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Concordance of health states in couples: Analysis of self-reported, nurse administered and blood-based biomarker data in the UK Understanding Society panel

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  • Davillas, Apostolos
  • Pudney, Stephen

Abstract

We use self-reported health measures, nurse-administered measurements and blood-based biomarkers to examine the concordance between health states of partners in marital/cohabiting relationships in the UK. A model of cumulative health exposures is used to interpret the empirical pattern of between-partner health correlation in relation to elapsed relationship duration, allowing us to distinguish non-causal correlation due to assortative mating from potentially causal effects of shared lifestyle and environmental factors. We find important differences between the results for different health indicators, with strongest homogamy correlations observed for adiposity, followed by blood pressure, heart rate, inflammatory markers and cholesterol, and also self-assessed general health and functional difficulties. We find no evidence of a “dose–response relationship” for marriage duration, and show that this suggests – perhaps counterintuitively – that shared lifestyle factors and homogamous partner selection make roughly equal contributions to the concordance we observe in most of the health measures we examine.

Suggested Citation

  • Davillas, Apostolos & Pudney, Stephen, 2017. "Concordance of health states in couples: Analysis of self-reported, nurse administered and blood-based biomarker data in the UK Understanding Society panel," Journal of Health Economics, Elsevier, vol. 56(C), pages 87-102.
    • Handle: RePEc:eee:jhecon:v:56:y:2017:i:c:p:87-102
    DOI: 10.1016/j.jhealeco.2017.09.010
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    Citations

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    Cited by:

    1. Davillas, Apostolos & Pudney, Stephen, 2020. "Using biomarkers to predict healthcare costs: Evidence from a UK household panel," Journal of Health Economics, Elsevier, vol. 73(C).
    2. Angelini, Viola & Costa-Font, Joan, 2023. "Health and wellbeing spillovers of a partner's cancer diagnosis," Journal of Economic Behavior & Organization, Elsevier, vol. 212(C), pages 422-437.
    3. Byrne, Dominic & Kwak, Do Won & Tang, Kam Ki & Yazbeck, Myra, 2023. "Spillover effects of retirement: Does health vulnerability matter?," Economics & Human Biology, Elsevier, vol. 48(C).
    4. Seetha Menon, 2023. "The effect of domestic violence on cardiovascular risk," Review of Economics of the Household, Springer, vol. 21(2), pages 371-395, June.
    5. Arni, Patrick & Dragone, Davide & Goette, Lorenz & Ziebarth, Nicolas R., 2021. "Biased health perceptions and risky health behaviors—Theory and evidence," Journal of Health Economics, Elsevier, vol. 76(C).
    6. Sinha, K.; & Davillas, A.; & Jones, A.M.; & Sharma, A.;, 2018. "Distributional analysis of the role of breadth and persistence of multiple deprivation in the health gradient measured by biomarkers," Health, Econometrics and Data Group (HEDG) Working Papers 18/31, HEDG, c/o Department of Economics, University of York.
    7. Davillas, Apostolos & de Oliveira, Victor Hugo & Jones, Andrew M., 2022. "Model of Errors in BMI Based on Self‐reported and Measured Anthropometrics with Evidence from Brazilian Data," CINCH Working Paper Series (since 2020) 76143, Duisburg-Essen University Library, DuEPublico.
    8. Davillas, Apostolos & Pudney, Stephen, 2020. "Biomarkers, disability and health care demand," Economics & Human Biology, Elsevier, vol. 39(C).
    9. Davillas, Apostolos & Pudney, Stephen, 2019. "Baseline health and public healthcare costs five years on: a predictive analysis using biomarker data in a prospective household panel," ISER Working Paper Series 2019-01, Institute for Social and Economic Research.
    10. Zhao, Yuejun, 2023. "Job displacement and the mental health of households: Burden sharing counteracts spillover," Labour Economics, Elsevier, vol. 81(C).
    11. Vincenzo Carrieri & Apostolos Davillas & Andrew M. Jones, 2020. "A latent class approach to inequity in health using biomarker data," Health Economics, John Wiley & Sons, Ltd., vol. 29(7), pages 808-826, July.
    12. Davillas, Apostolos & Jones, Andrew M, 2020. "Ex ante inequality of opportunity in health, decomposition and distributional analysis of biomarkers," Journal of Health Economics, Elsevier, vol. 69(C).
    13. Fumagalli, Elena & Fumagalli, Laura, 2022. "Subjective well-being and the gender composition of the reference group: Evidence from a survey experiment," Journal of Economic Behavior & Organization, Elsevier, vol. 194(C), pages 196-219.
    14. James Banks & Iris Kesternich & James P. Smith, 2021. "International differences in interspousal health correlations," Health Economics, John Wiley & Sons, Ltd., vol. 30(5), pages 1152-1177, May.
    15. Barry, L.E. & O'Neill, S. & Heaney, L.G. & O'Neill, C., 2021. "Stress-related health depreciation: Using allostatic load to predict self-rated health," Social Science & Medicine, Elsevier, vol. 283(C).
    16. Francetic, Igor & Meacock, Rachel & Sutton, Matt, 2022. "Understanding Concordance in Health Behaviours among Couples: Evidence from the Bowel Cancer Screening Programme in England," Journal of Economic Behavior & Organization, Elsevier, vol. 201(C), pages 310-345.
    17. Steele, Fiona & Clarke, Paul & Kuha, Jouni, 2019. "Modeling within-household associations in household panel studies," LSE Research Online Documents on Economics 88162, London School of Economics and Political Science, LSE Library.

    More about this item

    Keywords

    C2; C8; I10; Biomarkers; Health; Homogamy; Spousal concordance; Understanding Society;
    All these keywords.

    JEL classification:

    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • I10 - Health, Education, and Welfare - - Health - - - General

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