Full details of the randomised controlled trial as intention-to-treat have been published previously [1, 2]. In brief, the trial was conducted to compare the differences in sickness absence during the 12 month follow-up between the targeted OH intervention program and usual care for employees at high risk of sickness absence. The subjects came from one corporation in Finland. Forty-nine% of them were employed in construction industry (civil engineering, building contracting, technical building services, and building materials industry). The remaining 51% were employed in repair, service and maintenance of buildings, industrial installations, or communications networks. The Helsinki University Research Ethics Board approved the study, and it was performed according to the Declaration of Helsinki.
At the beginning of the study health risk appraisals [5] were sent to a cohort of 3,115 employees with permanent employment and age between 18 and 60 years. The proposed study design, implications of the trial, and alternative options were explained in the cover letter. The letter also emphasised that taking part in the trial is voluntary, and employees will get the best treatment available and full attention of the occupational physician even if they do not want to participate. In addition, it was explained that participants are free to withdraw from the trial at any point, and it would not prejudice their treatment.
The eligible employees who had given their informed consent (n = 1,341; 88% males; 62% blue-collar) were divided into three study groups ‘Low Risk’ (n = 386), ‘Intermediate Risk’ (n = 537) and ‘High Risk’ (n = 418) of sickness absence on the basis of the health risk appraisal based on a priori defined interpretation cut-off limits. Subjects who reported problems with future working ability, pain, impairment due to musculoskeletal problems, insomnia or insufficient sleep, frequent stress or fatigue, or had a high depression score, were classified into the ‘High Risk’ group. Table 1 shows the items used and their cut-off limits for identifying the employees at high risk of sickness absence. Employees were included in the study regardless of their sickness absence status at the time of performing the health risk appraisal, but employees who had been granted a disability pension (part-time or full-time) were excluded.
Table 1 ‘High risk’: findings in one or more of these topics
Of the employees who met the trial eligibility criteria, 209 were randomised to the targeted OH intervention and 209 to control group receiving usual care at occupational health. 382 observations were eligible for the subgroup analyses.
Interventions
The employees in the targeted OH intervention group attended the occupational health program operated by their own occupational nurses and physicians. They received personal feedback of their survey results and an invitation to a consultation at their local occupational health service (OHS). The main purpose of the consultation was the construction of an action plan, and if appropriate, referral to a further consultation by a medical specialist or psychologist. The visits had a predefined content including the procedures how to further diagnose diseases and rules for further actions according to the process description. The occupational nurse wrote for each employee in the intervention group a personal file, which included information about the treatments and health advice received at the OHS, the referrals to further evaluation or interventions, the considerations of OHS professionals that no further actions were needed, and the refusals of some employees to take further action. Attendance (yes/no) in the consultation was used as the indicator variable “adherence as intended” in the present study. Altogether 142 (68%) subjects participated in the OH intervention. Forty-eight occupational health centres were involved in the intervention program.
The employees in control group could consult their occupational nurse or physician on request, but they did not get feedback of their health survey results and were not invited for a consultation.
Outcome measures
Effectiveness was measured by the difference in the change in sickness absence days between the two treatment arms, i.e., the difference in sickness absence days between follow-up and prior year was calculated. Employee-specific sickness absence data, without medical diagnosis, were obtained from the employer’s records, covering two consecutive periods from 1st October 2003 to 30th September 2004 (prior year) and from 1st October 2004 to 30th September 2005 (follow-up).
Statistical methods
Interaction tests are regarded as the most efficient tests to identify modifying factors for the effectiveness of treatment [11]. Analyses were carried out by using ordinary least squares (OLS) regression using both change scores and analysis of covariance (ANCOVA) methods. In the change score analysis we used the difference of sickness absence days during the year preceding the intervention and during the follow-up year as the dependent variable (“Gain Score”). This is a simple way to control relationship between two consecutive year measures [12]. For ANCOVA models sickness absence days during follow-up year was the dependent variable and sickness absence days during preceding year was included as a covariate.
Models were written in general form as GainScore
i
= β0 + β1 × Intervention1i
+ β2 × Modifier
ji
+ β3 × (Intervention1i
× Modifier
ji
) + μ
i
, where i = 1,…,382 and j = {Modifier1, Modifier2….} for all the pre-specified effect modifiers and the mediator.
Treatment effect of intervention with modifier was calculated from the β
1 × Intervention1i
+ β3 × (Intervention1i
× Modifier
ji
), where β3 assess the variation caused by j modifier, i.e., is the estimated modification effect.
All chosen modifiers were added to the model one by one. A level of significance of P < 0.10 was considered to be relevant for modifiers [13]. Main results are reported treating modifiers as continuous variables when applicable. For mediator models the p-values were calculated from bootstrapped coefficients and standard errors [14].
For further illustrations we dichotomized continuous variables using cut-off limits that were based on our previous study in the same population concerning the determinants of sickness absence [5], or an arbitrary cut-off 14 days for prior sickness absence days (see Table 1). These results are presented as a forest plot, for which we included also the mediator variable where recorded participation in intervention was treated as acceptance-mediator (1: intervention group as intended to treat, 0: not as intended to treat).
Sensitivity of the results was assessed by using informal Bayesian inference [15]. Here the idea is to assess the sensitivity of the base case results by generating random simulations from the normal distributions related to associated β:s and the residual standard error σ [12]. These results from the sensitivity analysis are reported only verbally. Statistical analyses were performed using SPSS 14.0 (SPSS Inc, Chicago, Illinois) and R 2.8.1 software.