Skip to main content

Table 1 The unweighted and weighted level of health literacy by selected characteristics of participants in Nanjing, China

From: The relationship between socioeconomic position and health literacy among urban and rural adults in regional China

Characteristics

Overall participants within each category (n and %)

Participants having adequate HL

n

Unweighted

Weighted

%(95%CI)

P-value*

%(95%CI)

P-value*

Overall

8698 (100)

1566

18.0 (17.2, 18.8)

–

19.9 (16.6, 23.6)

–

Area

 Urban

6241 (71.8)

1334

21.4 (20.4, 22.4)

<0.001

20.8 (17.1, 24.0)

<0.001

 Rural

2457 (28.2)

232

9.4 (8.3, 10.6)

 

11.5 (8.9, 14.7)

 

Gender

 Man

4255 (48.9)

729

17.1 (16.0, 18.3)

0.038

19.5 (16.0, 23.6)

0.048

 Women

4443 (51.1)

837

18.8 (17.7, 20.0)

 

20.3 (17.0, 24.1)

 

Age (years)

 25–39

2554 (29.4)

701

27.4 (25.7, 29.2)

<0.001

25.1 (21.5, 29.1)

<0.001

 40–54

3255 (37.4)

544

16.7 (15.4, 18.0)

 

18.2 (14.2, 22.9)

 

 55–69

2889 (33.2)

321

11.1 (10.0, 12.3)

 

13.9 (10.7, 17.8)

 

Education in years

 0–9

3930 (45.2)

344

8.8 (7.9, 9.6)

<0.001

10.2 (7.6, 13.6)

<0.001

 10–12

2198 (25.3)

410

18.7 (17.0, 20.3)

 

18.0 (15.1, 21.3)

 

  ≥ 13

2570 (29.5)

812

31.6 (29.8, 33.4)

 

28.4 (23.4, 34.1)

 

Family average income

 Low

2978 (34.2)

265

8.9 (7.9, 9.9)

<0.001

10.9 (8.3, 14.3)

<0.001

 Middle

2478 (28.5)

441

17.8 (16.3, 19.3)

 

19.0 (15.5, 23.2)

 

 High

2998 (34.5)

816

27.2 (25.6, 28.8)

 

25.8 (20.7, 31.7)

 

Occupation†

 Blue collar

7426 (85.4)

1170

15.8 (14.9, 16.6)

<0.001

17.9 (14.9, 21.3)

<0.001

 White collar

1272 (14.6)

396

31.1 (28.6, 33.7)

 

29.3 (23.5, 36.0)

 

Chronic disease

 No

6472 (74.4)

1269

19.6 (18.6, 20.6)

<0.001

20.9 (17.5, 24.8)

0.003

 Yes

2226 (25.6)

297

13.3 (11.9, 14.8)

 

15.9 (12.3, 20.3)

 
  1. * Chi-square was used to make comparisons between subgroups of each variable based on weighted data
  2. † Blue collar = farmer, factory worker, forestry worker, fisher, service stuff, salesperson, house-worker and vehicle driver. White collar = office worker, teacher, doctor, academic researcher and government official