally, assessing the impact of individual diagnosis groups and the clustering of diagnosis groups on QoL are important in future research as it acknowledges that different diseases have varying impacts on QoL. Failing to account for this can skew the association. Further research is also needed to determine if the strength of the associations between multimorbidity and QoL remains consistent when diagnoses are not self-reported. Finally, future research could benefit from qualitative studies that explore patients’ lived experiences of multimorbidity and how it impacts different domains of QoL. Such studies could provide deeper insights into the sociodemographic factors that mediate or modify these associations, and help clarify the complex pathways linking multimorbidity to perceived QoL.

Appendix A. Willadsen et al.’s diagnostic groupings and definition of multimorbidity [14].

Original Diagnostic Groupings

Diagnostic Group	Examples of Included Conditions
Lung diseases	Asthma, Chronic Obstructive Pulmonary Disease (COPD), Allergy
Musculoskeletal diseases	Osteoarthritis, Rheumatoid arthritis
Endocrine diseases	Diabetes, Thyroid disorders
Mental disorders	Depression, Anxiety, Schizophrenia
Cancer	Breast cancer, Prostate cancer, Lung cancer
Neurological diseases	Multiple sclerosis, Epilepsy, Parkinson’s disease
Gastrointestinal diseases	Irritable bowel syndrome, Chronic hepatitis
Cardiovascular diseases	Hypertension, Heart failure, Atrial fibrillation
Kidney diseases	Chronic kidney disease
Diseases of sensory organs	Hearing impairment, Visual impairment

Definition of multimorbidity

In order to have multimorbidity the patient needs at least one diagnosis from two different bodily systems; for instance Diabetes from Endocrine diseases and Hypertension from Cardiovascular diseases.

Appendix B. The unadjusted association between the number of self-reported diagnosis groups and mean score in the MMQ1 domains.

		Worries	My social life	Self-image	Limitations in everyday life	Personal finances	Physical ability
Number of self-reported diagnosis groups	n	Mean (CI 95%)
1	5453	0.50(0.48; 0.52)	0.15(0.14; 0.16)	0.25(0.24; 0.27)	0.29(0.27; 0.30)	0.19(0.18; 0.21)	0.40(0.39; 0.42)
2	7666	0.71(0.69; 0.73)	0.23(0.22; 0.24)	0.37(0.35; 0.38)	0.45(0.43; 0.46)	0.28(0.27; 0.29)	0.58(0.56; 0.59)
3	7250	0.90(0.89; 0.92)	0.31(0.30; 0.92)	0.48(0.47; 0.50)	0.60(0.59; 0.62)	0.37(0.35; 0.39)	0.77(0.75; 0.79)
4	5529	1.12(1.11; 1.15)	0.45(0.44; 0.47)	0.63(0.61; 0.65)	0.81(0.79; 0.83)	0.49(0.47; 0.51)	0.99(0.97; 1.01)
5	3135	1.33(1.30; 1.36)	0.59(0.56; 0.61)	0.79(0.76; 0.82)	1.00(0.97; 1.03)	0.66(0.63; 0.69)	1.19(1.16; 1.21)
6	1660	1.59(1.55; 1.63)	0.83(0.79; 0.86)	1.00(0.96; 1.04)	1.29(1.25; 1.33)	0.84(0.79; 0.89)	1.42(1.39; 1.46)
7	706	1.76(1.70; 1.82)	0.96(0.90; 1.02)	1.16(1.09; 1.22)	1.47(1.41; 1.53)	0.95(0.87; 1.03)	1.60(1.54; 1.66)
8	277	2.07(1.98; 2.16)	1.30(1.20; 1.40)	1.48(1.37; 1.58)	1.78(1.69; 1.88)	1.32(1.20; 1.45)	1.88(1.80; 1.97)
9 +	77	2.18(2.02; 2.33)	1.43(1.22; 1.64)	1.59(1.38; 1.80)	1.86(1.67; 2.06)	1.56(1.29; 1.83)	1.98(1.82; 2.14)

The numbers represent the mean MMQ1- score including confidence intervals across the domains based on the number of diagnosis groups.

Appendix C. The association between the number of self-reported diagnosis groups and MMQ1 domain scores: Comparison of adjusted models.

