Tag: status

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.

REFERENCES

1. Howlader N, Noone A, Krapcho M, et al. SEER Cancer Statistics Review, 1975-2010. Bethesda, MD: National Cancer Institute;2013. 2. Bhatia S. Influence of race and socioeconomic status on outcome of children treated for childhood acute lymphoblastic leukemia. Curr Opin Pediatr. 2004;16:9-14. 3. Hossain MJ, Xie L, Caywood EH. Prognostic factors of childhood and adolescent acute myeloid leukemia (AML) survival: evidencefrom four decades of US population data. Cancer Epidemiol. 2015; 39:720-726. 4. Grubb W, Neboori H, Diaz A, Li H, Kwon D, Panoff J. Racial and ethnic disparities in the pediatric Hodgkin lymphoma population.Pediatr Blood Cancer. 2016;63:428-435. 5. Kent EE, Breen N, Lewis DR, de Moor JS, Smith AW, Seibel NL. US trends in survival disparities among adolescents and young adultswith non-Hodgkin lymphoma. Cancer Causes Control. 2015;26: 1153-1162. 6. Austin MT, Hamilton E, Zebda D, et al. Health disparities and impact on outcomes in children with primary central nervous systemsolid tumors. J Neurosurg Pediatr. 2016;18:585-593. 7. Henderson TO, Bhatia S, Pinto N, et al. Racial and ethnic dispar- ities in risk and survival in children with neuroblastoma: a Child-ren’s Oncology Group study. J Clin Oncol. 2010;29:76-82. 8. Johnson KA, Aplenc R, Bagatell R. Survival by race among children with extracranial solid tumors in the United States between 1985and 2005. Pediatr Blood Cancer. 2011;56:425-431. 9. Waxweiler TV, Rusthoven CG, Proper MS, et al. Non-rhabdomyo- sarcoma soft tissue sarcomas in children: a Surveillance, Epidemiol-ogy, and End Results analysis validating COG risk stratifications. Int J Radiat Oncol Biol Phys. 2015;92:339-348. 10. Bhatia S. Disparities in cancer outcomes: lessons learned from chil- dren with cancer. Pediatr Blood Cancer. 2011;56:994-1002. 11. Gupta S, Wilejto M, Pole JD, Guttmann A, Sung L. Low socioeco- nomic status is associated with worse survival in children with can-cer: a systematic review. PLoS One. 2014;9:e89482. 12. Jorde LB, Wooding SP. Genetic variation, classification and ‘race’. Nat Genet. 2004;36:S28-S33. 13. Jones CP. Levels of racism: a theoretic framework and a gardener’s tale. Am J Public Health. 2000;90:1212-1215. 14. Adamo M, Dickie L, Ruhl J. SEER Program Coding and Staging Manual 2016. Bethesda, MD: National Cancer Institute; 2016. 15. Surveillance, Epidemiology, and End Results. SEER race recode. http://seer.cancer.gov/seerstat/variables/seer/race_ethnicity. AccessedJanuary 2, 2018.

4096 Cancer October 15, 2018

Original Article 16. NAACCR Race and Ethnicity Work Group. NAACCR Guideline for Enhancing Hispanic/Latino Identification: Revised NAACCR Hispanic/Latino Identification Algorithm [NHIA v2. 2.1]. Spring- field, IL: North American Association of Central Cancer Registries;2011. 17. Steliarova-Foucher E, Stiller C, Lacour B, Kaatsch P. International Classification of Childhood Cancer. Cancer. 2005;103:1457-1467. 18. Yu M, Tatalovich Z, Gibson JT, Cronin KA. Using a composite index of socioeconomic status to investigate health disparities whileprotecting the confidentiality of cancer registry data. Cancer Causes Control. 2014;25:81-92. 19. Kish JK, Yu M, Percy-Laurry A, Altekruse SF. Racial and ethnic dis- parities in cancer survival by neighborhood socioeconomic status inSurveillance, Epidemiology, and End Results (SEER) registries. J Natl Cancer Inst Monogr. 2014;2014:236-243. 20. Yost K, Perkins C, Cohen R, Morris C, Wright W. Socioeconomic status and breast cancer incidence in California for different race/eth-nic groups. Cancer Causes Control. 2001;12:703-711. 21. Liu L, Deapen D, Bernstein L. Socioeconomic status and cancers of the female breast and reproductive organs: a comparison acrossracial/ethnic populations in Los Angeles County, California (UnitedStates). Cancer Causes Control. 1998;9:369-380. 22. Young J, Roffers S, Ries L, Fritz A, Hurlbut A. SEER Summary Staging Manual 2000: Codes and Coding Instructions. https://seer.cancer.gov/tools/ssm/. Accessed January 2, 2018. 23. Tchetgen Tchetgen EJ, Shpitser I. Semiparametric Estimation of Models for Natural Direct and Indirect Effects. Cambridge, MA: Harvard Uni- versity; 2011. Harvard University Biostatistics Working Paper 129. 24. Tchetgen Tchetgen EJ. Inverse odds ratio–weighted estimation for causal mediation analysis. Stat Med. 2013;32:4567-4580.

25. Baron RM, Kenny DA. The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J Pers Soc Psychol. 1986;51:1173-1182. 26. Nguyen QC, Osypuk TL, Schmidt NM, Glymour MM, Tchetgen Tchetgen EJ. Practical guidance for conducting mediation analysiswith multiple mediators using inverse odds ratio weighting. Am J Epidemiol. 2015;181:349-356. 27. Pearl J. Direct and indirect effects. In: Breese J, Koller D, eds. Pro- ceedings of the Seventeenth Conference on Uncertainty in ArtificialIntelligence. San Francisco, CA: Morgan Kaufmann Publishers Inc; 2001:411-420. 28. StataCorp. Stata Statistical Software. College Station, TX: StataCorp; 2011. 29. Aplenc R, Alonzo TA, Gerbing RB, et al. Ethnicity and survival in childhood acute myeloid leukemia: a report from the Children’s Oncology Group. Blood. 2006;108:74-80. 30. Kawachi I, Daniels N, Robinson DE. Health disparities by race and class: why both matter. Health Aff (Millwood). 2005;24:343-352. 31. Bhatia S, Landier W, Shangguan M, et al. Nonadherence to oral mercaptopurine and risk of relapse in Hispanic and non-Hispanicwhite children with acute lymphoblastic leukemia: a report fromthe Children’s Oncology Group. J Clin Oncol. 2012;30:2094- 2101. 32. Krieger N. Overcoming the absence of socioeconomic data in medi- cal records: validation and application of a census-based methodol-ogy. Am J Public Health. 1992;82:703-710. 33. Geronimus AT, Bound J. Use of census-based aggregate variables to proxy for socioeconomic group: evidence from national samples. Am J Epidemiol. 1998;148:475-486.

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