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RESEARCH Open AccessRace, gender, class, and sexual orientation:intersecting axes of inequality and self-ratedhealth in CanadaGerry VeenstraAbstractBackground: Intersectionality theory, a way of understanding social inequalities by race, gender, class, andsexuality that emphasizes their mutually constitutive natures, possesses potential to uncover and explicatepreviously unknown health inequalities. In this paper, the intersectionality principles of “directionality,”“simultaneity,” “multiplicativity,” and “multiple jeopardy” are applied to inequalities in self-rated health by race,gender, class, and sexual orientation in a Canadian sample.Methods: The Canadian Community Health Survey 2.1 (N = 90,310) provided nationally representative data thatenabled binary logistic regression modeling on fair/poor self-rated health in two analytical stages. The additivestage involved regressing self-rated health on race, gender, class, and sexual orientation singly and then as a set.The intersectional stage involved consideration of two-way and three-way interaction terms between the inequalityvariables added to the full additive model created in the previous stage.Results: From an additive perspective, poor self-rated health outcomes were reported by respondents claimingAboriginal, Asian, or South Asian affiliations, lower class respondents, and bisexual respondents. However, each axisof inequality interacted significantly with at least one other: multiple jeopardy pertained to poor homosexuals andto South Asian women who were at unexpectedly high risks of fair/poor self-rated health and mitigating effectswere experienced by poor women and by poor Asian Canadians who were less likely than expected to report fair/poor health.Conclusions: Although a variety of intersections between race, gender, class, and sexual orientation wereassociated with especially high risks of fair/poor self-rated health, they were not all consistent with the predictionsof intersectionality theory. I conclude that an intersectionality theory well suited for explicating health inequalitiesin Canada should be capable of accommodating axis intersections of multiple kinds and qualities.BackgroundSizeable health inequalities by race [1,2], gender [3,4]and class [5] have been recorded in Canada. Consistentwith traditional sociological understandings of socialinequality, these axes of inequality have for the mostpart been considered individually, with researchers onlyconsidering potential interconnectedness when investi-gating whether class mediates associations between raceand health or gender and health. Whether class influ-ences health differently for visible minority Canadiansand White Canadians or race influences health differ-ently for men and women, for example, has not yetbeen investigated. When statistical interactions such asthese have received analytical attention - for example,whether class influences health differently for Canadianmen and women [3] - they have not been adequatelytheorized. Intersectionality theory, an influential theore-tical tradition inspired by the feminist and antiracist tra-ditions, demands that inequalities by race, gender, andclass (and sexuality as well) be considered in tandemrather than distinctly. This is because these fundamentalaxes of inequality in contemporary societies are consid-ered to be intrinsically entwined; they mutually consti-tute and reinforce one another and as such cannot beCorrespondence: gerry.veenstra@ubc.caDepartment of Sociology, University of British Columbia, Vancouver, BritishColumbia, CanadaVeenstra International Journal for Equity in Health 2011, 10:3http://www.equityhealthj.com/content/10/1/3© 2011 Veenstra; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative CommonsAttribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction inany medium, provided the original work is properly cited.disentangled from one another. Intersectionality theorypresents a new way of understanding social inequalitiesthat possesses potential to uncover and explicate pre-viously unknown health inequalities. This paperdescribes the results of an original empirical investiga-tion of the degree to which the self-rated health ofCanadians varies by race, gender, class, and/or sexualorientation in ways that are consistent with predictionsof intersectionality theory. The remainder of this back-ground section describes some of the central principlesof this theoretical tradition followed by a description ofthe analytical strategy used to apply these principles inan empirical investigation of inequalities in self-ratedhealth in Canada.Intersectionality theoryIn the forward to a recent book on new theories andmethods for studying race, class, and gender, LynnWeber [6] describes how American women of color inthe 1970s and early 1980s, many from working classbackgrounds, came to critique the patriarchy traditionwithin gender studies for privileging gender over raceand class (and subsequently critiqued the stratificationtradition for privileging class over gender and race, etc.).They argued that these axes of inequality are in factanalytically inseparable, and that “the multidimensional-ity and interconnected nature of race, class, and genderhierarchies were especially visible to those who facedoppression along more than one dimension of inequal-ity” [6:xii]. These scholars envisioned axes of inequalitypertaining to gender, race, and class that intersect withone another, i.e., that are interlocked, dependent uponone another, and mutually constituted [7]. Power rela-tionships along the lines of gender, race, and class werethought to be mutually defining and mutually reinfor-cing rather than analytically distinct systems of oppres-sion, together forming a “matrix of domination” [8]. Bythe mid-1980s, lesbians of color had bridged the gapbetween gay and lesbian studies and the growing bodyof race, gender, and class research that had to that pointignored heterosexism [6], and axes of inequality pertain-ing to national origin, citizenship status, religion, dis-ability, and age also received some attention. Thecontributions of these various scholars gave rise to whatis now known as “intersectionality theory.” Landry [9]notes, however, that intersectionality theory does notprovide a set of propositions that together form anexplanation; rather, intersectionality theory currentlyconsists of a loose set of principles or assumptions thatare being applied and tested by many researchers in avariety of contexts.Founded upon analyses of relations of power in generaland inspired by theories of racism, patriarchy, classism, andheterosexism in particular, in American intersectionalitydiscourse the disadvantaged groups along the inequalityaxes of race, gender, class, and sexual orientation areassumed to be visible minorities from various backgrounds(especially African Americans), women, members of thelower and working classes, and gays, lesbians, and bisex-uals. These comprise implicit intersectionality assumptionsof “directionality.”Intersectionality theorists argue that our identitiesbased on race, gender, class, and sexuality accompany usin every social interaction [7]. The principle of “simulta-neity” maintains that all of the axes and their corre-sponding identities should be incorporated into socialanalyses.