Skip to main content

Table 4 The moderating effects of the number of qualified hospitals on the relationship between income and health (N=122061)

From: Does the immediate reimbursement of medical insurance reduce the socioeconomic inequality in health among the floating population? Evidence from China

 

OR (95% CI)

P value

OR (95% CI)

P value

Total hospitals

1.043 (1.017–1.069)

0.001

-

-

Primary hospitals

-

-

0.899 (0.812–0.994)

0.038

Secondary hospitals

-

-

1.094 (0829–1.443)

0.527

Tertiary hospitals

-

-

2.211 (1.528–3.201)

0.000

Income

1.280 (1.221–1.342)

0.000

1.332 (1.257–1.411)

0.000

Hospital × Income

0.955 (0.940–0.971)

0.000

-

-

Primary hospitals × Income

-

-

1.043 (0.753–1.150)

0.366

Secondary hospitals × Income

-

-

0.930 (0.753–1.150)

0.504

Tertiary hospitals × Income

-

-

0.641 (0.485–0.847)

0.002

Age

0.941 (0.939–0.943)

0.000

0.941 (0.939–0.943)

0.000

Gender (Male = 1)

1.219 (1.175–1.265)

0.000

1.219 (1.1747–1.264)

0.000

Hukou type (Rural = 1)

1.065 (1.008–1.126)

0.026

1.065 (1.007–1.126)

0.027

Marriage (Unmarried = 1)

1.179 (1.060–1.312)

0.002

1.180 (1.060–1.312)

0.002

Marriage (Married = 1)

1.166 (1.074–1.265)

0.000

1.167 (1.075–1.266)

0.000

Education (Primary school = 1)

1.299 (1.184–1.424)

0.000

1.296 (1.182–1.422)

0.000

Education (Junior high school = 1)

1.695 (1.548–1.857)

0.000

1.693 (1.545–1.854)

0.000

Education (High school = 1)

1.750 (1.585–1.932)

0.000

1.749 (1.584–1.930)

0.000

Education (College or above = 1)

1.772 (1.586–1.979)

0.000

1.773 (1.587–1.981)

0.000

Migrating reasons (Work = 1)

1.474 (1.348–1.612)

0.000

1.473 (1.347–1.610)

0.000

Migrating reasons (Business = 1)

1.792 (1.629–1.970)

0.000

1.789 (1.627–1.967)

0.000

Migrating reasons (Family = 1)

0.974 (0.884–1.073)

0.595

0.972 (0.883–1.071)

0.571

Willingness to settle (Yes = 1)

1.168 (1.117–1.221)

0.000

1.167 (1.117–1.220)

0.000

Year fixed effects

control

control

District fixed effects

control

control

  1. The dependent variable is SRH. Here, we used individual-level cross-sectional data. The ordered logistic model was employed, and data were also adjusted for year and district effects. The standard errors were clustered at the individual level. The 95% confident level were reported in parentheses