Does Socioeconomic Status Account for Racial and Ethnic
Disparities in Childhood Cancer Survival?
Rebecca D. Kehm, PhD 1; Logan G. Spector, PhD 2; Jenny N. Poynter, PhD 2; David M. Vock, PhD 3;
Sean F. Altekruse, PhD 4,5; and Theresa L. Osypuk, SD 1
BACKGROUND: For many childhood cancers, survival is lower among non-Hispanic blacks and Hispanics in comparison with non- Hispanic whites, and this may be attributed to underlying socioeconomic factors. However, prior childhood cancer survival studies
have not formally tested for mediation by socioeconomic status (SES). This study applied mediation methods to quantify the role of SES in racial/ethnic differences in childhood cancer survival. METHODS: This study used population-based cancer survival data from
the Surveillance, Epidemiology, and End Results 18 database for black, white, and Hispanic children who had been diagnosed at the ages of 0 to 19 years in 2000-2011 (n 531,866). Black-white and Hispanic-white mortality hazard ratios and 95% confidence intervals, adjusted for age, sex, and stage at diagnosis, were estimated. The inverse odds weighting method was used to test for mediation by
SES, which was measured with a validated census-tract composite index. RESULTS: Whites had a significant survival advantage over blacks and Hispanics for several childhood cancers. SES significantly mediated the race/ethnicity–survival association for acute lym-phoblastic leukemia, acute myeloid leukemia, neuroblastoma, and non-Hodgkin lymphoma; SES reduced the original association
between race/ethnicity and survival by 44%, 28%, 49%, and 34%, respectively, for blacks versus whites and by 31%, 73%, 48%, and 28%, respectively, for Hispanics versus whites ((log hazard ratio total effect – log hazard ratio direct effect)/log hazard ratio total
effect). CONCLUSIONS: 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 varies between black-white and Hispanic-white com- parisons for some cancers, and this suggests that mediation by other factors differs across groups.
2018 American Cancer Society .
KEYWORDS: cancer survival, childhood cancer, mediation, racial and ethnic disparities, socioeconomic status.
INTRODUCTION
Despite improvements over the last 4 decades in cancer survival in the US pediatric population, marked racial and ethnic
disparities persist. 1Compared with non-Hispanic white (white) children, non-Hispanic black (black) and Hispanic chil-
dren experience lower survival from many cancers, including leukemias, 2,3 lymphomas, 4,5 central nervous system (CNS)
tumors, 6and extracranial solid tumors. 7-9 The underlying causes of racial/ethnic survival differences are not well under-
stood and may vary by cancer type. As outlined in Figure 1,bothbiologicalandsocioeconomicpathwayshavebeenpro-
posed in the literature. 10,11 Underlying genetic variations associated with ancestry may lead to differences in tumor
biology and pharmacogenetics for some childhood cancers. 10 However, race/ethnicity is a socially constructed taxonomy
that is not synonymous with ancestry. 12 Race/ethnicity is highly correlated with socioeconomic status (SES), especially in
the United States, where embedded, institutionalized racism continues to place racial and ethnic minorities at high risk for
low SES. 13 Because of emerging evidence for a positive association between SES and survival from some childhood can-
cers, 11racial/ethnic survival disparities may also be explained by socioeconomic differences.
Quantifying the relative role of SES in explaining racial/ethnic survival disparities will help to inform practice and
intervention efforts. If SES accounts for racial/ethnic survival differences, then interventions addressing social and eco-
nomic barriers to treatment and care are warranted. However, if SES does not fully account for survival differences by
race/ethnicity, then other social factors (eg, immigration) and biological mechanisms (eg, tumor biology) must be consid-
ered. To date, formal mediation methods have not been used to disentangle racial/ethnic disparities in childhood cancer
Corresponding author: Rebecca D. Kehm, PhD, Division of Epidemiology and Community Health, University of Minnesota School of Public Health, 13,000 South Second Street, West Bank Office Building, Minneapolis, MN 55455; kehmx003@umn.edu
1Division of Epidemiology and Community Health, University of Minnesota School of Public Health, Minneapolis, Minnesota; 2Division of Epidemiology and Clini- cal Research, Department of Pediatrics, University of Minnesota, Minneapolis, Minnesota; 3Division of Biostatistics, University of Minnesota School of Public Health, Minneapolis, Minnesota; 4National Cancer Institute, Bethesda, Maryland; 5Epidemiology Branch, Prevention and Population Sciences Program, Division of Cardio- vascular Sciences, National Heart, Lung, and Blood Institute, Bethesda, Maryland.
