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Open accessJournal ArticleDOI: 10.1016/J.LANEPE.2021.100047

Identifying longitudinal clusters of multimorbidity in an urban setting: A population-based cross-sectional study

02 Mar 2021-Vol. 3, pp 100047
Abstract: Background Globally, there is increasing research on clusters of multimorbidity, but few studies have investigated multimorbidity in urban contexts characterised by a young, multi-ethnic, deprived populations. This study identified clusters of associative multimorbidity in an urban setting. Methods This is a population-based retrospective cross-sectional study using electronic health records of all adults aged 18 years and over, registered between April 2005 to May 2020 in general practices in one inner London borough. Multiple correspondence analysis and cluster analysis was used to identify groups of multimorbidity from 32 long-term conditions (LTCs). Results The population included 41 general practices with 826,936 patients registered between 2005 and 2020, with mean age 40 (SD15·6) years. The prevalence of multimorbidity was 21% (n = 174,881), with the median number of conditions being three and increasing with age. Analysis identified five consistent LTC clusters: 1) anxiety and depression (Ratio of within- to between- sum of squares (WSS/BSS Interpretation Mental health problems, pain, and at-risk behaviours leading to cardiovascular diseases are the important clusters identified in this young, urban population. Funding Impact on Urban Health, United Kingdom.

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Topics: Population (56%), Cross-sectional study (50%)
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Journal ArticleDOI: 10.12968/INDN.2021.4.12
02 Apr 2021-Independent Nurse
Abstract: Mark Greener summarises the latest research on how long-term conditions cluster together

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1 Citations


Open accessJournal ArticleDOI: 10.1016/J.LANEPE.2021.100064
D. Scott Kehler1Institutions (1)
25 Feb 2021-
Abstract: While populations around the globe are living longer, so too have the number of years living in poor health [1]. Indeed, the burden of chronic health problems has steadily increased over previous decades, which are not exclusive to old age [2]. In consequence, there will be more individuals who can expect to live longer with multiple conditions, otherwise known as multimorbidity [3]. Against this background, there has been increasing interest in understanding nonrandom patterns, or clusters, of multimorbidity to understand the expression of chronic conditions in adulthood [4]. In The Lancet Regional Health Europe, Alessandra Bisquera and colleagues characterised multimorbidity clustering of chronic health conditions across the adult lifespan [5]. Here, they included 32 chronic health conditions, which were analysed from assembling 826,936 patient electronic health records (mean age 40 years old; 52% female) from 2005 to 2020 across 41 urban general practices in London. From patient record data, the authors used a statistical method, called multiple correspondence analysis, to reveal unique clusters of multimorbidity. In this cohort, 41% (n = 339,044) had at least one chronic health problem and 21% (n = 174,881) had multimorbidity. Females (23%) were more likely than men (20%) to have multimorbidity. The number of chronic conditions increased with age, where people aged 80 years or older had a median 5 chronic conditions versus those who were 18 39 years old (median 2 conditions). The prevalence of multimorbidity increased from 15¢5% in the 2005 2010 study sample versus 25¢2% which supports previous data that the burden of disease and poor health is increasing in more recent years [2]. The increase in multimorbidity prevalence shown here poses major challenges for health care and policy planning. In addition to demonstrating a rise in multimorbidity rates, the study by Bisquera et al. offer insights into commonly occurring multimorbidity clusters [5]. It is noteworthy that these clusters have

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Topics: Cluster analysis (63%)

Open accessJournal ArticleDOI: 10.1016/J.JCLINEPI.2021.09.005
Abstract: Objective To estimate the prevalence and determinants of multimorbidity in an urban, multi-ethnic area over 15-years and investigate the effect of applying resolved/remission codes on prevalence estimates. Study design and setting This is a population-based retrospective cross-sectional study using electronic health records of adults registered between 2005 –2020 in general practices in one inner London borough (n = 826,936). Classification of resolved/remission was based on clinical coding defined by the patient's general practitioner. Results The crude and age-adjusted prevalence of multimorbidity over the study period were 21.2% (95% CI: 21.1 –21.3) and 30.8% (30.6 –31.0), respectively. Applying resolved/remission codes decreased the crude and age-adjusted prevalence estimates to 18.0% (95% CI: 17.9 –18.1) and 27.5% (27.4 –27.7). Asthma (53.2%) and depression (20.2%) were responsible for most resolved and remission codes. Substance use (Adjusted Odds Ratio 10.62 [95% CI: 10.30 –10.95]), high cholesterol (2.48 [2.44 –2.53]), and moderate obesity (2.19 [2.15 –2.23]) were the strongest risk factor determinants of multimorbidity outside of advanced age. Conclusion Our study highlights the importance of applying resolved/remission codes to obtain an accurate prevalence and the increased burden of multimorbidity in a young, urban, and multi-ethnic population. Understanding modifiable risk factors for multimorbidity can assist policymakers in designing effective interventions to reduce progression to multimorbidity.

