Differences in patient-reported outcomes across multimorbidity profiles in primary care: Evidence from the OECD Patient Reported Indicator Surveys (PaRIS)

Differences in patient-reported outcomes across multimorbidity profiles in primary care: Evidence from the OECD Patient Reported Indicator Surveys (PaRIS) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Differences in patient-reported outcomes across multimorbidity profiles in primary care: Evidence from the OECD Patient Reported Indicator Surveys (PaRIS) Candan Kendir, Nicolas Larrain, Michael van den Berg, Frederico Guanais, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9424991/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background Multimorbidity is a growing challenge for healthcare systems worldwide, yet policymakers and healthcare professionals often lack evidence on how distinct multimorbidity profiles (capturing co-occurrence of chronic conditions) relate to patient-reported outcomes and experiences. This study examines how mutually exclusive multimorbidity profiles are associated with physical health, mental health, and well-being and whether these associations are modified by healthcare experiences Methods We analysed data from 102 786 primary care patients aged ≥ 45 years participating in the OECD Patient-Reported Indicator Surveys (PaRIS) across 19 countries (2023–2024). Self‑reported chronic conditions were grouped into five mutually exclusive multimorbidity profiles. Physical and mental health outcomes were assessed using PROMIS® Global Health T-scores, and well-being using WHO-5 Well-being Index. Multilevel mixed-effects models (estimated associations between multimorbidity profiles and outcomes, controlling for age, gender, education and number of chronic conditions. Interactions with patient-reported person-centredness and continuity of care were examined. Results Multimorbidity profiles showed differences in self-reported health, with profiles including mental health conditions associated with both poorer physical and mental health outcomes. Compared with the cardiometabolic-only reference group, physical health scores were significantly lower among patients with cardiometabolic + mental (b=–4.74) and mental and/or other‑only profiles (b=–4.10) (p < 0.05). Profiles including mental health conditions were associated with lower scores on mental health outcomes, (b range: − 4.24 to − 6.39). Well‑being was similarly poorer, with the largest declines observed in cardiometabolic + mental (b=–12.65) and cardiometabolic + mental + other profiles (b=–12.08). While person‑centredness was positively associated with all health outcomes, it did not meaningfully modify the relationship between the health outcomes and multimorbidity profiles, with only limited interaction effects observed. Continuity of care showed overall limited effects. Conclusion Differences in patient-reported outcomes suggest that health systems may need to better adapt to the needs of people with multimorbidity. Profiles involving mental health conditions show the poorest patient‑reported outcomes, stressing the importance of implementing person-centred integrated care models within primary care and beyond. Investing in systems that systematically assess patient-reported outcomes among people with multiple chronic conditions in primary care may support policymakers and practitioners in improving care delivery. Patient-reported outcome measures patient-reported experience measures multimorbidity primary care healthcare system performance quality of care Figures Figure 1 Figure 2 Background Multimorbidity, defined as the presence of two or more chronic conditions in an individual, is a growing challenge for healthcare systems, healthcare professionals and patients (1; 2). People with multimorbidity need more healthcare, leading to increased costs and strain on healthcare systems (3; 4; 5). Multimorbidity also increases the complexity of care delivery, requiring more time and coordination from healthcare professionals, while imposing substantial physical, emotional, and financial burdens on patients. Despite this, most chronic care delivery models remain structured around single-disease frameworks, which are ill-suited to address the complexity and interactions inherent in multimorbidity (6). This misalignment increases the risk that people with multimorbidity receive fragmented or suboptimal care. In recent years, person-centred integrated care has been widely promoted to help provide services for people with multimorbidity (7). Patient-reported outcome measures (PROMs), defined as measures reported directly by patients without interpretation of anyone else, can serve as critical tools to assess outcomes that are highly valued by patients, e.g., their perceived health and well-being, which can guide health authorities, health services and stakeholders in countries in their efforts to make healthcare more person-centred. Generic PROMs are particularly valuable in the context of multimorbidity because they capture overall physical, mental, and social functioning, offering a holistic assessment that clinical, disease-specific indicators cannot provide. When used at the group level (aggregated), PROMs can help health system decision-makers identify the priority areas for action and support the redesign of services to better address the needs of their population (8; 9; 10). The limited collection of systematic PROMs data among people with multimorbidity remains a missed opportunity to inform health policy decisions and care delivery (10; 9). Not only the number but also the type and combination of chronic conditions influence patient outcomes CITATION XRef_s87dWRStRDa6eA_09__E5Ig \l 2057 \m Kapp_c8af05a5 \m XRef_Qa__90LtbWAEAim1bjk9ipg (11; 12; 13) . Emerging evidence shows that specific combinations of chronic conditions are associated with distinct levels of healthcare costs, care burden, and functional outcomes (5). Some combinations of conditions are more prevalent in specific sociodemographic groups such as older people, women and people with lower income, who are more at risk of poorer health outcomes (14; 15; 16). Several literature reviews exploring the consistency of diseases patterns using various statistical tests across different settings and populations identified two main (replicable) groups: cardiometabolic conditions and mental health (17; 18; 15; 16; 19; 20). At the same time, literature reviews indicate a persistent paucity of evidence linking multimorbidity patterns to patient-reported outcomes, especially in primary care settings. Primary care plays a critical role in managing multimorbidity (21). It is often the first and most frequently attended point of contact for patients and is uniquely positioned to deliver continuous, coordinated, comprehensive and person-centred care, which is essential in multimorbidity management (22; 23). It is usually also closely located to people and their communities, supporting individuals’ capacity to self-manage their own health and well-being. Primary care functions such as person-centred‑ness of care and continuity of care are often considered core components of high-quality‑ primary care for people with chronic conditions and may influence how patients perceive their physical and mental health. Yet, both primary care patients and professionals face substantial challenges related to multimorbidity, including fragmented care, and clinical guidelines (and their evidence base) that fail to account for the complexity of multimorbidity clusters (24). While studies have addressed the burden and patterns of multimorbidity clusters and their influence on patient-reported outcomes, most of these studies relied on reviews of literature and meta-analysis spanning different healthcare settings and data sources (1; 19; 15; 17; 16). Analyses using comparable PROMs data from primary care populations remain scarce. Research on multimorbidity clusters using harmonised comparable PROMs data from primary care users can provide actionable insights for health policy and practice, supporting the development of more responsive and equitable primary care systems for people with multimorbidity (20). Recognising the evidence gap, the Organisation for Economic Co‑operation and Development (OECD) developed the Patient Reported Indicator Surveys (PaRIS) to collect harmonised PROMs data, as well as data assessed with patient-reported experience measures (PREMs), from primary care patients aged 45 years and older living in 19 countries (12). In addition, the survey gathered detailed information on patients’ sociodemographic characteristics, healthcare utilisation, health behaviours and self-management capabilities. The present study assesses how distinct multimorbidity profiles are associated with patient-reported outcomes among people aged 45 years and older in primary care. Building on previous studies and observed differences in the initial PaRIS results, this study extends the evidence in two ways. First, it moves beyond simple disease counts by comparing mutually exclusive multimorbidity profiles (25). Second, it uses a harmonised international dataset, which has been developed specifically for people with chronic conditions using primary care. The study addresses the following research questions: How do patient-reported outcomes, in particular physical health, mental health and well-being, vary across patient groups with different and mutually exclusive multimorbidity patterns? To what extent do continuity of care relationship and person-centredness of care modify associations between patient-reported outcomes and multimorbidity profiles? Methods Analyses are based on secondary analysis of anonymised data from the OECD PaRIS database, which includes over 107 011 patients nested in 1 807 primary care practices across 19 countries. Data were collected in 2023-2024 using the PaRIS Patient Questionnaire (PaRIS-PQ), which was developed for the PaRIS study (26). The study was exempted from ethical review based on the decision of the non-WMO Committee of the Medical Ethics Review Committee of Amsterdam University Medical Centres (FWA00032965). Participation in this OECD-led research involved official national project management teams, who are appointed by their respective Ministries of Health to implement the OECD PaRIS study in their respective countries. Each participating national project management team has been responsible for preparing the information letter and consent form, as well as acquiring ethical clearance in accordance with their national requirements. Study population characteristics People aged 45 and over who had contact with their primary care practice in the six months prior to sampling were included in the study. Further study population characteristics as well as PaRIS in-/exclusion criteria and response rates have been described elsewhere (12). An analytical framework was developed to structure the multilevel analysis by organising variables into patient characteristics, clinical burden, and clinical and functional enablers (figure 1). The Wilson and Cleary model of patient outcomes (27), using the 2005 revision of the conceptual model of health-related quality of life by Ferrans et al. (28), was adopted as the primary theoretical framework linking clinical burden to patient‑reported outcomes through symptoms, functioning and health perceptions, with individual and environmental characteristics influencing these relationships. In our data, multimorbidity profiles represent clinical burden and PROMIS Global Health scores index general health perceptions (physical and mental). Symptoms and functional status mediators were not included explicitly as these were considered to be taken into account in the overall physical and mental health outcomes reported by patients. Consistent with Ferrans’s revision, patient-reported person‑centredness and continuity of care were conceptualised as enabling/contextual factors that may modify associations across multiple nodes. Multilevel nuance represents between-country and between-practice variance as an “environmental/contextual” layer. Multimorbidity profiles Chronic conditions were self-reported by the respondents to the question “Have you ever been told by a doctor that you have any of the following health conditions? Please select all the options that apply”. Conditions were grouped based on well‑established multimorbidity patterns into five mutually exclusive profiles (20; 17; 15; 18; 16; 29). Cardiometabolic conditions served as the reference category due to shared pathophysiology, common risk factors, and the well‑established care pathways available in primary care. This makes the cardiometabolic group an analytically robust and policy-relevant reference point for comparing the additional‑ burden associated with mental health or other chronic conditions. Mental health conditions were treated as a distinct multimorbidity domain due to their unique influence on symptom perception, functional impairment, self-management capacity and care utilisation. In contrast, the “other chronic conditions” category includes musculoskeletal, respiratory, gastrointestinal and other heterogeneous conditions that vary widely in severity and functional impact. Distinguishing mental health allows clearer identification of its specific role in shaping patient-reported outcomes, while the “other” category captures additional‑ heterogeneity and complexity. The final five multimorbidity profiles were mutually exclusive (figure 2). Patient-reported outcomes Patient-reported physical health, mental health and well-being were included as outcome measures and separate analyses were conducted for each outcome. Physical health and mental health were measured by PROMIS® Scale v1.2 – Global Health (30) components. Physical health combines responses to four questions measuring physical function, pain and fatigue, on a Likert scale of 1‑5 (poor-excellent), while mental health combines four questions on quality of life, emotional distress and social health. The raw scales (4‑20) were converted to T-score metric, ranging between 16.2‑67.7 for physical health and 21.2‑67.6 for mental health. Well-being was measured by the WHO-5 Well-being Index (31), capturing subjective psychological