Use of nation-wide and longitudinal register data for research purposes has many advantages (1). One challenge though is the lack of information on exposures to job hazards of different types (4). One solution to these problems is to use a Job Exposure Matrix (JEM). A JEM is used to assign exposures on the basis of occupational titles. Hence, a JEM is potentially convenient when information on individual occupation is available, but there is no information on job exposures or job hazards, as is the case in Norwegian register data. The JEM method is quite extensively used and has proved useful in several contexts (6; 8; 9). As indicated JEMs have been produced to capture several types of exposures and stressors, such as biological, mechanical, chemical and psychosocial. Since our JEM addresses mechanical exposures only, we will primarily review empirical studies that have assessed the reliability and validity of mechanical JEMs.
In the Netherlands Rijs et al. (10) found that use of force and work in uncomfortable positions were significantly associated with functional limitations and self-perceived health. A moderate probability of repetitive movements was associated with functional limitations in former workers. A high probability of repetitive movements was associated with functional limitations in current and former workers as well as with SPH and hip and knee. The authors conclude that the results suggest that the JEM accurately classifies jobs according to physical demands.
In Finland Solovieva et al. (5) reported that the specificity of the mechanical JEM was, in particular among women. The degrees of agreement, measured by kappa, were fair for most exposures. For men, all JEM exposures were significantly associated with one month prevalence of low back pain. For women, this applied to four out of six JEM exposures. The researchers conclude that the JEM can be «considered as a valid instrument for exposure assessment in large-scale epidemiological studies, when more precise but more labour-intensive methods are not feasible» (5: p 1).
In Norway Hanvold et al. (7) constructed and validated JEMs capturing mechanical as well as psychosocial work exposures. They found a general fair to moderate agreement between the JEM and individual work exposures. The JEM performed better for mechanical work exposures than for psychosocial stressors. The predictive validity of the mechanical JEM showed an acceptable relationship with the risk of low-back pain. The authors conclude that JEM «may be useful in large epidemiological register studies» (7: 239).
Against this background, the aim of this article is to propose a mechanical JEM for use in Norwegian register data. This implies to assess its statistical properties in various ways, as described below. The idea is to use this JEM for different purposes in our «research program» on work, health and welfare trajectories among vulnerable groups. Hitherto, available information in Norwegian register data has been limited to occupation (job titles), social class and employment status. Our ambition is to add a reliable and validated index variable describing mechanical exposures to this list. Specifically, we will construct a composite mechanical job exposure index (MJEI), compromising eight different mechanical job exposures, and validate it by the assessment of a confirmative factor analysis, by investigating the correspondence between the individual reported exposures and the occupational exposures, by judging sensitivity and specificity measures, and lastly by examining whether the MJEI predicts self-reported lower-back pain and long-term sick leave using survey data, and disability and long-term sick leave using register data.
Study population
The populations included in the analysis are described according to age, educational level and major occupational groups in Table 1 – the survey data and Table 2 – the register data. As shown in Table 1 the total population based on the survey data includes 43 977 individuals and the population based on the register data includes 1 589 535 individuals. The survey population includes all those who participated in the 2006, 2009, 2013, 2016 and 2019 Norwegian nationwide Survey of Living Conditions on work environment and had a valid occupational code. The high number of observations achieved by using respondents in five surveys is likely to increase the precision of the JEM estimates (11). The register data population includes all those who were between 18 and 55 years of age in 2007 and had a valid occupational code.
