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¹Health Management Research Center, Division of Kinesiology, University of Michigan, Ann Arbor, USA.

American Journal of Health Promotion : AJHP

|August 15, 2001

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Corporate wellness programs show significant trends in health risk reduction and cost savings. Investing in employee health, especially for those with multiple risk factors, yields the greatest employer returns and improved productivity.

Area of Science:

Health Management Research
Occupational Health
Data Analytics in Healthcare

Background:

A large-scale database of over 2,000,000 individuals, including 7-18 years of data for corporate employees, was analyzed.
The Health Management Research Center at the University of Michigan compiled data on health risks, medical costs, and productivity.

Purpose of the Study:

To identify emerging trends in employee health risks, medical costs, and productivity.
To evaluate the effectiveness of corporate wellness programs and identify optimal strategies for employers.

Main Methods:

Analysis of longitudinal data from a large employee database.
Identification and tracking of health risk factors, participation rates in health programs, medical costs, and productivity measures.

Related Experiment Videos

Exploration of data mining techniques for future analysis.

Main Results:

Typical annual participation in Health Risk Appraisal is 20-30%, with high long-term engagement.
Excess risk factors account for 21-31% of medical costs, with costs increasing alongside risk factors and age.
Programs focusing on both high-risk individuals and maintaining healthy employees offer the greatest employer return; cost savings are highest with multiple program participations.
Health risk factors correlate with increased absenteeism and lower productivity.

Conclusions:

Targeting employees with multiple risk factors and low perceived health status may yield the best results.
A 'corporate wellness score' combining risk factors and participation can benchmark employer health.
Future breakthroughs in analysis may arise from advanced data techniques and longitudinal datasets.