Housing Discrimination, Residential RacialSegregation, and Colorectal Cancer Survival inSoutheastern WisconsinYuhong Zhou, Amin Bemanian, and Kirsten M.M. Beyer
Abstract
Background: Residential racial segregation is still neglected incontemporary examinations of racial health disparities, includingstudies of cancer. Even fewer studies examine the processes bywhich segregation occurs, such as through housing discrimina-tion. This study aims to examine relationships among housingdiscrimination, segregation, and colorectal cancer survival insoutheastern Wisconsin.
Methods: Cancer incidence data were obtained from the Wis-consin Cancer Reporting System for two southeastern Wisconsinmetropolitan areas. Two indices of mortgage discrimination werederived fromHomeMortgage Disclosure Act data, and a measureof segregation (the location quotient) was calculated from U.S.census data; all predictors were specified at the ZIP Code Tabu-lation Area level. Cox proportional hazards regressionwas used toexamine associations between mortgage discrimination, segrega-tion, and colorectal cancer survival in southeastern Wisconsin.
Results: For all-causemortality, racial bias inmortgage lendingwas significantly associated with a greater hazard rate among
blacks [HR¼ 1.37; 95% confidence interval (CI), 1.06–1.76] andamong black women (HR ¼ 1.53; 95% CI, 1.06–2.21), but notblack men in sex-specific models. No associations were identifiedfor redlining or the location quotient. Additional work is neededto determine whether these findings can be replicated in othergeographical settings.
Conclusions: Our findings indicate that black women in par-ticular experience poorer colorectal cancer survival in neighbor-hoods characterized by racial bias inmortgage lending, ameasureof institutional racism. These findings are in line with previousstudies of breast cancer survival.
Impact: Housing discrimination and institutional racism maybe important targets for policy change to reduce health disparities,including cancer disparities. Cancer Epidemiol Biomarkers Prev; 26(4);561–8. �2017 AACR.
See all the articles in this CEBP Focus section, "Geo-spatial Approaches to Cancer Control and PopulationSciences."
IntroductionColorectal cancer is the third leading cause of cancer-related
death in both men and women in the United States (1) andcolorectal cancer survival disparities, including by race and geog-raphy, have been extensively documented (2, 3). Despite thecontinuing decrease in colorectal cancer death rates over the pasttwo decades, racial/ethnic minority groups, particularly blacks/African Americans, continue to have higher death rates comparedwith whites (1, 4, 5). The gap in colorectal cancer survival rates byrace has persisted since the early 1980s (2, 3) andmay be growingwider (6–8). Studies have shown that diagnosis stage, tumorbiology and genetics, comorbidities, lifestyle factors, differencesin screening and treatment, and socioeconomic status all can playa role in generating racial disparities in colorectal cancermortalityand survival (9–12). However, even after controlling these knowncontributing factors, the racial survival gap for colorectal cancer isnot fully eliminated (9).
Researchers are beginning to explore additional factors thatmay contribute to racial disparities in cancer outcomes, includingresidential racial segregation (13–15). Research on breast cancerhas hypothesized linkages between segregation and survivalthrough health care access, exposure to stressors, and local healthbehavioral norms, including physical activity, nutrition, tobacco,and alcohol use (16, 17), and a few studies have indicated thatsegregation may contribute to racial disparities in cancer mortal-ity, although findings have been mixed (14, 17–19). There are farfewer publications investigating the effects of racial residentialsegregation on colorectal cancer outcomes. In a study in the TwinCities 7-county Metropolitan area, Shen (20) found that facilitiesthat were located closer to minority-segregated census tracts hadpoorer colorectal cancer screening performance. In a study explor-ing the association of segregation with disparities in diagnosisstage for breast, colorectal, prostate, and lung cancer, Haas andcolleagues (21) found that the black/white disparity was actuallysmaller in more segregated areas, after adjusting for individual-level factors and an area-level urban/rural indicator. Given theplausibility of the influence of segregation on colorectal cancersurvival (13, 22), additional work is needed to better understandthese relationships.