Table 1
Background characteristics of the study population (survey data)
| | All (N= 43 977) | Men (N=23 062) | Women (N= 20 915) |
| Age (years) | N | % | N | % | N | % |
| 17-24 | 4 484 | 10,2 | 2 308 | 10,0 | 2 176 | 10,4 |
| 25-44 | 19 160 | 43,6 | 9 880 | 42,8 | 9 280 | 44,4 |
| 45-69 | 20 333 | 46,2 | 10 874 | 47,2 | 9 459 | 45,2 |
| Educational level | N | % | N | % | N | % |
| Primary school | 11 116 | 25,3 | 5 979 | 25,9 | 5 137 | 24,6 |
| Secondary/High school | 14 007 | 31,9 | 8 524 | 37,0 | 5 483 | 26,2 |
| College/university 4 years | 13 328 | 30,3 | 5 508 | 24,9 | 7 820 | 37,4 |
| College/university > 4 years | 5 366 | 12,2 | 2 969 | 12,9 | 2 397 | 11,5 |
| Major occupational groups (STYRK-98) | N | % | N | % | N | % |
| Legislator, senior officials, and mangers | 4 569 | 10,4 | 3 032 | 13,1 | 1 537 | 7,4 |
| Professionals | 7 921 | 18,0 | 4 170 | 18,1 | 3 751 | 17,9 |
| Technicians and associate professionals | 11 818 | 26,9 | 5 236 | 22,7 | 6 582 | 31,5 |
| Clerks | 2 743 | 6,2 | 1 100 | 4,8 | 1 643 | 7,9 |
| Service workers, shop, and market sales workers | 8 480 | 19,3 | 2 514 | 10,9 | 5 966 | 28,5 |
| Skilled agricultural and fishery workers | 822 | 1,9 | 670 | 2,9 | 152 | 0,7 |
| Craft and related trade workers | 3 911 | 8,9 | 3 665 | 15,9 | 246 | 1,2 |
| Plant and machine operators and assemblers | 2 552 | 5,8 | 2 235 | 9,7 | 317 | 1,5 |
| Elementary occupations | 1 161 | 2,6 | 440 | 1,9 | 721 | 3,5 |
| Low-back pain (previous month) | N | % | N | % | N | % |
| Severely/somewhat | 5 069 | 11,5 | 2 245 | 9,7 | 2 824 | 13,5 |
| A little/not at all | 38 908 | 88,5 | 20 817 | 90,3 | 18 091 | 86,5 |
| Long-term sick leave (previous month) | N | % | N | % | N | % |
| Yes | 7 046 | 16,0 | 2 946 | 12,8 | 4 100 | 19,6 |
| No | 36 931 | 84,0 | 20 116 | 87,2 | 16 815 | 80,4 |
Table 2
Background characteristics of the study population (register data)
| | All (N= 1 589 535) | Men (N= 819 232) | Women (N=770 303) |
| Age (years) | N | % | N | % | N | % |
| 18-24 | 221 568 | 13,9 | 113 520 | 13,9 | 108 048 | 14,0 |
| 25-44 | 903 754 | 56,9 | 472 831 | 57,7 | 430 923 | 55,9 |
| 45-55 | 464 213 | 29,2 | 232 881 | 28,4 | 231 332 | 30,0 |
| Educational level | N | % | N | % | N | % |
| Primary school | 321 207 | 20,2 | 176 392 | 21,5 | 144 815 | 18,8 |
| Secondary/High school | 714 616 | 45,0 | 399 202 | 48,7 | 315 414 | 41,0 |
| College/university 4 years | 424 436 | 26,7 | 167 405 | 20,4 | 257 031 | 33,4 |
| College/university > 4 years | 117 827 | 7,4 | 70 469 | 8,6 | 47 358 | 6,2 |
| Major occupational groups (STYRK-98) | N | % | N | % | N | % |
| Legislator, senior officials, and mangers | 174 674 | 11,0 | 93 566 | 11,4 | 81 108 | 10,5 |
| Professionals | 188 963 | 12,0 | 101 577 | 12,4 | 87 386 | 11,3 |
| Technicians and associate professionals | 326 718 | 20,6 | 147 123 | 18,0 | 179 595 | 23,3 |
| Clerks | 125 183 | 7,9 | 50 160 | 6,1 | 75 023 | 9,7 |
| Service workers, shop, and market sales workers | 383 242 | 24,1 | 111 858 | 13,6 | 271 384 | 35,2 |
| Skilled agricultural and fishery workers | 9 810 | 0,6 | 7 176 | 0,9 | 2 634 | 0,3 |
| Craft and related trade workers | 170 450 | 10,7 | 161 664 | 19,7 | 8 786 | 1,1 |
| Plant and machine operators and assemblers | 127 104 | 8,0 | 107 531 | 13,1 | 19 573 | 2,5 |
| Elementary occupations | 83 391 | 5,24 | 38 577 | 4,7 | 44 814 | 5,8 |
| Disability benefits (2008-2017) | N | % | N | % | N | % |
| Yes | 4 878 | 0,31 | 1 939 | 0,24 | 2 939 | 0,38 |
| No | 1 584 657 | 99,69 | 817 293 | 99,76 | 767 364 | 99,62 |
| Mortality (2008-2017) | N | % | N | % | N | % |
| Dead | 18 467 | 1,16 | 11 484 | 1,4 | 6 983 | 0,91 |
| Not dead | 157 068 | 98,84 | 807 748 | 98,60 | 763 320 | 99,09 |
| Ten long-term sick leave periods or more (2008-2015) | N | % | N | % | N | % |
| Yes | | | | | | |
| No | | | | | | |
The construction of the job exposure matrix (JEM)
The job exposure matrix, which forms the foundation for the Mechanical Job Exposure Index (MJEI), was developed by Hanvold et. al. (7) as a gender-specific matrix with group-based exposure estimates at each intersection between the occupations (rows) and the eight mechanical exposures (columns). To achieve reliable estimates, Hanvold et. al. decided to have at least ⩾ 19 respondents with the same occupational code when constructing the JEM groups. They report that two of the authors grouped the occupations and discussed further with a third author and two experts at the Norwegian Institute of Occupational Health. In total they constructed 268 JEM-groups based on occupational codes and the answers from 18 939 respondents in the 2006 and 2009 surveys. The job exposure matrix we used when constructing the MJEI is identical to the matrix developed by Hanvold et. al. except from the fact that we also included the 2013, 2016 and the 2019 Norwegian nationwide Survey of Living Conditions on work environment. Inclusion of these three survey populations increased the total N with 25 037 respondents and increased the mean number of respondents in each JEM group from 176 to 412. As shown in Table 3 the mean number of respondents per JEM group more than dobled in both men and woman.
