Skip to main content

Advertisement

SpringerLink
Log in

The Significance of Education for Mortality Compression in the United States

  • Published:
Demography

Abstract

Recent studies of old-age mortality trends assess whether longevity improvements over time are linked to increasing compression of mortality at advanced ages. The historical backdrop of these studies is the long-term improvement in a population’s socioeconomic resources that fueled longevity gains. We extend this line of inquiry by examining whether socioeconomic differences in longevity within a population are accompanied by old-age mortality compression. Specifically, we document educational differences in longevity and mortality compression for older men and women in the United States. Drawing on the fundamental cause of disease framework, we hypothesize that both longevity and compression increase with higher levels of education and that women with the highest levels of education will exhibit the greatest degree of longevity and compression. Results based on the Health and Retirement Study and the National Health Interview Survey Linked Mortality File confirm a strong educational gradient in both longevity and mortality compression. We also find that mortality is more compressed within educational groups among women than men. The results suggest that educational attainment in the United States maximizes life chances by delaying the biological aging process.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Notes

  1. Véron and Rohrbasser (2003) provide a detailed account of the approach taken by Lexis (1878).

  2. Lexis maintained that premature and normal deaths are etiologically distinct, but recognized that in practice, distinguishing between premature and normal deaths in the transitional region of the curve is difficult.

  3. We also estimated models that included all racial/ethnic groups (available on request). Although our substantive conclusions remain unchanged, in the models including all racial/ethnic groups, the modal ages of death were 0.09–0.81 years lower, and the SD above the mode were 0.08–0.34 higher, than those presented in Table 3.

  4. We also estimated a series of logit models to examine the sensitivity of our results to the model specification. The results from the logit and Gompertz models were virtually identical and did not alter our substantive conclusions. For example, depending on the data set and gender, the logit model produced modes that were 0.15–0.36 years higher and SD above the mode that were 0.07–0.18 years lower than those shown in Table 3.

  5. Eakin and Witten (1995) suggested normalizing age and the probability of survival to better facilitate interpretations over time and between different populations. Given that the current analyses are cross-sectional, this is technically not necessary. We do it, nonetheless, in the event that other researchers would like to compare their results with those presented herein. The results are interpreted the same regardless of whether normalization is performed.

References

  • Adler, N. E., & Stewart, J. (2010). Health disparities across the lifespan: Meaning, methods, and mechanisms. Annals of the New York Academy of Sciences, 1186, 5–23.

    Article  Google Scholar 

  • Baker, D. P., Leon, J., Smith Greenaway, E. G., Collins, J., & Movit, M. (2011). The education effect on population health: A reassessment. Population and Development Review, 37, 307–332.

    Article  Google Scholar 

  • Bongaarts, J. (2005). Long-range trends in adult mortality: Models and projection methods. Demography, 42, 23–49.

    Article  Google Scholar 

  • Bongaarts, J., & Feeney, G. (2002). How long do we live? Population Development Review, 28, 13–29.

    Article  Google Scholar 

  • Bongaarts, J., & Feeney, G. (2003). Estimating mean lifetime. Proceedings of the National Academy of Sciences, 100, 13127–13133.

    Article  Google Scholar 

  • Cai, L., Hayward, M. D., Saito, Y., Lubitz, J., Hagedorn, A., & Crimmins, E. (2010). Estimation of multi-state life table functions and their variability from complex survey data using the SPACE Program. Demographic Research, 22, 129–158. doi:10.4054/DemRes.2010.22.6

    Article  Google Scholar 

  • Canudas-Romo, V. (2008). The modal age at death and the shifting mortality hypothesis. Demographic Research, 19, article 30, 1179–1204. doi:10.4054/DemRes.2008.19.30

  • Canudas-Romo, V. (2010). Three measures of longevity: Time trends and record values. Demography, 47, 299–312.

