Background
Existing literature on intersectional stigma and health
Manifestations of intersectional stigma
Effects of intersectional stigma on health behaviors and outcomes
Measurement and analytical approaches for intersectional stigma
Qualitative approaches
Quantitative approaches
Measurement and instruments
Analytical strategies
Strategy | Description | Advantages | Limitations | Examples | Recommendations for use |
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Stratified analyses | The relationship between a measure of stigma and a health outcome is analyzed in separate samples disaggregated by an identity of interest (e.g., illness status, gender, race) | • Simple, easy to perform and interpret | • Cannot necessarily test for statistical significance [101] • Difficult to interpret and cumbersome to perform when multiple axes are considered • Cannot use for two continuous measures of health-related stigma | • Exploration of educational outcomes among individuals of Mexican origin only within a sample of women [102] | • Exploratory questions about how the relationship between stigma and health might vary in the presence of an additional identity-related factor that is discrete (e.g., HIV-status, gender) |
Factorial design | Vignettes that describe individuals with different combinations of characteristics or identities are presented, typically randomly, to a general population sample. Individuals’ responses to a measure of stigma (e.g., social distance) across vignettes are compared [103] | • Allows for decomposition of stigma related to different identities or factors into unique and shared components [103] • Experimental design | • Can reflect additive assumptions about the nature of intersectional stigma [52] • Difficult to interpret and cumbersome to perform when multiple axes are considered • Difficult to include explanatory or process variables [52] | • Disentanglement of stigma associated with HIV from stigma associated with risk practices (e.g., injection drug use) [104] | • How the level of community discrimination or stigmatizing attitudes and beliefs may vary based on the presence or absence of a small number of additional behavioral or identity-related factors |
Moderation analysis | The main effects of two (or more) stigma-related variables are modeled along with the product of those variables (e.g., race × gender × HIV status) | • Simple to do, and in the case of two-way interactions, to interpret • Flexible • Can assess positive or negative changes in magnitude and directionality of effects [63] | • When main effects explain much of the variance in the outcome, the ability to assess interactions between those terms is limited [20] • Three-way or higher-order interactions are difficult to depict and comprehend | • Examination of how social adversity, HIV status, and race interact to explain depression [105] • Assessment of how stigma related to HIV and substance use interact to explain depression [106] • Assessment of how weight discrimination interacts with race and socioeconomic status to shape mental health among women [107] | • When large sample sizes are available and variation is present within subgroups to test how the relationship between stigma and health might vary in the presence of an additional identity-related factor that is discrete (e.g., HIV-status, gender) |
Latent class or latent profile analysis | Identifies subpopulations of individuals based on their endorsement of different stigma or discrimination experiences Predictors of membership in these populations (such as identity characteristics like race or health status) can be evaluated and latent class regression can be used to assess how these different patterns of experiences differentially predict health outcomes | • A person-centered, rather than variable-centered, approach to assessing intersectionality • Treats different patterns of stigma experiences as latent and allows these to be empirically determined | • Can require large sample sizes • More difficult to explain to lay audiences, including policymakers and funders in some cases | • Identification of patterns of bullying and discrimination experiences related to different identities and assessed to what extent these patterns differentially predicted mental health outcomes [73] | • When large sample sizes are available and the question of interest is how the nature of stigma may vary based on the presence of different combinations of stigmatized behaviors or identities |
Multilevel models | In addition to fixed effects, random effects (intercepts and slopes) at the cluster level (e.g., neighborhood, city, country) are included in regression models Covariates can be included at both levels of analyses and cross-level interactions can be modeled | • Enables modeling of structural level influences on stigma and health • Can be used for analysis of intensive longitudinal data by accounting for correlation of observations within person over time | • More difficult to explain to lay audiences, including policymakers and funders in some cases • Requires data collection in multiple contexts and, in some cases, may require existing data at higher levels (e.g., state or country level data) | • Exploration of whether the relationship of gender, class, and race to self-rated health varied by neighborhood [63] • Assessment of how country-level and individual level factors interact to influence the mental health of male sexual minority European migrants [19] • Examination of how everyday experiences of discrimination impact internalized stigma among people living with HIV using a smartphone-based experience sampling method survey [64] | • When multiple time points are available or data is available from multiple clusters (the number necessary will vary, but 10–15 would be considered few clusters for an analysis [108]) and contextual influences on the relationship between stigma and health are of interest |