Table 3
Number of occupational titles according to number of repsondents and number of respondents per JEM group
| | All (N= 333 - all) (N= 330 – 06 & 09) | Men (N= 317) (N= 303 – 06 & 09) | Women (N=281) (N= 268 – 06 & 09) |
| Number of occupational titles according to number of repsondents (2006 and 2009 in brackets) | N | % | N | % | N | % |
| 1-18 | 90 (148) | 27 (45) | 126 (164) | 40 (54) | 151 (190) | 54 (71) |
| ⩾ 19 | 243 (182) | 73 (55) | 191 (139) | 60 (46) | 130 (78) | 46 (29) |
| Mean respondents per occupational title | 132 | 73 | 74 |
| Min - Max respondents per occupational title | 1 (1) | 2224 (1075) | 1 (1) | 831 (343) | 1 (1) | 1503 (732) |
| Respondents per JEM group (2006 and 2009 in brackets) | All (N= 268) | Men (N= 209) | Women (N= 195) |
| Median | 261 (97) | 218 (78) | 385 (132) |
| Mean | 412 (176) | 276 (109) | 562 (249) |
| Min - Max | 19 (19) | 1503 (732) | 19 (19) | 831 (343) | 19 (19) | 1503 (732) |
The 2006 and 2009 survey is not directly comparable to the 2013, 2016 and the 2019 in the sense that the two first surveys are based on 4-digit STYRK-98 occupational codes and the three later are based on 4-digit STYRK-08 codes. There is no official key of correspondence between the 4-digit STYRK-98 and the 4-digit STYRK-08 codes (confirmed in correspondence with Statistics Norway, section for labour market statistics), thus being able to append the five surveys we had to develop a key of correspondence. Since our register data includes the 4-digit STYRK-98 codes we choose to convert the 4-digit STYRK-08 codes in the 2013, 2016 and the 2019 survey into 4-digit STYRK-98 codes. When faced with the choice of having more than one STYRK-98 code to select, we chose to covert to the STYRK-98 code with the highest N in the 2006 and 2009 survey combined. This applied to 28 percent of the 4-digit STYRK-08 occupational codes, thus 72 percent remained unchanged.
Mechanical job exposures
The occupational-based mechanical exposure index is based on the same eight mechanical exposures Hanvold et.al. used when constructing their gender-specific job exposure matrix (JEM). The measures used for the self-reported mechanical exposures were developed by an expert group in a Nordic project (12) and based on the scientific literature (13), the eight mechanical exposures were dichotomized into exposed and not exposed at the individual level. The questions and cut-off values used are shown in Table 4 below.
