摘要：In the absence of scientific consensus on an appropriate theoretical framework, cumulative risk assessment and related research have relied on speculative conceptual models. We argue for the importance of theoretical backing for such models and discuss 3 relevant theoretical frameworks, each supporting a distinctive “family” of models. Social determinant models postulate that unequal health outcomes are caused by structural inequalities; health disparity models envision social and contextual factors acting through individual behaviors and biological mechanisms; and multiple stressor models incorporate environmental agents, emphasizing the intermediary role of these and other stressors. The conclusion is that more careful reliance on established frameworks will lead directly to improvements in characterizing cumulative risk burdens and accounting for disproportionate adverse health effects. FORMAL ASSESSMENT OF combined health effects from exposure to multiple environmental agents dates back at least several decades. 1 However, no cumulative risk assessment conducted by the US Environmental Protection Agency (EPA) has explicitly included nonchemical stressors (e.g., psychological and social factors), such as dilapidated housing, family conflict, and racial discrimination. 1 Strategies to assess cumulative risk fall into 2 general categories: a “bottom–up” approach, which attempts to calculate an aggregate risk estimate by summing risks of individual constituents, and a “top–down” approach, which works backward from observed health effects to disaggregate cumulative risk into its component parts. 2 Currently, principles and practices for conducting cumulative risk assessments are still in development, and there is no empirically verified theory guiding how best to combine and then assess risks from both chemical and nonchemical stressors. 3 , 4 In the subsequent discussion, we examine why decisions about theoretical frameworks matter for cumulative risk assessment, and identify 3 main families of conceptual models that can be used to understand and estimate combined health risks from environmental, social, and psychological factors.
