Marc Fadel

Remi Valter

Alexandre Quignette

Alexis Descatha
email: alexis.descatha@inserm.fr

Prof Alexis Descatha

Usefulness of a job-exposure matrix "MADE" as a decision tool for compensation of work-related musculoskeletal disorders

Keywords: public health, musculoskeletal, work, job exposure matrix, compensation, predictivity 2

Usefulness of a job-exposure matrix 'MADE' as a decision tool for compensation of work-related musculoskeletal disorders

Introduction

Obtaining valid estimates of occupational exposure is an important issue in many epidemiological studies dealing with such questions. The direct measurement of exposure through observation of workers is an accurate method but is limited to a short period of observation, and is very expensive and time-consuming. [START_REF] Hansson | Questionnaire versus direct technical measurements in assessing postures and movements of the head, upper back, arms and hands[END_REF] Self-reported questionnaires are easier to administer for large populations, but exposures are often less accurate, and responses may be subject to recall bias and altered perception of exposures in some cases. [START_REF] Plato | Methodology and utility of a job-exposure matrix[END_REF] In this context, job exposure matrices (JEMs) have been proposed for chemical exposures and some physical exposure. [START_REF] Plato | Methodology and utility of a job-exposure matrix[END_REF][START_REF] Rubak | An expert-based job exposure matrix for large scale epidemiologic studies of primary hip and knee osteoarthritis: the Lower Body JEM[END_REF] These matrices give a correspondence between job titles (generally defined by the combination of a profession and an activity sector) and probability, intensity and/or frequency of one or more exposure. Recently, JEM for biomechanical exposures has also been developed in Denmark, and France. [START_REF] Dalbøge | Upper arm elevation and repetitive shoulder movements: a general population job exposure matrix based on expert ratings and technical measurements[END_REF][START_REF] Descatha | Development of a French Job exposure matrix "MADE" for biomechanical exposure evaluation[END_REF] Interestingly, JEM has been also suggested not only for research purpose, but also for public health. [START_REF] Descatha | Job exposure matrix: From research to public health[END_REF] In France, tools based on JEM have been developed to help occupational health practitioners assess global exposure, which may lead to early retirement. [START_REF] Kerbrat | Pénibilité au travail en France et utilisation de l'outil matrice emplois-expositions pour son évaluation[END_REF] Compensation of musculoskeletal disorder as an occupational disease requires exposure assessment. JEM might be used to optimize the first evaluation.

We aimed to study the predictivity of a biomechanical job-exposure matrix compared to musculoskeletal data of National compensation health insurance for work-related disorders and injuries.

A c c e p t e d M a n u s c r i p t

Methods

French compensation system

The French system for recognition of the occupational nature of a disease is based on two possibilities: [START_REF] Choudat | Peut-on accroître la cohérence des avis pour la reconnaissance en maladie professionnelle ?[END_REF] A list system (called in French Tables): If the disease is listed as an occupational disease and if the "related conditions" (i.e. diagnosis criteria, time condition -diagnostic delay, sometime duration of exposure -type of exposure) are met, the disease is presumed to be occupational and the disease is compensated. For musculoskeletal disorder, there is a high recognition rate due to broad "related condition".

A complementary system: If the conditions are not met or if the disease does not appear in the list, compensation is possible if (1) victim has a predictable permanent disability rate over 25%; and (2) a committee determine that the disease is directly (and essentially if there is no existing list) related to work exposure.

Study

Blind compensation data of work-related musculoskeletal disorders in France were obtained from 2013 to 2015. We included the following disorders that are compensated: acute shoulder tendonitis (acute, chronic with or without rupture), elbow nerve entrapment, epicondylitis (bursitis not included), hand/wrist nerve entrapments, hand/wrist tendonitis, meniscus disorders, and chronic low back pain with sciatica. Job title is coded using the 2008 International Standard Classification of Occupations (ISCO).

The main outcome was the compensation results, i.e. acceptance or rejection. We only included rejection for lack of exposure. Other rejections for medical discrepancies, time from end of exposure or medical diagnosis were not included, as well as missing data for job title.

