IDEAS home Printed from https://ideas.repec.org/p/ese/iserwp/2018-11.html
   My bibliography  Save this paper

Biomarkers as precursors of disability

Author

Listed:
  • Davillas, Apostolos
  • Pudney, Stephen

Abstract

Some social surveys now collect physical measurements and markers derived from biological samples, in addition to self-reported health assessments. This information is expensive to collect; its value in medical epidemiology has been clearly established, but its potential contribution to social science research is less certain. We focused on disability, which results from biological processes but is defined in terms of its implications for social functioning and wellbeing. Using data from waves 2 and 3 of the UK Understanding Society panel survey as our baseline, we estimated predictive models for disability 2-4 years ahead, using a wide range of biomarkers in addition to self-assessed health (SAH) and other socio-economic covariates. We found a quantitatively and statistically significant predictive role for a large set of nurse-collected and blood-based biomarkers, over and above the strong predictive power of self-assessed health. We also applied a latent variable model accounting for the longitudinal nature of observed disability outcomes and measurement error in in SAH and biomarkers. Although SAH performed well as a summary measure, it has shortcomings as a leading indicator of disability, since we found it to be biased in the sense of over- or under-sensitivity to certain biological pathways.

Suggested Citation

  • Davillas, Apostolos & Pudney, Stephen, 2018. "Biomarkers as precursors of disability," ISER Working Paper Series 2018-11, Institute for Social and Economic Research.
  • Handle: RePEc:ese:iserwp:2018-11
    as

