title: Covariates of infant mortality in China: an exploratory approach.
abstract: To elucidate the nature of the relationship between infant mortality in China and a variety of covariates using data from the 2/1000 Chinese Fertility Survey, we use a logistic regression model where the covariates are transformed with the help of Alternating Conditional Expectation (ACE) algorithm. This approach is used to overcome the general problem in multivariate regression analysis of coding the independent variables so that relationship between independent variables and response variables is best described, rather than coding such variables in an arbitrary way. The study demonstrates the procedures and usefulness of the ACE guided transformation in multivariate analysis. The transformed covariates are then used to estimate the effects of a series of socioeconomic and demographic factors collected in the study of infant death in China. The study shows that after appropriate transformations, all the demographic and socioeconomic variables selected have statistically significant and direct influence on infant death.
authors.affiliation: London School of Economics, Great Britain.
authors.forename: D
authors.initials: D
authors.lastname: Wang
authors.forename: M
authors.initials: M
authors.lastname: Murphy
mesh_terms: D002681:China / Q000453:epidemiology; D006801:Humans; D007223:Infant; D007226:Infant Mortality; D016015:Logistic Models; D008423:Maternal Age; D015999:Multivariate Analysis; D017584:Reproductive History; D012959:Socioeconomic Factors