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BMC Health Serv Res. 2014 Dec 18;14:597. doi: 10.1186/s12913-014-0597-y.

Work productivity loss from depression: evidence from an employer survey.

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Department of Mental Health Law and Policy, College of Behavioral and Community Studies, University of South Florida, 13301 Bruce B. Downs Boulevard, Tampa, FL, 33612, USA.
School of Aging Studies, College of Behavioral and Community Studies, University of South Florida, 13301 Bruce B. Downs Boulevard, Tampa, FL, 33612, USA.
Biostatistics, Institute for Health Research, Kaiser Permanente, 10065 E. Harvard Avenue, Suite 300, Denver, CO, 80231, USA.
Department of Biostatistics and Informatics, School of Public Health, University of Colorado Denver, Denver, USA.



National working groups identify the need for return on investment research conducted from the purchaser perspective; however, the field has not developed standardized methods for measuring the basic components of return on investment, including costing out the value of work productivity loss due to illness. Recent literature is divided on whether the most commonly used method underestimates or overestimates this loss. The goal of this manuscript is to characterize between and within variation in the cost of work productivity loss from illness estimated by the most commonly used method and its two refinements.


One senior health benefit specialist from each of 325 companies employing 100+ workers completed a cross-sectional survey describing their company size, industry and policies/practices regarding work loss which allowed the research team to derive the variables needed to estimate work productivity loss from illness using three methods. Compensation estimates were derived by multiplying lost work hours from presenteeism and absenteeism by wage/fringe. Disruption correction adjusted this estimate to account for co-worker disruption, while friction correction accounted for labor substitution. The analysis compared bootstrapped means and medians between and within these three methods.


The average company realized an annual $617 (SD = $75) per capita loss from depression by compensation methods and a $649 (SD = $78) loss by disruption correction, compared to a $316 (SD = $58) loss by friction correction (p < .0001). Agreement across estimates was 0.92 (95% CI 0.90, 0.93).


Although the methods identify similar companies with high costs from lost productivity, friction correction reduces the size of compensation estimates of productivity loss by one half. In analyzing the potential consequences of method selection for the dissemination of interventions to employers, intervention developers are encouraged to include friction methods in their estimate of the economic value of interventions designed to improve absenteeism and presenteeism. Business leaders in industries where labor substitution is common are encouraged to seek friction corrected estimates of return on investment. Health policy analysts are encouraged to target the dissemination of productivity enhancing interventions to employers with high losses rather than all employers.




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