Redefining Multimorbidity in Older Surgical Patients.

BACKGROUND: Multimorbidity in surgery is common and associated with worse postoperative outcomes. However, conventional multimorbidity definitions (≥2 comorbidities) label the vast majority of older patients as multimorbid, limiting clinical usefulness. We sought to develop and validate better surgical specialty-specific multimorbidity definitions based on distinct comorbidity combinations. STUDY DESIGN: We used Medicare claims for patients aged 66 to 90 years undergoing inpatient general, orthopaedic, or vascular surgery. Using 2016 to 2017 data, we identified all comorbidity combinations associated with at least 2-fold (general/orthopaedic) or 1.5-fold (vascular) greater risk of 30-day mortality compared with the overall population undergoing the same procedure; we called these combinations qualifying comorbidity sets. We applied them to 2018 to 2019 data (general = 230,410 patients, orthopaedic = 778,131 patients, vascular = 146,570 patients) to obtain 30-day mortality estimates. For further validation, we tested whether multimorbidity status was associated with differential outcomes for patients at better-resourced (based on nursing skill-mix, surgical volume, teaching status) hospitals vs all other hospitals using multivariate matching. RESULTS: Compared with conventional multimorbidity definitions, the new definitions labeled far fewer patients as multimorbid: general = 85.0% (conventional) vs 55.9% (new) (p < 0.0001); orthopaedic = 66.6% vs 40.2% (p < 0.0001); and vascular = 96.2% vs 52.7% (p < 0.0001). Thirty-day mortality was higher by the new definitions: general = 3.96% (conventional) vs 5.64% (new) (p < 0.0001); orthopaedic = 0.13% vs 1.68% (p < 0.0001); and vascular = 4.43% vs 7.00% (p < 0.0001). Better-resourced hospitals offered significantly larger mortality benefits than all other hospitals for multimorbid vs nonmultimorbid general and orthopaedic, but not vascular, patients (general surgery difference-in-difference = -0.94% [-1.36%, -0.52%], p < 0.0001; orthopaedic = -0.20% [-0.34%, -0.05%], p = 0.0087; and vascular = -0.12% [-0.69%, 0.45%], p = 0.6795). CONCLUSIONS: Our new multimorbidity definitions identified far more specific, higher-risk pools of patients than conventional definitions, potentially aiding clinical decision-making.

Multimorbidity is associated with the income, education, employment and health domains of area-level deprivation in adult residents in the UK.

Evidence suggests that there are social inequalities in multimorbidity, with a recent review indicating that area levels of deprivation are consistently associated with greater levels of multimorbidity. Definitions of multimorbidity, the most common of which is the co-occurrence of more than one long term condition, can include long term physical conditions, mental health conditions or both. The most commonly used measure of deprivation in England and Wales is the Index of Multiple Deprivation (IMD), an index of seven different deprivation domains. It is unclear which features of IMD may be mediating associations with multimorbidity. Thus, there may be associations because of the individual characteristics of those living in deprived areas, characteristics of the areas themselves or overlap in definitions. Data from over 25,000 participants (aged 16+) of Understanding Society (Wave 10, 1/2018-3/2020) were used to understand the most salient features of multimorbidity associated with IMD and whether physical or mental conditions are differentially associated with the seven domains of IMD. 24% of participants report multimorbidity. There is an increased prevalence of multimorbidity composed of only long-term physical conditions in the most deprived decile of deprivation (22%, 95% CI[19,25]) compared to the least deprived decile (16%, 95% CI[14,18]). Mental health symptoms but not reporting of conditions vary by decile of IMD. Associations with multimorbidity are limited to the health, income, education and employment domains of IMD. We conclude that multimorbidity represents a substantial population burden, particularly in the most deprived areas in England and Wales.

Improving Stroke Risk Prediction in the General Population: A Comparative Assessment of Common Clinical Rules, a New Multimorbid Index, and Machine-Learning-Based Algorithms.

