Multiple systems estimation for studying over-coverage and its heterogeneity in population registers

Abstract

Abstract The growing necessity for evidence-based policy built on rigorous research has never been greater. However, the ability of researchers to provide such evidence is invariably tied to the availability of high-quality data. Bias stemming from over-coverage in official population registers, i.e. resident individuals whose death or emigration is not registered, can lead to serious implications for policymaking and research. Using Swedish Population registers and the statistical framework of multiple systems estimation, we estimate the extent of over-coverage among foreign-born individuals’ resident in Sweden for the period 2003–2016. Our study reveals that, although over-coverage is low during this period in Sweden, we observed a distinct heterogeneity in over-coverage across various sub-populations, suggesting significant variations among them. We also evaluated the implications of omitting each of the considered registers on real data and simulated data, and highlight the potential bias introduced when the omitted register interacts with the included registers. Our paper underscores the broad applicability of multiple systems estimation in addressing and mitigating bias from over-coverage in scenarios involving incomplete but overlapping population registers.