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Krauth SJ, Steell L, Ahmed S, McIntosh E, Dibben GO, Hanlon P, Lewsey J, Nicholl BI, McAllister DA, Smith SM, Evans R, Ahmed Z, Dean S, Greaves C, Barber S, Doherty P, Gardiner N, Ibbotson T, Jolly K, Ormandy P, Simpson SA, Taylor RS, Singh SJ, Mair FS, Jani BD. Association of latent class analysis-derived multimorbidity clusters with adverse health outcomes in patients with multiple long-term conditions: comparative results across three UK cohorts. EClinicalMedicine 2024; 74:102703. [PMID: 39045545 PMCID: PMC11261399 DOI: 10.1016/j.eclinm.2024.102703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 06/07/2024] [Accepted: 06/07/2024] [Indexed: 07/25/2024] Open
Abstract
Background It remains unclear how to meaningfully classify people living with multimorbidity (multiple long-term conditions (MLTCs)), beyond counting the number of conditions. This paper aims to identify clusters of MLTCs in different age groups and associated risks of adverse health outcomes and service use. Methods Latent class analysis was used to identify MLTCs clusters in different age groups in three cohorts: Secure Anonymised Information Linkage Databank (SAIL) (n = 1,825,289), UK Biobank (n = 502,363), and the UK Household Longitudinal Study (UKHLS) (n = 49,186). Incidence rate ratios (IRR) for MLTC clusters were computed for: all-cause mortality, hospitalisations, and general practice (GP) use over 10 years, using <2 MLTCs as reference. Information on health outcomes and service use were extracted for a ten year follow up period (between 01st Jan 2010 and 31st Dec 2019 for UK Biobank and UKHLS, and between 01st Jan 2011 and 31st Dec 2020 for SAIL). Findings Clustering MLTCs produced largely similar results across different age groups and cohorts. MLTC clusters had distinct associations with health outcomes and service use after accounting for LTC counts, in fully adjusted models. The largest associations with mortality, hospitalisations and GP use in SAIL were observed for the "Pain+" cluster in the age-group 18-36 years (mortality IRR = 4.47, hospitalisation IRR = 1.84; GP use IRR = 2.87) and the "Hypertension, Diabetes & Heart disease" cluster in the age-group 37-54 years (mortality IRR = 4.52, hospitalisation IRR = 1.53, GP use IRR = 2.36). In UK Biobank, the "Cancer, Thyroid disease & Rheumatoid arthritis" cluster in the age group 37-54 years had the largest association with mortality (IRR = 2.47). Cardiometabolic clusters across all age groups, pain/mental health clusters in younger groups, and cancer and pulmonary related clusters in older age groups had higher risk for all outcomes. In UKHLS, MLTC clusters were not significantly associated with higher risk of adverse outcomes, except for the hospitalisation in the age-group 18-36 years. Interpretation Personalising care around MLTC clusters that have higher risk of adverse outcomes may have important implications for practice (in relation to secondary prevention), policy (with allocation of health care resources), and research (intervention development and targeting), for people living with MLTCs. Funding This study was funded by the National Institute for Health and Care Research (NIHR; Personalised Exercise-Rehabilitation FOR people with Multiple long-term conditions (multimorbidity)-NIHR202020).
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Affiliation(s)
- Stefanie J. Krauth
- General Practice and Primary Care, School of Health and Wellbeing, University of Glasgow, Glasgow, United Kingdom
- School of Allied and Public Health Professions, Canterbury Christ Church University, Canterbury, United Kingdom
| | - Lewis Steell
- General Practice and Primary Care, School of Health and Wellbeing, University of Glasgow, Glasgow, United Kingdom
- AGE Research Group, Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
- NIHR Newcastle Biomedical Research Centre, Newcastle upon Tyne NHS Foundation Trust, Cumbria, Northumberland, Tyne and Wear NHS Foundation Trust and Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Sayem Ahmed
- Health Economics and Health Technology Assessment, School of Health and Wellbeing, University of Glasgow, Glasgow, United Kingdom
| | - Emma McIntosh
- Health Economics and Health Technology Assessment, School of Health and Wellbeing, University of Glasgow, Glasgow, United Kingdom
| | - Grace O. Dibben
- MRC/CSO Social & Public Health Sciences Unit, School of Health and Wellbeing, University of Glasgow, Glasgow, United Kingdom
| | - Peter Hanlon
- General Practice and Primary Care, School of Health and Wellbeing, University of Glasgow, Glasgow, United Kingdom
| | - Jim Lewsey
- Health Economics and Health Technology Assessment, School of Health and Wellbeing, University of Glasgow, Glasgow, United Kingdom
| | - Barbara I. Nicholl
- General Practice and Primary Care, School of Health and Wellbeing, University of Glasgow, Glasgow, United Kingdom
| | - David A. McAllister
- Health Economics and Health Technology Assessment, School of Health and Wellbeing, University of Glasgow, Glasgow, United Kingdom
| | - Susan M. Smith
- Discipline of Public Health and Primary Care, Trinity College Dublin, Dublin, Ireland
| | - Rachael Evans
- Department of Respiratory Sciences, University of Leicester, Leicester, United Kingdom
| | - Zahira Ahmed
- Department of Respiratory Sciences, University of Leicester, Leicester, United Kingdom
| | - Sarah Dean
- University of Exeter Medical School, Exeter, United Kingdom
| | - Colin Greaves
- School of Sport, Exercise and Rehabilitation Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Shaun Barber
- University of Exeter Medical School, Exeter, United Kingdom
- Clinical Trials Unit, University of Leicester, Leicester, United Kingdom
| | - Patrick Doherty
- Department of Health Science, University of York, York, United Kingdom
| | - Nikki Gardiner
- Department of Cardiopulmonary Rehabilitation, University Hospitals of Leicester NHS Trust, Leicester, United Kingdom
| | - Tracy Ibbotson
- General Practice and Primary Care, School of Health and Wellbeing, University of Glasgow, Glasgow, United Kingdom
| | - Kate Jolly
- Institute of Applied Health Research, University of Birmingham, Birmingham, United Kingdom
| | - Paula Ormandy
- School of Health and Society, University of Salford, Manchester, United Kingdom
| | - Sharon A. Simpson
- MRC/CSO Social & Public Health Sciences Unit, School of Health and Wellbeing, University of Glasgow, Glasgow, United Kingdom
| | - Rod S. Taylor
- MRC/CSO Social & Public Health Sciences Unit, School of Health and Wellbeing, University of Glasgow, Glasgow, United Kingdom
- Robertson Centre for Biostatistics, School of Health and Wellbeing, University of Glasgow, Glasgow, United Kingdom
| | - Sally J. Singh
- Department of Respiratory Sciences, University of Leicester, Leicester, United Kingdom
| | - Frances S. Mair
- General Practice and Primary Care, School of Health and Wellbeing, University of Glasgow, Glasgow, United Kingdom
| | - Bhautesh Dinesh Jani
- General Practice and Primary Care, School of Health and Wellbeing, University of Glasgow, Glasgow, United Kingdom
| | - PERFORM research team
- General Practice and Primary Care, School of Health and Wellbeing, University of Glasgow, Glasgow, United Kingdom
- School of Allied and Public Health Professions, Canterbury Christ Church University, Canterbury, United Kingdom
- AGE Research Group, Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
- NIHR Newcastle Biomedical Research Centre, Newcastle upon Tyne NHS Foundation Trust, Cumbria, Northumberland, Tyne and Wear NHS Foundation Trust and Newcastle University, Newcastle upon Tyne, United Kingdom
- Health Economics and Health Technology Assessment, School of Health and Wellbeing, University of Glasgow, Glasgow, United Kingdom
- MRC/CSO Social & Public Health Sciences Unit, School of Health and Wellbeing, University of Glasgow, Glasgow, United Kingdom
- Discipline of Public Health and Primary Care, Trinity College Dublin, Dublin, Ireland
- Department of Respiratory Sciences, University of Leicester, Leicester, United Kingdom
- University of Exeter Medical School, Exeter, United Kingdom
- School of Sport, Exercise and Rehabilitation Sciences, University of Birmingham, Birmingham, United Kingdom
- Clinical Trials Unit, University of Leicester, Leicester, United Kingdom
- Department of Health Science, University of York, York, United Kingdom
- Department of Cardiopulmonary Rehabilitation, University Hospitals of Leicester NHS Trust, Leicester, United Kingdom
- Institute of Applied Health Research, University of Birmingham, Birmingham, United Kingdom
- School of Health and Society, University of Salford, Manchester, United Kingdom
- Robertson Centre for Biostatistics, School of Health and Wellbeing, University of Glasgow, Glasgow, United Kingdom
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Ahrén J, Pirouzifard M, Holmquist B, Sundquist J, Sundquist K, Zöller B. Multimorbidity disease clusters are associated with venous thromboembolism: an extended cross-sectional national study. J Thromb Thrombolysis 2024; 57:898-906. [PMID: 38678153 PMCID: PMC11315723 DOI: 10.1007/s11239-024-02987-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/18/2024] [Indexed: 04/29/2024]
Abstract
Multimorbidity, i.e., two or more non-communicable diseases (NCDs), is an escalating challenge for society. Venous thromboembolism (VTE) is a common cardiovascular disease and it is unknown which multimorbidity clusters associates with VTE. Our aim was to examine the association between different common disease clusters of multimorbidity and VTE. The study is an extended (1997-2015) cross-sectional Swedish study using the National Patient Register and the Multigeneration Register. A total of 2,694,442 Swedish-born individuals were included in the study. Multimorbidity was defined by 45 NCDs. A principal component analysis (PCA) identified multimorbidity disease clusters. Odds ratios (OR) for VTE were calculated for the different multimorbidity disease clusters. There were 16% (n = 440,742) of multimorbid individuals in the study population. Forty-four of the individual 45 NCDs were associated with VTE. The PCA analysis identified nine multimorbidity disease clusters, F1-F9. Seven of these multimorbidity clusters were associated with VTE. The adjusted OR for VTE in the multimorbid patients was for the first three clusters: F1 (cardiometabolic diseases) 3.44 (95%CI 3.24-3.65), F2 (mental disorders) 2.25 (95%CI 2.14-2.37) and F3 (digestive system diseases) 4.35 (95%CI 3.63-5.22). There was an association between multimorbidity severity and OR for VTE. For instance, the occurrence of at least five diseases was in F1 and F2 associated with ORs for VTE: 8.17 (95%CI 6.32-10.55) and 6.31 (95%CI 4.34-9.17), respectively. In this nationwide study we have shown a strong association between VTE and different multimorbidity disease clusters that might be useful for VTE prediction.
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Affiliation(s)
- Jonatan Ahrén
- Center for Primary Health Care Research, Lund University/Region Skåne, Malmö, Sweden.
- University Clinic Primary Care Skåne, Region Skåne, Sweden.
| | - MirNabi Pirouzifard
- Center for Primary Health Care Research, Lund University/Region Skåne, Malmö, Sweden
- University Clinic Primary Care Skåne, Region Skåne, Sweden
| | | | - Jan Sundquist
- Center for Primary Health Care Research, Lund University/Region Skåne, Malmö, Sweden
- University Clinic Primary Care Skåne, Region Skåne, Sweden
| | - Kristina Sundquist
- Center for Primary Health Care Research, Lund University/Region Skåne, Malmö, Sweden
- University Clinic Primary Care Skåne, Region Skåne, Sweden
| | - Bengt Zöller
- Center for Primary Health Care Research, Lund University/Region Skåne, Malmö, Sweden
- University Clinic Primary Care Skåne, Region Skåne, Sweden
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de Aguiar RG, Simões D, Castro SS, Goldbaum M, Cesar CLG, Lucas R. Multimorbidity patterns and associated factors in a megacity: a cross-sectional study. Rev Saude Publica 2024; 58:26. [PMID: 39082597 PMCID: PMC11319032 DOI: 10.11606/s1518-8787.2024058006058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 01/25/2024] [Indexed: 08/15/2024] Open
Abstract
OBJECTIVE To identify empirical patterns of multimorbidity and quantify their associations with socioeconomic, behavioral characteristics, and health outcomes in the megacity of São Paulo. METHODS This was a cross-sectional study conducted through household interviews with residents aged 20 years or older in urban areas (n = 3,184). Latent class analysis was used to identify patterns among the co-existence of 22 health conditions. Age-adjusted prevalence ratios were estimated using Poisson regression. RESULTS The analysis of latent classes showed 4 patterns of multimorbidity, whereas 58.6% of individuals were classified in the low disease probability group, followed by participants presenting cardiovascular conditions (15.9%), respiratory conditions (12.8%), and rheumatic, musculoskeletal, and emotional conditions (12.8%). Older individuals, with lower schooling and lower household income, presented higher multimorbidity prevalence in cardiovascular, respiratory, rheumatic, musculoskeletal, and emotional conditions patterns compared with the low disease probability pattern. CONCLUSION The results showed four distinct patterns of multimorbidity in the megacity population, and these patterns are clinically recognizable and theoretically plausible. The identification of trends between patterns would make it feasible to estimate the magnitude of the challenge for the organization of health care policies.
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Affiliation(s)
- Ricardo Goes de Aguiar
- Universidade Federal de AlfenasInstituto de Ciências da MotricidadeAlfenasMGBrasilUniversidade Federal de Alfenas. Instituto de Ciências da Motricidade. Alfenas, MG, Brasil
- Universidade de São PauloFaculdade de Saúde PúblicaSão PauloSPBrasil Universidade de São Paulo. Faculdade de Saúde Pública. São Paulo, SP, Brasil
| | - Daniela Simões
- Universidade do PortoInstituto de Saúde PúblicaUnidade de Investigação em EpidemiologiaPortoPortugal Universidade do Porto. Instituto de Saúde Pública. Unidade de Investigação em Epidemiologia. Porto, Portugal
- Escola Superior de Saúde de Santa MariaPortoPortugal Escola Superior de Saúde de Santa Maria. Porto, Portugal
| | - Shamyr Sulyvan Castro
- Universidade Federal do CearáDepartamento de Fisioterapia. FortalezaCEBrasil Universidade Federal do Ceará. Departamento de Fisioterapia. Fortaleza, CE, Brasil
| | - Moises Goldbaum
- Universidade de São PauloFaculdade de MedicinaDepartamento de Medicina PreventivaSão PauloSPBrasil Universidade de São Paulo. Faculdade de Medicina. Departamento de Medicina Preventiva. São Paulo, SP, Brasil
| | - Chester Luiz Galvão Cesar
- Universidade de São PauloFaculdade de Saúde PúblicaSão PauloSPBrasil Universidade de São Paulo. Faculdade de Saúde Pública. São Paulo, SP, Brasil
| | - Raquel Lucas
- Universidade do PortoInstituto de Saúde PúblicaUnidade de Investigação em EpidemiologiaPortoPortugal Universidade do Porto. Instituto de Saúde Pública. Unidade de Investigação em Epidemiologia. Porto, Portugal
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Liu R, Nagel CL, Chen S, Newsom JT, Allore HG, Quiñones AR. Multimorbidity and associated informal care receiving characteristics for US older adults: a latent class analysis. BMC Geriatr 2024; 24:571. [PMID: 38956501 PMCID: PMC11221032 DOI: 10.1186/s12877-024-05158-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 06/18/2024] [Indexed: 07/04/2024] Open
Abstract
BACKGROUND Older adults with varying patterns of multimorbidity may require distinct types of care and rely on informal caregiving to meet their care needs. This study aims to identify groups of older adults with distinct, empirically-determined multimorbidity patterns and compare characteristics of informal care received among estimated classes. METHODS Data are from the 2011 National Health and Aging Trends Study (NHATS). Ten chronic conditions were included to estimate multimorbidity patterns among 7532 individuals using latent class analysis. Multinomial logistic regression model was estimated to examine the association between sociodemographic characteristics, health status and lifestyle variables, care-receiving characteristics and latent class membership. RESULTS A four-class solution identified the following multimorbidity groups: some somatic conditions with moderate cognitive impairment (30%), cardiometabolic (25%), musculoskeletal (24%), and multisystem (21%). Compared with those who reported receiving no help, care recipients who received help with household activities only (OR = 1.44, 95% CI 1.05-1.98), mobility but not self-care (OR = 1.63, 95% CI 1.05-2.53), or self-care but not mobility (OR = 2.07, 95% CI 1.29-3.31) had greater likelihood of being in the multisystem group versus the some-somatic group. Having more caregivers was associated with higher odds of being in the multisystem group compared with the some-somatic group (OR = 1.09, 95% CI 1.00-1.18), whereas receiving help from paid helpers was associated with lower odds of being in the multisystem group (OR = 0.36, 95% CI 0.19-0.77). CONCLUSIONS Results highlighted different care needs among persons with distinct combinations of multimorbidity, in particular the wide range of informal needs among older adults with multisystem multimorbidity. Policies and interventions should recognize the differential care needs associated with multimorbidity patterns to better provide person-centered care.
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Affiliation(s)
- Ruotong Liu
- Department of Family Medicine, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR, 97239, USA
| | - Corey L Nagel
- College of Nursing, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
| | - Siting Chen
- OHSU-PSU School of Public Health, Portland, OR, USA
| | - Jason T Newsom
- Department of Psychology, Portland State University, Portland, OR, USA
| | - Heather G Allore
- Department of Internal Medicine, Yale University, New Haven, Connecticut, USA
- Department of Biostatistics, Yale University, New Haven, Connecticut, USA
| | - Ana R Quiñones
- Department of Family Medicine, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR, 97239, USA.
- OHSU-PSU School of Public Health, Portland, OR, USA.
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Dell'Isola A, Recenti F, Englund M, Kiadaliri A. Twenty-year trajectories of morbidity in individuals with and without osteoarthritis. RMD Open 2024; 10:e004164. [PMID: 38955511 PMCID: PMC11256023 DOI: 10.1136/rmdopen-2024-004164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 05/21/2024] [Indexed: 07/04/2024] Open
Abstract
OBJECTIVES To identify multimorbidity trajectories over 20 years among incident osteoarthritis (OA) individuals and OA-free matched references. METHODS Cohort study using prospectively collected healthcare data from the Skåne region, Sweden (~1.4 million residents). We extracted diagnoses for OA and 67 common chronic conditions. We included individuals aged 40+ years on 31 December 2007, with incident OA between 2008 and 2009. We selected references without OA, matched on birth year, sex, and year of death or moving outside the region. We employed group-based trajectory modelling to capture morbidity count trajectories from 1998 to 2019. Individuals without any comorbidity were included as a reference group but were not included in the model. RESULTS We identified 9846 OA cases (mean age: 65.9 (SD 11.7), female: 58%) and 9846 matched references. Among both cases and references, 1296 individuals did not develop chronic conditions (no-chronic-condition class). We identified four classes. At the study outset, all classes exhibited a low average number of chronic conditions (≤1). Class 1 had the slowest progression towards multimorbidity, which increased progressively in each class. Class 1 had the lowest count of chronic conditions at the end of the follow-up (mean: 2.9 (SD 1.7)), while class 4 had the highest (9.6 (2.6)). The presence of OA was associated with a 1.29 (1.12, 1.48) adjusted relative risk of belonging to class 1 up to 2.45 (2.12, 2.83) for class 4. CONCLUSIONS Our findings suggest that individuals with OA face an almost threefold higher risk of developing severe multimorbidity.