BACKGROUND: There are few large studies examining and predicting the diversified cardiovascular/noncardiovascular comorbidity relationships with stroke. We investigated stroke risks in a very large prospective cohort of patients with multimorbidity, using two common clinical rules, a clinical multimorbid index and a machine-learning (ML) approach, accounting for the complex relationships among variables, including the dynamic nature of changing risk factors. METHODS: We studied a prospective U.S. cohort of 3,435,224 patients from medical databases in a 2-year investigation. Stroke outcomes were examined in relationship to diverse multimorbid conditions, demographic variables, and other inputs, with ML accounting for the dynamic nature of changing multimorbidity risk factors, two clinical risk scores, and a clinical multimorbid index. RESULTS: Common clinical risk scores had moderate and comparable c indices with stroke outcomes in the training and external validation samples (validation-CHADS2: c index 0.812, 95% confidence interval [CI] 0.808-0.815; CHA2DS2-VASc: c index 0.809, 95% CI 0.805-0.812). A clinical multimorbid index had higher discriminant validity values for both the training/external validation samples (validation: c index 0.850, 95% CI 0.847-0.853). The ML-based algorithms yielded the highest discriminant validity values for the gradient boosting/neural network logistic regression formulations with no significant differences among the ML approaches (validation for logistic regression: c index 0.866, 95% CI 0.856-0.876). Calibration of the ML-based formulation was satisfactory across a wide range of predicted probabilities. Decision curve analysis demonstrated that clinical utility for the ML-based formulation was better than that for the two current clinical rules and the newly developed multimorbid tool. Also, ML models and clinical stroke risk scores were more clinically useful than the "treat all" strategy. CONCLUSION: Complex relationships of various comorbidities uncovered using a ML approach for diverse (and dynamic) multimorbidity changes have major consequences for stroke risk prediction. This approach may facilitate automated approaches for dynamic risk stratification in the significant presence of multimorbidity, helping in the decision-making process for risk assessment and integrated/holistic management.

Socioeconomic variation of multimorbidity in Colombian older adults.

Multimorbidity (MM) prevalence among older adults is increasing worldwide. Variations regarding the socioeconomic characteristics of the individuals and their context have been described, mostly in high-income settings. However, further research is needed to understand the effect of the coexistence of infectious diseases along with socioeconomic factors regarding MM. This study aims to examine the variation of MM regarding infectious diseases mortality after adjusting for socioeconomic factors. A cross-sectional multilevel study with a nationally representative sample of 17,571 Colombian adults of 60 years of age or older was conducted. Individual socioeconomic, demographic, childhood and health related characteristics, as well as group level variables (multidimensional poverty index and infectious diseases mortality rate) were analyzed. A two-level stepwise structural equation model was used to simultaneously adjust for the individual and contextual effects. Multimorbidity prevalence was 62.3% (95% CI 61.7-62.9). In the multilevel adjusted models, age, female sex, having functional limitations, non-white ethnicity, high body mass index, higher income, physical inactivity and living in urban areas were associated with multimorbidity among the sample for this study. The median odds ratio for multidimensional poverty was 1.18 (1.16-1.19; p = 0.008) and for infectious diseases was 1.25 (1.22-1.28; p = 0.014). This paper demonstrates that MM varies regarding the mortality of infectious diseases and shows a strong association between MM and poverty in a low-middle income country. Differences in the factors involved in the etiology of multimorbidity are expected among wealthy and poor countries regarding availability and prioritization of health services.

The management of multi-morbidity in elderly patients: Ready yet for precision medicine in intensive care?

There is ongoing demographic ageing and increasing longevity of the population, with previously devastating and often-fatal diseases now transformed into chronic conditions. This is turning multi-morbidity into a major challenge in the world of critical care. After many years of research and innovation, mainly in geriatric care, the concept of multi-morbidity now requires fine-tuning to support decision-making for patients along their whole trajectory in healthcare, including in the intensive care unit (ICU). This article will discuss current challenges and present approaches to adapt critical care services to the needs of these patients.

International comparison of health spending and utilization among people with complex multimorbidity.

