Cancer Causes Control (2014) 25:633–637 DOI 10.1007/s10552-014-0357-7
BRIEF REPORT
The association between neighborhood socioeconomic status and ovarian cancer tumor characteristics Caryn E. Peterson • Garth H. Rauscher • Timothy P. Johnson Carolyn V. Kirschner • Richard E. Barrett • Seijeoung Kim • Marian L. Fitzgibbon • Charlotte E. Joslin • Faith G. Davis
•
Received: 7 December 2013 / Accepted: 4 February 2014 / Published online: 16 February 2014 Ó Springer International Publishing Switzerland 2014
Abstract Purpose Higher pathologic grade, suboptimal debulking surgery, and late-stage are markers of more aggressive and advanced ovarian cancer. Neighborhood socioeconomic status (SES) has been associated with more aggressive and advanced tumors for other cancer sites, and this may also be true for ovarian cancer. Methods We examined the association between neighborhood SES and ovarian cancer tumor characteristics using data on 581 women diagnosed with epithelial ovarian cancer in Cook County, Illinois. Two complementary measures (concentrated disadvantage and concentrated affluence) were used to estimate neighborhood SES. Prevalence differences and 95 % confidence intervals were estimated in logistic regression models adjusted for age and race.
Results Greater disadvantage was associated with higher grade tumors (p = 0.03) and suboptimal debulking (p = 0.05) and marginally associated with later tumor stage (p = 0.20). Greater affluence was inversely associated with stage at diagnosis (p = 0.004) and suboptimal debulking (p = 0.03) and (marginally) with tumor grade (p = 0.21). Conclusion Our findings suggest that lower SES, estimated by neighborhood SES, is associated with ovarian cancer tumor characteristics indicative of more advanced and aggressive disease.
C. E. Peterson (&) G. H. Rauscher F. G. Davis Division of Epidemiology and Biostatistics (MC 923), School of Public Health, University of Illinois at Chicago, 1603 West Taylor Street, Chicago, IL 60612-4336, USA e-mail:
[email protected]
S. Kim Division of Health Policy and Administration, School of Public Health, University of Illinois at Chicago, Chicago, IL, USA
T. P. Johnson Survey Research Lab, Public Administration, University of Illinois at Chicago, Chicago, IL, USA C. V. Kirschner Division of Gynecologic Oncology, NorthShore University HealthSystem, Evanston, IL, USA C. V. Kirschner Department of Obstetrics and Gynecology, University of Chicago, Chicago, IL, USA
Keywords Ovarian cancer Neighborhood socioeconomic status Tumor characteristics Socioeconomic disparities
M. L. Fitzgibbon Department of Medicine and School of Public Health, University of Illinois at Chicago, Chicago, IL, USA C. E. Joslin Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, IL, USA F. G. Davis Department of Public Health Sciences, School of Public Health, University of Alberta, Edmonton, AB, Canada
R. E. Barrett The Center for Health Behavior Research, University of Illinois at Chicago, Chicago, IL, USA
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634
Abbreviations FIGO Fe´deration Internationa`le de Gyne´cologie et d’Obste´trique PD Prevalence difference CI Confidence interval
Introduction Late-stage diagnosis, higher pathologic grade, and suboptimal surgical debulking are important prognostic factors for ovarian cancer [1]. Collectively, these correlated outcomes represent more aggressive ovarian cancer. [2, 3]. Neighborhood socioeconomic status (SES) has been associated with late-stage diagnosis for breast and cervical cancers [4, 5], and this association may also exist for ovarian cancer. Greater neighborhood disadvantage and lower affluence may be markers of individual resources, health behaviors, knowledge, access to, and attainment of high quality and consistent healthcare, all of which could lead to later stage at diagnosis. In addition, relative socioeconomic disadvantage has been associated with more biologically aggressive breast cancers [6–8], but to our knowledge, this relation has not previously been assessed with respect to ovarian cancer. We examined the association between neighborhood SES and ovarian cancer tumor characteristics in women diagnosed with epithelial ovarian cancer (‘‘ovarian cancer’’) in Cook County, Illinois. We used two well-established measures of neighborhood SES—concentrated disadvantage (disadvantage) and concentrated affluence (affluence) [9, 10]—to estimate the neighborhood SES of women at the time of diagnosis.
