Maryam J. Tabrizi

Promising practices in employer health and productivity management efforts: findings from a benchmarking study.

Objective: To identify key success factors related to employer-based health and productivity management (HPM) programs. Methods: Data regarding promising practices in HPM were gathered via literature review, discussions with subject matter experts, online inventory, and site visits. Results: Promising practices in HPM include 1) integrating HPM programs into the organization’s operations; 2) simultaneously addressing individual, environmental, policy, and cultural factors affecting health and productivity; 3) targeting several health issues; 4) tailoring programs to address specific needs; 5) attaining high participation; 6) rigorously evaluating programs; and 7) communicating successful outcomes to key stakeholders. Conclusion: Increased efforts should be directed at disseminating the experiences of promising practices. However, more research is needed in this area, so that additional public and private funding is made available for applied research in “real-life” business settings. Finally, employers should be provided effective tools and resources to support their HPM efforts.

How accurate are self-reports? Analysis of self-reported health care utilization and absence when compared with administrative data.

Objective: To determine the accuracy of self-reported health care utilization and absence reported on health risk assessments against administrative claims and human resource records. Methods: Self-reported values of health care utilization and absenteeism were analyzed for concordance to administrative claims values. Percent agreement, Pearson’s correlations, and multivariate logistic regression models examined the level of agreement and characteristics of participants with concordance. Results: Self-report and administrative data showed greater concordance for monthly compared with yearly health care utilization metrics. Percent agreement ranged from 30% to 99% with annual doctor visits having the lowest percent agreement. Younger people, males, those with higher education, and healthier individuals more accurately reported their health care utilization and absenteeism. Conclusions: Self-reported health care utilization and absenteeism may be used as a proxy when medical claims and administrative data are unavailable, particularly for shorter recall periods.

Using a return-on-investment estimation model to evaluate outcomes from an obesity management worksite health promotion program

Objective: Certain modifiable risk factors lead to higher health care costs and reduced worker productivity. A predictive return-on-investment (ROI) model was applied to an obesity management intervention to demonstrate the use of econometric modeling in establishing financial justification for worksite health promotion. Methods: Self-reported risk factors (n = 890) were analyzed using &khgr;2 and t test methods. Changes in risk factors, demographics, and financial measures comprised the model inputs that determined medical and productivity savings. Results: Over 1 year, 7 of 10 health risks decreased. Of total projected savings (

The relationship between 11 health risks and medical and productivity costs for a large employer.

311,755), 59% were attributed to reduced health care expenditures (

Health improvement from a worksite health promotion private-public partnership.

184,582) and 41% resulted from productivity improvements (

Employer integration of health promotion and health protection programs

127,173), a

The Predictive Validity of the HERO Scorecard in Determining Future Health Care Cost and Risk Trends

1.17 to

Estimating the Return on Investment From a Health Risk Management Program Offered to Small Colorado-Based Employers

1.00 ROI. Conclusions: Using an ROI model to project program savings is a practical way to provide financial justification for investment in worksite health promotion when risk reduction data are available.

Measuring the Prevalence and Incidence of Low Back Pain Disorders Among American Workers in the Aerospace and Defense Industry.

Objective: To evaluate the relationship between modifiable health risks, and health and productivity related expenditures and predict cost savings from improvements in the health risk profile of a large US employer. Methods: Information was collected on 11 modifiable health risks for active employees who completed a health assessment and enrolled in a noncapitated health plan. These risks were related to employer medical care costs and employee productivity. Multivariate analyses were performed to estimate costs associated with high risk, as well as potential savings from reducing risk prevalence among employees. Results: Health risks with the greatest impact on total medical care costs included obesity, high blood pressure, high blood glucose, high triglycerides, and inadequate exercise. Conclusions: Modifiable health risks are associated with higher employer costs. Targeted programs that address these risks are expected to yield substantial savings.

Current state and future directions for organizational health scorecards

Objective: To examine the impact of the New York City Department of Health and Mental Hygiene’s Wellness at Work program on health risks of employees from 10 New York City organizations at 26 worksites. Methods: Employer sites were matched and assigned to receive either moderate or high intensity health promotion interventions. Changes from time 1 to time 3 in employees’ risk status on 12 health risks were examined using &khgr;2 and t tests for a cohort group (N = 930). Comparisons between moderate and high intensity groups used multivariate methods, controlling for confounders. Results: From time 1 to time 3, both moderate and high intensity sites demonstrated significant risk reductions. Nevertheless, comparisons by intervention intensity did not reveal significant differences between treatment conditions. Conclusions: Private-public partnerships to promote employee health in the workplace have the potential to reduce health risks that are precursors to chronic disease.