Socioeconomic Status and Cancer Survival By David F. Cella, E. John Orayv, Alice B. Kornblith, Jimmie C. Holland, Peter M. Silberfarb, Kyu Won Lee, Robert L. Comis, Michael Perry, Robert Cooper, L. Herbert Maurer, Daniel F. Hoth, Marjorie Perloff, Clara D. Bloomfield, O. Ross McIntyre, Louis Leone, Gerson Lesnick, Nis Nissen, Arvin Glicksman, Edward Henderson, Maurice Barcos, Robert Crichlow, Charles S. Faulkner II,Walter Eaton, William North, Philip S. Schein, Florence Chu, Gerald King, and A. Philippe Chahinian for the Cancer and Leukemia Group B Survival data from eight Cancer and Leukemia Group B (CALGB) protocols were examined for patients with lung cancer (N = 961), multiple myeloma (N = 577), gastric cancer(N = 231), pancreatic cancer (N = 174), breast cancer (N = 87), and Hodgkin's disease (N = 58). After accounting for differences in survival rate attributable to type of cancer, initial performance status, age, and 14 other protocol-specific prognostic indicators, the additional predictive value of socioeconomic status (SES) was evaluated. Race (white v nonwhite) was not a significant predictor of survival time, but income and education were. People with lower annual incomes (below $5,000 per year in the years 1977 to 1981) and those with lower educational level (grade school only) showed survival times significantly shorter than those with higher income or education,

SOCIOECONOMIC

status (SES) has for some

time been linked to cancer survival."6 There is a growing disparity between white patients and nonwhite patients in cancer incidence and mortality rates, • ''57 and racial differences in cancer sur-

From Rush-Presbyterian-St. Luke's Medical Center, Chicago, IL; Harvard School of Public Health, Boston, MA; Dartmouth-Hitchcock Medical Center, Hanover, NH; Fox Chase Cancer Center, Philadelphia; US Bioscience Inc, Blue Bell, PA; University ofMissouri School ofMedicine, Columbia, MO; Bowman-Gray School of Medicine, Winston-Salem, NC; NationalCancerInstitute, Bethesda, MD; Rhode Island Hospital, Providence; Brown University School of Medicine, Providence, RI, Finsen Institute, Copenhagen, Denmark; Cancer Treatment Center, Greenville, SC; Roswell Park Cancer Institute, Buffalo; St. Luke's/Roosevelt HospitalCenter, New York; Memorial Sloan-Kettering Cancer Center, New York; Mount Sinai Hospital,New York; New York Hospital-CornellMedical Center, New York; and St. Luke's/Roosevelt Hospital Center, New York, NY. Submitted January 9, 1991; acceptedFebruary 19, 1991. Address reprintrequests to David F. Cella, PhD, Department of Psychology and Social Sciences, Rush-PresbyterianSt. Luke's Medical Center, 1653 West Congress Parkway, Chicago, IL 60612. C 1991 by American Society of ClinicalOncology. 0732-183X/91/0908-0009$3.00/0

1500

respectively. These survival differences were associated with, but could not be fully explained by, severity of disease at initial presentation. SES continued to exert a small but significant impact on cancer survival, even after controlling for all known prognostic variables. Economically and educationally disadvantaged cancer patients may require treatment programs that include education about treatment and compliance, even after an initial diagnosis is made and treatment is initiated. Because SES is related to survival independent of all known prognostic variables, it should be included in the data bases of clinical trial groups to provide a more accurate test of the effectiveness of new therapies. J Clin Oncol 9:1500-1509. © 1991 by American Society of Clinical Oncology. vival have been discussed at great length.1,2,5-10 In a large National Cancer Institute (NCI) study of nearly one million cancer patients, survival of black patients was consistently poorer than that of white patients.1 Using more recent data from the NCI Surveillance, Epidemiology and End Results (SEER) Program, McWhorter et al" showed that where black/white differences in cancer incidence occur, they are largely attributable to SES differences between races. In contrast to the NCI data, Page and Kuntz' found no black-white differences in 46,000 Veterans Administration (VA) patients with cancers in five major sites (stomach, colon, lung, rectum, and prostate gland). However, black patients with bladder cancer did have poorer survival rates. The most tenable explanation offered by the authors for this discrepancy from NCI statistics was that the VA system provides equal, "color-blind" care that is unaffected by SES, whereas the treatment received by the NCI sample was more variable, subject to different levels of care dictated by income or insurance status. It remains unclear whether or not SES indicators such as income and educational level are in themselves contributory