A c c e p t e d M a n u s c r i p t

A job-exposure matrix called "MADE" (for "difficult physical conditions and job matrix" in French) was developed for research purposes, based on the 2008 ISCO for 17 biomechanical exposures coded from 0 to 5 by expert assessment who weren't involved in the compensation process. [START_REF] Descatha | Development of a French Job exposure matrix "MADE" for biomechanical exposure evaluation[END_REF] (Appendix 1) The most relevant exposure variables were used for each locations considered: rating of exertion and shoulder postures for shoulder tendonitis, static strength for ulnar nerve entrapment at the elbow, elbow postures for epicondylitis, hand grip for hand/wrist nerve entrapments and tendonitis, kneeling or squatting for meniscus disorders and carrying heavy loads (i.e. >25kg) for chronic low back pain with sciatica.

Dataset was randomly divided into two subsamples in order to check the stability of the statistical results (main and validation subsample, respectively two third and one third of all sample). Disorders have been studied separately according to the exposure considered in the list/table system. Area Under Curves (AUC) of Receiver Operative Characteristic curves, as well as sensitivity, specificity, predictive values were calculated in each subsample using the compensation results as reference. The proportion of accepted and rejected cases was calculated based on the JEM using two thresholds that were estimated to have approximatively 0.90 sensitivity and specificity (appendix 2). The other subsample helped check the validity of the results.

Ethics

We worked on blind compensation data which required no ethic committee or consent.

A c c e p t e d M a n u s c r i p t

Results

In three years, 163,128 cases were available, with a high proportion of acceptance (94%), 110,000 in the main subgroup and 53,128 in the validation subgroup. In the main subgroup, for shoulder disorders, AUC effort and AUC shoulders postures were respectively 0.67[0.66;0.68] and 0.64[0.62;0.65]; for ulnar nerve entrapment at the elbow, AUC The threshold optimized for sensitivity and specificity was over 0.90 in most cases, but none reached both 0.90 sensitivity and specificity for the same threshold (Table ). If two thresholds are considered, 28.7% of the sample fit under or over those (examples in supplementary data 2). Results were very similar in the other subsample.

Discussion

Compared to the data of National compensation health insurance of work-related musculoskeletal disorders and injuries, the biomechanical job-exposure matrix "MADE" showed a fair predictivity, though two thresholds must be used for the matrix to be used as a decision tool for compensation. Some limitations should be discussed. Firstly, for non-specialists, coding the described job is complicated. [START_REF] Descatha | Job exposure matrix: From research to public health[END_REF] Though computerized approaches have been studied, they aren't implemented yet.[9] However, misclassification would lead to underestimating the accuracy because we A c c e p t e d M a n u s c r i p t used existing data, and the decision of compensation is made using the complete description of the job and not the coding job. Secondly, there are ongoing discussions about the homogeneity of response throughout France, with possible difference of compensation rate between some areas, which is likely caused by the variability of the expert's opinion. [START_REF] Chanson | Reconnaissance des psychopathologies par les comités régionaux de reconnaissance des maladies professionnelles de Lorraine entre 2005 et 2011[END_REF] However, because of the large number of cases, the weight of those variations has probably low incidence on the results. Similarly, the high acceptance rates might artificially increase predictive values. Nevertheless, in addition that these are what are expected in our country, results on sensitivity and specificity made us confident on similar results in another situation with a lower acceptance rate. Finally, a JEM reflects an average level of exposure of the factor considered for a job, and cannot summarize all the individual professional situations. [START_REF] Descatha | Job exposure matrix: From research to public health[END_REF] The aim here is clearly to have a decision tool, and not to replace expertise. This work is a unique way to transpose knowledge from research to Public health. A first attempt was using asbestos JEMs for compensation purpose in the ESPACES project and was continued in the framework of the ESPRIT and SPIRALE programs, extended to other carcinogens including wood dust. [START_REF] Descatha | Job exposure matrix: From research to public health[END_REF] The fact that the JEM provides a valid predictive answer in more than a quarter of situations will help clarify and document complex situations (and might be optimized later). This will also help harmonizing practices of experts in France.