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Affiliation(s)
- Andrea Dell'Isola
- Clinical Epidemiology Unit, Orthopedics, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
| | - Filippo Recenti
- Clinical Epidemiology Unit, Orthopedics, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genoa, Savona, Italy
| | - Martin Englund
- Clinical Epidemiology Unit, Orthopedics, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
| | - Ali Kiadaliri
- Clinical Epidemiology Unit, Orthopedics, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
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Tran T, Bliuc D, Abrahamsen B, Chen W, Eisman JA, Hansen L, Vestergaard P, Nguyen TV, Blank RD, Center JR. Multimorbidity clusters potentially superior to individual diseases for stratifying fracture risk in older people: a nationwide cohort study. Age Ageing 2024; 53:afae164. [PMID: 39078154 DOI: 10.1093/ageing/afae164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 05/27/2024] [Indexed: 07/31/2024] Open
Abstract
RATIONALE Comorbidities are common in fracture patients, but the interaction between fracture and comorbidities remains unclear. This study aimed to define specific multimorbidity clusters in older adults and quantify the association between the multimorbidity clusters and fracture risk. METHODS This nationwide cohort study includes 1.7 million adults in Denmark aged ≥50 years who were followed from 2001 through 2014 for an incident low-trauma fracture. Chronic diseases and fractures were identified from the Danish National Hospital Discharge Register. Latent class analysis and Cox's regression were conducted to define the clusters and quantify fracture risk, respectively. RESULTS The study included 793 815 men (age: 64 ± 10) and 873 524 women (65.5 ± 11), with a third having ≥1 chronic disease. The pre-existent chronic diseases grouped individuals into low-multimorbidity (80.3% in men, 83.6% in women), cardiovascular (12.5%, 10.6%), malignant (4.1%, 3.8%), diabetic (2.4%, 2.0%) and hepatic clusters (0.7%, men only). These clusters distinguished individuals with advanced, complex, or late-stage disease from those having earlier-stage disease. During a median follow-up of 14 years (IQR: 6.5, 14), 95 372 men and 212 498 women sustained an incident fracture. The presence of multimorbidity was associated with a significantly greater risk of fracture, independent of age and sex. Importantly, the multimorbidity clusters had the highest discriminative performance in assessing fracture risk, whereas the strength of their association with fracture risk equalled or exceeded that of both the individual chronic diseases most prevalent in each cluster and of counts-based comorbidity indices. CONCLUSIONS Future fracture prevention strategies should take comorbidities into account. Multimorbidity clusters may provide greater insight into fracture risk than individual diseases or counts-based comorbidity indices.
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Affiliation(s)
- Thach Tran
- Skeletal Diseases Program, Garvan Institute of Medical Research, Sydney, New South Wales 2010, Australia
- Faculty of Medicine, UNSW Sydney, New South Wales 2052, Australia
- School of Biomedical Engineering, University of Technology Sydney, New South Wales 2007, Australia
| | - Dana Bliuc
- Skeletal Diseases Program, Garvan Institute of Medical Research, Sydney, New South Wales 2010, Australia
- Faculty of Medicine, UNSW Sydney, New South Wales 2052, Australia
| | - Bo Abrahamsen
- Department of Medicine, Holbæk Hospital, 4300 Holbæk, Denmark
- Department of Clinical Research, Odense Patient Data Explorative Network, University of Southern Denmark, 5230 Odense, Denmark
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
| | - Weiwen Chen
- Skeletal Diseases Program, Garvan Institute of Medical Research, Sydney, New South Wales 2010, Australia
| | - John A Eisman
- Skeletal Diseases Program, Garvan Institute of Medical Research, Sydney, New South Wales 2010, Australia
- Faculty of Medicine, UNSW Sydney, New South Wales 2052, Australia
- School of Medicine Sydney, University of Notre Dame Australia, Sydney 2010, Australia
| | | | - Peter Vestergaard
- Department of Clinical Medicine, Aalborg University, 9260 Aalborg, Denmark
- Department of Endocrinology, Aalborg University Hospital, 9000 Aalborg, Denmark
- Steno Diabetes Center, North Jutland, 9000 Aalborg, Denmark
| | - Tuan V Nguyen
- School of Biomedical Engineering, University of Technology Sydney, New South Wales 2007, Australia
- School of Medicine Sydney, University of Notre Dame Australia, Sydney 2010, Australia
- Tam Anh Research Center, Ho Chi Minh City 736090, Vietnam
| | - Robert D Blank
- Skeletal Diseases Program, Garvan Institute of Medical Research, Sydney, New South Wales 2010, Australia
| | - Jacqueline R Center
- Skeletal Diseases Program, Garvan Institute of Medical Research, Sydney, New South Wales 2010, Australia
- Faculty of Medicine, UNSW Sydney, New South Wales 2052, Australia
- School of Medicine Sydney, University of Notre Dame Australia, Sydney 2010, Australia
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Yang J, Zhao ML, Jiang LH, Zhang YW, Ma TT, Lou CR, Lu WF, Zhao Y, Lu Q. Association between single and multiple cardiometabolic diseases and all-cause mortality among Chinese older adults: A prospective, nationwide cohort study. Nutr Metab Cardiovasc Dis 2024:S0939-4753(24)00244-8. [PMID: 39098378 DOI: 10.1016/j.numecd.2024.06.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Revised: 06/13/2024] [Accepted: 06/21/2024] [Indexed: 08/06/2024]
Abstract
BACKGROUND AND AIM Cardiometabolic diseases (CMDs) are leading causes of death and disability, but little is known about the additive mortality effects of multiple CMDs. This study aimed to examine the association between single and multiple CMDs and all-cause mortality among older Chinese population. METHODS AND RESULTS Using the Chinese Longitudinal Healthy Longevity Survey (CLHLS) database, we analyzed data from 2008 to 2018 to assess the relationship between CMDs and mortality. Cox regression models estimated hazard ratios (HRs) and 95% confidence intervals (CIs) for single and multiple CMDs. At baseline, 11,351 participants (56.9% female) aged 60 years or older were included. 11.91% of participants had a single CMD, 1.51% had two CMDs, and 0.22% had three CMDs. Over a decade follow-up, 8992 deaths (79.2%) were recorded. A dose-response relationship was observed, with the mortality risk increasing by 17% for each additional disease. The fully-adjusted HRs for all-cause mortality were 1.16, 1.36, and 2.03 for one, two, and three CMDs, respectively. Larger effects of single and multiple CMDs were observed in the male group (P = 0.015) and the younger senior group (P < 0.001). CONCLUSIONS This large-scale study found that CMDs multiply mortality risks, especially in younger seniors and males. The risk is highest when heart disease and stroke coexist, and diabetes further increases it. Public health efforts should prioritize evidence-based management and prevention of CMDs.
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Affiliation(s)
- Jin Yang
- School of Nursing, Tianjin Medical University, Tianjin, 300070, China
| | - Mei-Li Zhao
- Neurology Department, The Second Hospital of Tianjin Medical University, 300211, Tianjin, China
| | - Li-Hong Jiang
- Neurology Department, Tianjin Huanhu Hospital, Tianjin, 300350, China
| | - Yan-Wen Zhang
- Cardiology Department, Tianjin Medical University General Hospital, Tianjin, 300070, China
| | - Ting-Ting Ma
- School of Nursing, Tianjin Medical University, Tianjin, 300070, China
| | - Chun-Rui Lou
- School of Nursing, Tianjin Medical University, Tianjin, 300070, China
| | - Wen-Feng Lu
- School of Nursing, Tianjin Medical University, Tianjin, 300070, China
| | - Yue Zhao
- School of Nursing, Tianjin Medical University, Tianjin, 300070, China; Joint Research Centre for Primary Health Care, The Hong Kong Polytechnic University, Hong Kong, 100872, China.
| | - Qi Lu
- School of Nursing, Tianjin Medical University, Tianjin, 300070, China.
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Mao D, Li G, Li Y, Wang S, Zhang M, Ma M, Ren X. Study on the Impact of Dietary Patterns on Cardiovascular Metabolic Comorbidities among Adults. RESEARCH SQUARE 2024:rs.3.rs-4451883. [PMID: 38883798 PMCID: PMC11177970 DOI: 10.21203/rs.3.rs-4451883/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2024]
Abstract
Background The prevalence of cardiovascular metabolic comorbidities (CMM) among adults is relatively high, imposing a heavy burden on individuals, families, and society. Dietary patterns play a significant role in the occurrence and development of CMM. This study aimed to identify the combined types of CMM in adult populations and investigate the impact of dietary patterns on CMM. Methods Participants in this study were from the sixth wave of the China Health and Nutrition Survey (CHNS). Dietary intake was assessed using a three-day 24-hour dietary recall method among 4,963 participants. Latent profile analysis was used to determine dietary pattern types. Two-step cluster analysis was performed to identify the combined types of CMM based on the participants' conditions of hyperuricemia, dyslipidemia, diabetes, renal dysfunction, hypertension, and stroke. Logistic regression analysis with robust standard errors was used to determine the impact of dietary patterns on CMM. Results Participants were clustered into three dietary patterns (Pattern 1 to 3) and five CMM types (Class I to V). Class I combined six diseases, with a low proportion of diabetes. Class II also combined six diseases but with a high proportion of diabetes. Class III combined four diseases, with a high proportion of hypertension. Class IV combined three diseases, with the highest proportions of hyperuricemia, diabetes, and renal dysfunction. Class V combined two diseases, with high proportions of dyslipidemia and renal dysfunction. Patients with Class III CMM had a significantly higher average age than the other four classes (P ≤ 0.05). Compared to those with isolated dyslipidemia, individuals with a low-grain, high-fruit, milk, and egg (LCHFM) dietary pattern had a higher risk of developing dyslipidemia combined with renal dysfunction (Class V CMM) with an odds ratio of 2.001 (95% CI 1.011-3.960, P≤ 0.05). Conclusion For individuals with isolated dyslipidemia, avoiding a low-grain, high-fruit, milk, and egg (LCHFM) dietary pattern may help reduce the risk of developing dyslipidemia combined with renal dysfunction (Class V CMM).
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Affiliation(s)
- Danhui Mao
- Third Hospital of Shanxi Medical University, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital
| | - Gongkui Li
- Third Hospital of Shanxi Medical University, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital
| | - Yajing Li
- Third Hospital of Shanxi Medical University, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital
| | | | | | | | - Xiaojun Ren
- Third Hospital of Shanxi Medical University, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital
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Zhao Y, Liu J, Xia JH, Li C, Ma XQ. Dose-response relationship between sleep duration and cardiovascular metabolic multimorbidity among older adults in China: A nationwide survey. J Affect Disord 2024; 354:75-81. [PMID: 38479505 DOI: 10.1016/j.jad.2024.03.051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 03/04/2024] [Accepted: 03/09/2024] [Indexed: 03/17/2024]
Abstract
AIMS AND OBJECTIVES The purpose of this study was to explore the relationship between the duration of sleep per day and cardiovascular metabolic multimorbidity (CMM) in older adults and to identify how many hours of sleep per day can lead to a lower risk of CMM in older adults. BACKGROUND CMM are a common syndrome in the older adults. There may be an association between sleep duration and CMM in older adults, with both insomnia and sleep deprivation having an impact on the health of older adults. Therefore, it is important to explore the possibility that older adults who sleep for a few hours per day may have a lower prevalence of CMM. METHODS The study included 9710 older adults. The sleep duration in this study was assessed by the question "How many hours of sleep do you currently get in a day? ". Older adults were defined as having CMM when they had two or more of the five categories of hypertension, diabetes, heart disease, stroke or cardiovascular disease, dyslipidemia. We used multivariate logistic regression analysis to explore the association among sleep duration and CMM. Restrictive cubic splines were used to examine the shape of the association among sleep duration and the CMM. The STROBE checklist was used for this cross-sectional study. RESULTS The mean age was 84.78 ± 11.73 years, with 55.5 % being female. Of the total sample, 21.3 % were CMM. When all covariates were adjusted, there was dose-response relationship between sleep duration and CMM. The dose-response relationship between CMM and sleep duration showed that older adults had a lower risk of cardiovascular and metabolic multimorbidity when they slept 9 h and 10 h per day. CONCLUSION With the increasing population of older adults, the number of older adults suffering from CMM continues to rise, and adequate sleep time can effectively prevent the occurrence of CMM. We should pay attention to the sleep problem of the older adults. RELEVANCE TO CLINICAL PRACTICE This study provided information for healthcare providers to identify circumstances that increase cardiovascular metabolic multimorbidity and suggest the appropriate sleep duration per day to reduce the risk of disease in older adults. PATIENT OR PUBLIC CONTRIBUTION Because of the public database data used in this study, all data were collected by survey agency personnel, so this section is not applicable to this study.
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Affiliation(s)
- Yu Zhao
- HanZhong Central Hospital, HanZhong, China
| | - Juan Liu
- HanZhong Central Hospital, HanZhong, China
| | | | - Cui Li
- HanZhong Central Hospital, HanZhong, China
| | - Xiu-Qin Ma
- HanZhong Central Hospital, HanZhong, China.
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Lleal M, Baré M, Herranz S, Orús J, Comet R, Jordana R, Baré M. Trajectories of chronic multimorbidity patterns in older patients: MTOP study. BMC Geriatr 2024; 24:475. [PMID: 38816787 PMCID: PMC11137950 DOI: 10.1186/s12877-024-04925-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 03/27/2024] [Indexed: 06/01/2024] Open
Abstract
BACKGROUND Multimorbidity is associated with negative results and poses difficulties in clinical management. New methodological approaches are emerging based on the hypothesis that chronic conditions are non-randomly associated forming multimorbidity patterns. However, there are few longitudinal studies of these patterns, which could allow for better preventive strategies and healthcare planning. The objective of the MTOP (Multimorbidity Trajectories in Older Patients) study is to identify patterns of chronic multimorbidity in a cohort of older patients and their progression and trajectories in the previous 10 years. METHODS A retrospective, observational study with a cohort of 3988 patients aged > 65 was conducted, including suspected and confirmed COVID-19 patients in the reference area of Parc Taulí University Hospital. Real-world data on socio-demographic and diagnostic variables were retrieved. Multimorbidity patterns of chronic conditions were identified with fuzzy c-means cluster analysis. Trajectories of each patient were established along three time points (baseline, 5 years before, 10 years before). Descriptive statistics were performed together with a stratification by sex and age group. RESULTS 3988 patients aged over 65 were included (58.9% females). Patients with ≥ 2 chronic conditions changed from 73.6 to 98.3% in the 10-year range of the study. Six clusters of chronic multimorbidity were identified 10 years before baseline, whereas five clusters were identified at both 5 years before and at baseline. Three clusters were consistently identified in all time points (Metabolic and vascular disease, Musculoskeletal and chronic pain syndrome, Unspecific); three clusters were only present at the earliest time point (Male-predominant diseases, Minor conditions and sensory impairment, Lipid metabolism disorders) and two clusters emerged 5 years before baseline and remained (Heart diseases and Neurocognitive). Sex and age stratification showed different distribution in cluster prevalence and trajectories. CONCLUSIONS In a cohort of older patients, we were able to identify multimorbidity patterns of chronic conditions and describe their individual trajectories in the previous 10 years. Our results suggest that taking these trajectories into consideration might improve decisions in clinical management and healthcare planning. TRIAL REGISTRATION NUMBER NCT05717309.
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Affiliation(s)
- Marina Lleal
- Clinical Epidemiology and Cancer Screening Department, Parc Taulí Hospital Universitari, Institut d'Investigació i Innovació Parc Taulí (I3PT-CERCA), Universitat Autònoma de Barcelona, Sabadell, Spain
- Department of Paediatrics, Obstetrics and Gynaecology, Preventive Medicine and Public Health, Autonomous University of Barcelona (UAB), Bellaterra, Spain
- Research Network on Chronicity, Primary Care and Health Promotion (RICAPPS), Instituto de Salud Carlos III, Madrid, Spain
| | - Montserrat Baré
- Creu Alta Primary Care Centre, Institut Català de la Salut, Sabadell, Spain
| | - Susana Herranz
- Acute Geriatric Unit, Centre Sociosanitari Albada, Parc Taulí Hospital Universitari, Institut d'Investigació i Innovació Parc Taulí (I3PT-CERCA), Universitat Autònoma de Barcelona, Sabadell, Spain
| | - Josefina Orús
- Cardiology Department, Parc Taulí Hospital Universitari, Institut d'Investigació i Innovació Parc Taulí (I3PT-CERCA), Universitat Autònoma de Barcelona, Sabadell, Spain
| | - Ricard Comet
- Acute Geriatric Unit, Centre Sociosanitari Albada, Parc Taulí Hospital Universitari, Institut d'Investigació i Innovació Parc Taulí (I3PT-CERCA), Universitat Autònoma de Barcelona, Sabadell, Spain
| | - Rosa Jordana
- Internal Medicine Department, Parc Taulí Hospital Universitari, Institut d'Investigació i Innovació Parc Taulí (I3PT-CERCA), Universitat Autònoma de Barcelona, Sabadell, Spain
| | - Marisa Baré
- Clinical Epidemiology and Cancer Screening Department, Parc Taulí Hospital Universitari, Institut d'Investigació i Innovació Parc Taulí (I3PT-CERCA), Universitat Autònoma de Barcelona, Sabadell, Spain.
- Research Network on Chronicity, Primary Care and Health Promotion (RICAPPS), Instituto de Salud Carlos III, Madrid, Spain.
- Can Rull- Can Llong Primary Care Centre, Parc Taulí Hospital Universitari, Institut d'Investigació i Innovació Parc Taulí (I3PT-CERCA), Universitat Autònoma de Barcelona, Sabadell, Spain.
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Beaney T, Clarke J, Salman D, Woodcock T, Majeed A, Aylin P, Barahona M. Identifying multi-resolution clusters of diseases in ten million patients with multimorbidity in primary care in England. COMMUNICATIONS MEDICINE 2024; 4:102. [PMID: 38811835 PMCID: PMC11137021 DOI: 10.1038/s43856-024-00529-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Accepted: 05/20/2024] [Indexed: 05/31/2024] Open
Abstract
BACKGROUND Identifying clusters of diseases may aid understanding of shared aetiology, management of co-morbidities, and the discovery of new disease associations. Our study aims to identify disease clusters using a large set of long-term conditions and comparing methods that use the co-occurrence of diseases versus methods that use the sequence of disease development in a person over time. METHODS We use electronic health records from over ten million people with multimorbidity registered to primary care in England. First, we extract data-driven representations of 212 diseases from patient records employing (i) co-occurrence-based methods and (ii) sequence-based natural language processing methods. Second, we apply the graph-based Markov Multiscale Community Detection (MMCD) to identify clusters based on disease similarity at multiple resolutions. We evaluate the representations and clusters using a clinically curated set of 253 known disease association pairs, and qualitatively assess the interpretability of the clusters. RESULTS Both co-occurrence and sequence-based algorithms generate interpretable disease representations, with the best performance from the skip-gram algorithm. MMCD outperforms k-means and hierarchical clustering in explaining known disease associations. We find that diseases display an almost-hierarchical structure across resolutions from closely to more loosely similar co-occurrence patterns and identify interpretable clusters corresponding to both established and novel patterns. CONCLUSIONS Our method provides a tool for clustering diseases at different levels of resolution from co-occurrence patterns in high-dimensional electronic health records, which could be used to facilitate discovery of associations between diseases in the future.
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Affiliation(s)
- Thomas Beaney
- Department of Primary Care and Public Health, Imperial College London, London, W6 8RP, UK.
