Individual causal models and population system models in epidemiology

Abstract

A group of individuals behaves as a population system when patterns of connections among individuals influence population health outcomes. Epidemiology usually treats populations as collections of independent individuals rather than as systems of interacting individuals. An appropriate theoretical structure, which includes the determinants of connections among individuals, is needed to develop a 'population system epidemiology'. Infection transmission models and sufficient-component cause models provide contrasting templates for the needed theoretical structure. Sufficient- component cause models focus on joint effects of multiple exposures in individuals. They handle time and interactions between individuals in the definition of variables and assume that populations are the sum of their individuals. Transmission models, in contrast, model interactions among individuals over time. Their nonlinear structure means that population risks are not simply the sum of individual risks. The theoretical base for 'population system epidemiology' should integrate both approaches. It should model joint effects of multiple exposures in individuals as time related processes while incorporating the determinants and effects of interactions among individuals. Recent advances in G-estimation and discrete individual transmission model formulation provide opportunities for such integration.