Unemployment and the Detection of Early Stage Breast Tumors Among African Americans and Non-Hispanic Whites

Unemployment and the Detection of Early Stage Breast Tumors Among African Americans and Non-Hispanic Whites RALPH A. CATALANO, PHD, WILLIAM A. SATARIANO, PHD, AND ELIZABETH L. CIEMINS, MPH

PURPOSE: To test the hypothesis that high unemployment predicts reduced detection of local breast tumors among African American and non-Hispanic white women in the Detroit, Michigan and Atlanta, Georgia SEER catchment areas. METHODS: We test the hypothesis with data for the 156 months from January 1985 through December 1997. RESULTS: In situ and local breast tumors in African American and non-Hispanic white women were less likely to be detected during periods of high unemployment. CONCLUSIONS: Contracting labor markets may impede women with symptoms from getting proper medical attention or distract women from discovering symptoms they would otherwise detect. African American women appear at greatest risk of having a tumor going undetected by virtue of labor market performance. Ann Epidemiol 2003;13:8–15. © 2002 Elsevier Science Inc. All rights reserved. KEY WORDS:

Breast Neoplasms, Prevention and Control.

INTRODUCTION Hypotheses Stage of disease at diagnosis affects breast cancer survival (1). The 5-year relative survival rates for local, regional, and remote breast cancer are 97, 76, and 20 percent, respectively. Among survivors, moreover, women with cancer detected at a late stage report the greatest difficulty with upper-body limitations (2). Much research suggests that women subjected to economic hardship discover their breast tumors later than other women. African American women of low socioeconomic status, for example, have a high risk for diagnosis with late stage disease (3–10). These findings have led to the suspicion that the physical and social environments occupied by these women impede the detection of breast tumors (11, 12). Authors of this work have suggested that further research focus on the role the local economy may play in this impedance (12). Unemployment, for example, appears to be associated with many health indicators including symptoms of psychological distress and nonspecific physiological illness (13, 14). From the School of Public Health, University of California at Berkeley, Berkeley, CA. Address correspondences to: Ralph Catalano, PhD, Health Policy and Administration, School of Public Health, University of California at Berkeley, Berkeley, CA 94720, USA. Tel. (510) 642-3103; Fax (510) 6584066; E-mail: [email protected] Received October 2, 2001; revised March 29, 2002; accepted April 8, 2002. © 2002 Elsevier Science Inc. All rights reserved. 360 Park Avenue South, New York, NY 10010

Unemployment may affect the detection of early breast tumors through mechanisms arrayed in the matrix shown in Table 1. The first dimension of the matrix separates mechanisms into direct or indirect connections. Direct mechanisms pertain to unemployed women (i.e., those not working but looking for a job). Indirect mechanisms pertain to women socially or economically connected to unemployed persons though not unemployed themselves. The second dimension separates mechanisms by whether they assume that unemployment impedes women who want screening from being screened or whether unemployment distracts women who could otherwise be screened from pursuing screening. Several intuitive mechanisms fall into cell 1. Unemployed women will not have health insurance provided by an employer. Uninsured women are less likely to be screened for breast cancer than other women (15). Money may not be available for childcare or transportation needed to obtain medical attention (16, 17). Cell 2 includes similar impedance effects but these occur in women whose husbands, partners, or households lose health insurance or income. The effect of these losses could extend beyond households to social networks, such as extended families, that share the earned income of their members. Mechanisms in cells 3 and 4 arise from the assumption that we have limited time, energy, or attention with which to affect our experiences. We allocate these resources across domains of experience depending on our personal priorities. 1047-2797/03/$–see front matter PII S1047-2797(02)00273-9

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Selected Abbreviations and Acronyms CPS  Current Population Survey SEER  Surveillance, Epidemiology, and End Results

