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• W3176121444 abstract "Abstract Background Influenced by various factors such as socio-demographic characteristics, behavioral lifestyles and socio-cultural environment, the multimorbidity patterns in old adults remain complex. This study aims to identify their characteristics and associated multi-layered factors based on health ecological model. Methods In 2019, we surveyed a total of 7480 participants aged 60+ by using a multi-stage random cluster sampling method in Shanxi province, China. Latent class analysis was used to discriminate the multimorbidity patterns in old adults, and hierarchical regression was performed to determine the multi-layered factors associated with their various multimorbidity patterns. Results The prevalence of multimorbidity was 34.70% among the old patients with chronic disease. Over half (60.59%) of the patients with multimorbidity had two co-existing chronic diseases. “Degenerative/digestive diseases”, “metabolic diseases” and “cardiovascular diseases” were three specific multimorbidity patterns. Behavioral lifestyles-layered factors had the most explanatory power for the three patterns, whose proportions of explanatory power were 54.00, 43.90 and 48.15% individually. But the contributions of other multi-layered factors were different in different patterns; balanced diet, medication adherence, the size of family and friendship network, and different types of basic medical insurance might have the opposite effect on the three multimorbidity patterns ( p < 0.05). Conclusions In management of old patients with multimorbidity, we should prioritize both the “lifestyle change”-centered systematic management strategy and group-customized intervention programs." @default.
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• W3176121444 date "2021-06-19" @default.
• W3176121444 modified "2023-09-26" @default.
• W3176121444 title "Multimorbidity patterns in old adults and their associated multi-layered factors: a cross-sectional study" @default.
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• W3176121444 doi "https://doi.org/10.1186/s12877-021-02292-w" @default.
• W3176121444 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8214251" @default.
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