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Background: Robustly examining associations between long-term conditions may be important in identifying opportunities for intervention in multimorbidity but is challenging when evidence is limited. We have developed a Bayesian inference framework that is robust to sparse data and used it to quantify morbidity associations in the oldest old, a population with limited available data., Methods: We conducted a retrospective cross-sectional study of a representative dataset of primary care patients in Scotland as of March 2007. We included 40 long-term conditions and studied their associations in 12,009 individuals aged 90 and older, stratified by sex (3039 men, 8970 women). We analysed associations obtained with Relative Risk (RR), a standard measure in the literature, and compared them with our proposed measure, Associations Beyond Chance (ABC). To enable a broad exploration of interactions between long-term conditions, we built networks of association and assessed differences in their analysis when associations are estimated by RR or ABC., Findings: Our Bayesian framework was appropriately more cautious in attributing association when evidence is lacking, particularly in uncommon conditions. This caution in reporting association was also present in reporting differences in associations between sex and affected the aggregated measures of multimorbidity and network representations., Interpretation: Incorporating uncertainty into multimorbidity research is crucial to avoid misleading findings when evidence is limited, a problem that particularly affects small but important subgroups. Our proposed framework improves the reliability of estimations of associations and, more in general, of research into disease mechanisms and multimorbidity., Funding: National Institute for Health and Care Research., Competing Interests: Declaration of interests We declare that we have no conflicts of interest., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: Multimorbidity is one of the greatest challenges facing health and social care systems globally. It is associated with high rates of health service use, adverse healthcare events, and premature death. Despite its importance, little is known about the effects of contextual determinants such as household and area characteristics on health and care outcomes for people with multimorbidity. This study protocol presents a plan for the examination of associations between individual, household, and area characteristics with important health and social care outcomes., Methods: The study will use a cross-section of data from the SAIL Databank on 01 January 2019 and include all people alive and registered with a Welsh GP. The cohort will be stratified according to the presence or absence of multimorbidity, defined as two or more long-term conditions. Multilevel models will be used to examine covariates measured for individuals, households, and areas to account for social processes operating at different levels. The intra-class correlation coefficient will be calculated to determine the strength of association at each level of the hierarchy. Model outcomes will be any emergency department attendance, emergency hospital or care home admission, or mortality, within the study follow-up period., Discussion: Household and area characteristics might act as protective or risk factors for health and care outcomes for people with multimorbidity, in which case results of the analyses can be used to guide clinical and policy responses for effective targeting of limited resources., Competing Interests: None, (Copyright: © 2023 MacRae et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Introduction: Multimorbidity has emerged as a major healthcare challenge in low/middle-income countries (LMICs) such as India and Brazil. Life course epidemiology suggests that adverse events in early life contribute to an individual's later health in adulthood. However, little is known about the influence of early life health and social factors on the development of multimorbidity in adulthood in LMICs. We aimed to explore the association of adult multimorbidity with childhood health and social disadvantages among two LMICs, India and Brazil., Methods: We conducted a secondary data analysis of older adults aged ≥50 years using nationally representative surveys from Longitudinal Ageing Study in India, 2017-2018 (n=51 481) and 'Estudo Longitudinal da Saude e Bem-Estar dos Idosos Brasileirous', 2015-2016 (n=8730). We estimated the prevalence of multimorbidity along with 95% CI as a measure of uncertainty for all weighted proportions. Log link in generalised linear model was used to assess the association between childhood health and disadvantages with multimorbidity, reported as adjusted prevalence ratio (APR)., Results: The prevalence of multimorbidity was 25.53% and 55.24% in India and Brazil, respectively. Participants who perceived their childhood health as poor and missed school for a month or more due to illness had the highest level of multimorbidity across both countries. After adjusting for age and gender, a significant association between adult multimorbidity and poor self-rated childhood health (APR: (India: 1.38, 1.16 to 1.65) and (Brazil: 1.19, 1.09 to 1.30)); and missed school for a month due to illness (AOR: (India: 1.73, 1.49 to 2.01) and (Brazil: 1.16, 1.08 to 1.25)) was observed., Conclusion: Early life health, educational and economic disadvantages are associated with adult multimorbidity and appear to contribute to the later course of life. A life course approach to the prevention of multimorbidity in adulthood in LMICs may be useful in health programmes and policies., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY. Published by BMJ.)

Background: Measurement of multimorbidity in research is variable, including the choice of the data source used to ascertain conditions. We compared the estimated prevalence of multimorbidity and associations with mortality using different data sources., Methods: A cross-sectional study of SAIL Databank data including 2,340,027 individuals of all ages living in Wales on 01 January 2019. Comparison of prevalence of multimorbidity and constituent 47 conditions using data from primary care (PC), hospital inpatient (HI), and linked PC-HI data sources and examination of associations between condition count and 12-month mortality., Results: Using linked PC-HI compared with only HI data, multimorbidity was more prevalent (32.2% versus 16.5%), and the population of people identified as having multimorbidity was younger (mean age 62.5 versus 66.8 years) and included more women (54.2% versus 52.6%). Individuals with multimorbidity in both PC and HI data had stronger associations with mortality than those with multimorbidity only in HI data (adjusted odds ratio 8.34 [95% CI 8.02-8.68] versus 6.95 (95%CI 6.79-7.12] in people with ≥ 4 conditions). The prevalence of conditions identified using only PC versus only HI data was significantly higher for 37/47 and significantly lower for 10/47: the highest PC/HI ratio was for depression (14.2 [95% CI 14.1-14.4]) and the lowest for aneurysm (0.51 [95% CI 0.5-0.5]). Agreement in ascertainment of conditions between the two data sources varied considerably, being slight for five (kappa < 0.20), fair for 12 (kappa 0.21-0.40), moderate for 16 (kappa 0.41-0.60), and substantial for 12 (kappa 0.61-0.80) conditions, and by body system was lowest for mental and behavioural disorders. The percentage agreement, individuals with a condition identified in both PC and HI data, was lowest in anxiety (4.6%) and highest in coronary artery disease (62.9%)., Conclusions: The use of single data sources may underestimate prevalence when measuring multimorbidity and many important conditions (especially mental and behavioural disorders). Caution should be used when interpreting findings of research examining individual and multiple long-term conditions using single data sources. Where available, researchers using electronic health data should link primary care and hospital inpatient data to generate more robust evidence to support evidence-based healthcare planning decisions for people with multimorbidity., (© 2023. BioMed Central Ltd., part of Springer Nature.)

Background: Multimorbidity prevalence rates vary considerably depending on the conditions considered in the morbidity count, but there is no standardised approach to the number or selection of conditions to include., Methods and Findings: We conducted a cross-sectional study using English primary care data for 1,168,260 participants who were all people alive and permanently registered with 149 included general practices. Outcome measures of the study were prevalence estimates of multimorbidity (defined as ≥2 conditions) when varying the number and selection of conditions considered for 80 conditions. Included conditions featured in ≥1 of the 9 published lists of conditions examined in the study and/or phenotyping algorithms in the Health Data Research UK (HDR-UK) Phenotype Library. First, multimorbidity prevalence was calculated when considering the individually most common 2 conditions, 3 conditions, etc., up to 80 conditions. Second, prevalence was calculated using 9 condition-lists from published studies. Analyses were stratified by dependent variables age, socioeconomic position, and sex. Prevalence when only the 2 commonest conditions were considered was 4.6% (95% CI [4.6, 4.6] p < 0.001), rising to 29.5% (95% CI [29.5, 29.6] p < 0.001) considering the 10 commonest, 35.2% (95% CI [35.1, 35.3] p < 0.001) considering the 20 commonest, and 40.5% (95% CI [40.4, 40.6] p < 0.001) when considering all 80 conditions. The threshold number of conditions at which multimorbidity prevalence was >99% of that measured when considering all 80 conditions was 52 for the whole population but was lower in older people (29 in >80 years) and higher in younger people (71 in 0- to 9-year-olds). Nine published condition-lists were examined; these were either recommended for measuring multimorbidity, used in previous highly cited studies of multimorbidity prevalence, or widely applied measures of "comorbidity." Multimorbidity prevalence using these lists varied from 11.1% to 36.4%. A limitation of the study is that conditions were not always replicated using the same ascertainment rules as previous studies to improve comparability across condition-lists, but this highlights further variability in prevalence estimates across studies., Conclusions: In this study, we observed that varying the number and selection of conditions results in very large differences in multimorbidity prevalence, and different numbers of conditions are needed to reach ceiling rates of multimorbidity prevalence in certain groups of people. These findings imply that there is a need for a standardised approach to defining multimorbidity, and to facilitate this, researchers can use existing condition-lists associated with highest multimorbidity prevalence., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 MacRae et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Multimorbidity poses major challenges to healthcare systems worldwide. Definitions with cut-offs in excess of ≥2 long-term conditions (LTCs) might better capture populations with complexity but are not standardised., Aim: To examine variation in prevalence using different definitions of multimorbidity., Design and Setting: Cross-sectional study of 1 168 620 people in England., Method: Comparison of multimorbidity (MM) prevalence using four definitions: MM2+ (≥2 LTCs), MM3+ (≥3 LTCs), MM3+ from 3+ (≥3 LTCs from ≥3 International Classification of Diseases, 10th revision chapters), and mental-physical MM (≥2 LTCs where ≥1 mental health LTC and ≥1 physical health LTC are recorded). Logistic regression was used to examine patient characteristics associated with multimorbidity under all four definitions., Results: MM2+ was most common (40.4%) followed by MM3+ (27.5%), MM3+ from 3+ (22.6%), and mental-physical MM (18.9%). MM2+, MM3+, and MM3+ from 3+ were strongly associated with oldest age (adjusted odds ratio [aOR] 58.09, 95% confidence interval [CI] = 56.13 to 60.14; aOR 77.69, 95% CI = 75.33 to 80.12; and aOR 102.06, 95% CI = 98.61 to 105.65; respectively), but mental-physical MM was much less strongly associated (aOR 4.32, 95% CI = 4.21 to 4.43). People in the most deprived decile had equivalent rates of multimorbidity at a younger age than those in the least deprived decile. This was most marked in mental-physical MM at 40-45 years younger, followed by MM2+ at 15-20 years younger, and MM3+ and MM3+ from 3+ at 10-15 years younger. Females had higher prevalence of multimorbidity under all definitions, which was most marked for mental-physical MM., Conclusion: Estimated prevalence of multimorbidity depends on the definition used, and associations with age, sex, and socioeconomic position vary between definitions. Applicable multimorbidity research requires consistency of definitions across studies., (© The Authors.)

