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Adding interactions to models of intersectional health inequalities: Comparing multilevel and conventional methods

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  • Evans, Clare R.

Abstract

Examining health inequalities intersectionally is gaining in popularity and recent quantitative innovations, such as the development of intersectional multilevel methods, have enabled researchers to expand the number of dimensions of inequality evaluated while avoiding many of the theoretical and methodological limitations of the conventional fixed effects approach. Yet there remains substantial uncertainty about the effects of integrating numerous additional interactions into models: will doing so reveal statistically significant interactions that were previously hidden or explain away interactions seen when fewer dimensions were considered? Furthermore, how does the multilevel approach compare empirically to the conventional approach across a range of conditions? These questions are essential to informing our understanding of population-level health inequalities. I address these gaps using data from the National Longitudinal Study of Adolescent to Adult Health by evaluating conventional and multilevel intersectional models across a range of interaction conditions (ranging from six points of interaction to more than ninety, interacting gender, race/ethnicity/immigration status, parent education, family income, and sexual identification), different model types (linear and logistic), and seven diverse dependent variables commonly examined by health researchers: body mass index, depression, general self-rated health, binge drinking, cigarette use, marijuana use, and other illegal drug use. Findings suggest that adding categories to intersectional analyses will tend to reveal new points of interaction. Stratum-level results from the multilevel approach are robust to cross-classification by school context. Conventional and multilevel approaches differ substantially when tested empirically. I conclude with a detailed consideration of the origin of these differences and provide recommendations for future scholarship of intersectional health inequalities.

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  • Evans, Clare R., 2019. "Adding interactions to models of intersectional health inequalities: Comparing multilevel and conventional methods," Social Science & Medicine, Elsevier, vol. 221(C), pages 95-105.
    • Handle: RePEc:eee:socmed:v:221:y:2019:i:c:p:95-105
    DOI: 10.1016/j.socscimed.2018.11.036
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    References listed on IDEAS

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    1. Leckie, George & Charlton, Chris, 2013. "runmlwin: A Program to Run the MLwiN Multilevel Modeling Software from within Stata," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 52(i11).
    2. Evans, Clare R. & Erickson, Natasha, 2019. "Intersectionality and depression in adolescence and early adulthood: A MAIHDA analysis of the national longitudinal study of adolescent to adult health, 1995–2008," Social Science & Medicine, Elsevier, vol. 220(C), pages 1-11.
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    6. Evans, Clare R. & Onnela, Jukka-Pekka & Williams, David R. & Subramanian, S.V., 2016. "Multiple contexts and adolescent body mass index: Schools, neighborhoods, and social networks," Social Science & Medicine, Elsevier, vol. 162(C), pages 21-31.
    7. Evans, Clare R. & Williams, David R. & Onnela, Jukka-Pekka & Subramanian, S.V., 2018. "A multilevel approach to modeling health inequalities at the intersection of multiple social identities," Social Science & Medicine, Elsevier, vol. 203(C), pages 64-73.
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    Cited by:

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    3. Zubizarreta, Dougie & Beccia, Ariel L. & Trinh, Mai-Han & Reynolds, Colleen A. & Reisner, Sari L. & Charlton, Brittany M., 2022. "Human papillomavirus vaccination disparities among U.S. college students: An intersectional Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA)," Social Science & Medicine, Elsevier, vol. 301(C).
    4. Guozhang Yan & Lianyou Li & Muhammad Tayyab Sohail & Yanan Zhang & Yahui Song, 2024. "Mitigating Health Disparities among the Elderly in China: An Analysis of the Roles of Social Security and Family Support from a Perspective Based on Relative Deprivation," Sustainability, MDPI, vol. 16(18), pages 1-16, September.
    5. Valérie Lederer & Karen Messing & Hélène Sultan-Taïeb, 2022. "How Can Quantitative Analysis Be Used to Improve Occupational Health without Reinforcing Social Inequalities? An Examination of Statistical Methods," IJERPH, MDPI, vol. 20(1), pages 1-19, December.
    6. Philipp Jaehn & Emily Mena & Sibille Merz & Robert Hoffmann & Antje Gößwald & Alexander Rommel & Christine Holmberg & on behalf of the ADVANCE GENDER study group, 2020. "Non-response in a national health survey in Germany: An intersectionality-informed multilevel analysis of individual heterogeneity and discriminatory accuracy," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-17, August.
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    8. Robyn Feiss & Melissa M. Pangelinan, 2021. "Relationships between Physical and Mental Health in Adolescents from Low-Income, Rural Communities: Univariate and Multivariate Analyses," IJERPH, MDPI, vol. 18(4), pages 1-16, February.

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