OBJECTIVE: The objective of this study was to explore cross-country differences in spending and utilization across different domains of care for a multimorbid persona with heart failure and diabetes. DATA SOURCES: We used individual-level administrative claims or registry data from inpatient and outpatient health care sectors compiled by the International Collaborative on Costs, Outcomes, and Needs in Care (ICCONIC) across 11 countries: Australia, Canada, England, France, Germany, the Netherlands, New Zealand, Spain, Sweden, Switzerland, and the United States (US). DATA COLLECTION/EXTRACTION METHODS: Data collected by ICCONIC partners. STUDY DESIGN: We retrospectively analyzed age-sex standardized utilization and spending of an older person (65-90 years) hospitalized with a heart failure exacerbation and a secondary diagnosis of diabetes across five domains of care: hospital care, primary care, outpatient specialty care, post-acute rehabilitative care, and outpatient drugs. PRINCIPAL FINDINGS: Sample sizes ranged from n = 1270 in Spain to n = 21,803 in the United States. Mean age (standard deviation [SD]) ranged from 76.2 (5.6) in the Netherlands to 80.3 (6.8) in Sweden. We observed substantial variation in spending and utilization across care settings. On average, England spent $10,956 per person in hospital care while the United States spent $30,877. The United States had a shorter length of stay over the year (18.9 days) compared to France (32.9) and Germany (33.4). The United States spent more days in facility-based rehabilitative care than other countries. Australia spent $421 per person in primary care, while Spain (Aragon) spent $1557. The United States and Canada had proportionately more visits to specialist providers than primary care providers. Across almost all sectors, the United States spent more than other countries, suggesting higher prices per unit. CONCLUSION: Across 11 countries, there is substantial variation in health care spending and utilization for a complex multimorbid persona with heart failure and diabetes. Drivers of spending vary across countries, with the United States being the most expensive country due to high prices and higher use of facility-based rehabilitative care.

The changing health needs of the UK population.

The demographics of the UK population are changing and so is the need for health care. In this Health Policy, we explore the current health of the population, the changing health needs, and future threats to health. Relative to other high-income countries, the UK is lagging on many health outcomes, such as life expectancy and infant mortality, and there is a growing burden of mental illness. Successes exist, such as the striking improvements in oral health, but inequalities in health persist as well. The growth of the ageing population relative to the working-age population, the rise of multimorbidity, and persistent health inequalities, particularly for preventable illness, are all issues that the National Health Service (NHS) will face in the years to come. Meeting the challenges of the future will require an increased focus on health promotion and disease prevention, involving a more concerted effort to understand and tackle the multiple social, environmental, and economic factors that lie at the heart of health inequalities. The immediate priority of the NHS will be to mitigate the wider and long-term health consequences of the COVID-19 pandemic, but it must also strengthen its resilience to reduce the impact of other threats to health, such as the UK leaving the EU, climate change, and antimicrobial resistance.

Rising burden of multimorbidity and related socio-demographic factors: a repeated cross-sectional study of Ontarians.

OBJECTIVE: This study aimed to provide population-level data regarding trends in multimorbidity over 13 years. METHODS: We linked provincial health administrative data in Ontario, Canada, to create 3 cross-sectional panels of residents of any age in 2003, 2009, and 2016 to describe: (i) 13-year trends in multimorbidity prevalence and constellations among residents and across age, sex, and income; and (ii) chronic condition clusters. Multimorbidity was defined as having at least any 2 of 18 selected conditions, and further grouped into levels of 2, 3, 4, or 5 or more conditions. Age-sex standardized multimorbidity prevalence was estimated using the 2009 population as the standard. Clustering was defined using the observed combinations of conditions within levels of multimorbidity. RESULTS: Standardized prevalence of multimorbidity increased over time (26.5%, 28.8%, and 30.0% across sequential panels), across sex, age, and area-based income. Females, older adults and those living in lower income areas exhibited higher rates in all years. However, multimorbidity increased relatively more among males, younger adults, and those with 4 or 5 or more conditions. We observed numerous and increasing diversity in disease clusters, namely at higher levels of multimorbidity. CONCLUSION: Our study provides relevant and needed population-based information on the growing burden of multimorbidity, and related socio-demographic risk factors. Multimorbidity is markedly increasing among younger age cohorts. Also, there is an increasing complexity and lack of common clustering patterns at higher multimorbidity levels.