Journal of Clinical Oncology, Vol 9, No 8 (August), 1991: pp 1500-1509

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1501

SOCIOECONOMIC STATUS AND CANCER SURVIVAL

after controlling for clinical and environmental prognostic indicators. In addition to race, two SES indicators, income and education, have been identified as potentially important predictors of delay in diagnosis, response to treatment, and long-term survival.1-7,10,11 Early studies tended not to detect a relationship between SES indicators and cancer survival.'- 17 However, there have been significant improvements in cancer treatment over the past 3 decades. These improvements may have increased differences in cancer survival rates due to SES factors that influence treatment-related behaviors such as delay in seeking diagnosis, treatment noncompliance, and limited access to adequate health care.3,4, 10,11 Survival in states with higher median SES exceeds that in states with lower SES, and within states, the poor do not appear to survive as long as more affluent patients.3 Examining level of care across a range of cancer diagnoses, Lipworth et al"' found that 1,277 private patients survived longer than 822 nonprivate patients, even after adjusting for initial spread of disease. These survival differences began to arise even within 2 months postdiagnosis and were most dramatic for patients over 70 years of age. Similarly, a California Public Health Survey19 showed higher survival rates in cancer patients admitted to private hospitals versus city hospitals. Only some of these survival differences disappeared after controlling for extent of disease. It is uncertain whether the level of care received is significantly confounded by SES factors. It has been suggested that nonclinical SES factors such as educational level or insurance status enter into decisions for more aggressive treatment, at least in lung cancer. 20 This report addressed two issues: (1) Does race, income, or educational level predict the initial performance status (IPS; a known prognostic indicator) of patients entering a clinical trial? (2) Will these same three variables predict longer survival after adjusting for differences due to IPS and other prognostic indicators? Based on the aggregate of existing data, it was hypothesized that people with lower income and educational level would enter clinical trials at a prognostic disadvantage. These same people were expected to have shorter survival even after controlling for prognostic factors at entry to study. Barriers to early

detection, awareness of and belief in the value of treatment, frequency of follow-up, and factors linked to comorbidity and nutrition were expected

to be operating in socioeconomically disadvantaged patients. Race itself was not hypothesized as a factor related to IPS or survival time. If race were to show an effect, it was postulated to be secondary to its confounding with income and educational level. METHODS Study Setting Cooperative clinical trials are the foundation on which most recommended cancer treatments are based. CALGB is a cooperative clinical trials group with member institutions spread throughout the United States and Canada. The CALGB has collected SES data within some of its trials since 1977, making it uniquely able to address the relationship between SES factors and response to cancer treatment. All patients were treated using strict guidelines for study entry and adherence to treatment. This virtually guaranteed similar treatment to all patients, regardless of income or insurance status. Protocol requirements ensured standardized data collection on disease status and outcome. Patients who violated protocol requirements were removed from study and are excluded from survival analyses. The CALGB also collected IPS and other known prognostic factor data upon entry to study, so it was possible to control for known prognostic indicators across different protocols. The CALGB maintains follow-up data on nearly every patient entered into one of its studies. These circumstances permitted evaluation of SES factors on survival time, independent of established prognostic indicators.

