Comparability of Mortality Estimates from Social Surveys and Vital Statistics Data in the United States

  • Dustin C. BrownEmail author
  • Joseph T. Lariscy
  • Lucie Kalousová
Original Research


Social surveys prospectively linked with death records provide invaluable opportunities for the study of the relationship between social and economic circumstances and mortality. Although survey-linked mortality files play a prominent role in U.S. health disparities research, it is unclear how well mortality estimates from these datasets align with one another and whether they are comparable with U.S. vital statistics data. We conduct the first study that systematically compares mortality estimates from several widely used survey-linked mortality files and U.S. vital statistics data. Our results show that mortality rates and life expectancies from the National Health Interview Survey Linked Mortality Files, Health and Retirement Study, Americans’ Changing Lives study, and U.S. vital statistics data are similar. Mortality rates are slightly lower and life expectancies are slightly higher in these linked datasets relative to vital statistics data. Compared with vital statistics and other survey-linked datasets, General Social Survey-National Death Index life expectancy estimates are much lower at younger adult ages and much higher at older adult ages. Cox proportional hazard models regressing all-cause mortality risk on age, gender, race, educational attainment, and marital status conceal the issues with the General Social Survey-National Death Index that are observed in our comparison of absolute measures of mortality risk. We provide recommendations for researchers who use survey-linked mortality files.


Mortality Vital statistics Record linkage Survey-linked mortality files National Death Index 



An earlier draft of this paper was presented at the 2016 meeting of the Population Association of America meeting, Washington, DC. This research received support from NICHD Center (R24 HD041028) and NIA Training (T32 AG000221) grants to the Population Studies Center at the University of Michigan and an NIA Training (T32 AG000139) grant to the Duke Population Research Institute at Duke University. We thank the Americans’ Changing Lives working group at the University of Michigan, Audrey Dorelien, Benjamin Walker, and three anonymous PRPR reviewers for helpful comments. We also thank the Human Mortality Database, Minnesota Population Center and State Health Access Data Assistance Center, National Center for Health Statistics, and National Opinion Research Center for providing the datasets used in this analysis.


