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Understanding Society: health, biomarker and genetic data

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  • Michaela Benzeval
  • Edith Aguirre
  • Meena Kumari

Abstract

Understanding Society, the UK Household Longitudinal Study includes a wide range of health measures, and in particular biomarker and genetic data. This makes it a unique resource for research on the economics of health. We review the main features of the biomarker data, how they are collected, and evidence on data quality. We also discuss examples of how these data have been used in economic research to date.

Suggested Citation

  • Michaela Benzeval & Edith Aguirre & Meena Kumari, 2023. "Understanding Society: health, biomarker and genetic data," Fiscal Studies, John Wiley & Sons, vol. 44(4), pages 399-415, December.
    • Handle: RePEc:wly:fistud:v:44:y:2023:i:4:p:399-415
    DOI: 10.1111/1475-5890.12354
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    References listed on IDEAS

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    1. Arulsamy, Karen & Delaney, Liam, 2022. "The impact of automatic enrolment on the mental health gap in pension participation: Evidence from the UK," Journal of Health Economics, Elsevier, vol. 86(C).
    2. 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.
    3. Peter Lynn & Pablo Cabrera‐Álvarez & Paul Clarke, 2023. "Sample composition and representativeness on Understanding Society," Fiscal Studies, John Wiley & Sons, vol. 44(4), pages 341-359, December.
    4. Hughes, Amanda & Kumari, Meena, 2019. "Testosterone, risk, and socioeconomic position in British men: Exploring causal directionality," Social Science & Medicine, Elsevier, vol. 220(C), pages 129-140.
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    6. Michaela Benzeval & Meena Kumari & Andrew M. Jones, 2016. "How Do Biomarkers and Genetics Contribute to Understanding Society?," Health Economics, John Wiley & Sons, Ltd., vol. 25(10), pages 1219-1222, October.
    7. Hand, Chris, 2020. "Biology and being green: The effect of prenatal testosterone exposure on pro-environmental consumption behaviour," Journal of Business Research, Elsevier, vol. 120(C), pages 619-626.
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    Cited by:

    1. Davillas, Apostolos & Jones, Andrew M., 2021. "The implications of self-reported body weight and height for measurement error in BMI," Economics Letters, Elsevier, vol. 209(C).
    2. Davillas, Apostolos & Jones, Andrew M., 2025. "Biological age and predicting future health care utilisation," Journal of Health Economics, Elsevier, vol. 99(C).
    3. Davillas, Apostolos & M. Jones, Andrew, 2024. "Biological age and predicting future health care utilisation," ISER Working Paper Series 2024-03, Institute for Social and Economic Research.
    4. Michaela Benzeval & Thomas F. Crossley & Edith Aguirre, 2023. "A symposium on Understanding Society, the UK Household Longitudinal Study: introduction," Fiscal Studies, John Wiley & Sons, vol. 44(4), pages 317-340, December.
    5. 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.
    6. 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.

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