Factors Affecting the Frequency of Value-Focused Health Activities and Policies by Employers

A method for imputing the impact of health problems on at-work performance and productivity from available health data

OBJECTIVE

To develop a method for imputing the work performance and productivity impact of illness and treatment from available data.

METHODS

Using data from four studies of musculoskeletal disorders (eg, osteoarthritis) and pain, we modeled the relationships between scores from the Work Limitations Questionnaire (WLQ), a validated measure of health-related limitations in work performance and productivity, and a series of validated health measures (eg, a pain scale).

RESULTS

The 15 health and 5 WLQ variables were significantly associated in 115 of 116 study-specific models (P < 0.05).

CONCLUSION

Fifteen commonly collected health variables may be used to predict WLQ impact (increase or decrease) for samples with musculoskeletal pain and physical impairments to help fill information gaps.

Use of a normal impairment factor in quantifying avoidable productivity loss because of poor health

OBJECTIVE

Growing evidence demonstrates a relationship between excess health risk and preventable productivity loss. There is a need to quantify how much lost productivity is avoidable through employer-sponsored health management interventions. This study introduced the Normal Impairment Factor (NIF) to recognize the amount of productivity loss that cannot be mitigated through health management interventions.

METHODS

A health assessment questionnaire was administered to 772,750 employees, representing 106 employers within five industry sectors. Researchers used multivariate regression procedures to examine the association between preventable health risks and self-reported productivity loss.

RESULTS

Back pain, mental well being, and stress risk were the strongest predictors of on-the-job productivity loss. A strong association was also detected between the number of health risks and productivity loss ranging from 3.4% for those at lowest risk (the NIF group) to 24.0% loss for those at risk for eight risks.

CONCLUSIONS

This study demonstrated the utility of the NIF in estimating the level of productivity loss that cannot be regained through health management interventions.