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Deaths without denominators: using a matched dataset to study mortality patterns in the United States

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  • Alexander, Monica

Abstract

To understand national trends in mortality over time, it is important to study differences by demographic, socioeconomic and geographic characteristics. One issue with studying mortality inequalities, particularly by socioeconomic status, is that there are few micro-level data sources available that link an individual's SES with their eventual age and date of death. In this paper, a new dataset for studying mortality disparities and changes over time in the United States is presented. The dataset, termed 'CenSoc', uses two large-scale datasets: the full-count 1940 Census to obtain demographic, socioeconomic and geographic information; and that is linked to the Social Security Deaths Masterfile (SSDM) to obtain mortality information. This paper also develops mortality estimation methods to better use the 'deaths without denominators' information contained in CenSoc. Bayesian hierarchical methods are presented to estimate truncated death distributions over age and cohort, allowing for prior information in mortality trends to be incorporated and estimates of life expectancy and associated uncertainty to be produced.

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  • Alexander, Monica, 2018. "Deaths without denominators: using a matched dataset to study mortality patterns in the United States," SocArXiv q79ye_v1, Center for Open Science.
    • Handle: RePEc:osf:socarx:q79ye_v1
    DOI: 10.31219/osf.io/q79ye_v1
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