Background: Many European Health Systems are implementing or increasing levels of cost-sharing for medicine in response to the growing constrains on public spending on health despite their negative impact on population health due to delay in seeking care., Objective: This study aims to examine the relationships between multimorbidity (two or more coexisting chronic diseases, CDs), complex multimorbidity (three or more CDs impacting at least three different body systems), and out-of-pocket expenditure (OOPE) for medicine across European nations., Methods: This study utilized data on participants aged 50 years and above from two recent waves of the Survey of Health, Aging, and Retirement in Europe conducted in 2013 ( n = 55,806) and 2015 ( n = 51,237). Pooled cross-sectional and longitudinal study designs were used, as well as a two-part model, to analyse the association between multimorbidity and OOPE for medicine., Results: The prevalence of multimorbidity was 50.4% in 2013 and 48.2% in 2015. Nearly half of those with multimorbidity had complex multimorbidity. Each additional CD was associated with a 34% greater likelihood of incurring any OOPE for medicine (Odds ratio = 1.34, 95% CI = 1.31 - 1.36). The average incremental OOPE for medicine was 26.4 euros for each additional CD (95% CI = 25.1 - 27·7), and 32.1 euros for each additional body system affected (95% CI 30.6 - 33.7). In stratified analyses for country-specific quartiles of household income the average incremental OOPE for medicine was not significantly different across groups., Conclusion: Between 2013 and 2015 in 13 European Health Systems increased prevalence of CDs was associated with greater likelihood of having OOPE on medication and an increase in the average amount spent when one occurred. Monitoring this indicator is important considering the negative association with treatment adherence and subsequent effects on health., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Palladino, Pan, Mercer, Atun, McPake, Rubba, Triassi and Lee.)

In an observational population-based study including nearly four million participants, Kuan et al. examined frequencies of common combinations of diseases and identified non-random disease associations in people of all ages and multiple ethnicities., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

The co-occurrence of mental and physical chronic conditions is a growing concern and a largely unaddressed challenge in low-and-middle-income countries. This study aimed to investigate the independent and multiplicative effects of depression and physical chronic conditions on health-related quality of life (HRQoL) in China, and how it varies by age and gender. We used two waves of the China Health and Retirement Longitudinal Study (2011, 2015), including 9227 participants aged ≥ 45 years, 12 physical chronic conditions and depressive symptoms. We used mixed-effects linear regression to assess the effects of depression and physical multimorbidity on HRQoL, which was measured using a proxy measure of Physical Component Scores (PCS) and Mental Component Scores (MCS) of the matched SF-36 measure. We found that each increased number of physical chronic conditions, and the presence of depression were independently associated with lower proxy PCS and MCS scores. There were multiplicative effects of depression and physical chronic conditions on PCS (- 0.83 points, 95% CI - 1.06, - 0.60) and MCS scores (- 0.50 points, 95% CI - 0.73, - 0.27). The results showed that HRQoL decreased markedly with multimorbidity and was exacerbated by the presence of co-existing physical and mental chronic conditions., (© 2022. The Author(s).)

Background: Multimorbidity (the co-existence of two or more long-term conditions within an individual) is a complex management challenge, with a very limited evidence base. Theories can help in the design and operationalization of complex interventions., Objective: This article proposes self-determination theory (SDT) as a candidate theory for the development and evaluation of interventions in multimorbidity., Methods: We provide an overview of SDT, its use in research to date, and its potential utility in complex interventions for patients with multimorbidity based on the new MRC framework., Results: SDT-based interventions have mainly focused on health behaviour change in the primary prevention of disease, with limited use in primary care and chronic conditions management. However, SDT may be a useful candidate theory in informing complex intervention development and evaluation, both in randomized controlled trials and in evaluations of 'natural experiments'. We illustrate how it could be used multimorbidity interventions in primary care by drawing on the example of CARE Plus (a primary care-based complex intervention for patients with multimorbidity in deprived areas of Scotland)., Conclusions: SDT may have utility in both the design and evaluation of complex interventions for multimorbidity. Further research is required to establish its usefulness, and limitations, compared with other candidate theories., Patient or Public Contribution: Our funded research programme, of which this paper is an early output, has a newly embedded patient and public involvement group of four members with lived experience of long-term conditions and/or of being informal carers. They read and commented on the draft manuscript and made useful suggestions on the text. They will be fully involved at all stages in the rest of the programme of research., (© 2022 The Authors. Health Expectations published by John Wiley & Sons Ltd.)