“Race, class and gender may all structure a situationbut may not be equally visible and/or important in peo-ple’s self-definitions... This recognition that one categorymay have salience over another for a given time andplace does not minimize the theoretical importance ofassuming that race, class and gender as categories ofanalysis structure all relationships” [7:560-1].That is, while some axes and identities may be morepertinent to a specific social context or outcome thanare others, simultaneity implies that a social researchershould never discard an axis of inequality before investi-gating its potential relevance for the problem at hand.Intersections between axes are thought to create com-plex social locations that are more central to the natureof social experiences than are any of the axes of inequal-ity considered singly.“People experience race, class, gender and sexualitydifferently depending upon their social location in thestructures of race, class, gender and sexuality. For exam-ple, people of the same race will experience race differ-ently depending upon their location in the classstructure as working class, professional managerial classor unemployed; in the gender structure as female ormale; and in structures of sexuality as heterosexual,homosexual or bisexual” [10:326-7].Thus “multiplicativity” should supplant additivity [10].Racism x sexism x classism x sexism should replaceracism + sexism + classism + sexism [11,12]. A lower-class Black lesbian is necessarily all of these things, andtheir mutual manifestation represents a unique state ofbeing and a unique set of social experiences and struc-tural constraints.“Race, class, gender and sexuality are not reducible toindividual attributes to be measured and assessed fortheir separate contribution in explaining social out-comes, an approach that Elizabeth Spelman calls “pop-bead metaphysics,” where a woman’s identity consists ofthe sum of parts neatly divisible from one another. Thematrix of domination seeks to account for the multipleways that women experience themselves as gendered,raced, classed and sexualized” [10:327].Veenstra International Journal for Equity in Health 2011, 10:3http://www.equityhealthj.com/content/10/1/3Page 2 of 11Experiences of gender are racialized, sexualized, andclassed; experiences of class are gendered, racialized,and sexualized, etc.From the abovementioned principles of directionality,simultaneity, and multiplicativity arise new versions ofdouble jeopardy and triple jeopardy, renamed “multiplejeopardy” by Deborah King [11], wherein disadvantagedidentities experienced in tandem are seen to result ininordinate, i.e., even more than additive, amounts of dis-advantage. Thus complex social locations comprised ofdisadvantaged identities held in tandem are thought tolead to multiplicative disadvantage; that is, combinationsof these identities are thought to have an aggravatingrather than a simply cumulative or mitigating effect. Inaddition, because of the relational nature of intersec-tional theories, some complex locations, such as the oneinhabited by wealthy heterosexual White men, in turnexperience multiplicative advantage.Despite the immense popularity of intersectionalitytheory in humanities and social sciences circles and thelarge and growing body of intersectionality research thatincludes applications of both qualitative and quantitativemethodologies, very little quantitative research has expli-citly applied intersectionality theory to health outcomes.However, many health determinants researchers haveunintentionally addressed simultaneity and multiplicativ-ity by identifying two-way statistical interactionsbetween axes of inequality in regression modeling. InCanada, Zheng Wu and colleagues [2] identified interac-tions between race and socioeconomic status for depres-sion. In the United States, Ostrove and colleagues [13]identified interactions between socioeconomic statusand race as predictors of self-rated health and depres-sion, Nomagushi [14] found interactions between raceand gender on the effect of marital dissolution ondepression, and Read and Gorman [15] determined thatthe gender gap in health differs widely by racial/ethnicgroup. But only a few quantitative studies have explicitlystudied illness states associated with complex socialpositions arising from intersections between three axesof inequality [16-19], none of them Canadian, and nostudies have studied intersections between all four ofthe primary axes of inequality of intersectionality theory.Given the seeming complicity of all of race [2,20-23],gender [3,4,24], class [5,25-29], and sexual orientation[30-33] in North American health inequalities, this lackof attention to health inequalities that accrue to multiplecombinations of inequality identities represents animportant gap in the health determinants literature.Analytical strategyModeling the main effects of inequality identities (addi-tivity) and then statistical interactions between them(multiplicativity) in multivariate regression models onhealth can establish whether two-way or three-way statis-tical interactions (intersections) between axes of inequal-ity contribute to explaining variability in health aboveand beyond the additive approach to health inequalitiesthat currently dominates health determinants research.This paper uses a two-stage analytical strategy, the firstadditive and the second multiplicative, applied to a largerepresentative survey dataset from Canada in order toinvestigate health outcomes associated with intersectionsbetween race, gender, class, and sexual orientation.First, the strength and direction of the main effects inadditive regression models such as Race + Gender +Class + Sexual Orientation = Health addresses the prin-ciples of simultaneity and directionality. Simultaneitysuggests that all four identities will make significantcontributions to these