RéSUMé: OBJECTIF: Cette étude a pour but d'offrir des données populationnelles sur la multimorbidité et les tendances sur 13 années. MéTHODES: Cette étude transversale utilise des données administratives provinciales, incluant trois panels d'individus de tous âges, en 2003, 2009 et 2016, pour décrire : (i) les tendances de la multimorbidité en Ontario, et les différences entre âge, sexe et niveaux de revenus; ainsi que (ii) les combinaisons de maladies chroniques. La multimorbidité a été définie comme ayant au moins deux des 18 maladies chroniques sélectionnées, et ensuite groupée par niveau de 2, 3, 4 ou 5 maladies ou plus. Les taux de prévalence standardisés ont été estimés à partir de la population de 2009. Les combinaisons fréquentes de maladies chroniques observées par niveau de multimorbidité sont également arborées. RéSULTATS: La prévalence standardisée de multimorbidité a augmenté au fil des années (26,5 %, 28,8 % et 30,0 %). Elle était plus élevée chez les femmes, les personnes âgées, et celles vivant dans les endroits à faible revenus, peu importe l'année. Toutefois l'augmentation dans le temps était plus importante chez les hommes, les jeunes adultes, et pour les niveaux élevés de multimorbidité (4, 5 ou plus). Nous avons observé un nombre élevé de combinaisons de maladies, et une diversité grandissante spécialement pour les niveaux de multimorbidité élevés. CONCLUSION: Cette étude fournit des données épidémiologiques probantes sur le problème grandissant de la multimorbidité, notamment au sein des jeunes cohortes, et les facteurs sociodémographiques associés. Il existe également une complexité grandissante et pas de profils communs dans les combinaisons de maladies, aux niveaux élevés de multimorbidité.

Timing of Co-occurring Chronic Conditions in Children With Neurologic Impairment.

BACKGROUND: Children with neurologic impairment (NI) are at risk for developing co-occurring chronic conditions, increasing their medical complexity and morbidity. We assessed the prevalence and timing of onset for those conditions in children with NI. METHODS: This longitudinal analysis included 6229 children born in 2009 and continuously enrolled in Medicaid through 2015 with a diagnosis of NI by age 3 in the IBM Watson Medicaid MarketScan Database. NI was defined with an existing diagnostic code set encompassing neurologic, genetic, and metabolic conditions that result in substantial functional impairments requiring subspecialty medical care. The prevalence and timing of co-occurring chronic conditions was assessed with the Agency for Healthcare Research and Quality Chronic Condition Indicator system. Mean cumulative function was used to measure age trends in multimorbidity. RESULTS: The most common type of NI was static (56.3%), with cerebral palsy (10.0%) being the most common NI diagnosis. Respiratory (86.5%) and digestive (49.4%) organ systems were most frequently affected by co-occurring chronic conditions. By ages 2, 4, and 6 years, the mean (95% confidence interval [CI]) numbers of co-occurring chronic conditions were 3.7 (95% CI 3.7-3.8), 4.6 (95% CI 4.5-4.7), and 5.1 (95% CI 5.1-5.2). An increasing percentage of children had ≥9 co-occurring chronic conditions as they aged: 5.3% by 2 years, 10.0% by 4 years, and 12.8% by 6 years. CONCLUSIONS: Children with NI enrolled in Medicaid have substantial multimorbidity that develops early in life. Increased attention to the timing and types of multimorbidity in children with NI may help optimize their preventive care and case management health services.

Multimorbidity combinations, costs of hospital care and potentially preventable emergency admissions in England: A cohort study.

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.

Development of Multimorbidity Over Time: An Analysis of Belgium Primary Care Data Using Markov Chains and Weighted Association Rule Mining.

BACKGROUND: The prevalence of multimorbidity is increasing in recent years, and patients with multimorbidity often have a decrease in quality of life and require more health care. The aim of this study was to explore the evolution of multimorbidity taking the sequence of diseases into consideration. METHODS: We used a Belgian database collected by extracting coded parameters and more than 100 chronic conditions from the Electronic Health Records of general practitioners to study patients older than 40 years with multiple diagnoses between 1991 and 2015 (N = 65 939). We applied Markov chains to estimate the probability of developing another condition in the next state after a diagnosis. The results of Weighted Association Rule Mining (WARM) allow us to show strong associations among multiple conditions. RESULTS: About 66.9% of the selected patients had multimorbidity. Conditions with high prevalence, such as hypertension and depressive disorder, were likely to occur after the diagnosis of most conditions. Patterns in several disease groups were apparent based on the results of both Markov chain and WARM, such as musculoskeletal diseases and psychological diseases. Psychological diseases were frequently followed by irritable bowel syndrome. CONCLUSIONS: Our study used Markov chains and WARM for the first time to provide a comprehensive view of the relations among 103 chronic conditions, taking sequential chronology into consideration. Some strong associations among specific conditions were detected and the results were consistent with current knowledge in literature, meaning the approaches were valid to be used on larger data sets, such as National Health care Systems or private insurers.