Although studies of segregation are greatly needed, studyingonly segregation patterns may not provide sufficient informationto inform policy change to improve health and reduce disparities.Segregation measures reveal spatial distributions of populationgroups by race, but do not directly measure the underlying
Division of Epidemiology, Institute for Health and Equity, Medical College ofWisconsin, Milwaukee, Wisconsin.
Corresponding Author: Yuhong Zhou, Medical College of Wisconsin, 8701Watertown Plank Road, Milwaukee, WI 53226. Phone: 414-955-4302; Fax:414-955-0176; E-mail: [email protected]
doi: 10.1158/1055-9965.EPI-16-0929
�2017 American Association for Cancer Research.
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discriminatory and socioeconomic processes that create the pat-terns. Processes contributing to segregation patterns involve mul-tiple sectors of society, such as housing, education, and labor (23),and are ultimately the targets for policy changes to reduce resi-dential racial segregation. Recently, several studies have foundrelationships betweenmortgage discrimination and health status,including pregnancy health, preterm birth, and most recently,breast cancer survival (24–27), indicating that racial discrimina-tion in housing could be important in explaining racial/ethnicdisparities in health outcomes, including cancer. No studies havelooked at the relationship between patterns of housing discrim-ination and colorectal cancer survival, and none have concurrent-ly examined housing discrimination and segregation.
The purpose of this study is to examine relationships betweenhousing discrimination segregation, and colorectal cancer surviv-al, contributing to a growing body of work examining racism,segregation, and cancer outcomes.
Materials and MethodsStudy area
The study area includes two metropolitan statistical areas(Milwaukee-Waukesha-West Allis and Racine) in southeasternWisconsin. As the center of this region, the City of Milwaukeeis home to approximately 600,000 residents, of whom non-Hispanic black and non-Hispanic white populations sharesimilar percentages (39% and 38%). The population withinMilwaukee County and the City of Milwaukee experienceslower socioeconomic status (SES) than the state population,including lower incomes, higher poverty, greater unemploy-ment, lower educational attainment, lower home ownershiprates, and poorer housing stability (28). The long-termentrenched poverty and residential racial segregation in Mil-waukee, its history of discriminatory housing policies (29), andobserved disparities in colorectal cancer incidence and mortal-ity rates (30) make the area an appropriate setting for thisstudy. Figure 1 displays the geographic extent of the study area,as well as patterns of colorectal cancer incidence and mortalityin the region, to provide context for the study.
Data and variablesOur analyses are based on three data sources. Cancer incidence
data were provided by theWisconsin Cancer Reporting System forthe years 2002 to 2011 for invasive colorectal cancers for south-eastern Wisconsin. Segregation metrics were calculated from U.S.Census Bureau population and demographic data (31). Indices ofmortgage discrimination were derived from Home Mortgage Dis-closure Act (HMDA) data (2004–2011) available on the FederalFinancial Institutions Examination Council HMDA website (32).
Reported by hospitals, physicians, and clinics directly to Wis-consinDepartment ofHealth Services (DHS), cancer cases includeimportant information about patients' demographics, tumorcharacteristics and treatment, date and cause of death via linkagesto the Wisconsin Vital Records resident death file and NationalDeath Index. The sample used was limited to individuals who areblack or non-Hispanic white and resided in the study area atdiagnosis. Casesmissing diagnosis stage informationwere exclud-ed (<3%). This study was approved by the institutional reviewboard at the local institution and authorized and approved by theDHS Research Review Board for the release of cancer data for the
purpose of cancer prevention and control as defined in Statute255.04(3)(c).
