Investigations of age, period, and cohort effects are difficult because the 3 factors are linearly dependent. In a novel application, Kramer et al. (Am J Epidemiol. 2015;182(4):302-312) have used graphical analysis and statistical models to estimate the impact that age, period, and cohort effects have had on trends in black-white inequalities in heart disease mortality. Using a constrained regression approach (with the first 2 periods' effects constrained to zero), Kramer et al. find evidence that age and cohort effects figure more prominently than do period effects in contributing to relative black-white mortality differences, and they argue that early-life exposures should be given greater consideration for mitigation of racial differences in heart disease. In this invited commentary, I argue that the utility of age-period-cohort models for understanding health inequalities depends on the plausibility of the assumptions used to break the link between the 3 factors. Based on the existing age-period-cohort literature, alternative assumptions seem likely to produce substantially different results. I also argue that interpretations of the impacts of age, period, and cohort effects on racial inequalities in heart disease mortality may depend on whether inequalities are assessed on the absolute scale or the relative scale.