Subjects Between 1977 and 1983, each of 2,451 patients were entered into one of eight clinical protocols for Hodgkin's disease, multiple myeloma, breast cancer, small-cell lung cancer, gastric cancer, and pancreatic cancer. Of this group, after exclusion due to protocol violations or withdrawal from study, 2,139 (87%) patients remained eligible for study evaluation. Of these 2,139 eligible patients, 2,089 (98%) had age, sex, race, IPS, and protocol-specific clinical prognostic data available. The treatment protocols of these 2,089 patients are listed in Table 1.

IPS The CALGB uses the 5-point scale developed by Zubrod et at as its measure of IPS.21 Also used by the Eastern Cooperative Oncology Group and World Health Organization, it is an observer rating of activity level, ranging from 0 (fully ambulatory) to 4 (bedridden). Score on this simple scale at entry to study is related to extent of disease, and it has been linked to survival time in many tumor types. Along with other disease-specific prognostic indicators, it was used as a proxy measure of extent of disease upon entry to study.

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1502

CELLA ET AL Table 1. CALGB Treatment Protocols Included in Study Protocol No.

Site/Type

7751 7761 7781 7782 7784 7981 7982 8083

Hodgkin's disease Multiple myeloma Small-cell lung-limited Small-cell lung-extensive Breast Gastric Pancreatic Small-cell lung-limited

N (%) 58 577 291 303 87 231 174 368

(3) (28) (14) (14) (4) (11) (8) (18)

Specific Prognostic Factors* Age Treatment arm, creatinine level Sex, sex by treatment Sex, no. of metastatic sites Estrogen receptor status Disease extent, measurability Age, prior surgery Weight loss, treatment arm

22

NOTE. N = 2,089. The results of most of these studies have been published. -29 However, more detailed technical reports may be requested from the Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115. *Performance status is treated as a prognostic factor in all protocols.

Protocol-SpecificPredictorsof Survival Table 1 presents protocol-specific prognostic factors. Clinical results from these CALGB studies are presented elsewhere.2 - From those presentations and from unreported analyses (data on file, CALGB central office), significant prognostic variables in addition to age and IPS were identified. They were then included in this study as protocol-specific adjustments for survival time. Age and IPS were included as general predictors of survival across all eight studies. Summary ofAvailable Data Demographic data are presented in Table 2. Because they were coded by the study registrar, data on sex, age, and race were available on the entire sample of 2,089. However, not all patients reported income (N = 938), education (N = 1,052), and marital status (N = 1,073). Completion of the demographic questionnaire by the patients before treatment was not mandatory and was coupled with completion of self-report measures of mood disturbance and psychosocial functioning, which may have hindered overall compliance. To assess whether there might be some systematic bias introduced by the nonadherence to completion of the pretreatment demographic questionnaire, the patients who provided SES information were compared with those who did not. There were no statistically significant differences on IPS and length of survival between patients who provided information about SES and those who did not. Patients in two protocols (7781, limited small-cell lung carcinoma; and 7982, pancreatic carcinoma) were more likely to have completed the demographic data form, as were younger patients. However, the difference in age between those who did and those who did not complete the form was only 1 year, suggesting that this statistically significant difference was not clinically relevant. StatisticalMethods Relationships among the SES predictor variables were examined using the Wilcoxon rank-sum test (race to education; race to income) or the Mantel-Haenzel test for trend (education to income). A polychotomous logistic regression"' was used to assess the relationship between SES predictors and IPS. This procedure treated IPS as an ordered, five-category, depen-

dent variable with category intervals that were not necessarily equal. Predictor variables (SES, age, sex, marital status) were evaluated to see if any of them significantly altered the probability of a patient falling into a particular prognostic category. Sex, age, and marital status were first entered into the model, after which a likelihood ratio test was used to determine whether SES variables added significantly to the explanatory power of the model. The predictive power of each individual covariate was assessed using either the Wilcoxon rank-sum test (sex, race, marital status) or the Kruskal-Wallis test (age bracket, educational level, income bracket), with IPS as the outcome measure.