models before and/or after con-trolling for one another while directionality implies thatnon-Whites, women, lower-class people, and non-het-erosexuals will manifest the poorer health outcomes.Second, simultaneity and multiplicativity imply that theinequality identities should interact meaningfully withone another as predictors of health, that is, statisticalinteractions between the inequality variables of race, gen-der, class, and sexual orientation should manifest signifi-cant effects above and beyond their main effects in theabovementioned additive models. The existence of inter-actions speaks to multiplicativity. The qualities of theinteractions themselves speak to multiple jeopardy anddirectionality. At least three multiplicative scenarios arepossible for a given statistical interaction: 1. two or moreaxes of inequality manifest directions of some kind orother in additive models and then display an aggravatingeffect in the interaction between them, 2. two or moreaxes manifest given directions in additive models andthen display a mitigating effect in their interaction, and 3.an interaction manifests itself between two or more axesbut not all of the axes display independent effects inadditive models. Aggravating effects support the assump-tion of multiple jeopardy and reinforce the directionalityidentified in the additive models whereas non-aggravat-ing effects run contrary to the assumption of multiplejeopardy and complicate directionality. Finally, contribu-tions to predicted variability in the models address multi-plicativity by providing an indication of the “value added”of the statistical interactions; comparisons of R2 valuesbetween regression models with and without the cross-product terms can be used to assess the magnitude oftheir contributions to explaining variability in healthabove and beyond the contributions of the main effects.MethodsSurvey sampleThe Canadian Community Health Survey 2.1 datasetwas collected by Statistics Canada in 2003. The targetVeenstra International Journal for Equity in Health 2011, 10:3http://www.equityhealthj.com/content/10/1/3Page 3 of 11population for this cross-sectional survey was all persons12 years of age and older residing in Canada, excludingindividuals living on Indian Reserves and on CrownLands, institutional residents, fulltime members of theCanadian Armed Forces, and residents of some remoteregions. Sampling considered province or territory andhealth region of residence and applied three samplingframes (a multistage stratified cluster design in an areaframe, a list frame of telephone numbers, and a randomdigit dialing frame) to select the sample of households.One person was chosen randomly from each householdto complete the survey. A total of 134,072 usableresponses were obtained, representing a nationalresponse rate of 80.7%. Final person estimation weightswere provided by Statistics Canada.This investigation focuses on survey respondentswho were aged 25 and older at the time of the survey.Table 1 describes socio-demographic characteristics ofthis sample of 109,967 respondents. The logistic regres-sion models were applied to the 90,310 respondentswith valid information for the age, race, gender, educa-tion, household income, sexual orientation, and self-rated health variables. Household income (N = 15,481)and sexual orientation (N = 7,676) were the main con-tributors to the loss of cases from listwise deletion. Incomparison with the working sample, the sample ofmissing cases was older, poorer, and less educated onaverage and contained proportionately more widows,non-Whites, and adult immigrants to Canada.Survey measuresSurvey respondents were asked the following question:“People living in Canada come from many different cul-tural and racial backgrounds. Are you: White? Chinese?South Asian (e.g., East Indian, Pakistani, Sri Lankan)?Black? Filipino? Latin American? Southeast Asian (e.g.,Cambodian, Indonesian, Laotian, Vietnamese)? Arab?West Asian (e.g., Afghan, Iranian)? Japanese? Korean?Aboriginal (North American Indian, Métis or Inuit)?Other - specify.” The interviewer was instructed to readall of the possible responses and record all of them thatapplied. Due to small sample sizes for some responsesthis variable was recoded as follows: Aboriginal, Asian(combining the Chinese, Korean and Japanese cate-gories), Black, South Asian, and White, as well as a resi-dual category created by combining the remainingcategories, including the original “other” category, into asingle un-interpretable category labeled “other.”Highest educational attainment and household incomewere used to assess class standing. Statistics Canadaasked a series of survey questions pertaining to educa-tional attainment to generate the education variabledescribed in Table 1. To assess household income,respondents were asked: “What is your best estimateof the total income, before taxes and deductions, ofall household members from all sources in the past12 months?” Follow-up questions determined the rangewithin which their household income fell for thoserespondents unable or unwilling to provide a precisedollar value.Sexual orientation was assessed as follows: “Do youconsider yourself to be: Heterosexual? (sexual relationswith people of the opposite sex); Homosexual, that islesbian or gay? (sexual relations with people your ownsex); Bisexual? (sexual relations with people of bothsexes)” Approximately 0.6% of women and 0.5% of menself-reported as bisexual and 0.7% of women and 1.2%of men self-reported as homosexual, values that areslightly lower than numbers reported by similar studiesin the United States [32], Australia [34], and the Nether-lands [35] where approximately 2-3% of the generalpopulation reported being homosexual or bisexual.Global self-rated health, a variable known to encom-pass both physical and mental well-being and to reliablypredict other, more objective, measures of health [36] aswell as mortality [37], was assessed as follows: “I’ll startwith a few questions about your health in general. Ingeneral, would you say your health is: Excellent? Verygood? Good? Fair? Poor?”Regression modelingSelf-rated health was dichotomized so that fair and poorresponses were contrasted with good, very good, andexcellent responses and binary logistic regression model-ing was then used to predict fair/poor health. Eachnominal independent variable in a regression model wastreated as a set of dummy variables with one (missing)dummy variable serving as the reference. Because the Nfor a reference category should be large in order to pro-vide a stable reference point, “White” was assigned thereference