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The ecological r's of neighborhood mortality levels in a large Ur center are examined. The focus is upon the identification of the relative contribution of demographic, environmental, & soc org'al variables to the explanation of diff's in Ur subarea death rates. Data was derived from the city of Columbus, Ohio. A causal model of path analysis is developed that links mortality to a network of demographic, environmental, & soc phenomena. Through the study of the total network of relationships among the variables, more insight may be gained into the mechanisms through which soc, demographic, & environmental factors influence Ur mortality rates. It is hoped that this approach will aid in a fuller theoretical understanding of diff'ial Ur mortality & lay the ground-work for more comprehensive attacks upon excessive local mortality levels. Analysis involved multiple r regression from which the collective impact of the variables upon mortality as well as the general contribution of each independent variable to mortality's explanation were assessed. Then a causal ordering of key independent variables was hyp'ed on the basis of both the r analysis & past res. This was graphically depicted in a path diagram. Then the path coefficients called for in the model were solved, the model was modified on the basis of the calculated path values by eliminating low values & the coefficients specified in the revised model were calculated. The r matrix based on the revised model was developed, & the predicted r was compared with those originally observed; the adequacy of the model was thereby confirmed. Through tabular analysis of combinations of key independent variables the conditions were specified under which Ur mortality levels are the highest. It is found that mortality rates are sensitive to the total soc & ecological system in general, & to problems of poverty, poor housing, & aging in particular. 3 Tables, 2 Figures. M. Maxfield.

The area of mortality modelling has received significant attention over the last 20 years owing to the need to quantify and forecast improving mortality rates. This need is driven primarily by the concern of governments, professionals, insurance and actuarial professionals and individuals to be able to fund their old age. In particular, to quantify the costs of increasing longevity we need suitable model of mortality rates that capture the dynamics of the data and forecast them with sufficient accuracy to make them useful. In this paper we test several of those models by considering the fitting quality and in particular, testing the residuals of those models for normality properties. In a wide ranging study considering 30 countries we find that almost exclusively the residuals do not demonstrate normality. Further, in Hurst tests of the residuals we find evidence that structure remains that is not captured by the models.

SummaryIn order to determine the validity of infant mortality estimates based on retrospective reporting, the Honduran Ministry of Health carried out a follow-up survey of women interviewed in a 1987 national survey. Women were interviewed approximately 14 months after the baseline survey and were asked about the outcomes of their pregnancies and the survival status of their young children. The overall infant mortality rate calculated from the follow-up survey was lower than that obtained from the baseline survey, due to the particularly low rate among the group of women who were pregnant at the time of the baseline survey. Possible explanations for this low rate are discussed.