Domain (MID)	Worries (0.59)			My social life (0.57)			Self-image (0.60)
Model	1	2	3	1	2	3	1	2	3
Number of diagnosis groups
1 (ref)	–	–	–	–	–	–	–	–	–
2	0.21(0.18; 0.24)	0.20(0.17; 0.22)	0.18(0.16; 0.21)	0.08(0.06; 0,10)	0,07(0.05; 0.10)	0.06(0.04; 0.08)	0.11(0.10; 0.14)	0.10(0.10; 0.12)	0.09(0.10; 0.11)
3	0.40(0.40; 0.43)	0.38(0.40; 0.41)	0.35(0.32; 0.37)	0.16(0.14; 0,20)	0,13(0.11; 0.15)	0.11(0.10; 0.13)	0.23(0.21; 0.30)	0.21(0.20; 0.23)	0.18(0.18; 0.20)
4	0.63(0.61; 0.66)	0.60(0.60; 0.63)	0.54(0.51; 0.56)	0.31(0.30; 0,33)	0,26(0.24; 0.30)	0.23(0.21; 0.25)	0.38(0.35; 0.40)	0.34(0.32; 0.40)	0.30(0.30; 0.32)
5	0.82(0.80; 0.90)	0.77(0.74; 0.81)	0.69(0.65; 0.72)	0.43(0.41; 0,46)	0,37(0.35; 0.40)	0.32(0.30; 0.32)	0.54(0.51; 0.60)	0.48(0.50; 0.51)	0.42(0.40; 0.50)
6	1.08(1.04; 1.12)	1.02(1.00; 1.10)	0.90(0.90; 0.94)	0.67(0.64; 0,70)	0,59(0.60; 0.62)	0.52(0.50; 0.55)	0.73(0.70; 0.80)	0.67(0.63; 0.70)	0.58(0.55; 0.62)
7	1.24(1.18; 1.30)	1.17(1.11; 1.23)	1.01(1.00; 1.10)	0.80(0.80; 0,84)	0,71(0.70; 0.75)	0.61(0.60; 0.70)	0.87(0.82; 0.92)	0.80(0.75; 0.84)	0.69(0.64; 0.74)
8	1.52(1.43; 1.80)	1.40(1.31; 1.50)	1.23(1.14; 1.32)	1.09(1.03; 1,20)	0,95(0.90; 1.01)	0.83(0.80; 0.90)	1.14(1.10; 1.22)	1.02(0.94; 1.10)	0.90(0.82; 1.00)
+9	1.60(1.43; 1.80)	1.47(1.30; 1.64)	1.27(1.11; 1.44)	1.22(1.10; 1,34)	1,07(0.95; 1.20)	0.94(0.82; 1.10)	1.25(1.10; 1.40)	1.11(1.00; 1.30)	0.98(0.82; 1.12)
Domain	Limitations in everyday life (0.68)			Personal finances (0.93)			Physical ability (0.39)
Model	1	2	3	1	2	3	1	2	3
Number of diagnosis groups
1 (ref)	–	–	–	–	–	–	–	–	–
2	0.16(0.14; 0.20)	0.14(0.12; 0.17)	0.13(0.10; 0.15)	0.10(0.10; 0.12)	0.08(0.06; 0.10)	0.07(0.05; 0.10)	0.17(0.15; 0.20)	0.16(0.14; 0.18)	0.14(0.12; 0.17)
3	0.31(0.30; 0.33)	0.27(0.25; 0.30)	0.24(0.21; 0.30)	0.19(0.20; 0.21)	0.14(0.12; 0.20)	0.13(0.12; 0.20)	0.36(0.34; 0.39)	0.33(0.31; 0.36)	0.29(0.30; 0.31)
4	0.52(0.50; 0.55)	0.47(0.44; 0.50)	0.40(0.40; 0.43)	0.32(0.30; 0.34)	0.25(0.23; 0.30)	0.22(0.20; 0.25)	0.58(0.55; 0.61)	0.54(0.51; 0.60)	0.46(0.43; 0.50)
5	0.71(0.70; 0.74)	0.63(0.60; 0.70)	0.54(0.51; 0.60)	0.48(0.45; 0.51)	0.39(0.40; 0.42)	0.35(0.32; 0.40)	0.77(0.74; 0.80)	0.71(0.70; 0.74)	0.60(0.60; 0.63)
6	0.98(0.95; 1.02)	0.89(0.90; 0.93)	0.76(0.72; 0.80)	0.64(0.60; 0.70)	0.53(0.50; 0.50)	0.47(0.44; 0.51)	1.00(1.00; 1.04)	0.92(0.90; 1.00)	0.77(0.73; 0.81)
7	1.17(1.11; 1.22)	1.05(1.00; 1.11)	0.88(0.83; 0.93)	0.76(0.70; 0.81)	0.62(0.60; 0.70)	0.55(0.50; 0.60)	1.18(1.12; 1.23)	1.09(1.03; 1.14)	0.89(0.83; 0.94)
8	1.45(1.40; 1.53)	1.27(1.90; 1.35)	1.07(1.00; 1.15)	1.03(0.95; 1.12)	0.82(0.74; 1.00)	0.74(0.70; 0.82)	1.44(1.40; 1.53)	1.29(1.21; 1.40)	1.07(1.00; 1.15)
+9	1.53(1.40; 1.70)	1.34(1.20; 1.50)	1.12(1.00; 1.30)	1.23(1.10; 1.40)	1.00(0.90; 1.15)	0.91(0.80; 1.10)	1.54(1.40; 1.70)	1.38(1.23; 1.54)	1.13(1.00; 1.30)

Estimate from model 1, 2 and 3. Model 1: Adjusted for age, sex and educational level, Model 2: Adjusted for living alone and occupation + model 1, Model 3: adjusted for medicine consumption + model 2. The numbers represent the increase in MMQ1- score compared to having conditions from 1 diagnosis group.

MID = Minimal Important Difference.

Missing data: Model 1: Observations read: 31 753, observations used: 31 090; Model 2: Observations read: 31 753, observations used: 31 087; Model 3: Observations read: 31 753, observations used: 31 087.

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Funding Statement

This study is based on data from MM600 trial [20], a cluster-randomized controlled trial, funded by the public agreement between The Danish Regions and the General Practitioners’ Organization for the period 2022–2024.

Disclosure statement

No potential conflict of interest was reported by the author(s).