Black–White Disparities in Adult Mortality: Implications of Differential Record Linkage for Understanding the Mortality Crossover
Mortality rates among black individuals exceed those of white individuals throughout much of the life course. The black–white disparity in mortality rates is widest in young adulthood, and then rates converge with increasing age until a crossover occurs at about age 85 years, after which black older adults exhibit a lower mortality rate relative to white older adults. Data quality issues in survey-linked mortality studies may hinder accurate estimation of this disparity and may even be responsible for the observed black–white mortality crossover, especially if the linkage of surveys to death records during mortality follow-up is less accurate for black older adults. This study assesses black–white differences in the linkage of the 1986–2009 National Health Interview Survey to the National Death Index through 2011 and the implications of racial/ethnic differences in record linkage for mortality disparity estimates. Match class and match score (i.e., indicators of linkage quality) differ by race/ethnicity, with black adults exhibiting less certain matches than white adults in all age groups. The magnitude of the black–white mortality disparity varies with alternative linkage scenarios, but convergence and crossover continue to be observed in each case. Beyond black–white differences in linkage quality, this study also identifies declines over time in linkage quality and even eligibility for linkage among all adults. Although linkage quality is lower among black adults than white adults, differential record linkage does not account for the black–white mortality crossover.
KeywordsMortality Race/ethnicity Record linkage Mortality crossover National Health Interview Survey
An earlier draft of this article was presented at the 2013 Southern Demographic Association meeting in Montgomery, Alabama. Research for this article was supported by training grants from the National Institute of Child Health and Human Development (5 T32 HD007081) and the National Institute on Aging (5 T32 AG000139). Patricia Barnes, Jennifer Parker, and Donna Miller of the National Center for Health Statistics assisted in acquiring a special request file of the National Health Interview Survey Linked Mortality File data. Analyses were conducted in the Texas Federal Statistical Research Data Center (RDC) in College Station, Texas, Triangle RDC in Durham, North Carolina, and Missouri RDC in Columbia, Missouri. The research in this article was conducted while the author was a Special Sworn Status researcher of the U.S. Census Bureau at the Center for Economic Studies. Research results and conclusions expressed are those of the author and do not necessarily reflect the views of the Census Bureau. This article has been screened to ensure that no confidential data are revealed.
- Anderson, M. J., & Fienberg, S. E. (1999). Who counts? The politics of census-taking in contemporary America. New York: Russell Sage Foundation.Google Scholar
- Arias, E., Schauman, W. S., Eschbach, K., Sorlie, P. D., & Backlund, E. L. (2008). The validity of race and Hispanic origin reporting on death certificates in the United States. Vital Health Stat, 2(148), 1–23.Google Scholar
- Bates, N., Dahlhamer, J., & Singer, E. (2008). Privacy concerns, too busy, or just not interested: Using doorstep concerns to predict survey nonresponse. J Off Stat, 24(4), 591–612.Google Scholar
- 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. In Proceedings of the federal committee on statistical methodology research conference. Washington, DC.Google Scholar
- Dahlhamer, J. M., Meyer, P. S., & Pleis, J. R. (2006). Questions people don’t like to answer: Wealth and social security numbers. In Proceedings of the American Statistical Association Joint Statistical Meetings. Seattle, WA.Google Scholar
- Data Linkage Team. (2015). Comparative analysis of the NHIS public-use and restricted-use linked mortality files: 2015 public-use data release. Hyattsville: National Center for Health Statistics. http://www.cdc.gov/nchs/data/datalinkage/nhis-public-restr-2011lmf-2-3-15.pdf. Accessed 26 May 2016.
- Elo, I. T., & Preston, S. H. (1997). Racial and ethnic differences in mortality at older ages. In L. G. Martin & B. J. Soldo (Eds.), Racial and ethnic differences in the health of older Americans. Washington, DC: National Academy Press.Google Scholar
- Harron, K., Goldstein, H., & Dibben, C. (Eds.). (2016). Methodological developments in data linkage. West Sussex: Wiley.Google Scholar
- Jackson, J. S., Hudson, D., Kershaw, K., Mezuk, B., Rafferty, J., & Tuttle, K. K. (2011). Discrimination, chronic stress, and mortality among black Americans: A life course framework. In R. G. Rogers & E. M. Crimmins (Eds.), International handbook of adult mortality. New York: Springer.Google Scholar
- Kochanek, K. D., Murphy, S. L., Xu, J., & Tejada-Vera, B. (2016). Deaths: Final data for 2014. Natl Vital Stat Rep, 65(4), 1.Google Scholar
- Manton, K. G., & Stallard, E. (1981). Methods for evaluating the heterogeneity of aging processes in human populations using vital statistics data: Explaining the black/white mortality crossover by a model of mortality selection. Hum Biol, 53(1), 47–67.Google Scholar
- Miller, E. A. (2012). What’s in a name? Accounting for naming conventions in NCHS data linkages. In Paper presented at the federal committee on statistical methodology (FCSM) statistical policy seminar, Washington, DC. http://www.copafs.org/UserFiles/file/seminars/2012FCSM/Session07FCSM2012Miller.pptx. Accessed 27 Feb 2013.
- Miller, E. A., McCarty, F., & Parker, J. D. (2015). Differential linkage by race/ethnicity and availability of a social security number in the linkage with the national death index. In Paper presented at the National Conference on Health Statistics, Bethesda, MD. http://www.cdc.gov/nchs/ppt/nchs2015/Ingram_Tuesday_SalonD_BB1_2nd.pdf. Accessed 12 Nov 2015.
- Nam, C. B. (1995). Another look at mortality crossovers. Soc Biol, 42(1–2), 133–142.Google Scholar
- National Center for Health Statistics. (2009). NHANES I epidemiologic follow-up study (NHEFS) calibration sample for NDI matching methodology. Hyattsville, MD. http://www.cdc.gov/nchs/data/datalinkage/mort_calibration_study.pdf. Accessed 27 Sept 2013.
- National Center for Health Statistics, Office of Analysis and Epidemiology. (2009). National health interview survey (1986–2004) linked mortality files, mortality follow-up through 2006: Matching methodology. Hyattsville, MD. http://www.cdc.gov/nchs/data/datalinkage/matching_methodology_nhis_final.pdf. Accessed 3 Dec 2010.
- National Center for Health Statistics, Office of Analysis and Epidemiology. (2013). NCHS 2011 linked mortality files matching methodology. Hyattsville, MD. http://www.cdc.gov/nchs/data/datalinkage/2011_linked_mortality_file_matching_methodology.pdf. Accessed 7 Apr 2016.
- National Research Council. (2013). Nonresponse in social science surveys: a research agenda. Washington, DC: National Academies Press.Google Scholar
- Pettit, B. (2012). Invisible men: Mass incarceration and the myth of black progress. New York: Russell Sage Foundation.Google Scholar
- Research Triangle Institute. (2012). SUDAAN language manual, volumes 1 and 2, release 11.0. Research Triangle Park: Research Triangle Institute.Google Scholar
- 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
- Rosenberg, H. M., Maurer, J. D., Sorlie, P. D., Johnson, N. J., MacDorman, M. F., Hoyert, D. L., et al. (1999). Quality of death rates by race and Hispanic origin: A summary of current research, 1999. Vital Health Stat, 2(128), 1–3.Google Scholar
- SAS Institute. (2011). The SAS system for windows. Release 9.2. Cary: SAS Institute Inc.Google Scholar