PDF
Full
Outline
Objective To analyze the differences in chronic disease multimorbidity trajectories and influencing factors between middle-aged and elderly agricultural and non-agricultural production operators. Methods Data from five waves of the China Health and Retirement Longitudinal Study (CHARLS) from 2011 to 2020 were used. Group-based trajectory modeling (GBTM) was applied to fit multimorbidity trajectories for both groups, and multivariable logistic regression analysis was conducted to identify factors influencing the trajectory groups. Results A total of 9 937 participants were included, with 5 010 in the agricultural group and 4 927 in the non-agricultural group. In the agricultural group, the multimorbidity trajectories were classified into three patterns: no-multimorbidity (19.22%, 963 cases), new-onset multimorbidity (56.45%,2 828 cases), and progressive multimorbidity (24.33%, 1 219 cases). In the non-agricultural group, four patterns were identified: no-multimorbidity (18.06%, 890 cases), new-onset multimorbidity (41.10%, 2 025 cases), progressive multimorbidity (33.65%, 1 658 cases), and high-growth multimorbidity (7.18%, 354 cases). Increasing age, obesity, sleep duration ≤6 hours, and poor psychological status significantly increased the risk of multimorbidity in both groups (P<0.05). Among agricultural group, overweight (OR=1.37, 95% CI: 1.14-1.65; OR=2.49, 95% CI: 2.01-3.08) and obesity(OR=2.21, 95%CI: 1.59-3.06; OR=5.58, 95% CI: 3.93-7.92) significantly increased the risks of multimorbidity. In the non-agricultural group, higher education levels reduced the risk of new-onset multimorbidity(OR=0.75, 95% CI: 0.60-0.95), and moderate physical activity decreased the risk of high-growth multimorbidity(OR=0.68, 95% CI: 0.52-0.90). Smoking cessation was significantly associated with increased risks of multimorbidity (OR=1.55, 95% CI: 1.04-2.32; OR=2.37, 95% CI: 1.57-3.57; OR=4.02, 95%CI: 2.31-7.00). Conclusion In agricultural populations, the new-onset multimorbidity type is dominant, driven by obesity and insufficient sleep. In non-agricultural populations, the high-growth multimorbidity type is associated with poor psychological status and health deterioration after smoking cessation. It is necessary to strengthen weight and sleep management for agricultural populations and focus on mental health as well as post-cessation monitoring for non-agricultural populations to reduce the risk of multimorbidity.
PDF
85
37
| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
关闭全屏
No. 86 Xueyuan South Road, Haidian District, Beijing
010-62199257
qkjq@cast.org.cn
Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House