    Article  Google Scholar 

  • Chang, V. W., & Lauderdale, D. S. (2009). Fundamental cause theory, technological innovation, and health disparities: The case of cholesterol in the era of statins. Journal of Health and Social Behavior, 50, 245–260.

    Article  Google Scholar 

  • Cheung, S. L. K., & Robine, J. M. (2007). Increase in common longevity and the compression of mortality: The case of Japan. Population Studies, 61, 85–97.

    Article  Google Scholar 

  • Cheung, S. L. K., Robine, J. M., & Caselli, G. (2008). The use of cohort and period data to explore changes in adult longevity in low mortality countries. Genus, LXIV(1–2), 101–129.

    Google Scholar 

  • Cheung, S. L. K., Robine, J. M., Tu, E. J. C., & Caselli, G. (2005). Three dimensions of the survival curve: Horizontalization, verticalization, and longevity extension. Demography, 42, 243–258.

    Article  Google Scholar 

  • Chiang, C. L. (1960). A stochastic study of the life table and its applications: I. Probability distributions of the biometric functions. Biometrics, 16, 618–635.

    Article  Google Scholar 

  • Crimmins, E. M., Hayward, M. D., & Seeman, T. E. (2004). Race/ethnicity, socioeconomic status and health. In N. Anderson, R. Bulato, & B. Cohen (Eds.), Critical perspectives on racial and ethnic differences in health in late life (pp. 310–352). Washington, DC: The National Academies.

    Google Scholar 

  • Crimmins, E. M., Hayward, M. D., Hagedorn, A., Saito, Y., & Brouard, N. (2009a). Change in disability-free life expectancy for Americans 70 years old and older. Demography, 46, 627–646.

    Article  Google Scholar 

  • Crimmins, E. M., Kim, J. K., & Seeman, T. E. (2009b). Poverty and biological risk: The earlier “aging” of the poor. Journal of Gerontology Series A: Biological and Medical Sciences, 64A, 286–292.

    Article  Google Scholar 

  • Crimmins, E. M., & Seeman, T. E. (2004). Integrating biology into the study of health disparities. Population and Development Review, 30, 89–107.

    Google Scholar 

  • Denney, J. T., Rogers, R. G., Hummer, R. A., & Pampel, F. C. (2010). Education inequality in morality: The age and gender specific mediating effects of cigarette smoking. Social Science Research, 39, 662–673.

    Article  Google Scholar 

  • Eakin, T., & Witten, M. (1995). How square is the survival curve of a given species? Experimental Gerontology, 30, 33–64.

    Article  Google Scholar 

  • Easterlin, R. A. (1998). Growth triumphant: The twenty-first century in historical perspective. Ann Arbor: University of Michigan Press.

    Google Scholar 

  • Edwards, R. D., & Tuljapurkar, S. (2005). Inequality in life spans and a new perspective on mortality convergence across industrialized countries. Population and Development Review, 31, 645–674.

    Article  Google Scholar 

  • Elo, I. T. (2009). Social class differentials in health and mortality: Patterns and explanations in comparative perspective. Annual Review of Sociology, 35, 553–572.

    Article  Google Scholar 

  • Engelman, M., Canudas-Romo, V., & Agree, E. M. (2010). The implications of increased survivorship for mortality variation in aging populations. Population and Development Review, 36, 511–539.

    Article  Google Scholar 

  • Fogel, R. W. (2004). The escape from hunger and premature death, 1700–2100: Europe, America, and the Third World. Cambridge, UK: Cambridge University Press.

    Book  Google Scholar 

  • Fogel, R. W., & Costa, D. L. (1997). A theory of technophysio evolution, with some implications for forecasting population, health care costs, and pension costs. Demography, 34, 49–66.

    Article  Google Scholar 

  • Fries, J. F. (1980). Aging, natural death, and the compression of morbidity. New England Journal of Medicine, 303, 130–135.

    Article  Google Scholar 

  • Fries, J. F. (1983). The compression of morbidity. Milbank Memorial Fund Quarterly, 61, 397–419.

