Socioeconomics, race, and ethnicity in childhood cancer survival: Accessing and addressing root causes of disparities

In this issue of Cancer, Kehm et al report on racial and ethnic differences in childhood cancer survival and quantify how socioeconomic status (SES) mediates these disparities. They show that SES accounts for 28% to 73% of racial and ethnic disparities for several childhood cancers, including acute lymphoblastic leukemia (ALL), acute myeloid leukemia (AML), neuroblastoma, and non-Hodgkin lymphoma. In addition, the authors note that there are still statistically significant racial and ethnic disparities in survival independent of SES, the sources of which remain unclear. Both of these statements represent important steps in the understanding of childhood cancer disparities; they are at once calls for further work to improve our understanding of inequalities as well as opportunities to address them. In this editorial, we explore the epidemiologic challenges of understanding social determinants of childhood cancer survival, specifically those concerning racial and ethnic disparities, and we discuss future directions for increasing health equity for childhood cancer patients. There is resounding evidence that social factors, including race, ethnicity, and SES, are associated with disparities in cancer survival. The current study makes important progress in deconvoluting these different factors while also highlighting the challenges of these efforts. Large databases such as the Surveillance, Epidemiology, and End Results (SEER) database provide powerful evidence that is widely generalizable and not prone to selection and survival biases. Compared with clinical trials, this population-based study provides a better opportunity for understanding possible differences in racial and ethnic populations, which are often underrepresented in clinical trials. However, there are some distinct limitations to consider with the SES classification in the SEER database. Because it is an ecological variable rather than an individual-level determination, there may be significant measurement error or misclassification of individuals, which may bias the socioeconomic effect toward the null; therefore, its contribution to disparities may be underestimated. Because individuals are uniformly labeled, variations in education, income, and occupation within the area-based grouping will not be detectable. The individuals who are worst off and in turn may have the worst survival outcomes will not be identified. Previous research has shown large variability between area-based and household incomes. Consortium trials may provide a balance between collecting individual data and being widely generalizable, but requiring access to advanced centers may limit the generalizability to geographically or economically isolated populations. Furthermore, with the adjustment of the SES status, there is the risk of residual confounding stemming from the broadness of the SES grouping parameters and errors in the classification of SES grouping. A more comprehensive analysis of health insurance status, rather than the crude measure used in this study, could provide an opportunity to further explore the role that SES and insurance play in racial/ethnic disparities in survival. A recent California Cancer Registry study found that compared with privately insured patients, both uninsured patients and patients with public insurance had significantly higher cancer-specific mortality. In conjunction with SES, a more in-depth analysis of insurance coverage may further highlight factors affecting survival, such as access to care and adherence to treatment. There is also concern about misclassification of race in large databases such as SEER. Although SEER has streamlined methods for identifying 4 key races and discriminating between race and ethnicity, there are still no clear guidelines for how institutions and providers should initially classify patients. Several bodies have evaluated the correlation between self-reported and documented race and found significant misclassification of American Indians/Alaska Natives as well as milder misclassification of Hispanic and Asian/Pacific Islander populations. The NAACCR Hispanic Identification Algorithm (version 2) has greatly improved correlation, but some degree of misclassification will continue to persist. These