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Table 4 Coefficients from two-level linear regression models for height among children, stratified by child’s age, 2011

From: The association between urbanization and child height: a multilevel study in China

effects 5–12 years 13–18 years
Model 5 Model 6 Model 7 Model 8
Coefficient SE P Coefficient SE P Coefficient SE P Coefficient SE P
Fixed effects
 Intercept −9.18 1.12 < 0.01 −9.26 1.17 < 0.01 −13.19 1.23 < 0.01 −12.23 1.27 < 0.01
Individual variables- Level-1
 Gender (ref. = female) 0.13 0.08 0.10 0.14 0.08 0.09 0.08 0.08 0.34 0.10 0.08 0.24
Ethnicity (ref. = Han)
 Miao −0.09 0.27 0.76 − 0.11 0.27 0.69 −0.30 0.36 0.56 −0.29 0.34 0.55
 Buyi −0.41 0.33 0.26 −0.28 0.34 0.43 −0.20 0.26 0.58 −0.05 0.25 0.87
 Tujia −0.57 0.35 0.15 −0.53 0.35 0.18 −0.13 0.36 0.78 −0.10 0.34 0.82
Maternal height 0.05 0.01 < 0.01 0.05 0.01 < 0.01 0.08 0.01 < 0.01 0.08 0.01 < 0.01
Maternal Education level (ref. = low school)
 middle school 0.12 0.10 0.24 0.09 0.10 0.38 0.06 0.11 0.58 −0.06 0.11 0.57
 high school 0.55 0.16 < 0.01 0.37 0.17 0.03 0.27 0.17 0.11 −0.07 0.18 0.68
Family income per capita (ref. = 1st Q)
 2nd Quintile 0.18 0.13 0.18 0.19 0.13 0.14 0.23 0.16 0.15 0.18 0.15 0.23
 3rd Quintile 0.35 0.13 < 0.01 0.35 0.13 0.01 0.14 0.15 0.36 0.10 0.15 0.50
 4th Quintile 0.31 0.13 0.02 0.30 0.14 0.03 0.11 0.15 0.47 0.07 0.15 0.63
 5th Quintile 0.29 0.14 0.04 0.24 0.14 0.09 0.31 0.15 0.04 0.21 0.15 0.16
Energy intake from protein (%) 3.44 1.25 < 0.01 2.83 1.26 0.03 1.65 1.40 0.24 1.22 1.36 0.37
Community variables- Level-2
Urbanization Index (ref. = 1st Q)
 2nd Quintile 0.10 0.16 0.51     0.14 0.18 0.42    
 3rd Quintile 0.24 0.16 0.14     0.19 0.17 0.25    
 4th Quintile 0.21 0.17 0.20     0.22 0.18 0.21    
 5th Quintile 0.36 0.17 0.04     0.35 0.18 0.05    
Urbanization components
 Communication     0.03 0.05 0.50     −0.02 0.04 0.67
 Population Density     0.05 0.04 0.23     0.09 0.04 0.01
 Diversity     −0.01 0.05 0.86     0.08 0.05 0.09
 Economic Activity     0.02 0.02 0.49     −0.01 0.02 0.83
 Health Infrastructure     < 0.01 0.02 0.88     < 0.01 0.02 0.85
 Housing     −0.03 0.05 0.54     − 0.11 0.05 0.03
 Traditional Markets     −0.01 0.02 0.55     −0.01 0.02 0.57
 Social Services     − 0.02 0.02 0.32     −0.02 0.02 0.15
 Transportation     −0.02 0.03 0.38     0.04 0.03 0.08
 Education     0.11 0.04 0.01     0.15 0.04 < 0.01
 Modern Markets     −0.03 0.03 0.22     −0.01 0.03 0.67
 Sanitation     0.03 0.03 0.31     0.02 0.03 0.57
Random effects
 Intercept 0.16 0.05 < 0.01 0.15 0.05 < 0.01 0.06 0.04 0.06 0.01 0.03 0.40
 Residual 1.15 0.07 < 0.01 1.13 0.06 < 0.01 0.70 0.06 < 0.01 0.70 0.06 < 0.01
-2LL 2491.5 2477.2 1118.9 1083.8
ICC 0.12 0.12 0.08 0.01
  1. Table 4 presents two-level linear regression analyses stratified by children’s age: Models 5 and 6 were fitted in the age stratum of 5–12 years old, and Models 7 and 8 were fitted in the age stratum of 13–18 years old. For exploratory variables, Models 5 and 7 included the composite urbanization index as community-level contextual factor, while Models 6 and 8 included the 12 urbanization components as community-level variables; all the models have adjusted individual-level factors. The inter-class correlation coefficient (ICC) is a ratio of between-community variance to total variance in children’s heights.-2LL: negative twice likelihood ratio.