- Department of Mathematics, Imperial College London, London, SW7 2AZ, UK.
| | - Jonathan Clarke
- Department of Mathematics, Imperial College London, London, SW7 2AZ, UK
| | - David Salman
- Department of Primary Care and Public Health, Imperial College London, London, W6 8RP, UK
- MSk Lab, Department of Surgery and Cancer, Imperial College London, London, W12 0BZ, UK
| | - Thomas Woodcock
- Department of Primary Care and Public Health, Imperial College London, London, W6 8RP, UK
| | - Azeem Majeed
- Department of Primary Care and Public Health, Imperial College London, London, W6 8RP, UK
| | - Paul Aylin
- Department of Primary Care and Public Health, Imperial College London, London, W6 8RP, UK
| | - Mauricio Barahona
- Department of Mathematics, Imperial College London, London, SW7 2AZ, UK
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12
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Lu H, Dong XX, Li DL, Nie XY, Wang P, Pan CW. Multimorbidity patterns and health-related quality of life among community-dwelling older adults: evidence from a rural town in Suzhou, China. Qual Life Res 2024; 33:1335-1346. [PMID: 38353890 DOI: 10.1007/s11136-024-03608-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/11/2024] [Indexed: 04/26/2024]
Abstract
PURPOSE The high prevalence of multimorbidity in aging societies has posed tremendous challenges to the healthcare system. The aim of our study was to comprehensively assess the association of multimorbidity patterns and health-related quality of life (HRQOL) among rural Chinese older adults. METHODS This was a cross-sectional study. Data from 4,579 community-dwelling older adults aged 60 years and above was collected by the clinical examination and questionnaire survey. Information on 10 chronic conditions was collected and the 3-Level EQ-5D (EQ-5D-3L) was adopted to measure the HRQOL of older adults. An exploratory factor analysis was performed to determine multimorbidity patterns. Regression models were fitted to explore the associations of multimorbidity patterns with specific health dimensions and overall HRQOL. RESULTS A total of 2,503 (54.7%) participants suffered from multimorbidity, and they reported lower HRQOL compared to those without multimorbidity. Three kinds of multimorbidity patterns were identified including cardiovascular-metabolic diseases, psycho-cognitive diseases and organic diseases. The associations between psycho-cognitive diseases/organic diseases and overall HRQOL assessed by EQ-5D-3L index score were found to be significant (β = - 0.097, 95% CI - 0.110, - 0.084; β = - 0.030, 95% CI - 0.038, - 0.021, respectively), and psycho-cognitive diseases affected more health dimensions. The impact of cardiovascular-metabolic diseases on HRQOL was largely non-significant. CONCLUSION Multimorbidity was negatively associated with HRQOL among older adults from rural China. The presence of the psycho-cognitive diseases pattern or the organic diseases pattern contributed to worse HRQOL. The remarkable negative impact of psycho-cognitive diseases on HRQOL necessiates more attention and relevant medical assistance to older rural adults.
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Affiliation(s)
- Heng Lu
- School of Public Health, Suzhou Medical College of Soochow University, Suzhou, China
| | - Xing-Xuan Dong
- School of Public Health, Suzhou Medical College of Soochow University, Suzhou, China
| | - Dan-Lin Li
- School of Public Health, Suzhou Medical College of Soochow University, Suzhou, China
| | - Xin-Yi Nie
- School of Public Health, Suzhou Medical College of Soochow University, Suzhou, China
| | - Pei Wang
- School of Public Health, Fudan University, Shanghai, China.
- Key Lab of Health Technology Assessment, National Health Commission of the People's Republic of China (Fudan University), Shanghai, China.
| | - Chen-Wei Pan
- School of Public Health, Suzhou Medical College of Soochow University, Suzhou, China.
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13
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He Q, Wang Y, Feng Z, Chu J, Li T, Hu W, Chen X, Han Q, Sun N, Liu S, Sun M, Sun H, Shen Y. Visceral adiposity associated with incidence and development trajectory of cardiometabolic diseases: A prospective cohort study. Nutr Metab Cardiovasc Dis 2024; 34:1235-1244. [PMID: 38331642 DOI: 10.1016/j.numecd.2023.12.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Revised: 12/19/2023] [Accepted: 12/25/2023] [Indexed: 02/10/2024]
Abstract
BACKGROUND AND AIMS There is a lack of literature concerning the effects of visceral adipose on the development of first cardiometabolic disease (FCMD) and its subsequent progression to cardiometabolic multimorbidity (CMM) and mortality. METHODS AND RESULTS 423,934 participants from the UK Biobank with different baseline disease conditions were included in the analysis. CMM was defined as the simultaneous presence of coronary heart disease, T2D, and stroke. Visceral adiposity was estimated by calculating the visceral adiposity index (VAI). Multistate models were used to assess the effect of visceral adiposity on the development of CMM. During a median follow-up of 13.5 years, 50,589 patients had at least one CMD, 6131 were diagnosed with CMM, whereas 24,634 patients died. We observed distinct roles of VAI with respect to different disease transitions of CMM. HRs (95 % CIs) of high VAI were 2.35 (2.29-2.42) and 1.64 (1.50-1.79) for transitions from healthy to FCMD and from FCMD to CMM, and 0.97 (0.93-1.02) for all-cause mortality risk from healthy, FCMD and CMM, respectively. CONCLUSIONS Our study provides the first evidence that visceral adipose may contribute to the development of FCMD and CMM in healthy participants. However, visceral adipose may confer resistance to all-cause mortality in participants with existing CMD or CMM. A better understanding of the relationship between visceral adipose and CMM can focalize further investigations on patients with CMD with high levels of visceral fat and help take targeted preventive measures to reduce the medical burden on individual patients and society.
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Affiliation(s)
- Qida He
- Department of Epidemiology and Biostatistics, School of Public Health, Medical College of Soochow University, 199 Renai Road, Suzhou City, Jiangsu Province, PR China
| | - Yu Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Medical College of Soochow University, 199 Renai Road, Suzhou City, Jiangsu Province, PR China
| | - Zhaolong Feng
- Department of Epidemiology and Biostatistics, School of Public Health, Medical College of Soochow University, 199 Renai Road, Suzhou City, Jiangsu Province, PR China
| | - Jiadong Chu
- Department of Epidemiology and Biostatistics, School of Public Health, Medical College of Soochow University, 199 Renai Road, Suzhou City, Jiangsu Province, PR China
| | - Tongxing Li
- Department of Epidemiology and Biostatistics, School of Public Health, Medical College of Soochow University, 199 Renai Road, Suzhou City, Jiangsu Province, PR China
| | - Wei Hu
- Department of Epidemiology and Biostatistics, School of Public Health, Medical College of Soochow University, 199 Renai Road, Suzhou City, Jiangsu Province, PR China
| | - Xuanli Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Medical College of Soochow University, 199 Renai Road, Suzhou City, Jiangsu Province, PR China
| | - Qiang Han
- Department of Epidemiology and Biostatistics, School of Public Health, Medical College of Soochow University, 199 Renai Road, Suzhou City, Jiangsu Province, PR China
| | - Na Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Medical College of Soochow University, 199 Renai Road, Suzhou City, Jiangsu Province, PR China
| | - Siyuan Liu
- School of Health Management, Southern Medical University, No.1023 1063 Shatai Road, Baiyun District, Guangzhou City, Guangdong Province, PR China
| | - Mengtong Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Medical College of Soochow University, 199 Renai Road, Suzhou City, Jiangsu Province, PR China
| | - Hongpeng Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Medical College of Soochow University, 199 Renai Road, Suzhou City, Jiangsu Province, PR China.
| | - Yueping Shen
- Department of Epidemiology and Biostatistics, School of Public Health, Medical College of Soochow University, 199 Renai Road, Suzhou City, Jiangsu Province, PR China.
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Xia X, Chen S, Tian X, Xu Q, Zhang Y, Zhang X, Li J, Wang P, Wu S, Wang A. Association of body mass index with risk of cardiometabolic disease, multimorbidity and mortality: a multi-state analysis based on the Kailuan cohort. Endocrine 2024; 84:355-364. [PMID: 37878230 DOI: 10.1007/s12020-023-03570-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Accepted: 10/10/2023] [Indexed: 10/26/2023]
Abstract
PURPOSE To evaluate the association of body mass index (BMI) with risk of first cardiometabolic disease (FCMD), cardiometabolic multimorbidity (CMM) and death. METHODS 87,512 participants free of CMD were included from the Kailuan cohort, which was established during 2006-2007 and followed up until 2020. BMI was classified as underweight ( < 18.5 kg/m2), healthy weight (18.5-23.9 kg/m2), overweight (24.0-27.9 kg/m2), mildly obese (28.0-31.9 kg/m2), and severely obese ( ≥ 32.0 kg/m2). FCMD was defined as the first onset of diabetes, heart disease, or stroke, and CMM as the coexistence of at least two CMD. The hazard ratio (HR) and 95% confidence interval (95%CI) were estimated with multi-state models. RESULTS 20,577 participants developed FCMD, 2232 developed CMM afterwards, and 10,191 died. Individuals with higher BMI was more likely to develop FCMD and CMM. Compared with healthy weight, the HR (95%CI) of severe obesity for transition from health to FCMD and from FCMD to CMM was 3.12 (2.91, 3.34) and 1.92 (1.60, 2.31), respectively. On the other hand, underweight was consistently associated with higher mortality risk regardless of initial status, whereas severe obesity was only related to increased risk for transition from health to death (HR: 1.36; 95%CI: 1.17, 1.56) but not for transition from FCMD (HR: 0.70; 95%CI: 0.57, 0.87) or CMM (HR: 0.80; 95%CI: 0.54, 1.19) to death. CONCLUSION Our findings highlighted the importance of maintaining healthy weight for primary and secondary prevention of CMD and reflected the demand for more accurate measurement and comprehensive management of obesity for CMD patients.
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Affiliation(s)
- Xue Xia
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
- China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
| | - Shuohua Chen
- Department of Cardiology, Kailuan General Hospital, North China University of Science and Technology, Tangshan, 063000, Hebei, China
| | - Xue Tian
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
- China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, 100069, China
| | - Qin Xu
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
- China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
| | - Yijun Zhang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
- China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, 100069, China
| | - Xiaoli Zhang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
- China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
| | - Jing Li
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
- China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
| | - Penglian Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
- China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
| | - Shouling Wu
- Department of Cardiology, Kailuan General Hospital, North China University of Science and Technology, Tangshan, 063000, Hebei, China.
| | - Anxin Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China.
- China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China.
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Stockmarr A, Frølich A. Clusters from chronic conditions in the Danish adult population. PLoS One 2024; 19:e0302535. [PMID: 38687772 PMCID: PMC11060538 DOI: 10.1371/journal.pone.0302535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 04/09/2024] [Indexed: 05/02/2024] Open
Abstract
Multimorbidity, the presence of 2 or more chronic conditions in a person at the same time, is an increasing public health concern, which affects individuals through reduced health related quality of life, and society through increased need for healthcare services. Yet the structure of chronic conditions in individuals with multimorbidity, viewed as a population, is largely unmapped. We use algorithmic diagnoses and the K-means algorithm to cluster the entire 2015 Danish multimorbidity population into 5 clusters. The study introduces the concept of rim data as an additional tool for determining the number of clusters. We label the 5 clusters the Allergies, Chronic Heart Conditions, Diabetes, Hypercholesterolemia, and Musculoskeletal and Psychiatric Conditions clusters, and demonstrate that for 99.32% of the population, the cluster allocation can be determined from the diagnoses of 4-5 conditions. Clusters are characterized through most prevalent conditions, absent conditions, over- or under-represented conditions, and co-occurrence of conditions. Clusters are further characterized through socioeconomic variables and healthcare service utilizations. Additionally, geographical variations throughout Denmark are studied at the regional and municipality level. We find that subdivision into municipality levels suggests that the Allergies cluster frequency is positively associated with socioeconomic status, while the subdivision suggests that frequencies for clusters Diabetes and Hypercholesterolemia are negatively correlated with socioeconomic status. We detect no indication of association to socioeconomic status for the Chronic Heart Conditions cluster and the Musculoskeletal and Psychiatric Conditions cluster. Additional spatial variation is revealed, some of which may be related to urban/rural populations. Our work constitutes a step in the process of characterizing multimorbidity populations, leading to increased comprehension of the nature of multimorbidity, and towards potential applications to individual-based care, prevention, the development of clinical guidelines, and population management.
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Affiliation(s)
- Anders Stockmarr
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
| | - Anne Frølich
- Innovation and Research Centre for Multimorbidity, Slagelse Hospital, Slagelse, Denmark
- Department of Public Health, University of Copenhagen, Copenhagen, Denmark
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Lee MS, Lee H. Chronic Disease Patterns and Their Relationship With Health-Related Quality of Life in South Korean Older Adults With the 2021 Korean National Health and Nutrition Examination Survey: Latent Class Analysis. JMIR Public Health Surveill 2024; 10:e49433. [PMID: 38598275 PMCID: PMC11043926 DOI: 10.2196/49433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 01/03/2024] [Accepted: 03/04/2024] [Indexed: 04/11/2024] Open
Abstract
BACKGROUND Improved life expectancy has increased the prevalence of older adults living with multimorbidities, which likely deteriorates their health-related quality of life (HRQoL). Understanding which chronic conditions frequently co-occur can facilitate person-centered care tailored to the needs of individuals with specific multimorbidity profiles. OBJECTIVE The study objectives were to (1) examine the prevalence of multimorbidity among Korean older adults (ie, those aged 65 years and older), (2) investigate chronic disease patterns using latent class analysis, and (3) assess which chronic disease patterns are more strongly associated with HRQoL. METHODS A sample of 1806 individuals aged 65 years and older from the 2021 Korean National Health and Nutrition Examination Survey was analyzed. Latent class analysis was conducted to identify the clustering pattern of chronic diseases. HRQoL was assessed by an 8-item health-related quality of life scale (HINT-8). Multiple linear regression was used to analyze the association with the total score of the HINT-8. Logistic regression analysis was performed to evaluate the odds ratio of having problems according to the HINT-8 items. RESULTS The prevalence of multimorbidity in the sample was 54.8%. Three chronic disease patterns were identified: relatively healthy, cardiometabolic condition, arthritis, allergy, or asthma. The total scores of the HINT-8 were the highest in participants characterized as arthritis, allergy, or asthma group, indicating the lowest quality of life. CONCLUSIONS Current health care models are disease-oriented, meaning that the management of chronic conditions applies to a single condition and may not be relevant to those with multimorbidities. Identifying chronic disease patterns and their impact on overall health and well-being is critical for guiding integrated care.
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Affiliation(s)
- Mi-Sun Lee
- Department of Preventive Medicine, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Hooyeon Lee
- Department of Preventive Medicine, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
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Mindlis I, Revenson TA. Above and Beyond Number of Illnesses: A Two-Sample Replication of Current Approaches to Depressive Symptoms in Multimorbidity. Clin Gerontol 2024:1-10. [PMID: 38431827 DOI: 10.1080/07317115.2024.2324323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/05/2024]
Abstract
OBJECTIVES To expand current models of depressive symptoms in older adults with multimorbidity (MM) beyond the number of illnesses as a predictor of worsened mental health. METHODS Two-sample replication study of adults ≥62 years old with ≥ two chronic illnesses, who completed validated questionnaires assessing depressive symptoms, and disease- and treatment-related stressors. Data were analyzed using hierarchical linear regression. RESULTS The model of cumulative number of illnesses was worse at explaining variance in depressive symptoms (Sample 1 R2 = .035; Sample 2 R2 = .029), compared to models including disease- and treatment-related stressors (Sample 1 R2 = .37; Sample 2 R2 = .47). Disease-related stressors were the strongest factor associated with depressive symptoms, specifically, poor subjective cognitive function (Sample 1: b = -.202, p = .013; Sample 2: b = -.288, p < .001) and greater somatic symptoms (b = .455, p < .001; Sample 2: b = .355, p < .001). CONCLUSIONS Using the number of illnesses to understand depressive symptoms in MM is a limited approach. Models that move beyond descriptive relationships between MM and depressive symptoms are needed. CLINICAL IMPLICATIONS Providers should consider the role of somatic symptom management in patients with MM and depressive symptoms.
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Affiliation(s)
- Irina Mindlis
- Weill Cornell Medicine, Division of Geriatrics and Palliative Medicine, New York, New York, USA
| | - Tracey A Revenson
- Psychology, Hunter College and The Graduate Center, City University of New York, New York, New York, USA
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18
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Zhang X, Yu SL, Qi LM, Xia LN, Yang QT. Association of educational attainment with hypertension and type-2 diabetes: A Mendelian randomization study. SSM Popul Health 2024; 25:101585. [PMID: 38283548 PMCID: PMC10821170 DOI: 10.1016/j.ssmph.2023.101585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 11/17/2023] [Accepted: 12/13/2023] [Indexed: 01/30/2024] Open
Abstract
BACKGROUNDDue to the long time interval between exposure and outcome, it is difficult to infer the causal relationship between educational attainment (EA) and common chronic diseases. Therefore, we utilized Mendelian randomization (MR) to predict the causal relationships of EA with hypertension and type-2 diabetes (T2DM). METHODSA two-sample MR analysis was conducted using genome-wide association studies (GWASs) combined with inferential measurements. A GWAS meta-analysis including 1,131,881 European individuals was used to identify instruments for EA. Hypertension and T2DM data were obtained from a Finnish database. MR analyses were performed using inverse-variance weighted meta-analysis (IVW), weighted median regression, MR‒Egger regression, simple mode regression, weighted mode regression and the MR-Pleiotropy RESidual Sum and Outlier test. Sensitivity analyses were further performed using the leave-one-out method to test the robustness of our findings. RESULTSUsing the MR approach, our results showed that EA was significantly associated with a reduced risk of hypertension (OR = 0.63; P = 2.94 × 10-47; [95% CI: 0.59, 0.67]) and type-2 diabetes (OR = 0.59; P = 1.25 × 10-16; [95% CI: 0.52, 0.67]). CONCLUSIONSThis study showed that EA is causally linked to the risk of chronic diseases, including high blood pressure and T2DM.
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Affiliation(s)
- Xin Zhang
- Rehabilitation Traditional Chinese Medicine Department, Nanping First Hospital Affiliated to Fujian Medical University, Nanping, Fujian, 353000, China
| | - Shi-liang Yu
- Rehabilitation Traditional Chinese Medicine Department, Nanping First Hospital Affiliated to Fujian Medical University, Nanping, Fujian, 353000, China
| | - Lu-ming Qi
- School of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, 610075, China
| | - Li-na Xia
- School of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, 610075, China
- State Administration of Traditional Chinese Medicine Key Laboratory of Traditional Chinese Medicine, Regimen and Health, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, 610075, China
| | - Qing-tang Yang
- Rehabilitation Traditional Chinese Medicine Department, Nanping First Hospital Affiliated to Fujian Medical University, Nanping, Fujian, 353000, China
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19
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Head A, O'Flaherty M, Kypridemos C. Multimorbidity research: where one size does not fit all. BMJ MEDICINE 2024; 3:e000855. [PMID: 38440404 PMCID: PMC10910389 DOI: 10.1136/bmjmed-2024-000855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Accepted: 02/19/2024] [Indexed: 03/06/2024]
Affiliation(s)
- Anna Head
- Department of Public Health, Policy, and Systems, University of Liverpool, Liverpool, UK
| | - Martin O'Flaherty
- Department of Public Health, Policy, and Systems, University of Liverpool, Liverpool, UK
| | - Chris Kypridemos
- Department of Public Health, Policy, and Systems, University of Liverpool, Liverpool, UK
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20
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Li QY, Hu HY, Zhang GW, Hu H, Ou YN, Huang LY, Wang AY, Gao PY, Ma LY, Tan L, Yu JT. Associations between cardiometabolic multimorbidity and cerebrospinal fluid biomarkers of Alzheimer's disease pathology in cognitively intact adults: the CABLE study. Alzheimers Res Ther 2024; 16:28. [PMID: 38321520 PMCID: PMC10848421 DOI: 10.1186/s13195-024-01396-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 01/21/2024] [Indexed: 02/08/2024]
Abstract
BACKGROUND Cardiometabolic multimorbidity is associated with an increased risk of dementia, but the pathogenic mechanisms linking them remain largely undefined. We aimed to assess the associations of cardiometabolic multimorbidity with cerebrospinal fluid (CSF) biomarkers of Alzheimer's disease (AD) pathology to enhance our understanding of the underlying mechanisms linking cardiometabolic multimorbidity and AD. METHODS This study included 1464 cognitively intact participants from the Chinese Alzheimer's Biomarker and LifestylE (CABLE) database. Cardiometabolic diseases (CMD) are a group of interrelated disorders such as hypertension, diabetes, heart diseases (HD), and stroke. Based on the CMD status, participants were categorized as CMD-free, single CMD, or CMD multimorbidity. CMD multimorbidity is defined as the coexistence of two or more CMDs. The associations of cardiometabolic multimorbidity and CSF biomarkers were examined using multivariable linear regression models with demographic characteristics, the APOE ε4 allele, and lifestyle factors as covariates. Subgroup analyses stratified by age, sex, and APOE ε4 status were also performed. RESULTS A total of 1464 individuals (mean age, 61.80 years; age range, 40-89 years) were included. The markers of phosphorylated tau-related processes (CSF P-tau181: β = 0.165, P = 0.037) and neuronal injury (CSF T-tau: β = 0.065, P = 0.033) were significantly increased in subjects with CMD multimorbidity (versus CMD-free), but not in those with single CMD. The association between CMD multimorbidity with CSF T-tau levels remained significant after controlling for Aβ42 levels. Additionally, significantly elevated tau-related biomarkers were observed in patients with specific CMD combinations (i.e., hypertension and diabetes, hypertension and HD), especially in long disease courses. CONCLUSIONS The presence of cardiometabolic multimorbidity was associated with tau phosphorylation and neuronal injury in cognitively normal populations. CMD multimorbidity might be a potential independent target to alleviate tau-related pathologies that can cause cognitive impairment.
