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


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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.

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