The intervention protocol was reported in full elsewhere (see 15).
The study was conducted in Pihlajalinna Työterveys, a large private occupational health services’ provider, which at the time of starting the study had 28 private healthcare units across Finland. In 2015, Pihlajalinna Työterveys had approximately 68370 employees on their register. Pihlajalinna Työterveys went through several rounds of mergers and corporate acquisitions during the study period, which led to a substantial increase in patient and health care unit numbers.
The intervention was multifaceted and implemented sequentially. First, a notice was sent to the entire organization informing all practitioners that the study would be conducted. The intervention consisted of two separate activities: one, training, mentoring and follow-up of trainees in intervention units on how to identify and record work related illnesses in primary care visits and how to identify and record risk of work disability. This also included training on the sequence of actions that was to be implemented when a marking was made. Second, an organization-wide change was made to the electronic health care system, clarifying the way in which work relatedness and risk of disability pension was recorded.
The trainees in the intervention sites were those occupational health physicians, who were responsible for collaboration with their own client companies, working at any of the 22 sites included in the study. The intervention sites had altogether 58 physicians working during the study, while control sites had in total 50 physicians during the study. These physicians can for example be involved in tailoring work tasks and other workplace interventions.
If the OH physician noted a visit as work related, or a patient as at risk of disability in the near future, a sequence of events was kicked off at the intervention sites. The OHS nurses responsible for the employer organisations were to collect the patients for which such reports were made, and initiate the recommended interventions, together with the physicians either for the patients or at the employer organization. The interventions could include e.g. an occupational health collaborative negotiation to modify the employee’s work tasks or timing, or organizational interventions on workplace ergonomics or teamwork counselling. Other interventions could e.g. starting medical or vocational rehabilitation for the individual patient, which involves both the workplace and the patient/employee. It was not possible to collect the number of these interventions in this study since the individual patients that received the note and the tasks following conducted could not be associated.
A fuller description of the intervention can be found in the TIDIER reporting guide for population health interventions: tidierguide.org/#/gen/pFqrFqw3M
Information about the study was sent to all sites in April 2016. The intervention training was conducted in May 2016. The electronic change to systems was implemented in 9.3.2017. Data collection ended in December 2017.
Inclusion and exclusion criteria:
We included 22 clinics within Pihlajalinna 2016 in collaboration with managers at the institution. Clients were included in the analysis if they attended any of the 22 clinics within the trial period, and were between 18 and 64 years of age.
Of all clinics within Pihlajalinna Työterveys in 2016, we included 22. We treated each healthcare unit as a cluster, as individual randomisation in this context would have been challenging. NT, the team statistician conducted initial simple randomization to randomize the first four clusters of healthcare units. Randomisation was conducted on an 1:1 ratio. After this, the minimisation approach was used to randomise the remaining 18 clusters using excel so that confounders including 1) the occupational sector (e.g. industrial, service sector, public service), 2) presence of a large industry client, and 3) client volume per site balanced across the intervention and control sites (see 15).
Occupational health professionals or the research team were not blinded to the intervention.
Our primary outcome was medium-length sickness absences. Deviating from the original protocol, we considered medium length sickness absences from 4 days to two weeks. This time was related to the Finnish Insurance Agency’s payment of sickness absences after more than 10 days of sickness absences. However, the Finnish Insurance Agency calculates working days, while sickness absences are registered on the system using calendar days.
Our secondary outcomes were
- Reduction in mean number of short term (1–3 days) sickness absences from the workplace per cluster from baseline after 1 year from the start of the intervention as measured by self-reported sickness absences that are recorded on OHS records or OHS records of sickness absence written in the OHS units.
- Reduction of mean number of any form of work disability pensions as measured by an employee registering as receiving a work disability pension on the central pensions register from baseline to up to 2 years from the intervention as measured by the entry on the central pensions register
- Reduction of mean number of long-term (15+ days) sickness absences from the workplace per cluster from baseline to 1 year after the intervention as measured by OHS records
This article focuses on reporting the primary outcome, medium term sickness absences, as we deemed the follow-up period too short to report on disability pensions or long-term sickness absences. We also report short-term sickness absences and the process indicators collected on recording consultations’ work relatedness and risk of work disability across control and intervention sites.
Our initial power calculation suggested that we would have 91% power to detect a 10% change in mean sickness absence rates across intervention and control clusters, if we had 22 occupational health units with 24892 patients. For the trial, we retained all 22 units, with 26804 patients recorded on the system.
We collected medical record data on patients’ healthcare related visits from Pihlajalinna Työterveys from 2015 to 2017. The medical records included between 68370 patients in 2015 and 107413 patients in 2017. The cohort was dynamic, in that patients could be added to the cohort as the study progressed. Data were pseudonymised, and researchers had no access to patient identifying data. All patients above the age of 18 and with a healthcare curative contract with Pihlajalinna Työterveys were included in the study.
The data were combined with pseudonymised data from the Finnish Centre for Pensions, where we obtained all participants’ pensions granted for the study period.
After data collection was complete, we noted that Pihlajalinna’s acquisition of another large occupational health services provider impacted on our outcomes. Therefore, we used all initially randomised sites in the intention to treat analyses and excluded them in the per protocol analyses.
We included data on curative patient visits to OHS physicians responsible for client organisations. This is because OHS services have many casual workers, who deal with primary care patients but are not occupational health specialists, most of whom were not exposed to training. We also excluded preventive visits such as health examinations. We analysed data 6 months before the intervention, during the intervention, and after the intervention for 6 months. After initial analysis we chose a period of 6 months after the intervention corresponding with the same yearly season of the 6 months preceding the intervention, to ensure that seasonal effects did not confound our analysis. We analysed data using ANCOVA, setting alpha at 0.05.
We also analysed process indicators among intervention and control clinics. These indicators included whether the recording of a visit’s relatedness to work remained the same as before or whether the physicians recording had changed practice. At each visit a physician would be asked to record whether or not the patient’s visit was related to work or not or whether it was not assessed. We analysed changes both after the educational intervention and after change in the electronic system using descriptive statistics.