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Affiliation(s)
- Qiong-Yao Li
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, No.5 Donghai Middle Road, Qingdao, China
| | - He-Ying Hu
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, No.5 Donghai Middle Road, Qingdao, China
| | - Gao-Wen Zhang
- Department of Thoracic Surgery, The Fourth Affiliated Hospital of China Medical University, Shenyang, China
| | - Hao Hu
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, No.5 Donghai Middle Road, Qingdao, China
| | - Ya-Nan Ou
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, No.5 Donghai Middle Road, Qingdao, China
| | - Liang-Yu Huang
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, No.5 Donghai Middle Road, Qingdao, China
| | - An-Yi Wang
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, No.5 Donghai Middle Road, Qingdao, China
| | - Pei-Yang Gao
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, No.5 Donghai Middle Road, Qingdao, China
| | - Li-Yun Ma
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, No.5 Donghai Middle Road, Qingdao, China
| | - Lan Tan
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, No.5 Donghai Middle Road, Qingdao, China.
| | - Jin-Tai Yu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, No. 12 Wulumuqi Road, Shanghai, China.
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21
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Seghieri C, Tortù C, Tricò D, Leonetti S. Learning prevalent patterns of co-morbidities in multichronic patients using population-based healthcare data. Sci Rep 2024; 14:2186. [PMID: 38272953 PMCID: PMC10810806 DOI: 10.1038/s41598-024-51249-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 01/02/2024] [Indexed: 01/27/2024] Open
Abstract
The prevalence of longstanding chronic diseases has increased worldwide, along with the average age of the population. As a result, an increasing number of people is affected by two or more chronic conditions simultaneously, and healthcare systems are facing the challenge of treating multimorbid patients effectively. Current therapeutic strategies are suited to manage each chronic condition separately, without considering the whole clinical condition of the patient. This approach may lead to suboptimal clinical outcomes and system inefficiencies (e.g. redundant diagnostic tests and inadequate drug prescriptions). We develop a novel methodology based on the joint implementation of data reduction and clustering algorithms to identify patterns of chronic diseases that are likely to co-occur in multichronic patients. We analyse data from a large adult population of multichronic patients living in Tuscany (Italy) in 2019 which was stratified by sex and age classes. Results demonstrate that (i) cardio-metabolic, endocrine, and neuro-degenerative diseases represent a stable pattern of multimorbidity, and (ii) disease prevalence and clustering vary across ages and between women and men. Identifying the most common multichronic profiles can help tailor medical protocols to patients' needs and reduce costs. Furthermore, analysing temporal patterns of disease can refine risk predictions for evolutive chronic conditions.
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Affiliation(s)
- Chiara Seghieri
- Management and Healthcare Laboratory, Institute of Management and Department EMbeDS, Sant'Anna School of Advanced Studies, Piazza Martiri della Libertà 33, 56127, Pisa, Italy
| | - Costanza Tortù
- Management and Healthcare Laboratory, Institute of Management and Department EMbeDS, Sant'Anna School of Advanced Studies, Piazza Martiri della Libertà 33, 56127, Pisa, Italy
| | - Domenico Tricò
- Department of Clinical and Experimental Medicine, University of Pisa, Via Roma 67, 56126, Pisa, Italy
| | - Simone Leonetti
- Management and Healthcare Laboratory, Interdisciplinary Research Center "Health Science", Sant'Anna School of Advanced Studies, Piazza Martiri della Libertà 33, 56127, Pisa, Italy.
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22
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Beridze G, Abbadi A, Ars J, Remelli F, Vetrano DL, Trevisan C, Pérez LM, López-Rodríguez JA, Calderón-Larrañaga A. Patterns of multimorbidity in primary care electronic health records: A systematic review. JOURNAL OF MULTIMORBIDITY AND COMORBIDITY 2024; 14:26335565231223350. [PMID: 38298757 PMCID: PMC10829499 DOI: 10.1177/26335565231223350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 12/12/2023] [Indexed: 02/02/2024]
Abstract
Background Multimorbidity, the coexistence of multiple chronic conditions in an individual, is a complex phenomenon that is highly prevalent in primary care settings, particularly in older individuals. This systematic review summarises the current evidence on multimorbidity patterns identified in primary care electronic health record (EHR) data. Methods Three databases were searched from inception to April 2022 to identify studies that derived original multimorbidity patterns from primary care EHR data. The quality of the included studies was assessed using a modified version of the Newcastle-Ottawa Quality Assessment Scale. Results Sixteen studies were included in this systematic review, none of which was of low quality. Most studies were conducted in Spain, and only one study was conducted outside of Europe. The prevalence of multimorbidity (i.e. two or more conditions) ranged from 14.0% to 93.9%. The most common stratification variable in disease clustering models was sex, followed by age and calendar year. Despite significant heterogeneity in clustering methods and disease classification tools, consistent patterns of multimorbidity emerged. Mental health and cardiovascular patterns were identified in all studies, often in combination with diseases of other organ systems (e.g. neurological, endocrine). Discussion These findings emphasise the frequent coexistence of physical and mental health conditions in primary care, and provide useful information for the development of targeted preventive and management strategies. Future research should explore mechanisms underlying multimorbidity patterns, prioritise methodological harmonisation to facilitate the comparability of findings, and promote the use of EHR data globally to enhance our understanding of multimorbidity in more diverse populations.
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Affiliation(s)
- Giorgi Beridze
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Aging Research Center, Stockholm, Sweden
| | - Ahmad Abbadi
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Aging Research Center, Stockholm, Sweden
| | - Joan Ars
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Aging Research Center, Stockholm, Sweden
- RE-FiT Barcelona Research group, Vall d'Hebron Institute of Research (VHIR) and Parc Sanitari Pere Virgili, Barcelona, Spain
- Medicine Department, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Francesca Remelli
- Department of Medical Sciences, University of Ferrara, Ferrara, Italy
| | - Davide L Vetrano
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Aging Research Center, Stockholm, Sweden
- Stockholm Gerontology Research Center, Stockholm, Sweden
| | - Caterina Trevisan
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Aging Research Center, Stockholm, Sweden
- Department of Medical Sciences, University of Ferrara, Ferrara, Italy
| | - Laura-Mónica Pérez
- RE-FiT Barcelona Research group, Vall d'Hebron Institute of Research (VHIR) and Parc Sanitari Pere Virgili, Barcelona, Spain
| | - Juan A López-Rodríguez
- Research Unit, Primary Health Care Management, Madrid, Spain
- Department of Medical Specialties and Public Health, Faculty of Health Sciences Rey Juan Carlos University, Madrid, Spain
- Research Network on Chronicity, Primary Care and Health Promotion (RICAPPS), Carlos III Health Institute, Madrid, Spain
| | - Amaia Calderón-Larrañaga
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Aging Research Center, Stockholm, Sweden
- Stockholm Gerontology Research Center, Stockholm, Sweden
- Research Network on Chronicity, Primary Care and Health Promotion (RICAPPS), Carlos III Health Institute, Madrid, Spain
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Beaney T, Clarke J, Salman D, Woodcock T, Majeed A, Barahona M, Aylin P. Assigning disease clusters to people: A cohort study of the implications for understanding health outcomes in people with multiple long-term conditions. JOURNAL OF MULTIMORBIDITY AND COMORBIDITY 2024; 14:26335565241247430. [PMID: 38638408 PMCID: PMC11025432 DOI: 10.1177/26335565241247430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Accepted: 03/25/2024] [Indexed: 04/20/2024]
Abstract
Background Identifying clusters of co-occurring diseases may help characterise distinct phenotypes of Multiple Long-Term Conditions (MLTC). Understanding the associations of disease clusters with health-related outcomes requires a strategy to assign clusters to people, but it is unclear how the performance of strategies compare. Aims First, to compare the performance of methods of assigning disease clusters to people at explaining mortality, emergency department attendances and hospital admissions over one year. Second, to identify the extent of variation in the associations with each outcome between and within clusters. Methods We conducted a cohort study of primary care electronic health records in England, including adults with MLTC. Seven strategies were tested to assign patients to fifteen disease clusters representing 212 LTCs, identified from our previous work. We tested the performance of each strategy at explaining associations with the three outcomes over 1 year using logistic regression and compared to a strategy using the individual LTCs. Results 6,286,233 patients with MLTC were included. Of the seven strategies tested, a strategy assigning the count of conditions within each cluster performed best at explaining all three outcomes but was inferior to using information on the individual LTCs. There was a larger range of effect sizes for the individual LTCs within the same cluster than there was between the clusters. Conclusion Strategies of assigning clusters of co-occurring diseases to people were less effective at explaining health-related outcomes than a person's individual diseases. Furthermore, clusters did not represent consistent relationships of the LTCs within them, which might limit their application in clinical research.
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Affiliation(s)
- Thomas Beaney
- Department of Primary Care and Public Health, Imperial College London, London, UK
- Centre for Mathematics of Precision Healthcare, Department of Mathematics, Imperial College London, London, UK
| | - Jonathan Clarke
- Centre for Mathematics of Precision Healthcare, Department of Mathematics, Imperial College London, London, UK
| | - David Salman
- Department of Primary Care and Public Health, Imperial College London, London, UK
| | - Thomas Woodcock
- Department of Primary Care and Public Health, Imperial College London, London, UK
| | - Azeem Majeed
- Department of Primary Care and Public Health, Imperial College London, London, UK
| | - Mauricio Barahona
- Centre for Mathematics of Precision Healthcare, Department of Mathematics, Imperial College London, London, UK
| | - Paul Aylin
- Department of Primary Care and Public Health, Imperial College London, London, UK
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24
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Dhafari TB, Pate A, Azadbakht N, Bailey R, Rafferty J, Jalali-Najafabadi F, Martin GP, Hassaine A, Akbari A, Lyons J, Watkins A, Lyons RA, Peek N. A scoping review finds a growing trend in studies validating multimorbidity patterns and identifies five broad types of validation methods. J Clin Epidemiol 2024; 165:111214. [PMID: 37952700 DOI: 10.1016/j.jclinepi.2023.11.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 10/14/2023] [Accepted: 11/05/2023] [Indexed: 11/14/2023]
Abstract
OBJECTIVES Multimorbidity, the presence of two or more long-term conditions, is a growing public health concern. Many studies use analytical methods to discover multimorbidity patterns from data. We aimed to review approaches used in published literature to validate these patterns. STUDY DESIGN AND SETTING We systematically searched PubMed and Web of Science for studies published between July 2017 and July 2023 that used analytical methods to discover multimorbidity patterns. RESULTS Out of 31,617 studies returned by the searches, 172 were included. Of these, 111 studies (64%) conducted validation, the number of studies with validation increased from 53.13% (17 out of 32 studies) to 71.25% (57 out of 80 studies) in 2017-2019 to 2022-2023, respectively. Five types of validation were identified: assessing the association of multimorbidity patterns with clinical outcomes (n = 79), stability across subsamples (n = 26), clinical plausibility (n = 22), stability across methods (n = 7) and exploring common determinants (n = 2). Some studies used multiple types of validation. CONCLUSION The number of studies conducting a validation of multimorbidity patterns is clearly increasing. The most popular validation approach is assessing the association of multimorbidity patterns with clinical outcomes. Methodological guidance on the validation of multimorbidity patterns is needed.
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Affiliation(s)
- Thamer Ba Dhafari
- Division of Informatics, Imaging & Data Sciences, School of Health Sciences, The University of Manchester, M13 9PL Manchester, UK
| | - Alexander Pate
- Division of Informatics, Imaging & Data Sciences, School of Health Sciences, The University of Manchester, M13 9PL Manchester, UK
| | - Narges Azadbakht
- Division of Informatics, Imaging & Data Sciences, School of Health Sciences, The University of Manchester, M13 9PL Manchester, UK
| | - Rowena Bailey
- Population Data Science, Swansea University Medical School, Faculty of Medicine, Health & Life Science, Swansea University, Singleton Park, SA2 8PP Swansea, UK
| | - James Rafferty
- Population Data Science, Swansea University Medical School, Faculty of Medicine, Health & Life Science, Swansea University, Singleton Park, SA2 8PP Swansea, UK
| | - Farideh Jalali-Najafabadi
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, M13 9PL Manchester, UK
| | - Glen P Martin
- Division of Informatics, Imaging & Data Sciences, School of Health Sciences, The University of Manchester, M13 9PL Manchester, UK
| | - Abdelaali Hassaine
- Division of Informatics, Imaging & Data Sciences, School of Health Sciences, The University of Manchester, M13 9PL Manchester, UK
| | - Ashley Akbari
- Population Data Science, Swansea University Medical School, Faculty of Medicine, Health & Life Science, Swansea University, Singleton Park, SA2 8PP Swansea, UK
| | - Jane Lyons
- Population Data Science, Swansea University Medical School, Faculty of Medicine, Health & Life Science, Swansea University, Singleton Park, SA2 8PP Swansea, UK
| | - Alan Watkins
- Population Data Science, Swansea University Medical School, Faculty of Medicine, Health & Life Science, Swansea University, Singleton Park, SA2 8PP Swansea, UK
| | - Ronan A Lyons
- Population Data Science, Swansea University Medical School, Faculty of Medicine, Health & Life Science, Swansea University, Singleton Park, SA2 8PP Swansea, UK
| | - Niels Peek
- Division of Informatics, Imaging & Data Sciences, School of Health Sciences, The University of Manchester, M13 9PL Manchester, UK; NIHR Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK.
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25
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Nazar G, Díaz-Toro F, Concha-Cisternas Y, Leiva-Ordoñez AM, Troncoso-Pantoja C, Celis-Morales C, Petermann-Rocha F. Latent class analyses of multimorbidity and all-cause mortality: A prospective study in Chilean adults. PLoS One 2023; 18:e0295958. [PMID: 38113219 PMCID: PMC10729966 DOI: 10.1371/journal.pone.0295958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 11/27/2023] [Indexed: 12/21/2023] Open
Abstract
Multimorbidity patterns can lead to differential risks for all-cause mortality. Within the Chilean context, research on morbidity and mortality predominantly emphasizes individual diseases or combinations thereof, rather than specific disease clusters. This study aimed to identify multimorbidity patterns, along with their associations with mortality, within a representative sample of the Chilean population. 3,701 participants aged ≥18 from the Chilean National Health Survey 2009-2010 were included in this prospective study. Multimorbidity patterns were identified from 16 chronic conditions and then classified using latent class analyses. All-cause mortality data were extracted from the Chilean Civil Registry. The association of classes with all-cause mortality was carried out using Cox proportional regression models, adjusting by sociodemographic and lifestyle variables. Three classes were identified: a) Class 1, the healthiest (72.1%); b) Class 2, the depression/cardiovascular disease/cancer class (17.5%); and c) Class 3, hypertension/chronic kidney disease class (10.4%). Classes 2 and 3 showed higher mortality risk than the healthiest class. After adjusting, Class 2 showed 45% higher mortality risk, and Class 3 98% higher mortality risk, compared with the healthiest class. Hypertension appeared to be a critical underlying factor of all-cause morbidity. Particular combinations of chronic diseases have a higher excess risk of mortality than others.
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Affiliation(s)
- Gabriela Nazar
- Departmento de Psicología, Universidad de Concepción, Concepción, Chile
| | - Felipe Díaz-Toro
- Facultad de Enfermería, Universidad Andres Bello, Santiago, Chile
| | - Yeny Concha-Cisternas
- Escuela de Kinesiología, Facultad de Salud, Universidad Santo Tomás, Santiago, Chile
- Pedagogía en Educación Física, Facultad de Educación, Universidad Autónoma de Chile, Providencia, Chile
| | - Ana María Leiva-Ordoñez
- Instituto Anatomía, Histología y Patología, Facultad de Medicina, Universidad Austral de Chile, Valdivia, Chile
| | - Claudia Troncoso-Pantoja
- Centro de Investigación en Educación y Desarrollo (CIEDE-UCSC), Departamento de Salud Pública, Facultad de Medicina, Universidad Católica de la Santísima Concepción, Concepción, Chile
| | - Carlos Celis-Morales
- School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, United Kingdom
- Human Performance Laboratory, Education, Physical Activity and Health Research Unit, Universidad Católica del Maule, Talca, Chile
| | - Fanny Petermann-Rocha
- School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, United Kingdom
- Centro de Investigación Biomédica, Facultad de Medicina, Universidad Diego Portales, Santiago, Chile
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26
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Ferris J, Fiedeldey LK, Kim B, Clemens F, Irvine MA, Hosseini SH, Smolina K, Wister A. Systematic review and meta-analysis of disease clustering in multimorbidity: a study protocol. BMJ Open 2023; 13:e076496. [PMID: 38070917 PMCID: PMC10729243 DOI: 10.1136/bmjopen-2023-076496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 11/09/2023] [Indexed: 12/18/2023] Open
Abstract
INTRODUCTION Multimorbidity is defined as the presence of two or more chronic diseases. Co-occurring diseases can have synergistic negative effects, and are associated with significant impacts on individual health outcomes and healthcare systems. However, the specific effects of diseases in combination will vary between different diseases. Identifying which diseases are most likely to co-occur in multimorbidity is an important step towards population health assessment and development of policies to prevent and manage multimorbidity more effectively and efficiently. The goal of this project is to conduct a systematic review and meta-analysis of studies of disease clustering in multimorbidity, in order to identify multimorbid disease clusters and test their stability. METHODS AND ANALYSIS We will review data from studies of multimorbidity that have used data clustering methodologies to reveal patterns of disease co-occurrence. We propose a network-based meta-analytic approach to perform meta-clustering on a select list of chronic diseases that are identified as priorities for multimorbidity research. We will assess the stability of obtained disease clusters across the research literature to date, in order to evaluate the strength of evidence for specific disease patterns in multimorbidity. ETHICS AND DISSEMINATION This study does not require ethics approval as the work is based on published research studies. The study findings will be published in a peer-reviewed journal and disseminated through conference presentations and meetings with knowledge users in health systems and public health spheres. PROSPERO REGISTRATION NUMBER CRD42023411249.
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Affiliation(s)
- Jennifer Ferris
- Gerontology Research Centre, Simon Fraser University, Burnaby, British Columbia, Canada
- BC Centre for Disease Control, Provincial Health Services Authority, Vancouver, British Columbia, Canada
| | - Lean K Fiedeldey
- Department of Public Health Sciences, Queen's University, Kingston, Ontario, Canada
| | - Boah Kim
- Gerontology Research Centre, Simon Fraser University, Burnaby, British Columbia, Canada
| | - Felicity Clemens
- BC Centre for Disease Control, Provincial Health Services Authority, Vancouver, British Columbia, Canada
| | - Mike A Irvine
- Gerontology Research Centre, Simon Fraser University, Burnaby, British Columbia, Canada
- BC Centre for Disease Control, Provincial Health Services Authority, Vancouver, British Columbia, Canada
| | - Sogol Haji Hosseini
- Gerontology Research Centre, Simon Fraser University, Burnaby, British Columbia, Canada
| | - Kate Smolina
- BC Centre for Disease Control, Provincial Health Services Authority, Vancouver, British Columbia, Canada
- School of Population and Public Health, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Andrew Wister
- Gerontology Research Centre, Simon Fraser University, Burnaby, British Columbia, Canada
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Khan N, Chalitsios CV, Nartey Y, Simpson G, Zaccardi F, Santer M, Roderick PJ, Stuart B, Farmer AJ, Dambha-Miller H. Clustering by multiple long-term conditions and social care needs: a cross-sectional study among 10 026 older adults in England. J Epidemiol Community Health 2023; 77:770-776. [PMID: 37620006 PMCID: PMC10646893 DOI: 10.1136/jech-2023-220696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 08/07/2023] [Indexed: 08/26/2023]
Abstract
BACKGROUND : People with multiple long-term conditions (MLTC) face health and social care challenges. This study aimed to classify people by MLTC and social care needs (SCN) into distinct clusters and quantify the association between derived clusters and care outcomes. METHODS : A cross-sectional study was conducted using the English Longitudinal Study of Ageing, including people with up to 10 MLTC. Self-reported SCN was assessed through 13 measures of difficulty with activities of daily living, 10 measures of mobility difficulties and whether health status was limiting earning capability. Latent class analysis was performed to identify clusters. Multivariable logistic regression quantified associations between derived MLTC/SCN clusters, all-cause mortality and nursing home admission. RESULTS: Our study included 9171 people at baseline with a mean age of 66.3 years; 44.5% were men. Nearly 70.8% had two or more MLTC, the most frequent being hypertension, arthritis and cardiovascular disease. We identified five distinct clusters classified as high SCN/MLTC through to low SCN/MLTC clusters. The high SCN/MLTC included mainly women aged 70-79 years who were white and educated to the upper secondary level. This cluster was significantly associated with higher nursing home admission (OR=8.71; 95% CI: 4.22 to 18). We found no association between clusters and all-cause mortality. CONCLUSIONS: We have highlighted those at risk of worse care outcomes, including nursing home admission. Distinct clusters of individuals with shared sociodemographic characteristics can help identify at-risk individuals with MLTC and SCN at primary care level.
