Abstract: Objective The study was conducted to identify socio-economic predictors of five major respiratory infectious diseases (Measles, Tuberculosis, Epidemic Cerebrospinal Meningitis, Pertussis and Scarlet Fever) in mainland China. Methods Cluster analysis and multidimensional scaling analysis were applied to explore the distribution patterns of these diseases, with principal component analysis used for extracting principal components from the original socio-economic data. Then, mantel test and matching analysis were conducted to quantify the predictor-outcome relationships. Results The results of cluster analysis and multidimensional scaling analysis showed that provinces in China can be classified into 6 groups according to disease burdens. The results of mantel test showed that the correlation coefficient between the disease dissimilarity matrix and the principal component matrix of socio-economic predictors ranged from 0.220 to 0.375, whereas results of matching analysis tended to be better, with the correlation coefficient ranging from 0.402 to 0.545. The best matching predictors identified were “Proportion of children under 15 years old”, “Average education years”, “Illiteracy rate among people beyond 15 years old”, “Urban residents proportion”, “Water consumption per capita per day” and “Unemployment rate”. Conclusions The distribution patterns of the main respiratory infectious diseases in China between 2009 to 2012 were relatively stable and closely related to the socio-economic predictors. And comprehensive control programs should be implemented.