progress and pitfalls in the social epidemiology of cancer

Progress and Pitfalls in the Social Epidemiology of CancerAuthor(s): Jay S. KaufmanSource: Cancer Causes & Control, Vol. 10, No. 6 (Dec., 1999), pp. 489-494Published by: SpringerStable URL: http://www.jstor.org/stable/3553734 .

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Cancer Causes and Control 10: 489-494, 1999. 489 @ 1999 Kluwer Academic Publishers. Printed in the Netherlands.

Editorial

Progress and pitfalls in the social epidemiology of cancer

Jay S. Kaufman1'2 'Department of Epidemiology, University of North Carolina School of Public Health, McGavran-Greenberg Hall, Pittsboro Road, Chapel Hill, NC 27599-7430; 2Carolinas Population Center, University of North Carolina at Chapel Hill, 123 West Franklin Street, Chapel Hill, NC 27516-3997

The unequal burden of cancer incidence and mortality experienced by racial/ethnic and socioeconomic sub- groups has been noted with increasing urgency, and is coming to be appreciated as one of the most pressing health disparities that we face at the close of the current

century [1]. Although epidemiologic study of social factors has a long and fruitful history in cardiovascular research, the social epidemiology of cancer still suffers from a curious obscurity. This perhaps stems from the traditional myopism of handling socially mitigated exposures - cigarette smoking, diet, and so forth - as isolated entities, divorced from their actual social context [2]. In reality, of course, these "proximal" exposures vary tremendously by social circumstances. Analyses that step back and view such exposures as elements of patterned trajectories allow for a more complete and ultimately useful description of disease occurrence in populations.

For example, we consider cigarette smoking to be a cause of lung cancer because we can contrast the rate of disease for a population that has a given distribution of smoking against the hypothetical rate that would be obtained under an alternative population distribution of smoking. We can take a more holistic point of view, however, and consider instead the rate of lung cancer for a population under a given social policy regime, such as the cigarette excise tax, contrasted with the hypothetical rate that would be obtained under an alternative policy regime. The behavior of cigarette smoking is sensitive to

price [3], which is a function of tax policy [4], and therefore smoking behavior becomes an intermediate variable in the causal chain between social policy and disease. Likewise, racial/ethnic identity and socioeconomic status are key factors associated with the distri- butions of many exposures of interest in cancer

epidemiology. The explicit consideration of these social variables, as accomplished in four articles included in the current issue [5-8], is therefore an exciting and

salutary development in the cancer research literature. As often occurs in scientific endeavors, progress

heralds new challenges. The papers in this issue are

sophisticated in their various approaches, bringing

thoughtful strategies to bear on questions regarding basic surveillance, etiology, and treatment utilization. Nonetheless, the particular nature of social variables leads to numerous methodologic, substantive and logical problems, many of which remain unresolved in these papers and elsewhere in the literature, and which therefore require further innovation and development. I discuss two of these issues here, using the papers in this series as examples in order to make more general points about the epidemiologic study of social variables and cancer.

Race, socioeconomic status, and cancer

Two of the papers in this issue focus on the etiology of racial/ethnic disparities in cancer incidence [6] or mortality [5]. The authors of both studies articulate the eminently sensible hypothesis that much or all of the observed racial/ethnic gap can be attributed to varia- tions in socioeconomic status (SES) which result from racial discrimination. Both sets of authors pursue this hypothesis by playing the adjustment game: what is the adjusted effect estimate for race once a measure of SES has been "controlled" in the analysis? The reasoning is that a large change in the effect estimate for race toward the null value, or an adjusted effect estimate that is close to the null value, are evidence in favor of the hypothesis that much of the race effect is through the SES pathway. In other words, the authors seek to distinguish between the indirect effects of race, as relayed through the SES variable, from the direct effects, which occur through some alternate pathway for which intermediates remain unmeasured (Figure 1). For example, if African-Amer- icans generally hold cultural beliefs that inhibit their screening behavior relative to whites, as suggested by some authors [9], then these cultural/behavioral factors would be unmeasured intermediates along a direct pathway.

Arbes and colleagues express some legitimate reser- vations about this strategy, pointing out, as have



490 J.S. Kaufman

INDIRECT

RACE SES CANCER

DIRECT

Fig. 1. Direct versus indirect effects in a causal chain.

previous authors as well (e.g., [10]), that SES is an intermediate and therefore not a good candidate for standard covariate adjustment methods. Indeed, although it is quite commonplace for authors to adjust for SES variables when examining racial/ethnic differ- entials, the logic behind this approach is seriously flawed, as demonstrated by the following thought exercise.