Table 2. Patient Characteristics Characteristic (N)

Sex (2,089) Female Male Age, years (2,089) Range < 59 > 60 Race (2,089) White Black IPS (2,089) 0 = fully ambulatory; asymptomatic 1 = ambulatory with symptoms 2 = in bed less than 50%of daytime 3 = in bed more than 50%of daytime 4 = bedridden Annual family income (935) < $5,000 $5,000-$15,000 $15,000-$30,000 > $30,000 Educational level (1,049) Grade school only High school degree College degree Professional degree Marital status (1,073) Married Unmarried

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n (%)

1,277 (61) 812 (39) 15-91 977 (47) 1,112(53) 1,843 (88) 246 (12) 606 812 400 203 68

(29) (39) (19) (10) (3)

247 (26) 393 (42) 205 (22) 90(10) 330(31) 517 (49) 154 (15) 48 (5) 771 (72) 302 (28)

1503

SOCIOECONOMIC STATUS AND CANCER SURVIVAL A stratified Cox regression model was used to assess the influence of the three SES predictors upon survival. The analysis was stratified by study protocol to allow different, nonproportional survival rates in each of the eight protocols. In this way, the multivariate model also allowed adjustment for the 14 protocol-specific prognostic variables listed in Table 1, as well as age, sex, marital status, and IPS, before assessing the effects of race, income, and educational level.

RESULTS RelationshipsAmong SES Predictors As expected, each of the three SES variables was found to be highly related to the others: black patients reported lower educational level (P < .0001) and lower income (P < .0001) than white patients. Also, patients with a lower educational level reported lower income (P < .0001).

Table 4. A Multivariate Model for IPS Predictor

Age Sex Marital status Education Grade school (baseline) High school College Graduate/professional school Annual income < $5,000 (baseline) $5,000-$14,999 $15,000-$29,999 > $30,000

Regression Coefficient

P (two-tailed)

.023 -. 063 -. 013

.0004 .64 .83

-. 036 -. 394 -. 025

.81 .06 .94

-. 247 -. 361 -. 758

.12 .06 .005

NOTE. A polychotomous logistic regression model was used to predict inclusion in the five-category outcome measure of IPS for 921 patients.

RelationshipBetween SES Variables and IPS Looking at the individual unadjusted effects of the SES predictors, two of the three, income and educational level, bore a statistically significant relationship with IPS (Table 3). Those with lower income and educational level were more likely to have greater physical impairment at entry to study, suggesting that these factors may contribute to extent of disease at study entry. Although a trend was evident, black patients were not significantly more likely than white patients to present with more impaired IPS. In examining the bivariate relationships between IPS and other demographic data (data not shown), no association was found for sex (P = .08) Table 3. Relationship Between SES Factors and IPS

PS(%) Factor (N) Race (2,089) White Black Income (935) < $5,000 $5,000-$15,000 $15,000-$30,000 > $30,000 Educational level (1,049) Grade school only High school degree College degree Professional degree *Wilcoxon. t Kruskal-Wallis.

0

1

2

3

4

(two-tailed)

29 30

40 30

18 25

10 9

3 6

.09*

21 29 33 45

39 40 42 37

27 19 17 9

10 10 6 5

3 3 2 3

.0001t

24 29 36 40

38 39 46 29

23 20 11 21

11 11 4 4

4 1 2 6

.0004t

or marital status (P = .19). However, younger patients did have significantly better IPS (P = .0001), supporting the strategy of forcing age into the final analysis before looking at the effect of SES factors. In the multivariate model to predict IPS, three demographic variables (age, sex, marital status) were forced into the initial equation. Adding educational level (categorized into four levels) contributed significantly to this baseline model (P < .001). Similarly, income bracket, when added to the baseline model, showed significant association with IPS (P < .001). Race was again unrelated to IPS (P = .23).