well‑being through five items scored on a Likert scale of 0–5. The responses are summed to a total score from 0 to 25 and transformed to a 0–100 scale. Higher scores indicated better outcomes. Case-mix variables and patient-reported experiences The study examined a set of factors of interest that were selected based on prior evidence of their influence on the association between patient-reported outcomes and multimorbidity profiles, as well as their relevance to primary care policy and practice (3). We encoded gender in two categories (female; male), age in four categories (45-54; 55-64; 65-74; 75+), education level in three categories (low; mid; high) and the number of chronic conditions as numerical. Patient‑reported experiences consisting of person‑centredness (P3CEQ) (32; 33) and continuity of care (having a usual primary care provider for most health problems: yes/no) were analysed both as main effects and as potential effect modifiers of the association between multimorbidity profiles and outcomes. Statistical Analysis Descriptive analyses summarised sample characteristics. Frequencies and proportions were calculated for categorical variables and across the multimorbidity profiles in the total sample and stratified by age, gender and education. Correlations among the two PREMs were examined before inclusion in the multilevel model. Cases with missing values were excluded from the analysis. Pairwise 2×2 cross‑tabulations of missingness across education level, gender, age, and count of chronic conditions were used to diagnose co-missingness patterns. The association between the PROMs and other variables were assessed using multi-level mixed-effects regression analysis, given the multi-level structure of the PaRIS data with patients nested in primary care practices, which are nested in countries/healthcare systems (Supplementary material 1). Mean PROM scores and 95% confidence intervals were calculated across multimorbidity profiles. Interaction terms tested whether person‑centredness or continuity of care moderated associations. The log-likelihood ratio and marginal pseudo-R2 offered insights into how well the model explains the variance in the outcomes and the contribution of the variables included. Log likelihood compared the goodness-of-fit between two multilevel models (e.g. with and without a specific variable of interest). It consisted of subtracting the deviance of the two models and comparing the resulting difference to a chi-squared distribution with degrees of freedom equal to the number of additional parameters. A significant likelihood ratio indicated that adding the variable improves the model fit (34). Marginal Pseudo-R2 quantified the proportion of variance explained by the covariates (fixed effect) in the model. It helped assess how much of the variability in the outcomes is attributed to the predictors, excluding random effects (35). P values below 0.05 were considered as statistically significant. To assess the robustness of our findings to alternative conceptualisations of multimorbidity, we conducted a sensitivity analysis in which hypertension was excluded from the definition of chronic conditions used to derive multimorbidity profiles. This was motivated by the consideration that hypertension may be viewed primarily as a risk factor rather than a chronic condition in some frameworks. The same analytical approach was applied, with multimorbidity profiles re‑estimated excluding hypertension, and all models were re‑run using these revised profiles to evaluate the consistency of associations with the outcomes of interest (Supplementary material 3). Results In the study sample (n=102786), 82% had at least one chronic condition and 52% had two or more chronic conditions. Overall, 28.8% were aged 55-64, 56.3% were female, and 43.2% had a higher education level (Table 1). Across the multimorbidity profiles, most participants (80%) were classified into profiles including cardiometabolic conditions. The percentage of patients with Profile 1 (cardiometabolic) was 11.8%, Profile 2 (cardiometabolic + mental) 3.6%, Profile 3 (cardiometabolic + other) 51.8%, Profile 4 (cardiometabolic + other + mental) 12.7% and Profile 5 (mental and/or other) 20.0%. Older people were most represented in Profile 1 (cardiometabolic) and Profile 3 (cardiometabolic + other), with around 60% of respondents aged 65 years or older. Whereas Profile 2 (cardiometabolic + mental) and Profile 5 (mental and/or other) included a larger proportion of younger people (66.4 and 67.2 below 65 years, respectively) (Table 1). Women were highly represented in Profile 4 (cardiometabolic + other + mental) and Profile 5 (mental and/or other) (67.1% and 72.6%, respectively), while men were more represented in Profile 1 (cardiometabolic) (68.2%). The mean number of chronic conditions was 1.8 in the total population and varied from 2.2 in Profile 1 (cardiometabolic) to 4.4 in Profile 4 (cardiometabolic + mental + other) across multimorbidity profiles. Supplementary material 2 includes further details on the distribution shape of total number of chronic conditions, including skewness and kurtosis, for the overall sample and within each multimorbidity profile. Table 1. Distribution across multimorbidity profiles Total sample n= 102786 % People with two or more chronic conditions n= 53581 % Profile 1: cardiometabolic n= 6346 Profile 2: cardiometabolic + mental n= 1907 Profile 3: cardiometabolic + other n= 27774 Profile 4: cardiometabolic + mental + other n= 6863 Profile 5: mental and/or other n= 10691 Age 45-54 55-64 65-74 75 and over 27.9 28.8 25.5 17.8 13.0 26.0 34.6 26.5 31.0 35.4 21.8 11.8 14.3 24.3 31.8 29.7 21.5 32.1 25.7 20.8 35.7 31.5 21.0 11.8 Gender Female Male 56.3 43.7 31.8 68.2 55.2 44.8 51.6 48.4 67.1 32.9 72.6 27.4 Education Lower Middle Higher 32.9 23.9 43.2 37.6 25.0 37.4 31.8 25.7 42.5 39.9 23.1 37.0 44.2 22.9 32.9 31.1 24.8 44.1 Total number of chronic conditions: mean (SD) 1.8 (1.4) 2.2(0.4) 2.4 (0.6) 3.0 (1.0) 4.4 (1.3) 2.4 (0.6) Physical health outcomes across multimorbidity profiles Results from the multilevel regression model indicated differences in patient‑reported physical health across multimorbidity profiles (Table 2). Men reported significantly better physical health than women (b=1.42, p<0.001). Compared with the youngest age group (45-54), respondents aged 55–64 (b=0.43, p<0.001) and 65–74 (b=1.46, p<0.001) reported better physical health, while those aged ≥75 had worse (b=−0.31, p<0.05). Using the lower education group as reference, higher educated people had better physical health, both for mid‑level (b=1.49, p<0.001) and high education (b=3.14, p<0.001). Physical health scores declined by 2.37 points on the mental health T-score for every additional chronic condition (b=−2.37, p<0.001). Using Profile 1 (cardiometabolic only) as the reference, all other multimorbidity profiles reported significantly poorer physical health. The largest differences were observed for Profile 5 (mental and/or other conditions; b=−2.94, p<0.001) and Profile 4 (cardiometabolic + mental + other conditions; b=−2.07, p<0.001). Profile 3 (cardiometabolic + other) also reported significantly poorer physical health compared with Profile 1 (b=−1.97, p<0.001), while Profile 2 (cardiometabolic + mental) did not differ significantly (b=-0.55). Higher patient-reported person‑centredness was associated with better physical health (b=0.44, p<0.001), while having a usual care provider was not significantly associated with physical health outcomes (b=−0.49). The positive association between patient-reported person‑centredness and physical health was attenuated for patients in Profile 4 (cardiometabolic + mental + other; b=−0.09, p<0.05). No other significant interactions were observed with patient-reported experiences. Table 2. Multimorbidity profiles and physical and mental health outcomes; estimates of multi-level mixed-effects regression analyses (only fixed effects shown in the table) Physical health T-score Estimate (Standard error) Mental health T-score Estimate (Standard error) WHO-5 Well-being Index Total score Estimate (Standard error) Intercept 42.77 (0.59) 42.03 (0.66) 44.21 (1.39) Male (ref: female) 1.42 (0.07) *** 0.41 (0.06) *** 2.26 (1.19) *** Age 55–64 (ref: age 45-54) 0.43 (0.10) *** 0.49 (0.09) *** 2.72 (0.27) *** Age 65–74 1.40 (0.11) *** 1.14 (0.10) *** 6.71 (0.28) *** Age ≥75 -0.31 (0.12) ** 0.64 (0.11) *** 4.34 (0.31) *** Education mid (ref: lower level) 1.49 (0.10) *** 1.14 (0.09) *** 2.32 (0.25) *** Education high 3.14 (0.08) *** 2.48 (0.07) *** 4.82 (0.22) *** Total number of chronic conditions (per +1) - 2.37 (0.04) *** - 1.20 (0.03) *** - 3.83 (0.10) *** Profile 2 (cardiometabolic + mental) (ref: Profile 1) - 0.55 (0.85) - 5.75 (0.76) *** -12.65 (2.21) *** Profile 3 (cardiometabolic + other) (ref: Profile 1) - 1.96 (0.50) *** - 0.30 (0.45) -2.91 (1.29) * Profile 4 (cardiometabolic + mental + other) (ref: Profile 1) - 2.07 (0.58) *** - 6.10 (0.52) *** -12.08 (1.51) *** Profile 5 (mental +/ other) (ref: Profile 1) - 2.94 (0.54) *** - 3.73 (0.48) *** -9.63 (1.40) *** Person-centredness 0.44 (0.02) *** 0.47 (0.02) *** 1.36 (0.06) *** Having a usual care provider - 0.49 (0.02) - 0.56 (0.26) * 0.77 (0.75) Person-centredness * Profile 4 (cardiometabolic + mental + other) - 0.09 (0.03) ** - 0.02 (0.03) - 0.10 (0.08) Usual care provider * Profile 2 (cardio + mental) - 0.72 (0.58) 0.01 (0.52) - 3.74 (1.50) * Pseudo-R 2 0.26 0.26 0.26 Note: Results are shown for the final model (Yijk=b0+∑b(Demographicsijk)+∑b(Profileijk)+∑b(Care_experienceijk)+∑b(Profile×Care_experience)ijk+u0k+v0jk+εijk). Demographics include gender, age group, education, and chronic condition count; Profile includes multimorbidity profiles 2–5 (profile 1 = reference); Care experience includes person ‑ centredness and continuity; Random intercepts are specified for country (u ₀ k), practice (v ₀ jk), and patient (εᵢⱼ ₖ ). Regression-coefficients (b) and standard errors are shown; *** p<0.001, ** p<0.01, * p<0.05. Physical health T score range: 16.2-67.7; Mental health T score range: 21.2-67.6; WHO-5 Well-being Index range: 0-100. Pseudo ‑ R² represents the percentage reduction in unexplained variance compared with the null (intercept ‑ only) model. For the interactions between patient-reported experiences and multimorbidity profiles, only those which had significant association with at least one of the health outcomes (physical, mental, well-being) are presented. Mental health outcomes across multimorbidity profiles Regarding mental health outcomes, results from multilevel analysis showed that men reported better mental health than women (b=0.41, p<0 .001). Compared with the youngest group (45-54), mental health scores were higher among the older age categories of 55–64 (b= 0.49, p < 0.001), 65-74 (b= 1.14, p<0.001), and ≥75 (b=0.64, p<0.001). Patients with mid‑level (b=0.14) and higher education (b=2.48) had better mental health outcomes (both p<0 .001). Mental health scores were 1.20 points lower on the mental health T score for every additional chronic condition (b=−1.20, p<0.001). Profiles including mental health conditions reported significantly poorer mental health outcomes compare to Profile 1 (cardiometabolic), which were Profile 2 (cardiometabolic + mental; b=−5.75, p<0.001), Profile 4 (cardiometabolic + mental + other; b=−6.10, p<0.001), and Profile 5 (mental +/ other; b=−3.73, p<0.001). Profile 3 (cardiometabolic + other) did not differ significantly (b=−0.30). Higher patient-reported person‑centredness of care was associated with better mental health outcomes (b=0.47, p<0.001). Having a usual care provider was negatively associated with mental health outcomes (b=−0.56, p0.05). Well-being across multimorbidity profiles Results from the multilevel regression model showed variation in well‑being across multimorbidity profiles (Table 2). Men reported significantly higher well‑being than women (b=2.26, p<0.001). Compared with people aged 45–54, respondents aged 55–64 (b=2.72, p<0.001), 65–74 (b=6.72, p<0.001), and ≥75 (b=4.35, p<0.001) all reported better well‑being. Both mid‑level education (b=2.32, p<0.001) and high education (b=4.83, p<0.001) groups reported significantly higher scores. Well‑being scores were 3.84 points lower for every additional chronic condition (b=−3.84, p<0.001). Using Profile 1 (cardiometabolic only) as the reference, well‑being was significantly lower in all other profiles. The largest differences were observed in Profile 2 (cardiometabolic + mental; b=−12.65, p<0.001), Profile 4 (cardiometabolic + mental + other; b=−12.08, p<0.001) and Profile 5 (mental ± other conditions; b=−9.63, p<0.001). Profile 3 (cardiometabolic + other) showed a smaller decrease (b=−2.91, p<0.05). Higher patient-reported person‑centredness was associated with better well‑being (b=1.36, p<0.001). No significant interactions were observed between multimorbidity profiles and patient-reported person‑centredness. While having a usual care provider was not significantly associated with well-being outcomes (b=0.77, p>0.05), having a usual provider attenuated the positive relationship between well-being and Profile 2 (cardiometabolic + mental; b=−3.74, p<0.05). Intraclass correlation and variance explained by the model For physical health, the null model showed that most of the variation was located at the patient level (93.3%), with smaller shares at the practice (2.5%) and country levels (4.2%) (Supplementary material 2). For mental health 89.9% of the variance was at the patient level, while country‑level variation was more pronounced (8.5%) and practice‑level variation remained limited (1.7%). For well‑being, 95.2% of the variance was at the patient level, with minimal clustering at the country and practice levels (2.8% and 2.0%, respectively). Explained variance increased progressively with the inclusion of covariates (Supplementary material 2). Among people with multimorbidity, age, gender, education level and number of chronic conditions (Model 1) explained 18.2% of the variance in physical health, 10.5% in mental health, and 12.8% in well‑being compared with the null model. Adding multimorbidity profiles (Model 2) improved the explained variance across outcomes (19.7% for physical health, 17.4% for mental health, and 17.5% for