Seeeditorialonpages thisissue. Additional supporting information may be found in the online version of this article.
DOI: 10.1002/cncr.31560, Received: December 8, 2017; Revised: January 11, 2018; Accepted: February 2, 2018, Published online Online Library (wileyonlinelibrary.com)
Original Article
Cancer 2018;124:40 –
Cancer October 15, 2018 4090
August 20, 2018 in Wiley
90
4097.
VC
3975-8, survival. Therefore, we conducted a mediation analysis
using population-based data, representative of the US
pediatric cancer population, to measure the role of SES in
racial and ethnic childhood cancer survival disparities. We
assessed survival from several childhood cancers to deter-
mine whether mediation by SES differs across cancer
types.
MATERIALS AND METHODS
Study Population
We obtained population-based cancer registry data from
the Surveillance, Epidemiology, and End Results (SEER)
18 database; the Alaska Native Tumor Registry was
excluded. We restricted the analysis to black, Hispanic,
and white cases aged 0 to 19 years with microscopically
confirmed first primary malignancies. Race was assigned
in SEER through medical record abstraction. 14,15 His-
panic ethnicity was assigned in SEER on the basis of self-
report/guardian report of Spanish origin in the medical
record or by a computer algorithm that searches surnames
and maiden names to determine Spanish origin. 14,16 We
assessed race/ethnicity with mutually exclusive categories
(non-Hispanic white, non-Hispanic black, and Hispanic);
individuals of Spanish origin were categorized as His-
panic, regardless of racial background. SES data were
available in SEER for diagnostic years 2000-2012. There-
fore, we restricted our sample to cases diagnosed in 2000-
2011 and followed through December 31, 2012, to allow
for at least 1 year of follow-up. We excluded 45 cases with
in situ tumors, 707 cases with missing/zero months of
follow-up, and 725 cases missing SES data. We assessed
cancers with 200 cases for each racial/ethnic group; they
were classified with the International Classification of
Childhood Cancer, third edition. 17 Our final analytic
sample consisted of 31,866 cases. This study was
approved by the Surveillance Research Program in the
National Cancer Institute’s Division of Cancer Control
and Population Sciences.
Measures
Overall survival was calculated in SEER as months from
the date of the cancer diagnosis to the date of death from
any cause or was censored at the date of last contact.
SES was measured at the neighborhood level (based
on the residential address at the date of the cancer diagno-
sis) with a validated census-tract composite index. 18 As
described in the prior literature, 19 the index was con-
structed through a factor analysis of nationwide 2000
decennial census data and 2005-2009 American Commu-
nity Survey data. 18 Seven indicators of neighborhood
SES, previously specified by Yost et al, 20 were included in
the index: proportion employed in working-class occupa-
tions, proportion aged 16 years or older and unemployed,
education index, 21median household income, proportion
below the 200% poverty level, median rent, and median
house value. Addresses were geocoded to census tracts
(2000 geographic boundaries). The 2000 census values
were assigned to cases diagnosed in 2000-2003; 2005-
2009 American Community Survey values were assigned
to cases diagnosed in 2004-2011. 18 The index is available
in SEER as a 5-level variable categorized into quintiles
(quintile 1 is the lowest SES quintile, and quintile 5 is the
highest SES quintile).
As for covariates, we controlled for diagnostic age
group ( <1, 1-4, 5-9, 10-14, or 15-19 years), sex, and stage
Figure 1. Proposed mediating pathways between race/ethnicity and childhood cancer survival.
4091 Cancer October 15, 2018
Mediation of Childhood Cancer Survival/Kehm et al at diagnosis (SEER Summary Stage 2000 [1998 1]: local-
ized, regional, distant, or unknown/unstaged). 22
Statistical Analysis
For each cancer type, we estimated black-white and
Hispanic-white mortality hazard ratios (total effects) from
multivariate Cox proportional hazards regression models.
No substantial violations of the proportional hazards
assumption were identified. For cancers with a statistically
significant total effect, we used the inverse odds weighting
(IOW) method to test for mediation by SES. 23,24 IOW
analyses were conducted separately for black-white and
Hispanic-white comparisons to account for the possibility
that SES may mediate differently by race/ethnicity,
although sensitivity analyses using multinomial models of
all 3 racial/ethnic groups documented comparable results.
