In this issue of Cancer, Kehm et al 1 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. 2,3 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. 4,5 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. 4,6 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 7,8 ; 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. 9 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. 10 A more comprehensive analysis of health insurance status, rather than the crude measure used in th...
Background Adolescents and young adults (AYA), patients age 15‐39, may experience worse outcomes than pediatric and adult patients. The aim of this paper was to document survival disparities associated with insurance status across the AYA age continuum in the United States. Methods We utilized the Surveillance, Epidemiologic, and End Results database to identify 66 556 AYA patients between 2007 and 2014 with 10 International Classification of Childhood Cancer diagnoses and calculated the Cox proportional hazard ratios of death for those with public or no insurance status compared to private insurance. The odds ratios of having a late stage of diagnosis by insurance status were also calculated. Results Insurance status was a statistically significant predictor of death for lymphoid leukemia (age 15‐19, 30‐34, and 35‐39), acute myeloid leukemia (age 15‐19 and 25‐29), Hodgkin lymphoma (all ages), non‐Hodgkin lymphoma (age 20‐24, 25‐29, 30‐34, and 35‐39), astrocytomas (age 30‐34), other gliomas (age 25‐29, 30‐34, and 35‐39), hepatic carcinomas (age 25‐29), fibrosarcomas, peripheral nerve and other fibrous tumors (age 30‐34), malignant gonadal germ cell tumors (age 20‐24, 25‐29, 30‐34, and 35‐39), and other and unspecified carcinomas (age 20‐24, 25‐29, 30‐34, and 35‐39), independent of stage at diagnosis. This hazard increased with age for most cancer types. Insurance status strongly predicted the odds of a metastatic cancer diagnosis for lymphoma, fibrosarcomas (age 15‐19), germ cell tumors, and other carcinomas. Conclusions AYA in the US experience disparities in cancer survival based on insurance status, independent of late stage of presentation. Patients age 26‐39 may be especially vulnerable to health outcomes associated with poor socioeconomic status, treatment disparities, and poor access to care.
Osteosarcoma (OST) and Ewing sarcoma (ES) are the most common pediatric bone cancers. Patients with metastatic disease at diagnosis have poorer outcomes compared with localized disease. Using the Surveillance, Epidemiology, and End Results registries, we identified children and adolescents diagnosed with OST or ES between 2004 and 2015. We examined whether demographic and socioeconomic disparities were associated with a higher likelihood of metastatic disease at diagnosis and poor survival outcomes. In OST, Hispanic patients and those living in areas of high language isolation were more likely to have metastatic disease at diagnosis. Regardless of metastatic status, OST patients with public insurance had increased odds of death compared to those with private insurance. Living in counties with lower education levels increased odds of death for adolescents with metastatic disease. In ES, non-White adolescents had higher odds of death compared with white patients. Adolescents with metastatic ES living in higher poverty areas had increased odds of death compared with those living in less impoverished areas. Disparities in both diagnostic and survival outcomes based on race, ethnicity, and socioeconomic factors exist in pediatric bone cancers, potentially due to barriers to care and treatment inequities.
The International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM), implemented in 2015, has more codes than ICD-9-CM for events involving cannabis. We examined cannabis indicator trends across the transition from ICD-9-CM to ICD-10-CM in Colorado, where state law regulates adult cannabis use. Using 2011 to 2018 data from hospital and emergency department (ED) discharges, we calculated monthly rates per 1000 discharges for two indicators: (1) cannabis use disorders and (2) poisoning and adverse effects of psychodysleptics. Immediate, point-of-transition (level) and gradual, post-transition (slope) changes across the ICD-9-CM to ICD-10-CM transition were tested using interrupted time series models adjusted for legalisation, seasonality and autocorrelation. We observed a level increase and slope increase in the rate of ED discharges with cannabis use disorders. Hospital discharges with cannabis use disorders had a negative slope change after the transition and no level change. ED discharges with poisoning and adverse effects of psychodysleptics showed an increase in slope after the transition. No effects of the transition were observed on hospital discharges with poisoning and adverse effects of psychodysleptics. Shifts in the level and slope of cannabis indicator rates after implementation of the new coding scheme suggest the use of caution when interpreting trends spanning the ICD-9-CM to ICD-10-CM transition.
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