well‑being). The full model, also including the two patient-experience measures and interaction terms (Model 3), provided the greatest explanatory power, accounting for 25.5% of the variance in physical health, 25.8% in mental health, and 26.4% in well‑being (Table 2). Discussion Main findings Our profile‑based multimorbidity approach mirrors recent efforts to move beyond simple counts by describing co‑occurring morbidity clusters and their distinct outcome patterns. This study demonstrates that these profiles are differently associated with patient-reported outcomes in a large, international primary care study population. Multimorbidity profiles were significantly associated with physical health, mental health and well-being outcomes, with profiles including mental health conditions showing the poorest outcomes. Relative to Profile 1 (cardiometabolic), patients in profiles involving mental health conditions, especially patients in Profiles 4 (cardiometabolic + mental + other) and 5 (mental and/or other) reported much lower physical health outcomes and patients in Profile 2 (cardiometabolic + mental) and Profile 4 (cardiometabolic + mental + other) reported substantially poorer mental health and well-being outcomes. Higher patient-reported person‑centredness of care showed a positive association with all outcomes, although this association was attenuated for Profile 4 (cardiometabolic + mental + other) and Profile 2 (cardiometabolic + mental). Having a usual care provider showed no main effect on physical health or well-being, while it was negatively associated with mental health outcomes. This counterintuitive finding may reflect reverse causality, where individuals with poorer mental health are more likely to seek continuous care. These findings indicate that the type and combination of conditions provide explanatory value beyond the number of chronic conditions alone. Comparison with existing literature That mental health comorbidity compounds deficits in perceived physical health, mental health and well-being aligns with evidence that mental health conditions are linked with poorer outcomes. A systematic review of multimorbidity clusters found that certain disease groupings, notably those involving cardiometabolic and mental health conditions, were consistently linked to poorer health-related quality of life and higher mortality ( 3 ). Cardiometabolic conditions are prevalent and given their known shared mechanisms, management of these conditions is advanced through better adapted healthcare service design (e.g., guidelines and disease pathways) ( 25 ). Yet, mental health conditions, which frequently co-exist with cardiometabolic conditions, may exacerbate disease burden and impair quality of life (3; 12). This may reflect the challenges primary care systems face in adequately addressing the interaction between mental and physical conditions, which often require coordination beyond standard disease-oriented care models. Our findings extend this literature by demonstrating these associations using comparative patient-reported outcomes across countries, rather than relying on clinical or administrative data from single settings. The comparatively modest decreases observed in Profile 3 (cardiometabolic + other) may reflect heterogeneity in the “other” group, which includes conditions across musculoskeletal, respiratory and neurological conditions among others. Due to the heterogeneity of the combination of disease groups, the management of people with these other conditions are more complicated. In addition, the high prevalence of functional symptoms such as pain and fatigue among patients with musculoskeletal conditions is associated with poorer outcomes ( 36 ). Their functional impact varies greatly by severity and trajectory, leading to more modest average differences when grouped together. The positive association between patient-reported person‑centredness and outcomes is in line with systematic reviews showing that person-centred care is linked to better patient‑reported outcomes. However, the positive association with experiencing more person‑centredness did not remain across multimorbidity profiles, except where it attenuated the relationship for Profile 4 (cardiometabolic + mental + other) regarding physical health outcomes. This may reflect variations in people’s needs, expectations regarding their physical health, mental health and well-being (37; 38). Patient-reported care continuity as measured by having a usual care provider did not influence outcomes in this study, except for mental health outcomes where it showed a small negative association. In addition, having a usual care provider attenuated the relationship between well-being and Profile 2 (cardiometabolic + mental). This pattern may reflect variability in continuity definitions and care models across countries. In addition, our measure only included one aspect of relational care continuity compared to other dimension of continuity such as information, management and organisational. Existing literature links care continuity to lower utilisation and mortality and better experiences of care (39; 40; 41). The observed pattern in which older people report better outcomes, despite higher disease burden, aligns with international research describing the “well--being paradox of ageing” ( 42 ). Older people often adjust expectations regarding health, have stronger coping mechanisms and face fewer work or social stressors, contributing to more favourable self-ratings. Yet, such research also shows that after certain age well-being declines in very old ages due to health and partner loss ( 42 ). In our study, the oldest age group (75 and over) had lower physical health compared to the youngest group (45–54) but reported better mental health outcomes and well-being. However, such increase was smaller compared to the people aged 65–74, supporting existing literature ( 42 ). The variance decomposition highlights the multilevel nature of patient‑reported outcomes (43; 44). Consistent with previous studies, the largest proportion of variance in all PROMs was located at the individual level. Nonetheless, the contribution of higher‑level factors, particularly for perceived physical health, was modest but meaningful. By contrast, substantially less variance in perceived mental health was explained at the practice and country levels. This mirrors the wider literature indicating that mental health outcomes are more strongly driven by psychosocial, cultural and individual‑level factors, while healthcare system characteristics tend to have more modest effects on emotional well‑being or distress perceptions ( 45 ). While strengthening primary care systems may substantially improve physical health outcomes for patients with multimorbidity, improving mental health outcomes and well-being may require additional psychosocial and community‑based interventions beyond primary care restructuring. Strengths and limitations To our knowledge, this is the first study examining multimorbidity using a harmonised standardised dataset designed specifically for people with chronic conditions within primary care. Our study has a number of limitations. As participation was based on survey responses, selection bias may have occurred if individuals with poorer health or lower engagement were underrepresented. Chronic conditions were self-reported using predefined response categories, which may lead to under- or over-reporting ( 26 ). Nevertheless, “having “ever been told” in the question was designed to limit over-reporting. Our analysis gave equal weight to each chronic condition response lacking information regarding the severity, sequence, and the individual burden of different conditions on health outcomes. Other factors such as healthcare utilisation or polypharmacy could not be incorporated due to their complexity and multi-level influences at patient, practice and system factors. Although self-report measures of chronic conditions might introduce recall and reporting bias, they also have advantages: the categories were deliberately designed to be understood by lay persons, and because the same approach was applied consistently across all participating countries, the findings are unlikely to be affected by differences in disease registration practices across systems. The analysis was based on cross-sectional data; therefore, cannot support any causal inference. In addition, the measure of care continuity captured only one aspect (having a usual care provider) and did not reflect other dimensions of continuity such as information, management and organisational continuity. Nevertheless, the results were drawn on a harmonised standardised dataset, based on a survey designed for chronic care in primary care. In addition, all questionnaires were translated into national and minority languages using TRAP-D translation approach and cognitively tested in all countries. Such a rigorous approach to translation and cognitive testing of the instruments enhances the cross-cultural validity of the results. In relation to study design, we used multilevel analysis given the nested structure of the PaRIS dataset (patients in practices, which are nested in countries). Like branches of a tree, these observations are not independent from each other, allowing more precise results through the multi-level analysis. Multilevel modelling used in the study quantified where variation lied (patient, practice and country). Policy and practice recommendations The substantial reduction in both physical and mental health observed for people with profiles involving mental health conditions highlights the need for systematic collaborative, multidisciplinary, and integrated primary care models that address physical and psychological aspects of multimorbidity. Investing in person‑centredness is likely to yield broad improvements across patient-reported outcomes among multimorbid patient populations. Policies and systems should enable environments to enhance the implementation of patient-centred integrated care models within primary care and beyond. Systematic collection of PROMs within primary care can support continuous monitoring of multimorbidity profiles and associated outcomes, facilitating identification of individuals with complex care needs who may benefit from enhanced support. Developed to systematically assess healthcare system performance from people’s perspectives, in its future cycles, PaRIS can include broader capture of multimorbidity information by including measures regarding the severity and complexity. Policymakers and practitioners can use these findings to adapt care delivery to the needs of a growing population living with multimorbidity. Current policies and systems often do not account for the increasing population of people with multimorbidity and may underestimate the resources required to manage these conditions in primary care. Policymakers could provide incentives to primary care professionals, including ensuring adequate consultation time and adjusting payment models to better account for multimorbidity. Conclusion Future health systems should be adapted to the needs of people with multiple chronic conditions, especially those with complex needs. Such systems should facilitate the implementation of person-centred integrated care models within primary care and beyond. Multimorbidity profiles that include mental health conditions are consistently associated with poorer outcomes, highlighting a critical gap in addressing their needs. This underscores the importance of integrating mental health support into chronic care delivery in primary care, in addition to physical health management. Systematically assessing PROMs among people with chronic conditions in primary care, in addition to PREMs, may support policymakers and practitioners in improving outcomes for people living with multimorbidity. Declarations Ethics approval The study is based on secondary analysis of anonymised data from the OECD Patient‑Reported Indicator Surveys (PaRIS). Data collection was conducted by the OECD and participating countries in accordance with relevant national ethical regulations and the principles of the Declaration of Helsinki, with ethical approval and informed consent obtained prior to data collection, where needed. The study was exempted from ethical review based on the decision of the non-WMO Committee of the Medical Ethics Review Committee of Amsterdam University Medical Centres (FWA00032965). Consent for publication Participation in this OECD-led research involved official national project management teams, who are appointed by their respective Ministries of Health to implement the OECD PaRIS initiative in their respective countries. Each participating national project management team has been responsible for preparing the information letter and consent form, as well as acquiring ethical clearance in accordance with their national requirements. Availability of data and materials The datasets analysed during the current study are not available due to OECD data privacy and security policies. Interested researchers are encouraged to contact the authors for further information. Competing interests The authors declare that they have no competing interests. Funding The authors received no funding for this study. Authors’ contributions CK conceptualised the study; MvdB, NK, DK and JVM provided overall supervision. CK, NL and JVM developed the data analysis plan. CK analysed the data, and NL did quality checks on the data analysis. CK drafted the original manuscript. NL, MvdB, FG, NK, DK, MR, and JVM contributed to the interpretation of findings and critically revised the manuscript for intellectual content. All authors read and approved the final manuscript. Acknowledgement Authors thank to Amsterdam UMC Health Services Research Group for their valuable comments on the draft protocol of the study and the draft manuscript. The views expressed and arguments employed herein are solely those of the author(s) and do not necessarily reflect the views of the OECD or its member countries. The organisation cannot be held responsible for possible violations of copyright resulting from the posting of any written material on this website. References Chowdhury S, Chandra Das D, Sunna T, Beyene J, Hossain A. Global and regional prevalence of multimorbidity in the adult population in community settings: a systematic review and meta-analysis. eClinicalMedicine. 