Using Self-Report and Adverse Event Measures to Track Health’s Impact on Productivity in Known Groups

Abstract

The use of survey data to measure and monitor health and productivity differences between groups is an issue of increasing importance. This article examines the capacity of productivity self-reports (derived from surveys) and adverse event measures (derived from administrative sources) to differentiate groups with a priori known characteristics. A replication strategy is used to test the contributions that productivity self-reports make, alone as well as above and beyond measures of adverse events, to the discrimination of 5 pairs of groups classified by clinical, job type, and demographic criteria. These tests are conducted on representative samples of the active, largely blue-collar employee population at International Truck and Engine Corporation. The results show that both productivity self-reports and adverse event measures differentiate and track known groups. Even in the presence of highly significant effects from adverse event measures, self-reports improve the assessment of productivity. We conclude that: 1) although the joint use of self-reports and adverse event measures is the better approach, practitioners can use self-reports with the expectation that this method will track group differences in health and productivity when adverse event measures are not available; and 2) survey self-reports make unique and independent contributions when adverse events measures are used.