    Article  Google Scholar 

  • Geronimus, A. T., Hicken, M., Keene, D., & Bound, J. (2006). “Weathering” and age patterns of allostatic load scores among blacks and whites in the United States. American Journal of Public Health, 96, 826–833.

    Article  Google Scholar 

  • Glied, S., & Lleras-Muney, A. (2009). Technological innovation and inequality in health. Demography, 45, 741–761.

    Article  Google Scholar 

  • Go, C. G., Brustrom, J. E., Lynch, M. F., & Aldwin, C. M. (1995). Ethnic trends in survival curves and mortality. The Gerontologist, 35, 318–326.

    Article  Google Scholar 

  • Hatch, S. L., & Dohrenwend, B. P. (2007). Distribution of traumatic and other stressful events by race/ethnicity, gender, SES, and age: A review of the research. American Journal of Community Psychology, 40, 313–332.

    Article  Google Scholar 

  • Hayward, M. D., Crimmins, E., Miles, T., & Yu, Y. (2000). The significance of socioeconomic status in explaining the racial gap in chronic health conditions. American Sociological Review, 65, 910–930.

    Article  Google Scholar 

  • Hayward, M. D., Crimmins, E. M., & Zhang, Z. (2006). Consequences of educational change for the burden of chronic health problems in the population. In A. H. Gauther, C. Y. C. Chu, & S. Tuljapurkar (Eds.), Allocating public and private resources across generations (pp. 227–242). New York: Springer.

    Chapter  Google Scholar 

  • Hidajat, M. M., Hayward, M. D., & Saito, Y. (2007). Indonesia’s social capacity for population health: The educational gap in active life expectancy. Population Research and Policy Review, 26, 219–234.

    Article  Google Scholar 

  • HRS Data, Version H. (2008). Produced by the RAND Center for the Study of Aging, with funding from the National Institute on Aging and the Social Security Administration. Santa Monica, CA: RAND.

    Google Scholar 

  • Human Mortality Database. (n.d.). University of California, Berkeley (USA), and Max Planck Institute for Demographic Research (Germany). [Data downloaded on 12-09-2009]. Retrieved from www.mortality.org or www.humanmortality.de

  • Hummer, R. A., & Lariscy, J. T. (2011). Educational attainment and adult mortality. In R. G. Rogers & E. M. Crimmins (Eds.), International handbook of adult mortality (pp. 241–261). New York: Springer.

    Chapter  Google Scholar 

  • Ingram, D. D., Lochner, K. A., & Cox, C. S. (2008). Mortality experience of the 1986–2000 National Health Interview Survey linked mortality files participants (Vital Health Statistics 2(147)). Hyattsville, MD: National Center for Health Statistics.

    Google Scholar 

  • Kannisto, V. (2001). Mode and dispersion of length of life. Population: An English Selection, 13, 159–172.

    Google Scholar 

  • Lexis, W. (1878). Sur la durée normale de la vie humaine et sur la théorie de la stabilité des rapports statistiques [On the normal human lifespan and on the theory of the stability of the statistical ratios]. Annales de Démographie Internationale, 2, 447–460.

    Google Scholar 

  • Lin, C. C., Rogot, E., Johnson, N. J., Sorlie, P. D., & Arias, E. (2003). A further study of life expectancy by socioeconomic factors in the National Longitudinal Mortality Study. Ethnicity and Disease, 13, 240–247.

    Google Scholar 

  • Link, B. G. (2008). Epidemiological sociology and the social shaping of population health. Journal of Health and Social Behavior, 49, 367–384.

    Article  Google Scholar 

  • Link, B. G., & Phelan, J. (1995). Social conditions as a fundamental cause of disease. Journal of Health and Social Behavior, Extra Issue, 80–94.

  • Lochner, K., Hummer, R. A., Bartee, S., Wheatcroft, G., & Cox, C. (2008). The public-use National Health Interview Survey linked mortality files: Methods of re-identification risk avoidance and comparative analysis. American Journal of Epidemiology, 168, 336–344.