2023;57:101860. van den Akker M, Buntinx F, Knottnerus JA. Comorbidity or multimorbidity. Eur J Gen Pract. 1996;2(2):65–70. Makovski TT, Schmitz S, Zeegers MP, Stranges S, van den Akker M. 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Is quality of life determined by expectations or experience? BMJ. 2001;322(7296):1240–3. Baker R, Freeman G, Haggerty J, Bankart MJ, Nockels KH. Primary medical care continuity and patient mortality: a systematic review. Br J Gen Pract. 2020;70(698):e600–11. Prior A, Rasmussen LB, Virgilsen LF, Vedsted P, Vestergaard M. Continuity of care in general practice and patient outcomes in Denmark: a population-based cohort study. Lancet Prim Care. 2025;1(2):100016. Engström SG, André M, Arvidsson E, Östgren CJ, Troein M, Borgquist L. Personal GP continuity improves healthcare outcomes in primary care populations: a systematic review. Br J Gen Pract. 2025;75(757):e518–25. Hansen T. The age and well-being paradox revisited: a multidimensional perspective. Innovation Aging. 2020;4(Suppl 1):459. Groenewegen P, Spreeuwenberg P, Timans R, Groene O, Suñol R, Valderas J, et al. Data analysis plan of the OECD PaRIS survey: leveraging a multi-level approach to analyse data collected from people living with chronic conditions and their primary care practices in 20 countries. BMC Res Notes. 2024;17(1). Schäfer WLA, Boerma WGW, Kringos DS, De Maeseneer J, Greß S, Heinemann S, et al. QUALICOPC, a multi-country study evaluating quality, costs and equity in primary care. BMC Fam Pract. 2011;12(1). Lim YW, Al-Busaidi I, Caya R, Bricca A, Mangin D, Wilson R, et al. Effectiveness of interventions for the management of multimorbidity in primary care and community settings: systematic review and meta-analysis. Fam Pract. 2025;42(6). Makovski TT, Le Coroller G, Putrik P, Choi YH, Zeegers MP, Stranges S, et al. Role of clinical, functional and social factors in the association between multimorbidity and quality of life: findings from the Survey of Health, Ageing and Retirement in Europe (SHARE). PLoS ONE. 2020;15(10):e0240024. Aramrat C, Choksomngam Y, Jiraporncharoen W, Wiwatkunupakarn N, Pinyopornpanish K, Mallinson P, et al. Advancing multimorbidity management in primary care: a narrative review. Prim Health Care Res Dev. 2022;23. Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial1.Variablesusedintheanalysis.docx Additional file 1. Variables used in the data analysis Supplementarymaterial2.Additionalresults.docx Additional file 2. Complementary results Supplementarymaterial3.Sensitivityanalysis.docx Additional file 3. Sensitivity analysis Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 15 May, 2026 Reviewers agreed at journal 13 May, 2026 Reviewers agreed at journal 13 May, 2026 Reviewers agreed at journal 30 Apr, 2026 Reviewers invited by journal 27 Apr, 2026 Editor invited by journal 22 Apr, 2026 Editor assigned by journal 22 Apr, 2026 Submission checks completed at journal 22 Apr, 2026 First submitted to journal 22 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9424991","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":633008540,"identity":"573db6aa-6bc1-401d-94c2-050a18f44a67","order_by":0,"name":"Candan Kendir","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYJACCSA2AOEDDBUgfgJJWs6QqoWBsY0ILfztZw/e+LmDwZhfunnj4cp5NnL87QmMjyt+4bHhTF6yZe8ZBjPJOccKDp7dlmYsceYBs+HZPtxaDBhyzCR42xhsDG7kGBxs3HY4cYNEAptkYw8eLfxvzCT/ArXYg7XMOVxPWItEjpk00BYzIAOopeFwggFIS8MPPH658cbYWrZNwljiRlrBwYZjaYYzzjxsNmxswK2Fvz/H8ObbNhvD/hnJmz821NjI87cnH3zY8Ae3FphlyBzGBmgEkQYI2zIKRsEoGAUjBwAAIvxSb6LYv4QAAAAASUVORK5CYII=","orcid":"","institution":"Organisation For Economic Co-Operation and Development","correspondingAuthor":true,"prefix":"","firstName":"Candan","middleName":"","lastName":"Kendir","suffix":""},{"id":633008541,"identity":"2ee8d2a6-fa11-4ae3-b1cb-f03f23d1fa08","order_by":1,"name":"Nicolas Larrain","email":"","orcid":"","institution":"Organisation For Economic Co-Operation and Development","correspondingAuthor":false,"prefix":"","firstName":"Nicolas","middleName":"","lastName":"Larrain","suffix":""},{"id":633008542,"identity":"abc3e6ba-9317-4ad2-9162-1d866d9ec631","order_by":2,"name":"Michael van den Berg","email":"","orcid":"","institution":"Organisation For Economic Co-Operation and Development","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"van den","lastName":"Berg","suffix":""},{"id":633008543,"identity":"9425eb20-4849-41dc-b209-d42432830b6f","order_by":3,"name":"Frederico Guanais","email":"","orcid":"","institution":"Organisation For Economic Co-Operation and Development","correspondingAuthor":false,"prefix":"","firstName":"Frederico","middleName":"","lastName":"Guanais","suffix":""},{"id":633008544,"identity":"6ccab675-6786-4ffd-b3e9-e70053eeeff8","order_by":4,"name":"Niek Klazinga","email":"","orcid":"","institution":"Amsterdam UMC location University of Amsterdam","correspondingAuthor":false,"prefix":"","firstName":"Niek","middleName":"","lastName":"Klazinga","suffix":""},{"id":633008546,"identity":"c5287082-c619-45b2-b655-5900b81ae1c3","order_by":5,"name":"Dionne Kringos","email":"","orcid":"","institution":"Amsterdam UMC location University of Amsterdam","correspondingAuthor":false,"prefix":"","firstName":"Dionne","middleName":"","lastName":"Kringos","suffix":""},{"id":633008549,"identity":"d0ca9c7f-e808-4b95-9dc3-2f58e94397aa","order_by":6,"name":"Mieke Rijken","email":"","orcid":"","institution":"Netherlands Institute for Health Services Research","correspondingAuthor":false,"prefix":"","firstName":"Mieke","middleName":"","lastName":"Rijken","suffix":""},{"id":633008551,"identity":"f01b822e-f548-4a21-b25c-3ccb9e860d35","order_by":7,"name":"Jose Maria Valderas","email":"","orcid":"","institution":"National University Health System","correspondingAuthor":false,"prefix":"","firstName":"Jose","middleName":"Maria","lastName":"Valderas","suffix":""}],"badges":[],"createdAt":"2026-04-15 09:59:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9424991/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9424991/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108630548,"identity":"0c6b6f63-95b8-4414-b575-ea3dafc36c21","added_by":"auto","created_at":"2026-05-06 16:37:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":249790,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAnalytical framework and analysis plan. We estimated three‑level multilevel models (random intercepts for Country, Practice, Patient). Model 1 \u003c/em\u003e\u003cu\u003e\u003cem\u003eadjusted for case-mix \u003c/em\u003e\u003c/u\u003e\u003cem\u003echaracteristics (age, gender, education), and total number of chronic conditions, recent hospitalisation and emergency service use. Model 2 added the \u003c/em\u003e\u003cu\u003e\u003cem\u003emultimorbidity profile\u003c/em\u003e\u003c/u\u003e\u003cem\u003e. Model 3 added experienced person‑centredness and experienced continuity of care relationship and tested their \u003c/em\u003e\u003cu\u003e\u003cem\u003eeffect modification \u003c/em\u003e\u003c/u\u003e\u003cem\u003eof the multimorbidity–outcome relationship.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-9424991/v1/0423eea98861e75613866328.png"},{"id":108805105,"identity":"54b285ab-2ec8-46ec-9f0c-fb2b2df4d8d7","added_by":"auto","created_at":"2026-05-08 15:24:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":72332,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eDistribution of multimorbidity profiles in the PaRIS sample. Chronic conditions were grouped as follows: \u003c/em\u003e\u003cu\u003e\u003cem\u003eCardiometabolic conditions\u003c/em\u003e\u003c/u\u003e\u003cem\u003e: high blood pressure; cardiovascular or heart condition; diabetes (type 1 or 2); chronic kidney disease. \u003c/em\u003e\u003cu\u003e\u003cem\u003eMental health conditions\u003c/em\u003e\u003c/u\u003e\u003cem\u003e: depression, anxiety, or other ongoing mental health conditions (e.g. bipolar disorder or schizophrenia). \u003c/em\u003e\u003cu\u003e\u003cem\u003eOther chronic conditions\u003c/em\u003e\u003c/u\u003e\u003cem\u003e: arthritis or ongoing back or joint problems; breathing conditions (e.g. asthma or COPD); Alzheimer’s disease or other causes of dementia; neurological conditions (e.g. epilepsy or migraine); chronic liver disease; cancer (diagnosis or treatment within the last five years); and other long‑term conditions.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"22.png","url":"https://assets-eu.researchsquare.com/files/rs-9424991/v1/fc39f98c4236226d6d9ce01c.png"},{"id":109206523,"identity":"b32c137d-731c-4804-b086-f3e67189de35","added_by":"auto","created_at":"2026-05-13 15:13:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":564860,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9424991/v1/8cf7d587-b6fd-413f-bf04-96bb6ff42bba.pdf"},{"id":108805510,"identity":"3d32c57e-5efd-44c1-be50-f47c3cae36dd","added_by":"auto","created_at":"2026-05-08 15:26:08","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":39351,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 1. Variables used in the data analysis\u003c/p\u003e","description":"","filename":"Supplementarymaterial1.Variablesusedintheanalysis.docx","url":"https://assets-eu.researchsquare.com/files/rs-9424991/v1/bcc81269fd9517a240d88df8.docx"},{"id":108630550,"identity":"481b3d52-bcda-4404-919e-7be1ad2109a2","added_by":"auto","created_at":"2026-05-06 16:37:32","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":62190,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 2. Complementary results\u003c/p\u003e","description":"","filename":"Supplementarymaterial2.Additionalresults.docx","url":"https://assets-eu.researchsquare.com/files/rs-9424991/v1/6e7a7ef36363207c986e598a.docx"},{"id":108806197,"identity":"7f5518e2-4544-4ab8-8337-1bee39fa39d0","added_by":"auto","created_at":"2026-05-08 15:27:58","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":38172,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 3. Sensitivity analysis\u003c/p\u003e","description":"","filename":"Supplementarymaterial3.Sensitivityanalysis.docx","url":"https://assets-eu.researchsquare.com/files/rs-9424991/v1/ef6e6e058540b8c3131c22bf.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Differences in patient-reported outcomes across multimorbidity profiles in primary care: Evidence from the OECD Patient Reported Indicator Surveys (PaRIS)","fulltext":[{"header":"Background","content":"\u003cp\u003eMultimorbidity, defined as the presence of two or more chronic conditions in an individual, is a growing challenge for healthcare systems, healthcare professionals and patients (1; 2). People with multimorbidity need more healthcare, leading to increased costs and strain on healthcare systems (3; 4; 5). Multimorbidity also increases the complexity of care delivery, requiring more time and coordination from healthcare professionals, while imposing substantial physical, emotional, and financial burdens on patients. Despite this, most chronic care delivery models remain structured around single-disease frameworks, which are ill-suited to address the complexity and interactions inherent in multimorbidity\u0026nbsp;(6). This misalignment increases the risk that people with multimorbidity receive fragmented or suboptimal care. In recent years, person-centred integrated care has been widely promoted to help provide services for people with multimorbidity\u0026nbsp;(7).\u003c/p\u003e\n\u003cp\u003ePatient-reported outcome measures (PROMs), defined as measures reported directly by patients without interpretation of anyone else, can serve as critical tools to assess outcomes that are highly valued by patients, e.g., their perceived health and well-being, which can guide health authorities, health services and stakeholders in countries in their efforts to make healthcare more person-centred. Generic PROMs are particularly valuable in the context of multimorbidity because they capture overall physical, mental, and social functioning, offering a holistic assessment that clinical, disease-specific indicators cannot provide. When used at the group level (aggregated), PROMs can help health system decision-makers identify the priority areas for action and support the redesign of services to better address the needs of their population\u0026nbsp;(8; 9; 10). \u0026nbsp;The limited collection of systematic PROMs data among people with multimorbidity remains a missed opportunity to inform health policy decisions and care delivery\u003csub\u003e\u0026nbsp;\u003c/sub\u003e(10; 9).\u003c/p\u003e\n\u003cp\u003eNot only the number but also the type and combination of chronic conditions influence patient outcomes \u003c!--[if supportFields]\u003e\u003cspan style='mso-element:field-begin'\u003e\u003c/span\u003e\u003cspan style='mso-no-proof:yes'\u003e\u0026nbsp;CITATION XRef_s87dWRStRDa6eA_09__E5Ig \\l 2057\u0026nbsp;\u0026nbsp;\\m Kapp_c8af05a5 \\m XRef_Qa__90LtbWAEAim1bjk9ipg \u003c/span\u003e\u003cspan style='mso-element:field-separator'\u003e\u003c/span\u003e\u003c![endif]--\u003e(11; 12; 13)\u003c!--[if supportFields]\u003e\u003cspan style='mso-element:field-end'\u003e\u003c/span\u003e\u003c![endif]--\u003e. Emerging evidence shows that specific combinations of chronic conditions are associated with distinct levels of healthcare costs, care burden, and functional outcomes\u0026nbsp;(5). Some combinations of conditions are more prevalent in specific sociodemographic groups such as older people, women and people with lower income, who are more at risk of poorer health outcomes \u003csub\u003e\u0026nbsp;\u003c/sub\u003e(14; 15; 16). Several literature reviews exploring the consistency of diseases patterns using various statistical tests across different settings and populations identified two main (replicable) groups: cardiometabolic conditions and mental health\u003csub\u003e\u0026nbsp;\u003c/sub\u003e(17; 18; 15; 16; 19; 20). At the same time, literature reviews indicate a persistent paucity of evidence linking multimorbidity patterns to patient-reported outcomes, especially in primary care settings.