Explicitly modeling disparities between subgroups according to our proposed typology of cancer disparities allowed us to identify points of overlap among effectiveness, equity, and efficiency in the context of different options for cervical cancer prevention. Using a previously published cervical cancer model (32
), we showed that several new strategies would reduce the lifetime risk of cervical cancer averaged across all subgroups; however, although some strategies widened disparities between groups, others dramatically reduced them. Furthermore, we identified strategies associated with both favorable average and distributional outcomes. Adding vaccination to current screening patterns or implementing a new screening algorithm and targeting risk-based screening resulted in reduced cancer incidence across all groups; the targeted risk-based screening strategies, with or without vaccination, resulted in the greatest reduction in disparities for black and Hispanic women, as compared with white women. The proposed changes to screening (new screening algorithm and targeted risk-based screening) were more effective and less costly than current screening patterns; strategies that combined these screening changes with vaccination were more effective and more cost-effective than the status quo. These points of convergence are “win-win” in the sense that they have the biggest positive impact in worst-off groups as well as on population health overall. Our claim is that such win-win strategies are most desirable from the perspective of both goals of health policy, population health improvement, and health equity.
We also demonstrate a format for reporting results that allows a clear and explicit portrayal of the distributional effects of different strategies. Although our typology points us in the right direction in terms of identifying potential targets, it cannot help us describe or quantify the trade-offs between strategies that improve overall population health but widen disparities and those that may be marginally less effective or less cost-effective but reduce disparities. Presenting the results of both clinical and cost-effectiveness analyses in the format suggested here can make these trade-offs transparent. We believe that this will promote a more specific dialogue in those cases for which trade-offs between fairness and cost-effectiveness are unavoidable.
To illustrate this point, consider Mechanic’s (46
) claim that policies that improve aggregate health, even when increasing health disparities, are morally acceptable provided that they also improve the absolute level of health for the less-than-equal groups. This claim ignores the fact that there may be a range of strategies that could produce different absolute levels of improvement for less-than-equal groups with or without accompanying changes in the aggregate health improvement. Yet, policy makers must choose among these alternatives even though they have different implications for health equity. One reason for the vagueness of Mechanic’s (46
) claim is that we do not have good (ie, quantified) examples of how different intervention strategies may affect the health levels of worse-off groups relative to aggregate population health. Our discussion of cervical cancer suggests that quantified illustrations of these trade-offs and similar examples can be used to make clearer arguments about the ethics of following one intervention strategy rather than another. By providing quantified illustrations, modeling gets us beyond purely hypothetical cases, even if some hypotheticals are embedded in the modeling alternatives. Thus, modeling allows for more rigorous discussion of policies and their actual consequences.
We emphasize that our suggested typology and stylized example represent a preliminary exploration of how we might begin to better align the methods we use in model-based cost-effectiveness analysis with our policy goals of improved population health and fair distribution of that health. Accordingly, there are several limitations that should be noted. First, we present a simplified example of cervical cancer prevention in which we purposefully restrict our analysis to a select group of strategies. We chose to do so in part because the model is already well described (32
) but also to ensure that the most important point of this article does not get lost in the context of a complex analysis. Thus, the results of this particular example are not intended to inform current policy but rather to illustrate the potential advantage of including the distribution of outcomes in model-based analyses of cancer control. It is important to realize that policies may unintentionally widen disparities as well as be targeted to reduce disparities. The assessment of both of these possibilities is critical to good health policy.
A second limitation is that although our typology assumes that disparities are those inequalities that result from an ethically problematic distribution of socially controllable factors, cancer outcomes that differ as a result of informed voluntary choices can be challenging to disentangle. For example, we view most smoking as a socially controllable factor amenable to policy decisions rather than an informed voluntary behavior because it is primarily the result of behaviors in adolescents who then become addicted to nicotine.
Third, our judgments about relative tractability are admittedly uncertain. For example, some treatment or screening interventions that have poor uptake by certain groups may seem to be tractable; whereas we ought to be able to improve access to them in theory, the obstacles to doing so may be difficult in practice. Even so, favorable results that assume tractability may indeed motivate more intensive efforts to remove barriers to implementation.
Fourth, we did not include all possible strategies that might indeed be tractable; for example, we did not model the costs and benefits of improved access to high-quality cervical cancer treatment. However, in one of our examples of targeted screening in which disparities were essentially eradicated for Hispanic women but only reduced by 50% for black women, the favorable economic profile of this strategy coupled with the explicit and persistent racial disparity could motivate targeted investments to improve high-quality stage-specific cancer treatment for black women. Similarly, as better data become available, strategies that address factors such as race-specific health-seeking behavior, differential access to care based on location (eg, rural vs urban), and the relative influence of socioeconomic factors such as insurance status would allow for refinement of this framework.
Fifth, we did not include specific incremental programmatic costs that might be required for implementation of targeted programs to reduce disparities, which are high-priority empirical data that will be needed in future analyses. Sixth, for this initial work, we have focused on health-care interventions, avoiding the somewhat less tractable social determinants of health. However, if we develop a better understanding of how to quantify trade-offs involving interventions for which we have a clearer idea of the mechanisms involved and which are tractable features of health policy, we will be in a better position to address how to include interventions that modify social determinants of cancer. The extension of this work clearly requires a robust research agenda.
In line with a point made by Neumann and Weinstein (47
) about the use of cost-effectiveness analysis in policy decisions, we cannot avoid difficult trade-offs by simply disregarding the costs and benefits of our choices. Furthermore, the socioeconomic and demographic changes we expect in coming years and the potential for widening disparities in the United States (48
), coupled with anticipated changes in how health care is delivered and financed, support the critical importance of reliable information about the distributional effects (as well as average costs and benefits) of interventions to prevent and treat cancer.
In conclusion, this preliminary work represents a potential framework from which we can consider how to prioritize interventions that reduce inequalities that are most unjust while also considering their tractability. Use of decision analytic methods to explicitly quantify and examine both the population benefits and impact on disparities associated with different strategies allows us to identify those policy choices that might contribute to both equity and efficiency. More importantly, in the majority of cases in which trade-offs between equity and efficiency are inevitable, illuminating the nature and magnitude of these trade-offs will reveal influential data gaps, promote critical dialogue and debate, broaden the conversation about what we value as a society, and move us one step closer to having those values reflected in our policies.