Int Arch Occup Environ Health (2011) 84:705–712 materials handling, awkward back postures in which the back is bent or twisted, static work postures, repetitive movements, and bending and/or twisting of the upper body. For all physical loads, a four-point scale was used with rat- ing ‘seldom or never’, ‘now and then’, ‘often’, and ‘always’ during a normal workday. The answers ‘often’ and ‘always’ were classiWed as high exposure (Elders and Burdorf 2001). The psychosocial workload was measured according to the demand-control model by Karasek et al. (1981, 1998). The three dimensions job control (5 items), skill discretion (3 items), and work demands (5 items) were assessed using an abbreviated version of the original questionnaire (Cron- bach’s alpha = 0.76) (Pelfrene et al. 2001). Questions on job control concerned workers’ inXuence on the planning of tasks, ability to interrupt work if necessary, and whether or not they had a say on completion of deadlines. Skill discre- tion covered creativity, varied work, and required skills and abilities. Work demands related to excessive work, working hard, working fast, insuYcient time to complete the work, and conXicting demands. For each question, a four-point scale was used with ratings ‘seldom or never’, ‘now and then’, ‘often’, and ‘always’ during a normal workday. The sum score was calculated for each dimension separately, and workers with a median sum score or higher were regarded as exposed to the psychosocial risk factor (Alavinia et al. 2009). Statistical analysis Descriptive statistics were used to describe the charac- teristics of the study population. Since the magnitude of productivity loss was not normally distributed, the dichot- omous variable productivity loss at work (yes/no) was used as dependent variable in the logistic regression anal- ysis to explore the association between productivity loss at work and independent variables individual characteris- tics, work ability, work ability dimensions, psychosocial work demands, and physical workload. The odds ratio (OR) was estimated as measure of association with corre- sponding 95% conWdence intervals (95% CI). In the Wrst step of the analysis, univariate associations were evalu- ated. Subsequently, all variables in the univariate analyses with p < 0.05 were investigated in a multivariate analysis using a forward technique with signiWcance level p < 0.05. Population attributable fractions (PAFs) were calculated for less than good work ability, using the formula PAF = Pe (OR ¡ 1)/(1 + Pe(OR ¡ 1)), whereby Pe is the prevalence in the study population (Hennekens et al. 1987). We were interested in the potential additive interaction between a decreased work ability and poor working condi- tions on the presence of productivity loss. Therefore, inter- actions between work ability and work-related factors were 707 estimated for work-related factors which remained statisti- cally signiWcant at p < 0.05 in the multivariate model. Inter- action was considered to be present when the combined association of both factors (decreased work ability as well as poor working conditions) was larger than the sum of the independent associations of decreased work ability and poor working conditions. Interaction terms were deWned by product terms of dichotomized variables, resulting in four exposure categories. Subjects with a good or excellent work ability and good working conditions were deWned as refer- ence category. The relative excess risk due to interaction (RERI) was estimated as measure for interaction with con- Wdence levels based on covariances in line with the delta method of Hosmer and Lemeshow (1992), using the fol- lowing formula: RERI = RR (Decreased WAI and poor working condition) ¡ RR (Decreased WAI and good work- ing condition) ¡ RR (Good WAI and poor working condition) + 1 (Andersson et al. 2005). In order to calculate RERI from a logistic regression analysis, we assumed that the odds ratios could be used as a fair approximation of relative risks. RERI can be interpreted as a measure of departure from additivity adjusted for confounders, in which a RERI of zero means no departure from additivity. The additive interaction is considered statistically signiW- cant when zero is outside the 95% conWdence interval (CI). All analyses were carried out with the Statistical Package for Social Sciences version 15.0 for Windows (1999). Results About 44% of the subjects reported productivity loss at work during the last workday, with an average loss of 11.4% compared with a regular workday (Table 1). This indicates an average loss of 0.9 h on an 8-h workday. The mean age of the study population was about 44 years, rang- ing from 18 to 68 years. The distribution of excellent, good, moderate, and poor work ability was 32.8, 47.4, 16.4, and 3.4%, respectively. Work-related factors were moderate interrelated with Pearson correlations ranging from ¡0.10 to 0.39 for psychosocial work characteristics, whereas Pearson correlations between physical work factors ranged from ¡0.11 to 0.52, and Pearson correlations between psy- chosocial and physical work factors ranged from 0.03 to 0.26. The odds ratios and 95% conWdence intervals (CI) for the likelihood of productivity loss were 2.03 (1.85–2.22), 3.50 (3.10–3.95), and 5.54 (4.37–7.03) for a good, moder- ate, and poor work ability, compared with an excellent work ability (reference group). The population attributable fraction for productivity loss at work due to less than good work ability was 10%. Asso- ciations between decreased work ability and productivity 123