As shown in Table 4, when income and educational level were added simultaneously to the baseline model, better IPS (ie, lower score) was associated with being in the highest income bracket (P = .005). Trends toward improved IPS were seen in patients in the second highest income range (P = .06) and in patients with a college degree (P = .06).

Survival Analysis Figure 1 shows the Kaplan-Meier survival curves for the eight protocols, unadjusted for any covariates. Survival was poorest in gastric, pancreatic, and extensive small-cell lung patients and slightly better for those in both of the limited small-cell lung protocols. Survival improved notably for the breast cancer and myeloma patients and was highest in the Hodgkin's disease (lymphoma) patients. This clearly emphasizes the disparate sur-

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1504

CELLA ET AL

1.00.8So.06 S0.4C

0.20.02 0

4

6

8

1

Years Fig 1. Kaplan-Meier plot of survival time for each CALGB clinical protocol (N = 2,089): (-) 7751, Hodgkin's disease, n = 58; (---.) 7761, multiple myeloma, n = 577; (- -)7781, limited small-cell lung, n = 291; (----) 7782, extensive smallcell lung, n = 303; (- -) 7784, breast, n = 87; (-----) 7781, gastric, n = 231; (--) 7982, pancreatic, n = 174; (-....) 8083, limited small-cell lung, n = 368.

vival curves across diseases and, therefore, the need to stratify the analysis by study protocol. A Cox regression was performed by first entering age and IPS because they are data that are common to all protocols and they may have prognostic value independent of the 14 clinical prognostic indicators listed in Table 1. After entry of age and IPS, all 14 of the clinical predictors were added to the model. With this simultaneous adjustment for IPS and age (variables that were not included in all previous reports on these protocols), not all of the previously significant, protocol-specific covariates were statistically significant in the regression model. However, to eliminate the possibility of bias, all of these covariates were retained in the model for adjustment in subsequent analyses. Table 5 presents the survival regression results after adjusting for age and all clinical variables. The effects of education, income, and race were tested separately in three Cox regression models to provide an illustration of the additional individual impact of each factor. Model 1 shows that, after adjustment for clinical variables and age, education is a significant predictor of survival. Patients with either a high school or college education survived significantly longer than patients with only a grade school education. Those who attended graduate or professional school showed a reduced risk of death, but the sample size (n = 48) was too small to attain statistical significance. The survival differences between the

four levels of education are small but persistent in time and across all diseases and are statistically significant after covariate adjustment. Model 2 in Table 5 reports the result of adding income to the predictive model. Patients whose annual family income was between $5,000 and $30,000 had significantly longer survival times than those earning less than $5,000. Again, a slightly reduced risk reduction in the limited number of patients earning over $30,000 (Table 2) does not attain statistical significance. Model 3 in Table 5 indicates that racial background did not predict survival after adjustment for clinical status and age. Marital status (married v nonmarried) also was not significant when entered into the adjusted model (P = .18). Table 6 presents a final protocol-stratified Cox regression model that includes all 14 protocolspecific predictors (Table 1), age, IPS, sex, marital status, and the three SES variables (education, income, race). For clarity, only the SES variables are presented in the table. With all SES variables entered together, only income emerged as significant: the $5,000 to $15,000 income bracket preTable 5. Separate Effects of Education, Income, and Race on Cancer Survival After Controlling for Clinical Variables

Predictor

Risk Baseline Regression P Relative to Relative Coefficient (two-tailed) Baseline* Risk*

Model 1: education Grade school (baseline) High school -. 185 College -. 271 Graduate school -. 177 Model 2: annual income < $5,000 (baseline) $5,000-$14,999 -. 277 $15,000-$29,999 -. 218 > $30,000 -. 188 Model 3: race White (baseline) Black .016

