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Table 1 Binary logistic regression to identify key determinants of malaria knowledge and personal protection use

From: Border malaria in China: knowledge and use of personal protection by minority populations and implications for malaria control: a questionnaire-based survey

Variable

n (%)1

Know

mosquito

transmits

malaria2

Odds

ratio

Recently

heard

info on

malaria3

Odds

ratio

Uses

bednets4

Odds

ratio

Uses

coils5

Odds

ratio

Uses

repellents6

Odds

ratio

Gender

  

p = 0.001

 

p = 0.003

 

p = 0.092

 

p < 0.001

 

p < 0.0001

Male

480 (62.7)

180 (37.5)

1

085 (17.7)

1

313 (65.3)

1

234 (48.8)

1

232 (48.3)

1

Female

268 (35.0)

052 (19.4)

0.443 (0.279, 0.703)

017 (06.3)

0.352 (0.176, 0.706)

185 (69.0)

1.457 (0.938, 2.321)

160 (59.7)

2.331(1.500, 3.624)

172 (64.2)

2.578 (1.664, 3.994)

Age

  

p = 0.978

 

p = 0.342

 

p = 0.711

 

p = 0.143

 

p = 0.066

< 30

119 (14.4)

030 (27.3)

1

011 (10.0)

1

078 (70.9)

1

048 (43.6)

1

048 (43.6)

1

30 to 50

378 (49.4)

116 (30.7)

1.025 (0.650, 1.684)

062 (16.4)

0.628 (0.330, 1.195)

260 (68.8)

0.858 (0.546, 0.346)

205 (54.2)

1.332 (0.891, 1.991)

202 (53.4)

1.574 (1.002, 2.471)

> 50

255 (33.3)

084 (32.9)

1.072 (0.529, 2.174)

029 (11.4)

0.830 (0.329, 2.095)

158 (62.0)

0.795 (0.432, 1.464)

138 (54.1)

1.808 (0.952, 3.431)

150 (58.8)

2.022 (1.002, 2.471)

Income

  

p = 0.656

 

p = 0.127

 

p = 0.004

 

p < 0.0001

 

p < 0.0001

< 600 Y

013 (01.7)

002 (15.4)

1

003 (23.1)

1

003 (23.1)

1

001 (07.7)

1

001 (07.7)

1

600–1200 Y

056 (07.3)

010 (17.9)

1.313 (0.619, 2.785)

003 (05.4)

2.864 (0.953, 8.613)

024 (42.9)

1.774 (0.911, 03.454)

010 (17.9)

04.257 (1.838, 009.857)

015 (26.8)

02.849 (1.347, 006.024)

Over 1200 Y

665 (86.9)

216 (32.5)

1.858 (0.415, 8.320)

094 (14.1)

0.374 (0.065, 2.138)

461 (69.3)

8.012 (1.784, 35.977)

380 (57.1)

21.584 (2.373, 196.316)

385 (57.9)

27.993 (3.212, 243.985)

Housing

  

p = 0.629

 

p = 0.015

 

p = 0.386

 

p = 0.0001

 

p = 0.012

Temporary

059 (07.7)

009 (15.3)

1

009 (15.3)

1

033 (55.9)

1

013 (22.0)

1

019 (32.2)

1

Semi-perm

057 (07.5)

015 (26.3)

1.347 (0.645, 2.813)

010 (17.5)

1.049 (0.364, 03.020)

029 (50.9)

0.818 (0.393, 1.702)

014 (24.6)

1.586 (0.789, 03.186)

015 (26.3)

1.654 (0.891, 03.069)

Perm Open

539 (70.5)

166 (30.8)

1.617 (0.611, 4.279)

058 (10.8)

2.852 (1.360, 05.983)

383 (71.1)

1.418 (0.511, 3.934)

305 (56.6)

4.847 (1.769, 13.283)

308 (57.1)

4.955 (1.765, 13.916)

Perm Closed

088 (11.5)

040 (45.5)

2.240 (0.575, 8.742)

024 (27.3)

2.476 (0.607, 10.094)

050 (56.8)

1.215 (0.481, 3.070)

058 (65.9)

5.895 (2.002, 17.361)

057 (64.8)

3.222 (1.14, 09.075)

Education

  

p = 0.001

 

p = 0.002

 

p = 0.01

 

p < 0.0001

 

p = 0.004

None

232 (30.3)

043 (18.5)

1

015 (06.5)

1

139 (59.9)

1

096 (41.4)

1

114 (49.1)

1

Primary

291 (38.0)

088 (30.2)

1.548 (0.944, 2.536)

040 (13.7)

2.020 (1.122, 3.637)

194 (66.7)

2.063 (1.218, 3.493)

149 (51.2)

2.171 (1.311, 3.594)

057 (54.0)

1.241 (0.751, 2.049)

Secondary

209 (27.3)

091 (43.5)

2.980 (1.666, 5.332)

045 (21.5)

3.412 (1.677, 6.942)

160 (76.6)

2.496 (1.352, 4.610)

142 (67.9)

4.216 (2.445, 7.268)

128 (61.2)

2.465 (1.437, 4.229)

Occupation

  

p = 0.001

 

Not calculated

 

p = 0.168

 

p = 0.1

 

p = 0.001

Agriculture

562 (73.5)

157 (28.9)

1

071 (13.1)

 

369 (68.0)

1

284 (51.1)

1

300 (54.0)

1

Forestry

031 (04.0)

019 (61.3)

2.194 (0.373,12.909)

008 (25.8)

 

017 (63.0)

2.196 (0.255, 018.925)

011 (40.7)

0.374 (0.041, 3.441)

006 (19.4)

0.210 (0.038, 01.150)

Plantation

027 (03.5)

005 (18.5)

0.131 (0.030, 00.567)

008 (29.6)

 

023 (74.2)

4.009 (0.101, 158.693)

009 (29.0)

0.348 (0.040, 3.056)

005 (18.5)

0.680 (0.019, 23.960)

Labourer

014 (01.8)

003 (25.0)

0.722 (0.210, 02.475)

000 (00.0)

 

009 (75.0)

4.051 (0.694, 023.657)

004 (28.6)

0.152 (0.023, 1.015)

005 (35.7)

0.295 (0.054, 01.621)

Other

096 (12.5)

044 (37.9)

0.645 (0.173, 02.407)

013 (11.0)

 

066 (56.9)

2.206 (0.362, 013.452)

075 (74.3)

0.113 (0.017, 0.767)

077 (76.2)

1.746 (0.230, 13.242)

Altitude

  

P < 0.0001

 

p < 0.0001

 

P < 0.0001

 

p = 0.183

 

p < 0.0001

< 1200 m

314 (42.0)

134 (42.7)

1

060 (19.3)

1

238 (75.8)

1

172 (54.8)

1

143 (45.5)

1

> 1200 m

424 (58.0)

098 (22.6)

0.392 (0.285, 0.538)

042 (09.7)

0.449 (0.294, 0.687)

260 (59.9)

0.447 (0.346, 0.658)

222 (51.2)

0.865 (0.646, 1.157)

261 (60.1)

1.804 (1.345, 2.420)

  1. The number and percentage of respondents in each socioeconomic category was stratified by gender and age of respondent, housing quality, annual household income, occupation of the head of the household, educational level of respondent, occupation of the head of household and the village altitude (> or < 1500 m). The likelihood of those respondents in each category correctly identifying night-biting mosquitoes as the cause of malaria1; having heard information on malaria in the last year2; and answering yes to the question do you use bednets3, coils4, and repellents5 to protect yourself against mosquitoes were calculated with binary logistic regression.