Measuring Disease Prevalence in Surveys: A Comparison of Diabetes Self-Reports, Biomarkers, and Linked Insurance Claims

Reliable measures of disease prevalence are crucial for answering many empirical research questions in health economics, including the causal structures underlying the correlation between health and wealth. Much of the existing literature on the health-wealth nexus relies on survey data, for example those from the Health and Retirement Study (HRS). Such survey data typically contain self-reported measures of disease prevalence, which are known to suffer from reporting error. Two more recent developments—the collection of biomarkers and the linkage with data from administrative sources such as insurance claims—promise more reliable measures of disease prevalence. In this paper, we systematically compare these three measures of disease prevalence. This work extends an existing literature that compares survey self‐reports and biomarker‐based measures of disease prevalence.