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Affiliation(s)
- Nusrat Khan
- Primary Care Research Centre, University of Southampton, Southampton, UK
| | | | - Yvonne Nartey
- Primary Care Research Centre, University of Southampton, Southampton, UK
| | - Glenn Simpson
- Primary Care Research Centre, University of Southampton, Southampton, UK
| | - Francesco Zaccardi
- Leicester Real World Evidence Unit, Leicester Diabetes Centre, University of Leicester, Leicester, UK
| | - Miriam Santer
- Primary Care Research Centre, University of Southampton, Southampton, UK
| | - Paul J Roderick
- Primary Care Research Centre, University of Southampton, Southampton, UK
| | - Beth Stuart
- Centre for Evaluation and Methods, Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | - Andrew J Farmer
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
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Alvarez-Galvez J, Ortega-Martin E, Ramos-Fiol B, Suarez-Lledo V, Carretero-Bravo J. Epidemiology, mortality, and health service use of local-level multimorbidity patterns in South Spain. Nat Commun 2023; 14:7689. [PMID: 38001107 PMCID: PMC10673852 DOI: 10.1038/s41467-023-43569-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 11/14/2023] [Indexed: 11/26/2023] Open
Abstract
Multimorbidity -understood as the occurrence of chronic diseases together- represents a major challenge for healthcare systems due to its impact on disability, quality of life, increased use of services and mortality. However, despite the global need to address this health problem, evidence is still needed to advance our understanding of its clinical and social implications. Our study aims to characterise multimorbidity patterns in a dataset of 1,375,068 patients residing in southern Spain. Combining LCA techniques and geographic information, together with service use, mortality, and socioeconomic data, 25 chronicity profiles were identified and subsequently characterised by sex and age. The present study has led us to several findings that take a step forward in this field of knowledge. Specifically, we contribute to the identification of an extensive range of at-risk groups. Moreover, our study reveals that the complexity of multimorbidity patterns escalates at a faster rate and is associated with a poorer prognosis in local areas characterised by lower socioeconomic status. These results emphasize the persistence of social inequalities in multimorbidity, highlighting the need for targeted interventions to mitigate the impact on patients' quality of life, healthcare utilisation, and mortality rates.
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Affiliation(s)
- Javier Alvarez-Galvez
- Department of General Economy (Health Sociology area), Faculty of Nursing and Physiotherapy, University of Cadiz, Cadiz, Spain.
- Computational Social Science DataLab, University Institute for Sustainable Social Development, University of Cádiz, Jerez de la Frontera, Spain.
- Biomedical Research and Innovation Institute of Cadiz (INiBICA), Hospital Puerta del Mar, Cadiz, Spain.
| | - Esther Ortega-Martin
- Department of General Economy (Health Sociology area), Faculty of Nursing and Physiotherapy, University of Cadiz, Cadiz, Spain
- Computational Social Science DataLab, University Institute for Sustainable Social Development, University of Cádiz, Jerez de la Frontera, Spain
| | - Begoña Ramos-Fiol
- Department of General Economy (Health Sociology area), Faculty of Nursing and Physiotherapy, University of Cadiz, Cadiz, Spain
- Computational Social Science DataLab, University Institute for Sustainable Social Development, University of Cádiz, Jerez de la Frontera, Spain
| | - Victor Suarez-Lledo
- Computational Social Science DataLab, University Institute for Sustainable Social Development, University of Cádiz, Jerez de la Frontera, Spain
- Department of Sociology, University of Granada, Granada, Spain
| | - Jesus Carretero-Bravo
- Department of General Economy (Health Sociology area), Faculty of Nursing and Physiotherapy, University of Cadiz, Cadiz, Spain
- Computational Social Science DataLab, University Institute for Sustainable Social Development, University of Cádiz, Jerez de la Frontera, Spain
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Jin Y, Xu Z, Zhang Y, Zhang Y, Wang D, Cheng Y, Zhou Y, Fawad M, Xu X. Serum/plasma biomarkers and the progression of cardiometabolic multimorbidity: a systematic review and meta-analysis. Front Public Health 2023; 11:1280185. [PMID: 38074721 PMCID: PMC10701686 DOI: 10.3389/fpubh.2023.1280185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Accepted: 10/27/2023] [Indexed: 12/18/2023] Open
Abstract
Background The role of certain biomarkers in the development of single cardiometabolic disease (CMD) has been intensively investigated. Less is known about the association of biomarkers with multiple CMDs (cardiometabolic multimorbidity, CMM), which is essential for the exploration of molecular targets for the prevention and treatment of CMM. We aimed to systematically synthesize the current evidence on CMM-related biomarkers. Methods We searched PubMed, Embase, Web of Science, and Ebsco for relevant studies from inception until August 31st, 2022. Studies reported the association of serum/plasma biomarkers with CMM, and relevant effect sizes were included. The outcomes were five progression patterns of CMM: (1) no CMD to CMM; (2) type 2 diabetes mellitus (T2DM) followed by stroke; (3) T2DM followed by coronary heart disease (CHD); (4) T2DM followed by stroke or CHD; and (5) CHD followed by T2DM. Newcastle-Ottawa Quality Assessment Scale (NOS) was used to assess the quality of the included studies. A meta-analysis was conducted to quantify the association of biomarkers and CMM. Results A total of 68 biomarkers were identified from 42 studies, which could be categorized into five groups: lipid metabolism, glycometabolism, liver function, immunity, and others. Lipid metabolism biomarkers were most reported to associate with CMM, including TC, TGs, HDL-C, LDL-C, and Lp(a). Fasting plasma glucose was also reported by several studies, and it was particularly associated with coexisting T2DM with vascular diseases. According to the quantitative meta-analysis, HDL-C was negatively associated with CHD risk among patients with T2DM (pooled OR for per 1 mmol/L increase = 0.79, 95% CI = 0.77-0.82), whereas a higher TGs level (pooled OR for higher than 150 mg/dL = 1.39, 95% CI = 1.10-1.75) was positively associated with CHD risk among female patients with T2DM. Conclusion Certain serum/plasma biomarkers were associated with the progression of CMM, in particular for those related to lipid metabolism, but heterogeneity and inconsistent findings still existed among included studies. There is a need for future research to explore more relevant biomarkers associated with the occurrence and progression of CMM, targeted at which is important for the early identification and prevention of CMM.
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Affiliation(s)
- Yichen Jin
- School of Public Health, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Ziyuan Xu
- School of Public Health, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Yuting Zhang
- School of Public Health, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Yue Zhang
- School of Public Health, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Danyang Wang
- School of Public Health, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Yangyang Cheng
- School of Public Health, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Yaguan Zhou
- School of Public Health, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Muhammad Fawad
- School of Public Health, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Xiaolin Xu
- School of Public Health, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, Zhejiang, China
- School of Public Health, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
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Li H, Wang S, Yang S, Liu S, Song Y, Chen S, Li X, Li Z, Li R, Zhao Y, Zhu Q, Ning C, Liu M, He Y. Multiple cardiometabolic diseases enhance the adverse effects of hypoalbuminemia on mortality among centenarians in China: a cohort study. Diabetol Metab Syndr 2023; 15:231. [PMID: 37957767 PMCID: PMC10644513 DOI: 10.1186/s13098-023-01201-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 10/30/2023] [Indexed: 11/15/2023] Open
Abstract
BACKGROUND Although hypoalbuminemia was associated with high risk of mortality in community-dwelling older adults, as well as in the hospitalized older adults, little is known among centenarians. And there are limited data on whether having cardiometabolic diseases (CMDs) is associated with additive effects. METHODS Baseline examinations including a determination of albumin levels were performed in 1002 Chinese centenarians from January 2014 through to December 2016, and the survival status was subsequently ascertained until 31 May 2021. Cox proportional risk model was performed to assess the risk of all-cause mortality associated with albumin levels and hypoalbuminemia combined with CMDs. RESULTS Of 1002 participants included in the analysis, the mean level of albumin was 38.5 g/L (± standard deviation, 4.0 g/L), and 174 (17.4%) had hypoalbuminemia (albumin < 35 g/L). The multivariable analyses showed that albumin level was negatively associated with all-cause mortality (Ptrend < 0.05). Compared to normoalbuminemia, hypoalbuminemia was associated with an increased mortality risk in the overall participants (hazard ratio [HR]: 1.55, 95% confidence interval [CI]: 1.22-1.97). Furthermore, the HR (95% CI) of hypoalbuminemia combined with multiple CMDs was 2.15 (1.14-4.07). There was evidence of an additive deleterious dose effect of an increasing number of CMDs (Ptrend = 0.001). CONCLUSIONS Hypoalbuminemia is associated with an increased risk of all-cause mortality in Chinese centenarians, and this risk is more pronounced among centenarians with multiple cardiometabolic diseases. Our findings suggest that older adults with hypoalbuminemia, especially comorbid multiple CMDs warrant early identification and management.
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Affiliation(s)
- Haowei Li
- Institute of Geriatrics, Beijing Key Laboratory of Aging and Geriatrics, National Clinical Research Center for Geriatrics Diseases, Second Medical Center of Chinese, PLA General Hospital, 28 Fuxing Road, Beijing, 100853, China
| | - Shengshu Wang
- Institute of Geriatrics, Beijing Key Laboratory of Aging and Geriatrics, National Clinical Research Center for Geriatrics Diseases, Second Medical Center of Chinese, PLA General Hospital, 28 Fuxing Road, Beijing, 100853, China
- Department of Healthcare, Agency for Offices Administration, Central Military Commission, People's Republic of China, Beijing, 100082, China
| | - Shanshan Yang
- Department of Disease Prevention and Control, Chinese PLA General Hospital, The 1St Medical Center, Beijing, 100853, China
| | - Shaohua Liu
- Institute of Geriatrics, Beijing Key Laboratory of Aging and Geriatrics, National Clinical Research Center for Geriatrics Diseases, Second Medical Center of Chinese, PLA General Hospital, 28 Fuxing Road, Beijing, 100853, China
| | - Yang Song
- Institute of Geriatrics, Beijing Key Laboratory of Aging and Geriatrics, National Clinical Research Center for Geriatrics Diseases, Second Medical Center of Chinese, PLA General Hospital, 28 Fuxing Road, Beijing, 100853, China
- Special Combat Detachment of Xinjiang Armed Police Crops, Health Corps, Aksu, 843000, China
| | - Shimin Chen
- Institute of Geriatrics, Beijing Key Laboratory of Aging and Geriatrics, National Clinical Research Center for Geriatrics Diseases, Second Medical Center of Chinese, PLA General Hospital, 28 Fuxing Road, Beijing, 100853, China
| | - Xuehang Li
- Institute of Geriatrics, Beijing Key Laboratory of Aging and Geriatrics, National Clinical Research Center for Geriatrics Diseases, Second Medical Center of Chinese, PLA General Hospital, 28 Fuxing Road, Beijing, 100853, China
| | - Zhiqiang Li
- Chinese PLA Center for Disease Control and Prevention, Beijing, 100071, China
| | - Rongrong Li
- Institute of Geriatrics, Beijing Key Laboratory of Aging and Geriatrics, National Clinical Research Center for Geriatrics Diseases, Second Medical Center of Chinese, PLA General Hospital, 28 Fuxing Road, Beijing, 100853, China
| | - Yali Zhao
- Central Laboratory of Hainan Hospital, Chinese PLA General Hospital, Sanya, 572013, China
| | - Qiao Zhu
- Central Laboratory of Hainan Hospital, Chinese PLA General Hospital, Sanya, 572013, China
| | - Chaoxue Ning
- Central Laboratory of Hainan Hospital, Chinese PLA General Hospital, Sanya, 572013, China
| | - Miao Liu
- Department of anti-NBC Medicine, Graduate School of Chinese PLA General Hospital, 28 Fuxing Road, Beijing, 100853, China.
| | - Yao He
- Institute of Geriatrics, Beijing Key Laboratory of Aging and Geriatrics, National Clinical Research Center for Geriatrics Diseases, Second Medical Center of Chinese, PLA General Hospital, 28 Fuxing Road, Beijing, 100853, China.
- State Key Laboratory of Kidney Diseases, Chinese PLA General Hospital, 100853, Beijing, China.
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Zhang Z, Zhao L, Lu Y, Meng X, Zhou X. Relationship of triglyceride-glucose index with cardiometabolic multi-morbidity in China: evidence from a national survey. Diabetol Metab Syndr 2023; 15:226. [PMID: 37926824 PMCID: PMC10626797 DOI: 10.1186/s13098-023-01205-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Accepted: 11/01/2023] [Indexed: 11/07/2023] Open
Abstract
BACKGROUND Cardiometabolic multi-morbidity (CMM) is emerging as a global healthcare challenge and a pressing public health concern worldwide. Previous studies have principally focused on identifying risk factors for individual cardiometabolic diseases, but reliable predictors of CMM have not been identified. In the present study, we aimed to characterize the relationship of triglyceride-glucose (TyG) index with the incidence of CMM. METHODS We enrolled 7,970 participants from the China Health and Retirement Longitudinal Study (CHARLS) and placed them into groups according to quartile of TyG index. The endpoint of interest was CMM, defined as the presence of at least two of the following: stroke, heart disease, and diabetes mellitus. Cox regression models and multivariable-adjusted restricted cubic spline (RCS) curves were used to evaluate the relationship between TyG index and CMM. RESULTS In total, 638 (8.01%) incident cases of CMM were recorded among the participants who did not have CMM at baseline (2011) during a median follow-up of 84 months (interquartile range, 20‒87 months). The incidences of CMM for the participants in quartiles (Q) 1-4 of TyG index were 4.22%, 6.12%, 8.78%, and 12.60%, respectively. A fully adjusted Cox model showed that TyG index was closely associated with the incidence of CMM: the hazard ratio (HR) [95% confidence interval (CI)] for each 1.0-unit increment in TyG index for CMM was 1.54 (1.29-1.84); and the HRs (95% CIs) for Q3 and Q4 (Q1 as reference) of the TyG index for CMM were 1.41 (1.05-1.90) and 1.61 (1.18-2.20), respectively. The association of TyG index with the incidence of CMM was present in almost all the subgroups, and persisted in the sensitivity analyses and additional analyses. Multivariable-adjusted RCS analysis revealed a significant dose-response relationship of TyG index with the risk of CMM (overall P < 0.001; non-linear P = 0.129). CONCLUSIONS We found that a high TyG index is associated with a higher risk of incident CMM. This finding may have significance for clinical practice and facilitate the creation of a personalized prevention strategy that involves monitoring the TyG index.
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Affiliation(s)
- Zenglei Zhang
- Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, No.167, Beilishi Road, Xicheng District, Beijing, China
| | - Lin Zhao
- Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, No.167, Beilishi Road, Xicheng District, Beijing, China
| | - Yiting Lu
- Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, No.167, Beilishi Road, Xicheng District, Beijing, China
| | - Xu Meng
- Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, No.167, Beilishi Road, Xicheng District, Beijing, China.
| | - Xianliang Zhou
- Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, No.167, Beilishi Road, Xicheng District, Beijing, China.
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Pineda-Moncusí M, Dernie F, Dell’Isola A, Kamps A, Runhaar J, Swain S, Zhang W, Englund M, Pitsillidou I, Strauss VY, Robinson DE, Prieto-Alhambra D, Khalid S. Classification of patients with osteoarthritis through clusters of comorbidities using 633 330 individuals from Spain. Rheumatology (Oxford) 2023; 62:3592-3600. [PMID: 36688706 PMCID: PMC10629784 DOI: 10.1093/rheumatology/kead038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 12/02/2022] [Accepted: 01/18/2023] [Indexed: 01/24/2023] Open
Abstract
OBJECTIVES To explore clustering of comorbidities among patients with a new diagnosis of OA and estimate the 10-year mortality risk for each identified cluster. METHODS This is a population-based cohort study of individuals with first incident diagnosis of OA of the hip, knee, ankle/foot, wrist/hand or 'unspecified' site between 2006 and 2020, using SIDIAP (a primary care database representative of Catalonia, Spain). At the time of OA diagnosis, conditions associated with OA in the literature that were found in ≥1% of the individuals (n = 35) were fitted into two cluster algorithms, k-means and latent class analysis. Models were assessed using a range of internal and external evaluation procedures. Mortality risk of the obtained clusters was assessed by survival analysis using Cox proportional hazards. RESULTS We identified 633 330 patients with a diagnosis of OA. Our proposed best solution used latent class analysis to identify four clusters: 'low-morbidity' (relatively low number of comorbidities), 'back/neck pain plus mental health', 'metabolic syndrome' and 'multimorbidity' (higher prevalence of all studied comorbidities). Compared with the 'low-morbidity' cluster, the 'multimorbidity' cluster had the highest risk of 10-year mortality (adjusted hazard ratio [HR]: 2.19 [95% CI: 2.15, 2.23]), followed by the 'metabolic syndrome' cluster (adjusted HR: 1.24 [95% CI: 1.22, 1.27]) and the 'back/neck pain plus mental health' cluster (adjusted HR: 1.12 [95% CI: 1.09, 1.15]). CONCLUSION Patients with a new diagnosis of OA can be clustered into groups based on their comorbidity profile, with significant differences in 10-year mortality risk. Further research is required to understand the interplay between OA and particular comorbidity groups, and the clinical significance of such results.
