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      Does Socioeconomic Status Account for Racial and Ethnic Disparities in Childhood Cancer Survival? : Mediation of Childhood Cancer Survival

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          Abstract

          <div class="section"> <a class="named-anchor" id="S1"> <!-- named anchor --> </a> <h5 class="section-title" id="d854145e170">Background</h5> <p id="P1">For many childhood cancers, survival is lower in non-Hispanic blacks and Hispanics compared to non-Hispanic whites, which may be attributed to underlying socioeconomic factors. However, prior childhood cancer survival studies have not formally tested for mediation by socioeconomic status (SES). We applied mediation methods to quantify the role of SES in racial/ethnic differences in childhood cancer survival. </p> </div><div class="section"> <a class="named-anchor" id="S2"> <!-- named anchor --> </a> <h5 class="section-title" id="d854145e175">Methods</h5> <p id="P2">We used population-based cancer survival data from the Surveillance, Epidemiology, and End Results 18 database for black, white, and Hispanic children, ages 0-19 years, diagnosed 2000-2011 (N=31,866). We estimated black-white and Hispanic-white mortality hazard ratios (HR) and 95% confidence intervals (CI), adjusted for age, sex, and stage at diagnosis. We used the inverse odds weighting (IOW) method to test for mediation by SES, measured with a validated census tract composite index. </p> </div><div class="section"> <a class="named-anchor" id="S3"> <!-- named anchor --> </a> <h5 class="section-title" id="d854145e180">Results</h5> <p id="P3">Whites had a significant survival advantage over blacks and Hispanics for several childhood cancers. SES significantly mediated the race/ethnicity-survival association for acute lymphoblastic leukemia, acute myeloid leukemia, neuroblastoma, and non-Hodgkin lymphoma; SES reduced the original association between race/ethnicity and survival by 44% ((log hazard ratio total effect – log hazard ratio direct effect)/log hazard ratio total effect), 28%, 49%, and 34% respectively for blacks vs. whites, and by 31%, 73%, 48%, and 28% respectively for Hispanics vs. whites. </p> </div><div class="section"> <a class="named-anchor" id="S4"> <!-- named anchor --> </a> <h5 class="section-title" id="d854145e185">Conclusions</h5> <p id="P4">SES significantly mediates racial/ethnic childhood cancer survival disparities for several cancers. However, the proportion of the total race/ethnicity-survival association explained by SES varied between black-white and Hispanic-white comparisons for some cancers, suggesting that mediation by other factors differs across groups. </p> </div>

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          Most cited references18

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          Genetic variation, classification and 'race'.

          New genetic data has enabled scientists to re-examine the relationship between human genetic variation and 'race'. We review the results of genetic analyses that show that human genetic variation is geographically structured, in accord with historical patterns of gene flow and genetic drift. Analysis of many loci now yields reasonably accurate estimates of genetic similarity among individuals, rather than populations. Clustering of individuals is correlated with geographic origin or ancestry. These clusters are also correlated with some traditional concepts of race, but the correlations are imperfect because genetic variation tends to be distributed in a continuous, overlapping fashion among populations. Therefore, ancestry, or even race, may in some cases prove useful in the biomedical setting, but direct assessment of disease-related genetic variation will ultimately yield more accurate and beneficial information.
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            Use of census-based aggregate variables to proxy for socioeconomic group: evidence from national samples.

            Increasingly, investigators append census-based socioeconomic characteristics of residential areas to individual records to address the problem of inadequate socioeconomic information on health data sets. Little empirical attention has been given to the validity of this approach. The authors estimate health outcome equations using samples from nationally representative data sets linked to census data. They investigate whether statistical power is sensitive to the timing of census data collection or to the level of aggregation of the census data; whether different census items are conceptually distinct; and whether the use of multiple aggregate measures in health outcome equations improves prediction compared with a single aggregate measure. The authors find little difference in estimates when using 1970 compared with 1980 US Bureau of the Census data or zip code compared with tract level variables. However, aggregate variables are highly multicollinear. Associations of health outcomes with aggregate measures are substantially weaker than with microlevel measures. The authors conclude that aggregate measures can not be interpreted as if they were microlevel variables nor should a specific aggregate measure be interpreted to represent the effects of what it is labeled.
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              Health disparities by race and class: why both matter.

              In this essay we examine three competing causal interpretations of racial disparities in health. The first approach views race as a biologically meaningful category and racial disparities in health as reflecting inherited susceptibility to disease. The second approach treats race as a proxy for class and views socioeconomic stratification as the real culprit behind racial disparities. The third approach treats race as neither a biological category nor a proxy for class, but as a distinct construct, akin to caste. We point to historical, political, and ideological obstacles that have hindered the analysis of race and class as codeterminants of disparities in health.
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                Author and article information

                Journal
                Cancer
                Cancer
                Wiley
                0008543X
                August 20 2018
                Affiliations
                [1 ]Division of Epidemiology and Community Health; University of Minnesota School of Public Health; Minneapolis Minnesota
                [2 ]Division of Epidemiology and Clinical Research, Department of Pediatrics; University of Minnesota; Minneapolis Minnesota
                [3 ]Division of Biostatistics; University of Minnesota School of Public Health; Minneapolis Minnesota
                [4 ]National Cancer Institute; Bethesda Maryland
                [5 ]Epidemiology Branch, Prevention and Population Sciences Program, Division of Cardiovascular Sciences; National Heart, Lung, and Blood Institute; Bethesda Maryland
                Article
                10.1002/cncr.31560
                6234050
                30125340
                243a2291-95cf-491d-b4ba-177435ebc735
                © 2018

                http://doi.wiley.com/10.1002/tdm_license_1.1

                http://onlinelibrary.wiley.com/termsAndConditions#vor

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