.018 .013 .309

0.83 0.76 0.84

1.20 1.32 1.19

.002 .040 .175

0.76 0.80 0.83

1.32 1.25 1.20

.831

1.02

0.98

NOTE. Each of the three models include the 14 protocolspecific clinical adjustment covariates plus age and IPS, as listed in Table 6. Coefficients and P values are not shown here. *Relative risk ratios are given to show the extent of the differences. In each model, the baseline level of the variable is the referent to which the others are compared. The figure in the last column presents the increase in risk incurred by the baseline group. Thus, over the duration of this study, patients with a grade school education were 1.20 times as likely (or 20% more likely) to die as those with a high school education. Their risk relative to college-educated patients is 1.32, or 32% more likely to die, and so on.

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1505

SOCIOECONOMIC STATUS AND CANCER SURVIVAL Table 6. Combined Effects of Education, Income, and Race on Cancer Survival After Controlling for Clinical Variables

Predictor

Education Grade school (baseline) High school College Graduate school Annual income < $5,000 (baseline) $5,000-$14,499 $15,00-$29,999 > $30,000 Race White (baseline) Black

Baseline Risk Relative Relative Regression P Coefficient (two-tailed) to Baseline* Risk*

-1.63 -. 248 -. 215

.067 .055 .259

0.85 0.78 0.81

1.18 1.28 1.23

-. 230 -. 119 -. 044

.019 .317 .780

0.79 0.89 0.96

1.27 1.12 1.04

-. 066

.572

0.94

1.06

NOTE. This model includes the 14 protocol-specific clinical adjustment covariates plus age, IPS, sex, and marital status. Coefficients and P values for these covariates are not shown here. *See notes to Table 5 for discussion of relative risk ratios.

dicted longer survival relative to the under $5,000 income bracket, P = .02. The positive effect of higher education relative to grade school education was similar in magnitude to that presented in Table 5, but the results were only marginally significant: P = .07 for high school and P = .06 for college education. Given the previously noted high degree of association among these three SES indicators, it is not surprising to see the impact of these covariates diluted when both education and income are included in the same model. Therefore, while it is clear that race played an insignificant role, it becomes difficult to differentiate the effects of income and education on survival time. In an attempt to differentiate between education and income, we considered only the 897 patients who reported both variables. Hierarchical modeling showed that, after adjusting for clinical factors, age, sex, and marital status, the addition of income had somewhat more impact on survival (reducing the log likelihood by 7.71 using three degrees of freedom) than education (which reduced the log likelihood by 6.03). With income in the survival model, the addition of education did not significantly improve the model (change in log likelihood, 4.83; P = .20). This suggests that income is somewhat more powerful than education in predicting survival; however, it does not shed light upon which of the two variables might have preceded the other. As an alternative model, we combined the three

categories of higher education (high school, college, graduate school) and found that this indicator was significantly predictive of longer survival (regression coefficient = -. 18; P = .038), even after adjusting for all clinical variables, age, sex, race, marital status, and the four levels of income. The differences obtained across levels of income and education pointed consistently toward identification of the lowest category of each variable as a high-risk group. For this reason, KaplanMeier survival curves were created to compare those with the lowest educational level with those in the other three levels combined (Fig 2), and those in the lowest income level (Fig 3) with those in the other three groups combined. These curves are unadjusted for protocol or other covariates. Both figures show that the influence of these factors is subtle and could be missed with smaller studies or without covariate adjustment. DISCUSSION This study demonstrated that the socioeconomic factors of income and education were associated with severity of functional impairment at initial presentation and with length of survival in a large sample of mixed cancer patients who were treated strictly and uniformly by clinical protocol. Race, independent of other SES factors, was not a significant predictor of IPS or survival. The direction of the SES effect obtained was such that lower educational level and lower annual family income predicted more impairment in IPS and earlier death. The results with IPS confirm the reports of many others'70'11 ,' 17,20,31that people of lower income and educational levels tend to enter cancer treatment with more advanced disease. This could 1.00.8.0

a ao .A

0.60.4-

n, 0.20.0-

Years Fig 2. Kaplan-Meier plot of survival by educational level, all cancer sites (N = 1,049). (-) Grade school only, n = 330; (....... ) high school or above, n = 719.