Structural equation modeling | Allows for simultaneous estimation of measurement and structural components, including pathways between observed and latent variables | • Appropriately models measurement error associated with inclusion of latent variables • Flexible strategy: can simultaneously assess the impact of multiple exposures on multiple outcomes, include group-based or multilevel modeling, and assess moderated mediation or mediated moderation [109] • Can assess how exposures and outcomes predict each other over time | • Modeled relationships may be inappropriately interpreted as causal • Depending on the number of parameters included, may require larger sample sizes to be estimable • Not all models may be identifiable and sensitive to model misspecifications | • Simultaneous assessment of experiences of racial discrimination and HIV-related stigma on quality of life among African and Caribbean Black women in Canada [74] | • For estimating complex models including multiple stigma-related factors as predictors or multiple related health outcomes of interest, particularly when including psychosocial variables that are not directly observable (e.g., stress, coping) |
Moderation approaches
Multilevel modeling
Latent variable, latent class, and latent profile methods
Structural equation modeling (SEM)
Mixed methods approaches
Discussion and future directions
Conclusions
Box 1 Intersectional stigma and mental health in 17 countries
Box 2 Tackling transphobia among healthcare providers in India
Box 3 Effects of HIV, race, and sexual orientation discrimination on depression in Alabama
Box 4 HIV-related stigma, sexual and gender identity stigma, and depressive symptoms among lesbian, gay, bisexual, and transgender (LGBT) persons in Jamaica
Box 5 Structural equation modeling to assess the impact of racial discrimination and HIV-related stigma on the well-being of African and Caribbean Black women living with HIV [99]
Box 6 Recommendations and priorities for intersectional stigma and health
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Further development of quantitative and qualitative tools for measuring/understanding intersectional stigma, including (but not limited to):
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○ Quantitative measures that capture complex and unique intersectional experiences for specific populations and health conditions.
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○ Valid parallel questionnaire measures that can capture the common elements of intersectional stigma across populations and health conditions.
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○ Qualitative interview/focus group guides that stimulate participants to explain and reflect on their experiences of intersectional stigma.
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Examination of how the effects of stigma change for different health conditions.
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○ Example research question: How do the effects of TB-related stigma differ from the effects of HIV-related stigma in influencing access to healthcare?
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Elucidation of how characteristics associated with stigmas (blame, concealability, perception of risk, etc.) change in the setting of intersectional stigma.
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○ Example research question: How does blame related to mental health stigma change according to whether the person is in a marginalized ethnic group?
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Characterization of how shifts in one type of stigma affect the burden of other stigmas.
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○ Example research question: How does reducing stigma around HIV in a community affect experiences of substance use stigma?
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Examination of how experiences and effects of intersectional stigma change based on historical, cultural, and socioeconomic context.
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○ Example research question: How do experiences of weight-related stigma and poverty stigma differ in impoverished settings versus high-resource settings?
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Characterization of potential positive effects of shared identity.
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○ Example research question: How does social support from people with similar intersectional identities change the way people react to and deal with stigma?
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Elucidation of drivers of intersectional stigma.
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○ Example research question: Are there common drivers of some co-occurring stigmas?
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Characterization of the interpersonal, psychological, and biological mechanisms for the effects of intersectional stigmas on health outcomes.
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○ Example research question: What are the pathways through which intersectional stigmas around cancer and race affect access to cancer treatment?
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Identification of the most salient pathways that can potentially be addressed in intersectional stigma interventions.
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○ Example research question: Is addressing mental health effects related to experiencing both sexual orientation- and HIV-related stigma a potentially effective way to improve health outcomes for men who have sex with men living with HIV?
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Developing strategies that address the barriers posed by intersectionality, while capitalizing on solidarity and social support of shared identities.
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Identifying what types of stigma are best addressed simultaneously/together in interventions.
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Deriving strategies that can be used to meaningfully and genuinely engage the people at the center of intersectional stigmas in the development of interventions.
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Funders should consider intersectional approaches to maximize the impact of investments.
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Policymakers should prioritize stigma reduction policies that consider multiple intersecting stigmas, when appropriate.