IOW is a semiparametric, weight-based approach that
overcomes many limitations of traditional parametric
mediation methods. 25 For example, IOW is appropriate
for any functional form (rather than just linear models),
can test multiple mediators simultaneously (as opposed to
testing them one by one), and is valid even in the presence
of exposure-mediator interactions. 26
Applying the IOW method, we estimated the natu-
ral27 direct effect (hereafter called the direct effect) of race/
ethnicity on survival by fitting a weighted, multivariate Cox
proportional hazards model. Weighting by the inverse odds
of exposure creates a pseudo-population in which the expo-
sure and the mediator are independent; thus, the race/eth-
nicity–survival association (direct effect) that remains after
accounting for the pathway through SES is estimated. To
obtain the IOW weights, we first estimated the odds of
exposure (ie, race/ethnicity) for each subject from a multi-
variate logistic regression model specifying SES and covari-
ates. We then took the inverse of the predicted odds to
create the IOW weight for whites; nonwhites were assigned
a weight of 1. The nonwhite racial/ethnic group was selected
as the reference to minimize extreme weighting values. Next,
we estimated the natural 27 indirect effect (hereafter called
the indirect effect) of race on survival operating through SES
by subtracting the direct effect (log hazard ratio [ b]) from
the total effect and bootstrapping to obtain standard errors
(500replications).Asignificantindirecteffectprovidessta-
tistical evidence of mediation. To quantify the magnitude of
mediation by SES, we calculated the percent reduction from
the total effect to the direct effect (( btotal –bdirect )/btotal ). Sta-
tistical significance was determined as P<.05 for a 2-sided
hypothesis test. Analyses were performed with Stata 14.2
(StataCorp, College Station, Texas). 28
Secondary analyses
The tract-level SES index likely captures an array of socio-
economic factors contributing to survival. One such factor
may be health insurance status. To empirically test this,
we compared indirect effects of mediation by tract SES
index and also by individual-level health insurance status
(private vs otherwise); we tested each of these mediators
separately and simultaneously. This analysis was confined
to cancers with a significant indirect tract SES effect in the
primary analysis and to cases diagnosed in 2007-2011,
when health insurance data were available in SEER. We
also explored whether we inadvertently overly adjusted
IOW models by including the stage at diagnosis as a
covariate. The stage at diagnosis could theoretically oper-
ate as a downstream mediator of the SES-survival associa-
tion if, for example, SES influences diagnostic timing. 10
We tested logistic models of SES predicting tumor stage
(local vs otherwise and distant vs otherwise), and we com-
pared SES indirect-effect estimates from IOW models
unadjusted and adjusted for the stage at diagnosis.
RESULTS
Descriptive characteristics by cancer type are provided in
Table 1. All-cause mortality from 2000 to 2012 varied
across cancers, ranging from 5.2% among Hodgkin lym-
phoma (HL) cases to 33.8% among acute myeloid leuke-
mia (AML) cases. The mean age at diagnosis varied across
cancers, ranging from 2.5 years (standard deviation, 3.3
years) among neuroblastoma cases to 14.9 years (standard
deviation, 3.8 years) among HL cases. There was a higher
proportion of males versus females for all cancers except
for Wilms tumors (53.4% female). The distribution of
tumor stages varied across cancers (stage does not apply to
leukemias). For example, only 1.9% of astrocytoma cases
were classified as distant stage at diagnosis, whereas
48.9% of neuroblastoma cases were. The distribution of
cases across SES categories was consistent across cancers.
Sample characteristics by race/ethnicity are available in
the supporting information (Supporting Table 1).
InTable 2, we compare all-cause mortality between
black and white cases (total effects). For cancers with sig-
nificant total effects, we also present IOW results for
mediation by SES. Compared with whites, black cases
had a statistically significant higher hazard of death for all
cancers except Wilms tumors, osteosarcomas, and germ
cell tumors. Across the 9 cancers with significant racial
disparities in mortality, black children exhibited a 38%
(neuroblastoma) to 95% (astrocytoma) higher risk of
mortality in comparison with white children ( P<.05).
SES was determined to be a significant mediator of the
4092 Cancer October 15, 2018
Original Article TABLE 1.
Characteristics of Childhood Cancer Cases Aged 0 to 19 Years and Diagnosed in 2000-2011 in the SEER 18 Registries
Cancer Type No.
Race/Ethnicity, No.
