Risk Assessment for Prolonged Sickness Absence Due to Musculoskeletal Conditions
Conditions: Musculoskeletal Pain Disorder
Sponsor: Oslo Metropolitan University

 
 
Statistical analysis plan (SAP) for:  
Modifiable prognostic factors of high costs related to 
healthcare utilization and productivity loss among people on 
sick leave due to musculoskeletal disorders (working title)   
 
 
Project  
 
The MI-NAV project  
NCT number   
NCT04196634   
Document date 
25.08.2022 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

SAP Modifiable prognostic factors of high cost related to healthcare utilization and productivity loss among people on sick leave due to musculoskeletal 
disorders (working title)   
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1. Introduction  
Scope 
This document is a supplement to the MI-NAV protocol (ClinicalTrials.gov Identifier: NCT04196634) 
and comprises a statistical analysis plan (SAP) for the article “Modifiable prognostic factors of high 
cost related to healthcare utilization and productivity loss among people on sick leave due to 
musculoskeletal disorders”. The current SAP has been written after data collection was finished. 
However, the SAP will be uploaded to ClinicalTrials.gov before we enter the study database for the 
subsequent analyses.  
2. Administrative information 
Version of SAP 
1.0 
Study sponsor 
This study is part of the MI-NAV project, a large-scale project funded by the Research Council of 
Norway, through the program “Sickness absence, work, and health” (280431/GE).  
Contributors to SAP  
 
Name 
Title 
Role  
Margreth Grotle1,2 
Professor 
Principal Investigator  
Rikke Munk Killingmo1 
PhD-student 
Main author of SAP  
Mauritz W. van Tulder3 
Professor 
Contributor to SAP  
Anne Therese Tveter4,1 
PhD 
Contributor to SAP 
Alexander Tingulstad1  
PhD-student 
Contributor to SAP 
Tarjei Rysstad1 
PhD-student 
Contributor to SAP 
1Oslo Metropolitan University, 2Oslo University Hospital, 3Vrije University Amsterdam, 4Diakonhjemmet Hospital 
 
 
Signatures  
I hereby declare that I have reviewed and approved the statistical analysis plan for this study  
 
 
 
________________________ 
______________________ 
Rikke Munk Killingmo 
Margreth Grotle 
Main author of the SAP 
Principal Investigator 
 
 

SAP Modifiable prognostic factors of high cost related to healthcare utilization and productivity loss among people on sick leave due to musculoskeletal 
disorders (working title)   
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3. Study aim   
The primary aim of this study is to identify modifiable prognostic factors of high costs related to 
healthcare utilization and productivity loss among people on sick leave due to musculoskeletal 
disorders. A secondary aim is to identify modifiable prognostic factors of high costs related to 
separately 1) healthcare utilization, and 2) productivity loss. 
4. Method  
Design and setting 
A prospective observational cohort study with one year of follow-up was conducted within the 
Norwegian Labour and Welfare Administration (NAV). This cohort study [1] is part of a large-scale 
project; the MI-NAV project (ClinicalTrials.gov Identifier: NCT04196634).  
Study population and recruitment 
Eligible participants were people, aged 18 to 67 years, on sick leave (50-100% sick leave rate, ≥ 4 
weeks) due to musculoskeletal disorders (diagnosis within the musculoskeletal (L) chapter of the 
International Classification of Primary Care, 2nd edition (ICPC-2)[2]). Exclusion criteria were 
insufficient Norwegian or English language skills to participate in the study. Participants were invited 
through a link on the participants' individual profile page on the NAV website between November 
2018 and February 2019. Participants entering the link were asked to consent digitally. 
Data collection, outcome, modifiable prognostic factors, and covariates  
At baseline, all participants responded to an electronic questionnaire including demographic 
variables and a set of patient-reported measures. Data on healthcare utilization were collected from 
public records including the Norwegian Patient Registry (NPR) and the Municipal Patient and User 
Registry (KPR). Data on productivity loss were collected from public records (NAV), containing dates 
and grading of absenteeism as well as the related diagnostic code. Data on healthcare utilization and 
productivity loss were collected in the period from baseline to 3 months retrospectively, and in the 
one-year follow-up period. All information is stored and will be analysed securely through the Service 
for sensitive data (TSD) at the University of Oslo, Norway.      
Outcomes 
The primary outcome of this study is costs related to healthcare utilization and productivity loss 
aggregated for one year of follow-up and dichotomized as high or low. Having high costs is defined as 
patients with costs in the top 25th percentile [3, 4]. As noted above, healthcare utilization was 
collected from public records (NPR, KPR) and included: consultation with healthcare professionals 
(type and frequency), consultations in emergency room, outpatient activity, number of days of 
hospitalization and/or institutionalisation, and surgery. Healthcare utilization during the one year of 
follow-up will be described as shown in Table 2. The total cost of healthcare utilization will be 
estimated based on reimbursement rates collected from NPR and KPR. Productivity loss was 
collected from public records (NAV) and included productivity loss related to absenteeism. The total 
costs of productivity loss will be estimated based on the number of days with absenteeism adjusted 
for the percentage of absenteeism and multiplied by the average wage rate (from official statistics) in 
Norway by sex and age groups.  
Secondary outcomes of this study are costs related to separately 1) healthcare utilization 
aggregated for one year of follow up and dichotomized as high and low, and 2) productivity loss 
aggregated for one year of follow up and dichotomized as high and low.  

