Objective: As a consequence of the epidemiological transition, multimorbidity has been identified as a critical public health challenge in India. The majority of the studies in the domain are grounded on hospital-based data or are based on small sample size, findings from which can only be generalized to a specific sub-group. These studies recommend exploring multimorbidity holistically at a national level to ensure adequate healthcare management in the country. Therefore, the present study examines the pattern and correlates of single and multimorbidity over the past two decades in India.
Methods: The study utilized data on 397901, 257519, and 399705 individuals from 52nd (1994-1995), 60th (2004-2005), and 75th (2018) rounds of cross-sectional data from the National Sample Survey (NSS). Univariate, bivariate, and multivariable statistical methods were applied to draw inferences from the data. The findings depict an increase in single and multimorbidity burden over individuals' age and NSS rounds.
Results: Hypertension and diabetes were the fastest-growing morbidities over time. Higher education, urban residence, and belonging to an affluent class were significantly associated with both single and multimorbidity occurrence over time.
Conclusion: The burden of single and multimorbidity increases over time among India's older adults. Therefore, there is an urgent need to recuperate chronic disease management strategies for older adults in the Indian healthcare infrastructure.
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http://dx.doi.org/10.1177/26335565211062756 | DOI Listing |
Clin Gerontol
January 2025
Division of Geriatrics and Palliative Medicine, Weill Cornell Medicine, New York, New York, USA.
Objectives: Arthritis is associated with poor quality of life (QOL) among older adults; and QOL is even worse among those with arthritis and multimorbidity (MM). Illness intrusiveness and perceived control have been identified in studies of single illnesses as modifiable mechanisms for QOL, but are understudied in older adults with arthritis and MM. We investigated the role of these potential mechanisms with QOL among older adults with arthritis and MM.
View Article and Find Full Text PDFClin Teach
February 2025
General Practice and Primary Care, University of Glasgow, Glasgow, UK.
Background: Multimorbidity and patient complexity are increasing, yet undergraduate medical education curricula remain dominated by single disease frameworks, where students are often shielded from exposure to this complexity. Why this shielding continues to occur is understandable; however, this may leave graduates feeling underprepared for real-world practice. This study aimed to explore medical students' experiences of encountering, managing and dealing with complexity and to provide informed recommendations for integrating complexity into clinical teaching.
View Article and Find Full Text PDFLancet Reg Health West Pac
January 2025
School of Public Health, Harbin Medical University, Harbin, China.
Background: In China, rising chronic diseases has coincided with the increasing burden of multimorbidity, particularly for vulnerable populations. Limited primary data are available to understand the prevalence and patterns of multimorbidity, especially in resource-limited rural areas. This study aims to conduct robust evaluations of the prevalence and patterns of multimorbidity among rural adults in China, and to compare the differences in prevalence and patterns when using primary data alone versus in combination with routinely collected data.
View Article and Find Full Text PDFHum Genomics
January 2025
Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Richards Building B304, 3700 Hamilton Walk, Philadelphia, PA, 19104, USA.
Background: Disease comorbidities and longer-term complications, arising from biologically related associations across phenotypes, can lead to increased risk of severe health outcomes. Given that many diseases exhibit sex-specific differences in their genetics, our objective was to determine whether genotype-by-sex (GxS) interactions similarly influence cross-phenotype associations. Through comparison of sex-stratified disease-disease networks (DDNs)-where nodes represent diseases and edges represent their relationships-we investigate sex differences in patterns of polygenicity and pleiotropy between diseases.
View Article and Find Full Text PDFComput Biol Med
January 2025
Applied Artificial Intelligence Institute, Deakin University, Geelong, Australia.
Multimorbidity, the co-occurrence of multiple chronic conditions within the same individual, is increasing globally. This is a challenge for the single patients, as these individuals are subject to a heavy disease and treatment burden, yet evidence on the epidemiology and consequences of multimorbidity remains underexplored. Historically, studies aiming to understand multimorbidity patterns predominantly utilized cross-sectional data, neglecting the essential temporal dynamics which shape multimorbidity progression.
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