The disease burden of multimorbidity and its interaction with educational level.

INTRODUCTION: Policies to adequately respond to the rise in multimorbidity have top-priority. To understand the actual burden of multimorbidity, this study aimed to: 1) estimate the trend in prevalence of multimorbidity in the Netherlands, 2) study the association between multimorbidity and physical and mental health outcomes and healthcare cost, and 3) investigate how the association between multimorbidity and health outcomes interacts with socio-economic status (SES). METHODS: Prevalence estimates were obtained from a nationally representative pharmacy database over 2007-2016. Impact on costs was estimated in a fixed effect regression model on claims data over 2009-2015. Data on physical and mental health and SES were obtained from the National Health Survey in 2017, in which the Katz-10 was used to measure limitations in activities of daily living (ADL) and the Mental Health Inventory (MHI) to measure mental health. SES was approximated by the level of education. Generalized linear models (2-part models for ADL) were used to analyze the health data. In all models an indicator variable for the presence or absence of multimorbidity was included or a categorical variable for the number of chronic conditions. Interactions terms of multimorbidity and educational level were added into the previously mentioned models. RESULTS: Over the past ten years, there was an increase of 1.6%-point in the percentage of people with multimorbidity. The percentage of people with three or more conditions increased with +2.1%-point. People with multimorbidity had considerably worse physical and mental health outcomes than people without multimorbidity. For the ADL, the impact of multimorbidity was three times greater in the lowest educational level than in the highest educational level. For the MHI, the impact of multimorbidity was two times greater in the lowest than in the highest educational level. Each additional chronic condition was associated with a greater worsening in health outcomes. Similarly, for costs, where there was no evidence of a diminishing impact of additional conditions either. In patients with multimorbidity total healthcare costs were on average €874 higher than in patients with a single morbidity. CONCLUSION: The impact of multimorbidity on health and costs seems to be greater in the sicker and lower educated population.

Examining multimorbidity differences across racial groups: a network analysis of electronic medical records.

Health disparities across ethnic or racial groups are typically examined through single behavior at a time. The syndemics and multimorbidity health disparities have not been well examined by race. In this study, we study health disparities by identifying the networks of multimorbidities among individuals from seven population groups based on race, including White, African American, Asian, Hispanic, Native American, Bi- or Multi-racial and Pacific Islander. We examined a large electronic medical record (EMR) containing health records of more than 18.7 million patients and created multimorbidity networks considering their lifetime history from medical records in order to compare the network properties among seven population groups. In addition, the networks at organ system level depicting the relationship among disorders belonging to different organ systems are also compared. Our macro analysis at the organ-level indicates that African-Americans have a stronger multimorbidity network followed by Whites and Native Americans. The networks of Asians and Hispanics are sparse. Specifically, the relationship of infectious and parasitic disorders with respiratory, circulatory and genitourinary system disorders is stronger among African Americans than others. On the other hand, the relationship of mental disorders with respiratory, musculoskeletal system and connective tissue disorders is more prevalent in Whites. Similar other disparities are discussed. Recognition and explanation of such differences in multimorbidities inform the public health policies, and can inform clinical decisions as well. Our multimorbidity network analysis identifies specific differences in diagnoses among different population groups, and presents questions for biological, behavioral, clinical, social science, and policy research.

What is the answer to the challenge of multimorbidity in New Zealand?