SAP Modifiable prognostic factors of high cost related to healthcare utilization and productivity loss among people on sick leave due to musculoskeletal 
disorders (working title)   
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Modifiable prognostic factors  
Potential modifiable prognostic factors are factors expected to have the potential to be modified or 
improved by appropriate care or treatment, and therefore classified as modifiable. Potential 
modifiable prognostic factors of high costs related to healthcare utilization and productivity loss are 
based on previous scientific literature on patients with back pain primarily [3-11], as well as people 
with musculoskeletal disorders [12-15] and non-diagnosis-specific studies [16-23], and includes the 
following self-reported variables measured at baseline: 
• 
Pain severity [3-7, 12-15] measured by the numeric rating scale (NRS) [24] from the Keele STarT 
MSK tool (STarT MSK) [25] 
• 
Disability [3-8, 11, 13-15] measured by a single item (Q4) from the Musculoskeletal Health 
Questionnaire (MSK-HQ) [26] 
• 
Health-related quality of life [7, 12] measured by the EuroQol 5 dimensions (EQ-5D-5L) [27] 
• 
Emotional well-being [3, 4, 8-10, 14] measured by a single item (Q11) from the MSK-HQ [26]  
• 
Sleep quality [21, 22] measured by a single item (Q4) from the Örebro Musculoskeletal Pain 
Screening Questionnaire Short Form (ÖMPSQ-SF) [28] 
• 
Health literacy [23] measured by a single item (Q12) from the MSK-HQ [26] 
• 
Future disease expectation measured by a single item (Q6) from the STarT MSK [25] 
• 
Return to work expectancy measured by a single item (Q8) from ÖMPSQ-SF [28] 
• 
Work satisfaction measured by a single item (numeric rating scale, 0=not satisfied, 10=satisfied) 
Covariates 
Prognostic factor research may vary depending on context (time, place, healthcare setting) and 
characteristics of the study population. We therefore plan to adjust for potential covariates when 
evaluating the modifiable prognostic factors. Potential covariates are based on previous scientific 
literature (as described above), and include the following self-reported variables measured at 
baseline: 
• 
Sex [5-7, 11, 15-19] 
• 
Age [5, 7, 11, 13, 15, 16, 18, 19] 
• 
Education level [5, 8, 17, 20] measured as the highest education completed, and categorised into 
low vs. high (university level) 
• 
Pain duration [3, 15] measured by a single item (Q1) from the ÖMPSQ-SF [28] 
In addition, the following public records variables will be included as covariates:    
• 
Absenteeism-related diagnosis type at baseline collected from the NAV registry and categorized 
into “upper/lower limb conditions”, “back and neck conditions”, “joint/inflammatory 
conditions”, “injury or trauma” or “other MSK conditions” 
• 
Total costs related to healthcare utilization during a period of 3 months prior to inclusion. 
Healthcare utilization prior to inclusion was collected from public records (NPR, KPR) as 
described above. Total costs of healthcare utilization will be estimated as described above. 
• 
Total costs related to productivity loss during a period of 3 months prior to inclusion [12, 14]. 
Productivity loss prior to inclusion was collected from public records (NAV) as described above. 
Total costs of productivity loss will be estimated as described above. 
Other variables 
Included participants will also be described with respect to the following baseline characteristics: 
mother tongue, days of absenteeism one year prior to inclusion, and healthcare utilization prior to 
inclusion. 