    Download full text from publisher

    File URL: https://www.iser.essex.ac.uk/wp-content/uploads/files/working-papers/iser/2018-11.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Bengtsson, Tommy & Nilsson, Anton, 2018. "Smoking and early retirement due to chronic disability," Economics & Human Biology, Elsevier, vol. 29(C), pages 31-41.
    2. Seeman, Teresa & Merkin, Sharon S. & Crimmins, Eileen & Koretz, Brandon & Charette, Susan & Karlamangla, Arun, 2008. "Education, income and ethnic differences in cumulative biological risk profiles in a national sample of US adults: NHANES III (1988-1994)," Social Science & Medicine, Elsevier, vol. 66(1), pages 72-87, January.
    3. Seeman, Teresa E. & Crimmins, Eileen & Huang, Mei-Hua & Singer, Burton & Bucur, Alexander & Gruenewald, Tara & Berkman, Lisa F. & Reuben, David B., 2004. "Cumulative biological risk and socio-economic differences in mortality: MacArthur Studies of Successful Aging," Social Science & Medicine, Elsevier, vol. 58(10), pages 1985-1997, May.
    4. Johnston, David W. & Propper, Carol & Shields, Michael A., 2009. "Comparing subjective and objective measures of health: Evidence from hypertension for the income/health gradient," Journal of Health Economics, Elsevier, vol. 28(3), pages 540-552, May.
    5. Lee, Jinkook & McGovern, Mark E. & Bloom, David E. & Arokiasamy, P. & Risbud, Arun & O’Brien, Jennifer & Kale, Varsha & Hu, Peifeng, 2015. "Education, gender, and state-level disparities in the health of older Indians: Evidence from biomarker data," Economics & Human Biology, Elsevier, vol. 19(C), pages 145-156.
    6. Jylhä, Marja, 2009. "What is self-rated health and why does it predict mortality? Towards a unified conceptual model," Social Science & Medicine, Elsevier, vol. 69(3), pages 307-316, August.
    7. Yong Zang & Suyu Liu & Ying Yuan, 2015. "Optimal marker-adaptive designs for targeted therapy based on imperfectly measured biomarkers," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 64(4), pages 635-650, August.
    8. Denise Doiron & Denzil G. Fiebig & Meliyanni Johar & Agne Suziedelyte, 2015. "Does self-assessed health measure health?," Applied Economics, Taylor & Francis Journals, vol. 47(2), pages 180-194, January.
    9. Pagan, Ricardo & Haro, Carmen Ordóñez de & Sánchez, Carlos Rivas, 2016. "Obesity, job satisfaction and disability at older ages in Europe," Economics & Human Biology, Elsevier, vol. 20(C), pages 42-54.
    10. Hernández-Quevedo, Cristina & Jones, Andrew M. & Rice, Nigel, 2008. "Persistence in health limitations: A European comparative analysis," Journal of Health Economics, Elsevier, vol. 27(6), pages 1472-1488, December.
    11. Kim, Hyuncheol Bryant & Lee, Suejin A. & Lim, Wilfredo, 2019. "Knowing is not half the battle: Impacts of information from the National Health Screening Program in Korea," Journal of Health Economics, Elsevier, vol. 65(C), pages 1-14.
    12. Zhao, Meng & Konishi, Yoshifumi & Glewwe, Paul, 2013. "Does information on health status lead to a healthier lifestyle? Evidence from China on the effect of hypertension diagnosis on food consumption," Journal of Health Economics, Elsevier, vol. 32(2), pages 367-385.
    13. Christensen, Vibeke Tornhøj & Datta Gupta, Nabanita, 2017. "Hearing loss and disability exit: Measurement issues and coping strategies," Economics & Human Biology, Elsevier, vol. 24(C), pages 80-91.
    14. Bound, John & Brown, Charles & Mathiowetz, Nancy, 2001. "Measurement error in survey data," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 59, pages 3705-3843, Elsevier.
    15. Walsemann, Katrina M. & Goosby, Bridget J. & Farr, Deeonna, 2016. "Life course SES and cardiovascular risk: Heterogeneity across race/ethnicity and gender," Social Science & Medicine, Elsevier, vol. 152(C), pages 147-155.
    16. Dana A. Glei & Noreen Goldman & Germán Rodríguez & Maxine Weinstein, 2014. "Beyond Self-Reports: Changes in Biomarkers as Predictors of Mortality," Population and Development Review, The Population Council, Inc., vol. 40(2), pages 331-360, June.
    17. Crossley, Thomas F. & Kennedy, Steven, 2002. "The reliability of self-assessed health status," Journal of Health Economics, Elsevier, vol. 21(4), pages 643-658, July.
    18. Jinkook Lee & McGovern, Mark E. & David E. Bloom & P. Arokiasamy & Arun Risbud & Jennifer O?Brien & Varsha Kale & Peifeng Hu, 2015. "Education, Gender, and State-Level Gradients in the Health of Older Indians: Evidence from Biomarker Data," Working Paper 228841, Harvard University OpenScholar.
    19. Vincenzo Carrieri & Andrew M. Jones, 2017. "The Income–Health Relationship ‘Beyond the Mean’: New Evidence from Biomarkers," Health Economics, John Wiley & Sons, Ltd., vol. 26(7), pages 937-956, July.
    20. Melanie Jones, 2016. "Disability and labor market outcomes," IZA World of Labor, Institute of Labor Economics (IZA), pages 253-253, April.
    21. Morciano, Marcello & Hancock, Ruth M. & Pudney, Stephen E., 2015. "Birth-cohort trends in older-age functional disability and their relationship with socio-economic status: Evidence from a pooling of repeated cross-sectional population-based studies for the UK," Social Science & Medicine, Elsevier, vol. 136, pages 1-9.
    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. 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. 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.
    3. Davillas, Apostolos & Pudney, Stephen, 2020. "Biomarkers, disability and health care demand," Economics & Human Biology, Elsevier, vol. 39(C).
    4. 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.
    5. Atkins, Rose & Turner, Alex James & Chandola, Tarani & Sutton, Matt, 2020. "Going beyond the mean in examining relationships of adolescent non-cognitive skills with health-related quality of life and biomarkers in later-life," Economics & Human Biology, Elsevier, vol. 39(C).
    6. Davillas, Apostolos & de Oliveira, Victor Hugo & Jones, Andrew M., 2023. "Is inconsistent reporting of self-assessed health persistent and systematic? Evidence from the UKHLS," Economics & Human Biology, Elsevier, vol. 49(C).
    7. 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).
    8. Davillas, Apostolos & Jones, Andrew M., 2020. "Regional inequalities in adiposity in England: distributional analysis of the contribution of individual-level characteristics and the small area obesogenic environment," Economics & Human Biology, Elsevier, vol. 38(C).