Background: Mobility disability is predictive of further functional decline and can itself compromise older people's capacity (and preference) to live independently. The world's population is also ageing, and multimorbidity is the norm in those aged ≥85. What is unclear in this age group, is the influence of multimorbidity on (a) transitions in mobility disability and (b) mobility disability-free life expectancy (mobDFLE)., Methods and Findings: Using multistate modelling in an inception cohort of 714 85-year-olds followed over a 10-year period (aged 85 in 2006 to 95 in 2016), we investigated the association between increasing numbers of long-term conditions and (1) mobility disability incidence, (2) recovery from mobility disability and (3) death, and then explored how this shaped the remaining life expectancy free from mobility disability at age 85. Models were adjusted for age, sex, disease group count, BMI and education. We defined mobility disability based on participants' self-reported ability to get around the house, go up and down stairs/steps, and walk at least 400 yards; participants were defined as having mobility disability if, for one or more these activities, they had any difficulty with them or could not perform them. Data were drawn from the Newcastle 85+ Study: a longitudinal population-based cohort study that recruited community-dwelling and institutionalised individuals from Newcastle upon Tyne and North Tyneside general practices. We observed that each additional disease was associated with a 16% increased risk of incident mobility disability (hazard ratio (HR) 1.16, 95% confidence interval (CI): 1.07 to 1.25, p < 0.001), a 26% decrease in the chance of recovery from this state (HR 0.74, 95% CI: 0.63 to 0.86, p < 0.001), and a 12% increased risk of death with mobility disability (HR: 1.12, 95% CI: 1.07- to .17, p < 0.001). This translated to reductions in mobDFLE with increasing numbers of long-term conditions. However, residual and unmeasured confounding cannot be excluded from these analyses, and there may have been unobserved transitions to/from mobility disability between interviews and prior to death., Conclusions: We suggest 2 implications from this work. (1) Our findings support calls for a greater focus on the prevention of multimorbidity as populations age. (2) As more time spent with mobility disability could potentially lead to greater care needs, maintaining independence with increasing age should also be a key focus for health/social care and reablement services., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2022 Davies et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Introduction: Using nationally representative survey data from China and India, this study examined (1) the distribution and patterns of multimorbidity in relation to socioeconomic status and (2) association between multimorbidity and out-of-pocket expenditure (OOPE) for medicines by socioeconomic groups., Methods: Secondary data analysis of adult population aged 45 years and older from WHO Study on Global Ageing and Adult Health (SAGE) India 2015 (n=7397) and China Health and Retirement Longitudinal Study (CHARLS) 2015 (n=11 570). Log-linear, two-parts, zero-inflated and quantile regression models were performed to assess the association between multimorbidity and OOPE for medicines in both countries. Quantile regression was adopted to assess the observed relationship across OOPE distributions., Results: Based on 14 (11 self-reported) and 9 (8 self-reported) long-term conditions in the CHARLS and SAGE datasets, respectively, the prevalence of multimorbidity in the adult population aged 45 and older was found to be 63.4% in China and 42.2% in India. Of those with any long-term health condition, 38.6% in China and 20.9% in India had complex multimorbidity. Multimorbidity was significantly associated with higher OOPE for medicines in both countries (p&lt;0.05); an additional physical long-term condition was associated with a 18.8% increase in OOPE for medicine in China (p&lt;0.05) and a 20.9% increase in India (p&lt;0.05). Liver disease was associated with highest increase in OOPE for medicines in China (61.6%) and stroke in India (131.6%). Diabetes had the second largest increase (China: 58.4%, India: 91.6%) in OOPE for medicines in both countries., Conclusion: Multimorbidity was associated with substantially higher OOPE for medicines in China and India compared with those without multimorbidity. Our findings provide supporting evidence of the need to improve financial protection for populations with an increased burden of chronic diseases in low-income and middle-income countries., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Objectives: This study aimed to examine the differences in multimorbidity between Aboriginal and Torres Strait Islander people and non-Indigenous Australians, and the effect of multimorbidity on health service use and work productivity., Setting: Cross-sectional sample of the Household, Income and Labour Dynamics in Australia wave 17., Participants: A nationally representative sample of 16 749 respondents aged 18 years and above., Outcome Measures: Multimorbidity prevalence and pattern, self-reported health, health service use and employment productivity by Indigenous status., Results: Aboriginal respondents reported a higher prevalence of multimorbidity (24.2%) compared with non-Indigenous Australians (20.7%), and the prevalence of mental-physical multimorbidity was almost twice as high (16.1% vs 8.1%). Multimorbidity pattern varies significantly among the Aboriginal and non-Indigenous Australians. Multimorbidity was associated with higher health service use (any overnight admission: adjusted OR=1.52, 95% CI=1.46 to 1.58), reduced employment productivity (days of sick leave: coefficient=0.25, 95% CI=0.19 to 0.31) and lower perceived health status (SF6D score: coefficient=-0.04, 95% CI=-0.05 to -0.04). These associations were found to be comparable in both Aboriginal and non-Indigenous populations., Conclusions: Multimorbidity prevalence was significantly greater among Aboriginal and Torres Strait Islanders compared with the non-Indigenous population, especially mental-physical multimorbidity. Strategies are required for better prevention and management of multimorbidity for the aboriginal population to reduce health inequalities in Australia., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Objectives: Multimorbidity is one of the greatest challenges facing healthcare internationally. Emergency department (ED) attendance and hospitalisation rates are higher in people with multimorbidity, but most research focuses on associations with individual characteristics, ignoring household or area mediators of service use., Design: Systematic review reported using the synthesis without meta-analysis framework., Data Sources: Twelve electronic databases (1 January 2000-21 September 2021): MEDLINE/OVID, Embase, Global Health, PsycINFO, ASSIA, CAB Abstracts, Science Citation Index Expanded/ISI Web of Science, Scopus, Cumulative Index to Nursing and Allied Health Literature, Sociological Abstracts, the Cochrane Library, and OpenGrey., Eligibility Criteria: Adults aged ≥16 years, with multimorbidity. Exposure(s) were household and/or area determinants of health. Outcomes were ED attendance and/or hospitalisation. The literature search was limited to publications in English., Data Extraction and Synthesis: Independent double screening of titles and abstracts to select relevant full-text studies. Methodological quality was assessed using an adaptation of the Newcastle-Ottawa Quality Assessment Scale tool. Given high study heterogeneity, narrative synthesis was performed., Results: After deduplication, 10 721 titles and abstracts were screened, and 142 full-text articles were reviewed, of which 10 were eligible for inclusion. In people with multimorbidity, household food insecurity was associated with hospitalisation (OR 1.58 (95% CI 1.06 to 2.36) in concordant multimorbidity). People with multimorbidity living in the most versus least deprived areas attended ED more frequently (8.9% (95% CI 8.6 to 9.1) in most versus 6.3% (95% CI 6.1 to 6.6) in least), had higher rates of hospitalisation (26% in most versus 22% in least), and higher probability of hospitalisation (6.4% (95% CI 5.8 to 7.2) in most versus 4.2% (95% CI 3.8 to 4.7) in least). There was non-conclusive evidence that household income is associated with ED attendance and hospitalisation. No statistically significant relationships were found between marital status, living with others with multimorbidity, or rurality with ED attendance or hospitalisation., Conclusions: There is some evidence that household and area contexts mediate associations of multimorbidity with ED attendance and hospitalisation, but firm conclusions are constrained by the small number of studies published and study design heterogeneity. Further research is required on large population samples using robust analytical methods., Prospero Registration Number: CRD42021283515., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ.)

Objective: (1) To estimate the pooled prevalence of multimorbidity in all age groups, globally. (2) To examine how measurement of multimorbidity impacted the estimated prevalence., Methods: In this systematic review and meta-analysis, we conducted searches in nine bibliographic databases (PsycINFO, Embase, Global Health, Medline, Scopus, Web of Science, Cochrane Library, CINAHL and ProQuest Dissertations and Theses Global) for prevalence studies published between database inception and 21 January 2020. Studies reporting the prevalence of multimorbidity (in all age groups and in community, primary care, care home and hospital settings) were included. Studies with an index condition or those that did not include people with no long-term conditions in the denominator were excluded. Retrieved studies were independently reviewed by two reviewers, and relevant data were extracted using predesigned pro forma. We used meta-analysis to pool the estimated prevalence of multimorbidity across studies, and used random-effects meta-regression and subgroup analysis to examine the association of heterogeneous prevalence estimates with study and measure characteristics., Results: 13 807 titles were screened, of which 193 met inclusion criteria for meta-analysis. The pooled prevalence of multimorbidity was 42.4% (95% CI 38.9% to 46.0%) with high heterogeneity (I 2 >99%). In adjusted meta-regression models, participant mean age and the number of conditions included in a measure accounted for 47.8% of heterogeneity in effect sizes. The estimated prevalence of multimorbidity was significantly higher in studies with older adults and those that included larger numbers of conditions. There was no significant difference in estimated prevalence between low-income or middle-income countries (36.8%) and high-income countries (44.3%), or between self-report (40.0%) and administrative/clinical databases (52.7%)., Conclusions: The pooled prevalence of multimorbidity was significantly higher in older populations and when studies included a larger number of baseline conditions. The findings suggest that, to improve study comparability and quality of reporting, future studies should use a common core conditions set for multimorbidity measurement and report multimorbidity prevalence stratified by sociodemographics. PROSPERO registration number CRD42020172409., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Patients with multimorbidity often experience treatment burden as a result of fragmented, specialist-driven healthcare. The 'family doctor team' is an emerging service model in China to address the increasing need for high-quality routine primary care., Objective: This study aimed to explore the extent to which treatment burden was associated with healthcare needs and patients' experiences., Methods: Multisite surveys were conducted in primary care facilities in Guangdong province, southern China. Interviewer-administered questionnaires were used to collect data from patients (N = 2160) who had ≥2 clinically diagnosed long-term conditions (multimorbidity) and had ≥1 clinical encounter in the past 12 months since enrolment registration with the family doctor team. Patients' experiences and treatment burden were measured using a previously validated Chinese version of the Primary Care Assessment Tool (PCAT) and the Treatment Burden Questionnaire, respectively., Results: The mean age of the patients was 61.4 years, and slightly over half were females. Patients who had a family doctor team as the primary source of care reported significantly higher PCAT scores (mean difference 7.2 points, p < .001) and lower treatment burden scores (mean difference -6.4 points, p < .001) when compared to those who often bypassed primary care. Greater healthcare needs were significantly correlated with increased treatment burden (β-coefficient 1.965, p < .001), whilst better patients' experiences were associated with lower treatment burden (β-coefficient -0.252, p < .001) after adjusting for confounders., Conclusion: The inverse association between patients' experiences and treatment burden supports the importance of primary care in managing patients with multimorbidity., Patient Contribution: Primary care service users were involved in the instrument development and data collection., (© 2021 The Authors. Health Expectations published by John Wiley & Sons Ltd.)