Methods Study population The study population consisted of 581 confirmed cases of ovarian cancer diagnosed between 1 June 1994 and 31 December 1998. Cases were recruited from hospitals in Cook County, Illinois, which includes the city of Chicago, and identified through hospital cancer registrar reports and the Illinois State Cancer Registry. (Eighty-three percent of Cook County hospitals participated in this study.) Cases of epithelial ovarian cancer (‘‘ovarian cancer’’) were eligible for inclusion if they were residents of Cook County, were 18–74 years old at the time of diagnosis, and self-reported their race as either ‘‘Black’’ or ‘‘White.’’ Among 1,210 cases available between 1 June 1994 and 31 December 1998, 702 met the eligibility criteria. Diagnosis was confirmed
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histologically after surgery using the International Histological Classification of Ovarian Tumors recommended by the Fe´deration Internationa`le de Gyne´cologie et d’Obste´trique (FIGO) [11]. A pathology review of 319 cases (55 %) was conducted by an independent gynecologic pathologist. Hospital pathology reports were used for histologic classification for the remaining 262 cases. Following a review of histology codes, 102 tumors were determined to be either benign or stromal, or of germ-cell origin, and were subsequently excluded from this analysis. Of the remaining 600 cases, vital status was valid for 581 women. The protocol was approved by the University of Illinois at Chicago Institutional Review Board. Outcome variables Three variables were evaluated as characteristics of tumor aggressiveness: late-stage (FIGO III/IV) versus early-stage (FIGO I/II) diagnosis; high pathologic grade (moderately to poorly differentiated) versus low-grade (well differentiated); and tumors that were considered suboptimally debulked following initial surgery (residual lesions greater than two centimeters, which was the definition at the time our cases were diagnosed) versus optimally debulked (two centimeters or less). Only epithelial tumors were included in this analysis. Neighborhood SES variables Neighborhood SES variables were constructed using U.S. Census data. Because the case-ascertainment period (i.e., 1994–1998) spanned two U.S. Census periods, both the 1990 and 2000 census periods were used to develop our two indices of neighborhood SES. Each patient’s residential address at the time of diagnosis was geocoded to the block level and then located within a census tract. Area disadvantage was constructed using six census variables: percent below poverty; percent unemployed; percent receiving public assistance; percent in female-headed households; percent under age 18; and percent African-American [10]. Area affluence was constructed using three census variables: percent of families with incomes above $75,000 (2000 Census period) or $50,000 (1990 Census period); percent of adults with college educations; and the percent of the civilian labor force employed in professional/managerial occupations [9]. Data from the 1990 and 2000 Census periods were used to create interpolated values representing the midpoint in the ascertainment period (i.e., 1996) for each of the nine census variables, and then each interpolated value was standardized (i.e., converted to z-scores). The variables used to construct the disadvantage and affluence variables were then summed with equal weighting and standardized to create the final disadvantage and affluence
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635
indices. Higher scores for each index represented greater concentrated disadvantage or greater concentrated affluence, and each was modeled as a continuous variable. Statistical analysis Differences in the distribution of tumor characteristics by quartile of disadvantage and affluence were tested using chi-square statistics. Three-level versions of the disadvantage and affluence variables were then defined by categorizing the original variables at \0.5 SD below the sample mean, within 0.5 SD of the sample mean, and [0.5 SD above the sample mean. These were evaluated separately in Table 1 Selected characteristics of the study population (n = 581) Characteristic
n
(%)
Black
100
17
White
481
83
Mean age, years (SD) 53.6 (13.04) Race
FIGO stage I
217
37
II
49
8
III
253
44
IV
62
11
176 405
30 70
Pathologic grade Low-grade High-grade Histologic sub-type Serous
283
49
Undifferentiated
49
8.5
Mucinous
99
17
Clear cell
36
6
Endometrioid
83
14
Unclassified
31
5.5
Table 2 Tumor characteristics by quartile of disadvantage and affluence
Neighborhood measure of socioeconomic status
age- and race-adjusted logistic regression models of each tumor characteristic. Prevalence differences (PD) were estimated using model-based standardization, and the corresponding 95 % confidence intervals were estimated using 1,000 bootstrapped samples with bias correction. PDs compared the highest levels of disadvantage and affluence ([0.50 SD above the mean) with the lowest levels (\0.50 SD below the mean).Analyses were performed using SAS (v9.3, Cary, NC) and STATA (v12, College Station, TX).