Suppose for the sake of simplicity that all effects are completely deterministic, which eliminates the need to accommodate stochastic issues, and that there are only two states of race/ethnicity (black, white), two states of SES (low, high) and two states of the outcome (disease, health). Furthermore, suppose that there exist only two causal types of people in the population of interest. Type A is defined causally as: black race leads invariably to low SES, and low SES leads invariably to disease. Type B is defined causally as: black race leads invariably to low SES, and black race leads invariably to disease, but SES has no effect on disease whatsoever. For Type A subjects, therefore, the entire effect of race is indirect (i.e. relayed through SES). For Type B subjects, on the other hand, the entire effect of race is direct; the effect of race on SES is incidental. The question regarding how much of the total observed effect is indirect (i.e. relayed through SES) and how much is direct is equivalent to determining the proportions of Types A and B in the population.

Now consider data on the three variables from an observational study, in which one would hope to separate the direct from the indirect effects of race on disease by controlling for SES. Every black subject presents with the vector of values: race = black, SES = low, outcome = disease, regardless of causal type. There is no logical way to determine the proportions of Types A and B, thus no way to separate out the two types of effects. Consequently, for the causal structure shown in Figure 1, there is no meaning to an estimate for the effect of race adjusted for SES,

regardless of the mechanism of control employed (even stratification). Using a more elaborate mix of causal types, Robins and Greenland [11] demonstrate that, in this scenario, attempts at statistical control from any method are prone to bias, and can easily indicate

"independent" effects even when none exist. The solu- tion to this problem is not methodologic, therefore, but must instead be conceptual; we must pose questions for which answers are meaningful and interpretable. For example, we may logically be able to show to what extent the SES-to-cancer relation is confounded by race, even if we cannot hope to identify what portion of the race effect is transmitted through SES.

SES and cancer over the life-course

Marshall and colleagues [7] are also concerned with etiology, and make the very important point that SES as measured at the time of diagnosis or death may be a poor indication of life-time exposure history. A number of recent epidemiologic investigations have utilized life- course oriented approaches, which seek to take into account the changing dynamics of social, economic and health factors at various ages [12, 13]. This is clearly an important conceptual advance for our field for many reasons, including the potentially long etiologic period for pathogenesis of cancers, the changing susceptibility to socioeconomic conditions and the potential for "critical periods" engendered by social or biological transitions or their interactions [14, 15], and the changing material and social significance of various. SES measures over the life-course [16]. Income may wane in importance relative to accumulated assets as an indi- vidual approaches middle-age and retirement, for example [17], and "reverse causation" (i.e. health impacting negatively on employment and income) may also act differentially over the life-course [18]. Because of critical periods of biological development, such as infancy and puberty, deprivation at one point in time might have a greater impact on healthy development than at another [19, 20], and because of psychological development and socialization, such deprivation may differentially impact on the patterning of health behaviors which later affect disease and mortality [15, 21]. Occupation, family income and accumulated assets are volatile quantities, and so the focus by Marshall and colleagues on social mobility, in addition to status at one point in time, represents a vital conceptual enrichment for cancer epidemiology [22, 23].

The usual methodologic implementation of this conceptual advance is to compare the magnitudes of relative risk estimates at various points in the life-



Progress and pitfalls in the social epidemiology of cancer 491

course for individuals in an identified cohort (e.g., [24]). This can be quite misleading, however. For example, previous researchers have made much of the observation that relative risk for mortality associated with lower SES is smaller in magnitude at older ages than at younger ages (e.g., [25]). Figure 2 shows a representation of this well-described pattern, with data drawn from the National Longitudinal Mortality Study (NLMS). The figure depicts relative risk of death for adult men in the 9-year follow-up period as a function of education, comparing risk in those with > 12 years with those who attained < 12 years. Clearly the effect is greatest at young ages and diminishes monotonically; mechanistic speculations follow readily from this observation. But wait. Probability of death is not constant over the life-course in the baseline SES group. The relative risk estimate of 3.0 in the 10-year interval from 20 to 29 years is the contrast of mortality probabilities of 0.008 for the highly educated and 0.024 for the lesser educated men. Is this really where the effect is greatgst? Figure 3 shows the same data, but now the contrast made is the risk difference rather than the risk ratio. Rather than seeing the SES effect to be greatest (in relative terms) at the youngest ages, it is now clear that the SES effect is greatest (in absolute

terms) when men have reached their 6th and 7th decades of life.

