This paper describes methods of calculating adjusted rates by using regression coefficients estimated from binary variable multiple regression analysis and multiple logistical regression analysis. The methods discussed are being applied to data collected in a cross-sectional study of respiratory diseases in a rural area of Beijing. The effects of social, biological and environmental factors on pulmonary functions have been examined. Other adjustment methods were also used to analyse the data for the purpose of comparison with the above methods. Of the potential risk factors, associated with impairment of pulmonary function before adjustment, only age and smoking were consistently after adjustment. These results suggest that the effects of sex and social economic status on impairment of pulmonary function were confounded by the other factors. In a further analysis of confounding factors, age and smoking were found to have distorted the risk estimates of social economic status and sex separately. The relative merits of each method are discussed. It is emphasized that when the sample size is relatively small or/and the number of influencing factors is large, regression analysis methods should be used, Mantel-Haenzszel method and logistic regression method are most appropriate for relative odds, and linear regression method is most appropriate for differences of rate in the evaluation of potential risk factors. Finally, regression models were developed to assess the relative risk on the basis of information available in this cross-sectional study, and the overall prevalent risk of impaired pulmonary function.
To investigate the factor relating to the average life span (e0) in Japan, relationships of annual transitions and geographical differences in eo to numbers of medical personnel, institutions and others were studided with serial and geographical correlations, and the following results were obtained. 1) The serial correlation coefficients between e0 and numbers of medical institutions and others, the trends of which were excluded by linear regression, were positive and significant, so was the partial correlation coefficient between e0 for women and number of hospitals. These suggested that the relation between e0 and number of medical institutions was strong, especially for women, number of hospitals and annual transitions in e0. 2) The geographical correlation coefficient between number of pharmacists and e0 for men, and that between number of general clinics and eo for women were positive and significant, so was the partial correlation coefficient between number of pharmacists and e0 for men. These suggested that strong were the relation between number of pharmacists and the geographical differences in eo for men, and that between general clinics and eo for women. 3) The geographical correlation coefficients between annual changes in eo and numbers of medical personnel, institutions and others were positive and significant, so was the partial correlation coefficient between annual changes in e0 for women and number of public health nurses. In the multiple regression analyses of annual changes in e0 on items, numbers of pharmacists and public health nurses were selected in this order as the variables for men, so did numbers of public health nurses and general clinics for women. These suggested that strong were the relations of numbers of medical personnel, institutions and others, especially numbers of pharmacists and public health nurses for men, and public health nurses and general clinics for women, to the geographical differences of annual changes in e0.
A cross national areal analysis of fertility and its relating socio-economic factors of the People's Republic of China in 1981-1982 revealed following findings. 1. Simple correlation showed the areal relationship between fertility and the selected socio-economic factors : the higher the level of?gmodernization" was or the lower the ratio of minority nationalities was, the lower the level of fertility was. 2. The multiple regression analysis and the path analysis showed the causal relationship to fertility, of the ratio of minority nationalities, the ratio of primary industrial population, and the ratio of women educated at junior high school level or above. This meant that "modernization" like improvement of women education level had played a certain role in fertility decline in modern China. 3. As a considerable part of fertility reducing effect of the ratio of minority nationalities and some part of the ratio of primary industrial population could be regarded as reflection of population policy, this cross-section analysis proved the considerable contribution of Chiness population policy to the dramatic fertility decline, that we had previously proved by time trend analysis. And this analysis also suggested that the fertility decline in China was probably accelerated by the combination of "modernization" with population policy.