Economic status and survivorship in digestive system cancers

Economic Status and Survivorship in Digestive System Cancers

THOMAS N. CHIRIKOS, PHD, AND RONNIE D. HORNER, PHD

This study investigates economic differentials in cancer survival in a sample of 1180 white men, focusing in particular on the relationship between income level and survivorship in the various subsites comprising the digestive system cancer category. Using the Cox proportional hazards model to control for confounding variables, the economic status-survivorship relationship is estimated for several subgroupings of primary malignancies. The results show significant variation in this relationship across different cancer sites, with a pronounced effect observed in carcinomas of the small intestine, peritoneum and, especially, colon and rectum. High-income patients with these malignancies had a significantly lower risk of dying from the disease (P < 0.05) than either their middle- or lower-income counterparts, controlling for age, stage, and initial course of treatment. Differences in immunologic status, tumor characteristics, and follow-up treatment may account for these economic effects.

Cuncer 56:210-217, 1985.

HREE RECENT REPORTS'-3 Cast doubt On, but fail to T refute, earlier findings4-* that socioeconomic factors influence cancer survivorship. Page and Kuntz' generally failed to detect a survival difference between white and black Veterans Administration (VA) cancer patients, even though the economic status of black VA patients differed significantly from their white counterparts. They did find, however, a racial difference in bladder cancer survivorship that might be attributable to economic factors. Wegner et al.' failed to detect significant main effects of socioeconomic status on the survivorship of colorectal cancer patients in five racial groups in Hawaii; modest interaction effects, however, were found for some racial groups, suggesting that part of the racial difference in survival might be explained by the socioeconomic variations across race. A study of Ohio cancer patients3 showed that economic effects are both easily confounded by differences in age, stage, severity, and initial course of treatment and quite sensitive to the way in which economic status is measured; still, the

From the Department of Preventive Medicine, College of Medicine, The Ohio State University. Columbus, Ohio.

Address for reprints: Thomas N. Chirikos, PHD, Department of Preventive Medicine, The Ohio State University, 320 West 10th. Columbus, OH 43210.

The authors thank, without implication, Nancy Reiches, Melvin Moeschberger, Michael Moser, and Jennie Nickel for their assistance in preliminary phases of this research. They also thank Professor Nathan Mantel of The American University and an anonymous referee for very helpful suggestions on an earlier draft of the paper, the OSU Instruction and Research Computer Center for providing computer time, and Carol Dowdell for secretarial help.

Accepted for publication July 13, 1984.

estimated parameters on various economic measures in multivariate, proportional hazard regressions controlling for these differences rarely reached even borderline statistical significance and were quite unstable. The exception was cancers of the digestive system where weak income effects on survival were found. In light of the mixed evidence on the role of economic status and cancer survival, more work on this relationship seems warranted.

This report presents the results of a more disaggregate analysis of the data used in the Ohio study. It is designed to ascertain whether weak economic effects on the survival experience of patients with digestive system cancers continue to be detected when the analysis is restricted to various sites constituting this broader category of malignancies. We investigated whether a subset of these cancers accounts for the measured impact of economic factors or whether the weak effects detected earlier are uniformly distributed across digestive system subsites. If the influence of economic variables on survivorship is uniformly weak, more weight might be given to the conclusion that measurement and specifi- cation errors have exaggerated the importance of these factors in cancer survival in previous studies. If, however, the analysis pinpoints specific cancer subsites as giving rise to the overall effects, useful information is provided for patient management and various etiologic investi- gations. Such findings would also have implications for evaluating public policies designed to increase access to high quality medical care for economically-disadvantaged individuals. On these several counts, then, testing the more detailed relationship between economic status and

210

No. 1 ECONOMIC STATUS AND CA SURVIVORSHIP * Chirikus and Hurner 21 1

survival for various types of digestive system cancers is worthwhile.

