EFFECTS OF EXPOSURE ON PREVALENCE AND CUMULATIVE RELATIVE RISK: DIRECT AND INDIRECT EFFECTS IN A RECURSIVE HAZARD MODEL

This paper outlines decomposition methods for assessing how exposure affects prevalence and cumulative relative risk. Let     x     denote a vector of exogenous covariates and suppose that a single dimension of time   t   governs two event processes     T ₁  and   T ₂.    If the occurrence of the event     T ₁    determines entry into the risk of the event     T ₂,    then subgroup variation in     T ₁    will affect the prevalence     T ₂,    even if subgroups in the population are otherwise identical. Although researchers often acknowledge this phenomenon, the literature has not provided procedures to assess the magnitude of an exposure effect of     T ₁    on the prevalence of     T ₂.    We derive decompositions that assess how variation in exposure generated by direct and indirect effects of the covariates     x     affect measures of absolute and relative prevalence of     T ₂.    We employ a parametric but highly flexible specification for baseline hazard for the     T ₁  and   T ₂    processes and use the resulting parametric proportional hazard model to illustrate the direct and indirect effects of family structure when     T ₁    is age at first sexual intercourse and     T ₂    is age at a premarital first birth for data on a cohort of non-hispanic white U.S. women.     

Confronting Anxiety in Couple and Family Therapy Supervision: A Developmental Supervisory Model Based on Attachment Theory

Referencing relevant supervisory literature and attachment theory, this article presents a developmental couple and family therapy supervisory model that emphasises the efficacy of the supervisory relationship. Issues concerning anxiety, cognition and learning theory are addressed and phases in the supervisory process are identified and described. Cognitive, emotional and social development are linked to attachment theory and discussed in the supervisory context.     

Alternative Methods for Computing the Sensitivity of Complex Surveillance Systems

Stochastic scenario trees are a new and popular method by which surveillance systems can be analyzed to demonstrate freedom from pests and disease. For multiple component systems—such as a combination of a serological survey and systematically collected observations—it can be difficult to represent the complete system in a tree because many branches are required to represent complex conditional relationships. Here we show that many of the branches of some scenario trees have identical outcomes and are therefore redundant. We demonstrate how to prune branches and derive compact representations of scenario trees using matrix algebra and Bayesian belief networks. The Bayesian network representation is particularly useful for calculation and exposition. It therefore provides a firm basis for arguing disease freedom in international forums.