pp 1–32 | Cite as

If My Blood Pressure Is High, Do I Take It to Heart? Behavioral Effects of Biomarker Collection in the Health and Retirement Study



Starting in 2006, respondents in the biennial U.S. Health and Retirement Study were asked to submit biomarkers every other wave and were notified of several results. Rates of undiagnosed high blood pressure and diabetes according to these biomarkers were 1.5 % and 0.7 %, respectively. An intent-to-treat analysis suggests that collection and notification had small effects on the average respondent and may have reduced health care utilization. Among respondents who received notification of potentially dangerous biomarker levels, subsequent rates of new diagnosis and associated pharmaceutical usage increased by 20 to 40 percentage points, an order of magnitude above baseline. High blood glucose A1C was associated with a 2.2 % drop in weight and an increase in exercise among respondents without a previous diagnosis of diabetes. Notifications appear also to have altered health behaviors by spouses, suggesting household responses to health maintenance. Biomarker collection seems to have altered circumstances for an interesting minority of HRS respondents.


Economics of aging Expectations Knowledge 

Supplementary material

13524_2018_650_MOESM1_ESM.pdf (640 kb)
ESM 1 (PDF 639 kb)


  1. Banks, J., Muriel, A., & Smith, J. P. (2010). Attrition and health in ageing studies: Evidence from ELSA and HRS. Longitudinal and Life Course Studies, 2, 101–126.Google Scholar
  2. Bindman, A. B., Grumbach, K., Osmond, D., Komaromy, M., Vranizan, K., Lurie, N., . . . Stewart, A. (1995). Preventable hospitalizations and access to health care. JAMA, 274, 305–311.Google Scholar
  3. Boozer, M. A., & Philipson, T. J. (2000). The impact of public testing for human immunodeficiency virus. Journal of Human Resources, 35, 419–446.CrossRefGoogle Scholar
  4. Case, A., & Deaton, A. (2015). Rising morbidity and mortality in midlife among white non-Hispanic Americans in the 21st century. Proceedings of the National Academy of Sciences, 112, 15078–15083.CrossRefGoogle Scholar
  5. Chetty, R., Stepner, M., Abraham, S., Lin, S., Scuderi, B., Turner, N., . . . Cutler, D. (2016). The association between income and life expectancy in the United States, 2001–2014. JAMA, 315, 1750–1766.Google Scholar
  6. Delavande, A., & Kohler, H.-P. (2012). The impact of HIV testing on subjective expectations and risky behavior in Malawi. Demography, 49, 1011–1036.CrossRefGoogle Scholar
  7. Falba, T. A., & Sindelar, J. L. (2008). Spousal concordance in health behavior change. Health Services Research, 43, 96–116.CrossRefGoogle Scholar
  8. Godlonton, S., & Thornton, R. L. (2013). Learning from others’ HIV testing: Updating beliefs and responding to risk. American Economic Review: Papers & Proceedings, 103, 439–444.CrossRefGoogle Scholar
  9. Goldman, D. P., & Smith, J. P. (2002). Can patient self-management help explain the SES health gradient? Proceedings of the National Academy of Sciences, 99, 10929–10934.CrossRefGoogle Scholar
  10. Goldman, N. (1993). Marriage selection and mortality patterns: Inferences and fallacies. Demography, 30, 189–208.CrossRefGoogle Scholar
  11. Gong, E. (2015). HIV testing and risky sexual behavior. Economic Journal, 125, 32–60.CrossRefGoogle Scholar
  12. Guner, N., Kulikova, Y., & Llull, J. (2014). Does marriage make you healthier? (IZA Discussion Paper No. 8633). Bonn, Germany: Institute for the Study of Labor.Google Scholar