Methods

Data and Variables

Our analysis draws on data from the Tumor Registry of the Ohio State University Hospitals (OSUH) on 1 I80 white men registered with first primary malignancies over the period from July 1977 to May 1981. Since this Registry and sample of patients are discussed in detail else~here,~ it suffices to note here that both the economic and cancer characteristics of these men are generally representative of all men in the United States in the 1970s. The only exception is that the OSUH Registry includes a slightly lower proportion of cancers of the digestive system than might have been expected using 1975 incidence rates of male cancers as the criterion. However, this difference is insignificant and, in our judgment, does not affect the generalizability of our findings.

The earlier analysis of the Ohio data strongly supports the view that gauging economic effects on survivorship requires suitable controls for disease severity and treatment confounders; it also suggests that the length of follow-up or survival time should be incorporated into the measure of survivorship. Accordingly, we create a set of variables for each Registry observation with a view toward building a multivariate model that can be used to compute the relative risk of different economic characteristics on survival, adjusted in at least a preliminary manner for these confounding factors. The variables included in the general survivorship model are:

Survival time: Follow-up interval or survival time was computed for each patient as the number of months between diagnosis and the most recent status report. A dummy variable taking the value of one was assigned to patients who died from cancer at the time of this report; a zero was assigned to all other, censored observations, i.e., patients who either were alive or had died from other causes.

Income categories: Analyzing the relationship between socioeconomic factors and survivorship requires a suitable measure of the long-term economic prospects of patient^.^ Economic researchers frequently use recent observations on the level of an individual's annual earnings or market income for such purposes. Since patient income is not measured directly in the OSUH Registry data, we use available information on occupation to proxy or characterize indirectly the annual earnings of each patient. The expected (mean) dollar income in 1970 reported in the 1970 Census of Popu- lation for each (three digit) occupation was attributed to the current occupational code of the patient (or the

most recent job category for the few patients who had retired). These expected dollar figures, measured in 1970 dollars, are then categorized arbitrarily by income level: high ($13,000 or more), middle ($6000-$12,999), and low income ($5999 or less). The high-income category includes occupations such as physician, dentist, lawyer, engineer, etc. Middle income occupations are illustrated by craftsmen and kindred workers, teachers, managers and administrators, and clerical personnel. The low income category includes service occupations as well as job functions for which no special skills are required such as manual laborer and farm worker. In the statistical work below, the threefold classification of income is used to create a pair (vector) of dichotomous variables characterizing the economic status of the patient. One of these variables is created by assigning a value of one to high income patients, zero otherwise; the other assigns a value of one to low income patients, zero otherwise. The middle income category, therefore, is the referent or omitted group for the vector of categorical income variables. This procedure permits testing nonlinear threshold effects of income level on survival and simpli- fies the computation of the risk ratios.

Age: Years of age at diagnosis, measured continuously. Supplemental statistical analyses also used the square of this variable to test for nonlinear effects of age at diagnosis on survival.

Stage: A dichotomous variable taking the value of one if the malignancy was diagnosed at a regional or distal stage or taking the value of zero if it was discovered at a local stage.

Surgery: A dichotomous treatment variable is created to characterize the first course of treatment received by the patient within 3 months of his admission to OSUH. Because we focus on cancers of the gastrointestinal tract, this variable takes the value of one if the treatment received was limited to surgery, zero otherwise. Although this variable does not account fully for therapeutic regimen, the aim here is to control for the general effects of treatment on survival rather than appraise the efficacy of treatment per se.

Site subgroupings and variables: In order to determine the source of the effects of economic status on survivorship, the Registry data were subdivided, or stratified, by the site of the primary malignancy. As discussed more fully below, the statistical analysis required testing the significance of these subdivisions. To accomplish this, a set of dichotomous site variables was created, each corresponding to one of the various subgroupings. These subgroupings and dummy variables are defined as follows: data and analyses referring to all I I80 patients are labeled all sites. The all sites category is then divided between digestive system (organ) cancers (International Classification of Diseases (ICD] 150- 158) and nondiges-

212 CANCER July 1 1985 Vol. 56

TABLE I . Adjusted Risk Ratios* for High- and Low-Income Patients by Selected Cancer Sites

Risk ratios (chi-square)

High Low Sample Sites* incomet incomet size

All sites Nondigestive system

Digestive system

Digestive system Severe

Less severe

Less severe Noncolorectal

Colorectal

1.070 (0.27) 0.595

(3.40)$

1.143 (0.14) 0.280 (6.44%

0.355 (0.86) 0.290

(4.81)§

1.119 998

1.004 182 (0.72)