\u003c/p\u003e\n\u003cp\u003ePrimary care plays a critical role in managing multimorbidity (21). It is often the first and most frequently attended point of contact for patients and is uniquely positioned to deliver continuous, coordinated, comprehensive and person-centred care, which is essential in multimorbidity management (22; 23). It is usually also closely located to people and their communities, supporting individuals\u0026rsquo; capacity to self-manage their own health and well-being. Primary care functions such as person-centred‑ness of care and continuity of care are often considered core components of high-quality‑ primary care for people with chronic conditions and may influence how patients perceive their physical and mental health. Yet, both primary care patients and professionals face substantial challenges related to multimorbidity, including fragmented care, and clinical guidelines (and their evidence base) that fail to account for the complexity of multimorbidity clusters\u0026nbsp;(24).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile studies have addressed the burden and patterns of multimorbidity clusters and their influence on patient-reported outcomes, most of these studies relied on reviews of literature and meta-analysis spanning different healthcare settings and data sources\u0026nbsp;(1; 19; 15; 17; 16). Analyses using comparable PROMs data from primary care populations remain scarce. Research on multimorbidity clusters using harmonised comparable PROMs data from primary care users can provide actionable insights for health policy and practice, supporting the development of more responsive and equitable primary care systems for people with multimorbidity\u0026nbsp;(20).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRecognising the evidence gap, the Organisation for Economic Co‑operation and Development (OECD) developed the Patient Reported Indicator Surveys (PaRIS) to collect harmonised PROMs data, as well as data assessed with patient-reported experience measures (PREMs), from primary care patients aged 45 years and older living in 19 countries (12). In addition, the survey gathered detailed information on patients\u0026rsquo; sociodemographic characteristics, healthcare utilisation, health behaviours and self-management capabilities.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe present study assesses how distinct multimorbidity profiles are associated with patient-reported outcomes among people aged 45 years and older in primary care. Building on previous studies and observed differences in the initial PaRIS results, this study extends the evidence in two ways. First, it moves beyond simple disease counts by comparing mutually exclusive multimorbidity profiles\u0026nbsp;(25). Second, it uses a harmonised international dataset, which has been developed specifically for people with chronic conditions using primary care.\u003c/p\u003e\n\u003cp\u003eThe study addresses the following research questions:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eHow do patient-reported outcomes, in particular physical health, mental health and well-being, vary across patient groups with different and mutually exclusive multimorbidity patterns?\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eTo what extent do continuity of care relationship and person-centredness of care modify associations between patient-reported outcomes and multimorbidity profiles?\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Methods","content":"\u003cp\u003eAnalyses are based on secondary analysis of anonymised data from the OECD PaRIS database, which includes over 107 011 patients nested in 1 807 primary care practices across 19 countries. Data were collected in 2023-2024 using the PaRIS Patient Questionnaire (PaRIS-PQ), which was developed for the PaRIS study (26). The study was exempted from ethical review based on the decision of the non-WMO Committee of the Medical Ethics Review Committee of Amsterdam University Medical Centres (FWA00032965). Participation in this OECD-led research involved official national project management teams, who are appointed by their respective Ministries of Health to implement the OECD PaRIS study in their respective countries. Each participating national project management team has been responsible for preparing the information letter and consent form, as well as acquiring ethical clearance in accordance with their national requirements.\u003c/p\u003e\n\u003ch3\u003eStudy population characteristics\u003c/h3\u003e\n\u003cp\u003ePeople aged 45 and over who had contact with their primary care practice in the six months prior to sampling were included in the study. Further study population characteristics as well as PaRIS in-/exclusion criteria and response rates have been described elsewhere (12). An analytical framework was developed to structure the multilevel analysis by organising variables into patient characteristics, clinical burden, and clinical and functional enablers (figure 1). The Wilson and Cleary model of patient outcomes (27), using the 2005 revision of the conceptual model of health-related quality of life by Ferrans et al. (28), was adopted as the primary theoretical framework linking clinical burden to patient‑reported outcomes through symptoms, functioning and health perceptions, with individual and environmental characteristics influencing these relationships. In our data, multimorbidity profiles represent clinical burden and PROMIS Global Health scores index general health perceptions (physical and mental). Symptoms and functional status mediators were not included explicitly as these were considered to be taken into account in the overall physical and mental health outcomes reported by patients. Consistent with Ferrans\u0026rsquo;s revision, patient-reported person‑centredness and continuity of care were conceptualised as enabling/contextual factors that may modify associations across multiple nodes. Multilevel nuance represents between-country and between-practice variance as an \u0026ldquo;environmental/contextual\u0026rdquo; layer.\u0026nbsp;\u003c/p\u003e\n\u003ch4\u003eMultimorbidity profiles\u003c/h4\u003e\n\u003cp\u003eChronic conditions were self-reported by the respondents to the question \u003cem\u003e\u0026ldquo;Have you ever been told by a doctor that you have any of the following health conditions? Please select all the options that apply\u0026rdquo;.\u0026nbsp;\u003c/em\u003eConditions were grouped based on well‑established multimorbidity patterns into five mutually exclusive profiles (20; 17; 15; 18; 16; 29). Cardiometabolic conditions served as the reference category due to shared pathophysiology, common risk factors, and the well‑established care pathways available in primary care. This makes the cardiometabolic group an analytically robust and policy-relevant reference point for comparing the additional‑ burden associated with mental health or other chronic conditions. Mental health conditions were treated as a distinct multimorbidity domain due to their unique influence on symptom perception, functional impairment, self-management capacity and care utilisation. In contrast, the \u0026ldquo;other chronic conditions\u0026rdquo; category includes musculoskeletal, respiratory, gastrointestinal and other heterogeneous conditions that vary widely in severity and functional impact. Distinguishing mental health allows clearer identification of its specific role in shaping patient-reported outcomes, while the \u0026ldquo;other\u0026rdquo; category captures additional‑ heterogeneity and complexity. The final five multimorbidity profiles were mutually exclusive (figure 2).\u003c/p\u003e\n\u003ch4\u003ePatient-reported outcomes\u003c/h4\u003e\n\u003cp\u003ePatient-reported physical health, mental health and well-being were included as outcome measures and separate analyses were conducted for each outcome. Physical health and mental health were measured by PROMIS\u0026reg; Scale v1.2 \u0026ndash; Global Health (30) components. Physical health combines responses to four questions measuring physical function, pain and fatigue, on a Likert scale of 1‑5 (poor-excellent), while mental health combines four questions on quality of life, emotional distress and social health. The raw scales (4‑20) were converted to T-score metric, ranging between 16.2‑67.7 for physical health and 21.2‑67.6 for mental health. Well-being was measured by the WHO-5 Well-being Index (31), capturing subjective psychological well‑being through five items scored on a Likert scale of 0\u0026ndash;5. The responses are summed to a total score from 0 to 25 and transformed to a 0\u0026ndash;100 scale. Higher scores indicated better outcomes.\u0026nbsp;\u003c/p\u003e\n\u003ch4\u003eCase-mix variables and patient-reported experiences\u003c/h4\u003e\n\u003cp\u003eThe study examined a set of factors of interest that were selected based on prior evidence of their influence on the association between patient-reported outcomes and multimorbidity profiles, as well as their relevance to primary care policy and practice (3). We encoded gender in two categories (female; male), age in four categories (45-54; 55-64; 65-74; 75+), education level in three categories (low; mid; high) and the number of chronic conditions as numerical. Patient‑reported experiences consisting of person‑centredness (P3CEQ) (32; 33) and continuity of care (having a usual primary care provider for most health problems: yes/no) were analysed both as main effects and as potential effect modifiers of the association between multimorbidity profiles and outcomes.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eStatistical Analysis\u003c/h3\u003e\n\u003cp\u003eDescriptive analyses summarised sample characteristics. Frequencies and proportions were calculated for categorical variables and across the multimorbidity profiles in the total sample and stratified by age, gender and education. Correlations among the two PREMs were examined before inclusion in the multilevel model. Cases with missing values were excluded from the analysis. Pairwise 2\u0026times;2 cross‑tabulations of missingness across education level, gender, age, and count of chronic conditions were used to diagnose co-missingness patterns.\u003c/p\u003e\n\u003cp\u003eThe association between the PROMs and other variables were assessed using multi-level mixed-effects regression analysis, given the multi-level structure of the PaRIS data with patients nested in primary care practices, which are nested in countries/healthcare systems (Supplementary material 1). Mean PROM scores and 95% confidence intervals were calculated across multimorbidity profiles. Interaction terms tested whether person‑centredness or continuity of care moderated associations. The log-likelihood ratio and marginal pseudo-R2 offered insights into how well the model explains the variance in the outcomes and the contribution of the variables included. Log likelihood compared the goodness-of-fit between two multilevel models (e.g. with and without a specific variable of interest). It consisted of subtracting the deviance of the two models and comparing the resulting difference to a chi-squared distribution with degrees of freedom equal to the number of additional parameters. A significant likelihood ratio indicated that adding the variable improves the model fit (34). Marginal Pseudo-R2 quantified the proportion of variance explained by the covariates (fixed effect) in the model. It helped assess how much of the variability in the outcomes is attributed to the predictors, excluding random effects (35). P values below 0.05 were considered as statistically significant.\u003c/p\u003e\n\u003cp\u003eTo assess the robustness of our findings to alternative conceptualisations of multimorbidity, we conducted a sensitivity analysis in which hypertension was excluded from the definition of chronic conditions used to derive multimorbidity profiles. This was motivated by the consideration that hypertension may be viewed primarily as a risk factor rather than a chronic condition in some frameworks. The same analytical approach was applied, with multimorbidity profiles re‑estimated excluding hypertension, and all models were re‑run using these revised profiles to evaluate the consistency of associations with the outcomes of interest (Supplementary material 3).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eIn the study sample (n=102786), 82% had at least one chronic condition and 52% had two or more chronic conditions. Overall, 28.8% were aged 55-64, 56.3% were female, and 43.2% had a higher education level (Table 1). Across the multimorbidity profiles, most participants (80%) were classified into profiles including cardiometabolic conditions. The percentage of patients with Profile 1 (cardiometabolic) was 11.8%, Profile 2 (cardiometabolic + mental) 3.6%, Profile 3 (cardiometabolic + other) 51.8%, Profile 4 (cardiometabolic + other + mental) 12.7% and Profile 5 (mental and/or other) 20.0%.\u003c/p\u003e\n\u003cp\u003eOlder people were most represented in Profile 1 (cardiometabolic) and Profile 3 (cardiometabolic + other), with around 60% of respondents aged 65 years or older. Whereas Profile 2 (cardiometabolic + mental) and Profile 5 (mental and/or other) included a larger proportion of younger people (66.4 and 67.2 below 65 years, respectively) (Table 1). Women were highly represented in Profile 4 (cardiometabolic + other + mental) and Profile 5 (mental and/or other) (67.1% and 72.6%, respectively), while men were more represented in Profile 1 (cardiometabolic) (68.2%). The mean number of chronic conditions was 1.8 in the total population and varied from 2.2 in Profile 1 (cardiometabolic) to 4.4 in Profile 4 (cardiometabolic + mental + other) across multimorbidity profiles. Supplementary material 2 includes further details on the distribution shape of total number of chronic conditions, including skewness and kurtosis, for the overall sample and within each multimorbidity profile.