category for race and “heterosexual” wasassigned the reference category for sexual orientation. Inaddition, “male” was assigned the reference category forgender and “postgraduate degree” was assigned thereference category for education. This strategy facilitatedready interpretation of how the other identities fare rela-tive to what are generally considered the more privi-leged identities in Canadian society. Nagelkerke pseudoR2, a rough measure of the proportion of variabilityexplained by a logistic regression model, was presentedfor each additive model.Introducing cross-product terms to hierarchically well-ordered models is a common approach to investigatingstatistical interactions in the context of logistic regres-sion [38]. Alpha was set at 0.05 with regards to the con-tributions of main effect terms in additive logisticregression models but at 0.10 for the interaction termsbecause of the lesser power of tests of significance forVeenstra International Journal for Equity in Health 2011, 10:3http://www.equityhealthj.com/content/10/1/3Page 4 of 11Table 1 Characteristics of the sample (weighted data)Variable Categories DistributionGender male 53,578 (48.7%)female 56,389 (51.3)Marital status married 68,255 (62.2%)living common-law 10,356 (9.4)widowed 6,916 (6.3)separated 3,048 (2.8)divorced 6,049 (5.5)single (never married) 15,135 (13.8)Age aged 25 - 34 21,639 (19.7%)aged 35 - 44 27,611 (25.1)aged 45 -54 23,839 (21.7)aged 55 - 64 17,155 (15.6)aged 65 and older 19,732 (17.9)Sexual orientation heterosexual 100,803 (98.5%)homosexual 945 (0.9)bisexual 543 (0.5)Educational attainment less than secondary 21,582 (20.1%)secondary graduate 26,463 (24.7)community college; technical school; some university (no degree) 36,496 (34.0)bachelor’s degree 15,466 (14.4)post-bachelor degree 7,237 (6.7)Household income < $10,000 2,291 (2.4%)$10,000 - 19,999 8,130 (8.6)$20,000 - 29,999 9,664 (8.8)$30,000 - 39,999 10,409 (11.0)$40,000 - 49,999 9,862 (10.4)$50,000 - 59,999 9,708 (10.3)$60,000 - 79,999 16,108 (17.0)$80,000 or more 28,313 (30.0)Race White 90,864 (85.5%)Chinese 3,676 (3.5)South Asian (e.g., East Indian, Pakistani, Sri Lankan) 2,758 (2.6)Black 1,617 (1.5)Aboriginal (North American Indian, Métis and Inuit) 1,028 (1.0)Filipino 998 (0.9)Latin American 848 (0.8)Southeast Asian (e.g., Cambodian, Indonesian, Laotian, Vietnamese) 594 (0.6)Arab 523 (0.5)West Asian (e.g., Afghan, Iranian) 311 (0.3)Korean 284 (0.3)Japanese 204 (0.2)other 1,509 (1.4)multiple origins 1,108 (1.0)Immigrant status immigrated to Canada as adult (aged 18 and older) 18,260 (17.2%)immigrated to Canada as child (under 18) 6,204 (5.8)born in Canada 81,834 (77.0)Self-rated health poor 3,361 (3.1%)fair 10,865 (9.9)good 33,919 (30.9)very good 38,138 (34.7)excellent 23,600 (21.5)Veenstra International Journal for Equity in Health 2011, 10:3http://www.equityhealthj.com/content/10/1/3Page 5 of 11interactions in general (some of the variation in thedependent variable explained by the interaction may bealready captured by the main effect test, measurementerror in the individual factors becomes compounded inan interaction term, etc.).The logistic regression models were implemented inSPSS 15.0. Because the sampling design for the CCHS2.1 was complex, the 500 bootstrapping weights andBOOTVAR program created for the CCHS 2.1 by Statis-tics Canada were used to generate more reliable var-iance estimates and thus more reliable tests ofsignificance and confidence intervals for individual vari-ables within regression models. Due to the limitations ofBOOTVAR, results from omnibus tests of significancefor categorical variables and interaction terms comprisedof sets of dummy variables and Model Chi-square testsof significance for logistic regression models in theirentirety could not be generated.ResultsAdditive modelsTable 2 describes the key features of a set of additivebinary logistic regression models on self-rated health.With regards to race, Table 2 indicates that Aboriginals,Asians, and South Asians were significantly more likelythan Whites to report fair/poor self-rated health. Thewomen of the sample were slightly more likely than themen to report fair or poor self-rated health, controllingfor age, but upon additionally controlling for the otherinequality variables gender was not significantly relatedto self-rated health. Educational attainment and house-hold income were both significantly associated withself-rated health, in the expected directions, before andafter controlling for the other variables. Finally, self-identified bisexual respondents were more likely thanheterosexuals to report fair or poor self-rated health,holding age constant, although the association weakenedto the point of non-significance after controlling for theother inequality variables. The decline in effect size forAboriginal identity compared to White identity fromModel I to Model V was mostly due to differences ineducation and income whereas the declines in effectsizes for female compared to male identity and bisexualorientation compared to heterosexual orientation wereprimarily due to differences in income (results notshown).Comparisons of odds ratios and Nagelkerke R2 valuesindicate that education and income followed by racewere the strongest predictors of self-rated health. Educa-tion and income were also implicated in some of the“hidden” explained variability in the regression models(results not shown). Regarding the overall contributionsof the main effects to predicted variability in health, as aset the five inequality variables produced an increase inNagelkerke R2 of 0.061 over the regression model onself-rated health containing age alone.In summary, the additive models of Table 2 describedpoorer health outcomes for bisexual respondents, non-White respondents, and respondents of lower classstanding. The health effects of gender were minimal andthe health scores of homosexuals did not differ signifi-cantly from those of heterosexuals. Class was the stron-gest distinct predictor of health of the four axes ofinequality. With regards to the principle of simultaneity,these results suggest that sexual orientation, race, andclass are especially relevant intersectionality axes ofinequality in this national context, with directions thatpoint to the negative health experiences of bisexuals,members of lower classes, and Canadians claimingAboriginal, Asian, or South Asian identities inparticular.Multiplicative modelsTwo-way and three-way interactions between the fiveinequality variables were individually added to the finaladditive model of Table 2. Interactions that includededucation and income, the