A domain that receives little time, energy, or attention will receive even less when new demands strain our capacity. Coping with unemployment, or unemployment in one’s household or social network requires time, energy, and attention. Other aspects of life must, therefore, get less attention. Screening for breast cancer may be neglected among women who do not rank health highly. Evidence supports both the direct and indirect mechanisms. The finding that unemployment induces demoralization and depression as well as other physical and behavioral correlates of distraction, supports the direct mechanisms (13, 14). The finding that women with unemployed husbands also suffer these conditions supports indirect distraction (18, 19). Illness induced in partners by unemployment may also prove distracting. Women with ill spouses, for example, are more likely to be diagnosed with advanced breast cancer than are other women (20). Unemployment in the community may also distract women. Unexpectedly high levels of unemployment cause fear of job loss among employed persons and their families (21). Fear, and the anxiety it engenders, may cause women to allocate time, energy, and attention to matters other than health. This mechanism may be more pervasive than intuition suggests because the people who fear job loss during periods of high unemployment far outnumber those who actually lose jobs (22). Indirect distraction may also affect women who remain employed during periods of corporate “downsizing.” The survivors of downsizing reportedly work harder than before and the fear of layoff further stresses them (23). Catalano and Satariano (24) measured the connection between unemployment and the detection of local breast tumors reported to the Greater San Francisco Bay Area (i.e., Alameda, Contra Costa, Marin, San Francisco, and

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San Mateo Counties) Cancer Registry from January 1983 through December 1993. They report that the likelihood of breast cancer being diagnosed at a local stage among nonHispanic white and African American women declined in the month after seasonally adjusted unemployment increased. The decline appeared more pronounced for African American than for non-Hispanic white women. The question of whether the San Francisco results generalize to other communities has not been addressed. We attempt to answer the question by repeating the test in two communities distant from San Francisco that include relatively large groups of African American women. These are Atlanta, Georgia (Clayton, Cobb, DeKalb, Fulton, and Gwinnett counties), and Detroit, Michigan (Macomb, Oakland, and Wayne counties). We test the hypothesis that the monthly number of in situ and local breast tumors discovered in the Atlanta and Detroit metropolitan areas varies inversely with seasonally adjusted changes in unemployment. We included in situ with local tumors to provide complete accounting of “early disease.” We conduct separate tests for non-Hispanic whites and African Americans. We control, as described below, for any confounders that affect the detection of in situ and local tumors in comparison populations as well as the detection of regional and distant tumors (“late disease”) in the test populations. We also control any variables that exhibit regular patterns, such as trends or cycles, over the test period.

METHODS Data We obtained data describing breast tumor stage at diagnosis for the 156 months beginning January, 1985 (first month for which economic data were available as consistent time series) and ending December, 1997 (last month for which complete tumor data were available at the time of our test) from the Surveillance, Epidemiology, and End Results (SEER) program (25, 26). The SEER data include demographic information (i.e., race, gender, age) as well as tumor

TABLE 1. Examples of mechanisms that connect unemployment to detection of early tumors.

Impedance - women want, but are impeded from, screening.

Distraction - women have access to, but are distracted from, screening.

Direct - Act only on the unemployed

Indirect - Act on those not in the labor market or the employed

1. Unemployed women do not have health insurance provided by employers Unemployment reduces income making transportation or childcare too expensive 3. Time, energy, or attention budget exceeded due to own unemployment

2. Women whose husbands are unemployed do not have insurance provided by employers Women whose husbands or partners are unemployed cannot afford transportation or childcare 4. Time, energy, or attention budget exceeded due to: a) demands of unemployed spouse, partner, or friends. b) excess demands on those left working

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characteristics (i.e., cancer site, stage at diagnosis, histology, grade of tumor). Tumors from all age groups were included in our analyses. The summary stages used by SEER include in situ (microscopic, no local spread), local (confined to breast), regional (spread to adjacent tissue and/or ipsilateral lymph nodes), and remote/distant (metastatic to nonadjacent tissue and /or contralateral lymph nodes. Monthly counts of registered in situ and local breast tumors detected among non-Hispanic white and African American women in Atlanta and Detroit are plotted in Figure 1 and Figure 2. The San Francisco analyses described above did not include in situ tumors because the focus was on invasive disease. We include them in our tests because screening should detect them and our theory concerns the effect of the economic climate on screening. We also tested our hypotheses without them to ensure strict comparability with the San Francisco tests. We obtained monthly counts of unemployed persons from the US Department of Labor and the state employment agencies of Michigan and Georgia. The data come primarily from the Current Population Survey (CPS) and measure the number of persons not working at the time of the survey but who are looking for work. Figure 3 shows the number of unemployed persons in the test communities plotted over the test months. Following Catalano and Satariano (24), we adjusted monthly changes in unemployment to remove seasonality.