India is witnessing an increase in the prevalence of multimorbidity. Oral health is related to overall health but is seldom included in the assessment of multimorbidity. Hence, this study aimed to estimate the prevalence of oral morbidity and explore its association with physical multimorbidity using data from Longitudinal Ageing Study in India (LASI). LASI is a nationwide survey amongst adults aged ≥ 45 years conducted in 2018. Descriptive analysis was performed on included participants ( n = 59,764) to determine the prevalence of oral morbidity. Multivariable logistic regression assessed the association between oral morbidity and physical multimorbidity. Self-rated health was compared between multimorbid participants with and without oral morbidity. Oral morbidity was prevalent in 48.56% of participants and physical multimorbidity in 50.36%. Those with multimorbidity were at a higher risk of having any oral morbidity (AOR: 1.60 (1.48-1.73)) than those without multimorbidity. Participants who had only oral morbidity rated their health to be good more often than those who had physical multimorbidity and oral morbidity (40.84% vs. 32.98%). Oral morbidity is significantly associated with physical multimorbidity. Multimorbid participants perceived their health to be inferior to those with only oral morbidity. The findings suggest multidisciplinary health teams in primary care should include the management of oral morbidity and physical multimorbidity.

Objectives: CARE Plus is a primary-care-based complex intervention for patients with multimorbidity living in areas of high socioeconomic deprivation. This study explores patients' experience of the intervention and whether self-determination theory is useful to understand reported impacts., Method: Thematic analysis of semistructured interviews of 14 participants conducted during a randomised controlled trial of CARE Plus. Improvement in wellbeing in daily lives following CARE Plus was estimated from participants' accounts of their experiences of the intervention., Findings: Participants valued the CARE Plus consultations irrespective of perceived improvements. Six participants reported changes in wellbeing that improved daily life, three reported slight improvement (not impacting daily life) and five no improvement. Evidence of satisfaction of the three major self-determination theory psychological needs - relatedness, competence and autonomy - was prominent in the accounts of those experiencing improved wellbeing in daily life; this group also spoke in ways congruent with more self-determined motivational regulation. These changes were not evident in those with little or no improvement in wellbeing., Discussion: This study suggests self-determination theory has utility in understanding the impact of CARE Plus on patients and may be a useful theory to inform development of future interventions to improve outcomes for patients with multimorbidity.

Multimorbidity - the occurrence of two or more long-term conditions in an individual - is a major global concern, placing a huge burden on healthcare systems, physicians, and patients. It challenges the current biomedical paradigm, in particular conventional evidence-based medicine's dominant focus on single-conditions. Patients' heterogeneous range of clinical presentations tend to escape characterization by traditional means of classification, and optimal management cannot be deduced from clinical practice guidelines. In this article, we argue that person-focused care based in complexity science may be a transformational lens through which to view multimorbidity, to complement the specialism focus on each particular disease. The approach offers an integrated and coherent perspective on the person's living environment, relationships, somatic, emotional and cognitive experiences and physiological function. The underlying principles include non-linearity, tipping points, emergence, importance of initial conditions, contextual factors and co-evolution, and the presence of patterned outcomes. From a clinical perspective, complexity science has important implications at the theoretical, practice and policy levels. Three essential questions emerge: (1) What matters to patients? (2) How can we integrate, personalize and prioritize care for whole people, given the constraints of their socio-ecological circumstances? (3) What needs to change at the practice and policy levels to deliver what matters to patients? These questions have no simple answers, but complexity science principles suggest a way to integrate understanding of biological, biographical and contextual factors, to guide an integrated approach to the care of people with multimorbidity., (© 2021 John Wiley & Sons, Ltd.)

Background: A systematic understanding of how multimorbidity has been constructed and measured is unavailable. This review aimed to examine the definition and measurement of multimorbidity in peer-reviewed studies internationally., Methods: We systematically reviewed studies on multimorbidity, via a search of nine bibliographic databases (Ovid [PsycINFO, Embase, Global Health, and MEDLINE], Web of Science, the Cochrane Library, CINAHL Plus, Scopus, and ProQuest Dissertations & Theses Global), from inception to Jan 21, 2020. Reference lists and tracked citations of retrieved articles were hand-searched. Eligible studies were full-text articles measuring multimorbidity for any purpose in community, primary care, care home, or hospital populations receiving a non-specialist service. Abstracts, qualitative research, and case series were excluded. Two reviewers independently reviewed the retrieved studies with conflicts resolved by discussion or a third reviewer, and a single researcher extracted data from published papers. To assess our objectives of how multimorbidity has been measured and examine variation in the chronic conditions included (in terms of number and type), we used descriptive analysis (frequencies, cross-tabulation, and negative binomial regression) to summarise the characteristics of multimorbidity studies and measures (study setting, source of morbidity data, study population, primary study purpose, and multimorbidity measure type). This systematic review is registered with PROSPERO, CRD420201724090., Findings: 566 studies were included in our review, of which 206 (36·4%) did not report a reference definition for multimorbidity and 73 (12·9%) did not report the conditions their measure included. The number of conditions included in measures ranged from two to 285 (median 17 [IQR 11-23). 452 (79·9%) studies reported types of condition within a single multimorbidity measure; most included at least one cardiovascular condition (441 [97·6%] of 452 studies), metabolic and endocrine condition (440 [97·3%]), respiratory condition (422 [93·4%]), musculoskeletal condition (396 [87·6%]), or mental health condition (355 [78·5%]) in their measure of multimorbidity. Chronic infections (123 [27·2%]), haematological conditions (110 [24·3%]), ear, nose, and throat conditions (107 [23·7%]), skin conditions (70 [15·5%]), oral conditions (19 [4·2%]), and congenital conditions (14 [3·1%]) were uncommonly included. Only eight individual conditions were included by more than half of studies in the multimorbidity measure used (diabetes, stroke, cancer, chronic obstructive pulmonary disease, hypertension, coronary heart disease, chronic kidney disease, and heart failure), with individual mental health conditions under-represented. Of the 566 studies, 419 were rated to be of moderate risk of bias, 107 of high risk of bias, and 40 of low risk of bias according to the Effective Public Health Practice Project quality assessment tool., Interpretation: Measurement of multimorbidity is poorly reported and highly variable. Consistent reporting of measure definitions should be required by journals, and consensus studies are needed to define core and study-dependent conditions to include in measures of multimorbidity., Funding: Health Data Research UK., (Copyright © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Background: The co-occurrence of mental and physical chronic conditions (mental-physical multimorbidity) is a growing and largely unaddressed challenge for health systems and wider economies in low-and middle-income countries. This study investigated the independent and combined (additive or synergistic) effects of mental and physical chronic conditions on disability, work productivity, and social participation in China., Methods: Panel data study design utilised two waves of the China Health and Retirement Longitudinal Study (2011, 2015), including 5616 participants aged ≥45 years, 12 physical chronic conditions and depression. We used a panel data approach of random-effects regression models to assess the relationships between mental-physical multimorbidity and outcomes., Results: After adjusting for socio-economic and demographic factors, an increased number of physical chronic conditions was independently associated with a higher likelihood of disability (Adjusted odds ratio (AOR) = 1.39; 95% CI: 1.33, 1.45), early retirement (AOR = 1.37 [1.26, 1.49]) and increased sick leave days (1.25 days [1.16, 1.35]). Depression was independently associated with disability (AOR = 3.78 [3.30, 4.34]), increased sick leave days (2.18 days [1.72, 2.77]) and a lower likelihood of social participation (AOR = 0.57 [0.47, 0.70]), but not with early retirement (AOR = 1.24 [0.97, 1.58]). There were small and statistically insignificant interactions between physical chronic conditions and mental health on disability, work productivity and social participation, suggesting an additive effect of mental-physical multimorbidity on productivity loss., Conclusion: Mental-physical multimorbidity poses substantial negative health and economic effects on individuals, health systems, and societies. More research that addresses the challenges of mental-physical multimorbidity is needed to inform the development of interventions that can be applied to the workplace and the wider community in China.

