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To explore the correlation between complex multimorbidity and their influencing factors and to reveal the interactions between diseases and factors using network inference methods to identify high-risk populations.
Based on longitudinal data from 2016 to 2022 in the Urumqi public health surveillance database and electronic medical record information database, this study collected information on the occurrence of complex multimorbidity and related variables. The structure of the Bayesian network was learned using the maximum-minimum hill-climbing algorithm combined with prior knowledge, and parameter learning was conducted using Bayesian estimation. Directed acyclic graphs were employed to identify confounding factors and guide the construction of regression models.
A total of 6 938 participants were included in the study, of which 12.96% (899/ 6 938) developed complex multimorbidity over the seven-year period. After screening influencing factors, six predictors were selected for model construction, resulting in a model with 7 nodes and 10 directed edges. The results indicated that age, gender, source, and BMI weredirectly related to the occurrence of complex multimorbidity, all serving as parent nodes in the model. The results of logistic regression based on DAG guidelines showed that the risk of complex multimorbidity would increase by 8.70% [OR=1.087 (95%CI:1.077-1.098)] for each year of age increase in patients with chronic diseases; the OR of complex multimorbidity for rural residents compared to urban residents=0.274 (95%CI: 0.237-0.317); the OR of complex multimorbidity for obese people compared with thenormal weight people=1.019 (95%CI: 1.008-1.504).
Bayesian networks effectively identify the relationships between complex multimorbidity and influencing factors, as well as the interactions among these factors, thus enabling inference about the risk of complex multimorbidity occurrence. Prevention and control of complex multimorbidity requires attention to aging, urban environments, and obesity management.
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| 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 |
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