(0.00)

1.320 80 (0.58) 0.812 102

(0.25)

0.805 18 (0.06) 0.780 84

(0.26)

* See text for definitions. t Middle-income is referent group. $ P 5 0.10. 4 P 5 0.05.

tive system, i.e., all other cancers (ICD 140-149; 160- 199). In the analyses of all sites data, patients whose primary malignancy is categorized as a digestive system cancer are assigned the value of one, zero otherwise. The digestive system grouping is then subdivided between tumors with relatively high fatality rates and those with lower or more average survival chances. The first subgroup includes cancers of the esophagus, stomach, liver, gallbladder, and pancreas (ICD 150-1 5 I ; 155- 157) and is arbitrarily labeled as severe digestive system cancers; the remaining digestive system cancers (ICD 152-154; 158) are labeled less severe and include neo- plasms of the small and large intestine, rectum, and peritoneal tissues. In the analyses of digestive system dam, patients with any less severe (digestive system) tumor are assigned the value of one, zero otherwise. Finally, the less severe grouping is divided between colorectal cancers (ICD 1 53- 154) and noncolorectal cancers (ICD 152; 158). In the analysis of less severe digestive system data, colorectal patients are assigned the value of one on the site variable, zero otherwise.

Adjusted Risk Ratios

We use a variant of Cox’s regression model’ to estimate the net effect of income level on survivorship, while controlling for age, disease severity, and treatment. We then use these regression results to compute adjusted risk ratios for the income category variables. The Cox model, which has been used in many survival studies, is ideally suited to our needs. It easily handles the

censoring commonly encountered in tumor registry data sets. It also posits that the natural logarithm of the ratio of the patient’s conditional probability of death at t or “hazard” A(t) to the baseline force of mortality b ( t ) is a simple linear function of some set of explanatory or regressor variables. The basic model uses the age, stage, and surgery measures described above as well as the dichotomous income category variables as regressors; thus, its general form is:

3

In [Mt)/b(t)l = P l Y H + PzYL + C Bixi ( 1 ) i= I

where YH and YL refer to high and low income, respec- tively, Xi refers to the ith control variable, and P I , P2, and Pi are regression coefficients to be estimated by partial likelihood methods.

In estimating Equation ( I ) , the influence of income status on survival is tested in terms of the magnitude and statistical significance of the coefficients (8, and 82) on the income category variables. These coefficients represent (approximate) risk ratios for patients with either high or low incomes relative to the referent, middle income group, i.e., exp (PI) and exp (&) measure risk differentials of higher and lower income, adjusted for the other variables in the statistical model. These exponentiated values are labeled adjusted risk ratios below. Clearly, interest centers on whether exp (PI) and exp (P2 ) differ significantly from one. A statistic approx- imately distributed as chi-square with one degree of freedom is used to test each coefficient under the null hypothesis. These test statistics are labeled chi-square and are presented in parentheses below their respective risk ratios in Table 1.

Initially, Equation ( 1 ) is estimqted several times for various site subgroupings, including all sites; digestive system and nondigestive system cancers; severe and less severe digestive system cancers; and the noncolorectal and colorectal subgroups of the less severe digestive category. As discussed above, these estimates are designed to pinpoint, if possible, where economic effects are most pronounced. The subgroupings used for the estimation derive from earlier research findings as much as they do from the simple divisions built into the ICD schemata. In particular, the initial breakdown between digestive and nondigestive system cancers as well as the separation of colorectal cancers from those essentially of the small intestine are suggested by earlier ~ t u d i e s . ~ . ~ Nonetheless, the estimation of such stratified regression models raises a question about the extent to which the results might be influenced (biased) by the choice of the site subgroup ings, especially given the small sample sizes for the final subdivision. Consequently, Equation (1 ) is respecified to

No. 1 ECONOMIC STATUS AND C A SURVIVORSHIP - Chirikos and Horner 213

test whether the subgroupings are statistically significant and, thereby, whether differences in economic effects detected across different sites are likely to be statistical artifacts. Thus, one variant of the respecified regression model, labeled Equation (2) below, adds one of the (respective) dichotomous site variables to the set of regressors. Another variant, labeled Equation (3) below, is conceptually equivalent to the stratified models, adding both site variables as main effects and site-income category products as interaction terms. Results of these estimates validate the original model when the main and/or interaction effects of the site variables are statistically significant; however, caution should be, and is, exercised in interpreting the results of Equation (1) when the variants incorporating site variables are statistically insignificant.