Background: Patients with multimorbidities have the greatest healthcare needs and generate the highest expenditure in the health system. There is an increasing focus on identifying specific disease combinations for addressing poor outcomes. Existing research has identified a small number of prevalent "clusters" in the general population, but the limited number examined might oversimplify the problem and these may not be the ones associated with important outcomes. Combinations with the highest (potentially preventable) secondary care costs may reveal priority targets for intervention or prevention. We aimed to examine the potential of defining multimorbidity clusters for impacting secondary care costs., Methods and Findings: We used national, Hospital Episode Statistics, data from all hospital admissions in England from 2017/2018 (cohort of over 8 million patients) and defined multimorbidity based on ICD-10 codes for 28 chronic conditions (we backfilled conditions from 2009/2010 to address potential undercoding). We identified the combinations of multimorbidity which contributed to the highest total current and previous 5-year costs of secondary care and costs of potentially preventable emergency hospital admissions in aggregate and per patient. We examined the distribution of costs across unique disease combinations to test the potential of the cluster approach for targeting interventions at high costs. We then estimated the overlap between the unique combinations to test potential of the cluster approach for targeting prevention of accumulated disease. We examined variability in the ranks and distributions across age (over/under 65) and deprivation (area level, deciles) subgroups and sensitivity to considering a smaller number of diseases. There were 8,440,133 unique patients in our sample, over 4 million (53.1%) were female, and over 3 million (37.7%) were aged over 65 years. No clear "high cost" combinations of multimorbidity emerged as possible targets for intervention. Over 2 million (31.6%) patients had 63,124 unique combinations of multimorbidity, each contributing a small fraction (maximum 3.2%) to current-year or 5-year secondary care costs. Highest total cost combinations tended to have fewer conditions (dyads/triads, most including hypertension) affecting a relatively large population. This contrasted with the combinations that generated the highest cost for individual patients, which were complex sets of many (6+) conditions affecting fewer persons. However, all combinations containing chronic kidney disease and hypertension, or diabetes and hypertension, made up a significant proportion of total secondary care costs, and all combinations containing chronic heart failure, chronic kidney disease, and hypertension had the highest proportion of preventable emergency admission costs, which might offer priority targets for prevention of disease accumulation. The results varied little between age and deprivation subgroups and sensitivity analyses. Key limitations include availability of data only from hospitals and reliance on hospital coding of health conditions., Conclusions: Our findings indicate that there are no clear multimorbidity combinations for a cluster-targeted intervention approach to reduce secondary care costs. The role of risk-stratification and focus on individual high-cost patients with interventions is particularly questionable for this aim. However, if aetiology is favourable for preventing further disease, the cluster approach might be useful for targeting disease prevention efforts with potential for cost-savings in secondary care., Competing Interests: The authors have declared that no competing interests exist.

Background: little is known about the relationship between multimorbidity and social care use (also known as long-term care). The aim of this study was to assess the relationship between receipt of formal social care services and multimorbidity., Methods: this retrospective data linkage, observational study included all individuals over the age of 65 in the population of Scotland in financial years 2014-15 and 2015-16 (n = 975,265). The main outcome was receipt of social care measured by presence in the Scottish Social Care Survey. Logistic regression models were used to assess the influence of multimorbidity, age, sex and socioeconomic position on the outcome reporting average marginal effects (AME)., Findings: 93.3% of those receiving social care had multimorbidity, 16.2% of those with multimorbidity received social care compared with 3.7% of those without. The strongest magnitudes of AME for receiving social care were seen for age and multimorbidity (respectively, 50 and 18% increased probability comparing oldest to youngest and most severe multimorbidity to none). A 5.5% increased probability of receiving social care was observed for the most-deprived compared with the least-deprived., Interpretation: higher levels of social care receipt are observed in those with increasing age, severe multimorbidity and living in more deprived areas. Multimorbidity does not fully moderate the relationship between social care receipt and either age or deprivation., (© The Author(s) 2020. Published by Oxford University Press on behalf of the British Geriatrics Society. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background: Multimorbidity, the presence of two or more mental or physical chronic non-communicable diseases, is a major challenge for the health system in China, which faces unprecedented ageing of its population. Here we examined the distribution of physical multimorbidity in relation to socioeconomic status; the association between physical multimorbidity, health-care service use, and catastrophic health expenditures; and whether these associations varied by socioeconomic group and social health insurance schemes., Methods: In this population-based, panel data analysis, we used data from three waves of the nationally representative China Health and Retirement Longitudinal Study (CHARLS) for 2011, 2013, and 2015. We included participants aged 50 years and older in 2015, who had complete follow-up for the three waves. We used 11 physical non-communicable diseases to measure physical multimorbidity and annual per-capita household consumption spending as a proxy for socioeconomic status., Findings: Of 17 708 participants in CHARLS, 11 817 were eligible for inclusion in our analysis. The median age of participants was 62 years (IQR 56-69) in 2015, and 5766 (48·8%) participants were male. 7320 (61·9%) eligible participants had physical multimorbidity in China in 2015. The prevalence of physical multimorbidity was increased with older age (odds ratio 2·93, 95% CI 2·71-3·15), among women (2·70, 2·04-3·57), within a higher socioeconomic group (for quartile 4 [highest group] 1·50, 1·24-1·82), and higher educational level (5·17, 3·02-8·83); however, physical multimorbidity was more common in poorer regions than in the more affluent regions. An additional chronic non-communicable disease was associated with an increase in the number of outpatient visits (incidence rate ratio 1·29, 95% CI 1·27-1·31), and number of days spent in hospital as an inpatient (1·38, 1·35-1·41). We saw similar effects in health service use of an additional chronic non-communicable disease in different socioeconomic groups and among those covered by different social health insurance programmes. Overall, physical multimorbidity was associated with a significantly increased likelihood of catastrophic health expenditure (for the overall population: odds ratio 1·29, 95% CI 1·26-1·32, adjusted for sociodemographic variables). The effect of physical multimorbidity on catastrophic health expenditures persisted even among the higher socioeconomic groups and across all health insurance programmes., Interpretation: Concerted efforts are needed to reduce health inequalities that are due to physical multimorbidity, and its adverse economic effect in population groups in China. Social health insurance reforms must place emphasis on reducing out-of-pocket spending for patients with multimorbidity to provide greater financial risk protection., Funding: None., (Copyright © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Background: Previous research has suggested a differential short-term effect of multimorbidity on hospitalization by age, with younger groups affected more. This study compares the nine-year hospitalization pattern by age and multimorbidity status in a retrospective cohort of discharged in-patients, who represent a high-need portion of the population., Methods: We examined routine clinical records of all patients aged 45+ years with chronic conditions discharged from public general hospitals in 2005 in Hong Kong. Patterns of annual frequencies of hospital admissions and number of hospitalized days over nine years (2005-2014) were compared by multimorbidity status (1, 2, 3+ conditions) and age group (45-64, 65-74, 75+)., Results: Among 121,188 included patients, 33.9% had 2+ conditions and 12.3% had 3+. Hospitalization patterns varied by age and multimorbidity status. For those having only 1 condition, annual number of admissions was similar by age, but older patients had more hospitalized days (4.40 days per person-year for the 45-64 group versus 10.29 for the 75+ group in the 5th year). For those with 3+ conditions, younger patients had more admissions (4.39 admissions per person-year for the 45-64 group versus 1.87 for the 75+ group in the 5th year) but similar number of hospitalized days with older patients. Interaction analysis showed effect of multimorbidity on hospitalization was stronger in younger groups (P < 0.05)., Conclusion: Middle-aged discharged in-patients with multimorbidity are admitted more often than their older counterparts and have similar total hospitalized days per year. Further research is needed to investigate chronic care needs of younger people with multimorbidity., (Copyright © 2018 European Federation of Internal Medicine. Published by Elsevier B.V. All rights reserved.)

Background: Multimorbidity, the coexistence of multiple health conditions, is a growing public health challenge. Research and intervention development are hampered by the lack of consensus regarding defining and measuring multimorbidity. The aim of this systematic review was to pool the findings of systematic reviews examining definitions and measures of multimorbidity., Methods: Medline, Embase, PubMed and Cochrane were searched from database inception to February 2017. Two authors independently screened titles, abstracts and full texts and extracted data from the included papers. Disagreements were resolved with a third author. Reviews were quality assessed., Results: Of six reviews, two focussed on definitions and four on measures. Multimorbidity was commonly defined as the presence of multiple diseases or conditions, often with a cut-off of two or more. One review developed a holistic definition including biopsychosocial and somatic factors as well as disease. Reviews recommended using measures validated for the outcome of interest. Disease counts are an alternative if no validated measure exists., Conclusions: To enable comparison between studies and settings, researchers and practitioners should be explicit about their choice of definition and measure. Using a cut-off of two or more conditions as part of the definition is widely adopted. Measure selection should be based on tools validated for the outcome being considered. Where there is no validated measure, or where multiple outcomes or populations are being considered, disease counts are appropriate.