Survival, Mean (SD), mo
All-Cause Mortality,
%
Age at Diagnosis, Mean (SD), y
Female,
%
Stage at Diagnosis, %
a
Tract-Level SES Index, %
b
Non- Hispanic White
Non- Hispanic Black Hispanic Localized Regional Distant
Unknown/ Unstaged Q1 Q2 Q3 Q4 Q5
Acute lymphoblastic leukemia
8492 4357 634 3501 70.2 (42.6) 12.9 6.9 (5.3) 43.1 N/A N/A N/A N/A 23.2 21.4 19.3 17.9 18.3
Acute myeloid leukemia 1832 965 253 614 54.6 (43.8) 33.8 8.9 (6.7) 48.0 N/A N/A N/A N/A 25.4 20.5 18.6 18.7 16.8 Neuroblastoma 1901 1214 264 423 63.5 (43.0) 21.6 2.5 (3.3) 47.6 20.8 25.3 48.9 5.0 20.5 19.7 21.0 18.6 20.3 Non-Hodgkin lymphoma 2065 1169 343 553 68.1 (44.2) 15.5 12.6 (5.0) 37.1 30.9 19.6 44.4 5.2 22.4 19.1 19.3 19.4 19.9 Hodgkin lymphoma 3078 1947 384 747 76.8 (41.5) 5.2 14.9 (3.8) 46.6 14.5 48.3 34.3 2.9 20.0 20.2 18.2 19.7 21.9Astrocytoma 3195 2080 360 755 69.2 (45.4) 17.2 9.1 (5.6) 47.9 82.7 11.5 1.9 3.8 19.2 19.2 19.5 20.3 21.9Non-astrocytoma CNS tumor
2827 1718 326 783 60.7 (45.1) 31.2 7.5 (5.8) 42.1 69.7 13.3 12.8 4.2 21.1 19.1 19.6 19.7 20.4
Non-rhabdomyosarcoma STS
1784 974 296 514 65.3 (44.9) 22.9 11.9 (5.9) 46.6 56.6 22.8 15.3 5.4 20.0 21.5 19.9 19.0 19.7
Rhabdomyosarcoma 1202 656 208 338 59.0 (43.1) 32.6 7.9 (5.7) 42.9 31.9 34.1 29.7 4.3 20.7 22.0 18.1 19.1 20.1 Wilms tumor 1430 789 254 387 71.0 (43.2) 8.3 3.4 (3.0) 53.4 42.4 29.7 24.8 3.2 22.5 21.1 19.7 18.1 18.6 Osteosarcoma 1247 619 217 411 60.8 (42.1) 32.6 13.2 (3.7) 45.1 32.3 44.0 20.6 3.1 21.3 22.5 20.2 18.3 17.6Germ cell tumor 2813 1549 231 1033 72.7 (43.5) 7.5 13.8 (6.0) 35.4 55.5 22.7 18.2 3.6 20.9 20.2 19.8 19.1 19.9 Abbreviations: CNS, central nervous system; N/A, not applicable; Q, quintile; SD, standard deviation; SEER, Surveillance, Epidemiology, and End R
esults; SES, socioeconomic status; STS, soft-tissue sarcoma.
aThe stage at diagnosis is N/A for leukemias.bHigher quintiles represent higher SES (ie, Q1 is the lowest SES quintile, and Q5 is the highest SES quintile).
4093 Cancer October 15, 2018
Mediation of Childhood Cancer Survival/Kehm et al race-survival association if the indirect effect of race on
survival operating through SES was statistically signifi-
cant. SES significantly mediated the black-white survival
disparity for acute lymphoblastic leukemia (ALL;
indirect-effect hazard ratio [iHR], 1.17; 95% confidence
interval [CI], 1.07-1.28; P<.01; 44% reduction from
the total effect to the direct effect of the racial disparity in
mortality), AML (iHR, 1.15; 95% CI, 1.03-1.29;
P 5 .01; 28% reduction), and neuroblastoma (iHR, 1.17;
95% CI, 1.03-1.33; P5 .02; 49% reduction). SES was a
marginally significant mediator of the black-white sur-
vival disparity for non-Hodgkin lymphoma (NHL; iHR,
1.16; 95% CI, 0.97-1.37; P5 .10; 34% reduction). First-
leg mediation results are available in the supporting infor-
mation (Supporting Table 2).
