Design and setting
The present study was part of a prospective observational cohort study among people on sick leave due to musculoskeletal disorders (the MI-NAV project), conducted within the Norwegian Labour and Welfare Administration (NAV) . Baseline data from the cohort study was compared with public registry data with respect to occurrence and duration of long-term absenteeism.
Participants and recruitment procedure
Eligible participants were people on sick leave for at least 4 weeks due to musculoskeletal disorders, aged 18 or above. Exclusion criteria were people being unable to read or write in Norwegian or English and people on sick leave longer than a 12-month period retrospectively from baseline. Recruitment of participants and consenting to participation was performed electronically through a link on everyone’s individual profile page at the NAV website. Recruitment was between November 2018 and Mars 2019.
The Mi-NAV project was classified as a quality assessment study by the Norwegian Regional Committee for Medical Research Ethics (Reference No. 2018/1326/REK sør-øst A) and approved by the Norwegian Centre for Research Data (NSD 861249) in 2018.
At baseline, the included participants completed a comprehensive questionnaire covering sociodemographic variables (sex, age, education level and mother tongue) and pain intensity in addition to self-reported long-term absenteeism by the iPCQ . The Numeric Rating Scale (NRS 0–10) was used to measure pain intensity . In addition, public registry data on long-term absenteeism as well as the related diagnostic code was collected from the Norwegian Labour and Welfare Administration (NAV), in the period from baseline to 12 months retrospectively.
The iMTA Productivity Cost Questionnaire
The iPCQ consists of 18 items and adopts a recall period of 4 weeks (except for item no. 5 and 6). In the introduction, nine items assess the date of reply and the following sociodemographic factors: age, sex, education level, work status, paid or unpaid work, profession, number of workdays and work hours per week of paid work. Further, productivity costs are measured in three separate index scores with individual sum scores: absence from paid work (absenteeism, with a distinction between short- (≤ 4 weeks) and long-term (> 4 weeks) absenteeism), reduced productivity at paid work (presenteeism) and productivity loss in unpaid work . The occurrence and duration of long-term absenteeism can be calculated from items no. 5 and 6 (“Did you miss work earlier than the period of 4 weeks due to being sick? This is referring to one whole uninterrupted period of missed work as a result of being sick.” (no, yes). “If yes, when did you call in sick?” (day, month, year).
The Norwegian versions as well as the manual for the iPCQ are available from the Institute for Medical Technology Assessment (iMTA) at Erasmus University Rotterdam .
NAV is the public welfare agency in Norway. Workers in Norway qualify for sickness benefits from NAV if they have been in paid work for the last 4 weeks before the sickness incident, and if the occupational disability is documented by a doctor’s sick leave certificate. In general, sickness benefit (100% of salary) can be received from the first day of reported sick and up to 1 year. If the person is still unable to work after 1 year, he or she may be entitled to work assessment allowance or disability benefits.
The data on absenteeism collected from the NAV registry contains dates and grading of absenteeism as well as the diagnostic codes related to the absence.
The outcomes in the present study will be occurrence and duration of long-term absenteeism. The occurrence of long-term absenteeism is defined as whether a continuous period of more than 4 weeks of absenteeism is recorded retrospectively from baseline (yes/no). The duration of long-term absenteeism is defined as the duration of a continuous period of absenteeism from baseline to maximum 12 months retrospectively. The duration of long-term absenteeism will be operationalized in two different ways (1) by calculating number of calendar days from start date until end date of sick leave (defined as the date the iPCQ was completed) (duration) and (2) by adjusting for grading of absenteeism, summarizing number of days with part-time sick leave to number of days with complete sick leave (adjusted duration) (e.g., 10 days with 50% sick leave equals absenteeism duration and adjusted duration of 10 and 5 days, respectively).
To assess criterion validity, the COSMIN group recommends evaluating the extent to which an instrument is an adequate reflection of a “gold standard” [16, 17]. To compare the occurrence of long-term absenteeism participants were classified according to whether a continuous period of long-term absenteeism had been recorded by the iPCQ (yes/no) and the registry (yes/no). The overall agreement between the two methods was expressed as follows: OA = (number of identical/total answers) × 100.
To compare the duration and adjusted duration of long-term absenteeism, we computed intraclass correlation coefficient (ICC) using two-way random average agreement. The acceptable level of ICC was set to > 0.70 . In addition, to illustrate the relationship between the two methods, we depicted the differences(iPCQ-registry) and averages of these using Blant–Altman plots. Also, the differences(iPCQ-registry) were described with medians and interquartile ranges and analyzed with the Wilcoxon signed rank test. To test whether differences between the two methods were associated with the length of sick leave, as recorded in the registry, stratified analyses for the following categories of absenteeism length were performed: ≤ 3 months, >3 months to ≤ 6 months and ≥6 months. In addition, Spearman’s rho was used to assess the correlation between the differences(iPCQ-registry) and the length of sick leave. Correlation coefficients smaller than 0.3, between 0.3 and 0.6 and greater than 0.6 were considered low, moderate and high, respectively .
To test credibility of the primary analyses, sensitivity analyses without outliers were performed. Outliers were identified with simple scatter plots by visual inspection.
All data analyses were performed using SPSS version 24 (SPSS Inc., Chicago, IL, USA).