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Table 2 Factors related to anemia: results from two-level logistic regression in 2001 and 2005 a

From: Assessment of maternal anemia in rural Western China between 2001 and 2005: a two-level logistic regression approach

Characteristics 2001 2005
  OR(95%CI) P-value OR(95%CI) P-value
Null model  
u 0j 0.7 ± 0.1 <0.001 0.8 ± 0.1 <0.001
VPC 16.5% - 20.4% -
Full model  
Fixed part  
Provinces b  
Ningxia 0.58(0.40,0.83) <0.01 0.63(0.33,1.22) 0.173
Xinjiang 0.63(0.34,1.14) 0.124 1.14(0.62,2.11) 0.663
Gansu 0.77(0.51,1.15) 0.201 0.31(0.13,0.74) <0.01
Inner Mongolia 0.85(0.59,1.23) 0.386 0.49(0.26,0.93) <0.05
Sichuan 1.04(0.72,1.51) 0.83 1.21(0.68,2.17) 0.515
Guizhou - - 1.56(0.82,2.96) 0.174
Chongqing 1.20(0.80,1.82) 0.378 2.00(1.03,3.85) <0.05
Qinghai 1.46(1.01,2.11) <0.05 1.86(1.00,3.48) <0.05
Guangxi 2.32(1.54,3.50) <0.001 1.31(0.71,2.44) 0.387
Individual characteristics  
Ethnicity  
Han 1.00   1.00  
Tibet 1.29(0.92,1.79) 0.136 1.23(0.79,1.92) 0.355
Uighur 2.35(1.28,4.30) <0.01 1.42(0.74,2.73) 0.288
Hui 1.36(0.99,1.87) 0.060 0.57(0.40,0.82) <0.01
Zhuang 1.06(0.68,1.63) 0.806 2.09(1.34,3.26) <0.01
Others 1.26(0.93,1.70) 0.133 1.42(1.05,1.93) <0.05
Parity  
1 1.00   1.00  
2 0.91(0.79,1.05) 0.194 0.84(0.71,0.98) <0.05
>2 0.76(0.57,0.99) <0.05 0.83(0.62,1.12) 0.228
Wealth index  
Poorest 1.00   1.00  
Middle 0.93(0.81,1.06) 0.280 1.05(0.91,1.22) 0.496
Wealthiest 0.90(0.80,0.99) <0.05 1.09(0.92,1.28) 0.313
Mothers’ age, years 1.00(0.99,1.03) 0.252 1.02(1.01,1.04) <0.01
Breastfeeding duration 0.99(0.98,1.00) <0.05 0.995(0.991,0.999) <0.05
County characteristics  
Altitude, m  
<500 1.00   1.00  
500 ~ 1500 1.03(0.70,1.53) 0.868 1.19(0.69,2.04) 0.530
>1500 2.47(1.58,3.84) <0.001 1.86(1.05,3.64) <0.05
Random part  
u 0j 0.4 + 0.1 <0.001 −1.7 ± 0.3 <0.001
VPC 10.2% - 16.2% -
  1. Abbreviation: VPC variance partition coefficient, u 0j , random effect.
  2. a Values were given as mean ± SD or odds ratio (95%CI) unless otherwise stated.
  3. b Effect coding was used for provinces. The reference group is the average of all 10 provinces.