Table 4
Exposures, Questions and Non-exposed/Exposed Composite Mechanical Job Exposure Index
| Exposures | Questions | Not exposed/Exposed |
| Heavy lifting (>20 kg) | Do you have to lift something that weighs more than 20 kg daily, and in the case of how many times per. day? “Yes, at least 20 times per. day”, “Yes, 5-19 times per. day”, “Yes, 1-4 times per day”, “No” | 0 = Not exposed (No), 1 = Exposed (≥1–4 times) |
| Hands above shoulder height | Do you work with your hands raised at shoulder height or higher? – “yes” or “no” If “yes” – Can you estimate how much of the workday you do this? “almost all the time”, “about 3/4 of the time”, “about half the time”, “about 1/4 of the time”, “very little part of the time” | 0 = Not exposed (no or very little of the workday), 1 = Exposed (≥1/4 of the workday) |
| Heavy physical work | Do you work so hard that you breathe faster? – “yes” or “no” If “yes” – Can you estimate how much of the workday you do this? “almost all the time”, “about 3/4 of the time”, “about half the time”, “about 1/4 of the time”, “very little part of the time” | 0 = Not exposed (no or very little of the workday), 1 = Exposed (≥1/4 of the workday) |
| Neck flexion | Do you work with your head forward bending? – “yes” or “no” If “yes” – Can you estimate how much of the workday you do this? “almost all the time”, “about 3/4 of the time”, “about half the time”, “about 1/4 of the time”, “very little part of the time” | 0 = Not exposed (no or very little of the workday), 1 = Exposed (≥1/4 of the workday) |
| Squatting/kneeling | Do you have to squat or kneel when you work? – “yes” or “no” If “yes” – Can you estimate how much of the workday you do this? “almost all the time”, “about 3/4 of the time”, “about half the time”, “about 1/4 of the time”, “very little part of the time” | 0 = Not exposed (no or very little of the workday), 1 = Exposed (≥1/4 of the workday) |
| Forward bending | Do you work in forward-leaning positions without supporting yourself with your hands or arms? – “yes” or “no” If “yes” – Can you estimate how much of the workday you do this? “almost all the time”, “about 3/4 of the time”, “about half the time”, “about 1/4 of the time”, “very little part of the time” | 0 = Not exposed (no or very little of the workday), 1 = Exposed (≥1/4 of the workday) |
| Awkward lifting | Do you have to lift in awkward positions? – “yes” or “no” If “yes” – Can you estimate how much of the workday you do this? “almost all the time”, “about 3/4 of the time”, “about half the time”, “about 1/4 of the time”, “very little part of the time” | 0 = Not exposed (no or very little of the workday), 1 = Exposed (≥1/4 of the workday) |
| Standing/walking | Do you work standing or walking? – “yes” or “no” If “yes” – Can you estimate how much of the workday you do this? “almost all the time”, “about 3/4 of the time”, “about half the time”, “about 1/4 of the time”, “very little part of the time” | 0 = Not exposed (≤1/4 of the workday), 1 = Exposed (≥1/2 of the workday) |
All the exposure variables are constructed as the proportion of individuals within each JEM-group that are exposed to the specific exposure. Thus, we have constructed variables that, in principle, goes from 0 to 100 percent based variables that are dichotomous (exposed = 1, not exposed = 0). This means that occupational codes with a value of 0 on one of the variables implies that none with these occupational codes, belonging to the same JEM-group, has provided an answer that involves exposure. In contrast, the value 100 means that all respondents with that occupational code, belonging to the same JEM-group, have provided an answer that involves exposure. In total, we have 323 unique occupational codes that are used when the index is merged to register data.
Constructing the composite mechanical job exposure index (MJEI)
In order to investigate the factorial validity of the occupational-based mechanical exposure index (MJEI), confirmatory factor analysis was performed. The CFA model was fitted in Stata v16 and for model estimation maximum likelihood was applied.
Model evaluation was based on chi-square tests for model fit and further model fit indices, including the root mean square error of approximation (RMSEA), the comparative fit index (CFI), the Tucker–Lewis index (TLI) and the standardised root mean square residual (SRMR). For model fit to be interpreted as ‘acceptable’, a RMSEA of < 0.05 was considered a close fit, while a RMSEA and a SRMR of up to 0.08 were considered acceptable. Comparing the fit of a target model to the fit of an independent or null model, the CFI has a cut-off for good fit CFI of ⩾0.90. A TLI of 0.95 indicates the model of interest improves the fit by 95% relative to the null model, and the cut-off for good fit was sat at TLI ⩾0.95. Furthermore, the correlations of residuals to improve model fit when fitting the nine one-factor models were considered. Correlated residuals < 0.2 were considered acceptable when fitting the model (14; 15). Potential model adjustments were based on modification indices as provided in the Stata output using the ‘estat gof, stats (all)’ command. To obtain a clearer idea of the data and potential problematic items, a one-factor model was fitted to the data. To test whether modifications, in terms of correlated within factor residuals, led to significant model improvement, modification indices were obtained using the ‘estat mindices’ command in Stata.