Several additional experiments were conducted in estimating Equation ( I ) , including a test for nonlinear age effects by adding a quadratic age term with the other regressors as well as a test of the basic regressors on deaths of Registry patients from causes other than their primary malignancy. Given space constraints, the results of these additional analyses are noted in the next section but are not reported in detail.

Results Table 1 sets out the adjusted risk ratios and their

associated test statistics for the income category variables for several estimates of Equation (1). The full regression results for Equation ( I ) as well as Equations (2) and (3), including the estimated coefficients for the covariates included in each model, their respective test statistics, and a model chi-square testing the significance of the estimated model relative to one excluding these covariates, are given in Table 2. Summary statistics for each of the regressor variables and sample sizes of each of the site subgroupings are presented in Table 3.

Table 1 shows that the patient’s income level influences survival differently across cancer sites, with the most pronounced effects deriving from patients with carcinomas of the small intestine, peritoneum and, especially, colon and rectum. None of the risk ratios for low- income patients differs significantly from the referent, the middle-income group; statistically, their survival experience is identical to those with middle income, controlling for age, stage, and treatment. In contrast, high-income patients have substantially better chances of surviving over middle- (and, by inference, low-income) individuals and this effect differs by site. The adjusted risk ratio for high-income patients with any type of digestive system cancer is about 60% of that of the middle-income group. This difference is detected for the high-income group in all of the models estimated and

is always statistically significant at the 10% level.* When digestive organ cancers are subdivided, a survival advantage for high-income patients is detected for those with less severe types. The estimated risk ratio falls to about 28% for high-income patients diagnosed with a malignancy of the small or large intestine, rectum, or peritoneal tissues in each of the several estimated equations. Of equal importance is that the statistical significance of the ratios increases to the 5% level. These results are not unexpected in view of the higher fatality rates of the malignancies included in the severe digestive system subgrouping such as stomach and pancreatic cancers; these patients have uniformly poor prognosis, so economic factors are not likely to play a significant role in survivorship. Moreover, the statistical confirmation of this expectation is unlikely to be artifactual. Significant differences in the survival experience of high- and middle- income patients in these two subgroups are evidenced by the significant coefficients on models including main and main plus site-income interaction effects (Table 2).

Further subdivision of the sample, however, yields only mixed results. On the one hand, the adjusted risk ratios suggest that income level confers a special survival advantage to patients with colorectal cancers but not to those with other malignancies in the less severe digestive category such as the small intestine. Interestingly, the magnitude of the risk ratio is almost identical to the previous estimate, although its statistic is somewhat lower. On the other hand, neither Equation (2), incorporating only the main effect of site nor Equation (3), with main and site-income interaction terms (Table 2), shows a significant difference in the survival of patients with colorectal and noncolorectal cancers. Although it

* It is noteworthy perhaps that economic effects are also detected in the experience of OSUH Registry patients who died from causes other than the diagnosed primary malignancy. In a separate analysis, death from all cancers other than the primary tumor were used to construct a dependent variable for the Cox regression model, deaths from the primary tumor and survivors at last follow-up being treated as censored observations. The regressors in Equation ( I ) were used in this analysis and the model was estimated for the entire sample of I180 patients. The coefficients and their corresponding chi-square values (in parentheses) are as follows:

High LOW Model Age Stage Surgery Income Income chi-square

-0.01 -0.028 0.218 0.035 0.315 16.86t

As can be seen, only surgery and low-income status are statistically significant at the 5% level, each variable raising the conditional probability of death. That low-income patients are less likely than middle-income (and, by inference, high-income) patients to survive is not unexpected, although the results contrast sharply with those reported for cancer deaths in Tables 1 and 2. The fact that low income plays a role here may be underscoring the nonlinear effects of high income on cancer survival.