\u003c/p\u003e\n\u003cp\u003eTable 1. Distribution across multimorbidity profiles\u0026nbsp;\u003c/p\u003e\n\u003ctable width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTotal sample\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003en= 102786\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003ePeople with two or more chronic conditions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003en= 53581\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eProfile 1: cardiometabolic\u003c/strong\u003e\u0026nbsp;\u003cbr\u003e\u0026nbsp;n= 6346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eProfile 2:\u0026nbsp;\u003cbr\u003e\u0026nbsp;cardiometabolic + mental\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;n= 1907\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eProfile 3: cardiometabolic + other\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;n= 27774\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eProfile 4: cardiometabolic + mental + other\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;n= 6863\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eProfile 5: mental and/or other\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;n= 10691\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e45-54\u003cbr\u003e\u0026nbsp;55-64\u003cbr\u003e\u0026nbsp;65-74\u003cbr\u003e\u0026nbsp;75 and over\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;27.9\u003cbr\u003e\u0026nbsp;28.8\u003cbr\u003e\u0026nbsp;25.5\u003cbr\u003e\u0026nbsp;17.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e13.0\u003cbr\u003e\u0026nbsp;26.0\u003cbr\u003e\u0026nbsp;34.6\u003cbr\u003e\u0026nbsp;26.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e31.0\u003cbr\u003e\u0026nbsp;35.4\u003cbr\u003e\u0026nbsp;21.8\u003cbr\u003e\u0026nbsp;11.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e14.3\u003cbr\u003e\u0026nbsp;24.3\u003cbr\u003e\u0026nbsp;31.8\u003cbr\u003e\u0026nbsp;29.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e21.5\u003cbr\u003e\u0026nbsp;32.1\u003cbr\u003e\u0026nbsp;25.7\u003cbr\u003e\u0026nbsp;20.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e35.7\u003cbr\u003e\u0026nbsp;31.5\u003cbr\u003e\u0026nbsp;21.0\u003cbr\u003e\u0026nbsp;11.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eFemale\u003cbr\u003e\u0026nbsp;Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e56.3\u003cbr\u003e\u0026nbsp;43.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e31.8\u003cbr\u003e\u0026nbsp;68.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e55.2\u003cbr\u003e\u0026nbsp;44.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e51.6\u003cbr\u003e\u0026nbsp;48.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e67.1\u003cbr\u003e\u0026nbsp;32.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e72.6\u003cbr\u003e\u0026nbsp;27.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eEducation\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eLower\u003cbr\u003e\u0026nbsp;Middle\u003cbr\u003e\u0026nbsp;Higher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e32.9\u003cbr\u003e\u0026nbsp;23.9\u003cbr\u003e\u0026nbsp;43.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e37.6\u003cbr\u003e\u0026nbsp;25.0\u003cbr\u003e\u0026nbsp;37.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e31.8\u003cbr\u003e\u0026nbsp;25.7\u003cbr\u003e\u0026nbsp;42.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;39.9\u003cbr\u003e\u0026nbsp;23.1\u003cbr\u003e\u0026nbsp;37.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e44.2\u003cbr\u003e\u0026nbsp;22.9\u003cbr\u003e\u0026nbsp;32.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e31.1\u003cbr\u003e\u0026nbsp;24.8\u003cbr\u003e\u0026nbsp;44.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTotal number of chronic conditions: mean (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.8 (1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.2(0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.4 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3.0 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4.4 (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.4 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003ePhysical health outcomes across multimorbidity profiles\u003c/h3\u003e\n\u003cp\u003eResults from the multilevel regression model indicated differences in patient‑reported physical health across multimorbidity profiles (Table 2). Men reported significantly better physical health than women (b=1.42, p\u0026lt;0.001). Compared with the youngest age group (45-54), respondents aged 55\u0026ndash;64 (b=0.43, p\u0026lt;0.001) and 65\u0026ndash;74 (b=1.46, p\u0026lt;0.001) reported better physical health, while those aged \u0026ge;75 had worse (b=\u0026minus;0.31, p\u0026lt;0.05). Using the lower education group as reference, higher educated people had better physical health, both for mid‑level (b=1.49, p\u0026lt;0.001) and high education (b=3.14, p\u0026lt;0.001). Physical health scores declined by 2.37 points on the mental health T-score for every additional chronic condition (b=\u0026minus;2.37, p\u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003eUsing Profile 1 (cardiometabolic only) as the reference, all other multimorbidity profiles reported significantly poorer physical health. The largest differences were observed for Profile 5 (mental and/or other conditions;\u0026nbsp;b=\u0026minus;2.94, p\u0026lt;0.001) and Profile 4 (cardiometabolic + mental\u0026nbsp;+\u0026nbsp;other conditions;\u0026nbsp;b=\u0026minus;2.07, p\u0026lt;0.001). Profile 3 (cardiometabolic + other) also reported significantly poorer physical health compared with Profile 1 (b=\u0026minus;1.97, p\u0026lt;0.001), while Profile 2 (cardiometabolic + mental) did not differ significantly (b=-0.55).\u003c/p\u003e\n\u003cp\u003eHigher patient-reported person‑centredness was associated with better physical health (b=0.44, p\u0026lt;0.001), while having a usual care provider was not significantly associated with physical health outcomes (b=\u0026minus;0.49). The positive association between patient-reported person‑centredness and physical health was attenuated for patients in Profile 4 (cardiometabolic + mental + other; b=\u0026minus;0.09, p\u0026lt;0.05). No other significant interactions were observed with patient-reported experiences.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 2. Multimorbidity profiles and physical and mental health outcomes; estimates of multi-level mixed-effects regression analyses (only fixed effects shown in the table)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable style=\"float: left;width: 100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePhysical health\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003eT-score\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eEstimate (Standard error)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMental health\u0026nbsp;\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003eT-score\u003c/em\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eEstimate (Standard error)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eWHO-5 Well-being Index\u0026nbsp;\u003cbr\u003e \u003cem\u003eTotal score\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eEstimate (Standard error)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e42.77 (0.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e42.03 (0.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e44.21 (1.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMale (ref: female)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.42 (0.07) ***\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.41 (0.06) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.26 (1.19) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAge 55\u0026ndash;64 (ref: age 45-54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.43 (0.10) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.49 (0.09) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.72 (0.27) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAge 65\u0026ndash;74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.40 (0.11) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.14 (0.10) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.71 (0.28) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAge \u0026ge;75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.31 (0.12) **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.64 (0.11) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.34 (0.31) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEducation mid (ref: lower level)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.49 (0.10) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.14 (0.09) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.32 (0.25) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEducation high\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.14 (0.08) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.48 (0.07) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.82 (0.22) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTotal number of chronic conditions (per +1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e- 2.37 (0.04) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e- 1.20 (0.03) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e- 3.83 (0.10) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eProfile 2 (cardiometabolic + mental)\u003cbr\u003e\u0026nbsp;(ref: Profile 1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e- 0.55 (0.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e- 5.75 (0.76) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-12.65 (2.21) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eProfile 3 (cardiometabolic + other)\u003cbr\u003e\u0026nbsp;(ref: Profile 1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e- 1.96 (0.50) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e- 0.30 (0.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-2.91 (1.29) *\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eProfile 4 (cardiometabolic + mental + other)\u003cbr\u003e\u0026nbsp;(ref: Profile 1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e- 2.07 (0.58) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e- 6.10 (0.52) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-12.08 (1.51) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eProfile 5 (mental +/ other)\u003cbr\u003e\u0026nbsp;(ref: Profile 1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e- 2.94 (0.54) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e- 3.73 (0.48) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-9.63 (1.40) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePerson-centredness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.44 (0.02) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.47 (0.02) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.36 (0.06) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHaving a usual care provider\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e- 0.49 (0.02)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e- 0.56 (0.26) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.77 (0.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePerson-centredness * Profile 4 (cardiometabolic + mental + other)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e- 0.09 (0.03) **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e- 0.02 (0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e- 0.10 (0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eUsual care provider * Profile 2 (cardio + mental)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e- 0.72 (0.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.01 (0.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e- 3.74 (1.50) *\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePseudo-R\u003csup\u003e2\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote: Results are shown for the final model (Yijk=b0+\u0026sum;b(Demographicsijk)+\u0026sum;b(Profileijk)+\u0026sum;b(Care_experienceijk)+\u0026sum;b(Profile\u0026times;Care_experience)ijk+u0k+v0jk+\u0026epsilon;ijk). Demographics include gender, age group, education, and chronic condition count; Profile includes multimorbidity profiles 2\u0026ndash;5 (profile 1 = reference); Care experience includes person\u003c/em\u003e\u003cem\u003e‑\u003c/em\u003e\u003cem\u003ecentredness and continuity; Random intercepts are specified for country (u\u003c/em\u003e\u003cem\u003e₀\u003c/em\u003e\u003cem\u003ek), practice (v\u003c/em\u003e\u003cem\u003e₀\u003c/em\u003e\u003cem\u003ejk), and patient (\u0026epsilon;ᵢⱼ\u003c/em\u003e\u003cem\u003eₖ\u003c/em\u003e\u003cem\u003e). Regression-coefficients (b) and standard errors are shown; *** p\u0026lt;0.001, ** p\u0026lt;0.01, * p\u0026lt;0.05. Physical health T score range: 16.2-67.7; Mental health T score range: 21.2-67.6; WHO-5 Well-being Index range: 0-100. Pseudo\u003c/em\u003e\u003cem\u003e‑\u003c/em\u003e\u003cem\u003eR\u0026sup2; represents the percentage reduction in unexplained variance compared with the null (intercept\u003c/em\u003e\u003cem\u003e‑\u003c/em\u003e\u003cem\u003eonly) model. For the interactions between patient-reported experiences and multimorbidity profiles, only those which had significant association with at least one of the health outcomes (physical, mental, well-being) are presented.\u003c/em\u003e\u003c/p\u003e\n\u003ch3\u003eMental health outcomes across multimorbidity profiles\u003c/h3\u003e\n\u003cp\u003eRegarding mental health outcomes, results from multilevel analysis showed that men reported better mental health than women (b=0.41, p\u0026lt;0 .001). Compared with the youngest group (45-54), mental health scores were higher among the older age categories of 55\u0026ndash;64 (b= 0.49, p \u0026lt; 0.001), 65-74 (b= 1.14, p\u0026lt;0.001), and \u0026ge;75 (b=0.64, p\u0026lt;0.001). Patients with mid‑level (b=0.14) and higher education (b=2.48) had better mental health outcomes (both p\u0026lt;0 .001). Mental health scores were 1.20 points lower on the mental health T score for every additional chronic condition (b=\u0026minus;1.20, p\u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003eProfiles including mental health conditions reported significantly poorer mental health outcomes compare to Profile 1 (cardiometabolic), which were Profile 2 (cardiometabolic + mental; b=\u0026minus;5.75, p\u0026lt;0.001), Profile 4 (cardiometabolic + mental + other; b=\u0026minus;6.10, p\u0026lt;0.001), and Profile 5 (mental +/ other; b=\u0026minus;3.73, p\u0026lt;0.001). Profile 3 (cardiometabolic + other) did not differ significantly (b=\u0026minus;0.30).