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Affiliation(s)
- Marta Pineda-Moncusí
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS), University of Oxford, Oxford, UK
| | - Francesco Dernie
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS), University of Oxford, Oxford, UK
| | - Andrea Dell’Isola
- Clinical Epidemiology Unit, Department of Clinical Sciences Lund, Orthopedics, Lund University, Lund, Sweden
| | - Anne Kamps
- Department of General Practice, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Jos Runhaar
- Department of General Practice, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Subhashisa Swain
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Weiya Zhang
- Academic Rheumatology, School of Medicine, University of Nottingham, UK; Pain Centre Versus Arthritis, University of Nottingham, Nottingham, UK
| | - Martin Englund
- Clinical Epidemiology Unit, Department of Clinical Sciences Lund, Orthopedics, Lund University, Lund, Sweden
| | - Irene Pitsillidou
- EULAR Patient Research Partner (PRP), Executive Secretary of Cyprus League Against Rheumatism, Nicosia, Cyprus
| | - Victoria Y Strauss
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS), University of Oxford, Oxford, UK
| | - Danielle E Robinson
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS), University of Oxford, Oxford, UK
| | - Daniel Prieto-Alhambra
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS), University of Oxford, Oxford, UK
| | - Sara Khalid
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS), University of Oxford, Oxford, UK
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Chen S, Marshall T, Jackson C, Cooper J, Crowe F, Nirantharakumar K, Saunders CL, Kirk P, Richardson S, Edwards D, Griffin S, Yau C, Barrett JK. Sociodemographic characteristics and longitudinal progression of multimorbidity: A multistate modelling analysis of a large primary care records dataset in England. PLoS Med 2023; 20:e1004310. [PMID: 37922316 PMCID: PMC10655992 DOI: 10.1371/journal.pmed.1004310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 11/17/2023] [Accepted: 10/09/2023] [Indexed: 11/05/2023] Open
Abstract
BACKGROUND Multimorbidity, characterised by the coexistence of multiple chronic conditions in an individual, is a rising public health concern. While much of the existing research has focused on cross-sectional patterns of multimorbidity, there remains a need to better understand the longitudinal accumulation of diseases. This includes examining the associations between important sociodemographic characteristics and the rate of progression of chronic conditions. METHODS AND FINDINGS We utilised electronic primary care records from 13.48 million participants in England, drawn from the Clinical Practice Research Datalink (CPRD Aurum), spanning from 2005 to 2020 with a median follow-up of 4.71 years (IQR: 1.78, 11.28). The study focused on 5 important chronic conditions: cardiovascular disease (CVD), type 2 diabetes (T2D), chronic kidney disease (CKD), heart failure (HF), and mental health (MH) conditions. Key sociodemographic characteristics considered include ethnicity, social and material deprivation, gender, and age. We employed a flexible spline-based parametric multistate model to investigate the associations between these sociodemographic characteristics and the rate of different disease transitions throughout multimorbidity development. Our findings reveal distinct association patterns across different disease transition types. Deprivation, gender, and age generally demonstrated stronger associations with disease diagnosis compared to ethnic group differences. Notably, the impact of these factors tended to attenuate with an increase in the number of preexisting conditions, especially for deprivation, gender, and age. For example, the hazard ratio (HR) (95% CI; p-value) for the association of deprivation with T2D diagnosis (comparing the most deprived quintile to the least deprived) is 1.76 ([1.74, 1.78]; p < 0.001) for those with no preexisting conditions and decreases to 0.95 ([0.75, 1.21]; p = 0.69) with 4 preexisting conditions. Furthermore, the impact of deprivation, gender, and age was typically more pronounced when transitioning from an MH condition. For instance, the HR (95% CI; p-value) for the association of deprivation with T2D diagnosis when transitioning from MH is 2.03 ([1.95, 2.12], p < 0.001), compared to transitions from CVD 1.50 ([1.43, 1.58], p < 0.001), CKD 1.37 ([1.30, 1.44], p < 0.001), and HF 1.55 ([1.34, 1.79], p < 0.001). A primary limitation of our study is that potential diagnostic inaccuracies in primary care records, such as underdiagnosis, overdiagnosis, or ascertainment bias of chronic conditions, could influence our results. CONCLUSIONS Our results indicate that early phases of multimorbidity development could warrant increased attention. The potential importance of earlier detection and intervention of chronic conditions is underscored, particularly for MH conditions and higher-risk populations. These insights may have important implications for the management of multimorbidity.
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Affiliation(s)
- Sida Chen
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
| | - Tom Marshall
- Institute of Applied Health Research, University of Birmingham, Birmingham, United Kingdom
| | | | - Jennifer Cooper
- Institute of Applied Health Research, University of Birmingham, Birmingham, United Kingdom
| | - Francesca Crowe
- Institute of Applied Health Research, University of Birmingham, Birmingham, United Kingdom
| | - Krish Nirantharakumar
- Institute of Applied Health Research, University of Birmingham, Birmingham, United Kingdom
| | - Catherine L. Saunders
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Paul Kirk
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
| | - Sylvia Richardson
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
| | - Duncan Edwards
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Simon Griffin
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Christopher Yau
- Nuffield Department for Women’s & Reproductive Health, University of Oxford, Oxford, United Kingdom
- Health Data Research, Oxford, United Kingdom
| | - Jessica K. Barrett
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
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Amirzada M, Buczak-Stec E, König HH, Hajek A. Multimorbidity patterns in the German general population aged 40 years and over. Arch Gerontol Geriatr 2023; 114:105067. [PMID: 37257215 DOI: 10.1016/j.archger.2023.105067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 05/08/2023] [Accepted: 05/17/2023] [Indexed: 06/02/2023]
Abstract
AIM The aim of this study was to identify and describe multimorbidity patterns among middle-aged and older community-dwelling individuals in Germany. Moreover, we aimed to determine potential gender differences in multimorbidity patterns. METHODS We analysed data from the most recent (sixth) wave (2017) of the large nationally representative German Ageing Survey (DEAS). Altogether n = 6,554 individuals participated, mean age was 62.0 (ranging from 43 to 92 years). Latent Class Analysis was performed to identify multimorbidity patterns, based on 13 chronic conditions and diseases. Multimorbidity was defined as the presence of at least two chronic conditions. RESULTS Altogether, 53.3% of individuals were multimorbid. We identified and clinically described five multimorbidity patterns: the relatively healthy class (45.1%), the high morbidity class (10.8%), the arthrosis/inflammatory/mental illnesses class (20.6%), the hypertension-metabolic illness class (21.7%), and the cardiovascular/cancer class (1.7%). Our analysis revealed that women compared to men have higher relative risk (IRR = 1.61, 95% CI 1.25-2.06) of being in the arthrosis/inflammatory/mental illnesses class, compared to the relatively healthy class. Furthermore, we found that, depending on which multimorbidity pattern individuals belong to, they differ greatly in terms of socio-demographic factors, health behaviour, and lifestyle factors. CONCLUSIONS We showed that the many chronic diseases cluster in a non-random way. Five clinically meaningful multimorbidity patterns were identified. Gender differences were apparent only in one class, namely in the arthrosis/inflammatory/mental illnesses class.
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Affiliation(s)
- Massuma Amirzada
- Department of Health Economics and Health Services Research, University Medical Center Hamburg-Eppendorf, Hamburg Center for Health Economics, Martinistr. 52, 20246, Hamburg, Germany.
| | - Elżbieta Buczak-Stec
- Department of Health Economics and Health Services Research, University Medical Center Hamburg-Eppendorf, Hamburg Center for Health Economics, Martinistr. 52, 20246, Hamburg, Germany.
| | - Hans-Helmut König
- Department of Health Economics and Health Services Research, University Medical Center Hamburg-Eppendorf, Hamburg Center for Health Economics, Martinistr. 52, 20246, Hamburg, Germany
| | - André Hajek
- Department of Health Economics and Health Services Research, University Medical Center Hamburg-Eppendorf, Hamburg Center for Health Economics, Martinistr. 52, 20246, Hamburg, Germany
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Verhoeff M, Weil LI, Chu H, Vermeeren Y, de Groot J, Burgers JS, Jeurissen PPT, Zwerwer LR, van Munster BC. Clusters of medical specialties around patients with multimorbidity - employing fuzzy c-means clustering to explore multidisciplinary collaboration. BMC Health Serv Res 2023; 23:975. [PMID: 37689648 PMCID: PMC10492354 DOI: 10.1186/s12913-023-09961-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 08/24/2023] [Indexed: 09/11/2023] Open
Abstract
BACKGROUND Hospital care organization, structured around medical specialties and focused on the separate treatment of individual organ systems, is challenged by the increasing prevalence of multimorbidity. To support the hospitals' realization of multidisciplinary care, we hypothesized that using machine learning on clinical data helps to identify groups of medical specialties who are simultaneously involved in hospital care for patients with multimorbidity. METHODS We conducted a cross-sectional study of patients in a Dutch general hospital and used a fuzzy c-means clustering algorithm for the analysis. We explored the patients' membership degrees in each cluster to identify subgroups of medical specialties that provide care to the same patients with multimorbidity. We used retrospectively collected electronic health record data from 2017. We extracted data from 22,133 patients aged ≥18 years who had received outpatient clinical care for two or more chronic and/ or oncological diagnoses. RESULTS We found six clusters of medical specialties and identified 22 subgroups. The clusters were labeled based on the specialties that most characterized them: 1. dermatology/ plastic surgery, 2. six specialties (gynecology/ rheumatology/ orthopedic surgery/ urology/ gastroenterology/ otorhinolaryngology), 3. pulmonology, 4. internal medicine/ cardiology/ geriatrics, 5. neurology/ physiatry (rehabilitation)/ anesthesiology, and 6. internal medicine. Most patients had a full or dominant membership to one of these clusters of medical specialties (11 subgroups), whereas fewer patients had a membership to two clusters. The prevalence of specific diagnosis groups, patient characteristics, and healthcare utilization differed between subgroups. CONCLUSION Our study shows that clusters and subgroups of medical specialties simultaneously involved in hospital care for patients with multimorbidity can be identified with fuzzy c-means cluster analysis using clinical data. Clusters and subgroups differed regarding the involved medical specialties, diagnoses, patient characteristics, and healthcare utilization. With this strategy, hospitals and medical specialists can further analyze which subgroups are target populations that might benefit from improved multidisciplinary collaboration.
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Affiliation(s)
- Marlies Verhoeff
- Department of Geriatric Medicine, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
- Knowledge Institute of the Federation of Medical Specialists, Utrecht, the Netherlands
| | - Liann I Weil
- Department of Geriatric Medicine, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands.
| | - Hung Chu
- Donald Smits Center for Information and Technology, University of Groningen, Groningen, the Netherlands
| | - Yolande Vermeeren
- Department of Internal Medicine, Gelre Hospitals, Apeldoorn/ Zutphen, the Netherlands
| | - Janke de Groot
- Knowledge Institute of the Federation of Medical Specialists, Utrecht, the Netherlands
| | - Jako S Burgers
- Maastricht University, Care and Public Health Research Institute (CAPHRI), Maastricht, the Netherlands
| | - Patrick P T Jeurissen
- Scientific Center for Quality of Healthcare (IQ healthcare), Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Leslie R Zwerwer
- Donald Smits Center for Information and Technology, University of Groningen, Groningen, the Netherlands
- Department of Health Sciences, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Barbara C van Munster
- Department of Geriatric Medicine, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
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Yang K, Yang S, Chen Y, Cao G, Xu R, Jia X, Hou L, Li J, Bi C, Wang X. Multimorbidity Patterns and Associations with Gait, Balance and Lower Extremity Muscle Function in the Elderly: A Cross-Sectional Study in Northwest China. Int J Gen Med 2023; 16:3179-3192. [PMID: 37533839 PMCID: PMC10392815 DOI: 10.2147/ijgm.s418015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 07/13/2023] [Indexed: 08/04/2023] Open
Abstract
Purpose Fall is a common geriatric syndrome leading to various adverse outcomes in the elderly. Gait and balance disorders and decreased lower extremity muscle function are the major intrinsic risk factors of falls, and studies suggested that they were closely related to the underlying chronic conditions. This study aimed to explore the patterns of multimorbidity and determine the associations of these multimorbidity patterns with gait, balance and lower extremity muscle function. Patients and Methods A cross-sectional survey of 4803 participants aged ≥60 years in Shaanxi Province, China was conducted and the self-reported chronic conditions were investigated. The 6-m walk test, timed-up-and-go test (TUG) and 5-sit-to-stand test (5-STS) were conducted to evaluate gait, balance, and lower extremity muscle function respectively. Latent class analysis was used to explore patterns of multimorbidity, and multivariate regression analysis was used to determine the associations of multimorbidity patterns with gait, balance, and lower extremity muscle function. Results Five multimorbidity patterns were identified: Degenerative Disease Class, Cardio-metabolic Class, Stroke-Respiratory-Depression Class, Gastrointestinal Class, and Very sick Class, and they were differently associated with gait and balance disorders and decreased lower extremity muscle function. In particular, the multimorbidity patterns of Degenerative Disease Class and Stroke-Respiratory-Depression Class were closely associated with all the three risk factors of falls. Conclusion There are significant differences in the impact of different multimorbidity patterns on the major intrinsic risk factors of falls in the elderly population, and appropriate multimorbidity patterns are closely related to the prediction of falls and can help to develop fall prevention strategies in the elderly.
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Affiliation(s)
- Kaikai Yang
- Department of Geriatrics, Xijing Hospital, Air Force Medical University, Xi’an, 710032, People’s Republic of China
| | - Shanru Yang
- Department of Geriatrics, Xijing Hospital, Air Force Medical University, Xi’an, 710032, People’s Republic of China
| | - Yang Chen
- Department of Geriatrics, Xijing Hospital, Air Force Medical University, Xi’an, 710032, People’s Republic of China
| | - Guihua Cao
- Department of Geriatrics, Xijing Hospital, Air Force Medical University, Xi’an, 710032, People’s Republic of China
| | - Rong Xu
- Department of Geriatrics, Xijing Hospital, Air Force Medical University, Xi’an, 710032, People’s Republic of China
| | - Xin Jia
- Department of Geriatrics, Xijing Hospital, Air Force Medical University, Xi’an, 710032, People’s Republic of China
| | - Liming Hou
- Department of Geriatrics, Xijing Hospital, Air Force Medical University, Xi’an, 710032, People’s Republic of China
| | - Jinke Li
- Department of Geriatrics, Xijing Hospital, Air Force Medical University, Xi’an, 710032, People’s Republic of China
| | - Chenting Bi
- Department of Geriatrics, Xijing Hospital, Air Force Medical University, Xi’an, 710032, People’s Republic of China
| | - Xiaoming Wang
- Department of Geriatrics, Xijing Hospital, Air Force Medical University, Xi’an, 710032, People’s Republic of China
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Zöller B, Pirouzifard M, Holmquist B, Sundquist J, Halling A, Sundquist K. Familial aggregation of multimorbidity in Sweden: national explorative family study. BMJ MEDICINE 2023; 2:e000070. [PMID: 37465436 PMCID: PMC10351236 DOI: 10.1136/bmjmed-2021-000070] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 06/01/2023] [Indexed: 07/20/2023]
Abstract
Objectives To examine whether multimorbidity aggregates in families in Sweden. Design National explorative family study. Setting Swedish Multigeneration Register linked to the National Patient Register, 1997-2015. Multimorbidity was assessed with a modified counting method of 45 chronic non-communicable diseases according to ICD-10 (international classification of diseases, 10th revision) diagnoses. Participants 2 694 442 Swedish born individuals (48.73% women) who could be linked to their Swedish born first, second, and third degree relatives. Twins were defined as full siblings born on the same date. Main outcome measures Multimorbidity was defined as two or more non-communicable diseases. Familial associations for one, two, three, four, and five or more non-communicable diseases were assessed to examine risks depending on the number of non-communicable diseases. Familial adjusted odds ratios for multimorbidity were calculated for individuals with a diagnosis of multimorbidity compared with relatives of individuals unaffected by multimorbidity (reference). An initial principal component decomposition followed by a factor analysis with a principal factor method and an oblique promax rotation was used on the correlation matrix of tetrachoric correlations between 45 diagnoses in patients to identify disease clusters. Results The odds ratios for multimorbidity were 2.89 in twins (95% confidence interval 2.56 to 3.25), 1.81 in full siblings (1.78 to 1.84), 1.26 in half siblings (1.24 to 1.28), and 1.13 in cousins (1.12 to 1.14) of relatives with a diagnosis of multimorbidity. The odds ratios for multimorbidity increased with the number of diseases in relatives. For example, among twins, the odds ratios for multimorbidity were 1.73, 2.84, 4.09, 4.63, and 6.66 for an increasing number of diseases in relatives, from one to five or more, respectively. Odds ratios were highest at younger ages: in twins, the odds ratio was 3.22 for those aged ≤20 years, 3.14 for those aged 21-30 years, and 2.29 for those aged >30 years at the end of follow-up. Nine disease clusters (factor clusters 1-9) were identified, of which seven aggregated in families. The first three disease clusters in the principal component decomposition were cardiometabolic disease (factor 1), mental health disorders (factor 2), and disorders of the digestive system (factor 3). Odds ratios for multimorbidity in twins, siblings, half siblings, and cousins for the factor 1 cluster were 2.79 (95% confidence interval 0.97 to 8.06), 2.62 (2.39 to 2.88), 1.52 (1.34 to 1.73), and 1.31 (1.23 to 1.39), and for the factor 2 cluster, 5.79 (4.48 to 7.48) 3.24 (3.13 to 3.36), 1.51 (1.45 to 1.57), and 1.37 (1.341.40). Conclusions The results of this explorative family study indicated that multimorbidity aggregated in Swedish families. The findings suggest that map clusters of diseases should be used for the genetic study of common diseases to show new genetic patterns of non-communicable diseases.
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Affiliation(s)
- Bengt Zöller
- Department of Clinical Science, Lund University, Malmö, Sweden
- Centre for Primary Health Care Research, Lund University, Malmö, Sweden
| | - MirNabi Pirouzifard
- Department of Clinical Science, Lund University, Malmö, Sweden
- Centre for Primary Health Care Research, Lund University, Malmö, Sweden
| | | | - Jan Sundquist
- Department of Clinical Science, Lund University, Malmö, Sweden
- Centre for Primary Health Care Research, Lund University, Malmö, Sweden
| | - Anders Halling
- Department of Clinical Science, Lund University, Malmö, Sweden
- Centre for Primary Health Care Research, Lund University, Malmö, Sweden
| | - Kristina Sundquist
- Department of Clinical Science, Lund University, Malmö, Sweden
- Centre for Primary Health Care Research, Lund University, Malmö, Sweden
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Owen RK, Lyons J, Akbari A, Guthrie B, Agrawal U, Alexander DC, Azcoaga-Lorenzo A, Brookes AJ, Denaxas S, Dezateux C, Fagbamigbe AF, Harper G, Kirk PDW, Özyiğit EB, Richardson S, Staniszewska S, McCowan C, Lyons RA, Abrams KR. Effect on life expectancy of temporal sequence in a multimorbidity cluster of psychosis, diabetes, and congestive heart failure among 1·7 million individuals in Wales with 20-year follow-up: a retrospective cohort study using linked data. Lancet Public Health 2023; 8:e535-e545. [PMID: 37393092 DOI: 10.1016/s2468-2667(23)00098-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 04/28/2023] [Accepted: 05/02/2023] [Indexed: 07/03/2023]
Abstract
BACKGROUND To inform targeted public health strategies, it is crucial to understand how coexisting diseases develop over time and their associated impacts on patient outcomes and health-care resources. This study aimed to examine how psychosis, diabetes, and congestive heart failure, in a cluster of physical-mental health multimorbidity, develop and coexist over time, and to assess the associated effects of different temporal sequences of these diseases on life expectancy in Wales. METHODS In this retrospective cohort study, we used population-scale, individual-level, anonymised, linked, demographic, administrative, and electronic health record data from the Wales Multimorbidity e-Cohort. We included data on all individuals aged 25 years and older who were living in Wales on Jan 1, 2000 (the start of follow-up), with follow-up continuing until Dec 31, 2019, first break in Welsh residency, or death. Multistate models were applied to these data to model trajectories of disease in multimorbidity and their associated effect on all-cause mortality, accounting for competing risks. Life expectancy was calculated as the restricted mean survival time (bound by the maximum follow-up of 20 years) for each of the transitions from the health states to death. Cox regression models were used to estimate baseline hazards for transitions between health states, adjusted for sex, age, and area-level deprivation (Welsh Index of Multiple Deprivation [WIMD] quintile). FINDINGS Our analyses included data for 1 675 585 individuals (811 393 [48·4%] men and 864 192 [51·6%] women) with a median age of 51·0 years (IQR 37·0-65·0) at cohort entry. The order of disease acquisition in cases of multimorbidity had an important and complex association with patient life expectancy. Individuals who developed diabetes, psychosis, and congestive heart failure, in that order (DPC), had reduced life expectancy compared with people who developed the same three conditions in a different order: for a 50-year-old man in the third quintile of the WIMD (on which we based our main analyses to allow comparability), DPC was associated with a loss in life expectancy of 13·23 years (SD 0·80) compared with the general otherwise healthy or otherwise diseased population. Congestive heart failure as a single condition was associated with mean a loss in life expectancy of 12·38 years (0·00), and with a loss of 12·95 years (0·06) when preceded by psychosis and 13·45 years (0·13) when followed by psychosis. Findings were robust in people of older ages, more deprived populations, and women, except that the trajectory of psychosis, congestive heart failure, and diabetes was associated with higher mortality in women than men. Within 5 years of an initial diagnosis of diabetes, the risk of developing psychosis or congestive heart failure, or both, was increased. INTERPRETATION The order in which individuals develop psychosis, diabetes, and congestive heart failure as combinations of conditions can substantially affect life expectancy. Multistate models offer a flexible framework to assess temporal sequences of diseases and allow identification of periods of increased risk of developing subsequent conditions and death. FUNDING Health Data Research UK.