Objective: Multimorbidity (the coexistence of two or more health conditions) is increasingly prevalent. No long-term cohort study has examined the impact of contemporaneously measured birth social class along with educational attainment on adult self-reported multimorbidity. We investigated the impact of educational attainment on the relationship between social class at birth and adult self-reported multimorbidity in the Aberdeen Children of the 1950s (ACONF) cohort., Methods: A prospective cohort study using the ACONF cohort. ACONF included 12 150 individuals born in Aberdeen, Scotland 1950-1956. In 2001, 7184 (64%) responded to a questionnaire providing information including self-reported morbidity and educational attainment. The exposure was father's social class at birth from birth records and the outcome was self-reported multimorbidity.Logistic regression assessed the association between social class and multimorbidity with adjustment for gender, then by educational attainment and finally by childhood cognition and secondary school type. ORs and 95% CIs were presented., Results: Of 7184 individuals (mean age 48, 52% female), 5.4% reported multimorbidity. Birth social class was associated with adult multimorbidity. For example, the OR of multimorbidity adjusted by gender was 0.62 (95% CI 0.39 to 1.00) in the highest social class group (I/II) in relation to the reference group (III (manual)) and was 1.85 (95% CI 1.19 to 2.88) in the lowest social class group. This was partially attenuated in all social class categories by educational attainment, for example, the OR was 0.74 (95% CI 0.45 to 1.21) in group I/II following adjustment., Conclusion: Lower social class at birth was associated with developing multimorbidity in middle age. This was partially mediated by educational attainment and future research should consider identifying the other explanatory variables. The results are relevant to researchers and to those aiming to reduce the impact of multimorbidity., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Objectives: Recent evidence has highlighted the high prevalence and impact of multimorbidity, but the evidence base for improving management is limited. We have tested a new complex intervention for multimorbidity (the 3D model). The paper describes the baseline characteristics of practices and patients in order to establish the external validity of trial participants. It also explores current 'usual primary care' for multimorbidity, against which the 3D intervention was tested., Design: Analysis of baseline data from patients in a cluster-randomised controlled trial and additional data from practice staff., Setting: Primary care in the UK., Participants: Patients with multimorbidity (n=5253) and 154 practice staff., Primary and Secondary Outcome Measures: Using surveys and routinely available data, we compared the characteristics of participating and non-participating practices and participating and non-participating eligible patients.Baseline questionnaire data from patient participants was used to examine participant illness burden, treatment burden and perceptions of receiving patient-centred care. We obtained data about usual care preintervention from practice staff using questionnaires and a structured pro forma., Results: Participating practices were slightly larger, in less deprived areas, and with slightly higher scores for patient satisfaction compared with non-participating practices. Patients with dementia or learning difficulties were likely to be excluded by their general practitioners, but comparison of participants with non-participants identified only minor differences in characteristics, suggesting that the sample was otherwise representative. Patients reported substantial illness burden, and an important minority reported high treatment burden. Although patients reported relatively high levels of satisfaction with care, many reported not having received potentially important components of care., Conclusion: This trial achieved good levels of external validity. Although patients were generally satisfied with primary care services, there was significant room for improvement in important aspects of care for multimorbidity that are targeted by the 3D intervention., Trial Registration Number: ISRCTN06180958; Post-results., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2018. Re-use permitted under CC BY. Published by BMJ.)

Background: The management of people with multiple chronic conditions challenges health-care systems designed around single conditions. There is international consensus that care for multimorbidity should be patient-centred, focus on quality of life, and promote self-management towards agreed goals. However, there is little evidence about the effectiveness of this approach. Our hypothesis was that the patient-centred, so-called 3D approach (based on dimensions of health, depression, and drugs) for patients with multimorbidity would improve their health-related quality of life, which is the ultimate aim of the 3D intervention., Methods: We did this pragmatic cluster-randomised trial in general practices in England and Scotland. Practices were randomly allocated to continue usual care (17 practices) or to provide 6-monthly comprehensive 3D reviews, incorporating patient-centred strategies that reflected international consensus on best care (16 practices). Randomisation was computer-generated, stratified by area, and minimised by practice deprivation and list size. Adults with three or more chronic conditions were recruited. The primary outcome was quality of life (assessed with EQ-5D-5L) after 15 months' follow-up. Participants were not masked to group assignment, but analysis of outcomes was blinded. We analysed the primary outcome in the intention-to-treat population, with missing data being multiply imputed. This trial is registered as an International Standard Randomised Controlled Trial, number ISRCTN06180958., Findings: Between May 20, 2015, and Dec 31, 2015, we recruited 1546 patients from 33 practices and randomly assigned them to receive the intervention (n=797) or usual care (n=749). In our intention-to-treat analysis, there was no difference between trial groups in the primary outcome of quality of life (adjusted difference in mean EQ-5D-5L 0·00, 95% CI -0·02 to 0·02; p=0·93). 78 patients died, and the deaths were not considered as related to the intervention., Interpretation: To our knowledge, this trial is the largest investigation of the international consensus about optimal management of multimorbidity. The 3D intervention did not improve patients' quality of life., Funding: National Institute for Health Research., (Copyright © 2018 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Purpose: Multimorbidity challenges health systems globally. New models of care are urgently needed to better manage patients with multimorbidity; however, there is no agreed framework for designing and reporting models of care for multimorbidity and their evaluation., Methods: Based on findings from a literature search to identify models of care for multimorbidity, we developed a framework to describe these models. We illustrate the application of the framework by identifying the focus and gaps in current models of care, and by describing the evolution of models over time., Results: Our framework describes each model in terms of its theoretical basis and target population (the foundations of the model) and of the elements of care implemented to deliver the model. We categorized elements of care into 3 types: (1) clinical focus, (2) organization of care, (3) support for model delivery. Application of the framework identified a limited use of theory in model design and a strong focus on some patient groups (elderly, high users) more than others (younger patients, deprived populations). We found changes in elements with time, with a decrease in models implementing home care and an increase in models offering extended appointments., Conclusions: By encouragin greater clarity about the underpinning theory and target population, and by categorizing the wide range of potentially important elements of an intervention to improve care for patients with multimorbidity, the framework may be useful in designing and reporting models of care and help advance the currently limited evidence base., Competing Interests: Conflicts of interest: authors report none., (© 2017 Annals of Family Medicine, Inc.)

Background: Multimorbidity is a major challenge to health systems globally and disproportionately affects socioeconomically disadvantaged populations. We examined socioeconomic inequalities in developing multimorbidity across the lifecourse and investigated the contribution of five behaviour-related risk factors., Methods: The Twenty-07 study recruited participants aged approximately 15, 35, and 55 years in 1987 and followed them up over 20 years. The primary outcome was development of multimorbidity (2+ health conditions). The relationship between five different risk factors (smoking, alcohol consumption, diet, body mass index (BMI), physical activity) and the development of multimorbidity was assessed. Social patterning in the development of multimorbidity based on two measures of socioeconomic status (area-based deprivation and household income) was then determined, followed by investigation of potential mediation by the five risk factors. Multilevel logistic regression models and predictive margins were used for statistical analyses. Socioeconomic inequalities in multimorbidity were quantified using relative indices of inequality and attenuation assessed through addition of risk factors., Results: Multimorbidity prevalence increased markedly in all cohorts over the 20 years. Socioeconomic disadvantage was associated with increased risk of developing multimorbidity (most vs least deprived areas: odds ratio (OR) 1.46, 95% confidence interval (CI) 1.26-1.68), and the risk was at least as great when assessed by income (OR 1.53, 95% CI 1.25-1.87) or when defining multimorbidity as 3+ conditions. Smoking (current vs never OR 1.56, 1.36-1.78), diet (no fruit/vegetable consumption in previous week vs consumption every day OR 1.57, 95% CI 1.33-1.84), and BMI (morbidly obese vs healthy weight OR 1.88, 95% CI 1.42-2.49) were strong independent predictors of developing multimorbidity. A dose-response relationship was observed with number of risk factors and subsequent multimorbidity (3+ risk factors vs none OR 1.91, 95% CI 1.57-2.33). However, the five risk factors combined explained only 40.8% of socioeconomic inequalities in multimorbidity development., Conclusions: Preventive measures addressing known risk factors, particularly obesity and smoking, could reduce the future multimorbidity burden. However, major socioeconomic inequalities in the development of multimorbidity exist even after taking account of known risk factors. Tackling social determinants of health, including holistic health and social care, is necessary if the rising burden of multimorbidity in disadvantaged populations is to be redressed.

The term multimorbidity is usually defined as the coexistence of two or more chronic conditions within an individual, whereas the term comorbidity traditionally describes patients with an index condition and one or more additional conditions. Multimorbidity of chronic conditions markedly worsens outcomes in patients, increases treatment burden and increases health service costs. Although patients with chronic respiratory disease often have physical comorbidities, they also commonly experience psychological problems such as depression and anxiety. Multimorbidity is associated with increased health-care utilisation and specifically with an increased number of prescription drugs in individuals with multiple chronic conditions such as chronic obstructive pulmonary disease. This npj Primary Care Respiratory Medicine Education Section case study involves a patient in a primary care consultation presenting several common diseases prevalent in people of this age. The patient takes nine different drugs at this moment, one or more pills for each condition, which amounts to polypharmacy. The problems related with polypharmacy recommend that a routine medication review by primary care physicians be performed to reduce the risk of adverse effects of polypharmacy among those with multiple chronic conditions. The primary care physician has the challenging role of integrating all of the clinical problems affecting the patient and reviewing all medicaments (including over-the-counter medications) taken by the patient at any point in time, and has the has the key to prevent the unwanted consequences of polypharmacy. Multimorbid chronic disease management can be achieved with the use of care planning, unified disease templates, use of information technology with appointment reminders and with the help of the wider primary care and community teams.

