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.
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We focus on potential sources of systematic bias. Sampling error will cause mortality estimates to vary between SLMFs when survey samples are different (Crimmins et al. 2004). However, sampling error should have modest effects on mortality estimate comparability because it is randomly distributed in surveys that contain large probability samples which are designed to be nationally representative of the non-institutionalized U.S. adult population. The survey samples contained in most SLMFs, including the ones analyzed herein, meet these criteria.
Respondents’ birth month is not available publicly in the 2000–2010 GSS, but the survey contains respondents’ zodiac sign. This information is used to randomly impute missing birth months in the 2000–2010 GSS. Zodiac sign is missing when birth month is missing in the 1986–1998 GSS. Births are assumed to occur mid-year when both birth month and zodiac sign are missing.
Table 6 in the Appendix displays mortality rates (per 100,000) at additional ages from U.S. vital statistics, NHIS-LMF, ACL, HRS, and the GSS-NDI for women, men, and both genders combined.
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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.
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Brown, D.C., Lariscy, J.T. & Kalousová, L. Comparability of Mortality Estimates from Social Surveys and Vital Statistics Data in the United States. Popul Res Policy Rev 38, 371–401 (2019). https://doi.org/10.1007/s11113-018-9505-1
- Vital statistics
- Record linkage
- Survey-linked mortality files
- National Death Index