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We used a method described in more detail in the Analysis section. The labor markets for Atlanta and Detroit are spatially larger than the SEER catchment areas. The Detroit unemployment data describe Lapeer, Macomb, Monroe, Oakland, St. Clair and Wayne Counties. The Atlanta data cover Barrow, Butts, Cherokee, Clayton, Cobb, Coweta, Dekalb, Douglas, Fayette, Forsyth, Fulton, Gwinnett, Henry, Newton, Paulding, Rockdale, Spalding, and Walton Counties. We believe that our hypotheses are well tested with unemployment measured at the labor market level. This is true because the labor market definitions are devised to reflect, in part, the fact that persons often work outside their county of residence. It is also the case that the number of respondents to the CPS who reside in the separate counties of a labor market can be very small. The confidence intervals of the point estimate of unemployment in these counties would, therefore, be very large. Design Correlational tests determine whether the observed values of two variables differ, as predicted by theory, from their statistically expected values in the same cases. Such tests typically assume that the statistically expected value of each variable is its mean. Time series, however, often exhibit trends, cycles, and the tendency to remain elevated or depressed after high or low values. These patterns compli-

FIGURE 1. Registered in situ and local tumors among African American and non-Hispanic white women in Atlanta, Georgia for the 156 months starting January, 1985.

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FIGURE 2. Registered in situ and local tumors among African American and non-Hispanic white women in Detroit, Michigan for the 156 months starting January 1985.

cate correlational tests because the expected value of a patterned series is not its mean. No one, in other words, would predict the next value of a series to be its mean if the previous values exhibited a cycle or trend. Researchers often solve this problem through the purely empirical approach of identifying patterns and expressing them as a product of earlier behavior in the dependent variable itself. Time-series equations used to test hypotheses, therefore, often include “lags” of the dependent variable among predictor variables. Any approach that removes patterns from the dependent variable before testing the effect of the independent variable has the added benefit of avoiding spurious associations due to shared trends and cycles. The estimated coefficients are net of shared patterns. Epidemiologists have offered an alternative method that measures the dependent variable in a comparison population and uses the series as a control variable in the test equation (27, 28). This provides the benefit of the purely empirical approach in that it removes patterns in the dependent variable induced by forces also at work in the comparison population. The approach also controls unspecified variables that affect both populations but exhibit no patterns. We combine the empirical and comparison population approaches. We model the dependent variable in one community as a function of the same phenomenon measured in the second. We inspect the residuals of the model for patterns. We assume that forces affecting the test, but not com-

parison, community induce any remaining patterns. We remove remaining patterns in the dependent variable by including, as in the purely empirical approach, the appropriate lags of the dependent variable among the predictors. We then add the unemployment variable to the equation. The coefficients of the unemployment variable are net of shared patterns as well as of any confounding effects of phenomena that affect both communities but exhibit no patterns. This approach leaves a class of third variables uncontrolled. These include phenomena that are peculiar to the test community and exhibit no patterns. We use two strategies to control such phenomena. First, we include the number of regional and distant tumors registered in the test community as a predictor variable. This controls any phenomena that are peculiar to the test community and affect the registration of all breast tumors. Such phenomena could include population growth or decline, extreme weather, and changes in record keeping policies. Second, we repeat the test but reverse the test and comparison communities. The argument that a third variable peculiar to a community spuriously induces the discovered effect becomes less plausible if we reject the null hypothesis in both communities. Data Analyses We describe our test equations in an appendix available from the first author. The tests proceeded through the following steps.

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FIGURE 3. Thousands of unemployed persons in Atlanta and Detroit for the 156 months beginning January 1985.

We modeled in situ and local breast tumors for either African Americans or non-Hispanic whites in the test community as a function of the regional and distant breast tumors for the same group in the test community and of in situ and local breast tumors for the same group in the comparison community. The residuals from step 1 were inspected for a mean and patterns. We added a constant to the equation if the mean was twice its standard error. Patterns, if any, in the residuals from step 1 were identified and specified using the augmented Dickey-Fuller test (29) to test for secular trends and the strategy attributed to Box and Jenkins (30) as well as Ljung and Box (31) to test for and model autoregressive and moving average effects. The strategy, Auto Regressive, Integrated, Moving Average (i.e., ARIMA) modeling, allows any of a large family of possible models to be empirically fit to serial measurements. ARIMA models mathematically express various filters through which series without patterns can pass. Each filter imposes a unique pattern. The Box-Jenkins approach uses a model-building process by which the researcher infers the filter that imposed the observed pattern. The differences between the values predicted by the inferred model and the observed series are assumed to be the unpatterned values that were filtered.