\u003c/p\u003e\n\u003cp\u003eHigher patient-reported person‑centredness of care was associated with better mental health outcomes (b=0.47, p\u0026lt;0.001). Having a usual care provider was negatively associated with mental health outcomes (b=\u0026minus;0.56, p\u0026lt;0.05). Patient-reported person-centredness and having a usual care provider did not influence the relationship between the mental health outcomes and multimorbidity profiles (p\u0026gt;0.05).\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eWell-being across multimorbidity profiles\u003c/h3\u003e\n\u003cp\u003eResults from the multilevel regression model showed variation in well‑being across multimorbidity profiles (Table\u0026nbsp;2). Men reported significantly higher well‑being than women (b=2.26, p\u0026lt;0.001). Compared with people aged 45\u0026ndash;54, respondents aged 55\u0026ndash;64 (b=2.72, p\u0026lt;0.001), 65\u0026ndash;74 (b=6.72, p\u0026lt;0.001), and\u0026nbsp;\u0026ge;75 (b=4.35, p\u0026lt;0.001) all reported better well‑being. Both mid‑level education (b=2.32, p\u0026lt;0.001) and high education (b=4.83, p\u0026lt;0.001) groups reported significantly higher scores. Well‑being scores were 3.84 points lower for every additional chronic condition (b=\u0026minus;3.84, p\u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003eUsing Profile 1 (cardiometabolic only) as the reference, well‑being was significantly lower in all other profiles. The largest differences were observed in Profile 2 (cardiometabolic + mental; b=\u0026minus;12.65, p\u0026lt;0.001), Profile 4 (cardiometabolic + mental + other; b=\u0026minus;12.08, p\u0026lt;0.001) and Profile 5 (mental \u0026plusmn; other conditions; b=\u0026minus;9.63, p\u0026lt;0.001). Profile 3 (cardiometabolic + other) showed a smaller decrease (b=\u0026minus;2.91, p\u0026lt;0.05).\u003c/p\u003e\n\u003cp\u003eHigher patient-reported person‑centredness was associated with better well‑being (b=1.36, p\u0026lt;0.001). No significant interactions were observed between multimorbidity profiles and patient-reported person‑centredness. While having a usual care provider was not significantly associated with well-being outcomes (b=0.77, p\u0026gt;0.05), having a usual provider attenuated the positive relationship between well-being and Profile 2 (cardiometabolic + mental; b=\u0026minus;3.74, p\u0026lt;0.05).\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eIntraclass correlation and variance explained by the model\u003c/h3\u003e\n\u003cp\u003eFor physical health, the null model showed that most of the variation was located at the patient level (93.3%), with smaller shares at the practice (2.5%) and country levels (4.2%) (Supplementary material 2). For mental health 89.9% of the variance was at the patient level, while country‑level variation was more pronounced (8.5%) and practice‑level variation remained limited (1.7%). For well‑being, 95.2% of the variance was at the patient level, with minimal clustering at the country and practice levels (2.8% and 2.0%, respectively).\u003c/p\u003e\n\u003cp\u003eExplained variance increased progressively with the inclusion of covariates (Supplementary material 2). Among people with multimorbidity, age, gender, education level and number of chronic conditions (Model 1) explained 18.2% of the variance in physical health, 10.5% in mental health, and 12.8% in well‑being compared with the null model. Adding multimorbidity profiles (Model 2) improved the explained variance across outcomes (19.7% for physical health, 17.4% for mental health, and 17.5% for well‑being). The full model, also including the two patient-experience measures and interaction terms (Model 3), provided the greatest explanatory power, accounting for 25.5% of the variance in physical health, 25.8% in mental health, and 26.4% in well‑being (Table 2).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMain findings\u003c/h2\u003e \u003cp\u003eOur profile‑based multimorbidity approach mirrors recent efforts to move beyond simple counts by describing co‑occurring morbidity clusters and their distinct outcome patterns. This study demonstrates that these profiles are differently associated with patient-reported outcomes in a large, international primary care study population. Multimorbidity profiles were significantly associated with physical health, mental health and well-being outcomes, with profiles including mental health conditions showing the poorest outcomes. Relative to Profile 1 (cardiometabolic), patients in profiles involving mental health conditions, especially patients in Profiles 4 (cardiometabolic\u0026thinsp;+\u0026thinsp;mental\u0026thinsp;+\u0026thinsp;other) and 5 (mental and/or other) reported much lower physical health outcomes and patients in Profile 2 (cardiometabolic\u0026thinsp;+\u0026thinsp;mental) and Profile 4 (cardiometabolic\u0026thinsp;+\u0026thinsp;mental\u0026thinsp;+\u0026thinsp;other) reported substantially poorer mental health and well-being outcomes. Higher patient-reported person‑centredness of care showed a positive association with all outcomes, although this association was attenuated for Profile 4 (cardiometabolic\u0026thinsp;+\u0026thinsp;mental\u0026thinsp;+\u0026thinsp;other) and Profile 2 (cardiometabolic\u0026thinsp;+\u0026thinsp;mental). Having a usual care provider showed no main effect on physical health or well-being, while it was negatively associated with mental health outcomes. This counterintuitive finding may reflect reverse causality, where individuals with poorer mental health are more likely to seek continuous care. These findings indicate that the type and combination of conditions provide explanatory value beyond the number of chronic conditions alone.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eComparison with existing literature\u003c/h2\u003e \u003cp\u003eThat mental health comorbidity compounds deficits in perceived physical health, mental health and well-being aligns with evidence that mental health conditions are linked with poorer outcomes. A systematic review of multimorbidity clusters found that certain disease groupings, notably those involving cardiometabolic and mental health conditions, were consistently linked to poorer health-related quality of life and higher mortality (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Cardiometabolic conditions are prevalent and given their known shared mechanisms, management of these conditions is advanced through better adapted healthcare service design (e.g., guidelines and disease pathways) (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Yet, mental health conditions, which frequently co-exist with cardiometabolic conditions, may exacerbate disease burden and impair quality of life (3; 12). This may reflect the challenges primary care systems face in adequately addressing the interaction between mental and physical conditions, which often require coordination beyond standard disease-oriented care models. Our findings extend this literature by demonstrating these associations using comparative patient-reported outcomes across countries, rather than relying on clinical or administrative data from single settings.\u003c/p\u003e \u003cp\u003eThe comparatively modest decreases observed in Profile 3 (cardiometabolic\u0026thinsp;+\u0026thinsp;other) may reflect heterogeneity in the \u0026ldquo;other\u0026rdquo; group, which includes conditions across musculoskeletal, respiratory and neurological conditions among others. Due to the heterogeneity of the combination of disease groups, the management of people with these other conditions are more complicated. In addition, the high prevalence of functional symptoms such as pain and fatigue among patients with musculoskeletal conditions is associated with poorer outcomes (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Their functional impact varies greatly by severity and trajectory, leading to more modest average differences when grouped together.\u003c/p\u003e \u003cp\u003eThe positive association between patient-reported person‑centredness and outcomes is in line with systematic reviews showing that person-centred care is linked to better patient‑reported outcomes. However, the positive association with experiencing more person‑centredness did not remain across multimorbidity profiles, except where it attenuated the relationship for Profile 4 (cardiometabolic\u0026thinsp;+\u0026thinsp;mental\u0026thinsp;+\u0026thinsp;other) regarding physical health outcomes. This may reflect variations in people\u0026rsquo;s needs, expectations regarding their physical health, mental health and well-being (37; 38).\u003c/p\u003e \u003cp\u003ePatient-reported care continuity as measured by having a usual care provider did not influence outcomes in this study, except for mental health outcomes where it showed a small negative association. In addition, having a usual care provider attenuated the relationship between well-being and Profile 2 (cardiometabolic\u0026thinsp;+\u0026thinsp;mental). This pattern may reflect variability in continuity definitions and care models across countries. In addition, our measure only included one aspect of relational care continuity compared to other dimension of continuity such as information, management and organisational. Existing literature links care continuity to lower utilisation and mortality and better experiences of care (39; 40; 41).\u003c/p\u003e \u003cp\u003eThe observed pattern in which older people report better outcomes, despite higher disease burden, aligns with international research describing the \u0026ldquo;well--being paradox of ageing\u0026rdquo; (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Older people often adjust expectations regarding health, have stronger coping mechanisms and face fewer work or social stressors, contributing to more favourable self-ratings. Yet, such research also shows that after certain age well-being declines in very old ages due to health and partner loss (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). In our study, the oldest age group (75 and over) had lower physical health compared to the youngest group (45\u0026ndash;54) but reported better mental health outcomes and well-being. However, such increase was smaller compared to the people aged 65\u0026ndash;74, supporting existing literature (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe variance decomposition highlights the multilevel nature of patient‑reported outcomes (43; 44). Consistent with previous studies, the largest proportion of variance in all PROMs was located at the individual level. Nonetheless, the contribution of higher‑level factors, particularly for perceived physical health, was modest but meaningful. By contrast, substantially less variance in perceived mental health was explained at the practice and country levels. This mirrors the wider literature indicating that mental health outcomes are more strongly driven by psychosocial, cultural and individual‑level factors, while healthcare system characteristics tend to have more modest effects on emotional well‑being or distress perceptions (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). While strengthening primary care systems may substantially improve physical health outcomes for patients with multimorbidity, improving mental health outcomes and well-being may require additional psychosocial and community‑based interventions beyond primary care restructuring.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eTo our knowledge, this is the first study examining multimorbidity using a harmonised standardised dataset designed specifically for people with chronic conditions within primary care.\u003c/p\u003e \u003cp\u003eOur study has a number of limitations. As participation was based on survey responses, selection bias may have occurred if individuals with poorer health or lower engagement were underrepresented. Chronic conditions were self-reported using predefined response categories, which may lead to under- or over-reporting (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Nevertheless, \u0026ldquo;having \u0026ldquo;ever been told\u0026rdquo; in the question was designed to limit over-reporting. Our analysis gave equal weight to each chronic condition response lacking information regarding the severity, sequence, and the individual burden of different conditions on health outcomes. Other factors such as healthcare utilisation or polypharmacy could not be incorporated due to their complexity and multi-level influences at patient, practice and system factors. Although self-report measures of chronic conditions might introduce recall and reporting bias, they also have advantages: the categories were deliberately designed to be understood by lay persons, and because the same approach was applied consistently across all participating countries, the findings are unlikely to be affected by differences in disease registration practices across systems. The analysis was based on cross-sectional data; therefore, cannot support any causal inference. In addition, the measure of care continuity captured only one aspect (having a usual care provider) and did not reflect other dimensions of continuity such as information, management and organisational continuity.\u003c/p\u003e \u003cp\u003eNevertheless, the results were drawn on a harmonised standardised dataset, based on a survey designed for chronic care in primary care. In addition, all questionnaires were translated into national and minority languages using TRAP-D translation approach and cognitively tested in all countries. Such a rigorous approach to translation and cognitive testing of the instruments enhances the cross-cultural validity of the results. In relation to study design, we used multilevel analysis given the nested structure of the PaRIS dataset (patients in practices, which are nested in countries). Like branches of a tree, these observations are not independent from each other, allowing more precise results through the multi-level analysis. Multilevel modelling used in the study quantified where variation lied (patient, practice and country).