  1. Andreev, E. M., & Shkolnikov, V. M. (2010). Spreadsheet for calculation of confidence limits for any life table or healthy-life table quantity. Max Planck Institute for Demographic Research Technical Report.Google Scholar
  2. Arias, E., Eschbach, K., Schauman, W. S., Backlund, E. L., & Sorlie, P. D. (2010). The Hispanic mortality advantage and ethnic misclassification on U.S. death certificates. American Journal of Public Health, 100(Suppl 1), S171–S177.CrossRefGoogle Scholar
  3. Black, D. A., Hsu, Y. C., Sanders, S. G., Schofield, L. S., & Taylor, L. J. (2017). The Methuselah effect: The pernicious impact of unreported deaths on old-age mortality estimates. Demography, 54(6), 2001–2024.CrossRefGoogle Scholar
  4. Blewett, L. A., Rivera Drew, J. A., Griffin, R., King, M. L., & Williams, K. C. W. (2018). IPUMS Health Surveys: National Health Interview Survey, version 6.3. Retrieved from
  5. Brown, D. C., Hayward, M. D., Montez, J. K., Hummer, R. A., Chiu, C. T., & Hidajat, M. M. (2012). The significance of education for mortality compression in the United States. Demography, 49(3), 819–840.CrossRefGoogle Scholar
  6. Bugliari, D., Campbell, N., Chan, C., Hayden, O., Hurd, M., Main, R., et al. (2016). RAND HRS data documentation, version P. Santa Monica: RAND Center for the Study of Aging.Google Scholar
  7. Chapman, B. P., Fiscella, K., Kawachi, I., Duberstein, P., & Muennig, P. (2013). Emotion suppression and mortality risk over a 12-year follow-up. Journal of Psychosomatic Research, 75(4), 381–385.CrossRefGoogle Scholar
  8. Crimmins, E. M., Hayward, M. D., & Seeman, T. E. (2004). Race/ethnicity, socioeconomic status, and health. In N. B. Anderson, R. A. Bulatao, & B. Cohen (Eds.), Critical perspectives on racial and ethnic differences in health in late life (pp. 310–352). Washington, DC: National Academies Press.Google Scholar
  9. Curb, J. D., Ford, C. E., Pressel, S., Palmer, M., Babcock, C., & Hawkins, C. M. (1985). Ascertainment of vital status through the National Death Index and the Social Security Administration. American Journal of Epidemiology, 121(5), 754–766.CrossRefGoogle Scholar
  10. Dahlhamer, J. M., & Cox, C. S. (2007). Respondent consent to link survey data with administrative records: Results from a split-ballot field test with the 2007 National Health Interview Survey. Proceedings of the Federal Committee on Statistical Methodology Research Conference. Washington, DC.Google Scholar
  11. Harron, K., Goldstein, H., & Dibben, C. (Eds.). (2015). Methodological developments in data linkage. West Sussex: Wiley.Google Scholar
  12. Hayward, M. D., & Gorman, B. K. (2004). The long arm of childhood: The influence of early-life social conditions on men’s mortality. Demography, 41(1), 87–107.CrossRefGoogle Scholar
  13. Hogan, H., Cantwell, P. J., Devine, J., Mule, V. T., & Velkoff, V. (2013). Quality and the 2010 Census. Population Research and Policy Review, 32(5), 637–662.CrossRefGoogle Scholar
  14. House, J. S. (2014). Americans’ Changing Lives: Waves I, II, III, IV, and V, 1986, 1989, 1994, 2002, and 2011. [Computer file] ICPSR04690-v7. Ann Arbor: Inter-university Consortium for Political and Social Research.Google Scholar
  15. Human Mortality Database. (2018). University of California, Berkeley (USA), and Max Planck Institute for Demographic Research (Germany).
  16. Hummer, R. A., Rogers, R. G., Nam, C. B., & Ellison, C. G. (1999). Religious involvement and U.S. adult mortality. Demography, 36(2), 273–285.CrossRefGoogle Scholar
  17. Ingram, D. D., Lochner, K. A., & Cox, C. S. (2008). Mortality experience of the 1986–2000 National Health Interview Survey Linked Mortality Files participants. Hyattsville: National Center for Health Statistics.Google Scholar
  18. Kim, J., Shin, H. C., Rosen, Z., Kang, J. H., Dykema, J., & Muennig, P. (2015). Trends and correlates of consenting to provide Social Security numbers: Longitudinal findings from the General Social Survey (1993–2010). Field Methods, 27(4), 348–362.CrossRefGoogle Scholar
  19. King, N. B., Harper, S., & Young, M. E. (2012). Use of relative and absolute effect measures in reporting health inequalities: Structured review. BMJ, 345(e5774), 1–8.Google Scholar
  20. Lariscy, J. T. (2011). Differential record linkage by Hispanic ethnicity and age in linked mortality studies: Implications for the epidemiologic paradox. Journal of Aging and Health, 23(8), 1263–1284.CrossRefGoogle Scholar
  21. Lariscy, J. T. (2017). Black-white disparities in adult mortality: Implications of differential record linkage for understanding the mortality crossover. Population Research and Policy Review, 36(1), 137–156.CrossRefGoogle Scholar
  22. Lariscy, J. T., Hummer, R. A., & Hayward, M. D. (2015). Hispanic older adult mortality in the United States: New estimates and an assessment of factors shaping the Hispanic paradox. Demography, 52(1), 1–14.CrossRefGoogle Scholar
  23. Lawrence, E. M., Rogers, R. G., & Wadsworth, T. (2015). Happiness and longevity in the United States. Social Science and Medicine, 145, 115–119.CrossRefGoogle Scholar
