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Identification of risk groups : study of infant mortality in Sri Lanka

University of British Columbia

Multivariate statistical methods, including recent computing-intensive techniques, are explained and applied in a medical sociology context to study infant death in relation to socioeconomic risk factors of households in Sri Lankan villages. The data analyzed were collected by a team of social scientists who interviewed households in Sri Lanka during 1980-81. Researchers would like to identify characteristics (risk factors) distinguishing those households at relatively high or low risk of experiencing an infant death. Furthermore, they would like to model temporal and structural relationships among important risk factors. Similar statistical issues and analyses are relevant to many sociological and epidemiological studies. Results from such studies may be useful to health promotion or preventive medicine program planning. With respect to an outcome such as infant death, risk groups and discriminating factors or variables can be identified using a variety of statistical discriminant methods, including Fisher's parametric (normal) linear discriminant, logistic linear discrimination, and recursive partitioning (CART). The usefulness of a particular discriminant methodology may depend on distributional properties of the data (whether the variables are dichotomous, ordinal, normal, etc.,) and also on the context and objectives of the analysis. There are at least three conceptual approaches to statistical studies of risk factors. An epidemiological perspective uses the notion of relative risk. A second approach, generally referred to as classification or discriminant analysis, is to predict a dichotomous outcome, or class membership. A third approach is to estimate the probability of each outcome, or of belonging to each class. These three approaches are discussed and compared; and appropriate methods are applied to the Sri Lankan household data. Path analysis is a standard method used to investigate causal relationships among variables in the social sciences. However, the normal multiple regression assumptions under which this method is developed are very restrictive. In this thesis, limitations of path analysis are explored, and alternative loglinear techniques are considered.

Text

2010-08-30

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http://hdl.handle.net/2429/27971

Statistics

University of British Columbia

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