  13. Gunton, J. E., Davies, L., Wilmshurst, E., Fulcher, G., & McElduff, A. (2002). Cigarette smoking affects glycemic control in diabetes. Diabetes Care, 25, 796–797.CrossRefGoogle Scholar
  14. Halpern-Manners, A., & Warren, J. R. (2012). Panel conditioning in longitudinal studies: Evidence from labor force items in the Current Population Survey. Demography, 49, 1499–1519.CrossRefGoogle Scholar
  15. Hu, Y., & Goldman, N. (1990). Mortality differentials by marital status: An international comparison. Demography, 27, 233–250.CrossRefGoogle Scholar
  16. Jiang, H. J., Russo, C. A., & Barrett, M. L. (2009). Nationwide frequency and costs of potentially preventable hospitalizations, 2006 (Healthcare Cost and Utilization Project Statistical Brief No. 72). Rockville, MD: U.S. Agency for Healthcare Research and Quality. Retrieved from
  17. Juster, F. T., & Suzman, R. (1995). An overview of the Health and Retirement Study. Journal of Human Resources, 30(Suppl.), S7–S56.Google Scholar
  18. Meara, E., & Skinner, J. (2015). Losing ground at midlife in America. Proceedings of the National Academy of Sciences, 112, 15006–15007.CrossRefGoogle Scholar
  19. Reczek, C., & Umberson, D. (2012). Gender, health behavior, and intimate relationships: Lesbian, gay, and straight contexts. Social Science & Medicine, 74, 1783–1790.CrossRefGoogle Scholar
  20. Rendall, M. S., Weden, M. M., Favreault, M. M., & Waldron, H. (2011). The protective effect of marriage for survival: A review and update. Demography, 48, 481–506.CrossRefGoogle Scholar
  21. Rosero-Bixby, L., & Dow, W. H. (2012). Predicting mortality with biomarkers: A population-based prospective cohort study for elderly Costa Ricans. Population Health Metrics, 10, 1–15. Scholar
  22. Sattar, N., Preiss, D., Murray, H. M., Welsh, P., Buckley, B. M., de Craen, A. J., . . . Ford, I. (2010). Statins and risk of incident diabetes: A collaborative meta-analysis of randomized statin trials. Lancet, 375, 735–742.Google Scholar
  23. Singleton, P. (2013). Health information and Social Security entitlements (Center for Policy Research Working Paper No. 164). Syracuse, NY: Syracuse University.Google Scholar
  24. Stevenson, B., & Wolfers, J. (2007). Marriage and divorce: Changes and their driving forces. Journal of Economic Perspectives, 21(2), 27–52.CrossRefGoogle Scholar
  25. Thornton, R. L. (2008). The demand for, and impact of, learning HIV status. American Economic Review, 98, 1829–1863.CrossRefGoogle Scholar
  26. Thornton, R. L. (2012). HIV testing, subjective beliefs and economic behavior. Journal of Development Economics, 99, 300–313.CrossRefGoogle Scholar
  27. Umberson, D., Crosnoe, R., & Reczek, C. (2010). Social relationships and health behavior across life course. Annual Review of Sociology, 36, 139–157.CrossRefGoogle Scholar
  28. Weinstein, M., Vaupel, J. W., & Wachter, K. W. (Eds.). (2007). Biosocial surveys. Washington, DC: National Academies Press.Google Scholar
  29. Weir, D. (2007). Elastic powers: The integration of biomarkers into the Health and Retirement Study. In M. Weinstein, J. W. Vaupel, & K. W. Wachter (Eds.), Biosocial surveys (pp. 78–95). Washington, DC: National Academies Press.Google Scholar
  30. Weir, D. R. (2010, April). Socio-economic status and mortality: Perceptions and outcomes. Paper presented at the annual meeting of the Population Association of America, Dallas, TX.Google Scholar

Copyright information

© Population Association of America 2018

Authors and Affiliations

  1. 1.Data Science Education ProgramUniversity of CaliforniaBerkeleyUSA
  2. 2.Berkeley Population CenterUniversity of CaliforniaBerkeleyUSA

Personalised recommendations