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The estimation of levels, trends, and differentials in demographic and health outcomes in developing countries has, over years, relied heavily on indirect methods that were devised to suit limited or deficient data. In recent decades, some world-wide surveys like the World Fertility Survey and its successor, the Demographic and Health Survey, have played important role in filling the gap in the availability of survey data in developing countries. While Demographic and Health Surveys, collected at enormous cost, enable investigators to make in-depth analyses that could guide policy intervention strategies, such analyses demand modern and more advanced statistical techniques.

The book presents both theoretical contributions and empirical applications of such advanced techniques. These include such techniques like Geo-additive models that link individual measures with area variables to account for spatial correlation; Multilevel models that address the issue of clustering within family and household; Multiprocess models that account for interdependencies over life-course events and non-random utilization of health services; and Flexible Parametric alternatives to existing intensity models. The techniques are illustrated mainly through modelling maternal and child health in the African context using data from Demographic and Health Surveys in several countries in the continent.