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No. 1 ECONOMIC STATUS AND CA SURVIVORSHIP - Chirikos and Horner 215

appears quite likely that economic effects arise from patients with carcinoma of the colon and rectum, the insignificant income effect for the noncolorectal equation might simply be attributable to the small sample size of this subgroup. Similarly, the extremely small subgroup sample sizes argue against further subdivision of the colorectal category. Thus, whereas the regression findings lead to the suspicion that the significant economic effects stem from their relationship with cancer of the colon and rectum, statistical criteria cannot rule out the pos- sibility that they play an equally important role in cancers of the small intestine and peritoneal tissues.

In interpreting the risk ratios presented in Table I , it should be understood that the high-income category variable makes a significant contribution to the explanation of survival independent of other important factors (Table 2). Statistically significant income effects are almost invariably associated with other significant regressors and the full models in which significant economic effects are detected are themselves statistically significant. It bears repeating here that our purpose was simply to control for factors likely to confound the relationship between the proxy income variable and survival. rather than to formulate and test a fully specified model of cancer survivorship. Nonetheless, it is noteworthy that the stage and surgery variables are either highly significant or, at least, of borderline significance (0.15 I P 5 0.10) in each of the models estimated. This is especially interesting given the possible connection between these variables in clinical decision-making. Age at diagnosis, as anticipated. is highly significant in most cancers. However, age tends to be insignificant at con- ventional levels for digestive system cancers, perhaps because of the similarity in ages of individuals diagnosed with these malignancies. The fact that experiments with nonlinear (quadratic) age terms failed suggests these results are not an artifact of the model’s assumptions.

In order to illustrate the importance of the economic status-survival relationship over time, we use the estimated regression coefficients from Equation ( 1 ) (Table 2) to compute survivor functions (survival curves) for patients in each income category for various site subgroupings. These calculations assume that men are as old as the mean age of the sample (i.e., 56 years), that their malignant disease was diagnosed at an ad- vanced stage, and that their treatment was not simply limited to surgery. The survival time estimates are, therefore, quite conservative. Table 4 presents the results of these computations, showing the percentage of high-, middle- and low-income patients in various site subgroups surviving at least 12, 24, and 36 months after diagnosis. As can be seen, the survival experience of high-income patients is indeed more favorable than either middle- or low-income patients. The proportion

TABLE 3. Means and Standard Deviations of Regressor Variables* by Site and Income Categories

~ ~ ~

Survival Sites and income time Age Stage Surgery Sample

categories (mo) (yr) (9) 6) size

All sites High income

Middle income

Low income

Nondigestive High income

Middle income

Low income

Digestive High income

Middle income

Low income

Severe High income

Middle income

Low income

Less severe High income

Middle income

Low income

Noncolorectal High income

Middle income

Low income

Colorectal High income

Middle income

Low income

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(50.73)

237

705

238

195

602

20 I

42

103

37

19

44

17

23

59

20

4

I I

3

19

48

17

See text for definition of variables. t Standard deviations in parentheses.

of high-income patients surviving either 12 months or 24 months is about 50% higher than the proportion of patients surviving in the other two income groups. By 36 months, the percentage of high-income patients surviving is double the respective percentages for either middle- or low-income patients. These relationships hold for all the site subgroupings where significant income effects are found.

216 CANCER July I 1985 Vol. 56

TABLE 4. Survival Experience Adjusted for Regressor Variables* by Selected Sites and Income Categories

Percentage of patients surviving time interval

Sites and income Diagnosis Diagnosis Diagnosis ategoriest 12 mo 24 mo 36 mo

Digestive system High income 60 48 33 Middle income 43 30 19 Low income 46 28 10

High income 90 88 53 Middle income 66 42 20 Low income 58 39 25

High income 88 14 58 Middle income 66 48 25 Low income 62 44 25

Less severe

Colorectal

See text for computational methods. t See text for definitions.