SAP Modifiable prognostic factors of high cost related to healthcare utilization and productivity loss among people on sick leave due to musculoskeletal 
disorders (working title)   
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Sample size 
This study contains secondary analyses embedded in the MI-NAV project. Details on sample size 
calculation are provided in the MI-NAV protocol [1]. To determine statistical power of this study we 
used number of events per parameter (EPP) [46-50] and the rule-of-thumb of “10 events per 
parameter included” [51-54]. With a fixed sample size of 549 participants included in the MI-NAV 
project, we anticipate 137 participants to be in the top 25th percentile of costs and categorised as 
having high costs (yes/no) (events). An EPP of 10 will allow a maximum of 13 parameters to be 
included in the final multivariable prediction model.  
Statistical analyses 
General analysis considerations  
All analyses described in this SAP are considered a priori in that they have been defined in the 
protocol and/or in this SAP. All post hoc analyses will be identified as such in the article if relevant. All 
analyses will be carried out using SPSS or Stata and controlled by a senior researcher/statistician. We 
consider our study as explanatory. Thus, no correction for multiple testing will be performed and p-
values < 0.05 will be considered statistically significant. All statistical tests will be two-sided. All 
confidence intervals will be reported as 95%. Preliminary analyses assessing the influence of missing 
data and assumptions of normality for continuous variables will be conducted. The assumption of 
normal distribution will be investigated using histograms and QQ-plots. Normally distributed data will 
be presented with means and standard deviations (SDs), skewed data with medians and interquartile 
range (IQR). Categorical data will be reported as counts and percentages.  
Description of study flow  
The flow of participants through the study will be reported with a flow chart according to the 
REMARK guidelines [29]. Reasons for dropout will be provided where known. Differences between 
responders and non-responders will be evaluated. 
Missing data 
We anticipate few missing values within this study. Information on the primary and secondary 
outcome will be obtained from public records (NRP, KPR, NAV) where all individuals receiving any 
form of benefits are registered by their social security number. Furthermore, we anticipate no or 
minimal missing data for the majority of the potential modifiable prognostic factors and the 
covariates, as a requirement to answer all questions on key questionnaires was implemented in the 
electronic baseline questionnaire. Nevertheless, missing value pattern will be visually explored and 
handled by multiple imputation if relevant (if >5% data is missing). 
Participant characteristics 
Baseline characteristics of included participants will be presented as shown in Table 1.  
Healthcare utilization, productivity loss and cost estimation 
Type and frequency of use of different healthcare resources will be calculated for the one-year 
follow-up period. Healthcare utilization will be presented as shown in Table 2. Costs related to 
healthcare utilization will be estimated based reimbursement rates collected from NPR and KPR. 
Days of absenteeism will be calculated for the one-year follow-up period and adjusted for percentage 
of absenteeism. Cost related to productivity loss will be estimated based number of days with 100% 
absenteeism and national average wage rates (from official statistics) in Norway by sex and age 
groups. All costs will be presented in euros (€) 2020 and estimated with both mean and median 
values with 95%CI, using bias-corrected and accelerated (BCa) bootstrapping as presented in table 3. 
The BCa will be conducted with a bootstrap sample size of 1000. Cost data are commonly skewed, 