two indicators of class, werenot considered. Insufficiently large cell sizes precludedinvestigation of the two-way interaction between raceand sexual orientation and the three-way cross-productterms that included sexual orientation and necessitateduse of a dichotomized version of education (has a uni-versity degree or not) in the two-way and three-wayinteractions that included education and race. Table 3contains odds ratios and p-values for the statistically sig-nificant interactions. Figure 1 depicts predicted probabil-ities for statistically significant interactions; theprobabilities labeled “additive” were generated fromadditive models that did not contain any interactionterms and the probabilities labeled “multiplicative” weregenerated from models that additionally contained theinteraction terms of interest. These visual depictions ofpredicted probabilities aid in determining whetheraggravating effects (multiplicative advantage or disad-vantage) or non-aggravating effects (such as mitigatingeffects) pertained to the multiplicative scenarios.Neither of the three-way interactions had a statisticallysignificant effect on self-rated health. However, each ofgender, race, and sexual orientation manifested signifi-cant two-way interactions with class and gender inter-acted significantly with race (Table 3). Consider first theinteraction between gender and income. Table 3 indi-cates that income manifested a stronger association withself-rated health among men (OR = 0.439) than amongwomen (OR = 0.502) and that the ratio of the two oddsratios differed significantly from 1 (p = .011). Figure 1Adepicts additive predicted probabilities of 0.305 for thepoorest women, 0.295 for the poorest men, 0.066 for theVeenstra International Journal for Equity in Health 2011, 10:3http://www.equityhealthj.com/content/10/1/3Page 6 of 11richest women and 0.063 for the richest men. These pre-dicted probabilities reflect the weak gender effect andstrong income effect evident in the final additive modelof Table 2. The plot also contains predicted probabilitiesfrom a multiplicative model incorporating the interactionbetween gender and income. Here we see that the pre-dicted probability of fair/poor health among the poorestwomen (0.286) was somewhat lower than we wouldexpect from an additive perspective. The interactionbetween gender and income on self-rated health thereforerepresents a mitigating effect for lower-class women.The marked change for the worse in risk of fair/poorhealth from the additive model to the multiplicative modelfor poor homosexuals depicted in Figure 1B is an aggra-vating effect in the form of multiplicative disadvantageexperienced by poor homosexuals. The self-rated health ofAsians was much less influenced by income than was theself-rated health of Whites; in particular, the risk of self-rated health depicted in Figure 1C was much lower thanexpected for the poorest Asians, a mitigating effect. Finally,South Asian women were more likely than White womento report fair/poor self-rated health while South Asianmen were no more likely than White men to do so(Table 3). The increase in risk of fair/poor self-rated healthamong South Asian women from the additive model tothe multiplicative model depicted in Figure 1D seeminglyrepresents a case of multiplicative disadvantage experi-enced by South Asian women.Adding all of the two-way cross-product terms to thefinal model of Table 2 produced an increase of 0.007 inthe Nagelkerke R2. Two-way interactions between thefour axes of inequality therefore contributed less thanone percent predicted variability in self-rated health.In summary, each of the four axes of inequality inter-acted significantly with at least one other, suggesting thatall four axes belong to the pantheon of intersectionalityaxes of inequality that contribute to health inequalities inCanada. The only instances of multiplicative disadvantagepertained to poor homosexuals and to South Asianwomen who were at an especially high risk of fair/poorself-rated health. Mitigating effects pertained to lowerclass women and to poorer Asians who were less likely toreport fair/poor health than expected. Lastly, the multipli-cative models contributed relatively little to overallTable 2 Binary logistic regression models on fair/poor self-rated healthModel I Model II Model III ModelIVModel VOR 95% CI OR 95% CI OR 95% CI OR 95% CI OR 95% CIRaceAboriginal 2.562*** [2.048 .. 3.206] —— —— —— 1.707*** [1.364 .. 2.136]Asian 1.426** [1.131 .. 1.798] —— —— —— 1.392** [1.093 .. 1.772]Black 1.186 [0.803 .. 1.753] —— —— —— 1.008 [0.670 .. 1.517]South Asian 1.313 [0.997 .. 1.729] —— —— —— 1.337* [1.010 .. 1.771]other 1.570*** [1.313 .. 1.871] —— —— —— 1.490*** [1.239 .. 1.792]White 1.000 —— —— —— 1.000Gender (female) —— 1.177*** [1.108 ..1.251]—— —— 1.051 [0.986 .. 1.120]Educational attainmentless than secondary —— —— 2.931*** [2.312 ..3.713]—— 3.018*** [2.386 .. 3.817]secondary graduate —— —— 2.033*** [1.606 ..2.576]—— 2.090*** [1.655 .. 2.640]cc/ts/some university —— —— 1.810*** [1.430 ..2.282]—— 1.872*** [1.485 .. 2.360]bachelor degree —— —— 1.192 [0.923 ..1.530]—— 1.187 [0.923 .. 1.525]postgraduate degree —— —— 1.000 —— 1.000Household income —— —— 0.458*** [0.430 ..0.487]—— 0.473*** [0.444 .. 0.504]Sexual orientationhomosexual —— —— —— 1.172 [0.858 ..1.601]1.239 [0.903 .. 1.700]bisexual —— —— —— 1.955** [1.293 ..2.954]1.534 [0.997 .. 2.361]heterosexual —— —— —— 1.000 1.000Nagelkerke R2 0.088 0.084 0.141 0.084 0.144Age controlled in all models; N = 90,310 for all models; *p < .05, **p < .01, ***p < .001 in two-tailed tests of significance.Veenstra International Journal for Equity in Health 2011, 10:3http://www.equityhealthj.com/content/10/1/3Page 7 of 11predicted variability in self-rated health over and above thecontribution of the full additive model.DiscussionFrom the perspective of intersectionality theory, byfocusing on a subset of the inequality identities or bytreating multiple axes of inequality as distinct ratherthan intersected processes, a social researcher is in dan-ger of misunderstanding the nature of social experiencesand identities manifested in specific contexts and thusin danger of producing results and interpretations thatare as misleading as they are incomplete. If this is truethen much of the literature on health effects of inequal-ities pertaining to race, gender, class, and/or sexualTable 3 Statistical interactions on self-rated health1. Income by genderFemale OR income = 0.502 (comparison with 0.439 produces p = .011)Male OR income = 0.4392. Education by genderFemale OR less than post-secondary = 2.428 (comparison