Discussion Although controlling for age, stage, and treatment in

a multivariate framework tends in the general case to dilute or reduce measured economic effects to zero, the results presented above indicate that the relationship between income level and survivorship in cancers of the gastrointestinal tract, especially colorectal cancers, is different. In this subgroup of cancers, there is reasonably convincing evidence that higher-income patients enjoy a more favorable survival experience than their middle- and low-income counterparts, all things being equal. Although a small advantage in survival might accrue to high-income status in other cancers, the relationship or interaction is most pronounced for the small group of malignancies pinpointed in the analysis of this report. Although we were unable to confirm that economic effects are most pronounced for colorectal cancers, the fact that we pinpoint the small intestine, peritoneum, colon, and rectum warrants the attention of other researchers in the cancer field.

In limited terms, our findings confirm similar results in at least two earlier ~tudies.~.’ Further analysis is needed, however, to assess whether nonlinear income effects of the sort detected in our statistical work show up in other data sets. More extensive analysis based on direct measurements of patient income would be partic- ularly useful in elucidating the relationship between survival and tumors at various sites. Even though the earlier literature suggests that the impact of economic variables on survival is greatest during the first year after diagnosis and that long-term survival curves for patients in different economic classes simply continue patterns of early advantage or disadvantage (i.e., the curves neither converge nor intersect), further analysis following

high- and low-income patients over much longer time periods than the period in the current study would also be useful.

Despite these limitations in the current analysis, we believe the finding that economic factors influence survivorship in some (less severe) digestive system cancers is quite robust. Why such differentials should be detected only for this small group of malignancies is far from clear. In attempting to understand this situation, at least three possible explanations warrant consideration.

First, it may be that high-income patients differ in terms of biologic characteristics favoring survival and that these characteristics play a more important role in surviving, say, carcinoma of the colon and rectum than in surviving any other cancers. For example, immunologic differences across economic groups as a potential explanation of economic effects has been postulated by several in~estigators.~.~ Wegner et al.’ note that evidence of a link between immunology and cancer survival is more prominent in the case of colon cancers. Why this should hold for colon cancers alone remains to be determined. Nonetheless, the prominent interaction effect of economic status on survival detected for cancers of the small intestine, colon, and rectum may result from immunologic differences between high- and middle- or low-income groups. Unfortunately, no indicators of immunologic status are available in the OSUH Registry data so these relationships could not be tested directly in the current analysis. Accordingly, implementing an earlier suggestion by Berg et al.’ to test for economic variations in laboratory findings such as low albumin or lymphocyte counts remains a high priority for future research on this topic. In view of the current study’s focus on white men, these studies should also include other sex-race groups for comparative purposes.

Second, it may be that the characteristics of the tumors differ across economic groups as a function of etiologic factors and these differences translate into differences in survivorship. Although we were able to rule out differences attributable to subsites in the less severe category, especially colorectal tumors, differences of a histologic nature cannot be ruled out. Available information on histologic type proved unwieldy in the context of the survival model, given the need for interaction terms between histologic code and income category and the relatively small number of sample cases. We were able, however, to construct simple contingency tables testing the association between income category (high versus middle-low) and histologic category (adenocarcinoma versus other types, e.g., spindle cell, mu- cinous, villous adenocarcinoma, etc.). These computations suggest that income is not a proxy for tumor characteristics favoring survival.

Finally, it is possible that despite controls for treatment

No. 1 ECONOMIC STATUS AND CA SURVIVORSHIP Chirikos and Horner 217

variations in the survival model, income status influences initial treatment in some qualitative fashion and/or the character of follow-up treatment. The fact that data in this study refer to only one center suggests that variations in initial treatment are not likely to differ greatly. Furthermore, auxiliary regressions incorporating treatment-income interaction terms showed consistently that such interactions were statistically insignificant. On both grounds, differences attributable to initial treatment can probably be ruled out. However, it is not possible to rule out variations in the character of follow-up treatment. Although higher-income patients may have fewer complications, they may also receive more prompt treatment for complications that do arise. Moreover, they may comply more fully with the follow-up treatment regimen. Follow-up data on the treatment regimen of patients characterized by economic status must be col- lected to test these hypotheses.

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Economic status and survivorship in digestive system cancers