Missing laboratory results data in electronic health databases: implications for monitoring diabetes risk

Abstract

Aim: Laboratory test (lab) results may be useful to detect incident diabetes in electronic health record and claims-based studies.

Research design & methods: Using the Mini-Sentinel distributed database, we assessed the value of lab results added to diagnosis codes and dispensing claims to identify incident diabetes.

Results: Inclusion of lab results increased the number of diabetes outcomes identified by 21%. In settings where capture of lab results was relatively complete, the absence of lab results was associated with implausibly low rates of the outcome.

Conclusion: Lab results can increase sensitivity of algorithms for detecting diabetes, and missing lab results are associated with much lower rates of diabetes ascertainment regardless of algorithm. Patterns of missing lab results may identify ascertainment bias.

Keywords: ascertainment bias; cohort studies; endocrinology; metabolism.