with 3.645 produces p = .090)OR secondary graduate = 1.944 (comparison with 2.110 produces p > .100)OR cc/ts/some university = 1.600 (comparison with 2.116 produces p > .100)OR bachelor degree = 1.166 (comparison with 1.145 produces p > .100)OR postgraduate degree = 1.000Male OR less than post-secondary = 3.645OR secondary graduate = 2.110OR cc/ts/some university = 2.116OR bachelor degree = 1.145OR postgraduate degree = 1.0003. Education by sexual orientation p > .100 in all comparisons4. Income by sexual orientationHomosexual OR income = 0.306 (comparison with 0.474 produces p = .050)Bisexual OR income = 0.605 (comparison with 0.474 produces p > .100)Heterosexual OR income = 0.4745. Education1 by race p > .100 in all comparisons6. Income by raceAboriginal OR income = 0.442 (comparison with 0.444 produces p > .100)Asian OR income = 0.804 (comparison with 0.444 produces p < .001)Black OR income = 0.731 (comparison with 0.444 produces p > .100)South Asian OR income = 0.335 (comparison with 0.444 produces p > .100)other OR income = 0.696 (comparison with 0.444 produces p = .004)White OR income = 0.4447. Sexual orientation by gender p > .100 in all comparisons8. Race by genderFemale OR Aboriginal = 1.628 (comparison with 1.818 produces p > .100)OR Asian = 1.597 (comparison with 1.185 produces p > .100)OR Black = 1.038 (comparison with 0.972 produces p > .100)OR South Asian = 1.808 (comparison with 1.031 produces p = .050)OR other = 1.667 (comparison with 1.323 produces p > .100)OR White = 1.000Male OR Aboriginal = 1.818OR Asian = 1.185OR Black = 0.972OR South Asian = 1.031OR other = 1.323OR White = 1.0009. Income by gender by race p > .100 in all comparisons10. Education1 by gender by race p > .100 in all comparisons1. Education in dichotomous form (has university degree).Veenstra International Journal for Equity in Health 2011, 10:3http://www.equityhealthj.com/content/10/1/3Page 8 of 11orientation is incomplete, and some of it may even bemisleading.The Canadian Community Health Survey dataset isespecially well suited to investigating the applicability ofintersectionality theory to health disparities in Canada.It is the first and only Statistics Canada survey datasetto assess sexual orientation, distinguishing betweenbisexuals, homosexuals, and heterosexuals, and unlikemost Canadian survey datasets it is large enough to pro-duce a multi-category measure of race. The analysisdescribed herein is therefore unique by virtue of its con-sideration of intersections between all four key inequal-ity axes of intersectionality theory, its consideration ofbisexual identities as well as homosexual and heterosex-ual identities, and its consideration of racialized identi-ties such as Aboriginal, Asian, and South Asian as wellas Black and White. In addition, the application of cen-tral principles of intersectionality theory to Canada,close neighbor to the United States, can contribute tofuture speculation about the portability of intersectional-ity assumptions across borders. Cross-contextual com-parisons are essential in light of the fact thatinstitutionalized race relations, gender relations, etc. arehistorically and contextually specific [39]. However, sev-eral important limitations of the study require acknowl-edgment. The validity of the sexual orientation surveyquestion is of some concern. The small percentage ofpeople who chose a non-heterosexual orientation ingeneral suggests that many survey respondents mayhave been unwilling to reveal a historically stigmatizedidentity to interviewers. The especially small percentagesof people reporting a non-heterosexual orientation inseveral of the non-White groups speaks to cultural dif-ferences in professing stigmatized non-heterosexualorientations, a knotty measurement problem for anystudy that seeks to investigate intersections between sex-ual orientation and race. Lastly, by virtue of excludingIndian Reserves from the sampling process the surveysample does not represent on-reserve Aboriginal peoplein Canada who are known to have even poorer healththan off-reserve Aboriginal Canadians [40].The intersectionality principle of simultaneity main-tains that all four axes of inequality should be consid-ered in an analysis while the principle of multiplicativitymaintains that intersections between axes should over-shadow or supplant the individual axes themselves intheir effects. Although we carry our identities into everysocial situation, not all of them are necessarily salient inor relevant to a particular encounter [7]. Even so, race,gender, class, and sexual orientation all manifested inde-pendent relationships with health at the additive stageof my analysis and each of the four axes intersectedmeaningfully with at least one other axis, suggestingthat all four of these intersectionality axes of inequalitywere operative for better or for worse in many of thesocial situations encountered by survey respondents intheir everyday lives. In short, the principles of simulta-neity and multiplicativity founded upon the inequalityfoursome of race, gender, class, and sexual orientationappear to be relevant for disparities in health in Canada.A B C D 0.4310.3130.2970.3000.0520.068 0.0640.0640.000.050.100.150.200.250.300.350.400.450.50Additive                     Multiplicative% Fair / Poor HealthPoorest homosexualsPoorest heterosexualsRichest homosexualsRichest heterosexuals0.1740.326 0.3020.2830.1030.0740.0580.0610.000.050.100.150.200.250.300.350.400.450.50Additive                 Multiplicative% Fair / Poor HealthPoorest AsiansPoorest WhitesRichest AsiansRichest Whites0.121 0.1440.1040.0870.0930.0910.080 0.0800.000.050.100.150.200.250.300.350.400.450.50Additive                 Multiplicative% Fair / Poor HealthSouth Asian womenSouth Asian menWhite womenWhite menFigure 1 Predicted Probabilities of Fair/Poor Self-rated Health.A: Income by gender; B: Income by sexual orientation; C: Income byrace; D: Race by gender.Veenstra International Journal for Equity in Health 2011, 10:3http://www.equityhealthj.com/content/10/1/3Page 9 of 11The intersectionality assumption of multiple jeopardymaintains that meaningful intersections manifest multi-plicative - inordinate amounts of - disadvantage oradvantage. While two intersections were to indeed tothe further detriment of certain complex social loca-tions, i.e., of poor homosexuals and South Asianwomen, two demonstrated a mitigating quality for cer-tain complex locations, i.e., for lower class women andpoor Asian Canadians. Many other possible interactionswere not large or statistically significant. It thereforeappears that, with regards to self-rated health