In Table 3,comparingall-causemortalitybetween
Hispanic and white cases, we present total effects and IOW
results for testing mediation by SES. Compared
with whites, Hispanic cases had a statistically significantly
or marginally significantly (AML and non-
rhabdomyosarcoma soft-tissue sarcomas) higher hazard of
death for all cancers except HL, non-astrocytoma CNS
tumors, rhabdomyosarcoma, and osteosarcoma. Among
the 6 cancers exhibiting significant ethnic disparities in
mortality, Hispanic children, compared with their white
counterparts, exhibited a 31% (neuroblastoma) to 65%
(NHL) higher risk of mortality ( P<.05). SES significantly
mediated the ethnic mortality disparity for ALL (iHR,
1.16; 95% CI, 1.08-1.26; P<.001; 31% reduction from
the total effect to the direct effect of the ethnic disparity in
mortality),AML(iHR,1.13;95%CI,1.03-1.25; P5 .01;
73% reduction), neuroblastoma (iHR, 1.14; 95% CI,
1.03-1.26; P5 .01; 48% reduction), and NHL (iHR,
1.15; 95% CI, 1.01-1.31; P5 .04; 28% reduction). Nota-
bly, SES significantly mediated both the racial and ethnic
disparities in survival for the same 4 cancers.
Secondary Analyses
Except for NHL, the mediating effect of tract-level SES
was greater than the mediating effect of health insurance
status among black-white and Hispanic-white compari-
sons (Supporting Table 3). For example, the indirect
effect of tract SES on the black-white mortality disparity
for ALL was 1.22 (95% CI, 1.01-1.48; P5 .04; 44%
reduction), whereas the indirect effect of health insurance
was 1.09 (95% CI, 0.94-1.27; P5 .24; 19% reduction).
Among cancers with significant SES indirect effects, SES
was not associated with the stage at diagnosis (Supporting
Table 4). The exclusion of the stage at diagnosis from
IOW models did not lead to notably stronger indirect
SES effects (Supporting Tables 5 and 6).
DISCUSSION
This is the first study to use formal mediation methods to
unpack childhood cancer survival disparities by race/eth-
nicity, and it generated several findings. We replicated
TABLE 2. Mediation by SES of Racial (Black vs White) Survival Disparities Among Childhood Cancer Cases
Aged 0 to 19 Years and Diagnosed in 2000-2011 in the SEER 18 Registries
Cancer Type
Total Effect of Race on Survival Through All Medi- ating Pathways
Direct Effect of Race on Survival After Blocking SES Pathway
Indirect Effect of Race on Survival OperatingThrough SES Pathway Reduction From Total Effect to Direct Effect, % b MortalityHR a 95% CI P Mortality HR a 95% CI P Mortality HR a 95% CI P
Acute lymphoblastic leukemia 1.43 1.15-1.77 <.01 1.22 0.96-1.54 .10 1.17 1.07-1.28 <.01 44 Acute myeloid leukemia 1.68 1.36-2.07 <.001 1.45 1.15-1.84 <.01 1.15 1.03-1.29 .01 28 Neuroblastoma 1.38 1.08-1.75 .01 1.18 0.91-1.52 .22 1.17 1.03-1.33 .02 49 Non-Hodgkin lymphoma 1.53 1.14-2.07 .01 1.33 0.94-1.88 .11 1.16 0.97-1.37 .10 34Hodgkin lymphoma 1.66 1.06-2.60 .03 1.50 0.87-2.58 .15 1.11 0.83-1.48 .50 20Astrocytoma 1.95 1.57-2.43 <.001 1.80 1.42-2.30 <.001 1.08 0.98-1.20 .12 12 Non-astrocytoma CNS tumor 1.53 1.25-1.88 <.001 1.41 1.11-1.78 <.01 1.09 0.97-1.22 .14 20 Non-rhabdomyosarcoma STS 1.40 1.06-1.84 .02 1.34 0.96-1.87 .08 1.04 0.87-1.26 .65 13Rhabdomyosarcoma 1.44 1.10-1.88 .01 1.33 0.98-1.81 .07 1.08 0.93-1.25 .31 21Wilms tumor 0.96 0.57-1.62 .88 Not applicable c
Osteosarcoma 0.88 0.67-1.16 .37 Not applicable c
Germ cell tumors 0.98 0.57-1.69 .94 Not applicable c
Abbreviations: b, log hazard ratio; CI, confidence interval; CNS, central nervous system; HR, hazard ratio; SEER, Surveillance, Epidemiology, and End Results; SES, socioeconomic status; STS, soft-tissue sarcomas.aAdjusted for age, sex, and stage at diagnosis (stage not applicable for leukemias). Bootstrapping was used for standard errors.b( btotal –bdirect )/btotal). cDirect and indirect effects were not estimated for cancers with a statistically nonsignificant total effect ( P>.05); bootstrapping was not used.