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003ePolicy and practice recommendations\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eThe substantial reduction in both physical and mental health observed for people with profiles involving mental health conditions highlights the need for systematic collaborative, multidisciplinary, and integrated primary care models that address physical and psychological aspects of multimorbidity.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eInvesting in person‑centredness is likely to yield broad improvements across patient-reported outcomes among multimorbid patient populations. Policies and systems should enable environments to enhance the implementation of patient-centred integrated care models within primary care and beyond.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSystematic collection of PROMs within primary care can support continuous monitoring of multimorbidity profiles and associated outcomes, facilitating identification of individuals with complex care needs who may benefit from enhanced support. Developed to systematically assess healthcare system performance from people\u0026rsquo;s perspectives, in its future cycles, PaRIS can include broader capture of multimorbidity information by including measures regarding the severity and complexity.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePolicymakers and practitioners can use these findings to adapt care delivery to the needs of a growing population living with multimorbidity. Current policies and systems often do not account for the increasing population of people with multimorbidity and may underestimate the resources required to manage these conditions in primary care. Policymakers could provide incentives to primary care professionals, including ensuring adequate consultation time and adjusting payment models to better account for multimorbidity.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eFuture health systems should be adapted to the needs of people with multiple chronic conditions, especially those with complex needs. Such systems should facilitate the implementation of person-centred integrated care models within primary care and beyond. Multimorbidity profiles that include mental health conditions are consistently associated with poorer outcomes, highlighting a critical gap in addressing their needs. This underscores the importance of integrating mental health support into chronic care delivery in primary care, in addition to physical health management. Systematically assessing PROMs among people with chronic conditions in primary care, in addition to PREMs, may support policymakers and practitioners in improving outcomes for people living with multimorbidity.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch3\u003eEthics approval\u003c/h3\u003e\n\u003cp\u003eThe study is based on secondary analysis of anonymised data from the OECD Patient‑Reported Indicator Surveys (PaRIS). Data collection was conducted by the OECD and participating countries in accordance with relevant national ethical regulations and the principles of the Declaration of Helsinki, with ethical approval and informed consent obtained prior to data collection, where needed. The study was exempted from ethical review based on the decision of the non-WMO Committee of the Medical Ethics Review Committee of Amsterdam University Medical Centres (FWA00032965).\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eConsent for publication\u003c/h3\u003e\n\u003cp\u003eParticipation in this OECD-led research involved official national project management teams, who are appointed by their respective Ministries of Health to implement the OECD PaRIS initiative in their respective countries. Each participating national project management team has been responsible for preparing the information letter and consent form, as well as acquiring ethical clearance in accordance with their national requirements.\u003c/p\u003e\n\u003ch3\u003eAvailability of data and materials\u003c/h3\u003e\n\u003cp\u003eThe datasets analysed during the current study are not available due to OECD data privacy and security policies. Interested researchers are encouraged to contact the authors for further information.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eCompeting interests\u003c/h3\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch3\u003eFunding\u003c/h3\u003e\n\u003cp\u003eThe authors received no funding for this study.\u003c/p\u003e\n\u003ch3\u003eAuthors\u0026rsquo; contributions\u003c/h3\u003e\n\u003cp\u003eCK conceptualised the study; MvdB, NK, DK and JVM provided overall supervision. CK, NL and JVM developed the data analysis plan. CK analysed the data, and NL did quality checks on the data analysis.\u0026nbsp;CK drafted the original manuscript. NL, MvdB, FG, NK, DK, MR, and JVM contributed to the interpretation of findings and critically revised the manuscript for intellectual content. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch3\u003eAcknowledgement\u003c/h3\u003e\n\u003cp\u003eAuthors thank to Amsterdam UMC Health Services Research Group for their valuable comments on the draft protocol of the study and the draft manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe views expressed and arguments employed herein are solely those of the author(s) and do not necessarily reflect the views of the OECD or its member countries. The organisation cannot be held responsible for possible violations of copyright resulting from the posting of any written material on this website.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eChowdhury S, Chandra Das D, Sunna T, Beyene J, Hossain A. 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Measuring patient-centred system performance: a scoping review of patient-centred care quality indicators. BMJ Open. 2019;9(1):e023596. \u003c/li\u003e\n\u003cli\u003eCarr AJ, Gibson B, Robinson PG. Is quality of life determined by expectations or experience? BMJ. 2001;322(7296):1240\u0026ndash;3. \u003c/li\u003e\n\u003cli\u003eBaker R, Freeman G, Haggerty J, Bankart MJ, Nockels KH. Primary medical care continuity and patient mortality: a systematic review. Br J Gen Pract. 2020;70(698):e600\u0026ndash;11. \u003c/li\u003e\n\u003cli\u003ePrior A, Rasmussen LB, Virgilsen LF, Vedsted P, Vestergaard M. Continuity of care in general practice and patient outcomes in Denmark: a population-based cohort study. Lancet Prim Care. 2025;1(2):100016. \u003c/li\u003e\n\u003cli\u003eEngstr\u0026ouml;m SG, Andr\u0026eacute; M, Arvidsson E, \u0026Ouml;stgren CJ, Troein M, Borgquist L. Personal GP continuity improves healthcare outcomes in primary care populations: a systematic review. Br J Gen Pract. 2025;75(757):e518\u0026ndash;25. \u003c/li\u003e\n\u003cli\u003eHansen T. The age and well-being paradox revisited: a multidimensional perspective. Innovation Aging. 2020;4(Suppl 1):459. \u003c/li\u003e\n\u003cli\u003eGroenewegen P, Spreeuwenberg P, Timans R, Groene O, Su\u0026ntilde;ol R, Valderas J, et al. Data analysis plan of the OECD PaRIS survey: leveraging a multi-level approach to analyse data collected from people living with chronic conditions and their primary care practices in 20 countries. BMC Res Notes. 2024;17(1). \u003c/li\u003e\n\u003cli\u003eSch\u0026auml;fer WLA, Boerma WGW, Kringos DS, De Maeseneer J, Gre\u0026szlig; S, Heinemann S, et al. QUALICOPC, a multi-country study evaluating quality, costs and equity in primary care. BMC Fam Pract. 2011;12(1). \u003c/li\u003e\n\u003cli\u003eLim YW, Al-Busaidi I, Caya R, Bricca A, Mangin D, Wilson R, et al. Effectiveness of interventions for the management of multimorbidity in primary care and community settings: systematic review and meta-analysis. Fam Pract. 2025;42(6). \u003c/li\u003e\n\u003cli\u003eMakovski TT, Le Coroller G, Putrik P, Choi YH, Zeegers MP, Stranges S, et al. Role of clinical, functional and social factors in the association between multimorbidity and quality of life: findings from the Survey of Health, Ageing and Retirement in Europe (SHARE). PLoS ONE. 2020;15(10):e0240024. \u003c/li\u003e\n\u003cli\u003eAramrat C, Choksomngam Y, Jiraporncharoen W, Wiwatkunupakarn N, Pinyopornpanish K, Mallinson P, et al. Advancing multimorbidity management in primary care: a narrative review. Prim Health Care Res Dev. 2022;23. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-primary-care","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"famp","sideBox":"Learn more about [BMC Primary Care](https://bmcprimcare.biomedcentral.com/)","snPcode":"","submissionUrl":"https://author-welcome.nature.com/12875","title":"BMC Primary Care","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Patient-reported outcome measures, patient-reported experience measures, multimorbidity, primary care, healthcare system performance, quality of care","lastPublishedDoi":"10.21203/rs.3.rs-9424991/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9424991/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMultimorbidity is a growing challenge for healthcare systems worldwide, yet policymakers and healthcare professionals often lack evidence on how distinct multimorbidity profiles (capturing co-occurrence of chronic conditions) relate to patient-reported outcomes and experiences. This study examines how mutually exclusive multimorbidity profiles are associated with physical health, mental health, and well-being and whether these associations are modified by healthcare experiences\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe analysed data from 102 786 primary care patients aged\u0026thinsp;\u0026ge;\u0026thinsp;45 years participating in the OECD Patient-Reported Indicator Surveys (PaRIS) across 19 countries (2023\u0026ndash;2024). Self‑reported chronic conditions were grouped into five mutually exclusive multimorbidity profiles. Physical and mental health outcomes were assessed using PROMIS\u0026reg; Global Health T-scores, and well-being using WHO-5 Well-being Index. Multilevel mixed-effects models (estimated associations between multimorbidity profiles and outcomes, controlling for age, gender, education and number of chronic conditions. Interactions with patient-reported person-centredness and continuity of care were examined.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eMultimorbidity profiles showed differences in self-reported health, with profiles including mental health conditions associated with both poorer physical and mental health outcomes. Compared with the cardiometabolic-only reference group, physical health scores were significantly lower among patients with cardiometabolic\u0026thinsp;+\u0026thinsp;mental (b=\u0026ndash;4.74) and mental and/or other‑only profiles (b=\u0026ndash;4.10) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Profiles including mental health conditions were associated with lower scores on mental health outcomes, (b range: \u0026minus;\u0026thinsp;4.24 to \u0026minus;\u0026thinsp;6.39). Well‑being was similarly poorer, with the largest declines observed in cardiometabolic\u0026thinsp;+\u0026thinsp;mental (b=\u0026ndash;12.65) and cardiometabolic\u0026thinsp;+\u0026thinsp;mental\u0026thinsp;+\u0026thinsp;other profiles (b=\u0026ndash;12.08). While person‑centredness was positively associated with all health outcomes, it did not meaningfully modify the relationship between the health outcomes and multimorbidity profiles, with only limited interaction effects observed. Continuity of care showed overall limited effects.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eDifferences in patient-reported outcomes suggest that health systems may need to better adapt to the needs of people with multimorbidity. Profiles involving mental health conditions show the poorest patient‑reported outcomes, stressing the importance of implementing person-centred integrated care models within primary care and beyond. Investing in systems that systematically assess patient-reported outcomes among people with multiple chronic conditions in primary care may support policymakers and practitioners in improving care delivery.\u003c/p\u003e","manuscriptTitle":"Differences in patient-reported outcomes across multimorbidity profiles in primary care: Evidence from the OECD Patient Reported Indicator Surveys (PaRIS)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-06 16:37:26","doi":"10.21203/rs.3.rs-9424991/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-15T19:09:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"216458970530988411882460942428552685211","date":"2026-05-14T01:34:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"82327869558830275298768199203570078496","date":"2026-05-13T12:55:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"290111514684552248449987252258364790288","date":"2026-04-30T11:25:28+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-27T09:37:08+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-22T19:33:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-22T19:29:30+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-22T10:55:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Primary Care","date":"2026-04-22T10:03:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-primary-care","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"famp","sideBox":"Learn more about [BMC Primary Care](https://bmcprimcare.biomedcentral.com/)","snPcode":"","submissionUrl":"https://author-welcome.nature.com/12875","title":"BMC Primary Care","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"96d70478-a8d0-415a-a975-d13e0764e1a0","owner":[],"postedDate":"May 6th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-15T19:09:30+00:00","index":81,"fulltext":""},{"type":"reviewerAgreed","content":"216458970530988411882460942428552685211","date":"2026-05-14T01:34:39+00:00","index":79,"fulltext":""},{"type":"reviewerAgreed","content":"82327869558830275298768199203570078496","date":"2026-05-13T12:55:49+00:00","index":76,"fulltext":""},{"type":"reviewerAgreed","content":"290111514684552248449987252258364790288","date":"2026-04-30T11:25:28+00:00","index":43,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-06T16:37:26+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-06 16:37:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9424991","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9424991","identity":"rs-9424991","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}