in Canadaat least, multiple jeopardy can be more or less than (ormost often simply equal to) cumulative double or triplejeopardy. This multiplicity of multiplicative possibilitiesdemands a kind of conceptual fluidity that is not accom-modated by the principle of multiple jeopardy as it isdepicted it in the introduction to this paper.Bart Landry [9] argues that while the notion ofoppression is useful and undoubtedly reflects realexperiences, for intersectionality theory to realize its fullpotential in social research it must accommodate moreneutral experiences of differences or variations inexperiences across social locations that are not inher-ently oppressive. The plight of poor homosexuals mayindeed reflect a multiple jeopardy that accrues at theintersection of the oppressive forces of heterosexismand capitalism. However, the interaction between genderand race reported here suggests that certain characteris-tics of South Asian communities are detrimental for thehealth of women and beneficial for the health of men. Ifpatriarchal gender relations within South Asian familiesare culpable [41] then inequality by gender is clearly afactor here but race relations perhaps are not. The inter-action between gender and class in turn points to theparticularly heavy penalty paid by lower class men; hereclass inequality among men [24] may be more pertinentthan gender relations between men and women. Theseprovocative findings point to the importance of applyingto health disparities in Canada a version or understand-ing of intersectionality theory that can accommodateintersections of different kinds and qualities.The theory of “invisible intersectionality” has this poten-tial. Valerie Purdie-Vaughns and Richard Eibach [42]argue that people with multiple subordinate-group identi-ties who do not fit the prototypes of their constituentgroups are “marginal members of marginal groups” whoare relegated to positions of “acute social invisibility.”While there are certainly disadvantages to holding multi-ple subordinate-group identities, they argue that there canbe advantages to social invisibility in that marginal mem-bers of marginal groups may be able to elude the moreactive forms of oppression which are directed at “prototy-pical” members of marginal groups. The multiplicity ofmultiplicative possibilities described in my analyses begsfor further investigation from an intersectional invisibilityperspective. For example, characteristics of workplacesand occupations, health behaviors, residential segregation,experiences with systemic, institutional, and interpersonaldiscrimination, adherence to different norms of masculi-nity and femininity, and encounters with the health caresystem may identify advantages and disadvantages adher-ing to various complex social locations and explicate vary-ing risks of poor health in Canada by intersecting axes ofinequality. However, acknowledging with Weber andParra-Medina [43] that intersectionality theory shouldfocus on the social construction of complex identities inspecific times and places and that survey data cannotexplicate the ways in which relations of power operate inindividual lives, some of these explanations may be amen-able to investigation by way of survey research but othersundoubtedly require other modes of investigation. Ethno-graphic investigation spanning interpersonal relations andinstitutional/structural arrangements may also be neededto substantiate and explicate the results described here.ConclusionsFrom an additive, non-intersectional perspective, poorself-rated health outcomes were reported by respondentsclaiming Aboriginal, Asian, or South Asian affiliations,lower class respondents, and bisexual respondents. How-ever, from an intersectional perspective, each axis ofinequality interacted significantly with at least one other:multiple jeopardy pertained to poor homosexuals and(possibly) South Asian women who were at an unex-pectedly high risk of fair/poor self-rated health and miti-gating effects were experienced by poor women and bypoor Asians who were less likely than expected to reportfair/poor health. I conclude from these varied resultsthat the intersectionality theory best suited for explicat-ing health inequalities in Canada should be theoreticallycapable of accommodating axis intersections of multiplekinds and qualities.AcknowledgementsGerry Veenstra is financially supported by a Senior Scholar careerinvestigator award from the Michael Smith Foundation for Health Research(2007-2012). Access to the master file of the Canadian Community HealthSurvey 2.1 was facilitated by the Canadian Initiative on Social Statistics whichis jointly administered by the Social Sciences and Humanities ResearchCouncil of Canada, the Canadian Institutes of Health Research, and StatisticsCanada. Special thanks go to Lee Grenon and Cheryl Fu at StatisticsCanada’s Research Data Centre at UBC and to the Vancouver chapter of theSchiesse Club. Cheryl Hon helped to review and summarize the Canadianhealth determinants literatures pertaining to race, gender, class, and sexualorientation.Competing interestsThe author declares that he has no competing interests.Received: 21 September 2010 Accepted: 17 January 2011Published: 17 January 2011Veenstra International Journal for Equity in Health 2011, 10:3http://www.equityhealthj.com/content/10/1/3Page 10 of 11References1. Veenstra G: Racialized identity and health in Canada: Results from anationally representative survey. Social Science and Medicine 2009,69:538-542.2. Wu Z, Noh S, Kaspar V, Schimmele CM: Race, ethnicity and depression inCanadian society. Journal of Health and Social Behaviour 2003, 44:426-441.3. McDonough P, Walters V: Gender and health: Reassessing patterns andexplanations. Social Science and Medicine 2001, 52:547-559.4. Spitzer DL: Engendering health disparities. Canadian Journal of PublicHealth 2005, 96:S78-S96.5. Humphries KH, van Doorslaer E: Income-related health inequality inCanada. Social Science & Medicine 2000, 50:663-671.6. Weber L: Forward. In Race, Gender and Class: Theory and Methods ofAnalysis. Edited by: Landry B. Upper Saddle River, NJ: Pearson Education;2007:xi-xiv.7. Collins PH: Toward a new vision: Race, class, and gender as categories ofanalysis and connection. Race, Sex, and Class 1993, 1:25-45.8. Collins PH: Black Feminist Thought: Knowledge, Consciousness, and the Politicsof Empowerment. 