    Article  Google Scholar 

  • Lynch, S. M., & Brown, J. S. (2001). Reconsidering mortality compression and deceleration: An alternative model of mortality rates. Demography, 38, 79–95.

    Article  Google Scholar 

  • Lynch, S. M., Brown, J. S., & Harmsen, K. G. (2003). Black-white differences in mortality compression and deceleration and the mortality crossover reconsidered. Research on Aging, 25, 456–483.

    Article  Google Scholar 

  • McEwen, B. S. (1998). Protective and damaging effects of stress mediators. New England Journal of Medicine, 338, 171–178.

    Article  Google Scholar 

  • McEwen, B. S., & Stellar, E. (1993). Stress and the individual mechanisms leading to disease. Archives of Internal Medicine, 153, 2093–2101.

    Article  Google Scholar 

  • Mirowsky, J., & Ross, C. E. (2003). Education, social status and health. New York: Aldine de Guyter.

    Google Scholar 

  • Montez, J. K., Hayward, M. D., Brown, D. C., & Hummer, R. A. (2009). Why is the educational gradient of mortality steeper for men? Journal of Gerontology Series B: Psychological Sciences and Social Sciences, 64B, 625–634.

    Article  Google Scholar 

  • Montez, J. K., Hummer, R. A., & Hayward, M. D. (2012). Educational attainment and adult mortality in the United States: A systematic analysis of functional form. Demography, 49, 315–336.

    Article  Google Scholar 

  • National Center for Health Statistics (NCHS). (1992). Vital Statistics of the United States, 1989 (Vol. II, Section 6). Washington, DC: Public Health Service.

    Google Scholar 

  • Ouellette, N., & Bourbeau, R. (2011). Changes in the age-at-death distribution in four low mortality countries: A nonparametric approach. Demographic Research, 25, article 19, 595–628. doi:10.4054/DemRes.2011.25.19

  • Pampel, F. C., Krueger, P. M., & Denney, J. T. (2010). Socioeconomic disparities in health behaviors. Annual Review of Sociology, 36, 349–370.

    Article  Google Scholar 

  • Phelan, J. C., Link, B. G., Diez-Roux, A., Kawachi, I., & Levin, B. (2004). “Fundamental causes” of social inequalities in mortality: A test of the theory. Journal of Health and Social Behavior, 45, 265–285.

    Article  Google Scholar 

  • Preston, S. H., & Wang, H. (2006). Sex mortality differences in the United States: The role of cohort smoking patterns. Demography, 43, 631–646.

    Article  Google Scholar 

  • Robine, J. M. (2001). Redefining the stages of the epidemiological transition by a study of the dispersion of life spans: The case of France. Population: An English Selection, 13, 173–194.

    Google Scholar 

  • Rostron, B. (2010, April). Socioeconomic differences in education reporting and their effect on estimates of life expectancy by educational attainment in the U.S. Paper presented at the annual meeting of the Population Association of America, Dallas, TX.

  • Seeman, T., Epel, E., Gruenewald, T., Karlamangla, A., & McEwen, B. S. (2010). Socio-economic differentials in peripheral biology: Cumulative allostatic load. Annals of the New York Academy of Sciences, 1186, 223–239.

    Article  Google Scholar 

  • Seeman, T., Merkin, S., Crimmins, E., Koretz, B., Charette, S., & Karlamangla, A. (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, 66, 72–87.

    Article  Google Scholar 

  • Servais, M. A. (2008). Overview of HRS public data files for cross-sectional and longitudinal analysis. Retrieved from http://hrsonline.isr.umich.edu/docs/dmgt/OverviewofHRSPublicData.pdf

  • Shkolnikov, V. M., Andreev, E. E., & Begun, A. Z. (2003). Gini coefficient as a life table function: Computation from discrete data, decomposition of differences and empirical examples. Demographic Research, 8, article 11, 305–358. doi:10.4054/DemRes.2003.8.11

  • Smits, J., & Monden, C. (2009). Length of life inequality around the globe. Social Science & Medicine, 68, 1114–1123.

