Mathematical models have facilitated our understanding of infectious diseases dynamics and proved useful tools to compare control scenarios when interventional studies are not feasible or ethical. Here, we summarize evidence linking social, economic and biologic determinants to tuberculosis (TB) and review modeling approaches that have been used to understand their contribution to the epidemic dynamics of TB. Specifically, we find evidence for associations between smoking, indoor air pollution, diabetes mellitus, alcohol, nutritional status, crowding, migration, aging and economic trends, and the occurrence of TB infection and/or disease. We outline some methodological problems inherent to the study of these associations; these include study design issues, reverse causality and misclassification of both exposure and outcomes. We then go on to review two existing approaches to modeling the impact of determinants and the effect of interventions: the population attributable fraction model, which estimates the proportion of the TB burden that would be averted if exposure to a risk factor were eliminated from the population, and deterministic epidemic models that capture transmission dynamics and the indirect effects of interventions. We conclude by defining research priorities in both the study of specific determinants and the development of appropriate models to assess the impact of addressing these determinants.