The increasing prevalence of multimorbidity, a growing ageing population and lack of success in addressing the negative effect of socioeconomic and cultural determinants of health are major challenges for New Zealand's primary care sector. Self-management support strategies, personalised care planning, integrated care and shared health records have all been proposed as mechanisms to address these challenges. The organisation of the health system, however, remains largely unchanged, with limited accommodation and few funding concessions made for the requirements of these different approaches and tools. As a result, the primary care system is no longer a good match for the population it serves. With one in four New Zealanders reporting multimorbidity, and people aged >65 years predicted to double in number by 2050, this article argues that over the next decade, New Zealand requires a health system focused on incorporating self-management support, personalised and integrated care and shared health records. This will require further educating of not only health professionals, but also patients in the purpose behind these approaches. In addition, it will mean transitioning to a primary care system more suited to the needs of people with long-term conditions. The key gain from a radical redesign will be a more equitable health system focused on a broader range of health needs.

Socioeconomic inequalities in the prevalence of complex multimorbidity in a Norwegian population: findings from the cross-sectional HUNT Study.

OBJECTIVES: Multimorbidity, the co-occurrence of multiple long-term conditions, is common and increasing. Definitions and assessment methods vary, yielding differences in estimates of prevalence and multimorbidity severity. Sociodemographic characteristics are associated with complicating factors of multimorbidity. We aimed to investigate the prevalence of complex multimorbidity by sex and occupational groups throughout adulthood. DESIGN: Cross-sectional study. SETTING: The third total county survey of The Nord-Trøndelag Health Study (HUNT), 2006-2008, Norway. PARTICIPANTS: Individuals aged 25-100 years with classifiable occupational data and complete questionnaires and measurements. OUTCOME MEASURE: Complex multimorbidity defined as 'the co-occurrence of three or more chronic conditions affecting three or more different body (organ) systems within one person without defining an index chronic condition'. ANALYSIS: Logistic regression models with age and occupational group were specified for each sex separately. RESULTS: 38 027 of 41 193 adults (55% women) were included in our analyses. 54% of the participants were identified as having complex multimorbidity. Prevalence differences in percentage points (pp) of those in the low occupational group (vs the high occupational group (reference)) were 19 (95% CI, 16 to 21) pp in women and 10 (8 to 13) pp in men at 30 years; 12 (10 to 14) pp in women and 13 (11 to 15) pp in men at 55 years; and 2 (-1 to 4) pp in women and 7 (4 to 10) pp in men at 75 years. CONCLUSION: Complex multimorbidity is common from early adulthood, and social inequalities persist until 75 years in women and 90 years in men in the general population. These findings have policy implications for public health as well as healthcare, organisation, treatment, education and research, as complex multimorbidity breaks with the specialised, fragmented paradigm dominating medicine today.

Association of Sustained Blood Pressure Control with Multimorbidity Progression Among Older Adults.

BACKGROUND/OBJECTIVES: Due to the high costs and excess mortality associated with multimorbidity, there is a need to develop approaches for delaying its progression. High blood pressure (BP) is a common chronic condition and a risk factor for many additional chronic conditions, making it an ideal target for intervention. The purpose of this analysis was to determine the association between the level of sustained BP control and the progression of multimorbidity. DESIGN: Retrospective cohort study. SETTING: Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT) linked to Medicare claims. PARTICIPANTS: A total of 6,591 ALLHAT participants with Medicare who had systolic BP (SBP) measurements at eight or more study visits. MEASUREMENTS: SBP control was categorized as lower than 140 mm Hg at less than 50%, 50% to less than 75%, 75% to less than 100%, and 100% of visits. Multimorbidity progression was defined by the number of incident chronic conditions, including arthritis, asthma, atrial fibrillation, cancer, chronic kidney disease, chronic obstructive pulmonary disease, coronary heart disease, dementia, depression, diabetes mellitus, heart failure, hyperlipidemia, osteoporosis, and stroke. Recurrent event survival analysis was used to calculate rate ratios (RRs) for the association of sustained SBP control with progression of multimorbidity. RESULTS: Rates of incident conditions per 10 person-years (95% CIs) were 5.2 (5.1-5.4), 4.7 (4.5-4.8), 4.4 (4.2-4.5), and 4.0 (3.8-4.2) for participants with SBP control at less than 50%, 50% to less than 75%, 75% to less than 100%, and 100% of visits, respectively, over a median follow-up of 9.0 years. Compared with participants with SBP control at less than 50% of visits, adjusted RRs (95% CIs) for multimorbidity progression were 0.90 (0.86-0.95), 0.85 (0.81-0.89), and 0.77 (0.72-0.82) for those with SBP control at 50% to less than 75%, 75% to less than 100%, and 100% of visits, respectively. CONCLUSIONS: Sustaining BP control may be an effective approach to slow multimorbidity progression and may reduce the population burden of multimorbidity.