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Affiliation(s)
- Rhiannon K Owen
- Population Data Science, Health Data Research, Swansea University Medical School, Faculty of Medicine, Health and Life Science, Swansea University, Swansea, UK.
| | - Jane Lyons
- Population Data Science, Health Data Research, Swansea University Medical School, Faculty of Medicine, Health and Life Science, Swansea University, Swansea, UK
| | - Ashley Akbari
- Population Data Science, Health Data Research, Swansea University Medical School, Faculty of Medicine, Health and Life Science, Swansea University, Swansea, UK
| | - Bruce Guthrie
- Advanced Care Research Centre, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Utkarsh Agrawal
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, Faculty of Engineering Sciences, University College London, London, UK
| | - Amaya Azcoaga-Lorenzo
- School of Medicine, University of St Andrews, St Andrews, UK; Hospital Rey Juan Carlos, Instituto de Investigación Sanitaria Fundación Jimenez Diaz, Madrid, Spain
| | | | - Spiros Denaxas
- Institute of Health Informatics, University College London, London, UK
| | - Carol Dezateux
- Clinical Effectiveness Group, Wolfson Institute of Population Health, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | | | - Gill Harper
- Clinical Effectiveness Group, Wolfson Institute of Population Health, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Paul D W Kirk
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK; Cambridge Institute of Therapeutic Immunology and Infectious Disease, University of Cambridge, Cambridge, UK
| | - Eda Bilici Özyiğit
- Centre for Medical Image Computing, Department of Computer Science, Faculty of Engineering Sciences, University College London, London, UK
| | | | - Sophie Staniszewska
- Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, UK
| | - Colin McCowan
- School of Medicine, University of St Andrews, St Andrews, UK
| | - Ronan A Lyons
- Population Data Science, Health Data Research, Swansea University Medical School, Faculty of Medicine, Health and Life Science, Swansea University, Swansea, UK
| | - Keith R Abrams
- Department of Statistics, University of Warwick, Coventry, UK; Centre for Health Economics, University of York, York, UK
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Carrasco-Ribelles LA, Cabrera-Bean M, Danés-Castells M, Zabaleta-Del-Olmo E, Roso-Llorach A, Violán C. Contribution of Frailty to Multimorbidity Patterns and Trajectories: Longitudinal Dynamic Cohort Study of Aging People. JMIR Public Health Surveill 2023; 9:e45848. [PMID: 37368462 PMCID: PMC10365626 DOI: 10.2196/45848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 05/02/2023] [Accepted: 05/25/2023] [Indexed: 06/28/2023] Open
Abstract
BACKGROUND Multimorbidity and frailty are characteristics of aging that need individualized evaluation, and there is a 2-way causal relationship between them. Thus, considering frailty in analyses of multimorbidity is important for tailoring social and health care to the specific needs of older people. OBJECTIVE This study aimed to assess how the inclusion of frailty contributes to identifying and characterizing multimorbidity patterns in people aged 65 years or older. METHODS Longitudinal data were drawn from electronic health records through the SIDIAP (Sistema d'Informació pel Desenvolupament de la Investigació a l'Atenció Primària) primary care database for the population aged 65 years or older from 2010 to 2019 in Catalonia, Spain. Frailty and multimorbidity were measured annually using validated tools (eFRAGICAP, a cumulative deficit model; and Swedish National Study of Aging and Care in Kungsholmen [SNAC-K], respectively). Two sets of 11 multimorbidity patterns were obtained using fuzzy c-means. Both considered the chronic conditions of the participants. In addition, one set included age, and the other included frailty. Cox models were used to test their associations with death, nursing home admission, and home care need. Trajectories were defined as the evolution of the patterns over the follow-up period. RESULTS The study included 1,456,052 unique participants (mean follow-up of 7.0 years). Most patterns were similar in both sets in terms of the most prevalent conditions. However, the patterns that considered frailty were better for identifying the population whose main conditions imposed limitations on daily life, with a higher prevalence of frail individuals in patterns like chronic ulcers &peripheral vascular. This set also included a dementia-specific pattern and showed a better fit with the risk of nursing home admission and home care need. On the other hand, the risk of death had a better fit with the set of patterns that did not include frailty. The change in patterns when considering frailty also led to a change in trajectories. On average, participants were in 1.8 patterns during their follow-up, while 45.1% (656,778/1,456,052) remained in the same pattern. CONCLUSIONS Our results suggest that frailty should be considered in addition to chronic diseases when studying multimorbidity patterns in older adults. Multimorbidity patterns and trajectories can help to identify patients with specific needs. The patterns that considered frailty were better for identifying the risk of certain age-related outcomes, such as nursing home admission or home care need, while those considering age were better for identifying the risk of death. Clinical and social intervention guidelines and resource planning can be tailored based on the prevalence of these patterns and trajectories.
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Affiliation(s)
- Lucía A Carrasco-Ribelles
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol I Gurina (IDIAPJGol), Barcelona, Spain
- Signal Processing and Communications Group (SPCOM), Department of Signal Theory and Communications, Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
- Unitat de Suport a la Recerca Metropolitana Nord, Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol I Gurina (IDIAPJGol), Mataró, Spain
- Grup de REcerca en Impacte de les Malalties Cròniques i les seves Trajectòries (GRIMTRA) (2021 SGR 01537), Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol I Gurina (IDIAPJGol), Barcelona, Spain
- Network for Research on Chronicity, Primary Care, and Health Promotion (RICAPPS) (RD21/0016/0029), Instituto de Salud Carlos III, Madrid, Spain
| | - Margarita Cabrera-Bean
- Signal Processing and Communications Group (SPCOM), Department of Signal Theory and Communications, Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
| | - Marc Danés-Castells
- Unitat de Suport a la Recerca Metropolitana Nord, Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol I Gurina (IDIAPJGol), Mataró, Spain
| | - Edurne Zabaleta-Del-Olmo
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol I Gurina (IDIAPJGol), Barcelona, Spain
- Grup de REcerca en Impacte de les Malalties Cròniques i les seves Trajectòries (GRIMTRA) (2021 SGR 01537), Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol I Gurina (IDIAPJGol), Barcelona, Spain
- Network for Research on Chronicity, Primary Care, and Health Promotion (RICAPPS) (RD21/0016/0029), Instituto de Salud Carlos III, Madrid, Spain
- Gerència Territorial de Barcelona, Institut Català de la Salut, Barcelona, Spain
- Nursing Department, Faculty of Nursing, Universitat de Girona, Girona, Spain
| | - Albert Roso-Llorach
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol I Gurina (IDIAPJGol), Barcelona, Spain
- Grup de REcerca en Impacte de les Malalties Cròniques i les seves Trajectòries (GRIMTRA) (2021 SGR 01537), Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol I Gurina (IDIAPJGol), Barcelona, Spain
- Network for Research on Chronicity, Primary Care, and Health Promotion (RICAPPS) (RD21/0016/0029), Instituto de Salud Carlos III, Madrid, Spain
- Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Spain
| | - Concepción Violán
- Unitat de Suport a la Recerca Metropolitana Nord, Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol I Gurina (IDIAPJGol), Mataró, Spain
- Grup de REcerca en Impacte de les Malalties Cròniques i les seves Trajectòries (GRIMTRA) (2021 SGR 01537), Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol I Gurina (IDIAPJGol), Barcelona, Spain
- Network for Research on Chronicity, Primary Care, and Health Promotion (RICAPPS) (RD21/0016/0029), Instituto de Salud Carlos III, Madrid, Spain
- Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Spain
- Fundació Institut d'Investigació en ciències de la Salut Germans Trias i Pujol (IGTP), Badalona, Spain
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Han Y, Hu Y, Yu C, Sun D, Pang Y, Pei P, Yang L, Chen Y, Du H, Liu J, Schmidt D, Avery D, Chen J, Chen Z, Li L, Lv J. Duration-dependent impact of cardiometabolic diseases and multimorbidity on all-cause and cause-specific mortality: a prospective cohort study of 0.5 million participants. Cardiovasc Diabetol 2023; 22:135. [PMID: 37308998 DOI: 10.1186/s12933-023-01858-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 05/12/2023] [Indexed: 06/14/2023] Open
Abstract
BACKGROUND The association of incident cardiometabolic multimorbidity (CMM) with mortality risk is rarely studied, and neither are the durations of cardiometabolic diseases (CMDs). Whether the association patterns of CMD durations with mortality change as individuals progress from one CMD to CMM is unclear. METHODS Data from China Kadoorie Biobank of 512,720 participants aged 30-79 was used. CMM was defined as the simultaneous presence of two or more CMDs of interest, including diabetes, ischemic heart disease, and stroke. Cox regression was used to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) for the duration-dependent associations of CMDs and CMM with all-cause and cause-specific mortality. All information on exposures of interest was updated during follow-up. RESULTS During a median follow-up of 12.1 years, 99,770 participants experienced at least one incident CMD, and 56,549 deaths were documented. Among 463,178 participants free of three CMDs at baseline, compared with no CMD during follow-up, the adjusted HRs (95% CIs) between CMM and all-cause mortality, mortality from circulatory system diseases, respiratory system diseases, cancer, and other causes were 2.93 (2.80-3.07), 5.05 (4.74-5.37), 2.72 (2.35-3.14), 1.30 (1.16-1.45), and 2.30 (2.02-2.61), respectively. All CMDs exhibited a high mortality risk in the first year of diagnosis. Subsequently, with prolonged disease duration, mortality risk increased for diabetes, decreased for IHD, and sustained at a high level for stroke. With the presence of CMM, the above association estimates inflated, but the pattern of which remained. CONCLUSION Among Chinese adults, mortality risk increased with the number of the CMDs and changed with prolonged disease duration, the patterns of which varied among the three CMDs.
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Affiliation(s)
- Yuting Han
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Yizhen Hu
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Canqing Yu
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Dianjianyi Sun
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Yuanjie Pang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Pei Pei
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Ling Yang
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Yiping Chen
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Huaidong Du
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Jingchao Liu
- NCDs Prevention and Control Department, Wuzhong CDC, Suzhou, Jiangsu, China
| | - Dan Schmidt
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Daniel Avery
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Junshi Chen
- China National Center for Food Safety Risk Assessment, Beijing, China
| | - Zhengming Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Liming Li
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China.
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China.
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China.
| | - Jun Lv
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China.
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China.
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China.
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Holm NN, Frølich A, Andersen O, Juul-Larsen HG, Stockmarr A. Longitudinal models for the progression of disease portfolios in a nationwide chronic heart disease population. PLoS One 2023; 18:e0284496. [PMID: 37079591 PMCID: PMC10118194 DOI: 10.1371/journal.pone.0284496] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 03/30/2023] [Indexed: 04/21/2023] Open
Abstract
BACKGROUND AND AIM With multimorbidity becoming increasingly prevalent in the ageing population, addressing the epidemiology and development of multimorbidity at a population level is needed. Individuals subject to chronic heart disease are widely multimorbid, and population-wide longitudinal studies on their chronic disease trajectories are few. METHODS Disease trajectory networks of expected disease portfolio development and chronic condition prevalences were used to map sex and socioeconomic multimorbidity patterns among chronic heart disease patients. Our data source was all Danish individuals aged 18 years and older at some point in 1995-2015, consisting of 6,048,700 individuals. We used algorithmic diagnoses to obtain chronic disease diagnoses and included individuals who received a heart disease diagnosis. We utilized a general Markov framework considering combinations of chronic diagnoses as multimorbidity states. We analyzed the time until a possible new diagnosis, termed the diagnosis postponement time, in addition to transitions to new diagnoses. We modelled the postponement times by exponential models and transition probabilities by logistic regression models. FINDINGS Among the cohort of 766,596 chronic heart disease diagnosed individuals, the prevalence of multimorbidity was 84.36% and 88.47% for males and females, respectively. We found sex-related differences within the chronic heart disease trajectories. Female trajectories were dominated by osteoporosis and male trajectories by cancer. We found sex important in developing most conditions, especially osteoporosis, chronic obstructive pulmonary disease and diabetes. A socioeconomic gradient was observed where diagnosis postponement time increases with educational attainment. Contrasts in disease portfolio development based on educational attainment were found for both sexes, with chronic obstructive pulmonary disease and diabetes more prevalent at lower education levels, compared to higher. CONCLUSIONS Disease trajectories of chronic heart disease diagnosed individuals are heavily complicated by multimorbidity. Therefore, it is essential to consider and study chronic heart disease, taking into account the individuals' entire disease portfolio.
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Affiliation(s)
- Nikolaj Normann Holm
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Anne Frølich
- Innovation and Research Centre for Multimorbidity, Slagelse Hospital, Slagelse, Denmark
- Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Ove Andersen
- Department of Clinical Research, Copenhagen University Hospital Amager and Hvidovre, Hvidovre, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Emergency Department, Copenhagen University Hospital Amager and Hvidovre, Hvidovre, Denmark
| | - Helle Gybel Juul-Larsen
- Department of Clinical Research, Copenhagen University Hospital Amager and Hvidovre, Hvidovre, Denmark
| | - Anders Stockmarr
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark
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Álvarez-Gálvez J, Ortega-Martín E, Carretero-Bravo J, Pérez-Muñoz C, Suárez-Lledó V, Ramos-Fiol B. Social determinants of multimorbidity patterns: A systematic review. Front Public Health 2023; 11:1081518. [PMID: 37050950 PMCID: PMC10084932 DOI: 10.3389/fpubh.2023.1081518] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 03/02/2023] [Indexed: 03/28/2023] Open
Abstract
Social determinants of multimorbidity are poorly understood in clinical practice. This review aims to characterize the different multimorbidity patterns described in the literature while identifying the social and behavioral determinants that may affect their emergence and subsequent evolution. We searched PubMed, Embase, Scopus, Web of Science, Ovid MEDLINE, CINAHL Complete, PsycINFO and Google Scholar. In total, 97 studies were chosen from the 48,044 identified. Cardiometabolic, musculoskeletal, mental, and respiratory patterns were the most prevalent. Cardiometabolic multimorbidity profiles were common among men with low socioeconomic status, while musculoskeletal, mental and complex patterns were found to be more prevalent among women. Alcohol consumption and smoking increased the risk of multimorbidity, especially in men. While the association of multimorbidity with lower socioeconomic status is evident, patterns of mild multimorbidity, mental and respiratory related to middle and high socioeconomic status are also observed. The findings of the present review point to the need for further studies addressing the impact of multimorbidity and its social determinants in population groups where this problem remains invisible (e.g., women, children, adolescents and young adults, ethnic groups, disabled population, older people living alone and/or with few social relations), as well as further work with more heterogeneous samples (i.e., not only focusing on older people) and using more robust methodologies for better classification and subsequent understanding of multimorbidity patterns. Besides, more studies focusing on the social determinants of multimorbidity and its inequalities are urgently needed in low- and middle-income countries, where this problem is currently understudied.
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Affiliation(s)
- Javier Álvarez-Gálvez
- Department of Biomedicine, Biotechnology and Public Health, University of Cadiz, Cádiz, Spain
- The University Research Institute for Sustainable Social Development (Instituto Universitario de Investigación para el Desarrollo Social Sostenible), University of Cadiz, Jerez de la Frontera, Spain
| | - Esther Ortega-Martín
- Department of Biomedicine, Biotechnology and Public Health, University of Cadiz, Cádiz, Spain
- *Correspondence: Esther Ortega-Martín
| | - Jesús Carretero-Bravo
- Department of Biomedicine, Biotechnology and Public Health, University of Cadiz, Cádiz, Spain
| | - Celia Pérez-Muñoz
- Department of Nursing and Physiotherapy, University of Cadiz, Cádiz, Spain
| | - Víctor Suárez-Lledó
- Department of Biomedicine, Biotechnology and Public Health, University of Cadiz, Cádiz, Spain
| | - Begoña Ramos-Fiol
- Department of Biomedicine, Biotechnology and Public Health, University of Cadiz, Cádiz, Spain
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Zhang Z, Yuan M, Shi K, Xu C, Lin J, Shi Z, Fang Y. Association between multimorbidity trajectories, healthcare utilization, and health expenditures among middle-aged and older adults: China Health and Retirement Longitudinal Study. J Affect Disord 2023; 330:24-32. [PMID: 36868387 DOI: 10.1016/j.jad.2023.02.135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 02/22/2023] [Accepted: 02/24/2023] [Indexed: 03/05/2023]
Abstract
BACKGROUND To identify the latent groups of multimorbidity trajectories among middle-aged and older adults and examine their associations with healthcare utilization and health expenditures. METHODS We included adults aged ≥45 years who participated in the China Health and Retirement Longitudinal Study from 2011 to 2015 and were without multimorbidities (<2 chronic conditions) at baseline. Multimorbidity trajectories underlying 13 chronic conditions were identified using group-based multi-trajectory modeling based on the latent dimensions. Healthcare utilization included outpatient care, inpatient care, and unmet healthcare needs. Health expenditures included healthcare costs and catastrophic health expenditures (CHE). Random-effects logistic regression, random-effects negative binomial regression, and generalized linear regression models were used to examine the association between multimorbidity trajectories, healthcare utilization, and health expenditures. RESULTS Of the 5548 participants, 2407 developed multimorbidities during follow-up. Three trajectory groups were identified among those with new-onset multimorbidity according to the increasing dimensions of chronic diseases: "digestive-arthritic" (N = 1377, 57.21 %), "cardiometabolic/brain" (N = 834, 34.65 %), and "respiratory/digestive-arthritic" (N = 196, 8.14 %). All trajectory groups had a significantly increased risk of outpatient care, inpatient care, unmet healthcare needs, and higher healthcare costs than those without multimorbidities. Notably, participants in the "digestive-arthritic" trajectory group had a significantly increased risk of incurring CHE (OR = 1.70, 95%CI: 1.03-2.81). LIMITATIONS Chronic conditions were assessed using self-reported measures. CONCLUSIONS The growing burden of multimorbidity, especially multimorbidities of digestive and arthritic diseases, was associated with a significantly increased risk of healthcare utilization and health expenditures. The findings may help in planning future healthcare and managing multimorbidity more effectively.
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Affiliation(s)
- Zeyun Zhang
- Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, China; Center for Aging and Health Research, School of Public Health, Xiamen University, China
| | - Manqiong Yuan
- Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, China; Center for Aging and Health Research, School of Public Health, Xiamen University, China
| | - Kanglin Shi
- Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, China; Center for Aging and Health Research, School of Public Health, Xiamen University, China
| | - Chuanhai Xu
- Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, China; Center for Aging and Health Research, School of Public Health, Xiamen University, China
| | - Jianlin Lin
- Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, China; Center for Aging and Health Research, School of Public Health, Xiamen University, China
| | - Zaixing Shi
- Center for Aging and Health Research, School of Public Health, Xiamen University, China
| | - Ya Fang
- Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, China; Center for Aging and Health Research, School of Public Health, Xiamen University, China.
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Gaitatzis A, Majeed A. Multimorbidity in People with Epilepsy. Seizure 2023; 107:136-145. [PMID: 37023627 DOI: 10.1016/j.seizure.2023.03.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 03/24/2023] [Accepted: 03/26/2023] [Indexed: 03/31/2023] Open
Abstract
Multimorbidity is an emerging priority in healthcare due to associations with the ageing population, frailty, polypharmacy, health and social care demands. It affects 60-70% of adults and 80% of children with epilepsy. Neurodevelopmental conditions are commonly seen in children with epilepsy, while cancer, cardiovascular and neurodegenerative conditions often afflict older people with epilepsy. Mental health problems are common across the lifespan. Genetic, environmental, social and lifestyle factors contribute to multimorbidity and its consequences. Multimorbid people with epilepsy (PWE) are at higher risk of depression and suicide, premature death, suffer lower health-related quality of life, and require more hospital admissions and health care costs. The best management of multimorbid PWE requires a paradigm shift from the traditional single disease-single comorbidity approach and a refocus on a person-centred approach. Improvements in health care must be informed by assessing the burden of multimorbidity associated with epilepsy, delineating disease clusters, and measuring the effects on health outcomes.
