IDEAS home Printed from https://ideas.repec.org/a/eee/socmed/v253y2020ics0277953620301878.html
   My bibliography  Save this article

Gender and sex independently associate with common somatic symptoms and lifetime prevalence of chronic disease

Author

Listed:
  • Ballering, Aranka V.
  • Bonvanie, Irma J.
  • Olde Hartman, Tim C.
  • Monden, Rei
  • Rosmalen, Judith G.M.

Abstract

Sex and gender influence health differently. Associations between sex and health have been extensively studied, but gender (i.e. psychosocial sex) has been largely neglected, partly due to the absence of gender measures in cohort studies. Therefore, our objective was to test the unique associations of gender and sex with common somatic symptoms and chronic diseases, using a gender index created from existing cohort data. We applied LASSO logistic regression to identify, out of 153 unique variables, psychosocial variables that were predictive of sex (i.e. gender-related) in the Dutch LifeLines Cohort Study. These psychosocial variables covered gender roles and institutionalized gender. Using the estimated coefficients, gender indexes were calculated for each adult participant in the study (n = 152,728; 58.5% female; mean age 44.6 (13.1) years). We applied multiple ordinal and logistic regression to test the unique associations of the gender index and sex, and their interactions, with common somatic symptoms assessed by the SCL-90 SOM and self-reported lifetime prevalence of chronic diseases, respectively. We found that in 10.1% of the participants the gender index was not in line with participants’ sex: 12.5% of men and 8.4% of women showed a discrepancy between gender index and sex. Feminine gender characteristics are associated with increased common somatic symptoms and chronic diseases, especially in men. Female sex is associated with a higher common somatic symptom burden, but not with a higher prevalence of chronic diseases. The study shows that gender and sex uniquely impact health, and should be considered in epidemiological studies. Our methodology shows that consideration of gender measures in studies is necessary and feasible, based on data generally present in cohort studies.