2 edition. New York: Routledge; 2000.9. Landry B: Race, Gender and Class: Theory and Methods of Analysis UpperSaddle River, NJ: Pearson Education; 2007.10. Zinn MB, Dill BT: Theorizing difference from multiracial feminism. FeministStudies 1996, 22:321-331.11. King DK: Multiple jeopardy, multiple consciousness. Signs 1988, 14:42-72.12. Brewer RM: Theorizing race, class and gender: The new scholarship ofblack feminist intellectuals and black women’s labor. In Theorizing BlackFeminisms: The Visionary Pragmatism of Black Women. Edited by: James SM,Busia APA. London: Routledge; 1993:13-30.13. Ostrove J, Feldman P, Adler N: Relations among socioeconomic statusindicators and health for African-Americans and whites. Journal of HealthPsychology 1999, 4:451-463.14. Nomaguchi KM: Are there race and gender differences in the effect ofmarital dissolution on depression? In Race, Gender and Class: Theory andMethods of Analysis. Edited by: Landry B. Upper Saddle River, NJ: PearsonEducation; 2007:394-410.15. Read JG, Gorman BK: Gender inequalities in US adult health: Theinterplay of race and ethnicity. Social Science & Medicine 2006,62:1045-1065.16. Jackson PB, Williams DR: The intersection of race, gender, and SES: healthparadoxes. In Gender, Race, Class, and Health: Intersectional Approaches.Edited by: Schulz AJ, Mullings L. San Francisco, CA: Jossey-Bass; 2006:131-162.17. Zambrana RE, Dill BT: Disparities in Latina health: An intersectionalanalysis. In Gender, Race, Class, and Health: Intersectional Approaches. Editedby: Schulz AJ, Mullings L. San Francisco, CA: Jossey-Bass; 2006:192-227.18. Roxburgh S: Untangling inequalities: Gender, race, and socioeconomicdifferences in depression. Sociological Forum 2009, 24:357-381.19. Sanchez-Vaznaugh EV, Kawachi I, Subramanian SV, Sanchez B, Acevedo-Garcia D: Do socioeconomic gradients in body mass index vary by race/ethnicity, gender, and birthplace? American Journal of Epidemiology 2009,169:1102-1112.20. Anand SS, Yusuf S, Jacobs R, Davis AD, Yi Q, Gerstein H, Montague PA,Lonn E: Risk factors, atherosclerosis, and cardiovascular disease amongAboriginal people in Canada: The study of health assessment and riskevaluation in Aboriginal peoples (SHARE-AP). The Lancet 2001,358:1147-1153.21. Shah CP: The health of aboriginal peoples. In Social determinants of health:Canadian perspectives. Edited by: Raphael D. Toronto, ON: CanadianScholars’ Press; 2004:267-280.22. Daniels J, Schulz AJ: Constructing whiteness in health disparities research.In Gender, Race, Class, and Health: Intersectional Approaches. Edited by:Schulz AJ, Mullings L. San Francisco, CA: Jossey-Bass; 2006:89-127.23. Geiger HJ: Health disparities: What do we know? What do we need toknow? What should we do? In Gender, Race, Class, and Health:Intersectional Approaches. Edited by: Schulz AJ, Mullings L. San Francisco,CA: Jossey-Bass; 2006:261-188.24. Courtenay W: Constructions of masculinity and their influence on men’swell-being: A theory of gender and health. Social Science and Medicine2000, 50:1385-1401.25. Adler NE, Boyce T, Chesney MA, Cohen S, Folkman S, Kahn RL, Syme SL:Socioeconomic status and health. American Psychologist 1994, 49:15-24.26. Adler NE, Newman K: Socioeconomic disparities in health: Pathways andpolicies. Health Affairs 2002, 21:60-76.27. Mirowsky J, Ross CE: Education, Social Status, and Health Hawthorne, NY:Aldine de Gruyter; 2003.28. Oakes JM, Rossi PH: The measurement of SES in health research: Currentpractice and steps towards a new approach. Social Science and Medicine2003, 56:769-784.29. Isaacs SL, Schroeder SA: Class - the ignored determinant of the nation’shealth. The New England Journal of Medicine 2004, 351:1137-1142.30. Dempsey CL: Health and social issues of gay, lesbian, and bisexualadolescents. Families in Society: The Journal of Contemporary HumanServices 1994, 75:160-167.31. Tremblay P, Ramsay R: Suicidal problems of youth with homosexual orbisexual orientations: Research, problems, and proposals. Vis-a-Vie 2000,10:5-8.32. Cochran SD, Sullivan JG, Mays VM: Prevalence of mental disorders,psychological distress and mental health services use among lesbian,gay, and bisexual adults in the United States. Journal of Consulting andClinical Psychology 2003, 71:53-61.33. Dworkin SL: Who is epidemiologically fathomable in the HIV/AIDSepidemic? Gender, sexuality, and intersectionality in public health.Culture, Health and Sexuality 2005, 7:615-623.34. Jorm A, Korten AE, Rodgers B, Jacomb PA, Christensen H: Sexualorientation and mental health: Results from a community survey ofyoung and middle-aged adults. British Journal of Psychiatry 2002,180:423-427.35. Bakker FC, Sandford TGM, Vanwesenbeeck I, Van Lindert H, Westert GP: Dohomosexual persons use health care services more frequently thanheterosexual persons? Findings from a Dutch population survey. SocialScience and Medicine 2006, 63:2022-2030.36. Idler EL, Benyamini Y: Self-rated health and mortality: A review of twenty-seven community studies. Journal of Health and Social Behavior 1997,38:21-37.37. Burstrom B, Fredlund P: Self rated health: Is it as good a predictor ofsubsequent mortality among adults in lower as well as in higher socialclasses? Journal of Epidemiology and Community Health 2001, 55:836-840.38. Jaccard J: Interaction Effects in Logistic Regression. Quantitative Applications inthe Social Sciences, Vol. 135 Thousand Oaks, CA: Sage Publications; 2001.39. Agyemang C, Bhopal R, Bruijnzeels M: Negro, Black, Black African, AfricanCaribbean, African American or what? Labelling African originpopulations in the health arena in the 21st century. Journal ofEpidemiology and Community Health 2005, 59:1014-1018.40. Curtis L: Health status of on and off-reserve Aboriginal peoples: Analysisof the Aboriginal Peoples Survey. Hamilton: McMaster University; 2007,SEDAP Research Paper No. 191.41. Talbani A, Hasanali P: Adolescent females between tradition andmodernity: Gender role socialization in South Asian immigrant culture.Journal of Adolescence 2000, 23:615-627.42. Purdie-Vaughns V, Eibach R: Intersectional invisibility: The distinctiveadvantages and disadvantages of multiple subordinate-group identities.Sex Roles 2008, 59:377-391.43. Weber L, Parra-Medina D: Intersectionality and women’s health: Chartinga path to eliminating health disparities. In Advances in Gender Research:Gender Perspectives on Health and Medicine. Edited by: Demos V, Segal MT.Oxford, UK: Elsevier Science; 2003:183-226.doi:10.1186/1475-9276-10-3Cite this article as: Veenstra: Race, gender, class, and sexual orientation:intersecting axes of inequality and self-rated health in Canada.International Journal for Equity in Health 2011 10:3.Veenstra International Journal for Equity in Health 2011, 10:3http://www.equityhealthj.com/content/10/1/3Page 11 of 11

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