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1506

CELLA ET AL Althruich

1.00.8 .c a•

M D.

0.6-

(n

0.4-

9

L

0.20.0 - 0.0

0

thc comalc

nr- c,-ntc r-rto;n

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tages of random assignment to standardized and carefully monitored treatment, and although it includes patients from diverse regions and communities throughout the United States, it may not be representative of the general population. Only 12% of the sample was nonwhite, clearly below what would be expected based on cancer incidence nr? Irin.

2

4

Yea6

8

10

np lllr

rplrnn 1I1

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nrlpnrr lnlll1lllil

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groups are placed in clinical trials is unknown.

Ironically, it may be related to the same problems Fig 3. Kaplan-Meier piot of survival by annual family income, all cancer sites (N = 935). (-) Under $5,000, n = 247; (...... ) over $5,000, n = 688.

be due to a lack of kn owledge about signs, symptoms, screening/early detection programs, and availability of treatment. It could also reflect deficiencies in transpo rtation, financial resources, or access to care affo rded by insurance benefits. Any of these reasons is potentially amenable to correction through pu blic education or outreach efforts. In the survival analyses, clinical prognostic indicaters of age and IPS were controlled across all eight protocols includ ed in this study. Fourteen other clinical progno stic indicators, empirically derived from earlier r eports of the study results, were controlled as wel I, leaving a conservative test of the impact of SES factors upon response to treatment. The combi nation of high income and higher educational lev el was the strongest predictor of longer survival. It was decided at the outset of these studies that participation in the tr eatment protocol would not be contingent upon patient completion of the demographic data forrm. Noncompletion of the demographic data forr n could be ascribed to many factors, such as staff neglect, physician or data manager unwillingness to participate, patient apathy or refusal, and in properly completed forms including missing resp onses. 32,33 Therefore, accrual to this component of 1the studies was significantly lower than accrual to the treatment studies. Although only half of th e treated patients supplied information about income and education, they were representative in terms of important outcomes: IPS and surviv al. Therefore, it is unlikely that any meaningful b ias was introduced into the study by virtue of acc rual deficits in this component of the protocols.

of access to care that so many previous reports have identified as a barrier to quality health care

for the economically disadvantaged. However, this concern is offset by the fact that over 25% of the sample reported annual income below $5,000. Even after accounting for inflation over the past 10 years, this remains a very low income. Patients with income below $5,000 (both white and non-

white) showed the most dramatic drop in survival. Many of these patients with very low income may have been unemployed, either voluntarily or due to their illness; these data are unavailable. Finally, because these protocols treated patients for a defined period, survival differences across SES may also reflect patients' experiences after they completed treatment within the CALGB study. Therefore, differences in survival related to SES may reflect patients' ability to seek help for early signs of recurrence, treatment side effects, new primary tumors, and iatrogenic or other unrelated diseases. However, the question of whether survival is compromised by SES during or after treatment is somewhat moot inasmuch as the recommendation would remain the same: improved patient education and awareness of treatment options. One can speculate about the mechanism(s) underlying less survival time of lower SES groups in this study. Haan et a134 have suggested five explanations: (1) The view that SES differences in health are a function of correlated risk behaviors such as smoking, alcohol consumption, and diet is most frequently espoused. (2) People who become more ill tend to drift into lower SES categories because they cannot maintain their occupational or social role. (3) Poor people adopt social and personal characteristics that foster illness and then transmit them from generation to generation, creating a vicious cycle. (4) SES differences in

