Development of a decision model to identify workers at risk of long-term disability in the construction industry.
OBJECTIVE
This study presents a decision model that predicts long-term disability among construction workers.


METHODS
Risk factors were identified in two cohort studies among construction workers and evaluated in validation samples of smaller cohort studies among Dutch construction workers. The risk estimates (odds ratios) were used in a logistic regression model to calculate the probability of long-term disability in the next 4 years for a particular construction worker, subject to a specific combination of risk factors. The a priori probability was set equal to the overall long-term disability risk among the youngest construction workers (< 30 years) with a relatively short exposure history.


RESULTS
According to literature findings, the risk estimate for work ability was set with the odds ratio at 2.0 for good work ability, 5.0 for moderate work ability, and 10.0 for bad work ability. Age-dependent risks were set at odds ratios of 1.5, 2.0, and 3.0 for the age groups of 30-34 years, 35-44 years, and 45-54 years, respectively. A sickness absence period of > or = 3 months had an odds ratio of 2.0, and severe musculoskeletal complaints had an odds ratio of 3.0. Since the number of construction workers older than 55 years was rather small and heavily biased by a healthy worker effect, it was decided to limit the applicability of the decision model to workers aged 20-55 years. The decision model used four risk factors and predicted a 40-fold difference in disability risk between construction workers with all four risk factors present (0.79) and those without any risk factor (0.02).


CONCLUSIONS
The decision model presented the combined effect of different risk factors on the risk of an individual worker becoming disabled within 4 years. Evaluation studies will need to demonstrate whether the application of this decision model is helpful in identifying workers at risk for long-term disability and will facilitate appropriate intervention at the individual level.
Burdorf
Alex
A
Department of Public Health, Erasmus MC, University Medical Center Rotterdam, PO Box 1738, NL-3000 DR Rotterdam, The Netherlands. a.burdorf@erasmusmc.nl
Frings-Dresen
Monique H W
MH
van Duivenbooden
Cor
C
Elders
Lex A M
LA
D000328:Adult; D003661:Decision Support Techniques; D006233:Persons with Disabilities; D005160:Facility Design and Construction; D005544:Forecasting; D006801:Humans; D016015:Logistic Models; D008875:Middle Aged; D018570:Risk Assessment; D012307:Risk Factors