Table 4 Associations between migration city type and health services use based on a logistic regression analysis
From: The health service use of aged rural-to-urban migrant workers in different types of cities in China
Variables | Health records | Health education | Medical treatment |
|---|---|---|---|
Independent variables | |||
Migration city type (third-tier cities and smaller cities) | |||
Second-tier cities | -0.28(0.04)*** | -0.29(0.04)*** | -0.24(0.06)*** |
First-tier cities | -1.12(0.09)*** | -0.58(0.06)*** | -0.39(0.09)*** |
Control variables | |||
Predisposing | |||
Age | 0.01(0.00)** | -0.02(0.00)*** | -0.00(0.00) |
Gender (male) | |||
Female | 0.07(0.04) | -0.12(0.04)** | 0.01(0.05) |
Education (illiterate or primary school) | |||
Middle school | 0.11(0.04)* | 0.25(0.04)*** | -0.07(0.05) |
High school and above | 0.03(0.06) | 0.31(0.06)*** | -0.04(0.08) |
Marital status (no spouse) | |||
Married | 0.22(0.07)** | 0.05(0.06) | 0.03(0.09) |
Enabling | |||
Migration range (across counties within a city) | |||
Across cities, within a province | 0.00(0.05) | -0.00(0.05) | -0.19(0.07)** |
Across province | -0.20(0.05)*** | -0.10(0.05) | -0.10(0.07) |
Medical insurance (no) | |||
Yes | 0.24(0.08)** | 0.20(0.07)** | 0.13(0.10) |
Social security card (no) | |||
Yes | 0.48(0.04)*** | 0.34(0.04)*** | 0.20(0.05)*** |
Needs | |||
Self-health assessment (unwell) | |||
Fair | 0.12(0.07) | 0.24(0.06)*** | -0.35(0.07)*** |
Health | 0.18(0.07)** | 0.31(0.06)*** | -0.62(0.07)*** |
Hypertension or diabetes (no) | |||
Yes | 0.25(0.05)*** | 0.09(0.05)* | 0.25(0.06)*** |
Constants | -2.31(0.24)*** | 1.15(0.22)*** | 0.47(0.30) |
Log likelihood | -8189.45*** | -9214.57*** | -4692.86** |
Sample | 14372 | 14372 | 6938 |