The study design employed by Marshall et al. [7] skillfully avoids this common problem by comparing a fixed outcome to indicators of past occupational position. The large variation in probability of cancer over the life-course is therefore not as potentially damaging for the relative risk comparisons as in many other studies. Nonetheless, the opportunity for considerable ambiguity remains when the results are interpreted on the basis of contrasts of effect magnitude between time points. Confounding between time windows often occurs, for example, when employment grade is correlated over time. Consider the sample data in Table 1, which represent a similar case-control design carried out in a fictitious occupational cohort. Employment grades, ranging from 1 (low) to 3 (high), are shown at three time points in the career, such that there are ten distinct career trajectories (Types A through J). The risk of outcome in this example is set to be a function of cumulative exposure only, which is the sum of the three grades, such that an employee who never rises above level I has a sum of 3 (highest risk) and an employee who begins and ends at grade 3 has a sum of 9 (lowest risk). All cases are included, along with a 20% sample of

Males (n = 209,556) 3.0

S 2.5 2

-. Education

Q: 2.0 High (> 12 years) versus Low (< 12

S1.5 . . years)

S........... Point-Wise S1.0 .. 95% Confidence

a. Intervals a 0.5

0.0

23 28 33 38 43 48 53 58 63 68 73 78 83 88 93

Age (Mid-point of 10-year Interval) Fig. 2. Relative risk of death for men in 9-year follow-up and point-wise 95% confidence intervals by educational level and 10-year age window: NLMS.



492 J.S. Kaufman

Males (n = 209,556)

0.10

) .Education 0.05 05 High (>12 years)

i5-> versus Low (< 12

" ooo -?*-. years) o ........... Point-W ise O) -0.05 95% Confidence a. Intervals a) 0) -0.10

23 29 35 41 47 53 59 65 71 77 83 89

Age (Mid-point of 10-year Interval) Fig. 3. Risk difference for death in men in 9-year follow-up and point-wise 95% confidence intervals by educational level and 10-year age window: NLMS.

Table 1. A case-control study from an occupational cohort in which employment grade is measured at three time points and disease risk is a function of cumulative exposure

Type N Time 1 Time 2 Time 3 Sum Risk* Cases Controls

A 1000 1 1 1 3 0.510 510 200 B 1000 1 1 2 4 0.321 321 200 C 1000 1 1 3 5 0.221 221 200 D 1000 1 2 2 5 0.221 221 200 E 1000 1 2 3 6 0.158 158 200 F 1000 2 2 2 6 0.158 158 200 G 1000 1 3 3 7 0.114 114 200 H 1000 2 2 3 7 0.114 114 200 I 1000 2 3 3 8 0.081 81 200 J 1000 3 3 3 9 0.055 55 200

Total 10000 1953 2000

Contrasts for Grade 1 versus Grade 3

Time 1 Time 2 Time 3

Pr(Grade = l Case) = 0.791 Pr(Grade = IlCase) = 0.539 Pr(Grade = liCase)= 0.261 Pr(Grade = 31Case) = 0.028 Pr(Grade = 31Case) = 0.128 Pr(Grade = 31Case) = 0.380 Pr(Grade = liCont) = 0.600 Pr(Grade = 1ICont) = 0.300 Pr(Grade = lICont)= 0.100 Pr(Grade = 31Cont) = 0.100 Pr(Grade = 3ICont) = 0.300 Pr(Grade = 3ICont) = 0.600

OR = 4.682 OR = 4.208 OR = 4.118 RR = 4.682 RR = 4.208 RR = 4.118 RD = 0.203 RD = 0.267 RD = 0.386

* Risk=(i1)-0.4



Progress and pitfalls in the social epidemiology of cancer 493

the cohort to serve as controls. The selection of controls as a random sample of the entire cohort leads to expected exposure odds ratios (OR) which equal the desired relative risks (RR). Note that in this example, comparing the RR magnitudes would lead one to conclude that Time 1 is most significant, whereas the absolute risk difference shows Time 3 to be the most significant. In truth, only cumulative exposure was used to determine disease risk.

Conclusion

"There is one thing even more vital to science than intelligent methods; and that is the sincere desire to find out the truth, whatever it may be." - Charles Sanders Pierce (1839-1914)

The fruitful development of a social epidemiology of cancer remains nascent, but through the contributions of the able investigators, including those represented here, promises to yield great insights. The methodologic challenges are formidable, but so too are the problems associated with nutritional, environmental and genetic factors, to name just a few. I have raised two relatively simple conceptual issues in this essay, which by no means exhausts the array of difficulties faced in these and other studies of social factors and health. Real progress, especially with respect to the more challenging issue of etiology, requires a sophisticated appreciation of biological, quantitative and social dimensions. Epide- miology is uniquely poised at the intersection of these three domains to address the profound social injustice of cancer inequalities. My comments regarding the new and promising work published in this issue of Cancer Causes and Control are offered in hopes of encouraging such progress. In an era of widening social inequality, the urgency for valid and insightful work on cancer incidence and mortality disparities between socioeconomic and racial/ethnic groups is ever greater.

Acknowledgements

Charles Poole commented on a draft of this editorial and provided several clarifications and improvements.

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