The HMDA database was initially created to collect data onmortgage-lending practices. It reports relevant information onmortgage applications, such as applicants' demographic andeconomic characteristics (race/ethnicity, sex, and income), prop-erty type, loan purpose, loan amount, andmortgage decision. Thecensus tract containing the residential address of the property forwhich a mortgage was requested is also included. Data werelimited to applications for purchasing an owner-occupied homeand without missing information on the primary race/ethnicity,sex and income of the primary applicant, loan amount, andwhether the loan was denied. Of a total of 396,032 total applica-tions for thepurchase of anowner-occupiedhome, approximately40% of applications were missing data on at least one of thesevariables; 32% of applications were missing approval/denialstatus. To mitigate common problems with estimation at studyarea boundaries, estimates calculated near the boundaries of thestudy area also included data from census tracts in countiesoutside of, but bordering, the study area.
The outcome variable is the survival time after colorectalcancer diagnosis, which is calculated as the number of monthsbetween initial diagnosis and either date of death or December31, 2011 (the last day of the study period). Two censoringvariables were used on the basis of cause of death informationto reflect (i) colorectal cancer as the underlying cause of deathand (ii) all causes of death among men and women diagnosedwith colorectal cancer. The first variable reflects censoring ofindividuals who died of causes other than colorectal cancer, orwere alive on the last day of the study period, whereas thesecond variable reflects censoring of only those alive on the lastday of the study period.
Primary predictors included two new indices of mortgagediscrimination and one segregation metric. Following the workof Beyer and colleagues (27), we calculated two indices, racial biasin mortgage lending and residential redlining, to measure hous-ing discrimination. Both indices were estimated by integratinglogistic regressionmodels with the adaptive spatial filtering (ASF)approach (33, 34). To apply ASF, a grid is first laid over the studyarea, and spatial filters symbolized by circles then are created andcentered at each grid point. The idea of ASF is to expand the radiusof the filter for each grid point until enough observations fromnearby geographic units (census tracts in this case) falling withinthe filter are obtained to calculate a stable statistic. The statistic ismapped as a continuous surface using inverse distance weightedmethod. The racial bias in mortgage lending index is the statisticestimated for each grid point using the observations within thefilter. It is the odds of denial of a mortgage application from ablack primary applicant compared with denial of a white primaryapplicant, while controlling for sex, and the ratio of the loanamount to the applicant's gross annual income. We used athreshold of a minimum of five denied black applicants and fivedenied white applicants to guide the filter size. In contrast, theresidential redlining index measures the bias against issuingmortgages in particular neighborhoods. Thus, the redlining indexis constructed by estimating the odds of denial of the mortgageapplication for individuals inside the filter, as compared withindividuals outside the filter. The same filter threshold wasapplied to calculate this index. We derived two variations of theredliningmeasure, one only adjusting for sex and loan amount toincome ratio and another controlling also the race and ethnicity of
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the primary applicant. For inclusion in survival models, bothindices represented by the interpolated continuous surfaces weresummarized (mean pixel value) by ZIP Code Tabulation Area(ZCTA). We also derived binary predictors from them. The binaryvariable of racial bias index is coded as 1 if the index value is equalto 2 or greater and as 0 otherwise. The cutoff value for coding thebinary variable of the redlining index is 1. Although the tworedlining measures were both tested, we only reported the resultsfor the one that is race/ethnicity adjusted, as results were similar.
Segregation was measured at the ZCTA level using the locationquotient (LQ), a measure of local area segregation (19, 35). Theequation for calculating LQ is as follows:
LQim ¼ xim=Xi
Xm=X
Where LQim is the value for the ith unit in a region forpopulation group m (black in our case), xim is the number ofindividuals of the mth group living in the ith unit, Xi is the totalnumber of residents in the ith unit of the region, Xm is the totalnumber of individuals from minority group m in the region,and X is the total number of residents living in the region. TheLQ relates the proportion of individuals in a local area of aparticular race to the same proportion at the regional level.Conceptually, the LQ represents the relative concentration of aracial group and can explain how the demographic makeup of asingle small unit contributes to the overall racial distribution inthe metropolitan area (35). The traditional measures of segre-gation, such as dissimilarity and exposure, as outlined by
Massey and Denton, quantify how groups are separated withina given area (36). Therefore, they are not suitable for measuringthe effect of small area's segregation on a larger region. The unitwe employed is ZCTA, to which the colorectal cancer cases weregeocoded, and the region is confined to the study area. LQranges from zero to infinity. An LQ equal to zero indicates thatthere are no residents of group m in the neighborhood unit,whereas an LQ less than one indicates that the proportion ofgroup m in the neighborhood is less than the proportion of thesame group in the region. On the basis of the work of Pruitt andcolleagues (19), the LQ was log(xþ1) normalized. Calculationof the LQ and mortgage discrimination metrics was completedin R (37) and ArcGIS (38).