4094 Cancer October 15, 2018
Original Article results from prior studies showing that whites have a sig-
nificant survival advantage over blacks and Hispanics for
several childhood cancers, including leukemias, 2,3 lym-
phomas, 4,5 CNS tumors, 6neuroblastomas, 7and non-
rhabdomyosarcoma soft-tissue sarcomas. 9In no instance
was survival among whites significantly worse than that of
either black or Hispanic children. Racial and ethnic sur-
vival differences were not uniform across cancers, and
some variability between black-white and Hispanic-white
comparisons was observed.
We demonstrated that SES significantly mediates
racial/ethnic survival disparities for several childhood can-
cers, including ALL, AML, neuroblastoma, and NHL.
For these cancers, indirect hazard ratios fell within a nar-
row range (1.13-1.17) for both black-white and Hispanic-
white comparisons. This suggests that the association
between SES and survival is not modified by, and may be
shared across, race/ethnicity. Conversely, the proportion
of the overall survival disparity explained by SES (ie, the
percent reduction) did vary by race/ethnicity for some
cancers. For example, among AML cases, SES explained
only 28% of the black-white survival disparity but 73% of
the Hispanic-white disparity. This may suggest a differen-
tial role of other mediating factors across racial/ethnic
groups for some cancers. For example, prior evidence sug-
gests that, among AML cases, a significantly lower pro-
portion of black children have matched family donors
available in comparison with white and Hispanic chil-
dren. 29Among other cancers with significant racial/ethnic
survival disparities (eg, CNS tumors and soft-tissue sarco-
mas), we found no significant evidence of mediation by
SES. Thus, for these cancers in particular, we cannot rule
out mediation by other factors such as differences in
tumor biology, pharmacogenomics, health care quality,
and other social factors not captured by the SES index (eg,
racism). 30
Because SES did not uniformly influence survival
across different types of childhood cancer, the mecha-
nisms through which SES influences survival may be
cancer-specific. For example, the strong association
between SES and ALL survival may be explained by differ-
ences in treatment adherence. 10 Unlike treatments for
other childhood cancers, the treatment of ALL requires a
prolonged maintenance phase composed of the oral
administration of antimetabolites, which may be difficult
for low-SES families to adhere to because of social and
economic constraints. 10 This is supported by prior evi-
dence of lower treatment adherence among children with
ALL living in a single-mother household versus a 2-parent
household. 31 Other cancer-specific mechanisms through
which SES may influence survival are less understood.
Secondary findings from this study suggest that factors
beyond health insurance status and stage at diagnosis con-
tribute to the SES-survival association, at least for some
TABLE 3. Mediation by SES of Ethnic (Hispanic vs White) Survival Disparities Among Childhood Cancer
Cases Aged 0 to 19 Years and Diagnosed in 2000-2011 in the SEER 18 Registries
Cancer Type
Total Effect of Ethnicity on Survival Through All Medi- ating Pathways
Direct Effect of Ethnicity on Survival After Blocking SES Pathway
Indirect Effect of Ethnicity on Survival OperatingThrough SES Pathway Reduction From Total Effect to Direct Effect, % b MortalityHR a 95% CI P Mortality HR a 95% CI P Mortality HR a 95% CI P
Acute lymphoblastic leukemia 1.63 1.43-1.86 <.001 1.40 1.21-1.63 <.001 1.16 1.08-1.26 <.001 31 Acute myeloid leukemia 1.19 0.99-1.43 .07 1.05 0.85-1.29 .66 1.13 1.03-1.25 .01 73 Neuroblastoma 1.31 1.04-1.65 .02 1.15 0.89-1.49 .27 1.14 1.03-1.26 .01 48 Non-Hodgkin lymphoma 1.65 1.29-2.12 <.001 1.44 1.08-1.92 .01 1.15 1.01-1.31 .04 28 Hodgkin lymphoma 1.11 0.76-1.64 .59 Not applicable c
Astrocytoma 1.34 1.10-1.64 <.01 1.26 1.01-1.56 .04 1.07 0.98-1.16 .12 23 Non-astrocytoma CNS tumor 1.07 0.92-1.25 .36 Not applicable c
Non-rhabdomyosarcoma STS 1.22 0.96-1.55 .10 1.13 0.87-1.46 .38 1.08 0.95-1.24 .25 41 Rhabdomyosarcoma 1.11 0.88-1.41 .37 Not applicable c
Wilms tumor 1.60 1.04-2.45 .03 1.57 0.95-2.52 .06 1.02 0.83-1.24 0.88 3 Osteosarcoma 0.99 0.79-1.23 .91 Not applicable c
Germ cell tumor 1.63 1.19-2.24 <.01 1.70 1.19-2.42 <.01 0.96 0.81-1.15 .69 –8
Abbreviations: b, log hazard ratio; CI, confidence interval; CNS, central nervous system; HR, hazard ratio; SEER, Surveillance, Epidemiology, and End Results; SES, socioeconomic status; STS, soft-tissue sarcoma.aAdjusted for age, sex, and stage at diagnosis (stage not applicable for leukemias).b( btotal –bdirect )/btotal). cDirect and indirect effects were not estimated for cancers with a statistically nonsignificant total effect ( P>.05); bootstrapping was not used.
