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Intersectionality and depression in adolescence and early adulthood: A MAIHDA analysis of the national longitudinal study of adolescent to adult health, 1995–2008

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  • Evans, Clare R.
  • Erickson, Natasha
Abstract
Depression in adolescents and young adults remains a pressing public health concern and there is increasing interest in evaluating population-level inequalities in depression intersectionally. A recent advancement in quantitative methods—multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA)—has many practical and theoretical advantages over conventional models of intercategorical intersectionality, including the ability to more easily evaluate numerous points of intersection between axes of marginalization. This study is the first to apply the MAIHDA approach to investigate mental health outcomes intersectionally in any population. We examine intersectionality and depression among adolescents and young adults in the U.S. along dimensions of gender, race/ethnicity, immigration status, and family income using a large, nationally representative sample—the National Longitudinal Study of Adolescent to Adult Health. We find evidence of considerable inequalities between social strata, with women, racial/ethnic minorities, immigrants, and low income strata experiencing elevated depression scores. Importantly, the majority of between-strata variation is explained by additive main effects, with no strata experiencing statistically significant residual “interaction” effects. We compare these findings to previous intersectional research on depression and discuss possible sources of differences between MAIHDA and conventional intersectional models.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:socmed:v:220:y:2019:i:c:p:1-11
    DOI: 10.1016/j.socscimed.2018.10.019
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    Cited by:

    1. Daniel Holman & Alan Walker, 2021. "Understanding unequal ageing: towards a synthesis of intersectionality and life course analyses," European Journal of Ageing, Springer, vol. 18(2), pages 239-255, June.
    2. Aránzazu Hernández-Yumar & Maria Wemrell & Ignacio Abásolo-Alessón & Beatriz González López-Valcárcel & Juan Merlo, 2023. "Impact of the Economic Crisis on Body Mass Index in Spain: An Intersectional Multilevel Analysis Using a Socioeconomic and Regional Perspective," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 42(4), pages 1-31, August.
    3. Evans, Clare & Leckie, George & Subramanian, SV & Bell, Andrew & Merlo, Juan, 2024. "A Tutorial for Conducting Intersectional Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA)," SocArXiv 635hx, Center for Open Science.
    4. Alvarez, Camila H. & Evans, Clare Rosenfeld, 2021. "Intersectional environmental justice and population health inequalities: A novel approach," Social Science & Medicine, Elsevier, vol. 269(C).
    5. Bell, Andrew & Evans, Clare & Holman, Daniel & Leckie, George, 2023. "Extending intersectional Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) for longitudinal data, with application to mental health trajectories in the UK," SocArXiv jq57s, Center for Open Science.
    6. Park, In Young & Speer, Rachel & Whitfield, Darren L. & Kattari, Leo & Walls, Eugene N. & Christensen, Candace, 2022. "Predictors of bullying, depression, and suicide attempts among youth: The intersection of race/ethnicity by gender identity," Children and Youth Services Review, Elsevier, vol. 139(C).
    7. Wilkes, Rima & Karimi, Aryan, 2024. "What does the MAIHDA method explain?," Social Science & Medicine, Elsevier, vol. 345(C).
    8. 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).
    9. Anna Persmark & Maria Wemrell & Sofia Zettermark & George Leckie & S V Subramanian & Juan Merlo, 2019. "Precision public health: Mapping socioeconomic disparities in opioid dispensations at Swedish pharmacies by Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA)," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-21, August.
    10. 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.
    11. Bell, Andrew & Evans, Clare & Holman, Dan & Leckie, George, 2024. "Extending intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) to study individual longitudinal trajectories, with application to mental health in the UK," Social Science & Medicine, Elsevier, vol. 351(C).
    12. Fagrell Trygg, Nadja & Månsdotter, Anna & Gustafsson, Per E., 2021. "Intersectional inequalities in mental health across multiple dimensions of inequality in the Swedish adult population," Social Science & Medicine, Elsevier, vol. 283(C).

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