National surveys are used to capture US health trends and set clinical guidelines, yet the sampling frame often includes those in noninstitutional households, potentially missing those most vulnerable for poor health. Declining response rates in national surveys also represent a challenge, and existing inputs to survey weights have limitations. We compared mortality rates between those who respond to surveys and the general population over time.
Survey respondents from 20 waves of the National Health Interview Survey from 1990 to 2009 who have been linked to death records through 31 December 2011 were included. For each cohort in the survey, we estimated their mortality rates along with that cohort’s mortality rate in the census population using vital statistics records, and differences were examined using Poisson models.
In all years, survey respondents had lower mortality rates compared with the general population when data were both weighted and unweighted. Among men, survey respondents in the weighted sample had 0.86 (95% confidence interval = 0.853, 0.868) times the mortality rate of the general population (among women, RR = 0.887; 95% confidence interval, 0.879, 0.895). Differences in mortality are evident along all points of the life course. Differences have remained relatively stable over time.
Survey respondents have lower death rates than the general US population, suggesting that they are a systematically healthier source population. Incorporating nonhousehold samples and revised weighting strategies to account for sample frame exclusion and nonresponse may allow for more rigorous estimation of the US population’s health.
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From the aDepartment of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY; bDepartment of Psychiatry, Columbia University Medical Center, New York, NY; and cMRC/CSO Social and Public Health Sciences Unit, University of Glasgow, Glasgow, United Kingdom.
Submitted December 7, 2016; accepted October 19, 2017.
The authors report no conflicts of interest.
Data and code for replication: Full SAS and Stata code for these analyses are included as an online supplement, and data files are available by request to the corresponding author.
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Correspondence: Katherine M. Keyes, Columbia University Department of Epidemiology, Mailman School of Public Health, 722 West 168th Street, Suite 503, New York, NY 10032. E-mail: email@example.com.
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