In conclusion, a decision tool based on a biomechanical JEM like "MADE" is useful. Every country can use their own JEM for Public health practice such as compensation. Improving compensation process is important for prevention.

  A c c e p t e d M a n u s c r i p t A c c e p t e d M a n u s c r i p t

Table :

 : Predictivity of the biomechanical job-exposure matrix "MADE" versus decision of compensation for work-related musculoskeletal disorders for the two randomized subsamples (regular font main subsample, and italic font for validation subsample with same threshold "idem")

		Number of	Thresholds (scales from 0 to 5)	Sensitivity	Specificity	Positive	Negative
		cases					predictive value	predictive value
	Shoulder, upper threshold	28027	Exertion >3		22.9%	94.5%	97.7%	10.9%
	Shoulder, upper threshold 13593	Idem		22.9%	94.0%	97.4%	10.9%
	Shoulder lower threshold	28027	Shoulder posture <0.6		96.1%	17.3%	92.1%	30.5%
	Shoulder, lower threshold 13593	idem		95.9%	17.7%	92.1%	30.3%
	UNEE, upper threshold UNEE, upper threshold 1477 3126 UNEE, lower threshold 3126 UNEE, lower threshold 1477 Epicondylitis, upper threshold 22202 Epicondylitis, upper threshold 10715 Epicondylitis, lower threshold 22202 Epicondylitis, lower threshold 10715 Hand, upper threshold 48428	Static strength >2.6 idem Static strength <1.05 Idem Elbow postures >2.6 Idem Elbow postures <0.8 Idem Hand grip >2.6	27.8% 29.6% 94.7% 95.7% 34.2% 34.2% 95.4% 95.2% 27.9% a n u s c r i p t 90.8% 90.1% 22.4% 23.8% 91.3% 86.5% 31.6% 35.7% 96.0% M	95.9% 95.5% 90.4% 90.0% 99.1% 98.7% 97.6% 97.9% 99.4%	14.0% 15.2% 35.1% 43.4% 4.5% 4.0% 19.0% 19.3% 5.6%
	Hand, upper threshold 23424 Hand, lower threshold 48428 Hand, lower threshold 23424 Meniscus, upper threshold 1545 Meniscus, upper threshold 742 Meniscus, lower threshold 1545 Meniscus, lower threshold 742	Idem Hand grip <0.3 Idem Kneeling/squatting >2.3 Idem Kneeling/squatting <1.0 Idem A c c e p t e d	28.0% 92.0% 91.8% 57.6% 56.3% 93.2% 94.4%	95.9% 38.4% 37.8% 91.4% 90.7% 40.0% 30.5%	99.4% 97.1% 97.2% 97.3% 97.0% 89.2% 87.8%	5.4% 17.5% 16.4% 28.9% 28.2% 52.7% 50.7%
	Sciatica, upper threshold	6268	Carrying heavy loads (i.e. >25kg)> 2.3	37.2%	91.1%	96.6%	17.5%
	Sciatica, upper threshold 2982	Idem		35.7%	90.3%	96.5%	15.7%
	Sciatica, lower threshold	6268	Carrying heavy loads (i.e. >25kg) <0.75	94.0%	22.3%	89.2%	35.2%
	Sciatica, lower threshold 2982	Idem		35.7%	90.3%	96.5%	15.7%
	UNEE Ulnar nerve entrapment at elbow
								10

Acknowledgments.

We thank Marine Jeantet, director of the French health insurance for work-related injuries and diseases, for giving permission to access compensation data.

Funding.

There was no special funding for this study. Regional compensation health insurance for work-related disorders and injuries funds the occupational health unit of Garches and had no influence on any results of the study.

Conflict of interest.

No conflict of interest declared.

In addition to our supportive institution, Alexis Descatha is also paid by Elsevier Masson as editor-in-chief of the journal "Les archives des maladies professionnelles et de l'environnement".

Key points:

• The biomechanical job-exposure matrix "MADE" showed a fair predictivity.

• Two thresholds must be used for the matrix to be used as a decision tool for compensation.

• Job-exposure matrix may help improving compensation process.