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Outline
To analyzes the influencing factors of outpatient and inpatient service utilization among middle-aged and elderly people in China based on the Andersen model.
Using the 2020 Chinese Health and Aging Tracking Survey Database as the basis, the study focused on middle-aged and elderly individuals aged 45 and above. Chi-square test, binary logistic regression, and CHAID decision tree were employed to predict the influencing factors.
The outpatient utilization rate among middle-aged and elderly people was 20.32%, and the inpatient utilization rate was 16.78%. Logistic regression showed that the main influencing factors for outpatient utilization were self-assessed health (general OR=0.310, 95%CI: 0.242-0.397 or good OR=0.611, 95%CI: 0.512-0.730), the number of chronic diseases (≥ 2 types OR=2.288, 95%CI: 1.877-2.790 or having 1 type OR=1.510, 95%CI: 1.272-1.794), and being affected by medical treatment during the epidemic (OR=2.755, 95%CI: 2.292-3.313). Urban employee medical insurance (OR=0.577, 95%CI: 0.361-0.923), having internet access in the past month (OR=1.198, 95%CI: 1.025-1.401), age 60-69 years old (OR=1.188, 95%CI: 1.007-1.401), and having running water (OR=0.739, 95%CI: 0.584-0.937). The main influencing factors for inpatient utilization were self-assessed health (general OR=0.238, 95%CI: 0.182-0.311 or good OR=0.400, 95%CI: 0.331-0.482), the number of chronic diseases (≥ 2 types OR=2.410, 95%CI: 1.957-2.967 or having 1 type OR=1.628, 95%CI: 1.352-1.959), IADL impairment (OR=1.346, 95%CI: 1.103-1.643), being affected by medical treatment during the epidemic (OR=1.657, 95%CI: 1.351-2.031), having internet access in the past month (OR=0.776, 95%CI: 0.653-0.922), and being elderly (70-79 years old OR=1.292, 95%CI: 1.017-1.641; ≥ 80 years old OR=1.476, 95%CI: 1.005-2.169). The CHAID decision tree further revealed that being affected by medical treatment during the epidemic was the primary stratification factor for outpatient utilization, and self-assessed health was the primary stratification factor for inpatient utilization. Both models demonstrated good predictive performance, with similar values of the area under the ROC curve.
Logistic regression and CHAID decision tree can complementarily analyze the influencing mechanisms of health service utilization. Policies should focus on middle-aged and elderly people with poor self-assessed health, chronic disease comorbidity, IADL impairment, and high age, and establish a medical service guarantee mechanism for the elderly in the event of a public health emergency.
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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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