4095 Cancer October 15, 2018
Mediation of Childhood Cancer Survival/Kehm et al childhood cancers. Additional research is needed to fur-
ther unpack the association between SES and childhood
cancer survival.
Limitations
We relied on an area-based variable as our primary measure
of SES because of the lack of individual-level SES measures
in SEER data; moreover, we selected an SES index to oper-
ationalize the SES construct over a meaningful period of
time. Although this improves upon many prior
population-based cancer studies that lacked any measures
of SES or relied on county-level measures, tract-level SES is
still a proxy for individual-level SES in this study because
we could not comprehensively control for SES at the indi-
vidual level. 32,33 Furthermore, we used a fairly crude mea-
sure of individual-level health insurance status (private vs
otherwise) in our secondary analysis. Because the tract-level
SES index was available in SEER only for the years 2000-
2012, the sample size and the follow-up time were limited.
This prevented us from testing more homogenized cancer
and racial/ethnic subgroups or stratifying by age. Addi-
tional research is thus needed for other smaller populations
of racial and ethnic groups not considered in this analysis
because of the rarity of childhood cancer, which limited
power. We also lacked geographic variables to explore
potential spatial variations in survival. Furthermore, the
lack of clinical data in SEER limited our ability to account
for diagnostic, therapeutic, and biological factors, such as
cytogenetic or molecular features. Finally, there is the
potential for differential loss to follow-up by race and SES.
In conclusion, through the application of formal
mediation methods, we have demonstrated that SES signif-
icantly contributes to racial and ethnic survival disparities
for several childhood cancers, including ALL, AML, neuro-
blastoma, and NHL. Thus, for these cancers in particular,
racial/ethnic survival disparities could theoretically be
addressed through initiatives that reduce social and eco-
nomic barriers to effective care. Such efforts may include
expanded health insurance coverage, improved patient care
coordination, increased health literacy, and supplementa-
tion of transportation and childcare costs during treatment.
However, because SES did not fully account for survival
disparities, we cannot rule out the potential role of other
mediating pathways, including tumor biology, pharmaco-
genomics,healthcarequality,andothersocialfactors.A
multipronged intervention approach that both addresses
socioeconomic barriers to care and invests in personalized
treatment regimens may ultimately be needed to fully elim-
inate childhood cancer survival disparities.
FUNDING SUPPORT
This work was supported by a National Institutes of Health
Translational Pediatric Cancer Epidemiology Training Grant
(T32CA099936).
CONFLICT OF INTEREST DISCLOSURES
The authors made no disclosures.
AUTHOR CONTRIBUTIONS
Rebecca D. Kehm : Conceptualization, data curation, formal anal-
ysis, methodology, writing–original draft, and writing–review and
editing. Logan G. Spector : Conceptualization, methodology, and
writing–review and editing. Jenny N. Poynter : Conceptualization,
methodology, and writing–review and editing. David M. Vock :
Conceptualization, methodology, and writing–review and editing.
Sean F. Altekruse : Conceptualization, methodology, and writing–
review and editing. Theresa L. Osypuk : Conceptualization, meth-
odology, and writing–review and editing.
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