Monthly counts of unemployed persons were seasonally adjusted by applying the Box-Jenkins routines described above. Seasonally adjusted unemployment was added to the equation resulting from steps 1 through 3. We estimated the equation resulting from step 4 and inspected the error terms for temporal patterns. If any were found, we added ARIMA parameters to the equation and estimated the resulting equation. We deleted any parameters with coefficients not different from 0 at P  0.05 (single-tailed test) and estimated the equation again. We measured the association between the error terms of the equation and the unemployment variable to ensure they were not related. We repeated steps 1 through 7 but reversed the communities such that Detroit became the test site and Atlanta the comparison.

RESULTS The analyses outlined above yielded the findings shown in Table 2, and Table 3. The results support our hypothesis in

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TABLE 2. Final coefficients (standard errors in parentheses) for predictors of registered in situ and local breast tumors in Atlanta (n  156 months starting January 1985) Unemployed persons  Coefficient Regional and distant tumors in test community In situ and local tumors in comparison community ARIMA parameters

African Americans

Non-Hispanic whites

Lag 1  0.1062* (0.0538) 0.5960* (0.3012) 0.2307* (0.1027) 0.4738** (0.0405) None

Lag 3  0.2185** (0.0784) 0.9170** (0.0729) 0.4994** (0.1601) 0.3588** (0.0299) None

*P  0.05; two-tailed test **P  0.01; two-tailed test

both communities. The discovery of in situ and local breast tumors appears to move inversely over time with unemployment, controlling for all autocorrelated third variables. The association also survives controlling for any third variables that exhibit no patterns but affect the detection of regional and distant tumors in the test community as well as in situ and local tumors in the comparison community. All the statistical effects of unemployment except that for non-Hispanic whites in Atlanta, exhibit the same delay. They appear one month after seasonally adjusted changes in unemployment and persist, though diminish, for at least two additional months. The effect for non-Hispanic whites in Atlanta appears in the third month after changes in unemployment and persists for at least two months. As shown in Table 3, the monthly discovery of in situ and local tumors among non-Hispanic white women in Detroit exhibited a pattern not shared with similar tumors in the comparison community or with regional and distant tumors in Detroit. The parameter means that the value of the dependent variable in any month can be predicted, at least in part, from the value nine months earlier. We know of no post hoc explanation for this pattern. Including the parameter in the models, however, ensures that the pattern cannot induce a type I error. The fact that all the tests yielded significant  coefficients suggests that the association between unemployment and the detection of in situ and local tumors persists over several months. The persistence, however, varies by group and test community. The association for non-Hispanic whites in Atlanta persists longer than that for African Americans while the pattern reverses in Detroit.

We estimated the number of early stage (i.e., in situ and local) tumors that our theory suggests were not detected due to increases in seasonally adjusted unemployment. We first combined the initial and persistent effects of unemployment into a single coefficient for each of the four ethnicity-by-place groups. For African Americans in Atlanta, for example, we multiplied 0.1062 (i.e., the initial effect) by 0.5960 (i.e., the  coefficient) and then multiplied the product again by 0.5906 and so on until the product was less than the initial effect less twice its standard error (i.e., less than 0.0018). The combined effect was 0.2295. We then multiplied the combined effect and the sum of seasonally adjusted unemployment in the months when unemployment increased. In Atlanta seasonally adjusted unemployment increased in 62 of the test months yielding a sum of 310,558 new episodes of unemployment. We, therefore, estimate that increases in unemployment may have caused about 71 African American women to lose the benefit of early detection of breast cancer (0.2295  310.558  71.27). Applying the above logic to non-Hispanic whites in Atlanta, we estimate that about 228 women had early stage tumors that went undiscovered due to unemployment. Seasonally adjusted unemployment in Detroit increased in 84 months yielding a net of about 776,000 incident episodes. These circumstances imply that, due to unemployment in the community, roughly 270 African American women lost the benefit of early detection. The calculations for non-Hispanic white women in Detroit suggest that about 421 had early tumors that went undiscovered as a result of unemployment.