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Zou H, Zhang S, Cai M, Qian ZM, Zhang Z, Chen L, Wang X, Arnold LD, Howard SW, Li H, Lin H. Ambient air pollution associated with incidence and progression trajectory of cardiometabolic diseases: A multi-state analysis of a prospective cohort. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 862:160803. [PMID: 36493826 DOI: 10.1016/j.scitotenv.2022.160803] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 12/05/2022] [Accepted: 12/05/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND Previous studies on the association between ambient air pollution and cardiometabolic diseases (CMDs) focused on a single disease, without considering cardiometabolic multimorbidity (CMM) and the progression trajectory of CMDs. METHODS Based on the UK Biobank cohort, we included 372,530 participants aged 37-73 years at baseline (2006-2010) with follow-up until September 2021. Incident CMDs cases were identified based on self-reported information and multiple health-related records in the UK Biobank. CMM was defined as the occurrence of at least two CMDs, including ischemic heart disease (IHD), stroke and type 2 diabetes (T2D). Exposure to ambient air pollutants, including particulate matter (PM) with aerodynamic diameter ≤2.5 μm (PM2.5), ≤10 μm (PM10), nitrogen dioxide (NO2), and nitrogen oxides (NOx) were estimated at participants' geocoded residential addresses based on the high-resolution (1 × 1 km) pollution data from 2001 to 2021 provided by UK Department for Environment, Food and Rural Affairs. Multi-state models with adjustment for potential confounders were used to examine the impact of long-term exposure to ambient air pollution on transitions from healthy to first CMD (FCMD), subsequently to CMM, and further to death. RESULTS During a median follow-up of 12.6 years, 40,112 participants developed at least one CMD, 3896 developed CMM, and 21,739 died. Among the four pollutants, PM2.5 showed the strongest associations with all transitions from healthy to FCMD, to CMM, and then to death [hazard ratios (95 % confidence intervals) per interquartile range (IQR) increment: 1.62 (1.60, 1.64) and 1.68 (1.61, 1.76) for transitions from healthy to FCMD and from FCMD to CMM, and 1.62 (1.59, 1.66), 1.67 (1.61, 1.73), and 1.52 (1.38, 1.67) for death risk from healthy, FCMD, and CMM, respectively]. After dividing FCMDs into three specific CMDs, we found that ambient air pollution had differential impacts on disease-specific transitions within the same transition phase. CONCLUSIONS Our findings indicate that there is potential for air pollution mitigation in contributing to the prevention of the development and progression of CMDs.
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Affiliation(s)
- Hongtao Zou
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Shiyu Zhang
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Miao Cai
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Zhengmin Min Qian
- Department of Epidemiology and Biostatistics, College for Public Health & Social Justice, Saint Louis University, Saint Louis, MO 63104, USA
| | - Zilong Zhang
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Lan Chen
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Xiaojie Wang
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Lauren D Arnold
- Department of Epidemiology and Biostatistics, College for Public Health & Social Justice, Saint Louis University, Saint Louis, MO 63104, USA
| | - Steven W Howard
- Department of Health Management and Policy, College for Public Health & Social Justice, Saint Louis University, Saint Louis, MO 63104, USA
| | - Haitao Li
- Department of Social Medicine and Health Service Management, Health Science Center, Shenzhen University, Shenzhen 518055, China
| | - Hualiang Lin
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.
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Luo Y, Chen Y, Wang K, De Fries CM, Huang Z, Xu H, Yang Z, Hu Y, Xu B. Associations between multimorbidity and frailty transitions among older Americans. J Cachexia Sarcopenia Muscle 2023; 14:1075-1082. [PMID: 36852679 PMCID: PMC10067509 DOI: 10.1002/jcsm.13197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 01/02/2023] [Indexed: 03/01/2023] Open
Abstract
BACKGROUND The associations of multimorbidity patterns with transitions between frailty states remain unclear in older individuals. METHODS We used data from the National Health and Aging Trends Study 2011-2019. Frailty was measured annually using the Fried frailty phenotype. Multimorbidity patterns at baseline were identified using latent class analysis based on 14 chronic conditions. We used the semi-Markov multi-state model to investigate the influences of multimorbidity characterized by condition counts and patterns on subsequent frailty transitions over follow-ups. RESULTS Among 9450 participants aged ≥65 years at baseline, 34.8% were non-frail, 48.1% were pre-frail and 17.0% were frail. Over a median follow-up of 4.0 years, 16 880 frailty transitions were observed, with 10 527 worsening and 6353 improving. For 7675 participants with multimorbidity, four multimorbidity patterns were identified: osteoarticular pattern (62.4%), neuropsychiatric-sensory pattern (17.2%), cardiometabolic pattern (10.3%) and complex multimorbidity pattern (10.1%). Compared with no disease, multimorbidity was significantly associated with an increased risk of worsening transitions, including from non-frail to pre-frail (hazard ratio [HR] = 1.35; 95% confidence interval [CI] = 1.21-1.52), from non-frail to frail (HR = 1.68; 95% CI = 1.04-2.73), from pre-frail to frail (HR = 2.19; 95% CI = 1.66-2.90) and from pre-frail to death (HR = 1.64; 95% CI = 1.11-2.41). Compared with the osteoarticular pattern, neuropsychiatric-sensory, cardiometabolic and complex multimorbidity patterns had a significantly higher risk of worsening frailty (all P < 0.05). CONCLUSIONS Multimorbidity was associated with dynamic transitions between frailty states and death among older American adults, and the associations varied across multimorbidity patterns. The findings could offer significant implications for public health policymakers in planning interventions and healthcare resources. They also might inform clinicians regarding providing targeted clinical treatment and health management based on multimorbidity patterns of older people.
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Affiliation(s)
- Yan Luo
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China.,Medical Informatics Center, Peking University, Beijing, China
| | - Yuming Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China.,Medical Informatics Center, Peking University, Beijing, China
| | - Kaipeng Wang
- Graduate School of Social Work, University of Denver, Denver, CO, USA
| | - Carson M De Fries
- Graduate School of Social Work, University of Denver, Denver, CO, USA
| | - Ziting Huang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China.,Medical Informatics Center, Peking University, Beijing, China
| | - Huiwen Xu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China.,Medical Informatics Center, Peking University, Beijing, China
| | - Zhou Yang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China.,Medical Informatics Center, Peking University, Beijing, China
| | - Yonghua Hu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China.,Medical Informatics Center, Peking University, Beijing, China
| | - Beibei Xu
- Medical Informatics Center, Peking University, Beijing, China
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McLoone P, Jani BD, Siebert S, Morton FR, Canning J, Macdonald S, Mair FS, Nicholl BI. Classification of long-term condition patterns in rheumatoid arthritis and associations with adverse health events: a UK Biobank cohort study. JOURNAL OF MULTIMORBIDITY AND COMORBIDITY 2023; 13:26335565221148616. [PMID: 36798088 PMCID: PMC9926377 DOI: 10.1177/26335565221148616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 12/08/2022] [Indexed: 06/18/2023]
Abstract
PURPOSE We aimed to classify individuals with RA and ≥2 additional long-term conditions (LTCs) and describe the association between different LTC classes, number of LTCs and adverse health outcomes. METHODS We used UK Biobank participants who reported RA (n=5,625) and employed latent class analysis (LCA) to create classes of LTC combinations for those with ≥2 additional LTCs. Cox-proportional hazard and negative binomial regression were used to compare the risk of all-cause mortality, major adverse cardiac events (MACE), and number of emergency hospitalisations over an 11-year follow-up across the different LTC classes and in those with RA plus one additional LTC. Persons with RA without LTCs were the reference group. Analyses were adjusted for demographic characteristics, smoking, BMI, alcohol consumption and physical activity. RESULTS A total of 2,566 (46%) participants reported ≥2 LTCs in addition to RA. This involved 1,138 distinct LTC combinations of which 86% were reported by ≤2 individuals. LCA identified 5 morbidity-classes. The distinctive condition in the class with the highest mortality was cancer (class 5; HR 2.66 95%CI (1.91-3.70)). The highest MACE (HR 2.95 95%CI (2.11-4.14)) and emergency hospitalisations (rate ratio 3.01 (2.56-3.54)) were observed in class 3 which comprised asthma, COPD & CHD. There was an increase in mortality, MACE and emergency hospital admissions within each class as the number of LTCs increased. CONCLUSIONS The risk of adverse health outcomes in RA varied with different patterns of multimorbidity. The pattern of multimorbidity should be considered in risk assessment and formulating management plans in patients with RA.
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Affiliation(s)
- Philip McLoone
- General Practice and Primary Care, School of Health and Wellbeing, University of Glasgow, UK
| | - Bhautesh D Jani
- General Practice and Primary Care, School of Health and Wellbeing, University of Glasgow, UK
| | - Stefan Siebert
- School of Infection and Immunity, University of Glasgow, Glasgow, UK
| | - Fraser R Morton
- School of Infection and Immunity, University of Glasgow, Glasgow, UK
| | - Jordan Canning
- General Practice and Primary Care, School of Health and Wellbeing, University of Glasgow, UK
| | - Sara Macdonald
- General Practice and Primary Care, School of Health and Wellbeing, University of Glasgow, UK
| | - Frances S Mair
- General Practice and Primary Care, School of Health and Wellbeing, University of Glasgow, UK
| | - Barbara I Nicholl
- General Practice and Primary Care, School of Health and Wellbeing, University of Glasgow, UK
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Ramos-Vera C, Barrientos AS, Vallejos-Saldarriaga J, Calizaya-Milla YE, Saintila J. Network Structure of Comorbidity Patterns in U.S. Adults with Depression: A National Study Based on Data from the Behavioral Risk Factor Surveillance System. DEPRESSION RESEARCH AND TREATMENT 2023; 2023:9969532. [PMID: 37096248 PMCID: PMC10122603 DOI: 10.1155/2023/9969532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 03/14/2023] [Accepted: 03/21/2023] [Indexed: 04/26/2023]
Abstract
Background People with depression are at increased risk for comorbidities; however, the clustering of comorbidity patterns in these patients is still unclear. Objective The aim of the study was to identify latent comorbidity patterns and explore the comorbidity network structure that included 12 chronic conditions in adults diagnosed with depressive disorder. Methods A cross-sectional study was conducted based on secondary data from the 2017 behavioral risk factor surveillance system (BRFSS) covering all 50 American states. A sample of 89,209 U.S. participants, 29,079 men and 60,063 women aged 18 years or older, was considered using exploratory graphical analysis (EGA), a statistical graphical model that includes algorithms for grouping and factoring variables in a multivariate system of network relationships. Results The EGA findings show that the network presents 3 latent comorbidity patterns, i.e., that comorbidities are grouped into 3 factors. The first group was composed of 7 comorbidities (obesity, cancer, high blood pressure, high blood cholesterol, arthritis, kidney disease, and diabetes). The second pattern of latent comorbidity included the diagnosis of asthma and respiratory diseases. The last factor grouped 3 conditions (heart attack, coronary heart disease, and stroke). Hypertension reported higher measures of network centrality. Conclusion Associations between chronic conditions were reported; furthermore, they were grouped into 3 latent dimensions of comorbidity and reported network factor loadings. The implementation of care and treatment guidelines and protocols for patients with depressive symptomatology and multimorbidity is suggested.
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Affiliation(s)
- Cristian Ramos-Vera
- Research Area, Faculty of Health Sciences, Universidad César Vallejo, Lima, Peru
| | | | | | - Yaquelin E. Calizaya-Milla
- Research Group for Nutrition and Lifestyle, School of Human Nutrition, Universidad Peruana Unión, Lima, Peru
| | - Jacksaint Saintila
- Research Group for Nutrition and Lifestyle, School of Human Nutrition, Universidad Peruana Unión, Lima, Peru
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Carretero-Bravo J, Ramos-Fiol B, Ortega-Martín E, Suárez-Lledó V, Salazar A, O’Ferrall-González C, Dueñas M, Peralta-Sáez JL, González-Caballero JL, Cordoba-Doña JA, Lagares-Franco C, Martínez-Nieto JM, Almenara-Barrios J, Álvarez-Gálvez J. Multimorbidity Patterns and Their Association with Social Determinants, Mental and Physical Health during the COVID-19 Pandemic. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:16839. [PMID: 36554719 PMCID: PMC9778742 DOI: 10.3390/ijerph192416839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 12/09/2022] [Accepted: 12/12/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND The challenge posed by multimorbidity makes it necessary to look at new forms of prevention, a fact that has become heightened in the context of the pandemic. We designed a questionnaire to detect multimorbidity patterns in people over 50 and to associate these patterns with mental and physical health, COVID-19, and possible social inequalities. METHODS This was an observational study conducted through a telephone interview. The sample size was 1592 individuals with multimorbidity. We use Latent Class Analysis to detect patterns and SF-12 scale to measure mental and physical quality-of-life health. We introduced the two dimensions of health and other social determinants in a multinomial regression model. RESULTS We obtained a model with five patterns (entropy = 0.727): 'Relative Healthy', 'Cardiometabolic', 'Musculoskeletal', 'Musculoskeletal and Mental', and 'Complex Multimorbidity'. We found some differences in mental and physical health among patterns and COVID-19 diagnoses, and some social determinants were significant in the multinomial regression. CONCLUSIONS We identified that prevention requires the location of certain inequalities associated with the multimorbidity patterns and how physical and mental health have been affected not only by the patterns but also by COVID-19. These findings may be critical in future interventions by health services and governments.
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Affiliation(s)
- Jesús Carretero-Bravo
- Department of Biomedicine, Biotechnology and Public Health, University of Cadiz, Avda. Ana de Viya 52, 11009 Cádiz, Spain
| | - Begoña Ramos-Fiol
- Department of Biomedicine, Biotechnology and Public Health, University of Cadiz, Avda. Ana de Viya 52, 11009 Cádiz, Spain
| | - Esther Ortega-Martín
- Department of Biomedicine, Biotechnology and Public Health, University of Cadiz, Avda. Ana de Viya 52, 11009 Cádiz, Spain
| | - Víctor Suárez-Lledó
- Department of Biomedicine, Biotechnology and Public Health, University of Cadiz, Avda. Ana de Viya 52, 11009 Cádiz, Spain
| | - Alejandro Salazar
- Department of Statistics and Operational Research, University of Cadiz, Polígono Río San Pedro, 11510 Puerto Real, Spain
| | | | - María Dueñas
- Department of Statistics and Operational Research, University of Cadiz, Polígono Río San Pedro, 11510 Puerto Real, Spain
| | - Juan Luis Peralta-Sáez
- Department of Statistics and Operational Research, University of Cadiz, Polígono Río San Pedro, 11510 Puerto Real, Spain
| | - Juan Luis González-Caballero
- Department of Statistics and Operational Research, University of Cadiz, Polígono Río San Pedro, 11510 Puerto Real, Spain
| | - Juan Antonio Cordoba-Doña
- Department of Biomedicine, Biotechnology and Public Health, University of Cadiz, Avda. Ana de Viya 52, 11009 Cádiz, Spain
- Preventive Medicine Area, Hospital of Jerez, Ctra. Trebujena, s/n, 11407 Jerez de la Frontera, Spain
| | - Carolina Lagares-Franco
- Department of Statistics and Operational Research, University of Cadiz, Polígono Río San Pedro, 11510 Puerto Real, Spain
| | | | - José Almenara-Barrios
- Department of Biomedicine, Biotechnology and Public Health, University of Cadiz, Avda. Ana de Viya 52, 11009 Cádiz, Spain
| | - Javier Álvarez-Gálvez
- Department of Biomedicine, Biotechnology and Public Health, University of Cadiz, Avda. Ana de Viya 52, 11009 Cádiz, Spain
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50
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Villén N, Roso-Llorach A, Gallego-Moll C, Danes-Castells M, Fernández-Bertolin S, Troncoso-Mariño A, Monteagudo M, Amado E, Violán C. Polypharmacy Patterns in Multimorbid Older People with Cardiovascular Disease: Longitudinal Study. Geriatrics (Basel) 2022; 7:geriatrics7060141. [PMID: 36547277 PMCID: PMC9777651 DOI: 10.3390/geriatrics7060141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 12/01/2022] [Accepted: 12/09/2022] [Indexed: 12/14/2022] Open
Abstract
(1) Introduction: Cardiovascular disease is associated with high mortality, especially in older people. This study aimed to characterize the evolution of combined multimorbidity and polypharmacy patterns in older people with different cardiovascular disease profiles. (2) Material and methods: This longitudinal study drew data from the Information System for Research in Primary Care in people aged 65 to 99 years with profiles of cardiovascular multimorbidity. Combined patterns of multimorbidity and polypharmacy were analysed using fuzzy c-means clustering techniques and hidden Markov models. The prevalence, observed/expected ratio, and exclusivity of chronic diseases and/or groups of these with the corresponding medication were described. (3) Results: The study included 114,516 people, mostly men (59.6%) with a mean age of 78.8 years and a high prevalence of polypharmacy (83.5%). The following patterns were identified: Mental, behavioural, digestive and cerebrovascular; Neuropathy, autoimmune and musculoskeletal; Musculoskeletal, mental, behavioural, genitourinary, digestive and dermatological; Non-specific; Multisystemic; Respiratory, cardiovascular, behavioural and genitourinary; Diabetes and ischemic cardiopathy; and Cardiac. The prevalence of overrepresented health problems and drugs remained stable over the years, although by study end, cohort survivors had more polypharmacy and multimorbidity. Most people followed the same pattern over time; the most frequent transitions were from Non-specific to Mental, behavioural, digestive and cerebrovascular and from Musculoskeletal, mental, behavioural, genitourinary, digestive and dermatological to Non-specific. (4) Conclusions: Eight combined multimorbidity and polypharmacy patterns, differentiated by sex, remained stable over follow-up. Understanding the behaviour of different diseases and drugs can help design individualised interventions in populations with clinical complexity.
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Affiliation(s)
- Noemí Villén
- Medicines Area and Pharmacy Service, Barcelona Territorial Management, Institut Català de la Salut, 08015 Barcelona, Spain
- Department of Pediatrics, Obstetrics, Gynecology, and Preventive Medicine, Universitat Autònoma de Barcelona, Bellaterra, 08193 Cerdanyola del Vallès, Spain
| | - Albert Roso-Llorach
- Department of Pediatrics, Obstetrics, Gynecology, and Preventive Medicine, Universitat Autònoma de Barcelona, Bellaterra, 08193 Cerdanyola del Vallès, Spain
- IDIAP Research Institute, 08007 Barcelona, Spain
| | | | - Marc Danes-Castells
- Germans Trias i Pujol Research Institute (IGTP), Camí de les Escoles, s/n, 08916 Badalona, Spain
- Sant Quirze del Vallès Primary Health Care Center Av. d′Ègara, s/n, Sant Quirze del Vallès, 08192 Barcelona, Spain
| | | | - Amelia Troncoso-Mariño
- Medicines Area and Pharmacy Service, Barcelona Territorial Management, Institut Català de la Salut, 08015 Barcelona, Spain
| | | | - Ester Amado
- Medicines Area and Pharmacy Service, Barcelona Territorial Management, Institut Català de la Salut, 08015 Barcelona, Spain
| | - Concepción Violán
- Department of Pediatrics, Obstetrics, Gynecology, and Preventive Medicine, Universitat Autònoma de Barcelona, Bellaterra, 08193 Cerdanyola del Vallès, Spain
- Germans Trias i Pujol Research Institute (IGTP), Camí de les Escoles, s/n, 08916 Badalona, Spain
- North Metropolitan Research Support Unit, IDIAP Research Institute, Mataró, 08303 Barcelona, Spain
- North Metropolitan Primary Health Care Administration, Institut Català de Salut, Ctra. de Barcelona, 473, Sabadell, 08204 Barcelona, Spain
- Correspondence:
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