Abstract: A limited number of studies have utilized multiple causes of death to investigate infant mortality patterns. The purpose of the present study was to examine the risk distribution of underlying and multiple causes of infant death for congenital anomalies, short gestation/low birth weight (LBW), respiratory conditions, infections, sudden infant death syndrome and external causes across four gestational age groups, namely ≤ 23, 24 − 30, 31 − 36, ≥ 37, and determine the extent to which mortality from each condition is underestimated when only the underlying cause of death is used. The data were obtained from the North Carolina linked birth/infant death files (1999 to 2003) and included 4908 death records. The findings of this study indicate that infants born less than 30 weeks old are more likely (odds ratio ranging from 1.99 to 6.03) to have multiple causes recorded when the underlying cause is congenital anomalies, respiratory conditions and infec tions in comparison to infants whose gestational age is at least 37 weeks. The underlying cause of death underestimated mortality for a number of cause specific deaths including short gestation/LBW, respiratory conditions, infec tions and external causes. This was particularly evident among infants born preterm. Based on these findings, it is recommended that multiple causes, whenever available, should be studied in conjunction with the underlying cause of death data.
Abstract: Searching for data structure and decision rules using classification and regression tree (CART) methodology is now well established. An alternative procedure, search partition analysis (SPAN), is less well known. Both provide classifiers based on Boolean structures; in CART these are generated by a hierarchical series of local sub-searches and in SPAN by a global search. One issue with CART is its perceived instability, another the awkward nature of the Boolean structures generated by a hierarchical tree. Instability arises because the final tree structure is sensitive to early splits. SPAN, as a global search, seems more likely to render stable partitions. To examine these issues in the context of identifying mothers at risk of giving birth to low birth weight babies, we have taken a very large sample, divided it at random into ten non-overlapping sub-samples and performed SPAN and CART analyses on each sub-sample. The stability of the SPAN and CART models is described and, in addition, the structure of the Boolean representation of classifiers is examined. It is found that SPAN partitions have more intrinsic stability and less prone to Boolean structural irregularities.