    Article  Google Scholar 

  • Teachman, J. D., & Hayward, M. D. (1993). Interpreting hazard rate models. Sociological Methods and Research, 21, 340–371.

    Article  Google Scholar 

  • Thatcher, A. R., Cheung, S. L. K., Horiuchi, S., & Robine, J.-M. (2010). The compression of deaths above the mode. Demographic Research, 22, article 17, 505–538. doi:10.4054/DemRes.2010.22.17

  • Thoits, P. A. (2010). Stress and health: Major findings and policy implications. Journal of Health and Social Behavior, 51, S41–S53.

    Article  Google Scholar 

  • Turner, R. J., & Avison, W. R. (2003). Status variations in stress exposure: Implications for the interpretation of research on race, socioeconomic status, and gender. Journal of Health and Social Behavior, 44, 488–505.

    Article  Google Scholar 

  • van Raalte, A. A., Kunst, A. E., Deboosere, P., Leinsalu, M., Lundberg, O., Martikainen, P., . . . Mackenbach, J. P. (2011). More variation in lifespan in lower educated groups: Evidence from 10 European countries. International Journal of Epidemiology, 40, 1703–1714.

    Article  Google Scholar 

  • Véron, J., & Rohrbasser, J.-M. (2003). Wilhelm Lexis: The normal length of life as an expression of the “nature of things.” Population: An English Selection, 58, 303–322.

    Google Scholar 

  • Wilmoth, J. R. (2000). Demography of longevity: Past, present, and future trends. Experimental Gerontology, 35, 1111–1129.

    Article  Google Scholar 

  • Wilmoth, J. W., & Horiuchi, S. (1999). Rectangularization revisited: Variability of age at death within human populations. Demography, 36, 475–495.

    Article  Google Scholar 

  • Zajacova, A., & Hummer, R. A. (2009). Gender differences in education effects on all-cause mortality for white and black adults in the United States. Social Science & Medicine, 69, 529–537.

    Article  Google Scholar 

Download references

Acknowledgments

This research was supported by a research grant from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (1 R01-HD053696, PI Robert A. Hummer) and by infrastructure (5 R24 HD042849) and training (5 T32 HD007081) grants awarded to the Population Research Center at the University of Texas at Austin by the Eunice Kennedy Shriver National Institute of Health and Child Development. The authors would like to thank the four anonymous reviewers and members of the health and mortality research group at the UT Population Research Center for their helpful suggestions and comments.

Author information

Authors and Affiliations

  1. Population Research Center and Department of Sociology, University of Texas at Austin, 110 Inner Campus Drive Stop G1800, Austin, TX, 78712-1699, USA

    Dustin C. Brown

  2. Population Research Center and Department of Sociology, University of Texas at Austin, Austin, TX, 78712, USA

    Mark D. Hayward, Robert A. Hummer & Chi-Tsun Chiu

  3. Harvard Center for Population and Development Studies, Harvard University, Cambridge, MA, USA

    Jennifer Karas Montez

  4. Institute of Public and International Affairs, University of Utah, Salt Lake City, UT, USA

    Mira M. Hidajat

Authors
  1. Dustin C. Brown
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mark D. Hayward
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jennifer Karas Montez
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Robert A. Hummer
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chi-Tsun Chiu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Mira M. Hidajat
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dustin C. Brown.

Appendix

Appendix

Table 4 Characteristics of the analytic samples in the HRS and NHIS-LMF
Full size table

Rights and permissions

Reprints and permissions

About this article

Cite this article

Brown, D.C., Hayward, M.D., Montez, J.K. et al. The Significance of Education for Mortality Compression in the United States. Demography 49, 819–840 (2012). https://doi.org/10.1007/s13524-012-0104-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13524-012-0104-1

Keywords

Search

Navigation