Statistical analysisWe used multivariable Cox proportional hazards regression to
model survival time for both colorectal cancer–specific mortalityand all-cause mortality among black and non-Hispanic whiteindividuals diagnosed with incident colorectal cancer in the studyarea between 2002 and 2011. In addition to the LQ for the blackpopulation and two new indices of mortgage discrimination(each incorporated into the survival model, one at a time),individual characteristics, such as age (18–44, 45–54, 55–64,65–74, and 75þ), sex (male and female), and stage at diagnosis(SEER Summary Stage 2000 categories, local, regional, and dis-tant), were included as control variables. Models also control fortwo neighborhood-level variables, ZCTA population density andan index of ZCTA socioeconomic status. The index was estimatedusing principle component analysis for selected American Com-munity Survey variables: median household income, percent
Figure 1.
Patterns of colorectal cancer incidence (A) and mortality (B) in the study area. The invasive colorectal cancer incidence/mortality rate is indirectly age-sexstandardized and smoothed using the ASF approach. A grid of points is used to estimate incidence/mortality rates continuously across the map, based on the30/20 closest diagnosed/colorectal cancer mortality cases. Darker areas indicate higher rates than expected and lighter areas indicate lower rates thanexpected, given the regional rate. Incidence data are from the Wisconsin Cancer Reporting System (WCRS), and mortality data are from the State VitalRecords Office.
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unemployed, percent renter households, percent families led bysingle female, and percent poverty. For the continuous redliningindex and LQ, two different models with/without adding popu-lation density as a covariate were tested due to a concern about themoderate correlation between population density and the twoprimary predictors. The results for modeling the effects of con-tinuous redlining index with/without controlling populationdensity are similar in terms of direction, magnitude, and signif-icance; thus, we only reported the model results with inclusion ofpopulation density. In total, there are ninemodels being tested foreach outcome variable (six of which are reported in the resultssection), examining the LQand continuous andbinary versions ofthe racial bias index and two redlining indices. Survival analyseswere implemented in Stata SE/13 (39) and R (37).The propor-tional hazards assumption was examined for the models with allthe predictors (age, sex, stage at diagnosis, population density,and segregation or mortgage discrimination index), and it wasfound that the stage variable often violated the assumption. Thus,we applied stratified Cox model to correct the problem, using thediagnosis stage as a stratification variable. In addition, we fittedmodels with frailty terms for ZCTAs to examine the possibility ofspatial clustering, but no frailty termswere statistically significant.Finally, we fitted additional, sex-specific models for the blackpopulation to determine whether observed relationships held forboth sexes.
ResultsFigure 2 displays the spatial distributions of the racial bias
index, redlining index while controlling for the race and ethnicityof the primary applicant, and black LQ.
Table 1 presents descriptive statistics for the population understudy. All individuals in the sample are black/African American ornon-Hispanic white individuals diagnosed with colorectal cancerbetween 2002 and 2011 in the study area. The sex composition isapproximately half females and half males. A relatively largeproportion of the individuals in the sample was diagnosed witha localized tumor (39.59% among blacks and 41.16% amongwhites). Of those deceased (265 blacks and 1,940 whites),67.17% and 59.74% died from colorectal cancer.