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Topics: Population (52%)

Open accessDOI: 10.1016/J.LANEPE.2021.100247
01 Jan 2022-
Abstract: Background Social and material deprivation accelerate the development of multimorbidity, yet the mechanisms which drive multimorbidity pathways and trajectories remain unclear. We aimed to examine the association between health inequality, risk factors and accumulation or resolution of LTCs, taking disease sequences into consideration. Methods We conducted a retrospective cohort of adults aged 18 years and over, registered between April 2005 and May 2020 in general practices in one inner London borough (n = 826,936). Thirty-two long term conditions (LTCs) were selected using a consensus process, based on a definition adapted to the demographic characteristics of the local population. sThe development and resolution of these LTCs were examined according to sociodemographic and clinical risk factors (hypertension; moderate obesity (BMI 30·0–39·9 kg/m2), high cholesterol (total cholesterol > 5 mmol/L), smoking, high alcohol consumption (>14 units per week), and psychoactive substance use), through the application of multistate Markov chain models. Findings Participants were followed up for a median of 4.2 years (IQR = 1·8 - 8·4); 631,760 (76%) entered the study with no LTCs, 121,424 (15%) with 1 LTC, 41,720 (5%) with 2 LTCs, and 31,966 (4%) with three or more LTCs. At the end of follow-up, 194,777 (24%) gained one or more LTCs, while 45,017 (5%) had resolved LTCs and 27,021 (3%) died. In multistate models, deprivation (hazard ratio [HR] between 1·30 to 1·64), female sex (HR 1·13 to 1·20), and Black ethnicity (HR 1·20 to 1·30; vs White) were independently associated with increased risk of transition from one to two LTCs, and shorter time spent in a healthy state. Substance use was the strongest risk factor for multimorbidity with an 85% probability of gaining LTCs over the next year. First order Markov chains identified consistent disease sequences including: chronic pain or osteoarthritis followed by anxiety and depression; alcohol and substance dependency followed by HIV, viral hepatitis, and liver disease; and morbid obesity followed by diabetes, hypertension, and chronic pain. Interpretation We examined the relations among 32 LTCs, taking the order of disease occurrence into consideration. Distinctive patterns for the development and accumulation of multimorbidity have emerged, with increased risk of transitioning from no conditions to multimorbidity and mortality related to ethnicity, deprivation and gender. Musculoskeletal disorders, morbid obesity and substance abuse represent common entry points to multimorbidity trajectories.

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Topics: Risk factor (51%)

Open accessJournal ArticleDOI: 10.3399/BJGP.2021.0325
Abstract: Background People with multimorbidity have complex healthcare needs. Some co-occurring diseases interact with each other to a larger extent than others and may have a different impact on primary care use. Aim To assess the association between multimorbidity clusters and primary care consultations over time. Design and setting A retrospective longitudinal (panel) study design was used. Data comprised electronic primary care health records of 826 166 patients registered at GP practices in an ethnically diverse, urban setting in London between 2005 and 2020. Method Primary care consultation rates were modelled using generalised estimating equations. Key controls included the total number of long-term conditions, five multimorbidity clusters, and their interaction effects, ethnic group, and polypharmacy (proxy for disease severity). Models were also calibrated by consultation type and ethnic group. Results Individuals with multimorbidity used two to three times more primary care services than those without multimorbidity (incidence rate ratio 2.30, 95% confidence interval = 2.29 to 2.32). Patients in the alcohol dependence, substance dependence, and HIV cluster (Dependence+) had the highest rate of increase in primary care consultations as additional long-term conditions accumulated, followed by the mental health cluster (anxiety and depression). Differences by ethnic group were observed, with the largest impact in the chronic liver disease and viral hepatitis cluster for individuals of Black or Asian ethnicity. Conclusion This study identified multimorbidity clusters with the highest primary care demand over time as additional long-term conditions developed, differentiating by consultation type and ethnicity. Targeting clinical practice to prevent multimorbidity progression for these groups may lessen future pressures on primary care demand by improving health outcomes.