  24. Lee, Y., Muennig, P., Kawachi, I., & Hatzenbuehler, M. L. (2015). Effects of racial prejudice on the health of communities: A multilevel survival analysis. American Journal of Public Health, 105(11), 2349–2355.CrossRefGoogle Scholar
  25. Morey, B. N., Gee, G. C., Muennig, P., & Hatzenbuehler, M. L. (2018). Community-level prejudice and mortality among immigrant groups. Social Science and Medicine, 199, 56–66.CrossRefGoogle Scholar
  26. Muennig, P., Johnson, G., Kim, J., Smith, T. W., & Rosen, Z. (2011). The General Social Survey-National Death Index: An innovative new dataset for the social sciences. BMC Research Notes, 4(385), 1–6.Google Scholar
  27. Muennig, P., Rosen, Z., & Johnson, G. (2013). Do the psychosocial risks associated with television viewing increase mortality? Evidence from the 2008 General Social Survey-National Death Index dataset. Annals of Epidemiology, 23(6), 355–360.CrossRefGoogle Scholar
  28. Muennig, P., Rosen, Z., Johnson, G., & Smith, T. W. (2016). Codebook for the 1978–2010 General Social Survey linked to mortality data through 12/31/2014 via the National Death Index. Retrieved from
  29. National Center for Health Statistics. (2018). The linkage of National Center for Health Statistics survey data to the National Death Index—2015 Linked Mortality File (LMF): Methodology overview and analytic considerations. Hyattsville, MD. Retrieved from
  30. National Center for Health Statistics, Office of Analysis and Epidemiology. (2009). National Health Interview Survey (19862004) Linked Mortality Files, mortality follow-up through 2006: Matching methodology. Hyattsville, MD. Retrieved from
  31. National Center for Health Statistics, Office of Analysis and Epidemiology. (2013). NCHS 2011 Linked Mortality Files matching methodology. Hyattsville.Google Scholar
  32. National Opinion Research Center. (2016). General Social Survey-National Death Index 19782010. Retrieved from
  33. Pabayo, R., Kawachi, I., & Muennig, P. (2015). Political party affiliation, political ideology, and mortality. Journal of Epidemiology and Community Health, 69(5), 423–431.CrossRefGoogle Scholar
  34. Pablos-Méndez, A. (1994). Mortality among Hispanics. Journal of the American Medical Association, 271(16), 1237.CrossRefGoogle Scholar
  35. Palloni, A., & Arias, E. (2004). Paradox lost: Explaining the Hispanic adult mortality advantage. Demography, 41(3), 385–415.CrossRefGoogle Scholar
  36. Preston, S. H., Elo, I. T., Rosenwaike, I., & Hill, M. (1996). African-American mortality at older ages: Results of a matching study. Demography, 33(2), 193–209.CrossRefGoogle Scholar
  37. Preston, S. H., & Taubman, P. (1994). Socioeconomic differences in adult mortality and health status. In L. G. Martin & S. H. Preston (Eds.), Demography of aging. Washington, DC: National Academies Press.Google Scholar
  38. Rogers, R. G., Carrigan, J. A., & Kovar, M. G. (1997). Comparing mortality estimates based on different administrative records. Population Research and Policy Review, 16(13), 213–224.CrossRefGoogle Scholar
  39. Rogers, R. G., Everett, B. G., Zajacova, A., & Hummer, R. A. (2010). Educational degrees and adult mortality risk in the United States. Biodemography and Social Biology, 56(1), 80–99.CrossRefGoogle Scholar
  40. Rogers, R. G., Hummer, R. A., & Nam, C. B. (2000). Living and dying in the USA: Behavioral, health, and social differentials of adult mortality. San Diego: Academic Press.Google Scholar
  41. Soldo, B. J., Hurd, M. D., Rodgers, W. L., & Wallace, R. B. (1997). Assets and health dynamics among the oldest old: An overview of the AHEAD study. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 52B(Special Issue), 1–20.CrossRefGoogle Scholar
  42. Sonnega, A., Faul, J. D., Ofstedal, M. B., Langa, K. M., Phillips, J. W. R., & Weir, D. R. (2014). Cohort profile: The Health and Retirement Study (HRS). International Journal of Epidemiology, 43(2), 576–585.CrossRefGoogle Scholar
  43. StataCorp. (2013). Stata statistical software: Release 13. College Station: StataCorp LP.Google Scholar
  44. Stewart, Q. T., Cobb, R. J., & Keith, V. M. (2018). The color of death: Race, observed skin tone, and all-cause mortality in the United States. Ethnicity & Disease. Scholar
  45. Teachman, J. D., & Hayward, M. D. (1993). Interpreting hazard rate models. Sociological Methods & Research, 21(3), 340–371.CrossRefGoogle Scholar
  46. Warner, D. F., & Hayward, M. D. (2006). Early-life origins of the race gap in men’s mortality. Journal of Health and Social Behavior, 47(3), 209–226.CrossRefGoogle Scholar
  47. Weir, D. R. (2016). Validating mortality ascertainment in the Health and Retirement Study. Retrieved from

Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  • Dustin C. Brown
    • 1
    Email author
  • Joseph T. Lariscy
    • 2
  • Lucie Kalousová
    • 3
  1. 1.Department of Sociology and Social Science Research CenterMississippi State UniversityMississippi StateUSA
  2. 2.Department of SociologyUniversity of MemphisMemphisUSA
  3. 3.Nuffield CollegeUniversity of OxfordOxfordUK

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