    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. Davillas, Apostolos & Pudney, Stephen, 2020. "Biomarkers, disability and health care demand," Economics & Human Biology, Elsevier, vol. 39(C).
    2. 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).
    3. 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.
    4. Chris Muris & Pedro Raposo & Sotiris Vandoros, 2020. "A dynamic ordered logit model with fixed effects," Papers 2008.05517, arXiv.org.
    5. Nesson, Erik T. & Robinson, Joshua J., 2019. "On the measurement of health and its effect on the measurement of health inequality," Economics & Human Biology, Elsevier, vol. 35(C), pages 207-221.
    6. Johnston, David W. & Lordan, Grace, 2012. "Discrimination makes me sick! An examination of the discrimination–health relationship," Journal of Health Economics, Elsevier, vol. 31(1), pages 99-111.
    7. Cheolmin Kang & Akira Kawamura & Haruko Noguchi, 2021. "Benefits of knowing own health status: effects of health check-ups on health behaviours and labour participation," Applied Economics Letters, Taylor & Francis Journals, vol. 28(11), pages 926-931, June.
    8. Federico Belotti & Joanna Kopinska & Alessandro Palma & Andrea Piano Mortari, 2022. "Health status and the Great Recession. Evidence from electronic health records," Health Economics, John Wiley & Sons, Ltd., vol. 31(8), pages 1770-1799, August.
    9. Gaggero, A. & Gil, J. & Jiménez-Rubio, D. & Zucchelli, E., 2021. "Health information and lifestyle behaviours: the impact of a diabetes diagnosis," Health, Econometrics and Data Group (HEDG) Working Papers 21/02, HEDG, c/o Department of Economics, University of York.
    10. van Ooijen, R. & Alessi, R. & Knoef, M., 2015. "Health status over the life cycle," Health, Econometrics and Data Group (HEDG) Working Papers 15/21, HEDG, c/o Department of Economics, University of York.
    11. Böckerman, Petri & Bryson, Alex & Viinikainen, Jutta & Hakulinen, Christian & Pulkki-Råback, Laura & Raitakari, Olli & Pehkonen, Jaakko, 2017. "Biomarkers and long-term labour market outcomes: The case of creatine," Journal of Economic Behavior & Organization, Elsevier, vol. 142(C), pages 259-274.
    12. Szanton, Sarah L. & Thorpe, Roland J. & Whitfield, Keith, 2010. "Life-course financial strain and health in African-Americans," Social Science & Medicine, Elsevier, vol. 71(2), pages 259-265, July.
    13. Alessio Gaggero & Joan Gil & Dolores Jiménez-Rubio & Eugenio Zucchelli, 2023. "Sick and depressed? The causal impact of a diabetes diagnosis on depression," Health Economics Review, Springer, vol. 13(1), pages 1-13, December.
    14. Davillas, Apostolos & de Oliveira, Victor Hugo & Jones, Andrew M., 2023. "Is inconsistent reporting of self-assessed health persistent and systematic? Evidence from the UKHLS," Economics & Human Biology, Elsevier, vol. 49(C).
    15. Joan Costa-Font & Frank Cowell & Xuezhu Shi & Joan Costa-i-Font, 2023. "Health Inequality and Health Insurance Coverage: The United States and China Compared," CESifo Working Paper Series 10807, CESifo.
    16. Gruenewald, Tara L. & Karlamangla, Arun S. & Hu, Perry & Stein-Merkin, Sharon & Crandall, Carolyn & Koretz, Brandon & Seeman, Teresa E., 2012. "History of socioeconomic disadvantage and allostatic load in later life," Social Science & Medicine, Elsevier, vol. 74(1), pages 75-83.
    17. Huynh, Kim P. & Jung, Juergen, 2015. "Subjective health expectations," Journal of Policy Modeling, Elsevier, vol. 37(4), pages 693-711.
    18. Schott, Whitney & Aurino, Elisabetta & Penny, Mary E. & Behrman, Jere R., 2019. "The double burden of malnutrition among youth: Trajectories and inequalities in four emerging economies," Economics & Human Biology, Elsevier, vol. 34(C), pages 80-91.
    19. Gaggero, Alessio, 2020. "The effect of type 2 diabetes diagnosis in the elderly," Economics & Human Biology, Elsevier, vol. 37(C).
    20. Bonsang, Eric & Caroli, Eve & Garrouste, Clémentine, 2021. "Gender heterogeneity in self-reported hypertension," Economics & Human Biology, Elsevier, vol. 43(C).

    More about this item

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:ese:iserwp:2018-11. 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: Jonathan Nears (email available below). General contact details of provider: https://edirc.repec.org/data/rcessuk.html .

    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.