Using routinely collected data to augment the management of health and productivity loss.
OBJECTIVE
To test models of productivity loss developed from data collected using a health risk appraisal (HRA) designed to examine health in the broader context of work, mental well-being, and the demands of organizational and family life.


METHODS
Secondary analyses of a data extract provided by the HRA's developer. These analyses focused on 17,821 respondents whose version of the HRA included the Work Limitation Questionnaire. Structural equation techniques were used to estimate a series of models featuring 38 measures and a four-step hypothesized sequence.


RESULTS
The tests confirmed the presence of two distinct but interrelated components driven by health issues--Presenteeism (impaired performance at work) and Absenteeism (time away from work)--posited to describe productivity loss. The tests also documented the predictive power of eight categories of measures in accounting for the phenomenon. Preeminent among these predictors was a heterogeneous set of measures encompassing current and future aspects of Health. But measures from seven other categories--Work-Life Balance, Personal Life Impact, Stress, Financial Concerns, and Job, Employee, and Company Characteristics--also made significant contributions. Combined, their unique contribution was five times that of Health alone.


CONCLUSIONS
This case study illustrates how data routinely captured via an instrument that is an example of a class of self-reports surveys increasingly being used to address a variety of workforce issues can be tapped to describe and predict productivity loss. The results confirm the key role that Health plays in determining the phenomenon. They also affirm the advisability of incorporating into interventions undertaken to reduce productivity loss an orientation that is paralleled by the recent emergence of the use of quality of life measures in provider settings. This orientation is predicated on the need to take into better account other contextual factors that exert considerable influences through as well as above and beyond Health.
Allen
Harris
H
Yale University School of Medicine, New Haven, CT, USA. harris-allen@msn.com
D000041:Absenteeism; D000328:Adult; D016208:Databases, Factual; D017598:Efficiency, Organizational / Q000706:statistics & numerical data*; D004651:Employment / Q000145:classification; D005260:Female; D006305:Health Status Indicators; D006801:Humans; D007588:Job Satisfaction; D008297:Male; D008875:Middle Aged; D017146:Models, Organizational; D015233:Models, Statistical; D012044:Regression Analysis; D018582:Sick Leave / Q000706:statistics & numerical data