SAP Modifiable prognostic factors of high cost related to healthcare utilization and productivity loss among people on sick leave due to musculoskeletal 
disorders (working title)   
6 
 
thus both mean and median values will be presented to support the result interpretation. Values in 
Norwegian kroner (NOK) were recalculated to euros using the exchange rate from February 2020 
(1€=NOK 10). 
Identification analysis  
Univariable and multivariable binary logistic regression models will be used to investigate 
associations (crude and adjusted for selected covariates) between each predefined modifiable 
prognostic factor and costs related to 1) healthcare utilization and productivity loss, 2) healthcare 
utilization, and 3) productivity loss. The cost score will be entered into the model as a dependent 
dichotomous variable (high cost defined as patients with cost in the top 25th percentile, yes/no). Non-
linear relationships in the modelling process will be explored using cubic splines or multivariable 
fraction polynomials, as these are recommended approaches for modelling continuous prognostic 
factors in prognosis research [30]. The results will be presented as crude and adjusted odds ratios 
(OR) with 95% confidence intervals (CI) as shown in table 4.  
Sensitivity analysis 
To assess credibility of the total cost calculation related to the primary analyses, the calculation will 
be conducted without outliers. Outliers will be identified with simple scatterplots by visual inspection 
and defined as patients with remarkably high total costs. If multiple imputation on missing data is 
conducted, the univariable and multivariable logistic regression analyses related to the primary 
analyses will be performed on complete case data to test credibility of the imputation procedure.  
6. Ethics approval 
This study is a part of the MI-NAV project [1]. The MI-NAV project (ClinicalTrials.gov Identifier: 
NCT04196634) has been 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 Social Science Data Service (reference no. 861249) in 2018. 
 
 
 
 
 
 
 
 
 
 
 
 

SAP Modifiable prognostic factors of high cost related to healthcare utilization and productivity loss among people on sick leave due to musculoskeletal 
disorders (working title)   
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Table 1. Participants characteristics and clinical status at baseline 
 
All participants  
(n=549) 
Missing, n (%) 
Female, n (%) 
 
 
Age in years 
 
 
Education at university level, n (%) 
 
 
Mother tongue Norwegian, n (%) 
 
 
Days of absenteeism last year* 
 
 
Diagnosis (ICPC-2)**, n (%) 
 
 
  Upper limb conditions  
 
 
  Lower limb conditions  
 
 
  Neck conditions  
 
 
  Back conditions  
 
 
  Joint or inflammatory conditions  
 
 
  Injurie or trauma  
 
 
  Other MSK conditions  
 
 
Pain severity average last week (NRS, 0-10) 
 
 
Pain duration, n (%) 
 
 
  < 3 months 
 
 
  3-6 months 
 
 
  > 6 months  
 
 
Disability (MSK-HQ, Q4) 
 
 
Health-related QOL (EQ-5D-5L, -0.59-1) 
 
 
Emotional well-being (MSK-HQ, Q11) 
 
 
Sleep quality (ÖMPSQ-SF, Q4) 
 
 
Health literacy (MSK-HQ, Q12) 
 
 
Future disease expectation (STarT MSK, Q6)  
 
 
Return to work expectancy (ÖMPSQ-SF, Q8) 
 
 
Work satisfaction (1-10) 
 
 
Healthcare utilization prior to inclusion***   
 
 
Primary care consultation last 3 months, n (%) 
 
 
  General practitioner 
 
 
  Physiotherapist 
 
 
  Chiropractor 
 
 
  Emergency room 
 
 
Secondary care consultation last 3 months, n (%) 
 
 
   Outpatient activity 
 
 
   Hospitalization (non-surgery) 
 
 
   Rehabilitation stay 
 
 
   Surgery 
 
 
EQ-5D-5L indicates EuroQol 5 dimensions; ICPC-2, International Classification of Primary Care 2ed edition; MSK-HQ, Musculoskeletal Health 
Questionnaire; NRS, Numeric Rating Scale; ÖMPSQ-SF, Örebro Musculoskeletal Pain Screening Questionnaire Short Form; STarT MSK, Keele 
STarT MSK tool. *collected from the Norwegian Labour and Welfare Administration (NAV) registry, measured as calendar days, and adjusted 
for percentage of absenteeism. **Absenteeism related diagnoses type at baseline, collected from the NAV registry. ***collected from public 
records; the Norwegian Patient Registry (NPR), the Municipal Patient and User Registry (KPR), and the Norwegian Control and Payment of 
Health Refunds Database (KUHR).  
 