Cost-effectiveness of a patient-centred approach to managing multimorbidity in primary care: a pragmatic cluster randomised controlled trial.

OBJECTIVE: Patients with multiple chronic health conditions are often managed in a disjointed fashion in primary care, with annual review clinic appointments offered separately for each condition. This study aimed to determine the cost-effectiveness of the 3D intervention, which was developed to improve the system of care. DESIGN: Economic evaluation conducted alongside a pragmatic cluster-randomised trial. SETTING: General practices in three centres in England and Scotland. PARTICIPANTS: 797 adults with three or more chronic conditions were randomised to the 3D intervention, while 749 participants were randomised to receive usual care. INTERVENTION: The 3D approach: comprehensive 6-monthly general practitioner consultations, supported by medication reviews and nurse appointments. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary economic evaluation assessed the cost per quality-adjusted life year (QALY) gained from the perspective of the National Health Service (NHS) and personal social services (PSS). Costs were related to changes in a range of secondary outcomes (QALYs accrued by both participants and carers, and deaths) in a cost-consequences analysis from the perspectives of the NHS/PSS, patients/carers and productivity losses. RESULTS: Very small increases were found in both QALYs (adjusted mean difference 0.007 (-0.009 to 0.023)) and costs (adjusted mean difference £126 (£-739 to £991)) in the intervention arm compared with usual care after 15 months. The incremental cost-effectiveness ratio was £18 499, with a 50.8% chance of being cost-effective at a willingness-to-pay threshold of £20 000 per QALY (55.8% at £30 000 per QALY). CONCLUSIONS: The small differences in costs and outcomes were consistent with chance, and the uncertainty was substantial; therefore, the evidence for the cost-effectiveness of the 3D approach from the NHS/PSS perspective should be considered equivocal. TRIAL REGISTRATION NUMBER: ISCRTN06180958.

Prevalence and Patterns of Multimorbidity in a Nationally Representative Sample of Older Chinese: Results From the China Health and Retirement Longitudinal Study.

BACKGROUND: Multimorbidity has become a prominent problem worldwide; however, few population-based studies have been conducted among older Chinese with multimorbidity. This study aimed to examine the prevalence of multimorbidity and explore its common patterns among a nationally representative sample of older Chinese. METHODS: This study used data from the China Health and Retirement Longitudinal Study and included 19,841 participants aged at least 50 years. The prevalence of individual chronic diseases and multimorbidity during 2011-2015 were evaluated among the entire cohort and according to residential regions and gender. The relationships between participants' demographic characteristics and multimorbidity were examined using logistic regression model. Patterns of multimorbidity were explored using hierarchical cluster analysis and association rule mining. RESULTS: Multimorbidity occurred in 42.4% of the participants. The prevalence of multimorbidity was higher among women (odds ratio [OR] = 1.31, 95% confidence interval [CI]: 1.13-1.51) and urban residents (OR = 1.14, 95% CI: 1.02-1.27) than their respective counterparts after accounting for potential confounders of age, education, smoking, and alcohol consumption. Hierarchical cluster analysis revealed four common multimorbidity patterns: the vascular-metabolic cluster, the stomach-arthritis cluster, the cognitive-emotional cluster, and the hepatorenal cluster. Regional differences were found in the distributions of stroke and memory-related disease. Most combinations of conditions and urban-rural difference in multimorbidity patterns from hierarchical cluster analysis were also observed in association rule mining. CONCLUSION: The prevalence and patterns of multimorbidity vary by gender and residential regions among older Chinese. Women and urban residents are more vulnerable to multimorbidity. Future studies are needed to understand the mechanisms underlying the identified multimorbidity patterns and their policy and interventional implications.