Suggested Citation

  • Ballering, Aranka V. & Bonvanie, Irma J. & Olde Hartman, Tim C. & Monden, Rei & Rosmalen, Judith G.M., 2020. "Gender and sex independently associate with common somatic symptoms and lifetime prevalence of chronic disease," Social Science & Medicine, Elsevier, vol. 253(C).
    • Handle: RePEc:eee:socmed:v:253:y:2020:i:c:s0277953620301878
    DOI: 10.1016/j.socscimed.2020.112968
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0277953620301878
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.socscimed.2020.112968?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. Springer, Kristen W. & Mager Stellman, Jeanne & Jordan-Young, Rebecca M., 2012. "Beyond a catalogue of differences: A theoretical frame and good practice guidelines for researching sex/gender in human health," Social Science & Medicine, Elsevier, vol. 74(11), pages 1817-1824.
    3. Singh-Manoux, A. & Guéguen, A. & Ferrie, J. & Shipley, M. & Martikainen, P. & Bonenfant, S. & Goldberg, M. & Marmot, M., 2008. "Gender differences in the association between morbidity and mortality among middle-aged men and women," American Journal of Public Health, American Public Health Association, vol. 98(12), pages 2251-2257.
    4. Bart Klijs & Salome Scholtens & Jornt J Mandemakers & Harold Snieder & Ronald P Stolk & Nynke Smidt, 2015. "Representativeness of the LifeLines Cohort Study," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-12, September.
    5. Cara Tannenbaum & Robert P. Ellis & Friederike Eyssel & James Zou & Londa Schiebinger, 2019. "Sex and gender analysis improves science and engineering," Nature, Nature, vol. 575(7781), pages 137-146, November.
    6. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    7. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
    8. Arber, Sara & McKinlay, John & Adams, Ann & Marceau, Lisa & Link, Carol & O'Donnell, Amy, 2006. "Patient characteristics and inequalities in doctors' diagnostic and management strategies relating to CHD: A video-simulation experiment," Social Science & Medicine, Elsevier, vol. 62(1), pages 103-115, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Gabriele Bolte & Katharina Jacke & Katrin Groth & Ute Kraus & Lisa Dandolo & Lotta Fiedel & Malgorzata Debiak & Marike Kolossa-Gehring & Alexandra Schneider & Kerstin Palm, 2021. "Integrating Sex/Gender into Environmental Health Research: Development of a Conceptual Framework," IJERPH, MDPI, vol. 18(22), pages 1-18, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Christopher J Greenwood & George J Youssef & Primrose Letcher & Jacqui A Macdonald & Lauryn J Hagg & Ann Sanson & Jenn Mcintosh & Delyse M Hutchinson & John W Toumbourou & Matthew Fuller-Tyszkiewicz &, 2020. "A comparison of penalised regression methods for informing the selection of predictive markers," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-14, November.
    2. Christopher Kath & Florian Ziel, 2018. "The value of forecasts: Quantifying the economic gains of accurate quarter-hourly electricity price forecasts," Papers 1811.08604, arXiv.org.
    3. Xiaojun Mao & Zhonglei Wang & Shu Yang, 2023. "Matrix completion under complex survey sampling," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 75(3), pages 463-492, June.
    4. Faisal Maqbool Zahid & Shahla Faisal & Christian Heumann, 2020. "Variable selection techniques after multiple imputation in high-dimensional data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(3), pages 553-580, September.
    5. Stephanie Houle & Ryan Macdonald, 2023. "Identifying Nascent High-Growth Firms Using Machine Learning," Staff Working Papers 23-53, Bank of Canada.
    6. Kath, Christopher & Ziel, Florian, 2018. "The value of forecasts: Quantifying the economic gains of accurate quarter-hourly electricity price forecasts," Energy Economics, Elsevier, vol. 76(C), pages 411-423.
    7. Danhyang Lee & Jae Kwang Kim, 2022. "Semiparametric imputation using conditional Gaussian mixture models under item nonresponse," Biometrics, The International Biometric Society, vol. 78(1), pages 227-237, March.
    8. Halewijn M. Drent & Barbara van den Hoofdakker & Jan K. Buitelaar & Pieter J. Hoekstra & Andrea Dietrich, 2022. "Factors Related to Perceived Stigma in Parents of Children and Adolescents in Outpatient Mental Healthcare," IJERPH, MDPI, vol. 19(19), pages 1-14, October.
    9. Hua Yun Chen & Hesen Li & Maria Argos & Victoria W. Persky & Mary E. Turyk, 2022. "Statistical Methods for Assessing the Explained Variation of a Health Outcome by a Mixture of Exposures," IJERPH, MDPI, vol. 19(5), pages 1-16, February.
    10. Tutz, Gerhard & Pößnecker, Wolfgang & Uhlmann, Lorenz, 2015. "Variable selection in general multinomial logit models," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 207-222.
    11. Carstensen, Kai & Heinrich, Markus & Reif, Magnus & Wolters, Maik H., 2020. "Predicting ordinary and severe recessions with a three-state Markov-switching dynamic factor model," International Journal of Forecasting, Elsevier, vol. 36(3), pages 829-850.
    12. Hou-Tai Chang & Ping-Huai Wang & Wei-Fang Chen & Chen-Ju Lin, 2022. "Risk Assessment of Early Lung Cancer with LDCT and Health Examinations," IJERPH, MDPI, vol. 19(8), pages 1-12, April.
    13. Wang, Qiao & Zhou, Wei & Cheng, Yonggang & Ma, Gang & Chang, Xiaolin & Miao, Yu & Chen, E, 2018. "Regularized moving least-square method and regularized improved interpolating moving least-square method with nonsingular moment matrices," Applied Mathematics and Computation, Elsevier, vol. 325(C), pages 120-145.
    14. Mkhadri, Abdallah & Ouhourane, Mohamed, 2013. "An extended variable inclusion and shrinkage algorithm for correlated variables," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 631-644.
    15. Lucian Belascu & Alexandra Horobet & Georgiana Vrinceanu & Consuela Popescu, 2021. "Performance Dissimilarities in European Union Manufacturing: The Effect of Ownership and Technological Intensity," Sustainability, MDPI, vol. 13(18), pages 1-19, September.
    16. Candelon, B. & Hurlin, C. & Tokpavi, S., 2012. "Sampling error and double shrinkage estimation of minimum variance portfolios," Journal of Empirical Finance, Elsevier, vol. 19(4), pages 511-527.
    17. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2022. "Specification Choices in Quantile Regression for Empirical Macroeconomics," Working Papers 22-25, Federal Reserve Bank of Cleveland.
    18. Kim, Hyun Hak & Swanson, Norman R., 2018. "Mining big data using parsimonious factor, machine learning, variable selection and shrinkage methods," International Journal of Forecasting, Elsevier, vol. 34(2), pages 339-354.
    19. Shuichi Kawano, 2014. "Selection of tuning parameters in bridge regression models via Bayesian information criterion," Statistical Papers, Springer, vol. 55(4), pages 1207-1223, November.
    20. Enrico Bergamini & Georg Zachmann, 2020. "Exploring EU’s Regional Potential in Low-Carbon Technologies," Sustainability, MDPI, vol. 13(1), pages 1-28, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:socmed:v:253:y:2020:i:c:s0277953620301878. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/315/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.