 
 
 
 
 
 
 
 
 

SAP Modifiable prognostic factors of high cost related to healthcare utilization and productivity loss among people on sick leave due to musculoskeletal 
disorders (working title)   
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Table 2. Healthcare utilization throughout one-year of follow-up 
 
All participants  
(n=549) 
Missing, 
 n (%) 
Primary care  
 
 
Patients with primary care consultation, n (%) 
 
 
  General practitioner 
 
 
  Physiotherapist 
 
 
  Chiropractor 
 
 
  Emergency room 
 
 
  No primary care consultations 
 
 
Numbers of consultations, median (IQR)* 
 
 
  General practitioner 
 
 
  Physiotherapist 
 
 
  Chiropractor 
 
 
  Emergency room 
 
 
Secondary care 
 
 
Patients with secondary care consultation, n (%) 
 
 
  Outpatient activity 
 
 
  Hospitalization (non-surgery) 
 
 
     Duration of stay in days, median (IQR) 
 
 
  Rehabilitation stay 
 
 
     Duration of stay in days, median (IQR) 
 
 
  Surgery 
 
 
  No secondary care consultations 
 
 
*Numbers of consultations is calculated on basis of patients who have reported primary care consultations.  
 
 
 
Table 3. Costs (€) due to healthcare utilization and productivity loss throughout one year of follow-up (n=) 
 
Mean 
(95% CI*) 
Median 
(95% CI*) 
Patients with zero 
cost, n (%) 
Healthcare utilization 
 
 
 
   Primary care  
 
 
 
   Secondary care 
 
 
 
Productivity loss 
 
 
 
   Absenteeism 
 
 
 
Total  
 
 
 
*Bias-corrected and accelerated bootstrapping (1000 simulations). 
 
 
 
 
 
 
 
 

SAP Modifiable prognostic factors of high cost related to healthcare utilization and productivity loss among people on sick leave due to musculoskeletal 
disorders (working title)   
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Table 4. Binary logistic regression analyses; individual associations between modifiable prognostic factors and high costs 
 
High costs related to healthcare 
utilization and productivity loss 
High costs related to  
healthcare utilization 
High costs related to 
productivity loss 
 
Crude OR  
(95% CI) 
Adjusted OR* 
(95% CI) 
Crude OR 
(95% CI) 
Adjusted OR* 
(95% CI) 
Crude OR 
(95% CI) 
Adjusted OR* 
(95% CI) 
Pain severity (NRS) 
 
 
 
 
 
 
Disability (MSK-HQ) 
 
 
 
 
 
 
Health-related QOL (EQ-
5D-5L) 
 
 
 
 
 
 
Emotional well-being 
(MSK-HQ) 
 
 
 
 
 
 
Sleep quality (ÖMPSQ-
SF) 
 
 
 
 
 
 
Health literacy (MSK-HQ) 
 
 
 
 
 
 
Future disease 
expectation (STarT MSK) 
 
 
 
 
 
 
Return to work 
expectancy (ÖMPSQ-SF) 
 
 
 
 
 
 
Work satisfaction 
 
 
 
 
 
 
EQ-5D-5L indicates EuroQol 5 dimensions; ÖMPSQ-SF, Örebro Musculoskeletal Pain Screening Questionnaire Short Form; NRS, Numeric Rating Scale; STarT 
MSK, Keele STarT MSK tool; MSK-HQ, Musculoskeletal Health Questionnaire. *Adjusted by sex, age, education level, absenteeism related diagnosis type, 
pain duration, and costs related to healthcare utilization and productivity loss (absenteeism) prior to inclusion. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

SAP Modifiable prognostic factors of high cost related to healthcare utilization and productivity loss among people on sick leave due to musculoskeletal 
disorders (working title)   
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