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Topics: Health care (59%), Population (51%)
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Journal ArticleDOI: 10.1080/01621459.1963.10500845
Abstract: A procedure for forming hierarchical groups of mutually exclusive subsets, each of which has members that are maximally similar with respect to specified characteristics, is suggested for use in large-scale (n > 100) studies when a precise optimal solution for a specified number of groups is not practical. Given n sets, this procedure permits their reduction to n − 1 mutually exclusive sets by considering the union of all possible n(n − 1)/2 pairs and selecting a union having a maximal value for the functional relation, or objective function, that reflects the criterion chosen by the investigator. By repeating this process until only one group remains, the complete hierarchical structure and a quantitative estimate of the loss associated with each stage in the grouping can be obtained. A general flowchart helpful in computer programming and a numerical example are included.

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Topics: Ward's method (54%)

15,609 Citations


Journal ArticleDOI: 10.1016/S0140-6736(12)60240-2
Karen Barnett1, Stewart W Mercer2, Michael Norbury1, Graham Watt2  +2 moreInstitutions (2)
07 Jul 2012-The Lancet
Abstract: Summary Background Long-term disorders are the main challenge facing health-care systems worldwide, but health systems are largely configured for individual diseases rather than multimorbidity. We examined the distribution of multimorbidity, and of comorbidity of physical and mental health disorders, in relation to age and socioeconomic deprivation. Methods In a cross-sectional study we extracted data on 40 morbidities from a database of 1 751 841 people registered with 314 medical practices in Scotland as of March, 2007. We analysed the data according to the number of morbidities, disorder type (physical or mental), sex, age, and socioeconomic status. We defined multimorbidity as the presence of two or more disorders. Findings 42·2% (95% CI 42·1–42·3) of all patients had one or more morbidities, and 23·2% (23·08–23·21) were multimorbid. Although the prevalence of multimorbidity increased substantially with age and was present in most people aged 65 years and older, the absolute number of people with multimorbidity was higher in those younger than 65 years (210 500 vs 194 996). Onset of multimorbidity occurred 10–15 years earlier in people living in the most deprived areas compared with the most affluent, with socioeconomic deprivation particularly associated with multimorbidity that included mental health disorders (prevalence of both physical and mental health disorder 11·0%, 95% CI 10·9–11·2% in most deprived area vs 5·9%, 5·8%–6·0% in least deprived). The presence of a mental health disorder increased as the number of physical morbidities increased (adjusted odds ratio 6·74, 95% CI 6·59–6·90 for five or more disorders vs 1·95, 1·93–1·98 for one disorder), and was much greater in more deprived than in less deprived people (2·28, 2·21–2·32 vs 1·08, 1·05–1·11). Interpretation Our findings challenge the single-disease framework by which most health care, medical research, and medical education is configured. A complementary strategy is needed, supporting generalist clinicians to provide personalised, comprehensive continuity of care, especially in socioeconomically deprived areas. Funding Scottish Government Chief Scientist Office.

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Topics: Comorbidity (56%), Health services research (54%), Health care (54%) ... show more

3,883 Citations


Journal ArticleDOI: 10.1016/J.ARR.2011.03.003
Abstract: A literature search was carried out to summarize the existing scientific evidence concerning occurrence, causes, and consequences of multimorbidity (the coexistence of multiple chronic diseases) in the elderly as well as models and quality of care of persons with multimorbidity. According to pre-established inclusion criteria, and using different search strategies, 41 articles were included (four of these were methodological papers only). Prevalence of multimorbidity in older persons ranges from 55 to 98%. In cross-sectional studies, older age, female gender, and low socioeconomic status are factors associated with multimorbidity, confirmed by longitudinal studies as well. Major consequences of multimorbidity are disability and functional decline, poor quality of life, and high health care costs. Controversial results were found on multimorbidity and mortality risk. Methodological issues in evaluating multimorbidity are discussed as well as future research needs, especially concerning etiological factors, combinations and clustering of chronic diseases, and care models for persons affected by multiple disorders. New insights in this field can lead to the identification of preventive strategies and better treatment of multimorbid patients.