Results A total of 581 women were included in this study. Most women (83 %) were White, and the mean age at diagnosis was 53.6 years (Table 1). Just over half of the sample was diagnosed with late-stage disease (54 %). The majority of women had high-grade tumors (70 %), and fewer than half had suboptimal debulking (42 %). Serous tumors were the most common histologic sub-type (49 %). Table 2 summarizes the association between tumor characteristics and quartile of disadvantage and affluence. There were statistically significant associations between higher disadvantage and both higher grade tumors (p = 0.04) and suboptimal debulking (p = 0.03). Comparing women in the highest versus lowest quartile, disadvantage was marginally associated with later stage at diagnosis (60 vs. 49 %, p = 0.10) and associated with higher grade tumors (79 vs. 67 %, p = 0.04) and suboptimal debulking (49 vs. 36 %, p = 0.03). The opposite pattern of associations was found with respect to affluence (Table 2). Table 3 presents the prevalence of tumor characteristics for the comparison levels of disadvantage and affluence, as well as prevalence differences (PD) and 95 % confidence intervals (95 % CIs) in race- and age-adjusted regression models. In age- and race-adjusted models comparing women with disadvantage score at least 0.5 SD above the sample mean
Later stagea n
%
1st quartile (lowest)
146
n
%
FIGO III/IV versus FIGO I/II
b
High-grade [Moderately to poorly differentiated] versus low-grade [Well differentiated]
2nd 3rd
49
146
145 145
56 52
145
60
c
Suboptimal debulking versus optimal debulking
1st quartile (lowest)
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d
Disadvantage and affluence divided into fourths at the quartiles of the sample distribution
2nd 3rd 4th quartile (highest)
4th quartile (highest)
p value
Surgical debulkingc n
%
67
144
36
145 145
66 67
139 141
41 42
145
79
132
49
64
146
79
136
51
146
51
146
64
138
40
145
51
145
67
140
39
144
51
144
69
142
38
Disadvantaged a
Higher gradeb
0.10
d
Affluence
p value 0.04
0.03
p value 0.03
0.11
0.04
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Table 3 Prevalence differences between highest and lowest levels of disadvantage and affluence for tumor characteristics, adjusted for age and race Neighborhood measure of socioeconomic status
Later stagea PD (95 % CI)
Higher gradeb p value
PD (95 % CI)
Surgical debulkingc p value
PD (95 % CI)
p value
Disadvantage (n = 464) Lowerd
0.52
0.66
Highere Difference
0.62 0.10 (-0.06, 0.24)
0.82 0.16 (0.03, 0.27)
0.20
0.38 0.03
0.55 0.17 (0.01, 0.31)
0.05
Affluence (n = 352) Lowerf
0.63
0.73
Higherg
0.48
0.67
Difference a
-0.15 (-0.26, -0.05)
0.004
-0.06 (-0.15, -0.03)
0.37 0.21
-0.12 (-0.24, -0.02)
0.03
FIGO III/IV versus FIGO I/II
b
High-grade [Moderately to poorly differentiated] versus low-grade [Well differentiated]
c
Suboptimal debulking versus optimal debulking
d
Lower disadvantage \0.50 SD below the mean
e
Higher disadvantage [0.50 SD above the mean
f
Lower affluence \0.50 SD below the mean Higher affluence [0.50 SD above the mean
g
0.49
versus at least 0.5 SD below the sample mean, greater disadvantage was associated with higher grade tumors and suboptimal debulking, and was marginally associated with later tumor stage. Conversely, greater affluence was inversely associated with stage at diagnosis and suboptimal debulking and (marginally) with tumor grade (Table 3).
Discussion Our findings suggest that lower SES is associated with greater tumor progression and with tumor characteristics representing a more aggressive form of disease. To our knowledge, this is the first analysis examining the association between neighborhood SES and more aggressive ovarian cancer. While our study lacked information on established measures of individual SES (e.g., income or education), we used two complementary measures to estimate neighborhood SES. Neighborhood disadvantage is based on the idea that structural disadvantage results in conditions that can adversely impact individuals [12]. Just as poverty is concentrated geographically, neighborhood affluence represents ‘‘spatial sorting’’ of residents by resources such as education, occupation, and income [9, 13]. While these two measures may represent different aspects of socioeconomic environment, we analyzed them in separate models in order to avoid attenuation of their respective associations given that they have a great deal of overlap. It is important to note that disadvantage and affluence as measured here reflect a mixture of individual and neighborhood level aspects of resources and opportunities. While
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neighborhood SES reflects the structural characteristics of an individual’s environment [9, 10], it is also a good indicator of individual residents’ circumstances [14]. The limited resources of women living in disadvantaged neighborhoods shape individual health behaviors and impact knowledge about, access to, and attainment of high quality and consistent healthcare [15, 16]. These factors would tend to delay diagnosis, and prior studies have shown these factors to be associated with late-stage diagnosis for a number of cancers [17–21]. Observed associations between socioeconomic disadvantage and more aggressive tumor characteristics (i.e., higher tumor grade and greater residual lesions) are less easily explained and more speculative in nature. Residents of disadvantaged neighborhoods are more likely to be exposed to chronic stressors such as neighborhood disruption, violence, and economic and family insecurity [22–24]. These sorts of environmental stress may impact immune function, contributing to more aggressive tumors [25–27]. Other factors associated with neighborhood disadvantage—including poor diet, reduced physical activity, and exposure to environmental carcinogens—may cause epigenetic changes, which may in turn lead to more aggressive tumors [28, 29]. The findings of this study should be interpreted with important limitations in mind. Eighty-three percent of hospitals in Cook County, Illinois participated in this study, encompassing a wide variety of institutions and contributing to the heterogeneity of the study. However, we found a smaller proportion of late-stage diagnoses than is typical in the general population of ovarian cancer patients, and we acknowledge the potential for selection bias in our sample.
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While our findings demonstrate an association between neighborhood SES and more aggressive ovarian cancer, the reasons for this potential influence remain speculative. Acknowledgments This research was funded by the NIH-NIMHD Training Program: Center of Excellence in Elimination Disparities (P60MD003424).
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