TABLE 3. Final coefficients (standard errors in parentheses) for predictors of registered in situ and local breast tumors in Detroit (n  156 months starting January 1985). Unemployed persons  Coefficient Regional and distant tumors in test community In situ and local tumors in comparison community ARIMA parameters *P  0.05; two-tailed test **P  0.01; two-tailed test

African Americans

Non-Hispanic Whites

Lag 1  0.0942**(0.0315) 0.9470**(0.0443) 0.7257**(0.0830) 0.8611**(0.0959) None

Lag 1  0.3665**(0.1053) 0.4822*(0.2331) 0.9040**(0.1171) 1.3321**(0.1096) B9  0.4250**(0.0776)

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Dividing the above estimates of undetected tumors by the number detected yields an approximation of the odds that an early stage tumor went undetected due to unemployment. The odds for African American women in Atlanta and Detroit were 0.077 and 0.147 respectively. For non-Hispanic whites the odds were 0.056 in Atlanta and 0.049 in Detroit. Consistent with the findings from the San Francisco Bay Area, African American women in the Atlanta and Detroit SEER catchment areas appear at greater risk of having early stage breast tumors go undetected by virtue of the mechanisms alluded to in Table 1. As noted in the Methods section, the San Francisco tests did not include in situ tumors. To ensure strict comparability between the San Francisco tests and ours, we estimated the equations shown in Tables 1 and 2 with local tumors only as the dependent variable. The results were essentially the same as those with in situ tumors included.

DISCUSSION Our results replicate those reported for the San Francisco Bay Area. We found that health care providers in the Atlanta and Detroit SEER catchment areas detected fewer early stage breast tumors following increases in seasonally adjusted unemployment. The association has now been found in three widely separated communities. The chances of these separate associations appearing by chance are quite small (i.e., 0.053 or less). The association also survives controlling for a wide array of rival hypotheses. In the case of the Atlanta and Detroit SEER catchment areas, we controlled any autocorrelated third variables and any that affect the discovery of regional and distant tumors in the test site or the discovery of in situ and local tumors in the comparison community. The discovered effects could be, of course, peculiar to Atlanta, Detroit, and San Francisco SEER catchment areas at the tested times. Only further replication can determine the external validity of the findings. Further tests should attempt to determine which of the mechanisms described in Figure 1, best describes the observed data. These tests could be at the aggregate level, such as ours, or the individual level. An appealing strategy at the aggregate level would be to separate tumors discovered in a country with universal health insurance into those from women whose age suggests they were unlikely to be in the labor force and those from all other women. Failure to replicate the association found in the United States would detract from the distraction mechanism. Distraction would remain viable if the effect were found in both age groups. Replicating the association only among the young would detract from indirect distraction mechanisms whereas replicating only among the old would have the opposite implication.

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The best way to estimate the relative parsimony of the mechanisms alluded to in Figure 1, is to supplement timeseries analyses with studies that interview women about health behaviors, such as breast self-examinations and use of mammography during periods of unexpected high unemployment. Together, these studies will enhance our understanding of the temporal association between periods of unemployment and the reduced likelihood that breast cancers will be diagnosed at an early stage. Further analyses should also focus on African American women to discover the reasons why they appear more responsive to economic perturbation. An intuitive strategy would specify unemployment among African Americans and non-Hispanic whites in our test equations. Coefficients in these equations would be rough estimates of the relative effects of distraction and impedance for each group. To our knowledge, however, monthly data comparable across local areas do not separate the unemployed into African Americans and others. A better understanding of the race differences found in the test sites probably awaits the individual level analyses alluded to above. The findings from the Atlanta, Detroit, and San Francisco SEER data suggest that breast cancer control efforts should be intensified during periods of unexpectedly high unemployment. Most programs intended to increase screening vary in intensity over time such that special efforts occur at regular intervals. In addition to regularly scheduled increases in effort, bursts of extra programming might be used strategically at times when the population is at elevated risk of distraction or impedance. Our research suggests that periods of unexpectedly high unemployment should trigger such bursts. The fact that we discovered a lagged association suggests that there would be time to mount bursts in programming. Such efforts would require cooperation among the programming organizations and the local, state, and federal agencies responsible for surveillance of unemployment. The US Bureau of Labor Statistics releases national counts of unemployment compensation claims, for example, with a lag of less than two weeks. While not currently published, these data should be available for smaller geographic areas. Testing our theories in additional localities may make diminishing contributions to basic science, but more tests would enhance the applied use of the work. Knowing, for example, why non-Hispanic whites responded later than the other tested groups, or why the persistence of the responses varied by community could make interventions better timed if not more effective. This work, of course, would require interdisciplinary teams including economists, epidemiologists, geographers, and perhaps others. The literature includes attempts (32) to integrate these perspectives but much more theoretical and empirical work remains before women enjoy an applied payoff.

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