Using data collected from 25,780 Hong Kong citizens in a household survey, this study aimed to investigate the association between having regular source of primary care and hospitalization amongst people with and without multimorbidity (two or more chronic conditions). Potential interaction effects of regular primary care with multimorbidity were also examined. Results revealed a significant association between having regular source of primary care from General Practitioners and reduced hospitalization amongst respondents with multimorbidity (RR = 0.772; 95% CI = 0.667-0.894), adjusting for other potential confounding factors (i.e., socio-demographic factors and medical insurance and benefits). In contrast, having regular Specialist care was significantly associated with increased risk of hospitalization among both people with multimorbidity (RR = 1.619; 95% CI = 1.256-2.087) and without multimorbidity (RR = 1.981; 95% CI = 1.246-3.149), adjusting for potential confounders. A dose-response relationship between the number of chronic diseases and hospitalization was also observed, regardless of whether participants had regular source of primary care or not; relative risks and predicted probabilities for hospitalization were generally greater for those without regular source of primary care. Further studies are warranted to explore the role of healthcare system, informatics, organizational and practice-related factors on healthcare and functional outcomes.

Introduction: As an increasing number of people are living with more than 1 long-term condition, identifying effective interventions for the management of multimorbidity in primary care has become a matter of urgency. Interventions are challenging to evaluate due to intervention complexity and the need for adaptability to different contexts. A process evaluation can provide extra information necessary for interpreting trial results and making decisions about whether the intervention is likely to be successful in a wider context. The 3D (dimensions of health, drugs and depression) study will recruit 32 UK general practices to a cluster randomised controlled trial to evaluate effectiveness of a patient-centred intervention. Practices will be randomised to intervention or usual care., Methods and Analysis: The aim of the process evaluation is to understand how and why the intervention was effective or ineffective and the effect of context. As part of the intervention, quantitative data will be collected to provide implementation feedback to all intervention practices and will contribute to evaluation of implementation fidelity, alongside case study data. Data will be collected at the beginning and end of the trial to characterise each practice and how it provides care to patients with multimorbidity. Mixed methods will be used to collect qualitative data from 4 case study practices, purposively sampled from among intervention practices. Qualitative data will be analysed using techniques of constant comparison to develop codes integrated within a flexible framework of themes. Quantitative and qualitative data will be integrated to describe case study sites and develop possible explanations for implementation variation. Analysis will take place prior to knowing trial outcomes., Ethics and Dissemination: Study approved by South West (Frenchay) National Health Service (NHS) Research Ethics Committee (14/SW/0011). Findings will be disseminated via a final report, peer-reviewed publications and practical guidance to healthcare professionals, commissioners and policymakers., Trial Registration Number: ISRCTN06180958., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/)

Background: The new Scottish GP contract introduced in April 2018 aims to improve quality of care through expansion of the multidisciplinary team (MDT) to enable GPs to spend more time as expert medical generalists with patients with complex needs. Aim: To explore patients' views on the changes in general practice in Scotland since the inception of the new contract. Design and setting: Qualitative study with 30 patients (10 living in urban deprived areas, 10 living in urban affluent/mixed urban areas, and 10 living in remote and rural areas). Method: In-depth semi-structured interviews with thematic analysis. Results: Patients were generally unaware of the new GP contract, attributing recent changes in general practice to the COVID-19 pandemic. Ongoing concerns included access to GP consultations (especially face-to-face ones), short consultation length with GPs, and damage to continuity of care and the GP–patient relationship. Most patients spoke positively about consultations with MDT staff but still wanted to see a known GP for health concerns that they considered potentially serious. These issues were especially concerning for patients with multiple complex problems, particularly those from deprived areas. Conclusion: Following the introduction of the new Scottish GP contract, patients in this study's sample were accepting of first contact care from the MDT but still wanted continuity of care and longer face-to-face consultations with GPs. These findings suggest that the expert generalist role of the GP is not being adequately supported by the new contract, especially in deprived areas, though further quantitative research is required to confirm this. [ABSTRACT FROM AUTHOR]

Background: Multimorbidity is rising in low-and middle-income countries such as Nepal, yet the research has not gained pace in this field. We aimed to systematically review the existing multimorbidity literature in Nepal and estimate the prevalence and map its risk factors and consequences. Methods: We reviewed data collated from PubMed, Embase and CINAHL by including original studies that reported prevalence of multimorbidity in Nepal. The quality of included studies was assessed using the Appraisal Tool for Cross-sectional Studies. The summary of the review is presented both qualitatively as well as through meta-analysis to give pooled prevalence. We prospectively registered in PROSPERO (CRD42024499598). Results: We identified 423 studies out of which seven were included in this review. All studies were conducted in a community setting except one which was hospital based. The prevalence reported across various studies ranged from 13.96% to 70.1%. The pooled prevalence of multimorbidity was observed to be 25.05% (95% CI: 16.99 to 34.09). The number of conditions used to assess multimorbidity ranged from four to nine. The major risk factors identified were increasing age, urban residence, and lower literacy rates. Conclusion: A wide variance in the prevalence of multimorbidity was observed. Moreover, multimorbidity assessment tool/conditions considered for assessing multimorbidity were heterogeneous. [ABSTRACT FROM AUTHOR]

Background: The Scottish Government's vision to transform primary care includes expansion of the primary care multidisciplinary team (MDT), formalised in the new GP contract in April 2018. Aim: To explore practitioners' views on the expansion of MDT working in Scotland. Design and setting: Qualitative study with GPs and a range of MDT staff working in three different population settings in Scotland. Method: In-depth semi-structured interviews were carried out by telephone with 8 GPs and 19 MDT staff between May and June 2022. Interviews were audio-recorded and transcribed verbatim. Thematic analysis was conducted to identify commonalities and divergences in the interviews. Results: Internal challenges facing MDT staff included adapting to the fast pace of primary care, building new relationships, training and professional development needs, line management issues, and monitoring and evaluation of performance. External challenges included the ongoing effects of the COVID-19 pandemic, lack of time, difficulties with hybrid working, and low staff morale. Most GPs reported that expansion of their roles as expert medical specialists had not yet happened because their workload had not decreased (and in many cases had increased). In deprived areas, insufficient resources to deal with the high numbers of patients with complex multimorbidity remained a key issue. Interviewees in remote and rural settings felt the new contract did not take into account the unique challenges of providing primary care services in such areas, and recruitment and accommodation were cited as particular problems. Conclusion: Although there has been substantial expansion of the primary care MDT, which most GPs welcome, many challenges to effective implementation remain that must be addressed if transformation of primary care in Scotland is to become a reality. [ABSTRACT FROM AUTHOR]

Background: Primary care transformation in Scotland aims to improve population health, reduce health inequalities, and reduce GP workload. Two key strategies (formalised in April 2018 in the new Scottish GP contract [Scottish General Medical Services contract], although started in early 2016) are the expansion of the multidisciplinary team (MDT) and GP cluster working. Aim: To explore progress in the implementation of the GP contract in Scotland in terms of the MDT and cluster working. Design and setting: Qualitative study with key national primary care stakeholders (PCSs) (n = 6) and cluster quality leads (CQLs) in clusters serving urban high deprivation areas (n = 4), urban mixed areas (n = 4), and remote and rural areas (n = 4). Method: Semi-structured interviews with thematic analysis. Results: There was general support for the initial aims of the new GP contract but all interviewees felt that progress on both MDT expansion and cluster working was slow, even before the pandemic. None of the CQLs (and few PCSs) felt that GP workload had reduced significantly, nor that the care of patients with complex needs had improved. Lack of time and poorly developed relationships were key barriers, as was a lack of relevant primary care data, and additional support (including guidance, administration, training, and protected time). Conclusion: Key PCSs and CQLs in different areas of Scotland report limited progress in primary care transformation, only partly related to the pandemic. There is a need for better workforce planning and support if the new GP contract is to succeed in transforming primary care in Scotland. [ABSTRACT FROM AUTHOR]

