Additional value can be gained through cross-cohort collaborations and comparisons (Larsen et al., 2013)(Paternoster et al., 2012)(Brion et al., 2011). By pooling data from multiple cohort studies, causal inferences can be made with greater confidence. In addition, through cross-country comparisons, heterogeneity in exposures is often increased making it more likely that an association between exposure and outcome will be observed. Similarly, cross-cohort comparisons enable researchers to investigate patterns associated with health, social, and economic outcomes in distinct regions of the world. These types of analyses may provide valuable insight into the underlying causes of global health inequalities.