Tables 2–4 show the results of Cox proportional hazardsmodels for the racial bias index, redlining, and the locationquotient, respectively. For all-cause mortality, the binary racialbias inmortgage lending variablewas significantly associatedwitha greater hazard rate for blacks [HR ¼ 1.37; 95% confidenceinterval (CI), 1.06–1.76 inModel 1.2, Table 2], but not for whites.The racial bias index was not significantly related to colorectalcancer–specific mortality. The redlining index and LQ (Tables 3and4)were not significantly associatedwith all-causemortality orcolorectal cancer–specificmortality.Of note, although itwas not aprimary predictor of interest, higher population density wassignificantly and consistently associated with a higher hazard rateamong whites.
Table 5 shows the results of Cox proportional hazards modelsfor all-cause mortality among black women diagnosed withcolorectal cancer. Racial bias in mortgage lending (binary) wasassociated with poorer colorectal cancer survival among blackwomen, with an HR comparable with but larger than thatobserved for the full sample (HR ¼ 1.53; 95% CI, 1.06–2.21 inModel 4.1.2, Table 5). Neither the redlining index nor the LQwassignificantly associated with survival. In male-only models, no
measures of segregation or mortgage discrimination were asso-ciated with colorectal cancer survival.
DiscussionThis study contributes new knowledge to a small but growing
body of research regarding institutional racism, segregation, andcancer outcomes, helping to shed light on possible directions forpolicy change andpublic health intervention. This is thefirst studyto examine linkages between elements of mortgage discrimina-tion and segregation concurrently, in their associationwith cancersurvival, and the first to examine the relationship between mort-gage discrimination and colorectal cancer survival. We found thatracial bias in mortgage lending (when measured as a binaryvariable) was related to poorer colorectal cancer survival amongblacks, but not amongwhites, and that this associationwas drivenby a strong relationship for black women. Neither redlining northe LQ exhibited any statistically significant associations, and theonly variable of importance for white patients was populationdensity, with higher density associated with a higher hazard rate.
The measures employed in this study are conceptually dis-tinct. Whereas the index of racial bias in mortgage lendingindicates the odds of denial of a mortgage application in aparticular area by race, the redlining index measures the odds ofdenial of a mortgage application in a particular neighborhood,regardless of race. The LQ seeks to relate the proportion of aracial group in the local area to the same proportion in thelarger region, providing a sense of the relative degree of seg-regation in a specific local area. In the study area, the LQ andredlining indices reveal more similar spatial patterns, withhigher values in Milwaukee's predominantly black central city.In contrast, the racial bias index tends to be higher in areasoutside of the central city, where fewer black residents reside,reinforcing their status as a racial minority.
There are a number of possible explanations for ourfinding thatinstitutional racism is associated with poorer survival after colo-rectal cancer diagnosis. The long-term presence of race-relatedmortgage discrimination in the Milwaukee area, as a manifesta-tionof institutional racism, could be apersistent source of stress ora barrier to health care access or utilization, thus promotingprogression of colorectal cancer or hindering recovery and leadingto shorter survival of black colorectal cancer patients. The higherlikelihood of black populations being denied to access to financ-ing for housing could indicate possible discrimination in othersectors, reducing access to other resources important to theirhealth and medical needs after being diagnosed with colorectalcancer. However, it is unclear why this relationship would affectblack women but not black men. The small sample size does notappear to have played a role in negative findings for males, as ahigher proportion of colorectal cancer patients in the databasewere male. The sex-specific aspects of these relationships requirefurther study.
Interestingly, results did not indicate that individuals living inredlined neighborhoods, or those characterized by high levels oflocal segregation, experienced poorer colorectal cancer–specificsurvival. These findings may seem to run counter to intuition, butdo reflect findings from some other studies (14, 27). It is possiblethat black patients could be exposed to protective factors inpredominantly black central city neighborhoods. In particular,the presence of strong social networks and social support maymitigate the effects of discrimination or generate protective effects
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The racial bias in mortgage lending index maps (A and B in the top row), the race- and ethnicity-adjusted redlining index maps (D and E in the bottom row),the black LQ map (C in the top row). The graphs (A, B, D, and E) for mortgage discrimination measures represent the time period from 2004 to 2011 and arebased on tract-level HMDA data. Each of these graphs in a row presents the average pixel value for the index for each ZCTA and the study area dividedinto two categories as described for binary indices.