Background: Although mobile health (mHealth) apps are increasingly being used to support patients with multiple chronic conditions (multimorbidity), most mHealth apps experience low interaction and eventual abandonment. To tackle this engagement issue, when developing an mHealth program, it is important to understand the social-behavioral factors that affect patients' use behavior. Objective: The aim of this study was to explore the social and behavioral factors contributing to patients' use behavior of an mHealth app called the electronic Patient-Reported Outcome (ePRO). The ePRO app supports goal-oriented care delivery in interdisciplinary primary care models. Methods: A descriptive qualitative study was used to analyze interview data collected for a larger mixed methods pragmatic trial. The original 15-month trial was conducted in 6 primary care teams across Ontario, Canada, between 2018 and 2019. The eligibility criteria for patients were being aged ≥60 years with ≥10 visits within the previous 12 months of study enrollment. For this analysis, patients were classified as long-term or short-term users based on their length of use of the ePRO app during the trial. The Social Cognitive Theory by Bandura was used to categorize social-behavioral factors that contributed to patients' decision to continue or discontinue using the app. Results: The patient-provider relationship emerged as a key factor that shaped patients' experiences with the app and subsequent decision to continue using the app. Other factors that contributed to patients' decision to continue using the app were personal and social circumstances, perceived usefulness, patients' previous experience with goal-related behaviors, and confidence in one's capability. There was an overlap of experience between long- and short-term app users but, in general, long-term users perceived the app to be more useful and their goals to be more meaningful than short-term app users. This observation was complicated by the fact that patient health-related goals were dynamic and changed over time. Conclusions: Complex patients' use behavior of a goal-supporting mHealth app is shaped by an array of sociobehavioral factors that can evolve. To tackle this dynamism, there should be an emphasis on creating adaptable health technologies that are easily customizable by patients and able to respond to their changing contexts and needs. [ABSTRACT FROM AUTHOR]

Background: Previous research has suggested an association between depression and subsequent acute stroke incidence, but few studies have examined any effect modification by sociodemographic factors. In addition, no studies have investigated this association among primary care recipients with hypertension. Methods: We examined the anonymized records of all public general outpatient visits by patients aged 45+ during January 2007–December 2010 in Hong Kong to extract primary care patients with hypertension for analysis. We took the last consultation date as the baseline and followed them up for 4 years (until 2011–2014) to observe any subsequent acute hospitalization due to stroke. Mixed-effects Cox models (random intercept across 74 included clinics) were implemented to examine the association between depression (ICPC diagnosis or anti-depressant prescription) at baseline and the hazard of acute stroke (ICD-9: 430–437.9). Effect modification by age, sex, and recipient status of social security assistance was examined in extended models with respective interaction terms specified. Results: In total, 396 858 eligible patients were included, with 9099 (2.3%) having depression, and 10 851 (2.7%) eventually hospitalized for stroke. From the adjusted analysis, baseline depression was associated with a 17% increased hazard of acute stroke hospitalization [95% confidence interval (CI) 1.03–1.32]. This association was suggested to be even stronger among men than among women (hazard ratio = 1.29, 95% CI 1.00–1.67). Conclusion: Depression is more strongly associated with acute stroke incidence among male than female primary care patients with hypertension. More integrated services are warranted to address their needs. [ABSTRACT FROM AUTHOR]

Objectives: To examine whether motivational interviewing is used by GPs in consultations with patients living with mental-physical multimorbidity. Methods: Secondary analysis of selected videos from an existing database of routine general practice consultations with adult patients in Glasgow, Scotland. Consultations involving patients with mental-physical multimorbidity were selected and coded using the Motivational Interviewing Treatment Integrity (MITI) coding system. Results: Sixty consultations were coded involving 32 GPs across 16 practices. Mean consultation length was 9.9 min. On average GPs asked 1.7 questions per minute and offered 1.2 pieces of information per minute. Using the MITI, five GPs met beginner proficiency for the relational global qualities of partnership and empathy; however, none of the GPs met beginner proficiency for the technical global rating of efforts made to encourage patients to discuss behaviour change. Simple reflections were observed in 67% of consultations and complex reflections in 28% of consultations. Confrontation, a technique inconsistent with motivational interviewing, was observed in 18% of consultations. Discussion: MI was not evident in these consultations with patients living with mental-physical multimorbidity. This study provides information about the baseline motivational interviewing-consistent skills of GPs working with multimorbid patients and may be helpful in informing motivational interviewing training efforts and future research. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic has impacted the psychological health and health service utilisation of older adults with multimorbidity, who are particularly vulnerable.Aim: To describe changes in loneliness, mental health problems, and attendance to scheduled medical care before and after the onset of the COVID-19 pandemic.Design and Setting: Telephone survey on a pre-existing cohort of older adults with multimorbidity in primary care.Method: Mental health and health service utilisation outcomes were compared with the outcomes before the onset of the COVID-19 outbreak in Hong Kong using paired t-tests, Wilcoxon's signed-rank test, and McNemar's test. Loneliness was measured by the De Jong Gierveld Loneliness Scale. The secondary outcomes (anxiety, depression, and insomnia) were measured by the 9-item Patient Health Questionnaire, the 7-item Generalized Anxiety Disorder tool, and the Insomnia Severity Index. Appointments attendance data were extracted from a computerised medical record system. Sociodemographic factors associated with outcome changes were examined by linear regression and generalised estimating equations.Results: Data were collected from 583 older (≥60 years) adults. There were significant increases in loneliness, anxiety, and insomnia, after the onset of the COVID-19 outbreak. Missed medical appointments over a 3-month period increased from 16.5% 1 year ago to 22.0% after the onset of the outbreak. In adjusted analysis, being female, living alone, and having >4 chronic conditions were independently associated with increased loneliness. Females were more likely to have increased anxiety and insomnia.Conclusion: Psychosocial health of older patients with multimorbidity markedly deteriorated and missed medical appointments substantially increased after the COVID-19 outbreak. [ABSTRACT FROM AUTHOR]

Purpose: To assess the effect of a primary care-based community-links practitioner (CLP) intervention on patients' quality of life and well-being.Methods: Quasi-experimental cluster-randomized controlled trial in socioeconomically deprived areas of Glasgow, Scotland. Adult patients (aged 18 years or older) referred to CLPs in 7 intervention practices were compared with a random sample of adult patients from 8 comparison practices at baseline and 9 months.Primary Outcome: health-related quality of life (EQ-5D-5L, a standardized measure of self-reported health-related quality of life that assesses 5 dimensions at 5 levels of severity).Secondary Outcomes: well-being (Investigating Choice Experiments for the Preferences of Older People Capability Measure for Adults [ICECAP-A]), depression (Hospital Anxiety and Depression Scale, Depression [HADS-D]), anxiety (Hospital Anxiety and Depression Scale, Anxiety [HADS-A]), and self-reported exercise. Multilevel, multiregression analyses adjusted for baseline differences. Patients were not blinded to the intervention, but outcome analysis was masked.Results: Data were collected on 288 and 214 (74.3%) patients in the intervention practices at baseline and follow-up, respectively, and on 612 and 561 (92%) patients in the comparison practices. Intention-to-treat analysis found no differences between the 2 groups for any outcome. In subgroup analyses, patients who saw the CLP on 3 or more occasions (45% of those referred) had significant improvements in EQ-5D-5L, HADS-D, HADS-A, and exercise levels. There was a high positive correlation between CLP consultation rates and patient uptake of suggested community resources.Conclusions: We were unable to prove the effectiveness of referral to CLPs based in primary care in deprived areas for improving patient outcomes. Future efforts to boost uptake and engagement could improve overall outcomes, although the apparent improvements in those who regularly saw the CLPs may be due to reverse causality. Further research is needed before wide-scale deployment of this approach. [ABSTRACT FROM AUTHOR]

This review discusses the interplay between multimorbidity (i.e. co-occurrence of more than one chronic health condition in an individual) and functional impairment (i.e. limitations in mobility, strength or cognition that may eventually hamper a person's ability to perform everyday tasks). On the one hand, diseases belonging to common patterns of multimorbidity may interact, curtailing compensatory mechanisms and resulting in physical and cognitive decline. On the other hand, physical and cognitive impairment impact the severity and burden of multimorbidity, contributing to the establishment of a vicious circle. The circle may be further exacerbated by people's reduced ability to cope with treatment and care burden and physicians' fragmented view of health problems, which cause suboptimal use of health services and reduced quality of life and survival. Thus, the synergistic effects of medical diagnoses and functional status in adults, particularly older adults, emerge as central to assessing their health and care needs. Furthermore, common pathways seem to underlie multimorbidity, functional impairment and their interplay. For example, older age, obesity, involuntary weight loss and sedentarism can accelerate damage accumulation in organs and physiological systems by fostering inflammatory status. Inappropriate use or overuse of specific medications and drug-drug and drug-disease interactions also contribute to the bidirectional association between multimorbidity and functional impairment. Additionally, psychosocial factors such as low socioeconomic status and the direct or indirect effects of negative life events, weak social networks and an external locus of control may underlie the complex interactions between multimorbidity, functional decline and negative outcomes. Identifying modifiable risk factors and pathways common to multimorbidity and functional impairment could aid in the design of interventions to delay, prevent or alleviate age-related health deterioration; this review provides an overview of knowledge gaps and future directions. [ABSTRACT FROM AUTHOR]