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SOCIOECONOMIC STATUS AND CANCER SURVIVAL exposure to toxic and carcinogenic agents leads to higher death rates. (5) Finally, a fifth view emphasizes the causal nature of material factors that are associated with poverty and can lead to a greater risk of mortality, such as limited access to medical care or lack of supplies such as food, transportation, or education. Although this study can neither prove nor disprove any of these explanations, explanations that refer to shorter-term impact are more plausible in light of the median survival times of this sample. This would include correlated risk behaviors and inadequate education. Group differences in treatment compliance, smoking, and diet were not controlled in this study and may in part account for the results. It should be kept in mind that the group of patients that did poorly would be considered by most to be extremely disadvantaged, even considering inflation over the past decade (income under $5,000, grade school education). Access to care is not an issue in this study, because all patients were entered into the same standardized treatment plan, regardless of SES. Recently, there has been a dramatic increase in differences between blacks and whites in cancer mortality. Over the past 30 years, after beginning at the same level, cancer mortality rates have increased four times as rapidly for blacks as they have for whites.7 Berg et al4 have speculated that survival differences across SES groupings are mediated by host factors such as immune competence, nutrition, and concurrent diseases. However, the short time frame of this shift virtually eliminates any reasonable explanation based upon genetic or inherently biologic factors alone. In their analysis of over 20,000 cases of 39 types of cancer treated in Iowa from 1940 to 1969, Berg et al found that 5-year crude survival rates for indigent patients were lower than those for nonindigent patients, especially in diseases with expected survival rates of 40% to 70%. Inasmuch as medical care for all patients was delivered by the same staff, there was control for quality of care. However, this and other studies did not evaluate the impact of educational level. Racial differences in education or income are important to examine because they explain other racial differences that more clearly mediate survivorship, such as delay in seeking treatment, extent of disease at diagnosis, malnutrition, quality of care, and compliance with

treatment.3,8-10,12-15,18,19,35 These SES factors are mutable with appropriate effort and program planning that has a national scope but a local emphasis. Although consistent with a growing number of studies, our study data conflict with other findings. Keirn and Metter35 found that among the four variables of age, sex, stage of disease, and indigence (based on insurance status), only stage of disease predicted for survival in 1,101 lung, colorectal, and breast cancer patients. Their patients were also treated equally, without prejudice toward income or insurance status. However, as a proxy variable for SES, insurance status is rather imprecise. Therefore, its use as an SES measure, may reduce the power to detect true differences in cancer survival between poor, less-educated people and those more fortunate. Ernster et al2 found results similar to those of Keirn and Metter. Using prostate cancer as a model of sharp racial differences in survival, they found that SES (defined as census tract educational level) added no significant predictive weight. However, Freeman and Wasfie'o reviewed the records of 708 indigent women with breast cancer94% of whom were black-and found significantly reduced survival compared with national statistics, primarily due to late detection. They concluded that this problem is directly tied to poverty, not race. These data further justify the direction of cancer control efforts with disadvantaged and medically underserved people. In thinking of types of interventions that might flow from these data, it becomes clear that level of education is important. Although educational level and annual income were intertwined in their predictive role, programs short of supplemental insurance for the disadvantaged cannot easily alter problems of access related to income. Educational level, by virtue of its link to an individual's fund of knowledge and awareness about cancer, is tied to prevention, early detection, and, it seems from this study, treatment results. It is possible that educational factors could exert a separate influence on attitude to health care and knowledge of the treatability of cancer, which could result in reduced compliance or a tendency to give up more easily in the fight against disease.

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It remains to be determined whether enhanced education would be better directed toward compliance with treatment itself or with medical follow-up after treatment has been completed. This study points to the importance of continued education and rehabilitation of patients in lower SES groups, even after the diagnosis is made and

treatment is initiated. As suggested by Haan et al, 34 future studies should first focus on measuring some of the patient parameters to identify the mechanisms by which education and income alter survival. From this knowledge, interventions to improve survival in lower SES patients can be designed and evaluated.

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