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(40). Furthermore, institutional racism in health care accessexperienced by black patients living in predominantly white areasmay be less of a barrier in areas with higher black populations, asencounters between black patients and physicians are likely to bemore common. Further study is needed to untangle the practicalmechanisms affecting disparate populations.
Our work has several limitations. First, the HMDA data donot include several variables that may affect denial rates,including current employment and credit scores. Second, thereare multiple variables for race and ethnicity in the HMDAdatabase and the sensitivity of modeling results to the choice ofone variable (race/ethnicity of the primary applicant) versus
another [of coapplicant(s)] when deriving indices has not yetbeen explored. It would be useful to examine the change inpatterns of mortgage discrimination when using differentdefinitions of race and ethnicity. Third, the measures of seg-regation and mortgage discrimination we used only capture thestatic condition of segregation and mortgage denial patternsduring a specific time period, while colorectal cancer patientswere diagnosed at different times and their exposures to theadverse effects of segregation and discrimination may changewith their mobility and life experience. Finally, as measure-ment is imperfect and not all important factors can be mea-sured with available data, there is of course the possibility thatresidual or unmeasured confounding could impact effect esti-mates for the exposures of interest. However, we did control forSES, a major potential source of unmeasured confounding.Furthermore, we did not find evidence of spatial clusteringusing frailties, providing little evidence of unmeasured spa-tially varying confounders.
Despite these limitations, this study presents a novel perspec-tive on the role of housing discrimination and segregation inracial disparities in colorectal cancer survival. Future work shouldexamine whether such findings hold for other racial and ethnicgroups, and in other geographic settings. In addition, moreresearch is needed to elucidate the pathways by which segregationinfluences cancer survival disparities and to move these findingstoward intervention and population health improvement. Inaddition, there are other social/economic processes that contrib-ute to segregation patterns and are worth exploring, such asdiscrimination in education and labor. Finally, it would beinteresting to explore survival between colon and rectum cancer,instead of combining them as colorectal cancer, as their risks formen and women are different (41, 42). More translationalresearch on policy development and intervention practices are
Table 1. Sample characteristics
White (n ¼ 4,699) Black (n ¼ 682)Frequency (%) Frequency (%)
Age group18–44 years 254 (5.40%) 66 (9.68%)45–54 years 663 (14.11%) 170 (24.93%)55–64 years 865 (18.41%) 184 (26.98%)65–74 years 1,005 (21.39%) 150 (21.99%)75þ years 1,912 (40.69%) 112 (16.42%)
needed to achieve the ultimate goal of reducing the impact ofinstitutional racism, including mortgage discrimination, on pop-ulation health.
Disclosure of Potential Conflicts of InterestNo potential conflicts of interest were disclosed.
Authors' ContributionsConception and design: Y. Zhou, K.M.M. BeyerDevelopment of methodology: K.M.M. BeyerAcquisition of data (provided animals, acquired and managed patients,provided facilities, etc.): Y. Zhou, K.M.M. BeyerAnalysis and interpretation of data (e.g., statistical analysis, biostatistics,computational analysis): Y. Zhou, A. Bemanian, K.M.M. BeyerWriting, review, and/or revision of the manuscript: Y. Zhou, A. Bemanian,K.M.M. Beyer
Administrative, technical, or material support (i.e., reporting or organizingdata, constructing databases): Y. Zhou, K.M.M. BeyerStudy supervision: K.M.M. BeyerOther (postdoctoral mentor of the first author): K.M.M. Beyer
Grant SupportAll authors received support from Research and Education Program Fund, a
component of the Advancing a Healthier Wisconsin endowment at the MedicalCollege of Wisconsin and in part by the Medical College of Wisconsin CancerCenter, Population Sciences Program (principal investigator, K.M.M. Beyer).
Received November 15, 2016; revised January 9, 2017; accepted February 10,2017; published OnlineFirst February 14, 2017.
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