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Intersectionality-Informed Sex/Gender-Sensitivity in Public Health Monitoring and Reporting (PHMR): A Case Study Assessing Stratification on an “Intersectional Gender-Score”

Author

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  • Emily Mena

    (Department of Social Epidemiology, Institute of Public Health and Nursing Research, Faculty of Human and Health Sciences, University of Bremen, 28359 Bremen, Germany
    Health Sciences Bremen, University of Bremen, 28359 Bremen, Germany)

  • Katharina Stahlmann

    (Department of Social Epidemiology, Institute of Public Health and Nursing Research, Faculty of Human and Health Sciences, University of Bremen, 28359 Bremen, Germany
    Department of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany)

  • Klaus Telkmann

    (Department of Social Epidemiology, Institute of Public Health and Nursing Research, Faculty of Human and Health Sciences, University of Bremen, 28359 Bremen, Germany
    Health Sciences Bremen, University of Bremen, 28359 Bremen, Germany)

  • Gabriele Bolte

    (Department of Social Epidemiology, Institute of Public Health and Nursing Research, Faculty of Human and Health Sciences, University of Bremen, 28359 Bremen, Germany
    Health Sciences Bremen, University of Bremen, 28359 Bremen, Germany)

  • on behalf of the AdvanceGender Study Group

    (Advance Gender Study Group are listed in acknowledgments.)

Abstract

To date, PHMR has often relied on male/female stratification, but rarely considers the complex, intersecting social positions of men and women in describing the prevalence of health and disease. Stratification on an Intersectional Gender-Score (IG-Score), which is based on a variety of social covariables, would allow comparison of the prevalence of individuals who share the same complex intersectional profile (IG-Score). The cross-sectional case study was based on the German Socio-Economic Panel 2017 (n = 23,269 age 18+). After stratification, covariable-balance within the total sample and IG-Score-subgroups was assessed by standardized mean differences. Prevalence of self-rated health, mental distress, depression and hypertension was compared in men and women. In the IG-Score-subgroup with highest proportion of males and lowest probability of falling into the ‘woman’-category, most individuals were in full-time employment. The IG-Score-subgroup with highest proportion of women and highest probability of falling into the ‘woman’-category was characterized by part-time/occasional employment, housewife/-husband, and maternity/parental leave. Gender differences in prevalence of health indicators remained within the male-dominated IG-Score-subgroup, whereas the same prevalence of depression and self-rated health was observed for men and women constituting the female-dominated IG-Score-subgroup. These results might indicate that sex/gender differences of depression and self-rated health could be interpreted against the background of gender associated processes. In summary, the proposed procedure allows comparison of prevalence of health indicators conditional on men and women sharing the same complex intersectional profile.

Suggested Citation

  • Emily Mena & Katharina Stahlmann & Klaus Telkmann & Gabriele Bolte & on behalf of the AdvanceGender Study Group, 2023. "Intersectionality-Informed Sex/Gender-Sensitivity in Public Health Monitoring and Reporting (PHMR): A Case Study Assessing Stratification on an “Intersectional Gender-Score”," IJERPH, MDPI, vol. 20(3), pages 1-15, January.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:3:p:2220-:d:1047480
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    References listed on IDEAS

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    1. van Buuren, Stef & Groothuis-Oudshoorn, Karin, 2011. "mice: Multivariate Imputation by Chained Equations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i03).
    2. Friedrich Scheller, 2011. "Bestimmung der Herkunftsnationen von Teilnehmern des Sozio-oekonomischen Panels (SOEP) mit Migrationshintergrund," SOEPpapers on Multidisciplinary Panel Data Research 407, DIW Berlin, The German Socio-Economic Panel (SOEP).
    3. Scheim, Ayden I. & Bauer, Greta R., 2019. "The Intersectional Discrimination Index: Development and validation of measures of self-reported enacted and anticipated discrimination for intercategorical analysis," Social Science & Medicine, Elsevier, vol. 226(C), pages 225-235.
    4. Bauer, Greta R., 2014. "Incorporating intersectionality theory into population health research methodology: Challenges and the potential to advance health equity," Social Science & Medicine, Elsevier, vol. 110(C), pages 10-17.
    5. Goebel Jan & Grabka Markus M. & Liebig Stefan & Kroh Martin & Richter David & Schröder Carsten & Schupp Jürgen, 2019. "The German Socio-Economic Panel (SOEP)," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 239(2), pages 345-360, April.
    6. Daniele Bottigliengo & Giulia Lorenzoni & Honoria Ocagli & Matteo Martinato & Paola Berchialla & Dario Gregori, 2021. "Propensity Score Analysis with Partially Observed Baseline Covariates: A Practical Comparison of Methods for Handling Missing Data," IJERPH, MDPI, vol. 18(13), pages 1-17, June.
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