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Topics: Health care (52%)

1,667 Citations


Open accessJournal ArticleDOI: 10.1016/S0140-6736(20)30925-9
Theo Vos1, Theo Vos2, Theo Vos3, Stephen S Lim  +2416 moreInstitutions (246)
17 Oct 2020-The Lancet
Abstract: Background: In an era of shifting global agendas and expanded emphasis on non-communicable diseases and injuries along with communicable diseases, sound evidence on trends by cause at the national level is essential. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) provides a systematic scientific assessment of published, publicly available, and contributed data on incidence, prevalence, and mortality for a mutually exclusive and collectively exhaustive list of diseases and injuries. Methods: GBD estimates incidence, prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) due to 369 diseases and injuries, for two sexes, and for 204 countries and territories. Input data were extracted from censuses, household surveys, civil registration and vital statistics, disease registries, health service use, air pollution monitors, satellite imaging, disease notifications, and other sources. Cause-specific death rates and cause fractions were calculated using the Cause of Death Ensemble model and spatiotemporal Gaussian process regression. Cause-specific deaths were adjusted to match the total all-cause deaths calculated as part of the GBD population, fertility, and mortality estimates. Deaths were multiplied by standard life expectancy at each age to calculate YLLs. A Bayesian meta-regression modelling tool, DisMod-MR 2.1, was used to ensure consistency between incidence, prevalence, remission, excess mortality, and cause-specific mortality for most causes. Prevalence estimates were multiplied by disability weights for mutually exclusive sequelae of diseases and injuries to calculate YLDs. We considered results in the context of the Socio-demographic Index (SDI), a composite indicator of income per capita, years of schooling, and fertility rate in females younger than 25 years. Uncertainty intervals (UIs) were generated for every metric using the 25th and 975th ordered 1000 draw values of the posterior distribution. Findings: Global health has steadily improved over the past 30 years as measured by age-standardised DALY rates. After taking into account population growth and ageing, the absolute number of DALYs has remained stable. Since 2010, the pace of decline in global age-standardised DALY rates has accelerated in age groups younger than 50 years compared with the 1990–2010 time period, with the greatest annualised rate of decline occurring in the 0–9-year age group. Six infectious diseases were among the top ten causes of DALYs in children younger than 10 years in 2019: lower respiratory infections (ranked second), diarrhoeal diseases (third), malaria (fifth), meningitis (sixth), whooping cough (ninth), and sexually transmitted infections (which, in this age group, is fully accounted for by congenital syphilis; ranked tenth). In adolescents aged 10–24 years, three injury causes were among the top causes of DALYs: road injuries (ranked first), self-harm (third), and interpersonal violence (fifth). Five of the causes that were in the top ten for ages 10–24 years were also in the top ten in the 25–49-year age group: road injuries (ranked first), HIV/AIDS (second), low back pain (fourth), headache disorders (fifth), and depressive disorders (sixth). In 2019, ischaemic heart disease and stroke were the top-ranked causes of DALYs in both the 50–74-year and 75-years-and-older age groups. Since 1990, there has been a marked shift towards a greater proportion of burden due to YLDs from non-communicable diseases and injuries. In 2019, there were 11 countries where non-communicable disease and injury YLDs constituted more than half of all disease burden. Decreases in age-standardised DALY rates have accelerated over the past decade in countries at the lower end of the SDI range, while improvements have started to stagnate or even reverse in countries with higher SDI. Interpretation: As disability becomes an increasingly large component of disease burden and a larger component of health expenditure, greater research and development investment is needed to identify new, more effective intervention strategies. With a rapidly ageing global population, the demands on health services to deal with disabling outcomes, which increase with age, will require policy makers to anticipate these changes. The mix of universal and more geographically specific influences on health reinforces the need for regular reporting on population health in detail and by underlying cause to help decision makers to identify success stories of disease control to emulate, as well as opportunities to improve. Funding: Bill & Melinda Gates Foundation.

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Topics: Disease burden (71%), Years of potential life lost (60%), Mortality rate (57%) ... show more

976 Citations


Journal ArticleDOI: 10.1038/NRCLINONC.2010.227
Leroy Hood1, Stephen H. Friend2Institutions (2)
Abstract: Medicine will move from a reactive to a proactive discipline over the next decade--a discipline that is predictive, personalized, preventive and participatory (P4) P4 medicine will be fueled by systems approaches to disease, emerging technologies and analytical tools There will be two major challenges to achieving P4 medicine--technical and societal barriers--and the societal barriers will prove the most challenging How do we bring patients, physicians and members of the health-care community into alignment with the enormous opportunities of P4 medicine? In part, this will be done by the creation of new types of strategic partnerships--between patients, large clinical centers, consortia of clinical centers and patient-advocate groups For some clinical trials it will necessary to recruit very large numbers of patients--and one powerful approach to this challenge is the crowd-sourced recruitment of patients by bringing large clinical centers together with patient-advocate groups

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Topics: Precision medicine (57%), MEDLINE (52%)

546 Citations


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