The conventional definition of multimorbidity may not address the complex treatment needs resulting from interactions between multiple conditions, impacting self-rated health (SRH). In India, there is limited research on healthcare use and SRH considering diverse disease combinations in individuals with multimorbidity. This study aims to identify multimorbidity clusters related to healthcare use and determine if it improves the self-rated health of individuals in different clusters. This study extracted information from cross-sectional data of the first wave of the Longitudinal Ageing Study in India (LASI), conducted in 2017-18. The study participants were 31,373 people aged ≥ 60 years. A total of nineteen chronic diseases were incorporated to identify the multimorbidity clusters using latent class analysis (LCA) in the study. Multivariable logistic regression was used to examine the association between identified clusters and healthcare use. A propensity score matching (PSM) analysis was utilised to further examine the health benefit (i.e., SRH) of using healthcare in each identified cluster. LCA analysis identified five different multimorbidity clusters: relatively healthy' (68.72%), 'metabolic disorder (16.26%), 'hypertension-gastrointestinal-musculoskeletal' (9.02%), 'hypertension-gastrointestinal' (4.07%), 'complex multimorbidity' (1.92%). Older people belonging to the complex multimorbidity [aOR:7.03, 95% CI: 3.54-13.96] and hypertension-gastrointestinal-musculoskeletal [aOR:3.27, 95% CI: 2.74-3.91] clusters were more likely to use healthcare. Using the nearest neighbor matching method, results from PSM analysis demonstrated that healthcare use was significantly associated with a decline in SRH across all multimorbidity clusters. Findings from this study highlight the importance of understanding multimorbidity clusters and their implications for healthcare utilization and patient well-being. Our findings support the creation of clinical practice guidelines (CPGs) focusing on a patient-centric approach to optimize multimorbidity management in older people. Additionally, finding suggest the urgency of inclusion of counseling and therapies for addressing well-being when treating patients with multimorbidity.
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http://dx.doi.org/10.1371/journal.pgph.0002330 | DOI Listing |
Nature
December 2024
State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, China.
J Multimorb Comorb
December 2024
Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA.
Background: Multimorbidity is common in patients with atrial fibrillation (AF), yet comorbidity patterns are not well documented.
Methods: The prevalence of 18 chronic conditions (6 cardiometabolic, 7 other somatic, 5 mental health) was obtained in patients with new-onset AF from 2013-2017 from a 27-county region and controls matched 1:1 on age, sex, and county of residence. For AF patients and controls separately, clustering of conditions and co-occurrence beyond chance was estimated (using the asymmetric Somers' D statistic), overall and for ages <65, 65-74, 75-84, and ≥85 years.
Am J Epidemiol
December 2024
CRONICAS Centre of Excellence in Chronic Diseases, Universidad Peruana Cayetano Heredia, Lima, Peru.
Multimorbidity data is typically analysed by tallying disease counts, which overlooks nuanced relationships among conditions. We identified clusters of multimorbidity and subpopulations with varying risks and examined their association with all-cause mortality using a data-driven approach. We analysed 8-year follow-up data of people ≥35 years who were part of the CRONICAS Cohort Study, a multisite cohort from Peru.
View Article and Find Full Text PDFBMC Geriatr
December 2024
Department of Nursing, Daqing Campus, Harbin Medical University, 39 Xinyang Road, Daqing, 163319, China.
Objectives: This study aimed to examine Intrinsic Capacity (IC) subgroups and the association of IC subgroups with IC predictors in Chinese urban empty nesters.
Methods: A convenient sample of 385 older adults aged 60 and above in Community Health Service Center was recruited from Hei Longjiang Province, China, between June 2023 and December 2023. Latent class Analysis (LCA) was conducted to explore IC subgroups using the sensory, cognition, locomotion, psychological, and vitality domains of IC as input variables.
BMC Prim Care
December 2024
School of Life Course & Population Sciences, Department of Population Health Sciences, King's College London, London, UK.
Background: We aimed to identify and characterise the longitudinal patterns of multimorbidity associated with stroke.
Methods: We used an unsupervised patient-oriented clustering approach to analyse primary care electronic health records (EHR) of 30 common long-term conditions (LTC) in patients with stroke aged over 18, registered in 41 general practices in south London between 2005 and 2021.
Results: Of 849,968 registered patients, 9,847 (1.
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