Table 5
Confirmatory Factor Analysis and internal consistency (Cronbach`s alpha) Composite Mechanical Job Exposure Index (one-factor model)
| Cronbach`s alpha: 0.89 | X² | p | RMSEA | CFI | TLI | SRMR | Correlated error |
| Original | 124.37 | 0.000 | 0.171 | 0.914 | 0.871 | 0.048 | |
| Heavy lifting (>20 kg) with Hands above shoulder height Heavy lifting (>20 kg) with Heavy physical work Heavy lifting (>20 kg) with Squatting/kneeling Hands above shoulder height with Awkward lifting Squatting/kneeling with Awkward lifting Forward bending with Standing/walking | 9.72 | 0.285 | 0.028 | 0.999 | 0.996 | 0.015 | .082 .055 .062 .095 .081 .072 |
| Exposures | *Standarised factor loading | Standard error |
| Share exposed - Heavy lifting (>20 kg) | .744 | .030 |
| Share exposed - Hands above shoulder height | .816 | .023 |
| Share exposed - Heavy physical work | .758 | .029 |
| Share exposed - Squatting/kneeling | .838 | .021 |
| Share exposed - Forward bending | .728 | .031 |
| Share exposed - Awkward lifting | .862 | .019 |
| Share exposed - Standing/walking | .762 | .028 |
| *no cross-loadings and no correlated residuals |
The results from fitting a one-factor model is shown in Table 5. The “Original” row shows the results when fitting the MJEI with no cross-loadings and no correlated residuals. All factor loadings were high (i.e. >0.7; see column “Standardised factor loading” in Table 4).
When fitting the one-factor model, correlated residuals were sequentially added to respective models, which improved each model fit significantly. As shown in Table 4, a model fit with ten modifications gave a satisfying model fit. All the correlated residuals were <0.2. The MJEI showed good internal consistency with a Cronbach’s alpha of 0.89 (see Table 5).
The MJEI performance
In order to assess the MJEI performance we used four different performance measures: Cohen`s Kappa, sensitivity, specificity and Spearman`s Rho. Cohen`s Kappa mesures agreement between the group-based exporsure estimates and the individual expsosure estimats, taking into account that agreement may occur by chance. According to Cohen (16) the kappa values can be classified as poor (<0.20), fair (0.21-0.40), moderate (0.41-0.60), good (0.61-0.80) and excellent (0.81-1) agreement. Sensitivity measures the proportion of individuals who are identified as exposed based on individual estimates, that are also identified as exposed using the group-based estimates. Specificity measures the proportion of individuals who are identified as unexposed based on individual estimates, that are also identified as unexposed using the group-based estimates. Spearman`r Rho measures the monotonic relationships, whether linear or not, between two variables. In this paper we use Spearman`r Rho to investigate the correspondence, i.e. the rank order, between the exposures reported by the individual employee and the exposures linked to the individual using their occupational code.
As shown in Table 6, Cohen`s Kappa is fare to good and moderate for all exposures except for “work with neck flexion” wich is poor (0.18 in men and 0.16 in woman). Using a cut-off of 20 percent for all the exposures except “standing/walking” with a cut-off of 50 percent, gave a sensitivity of >50 percent for six out of eight exposures for both men and women. For women the same cut-off`s gave a sensitivity of >50 percent for six out of eight exposures. The specificity was ⩾ 75 percent for six out of eight exposures for men and five for women.
Table 6
Cohen`s Kappa, Sensitivity and Specificity measures
| | Men | Woman |
| Exposures | Cut-off % | Kappa | Sensitivity | Specificity | Kappa | Sensitivity | Specificity |
| Heavy lifting (>20 kg) | 20 | 0.37 | 82 | 66 | 0.32 | 68 | 77 |
| Hands above shoulder height | 20 | 0.39 | 69 | 81 | 0.25 | 44 | 87 |
| Heavy physical work | 20 | 0.31 | 76 | 70 | 0.22 | 52 | 80 |
| Work with neck flexion | 20 | 0.18 | 43 | 79 | 0.16 | 51 | 70 |
| Squatting/kneeling | 20 | 0.43 | 75 | 80 | 0.32 | 77 | 71 |
| Forward bending | 20 | 0.24 | 45 | 87 | 0.22 | 48 | 83 |
| Awkward lifting | 20 | 0.29 | 63 | 79 | 0.27 | 77 | 70 |
| Standing/walking | 50 | 0.56 | 77 | 81 | 0.61 | 86 | 75 |
Hanvold et. al. (7) used cut-off values when constructing their final Job Exposure Matrix, being as they`re goal was to investigate each exposures association with lower-back pain, our goal however is to construct a Mechanical Job Exposure Index for the use in register data analysis. Thus, we choose not to reduce the information in the exposures using cut-off`s values but have instead use the exposure variables measuring the percentage within each occupational code that is exposed. The sensitivity and specificity measures provide a valuable insight into the different exposures performance in identifying exposed and non-exposed individuals. However, since our goal is to measure the overall mechanical exposure in each occupation, it seems more fruitful to consider occupations as more or less exposed based on the percentage reporting to be exposed in each occupation. Thus, we have chosen to keep the measures, measuring the percentage exposed and used in the factor analysis, as is when constructing the Mechanical Job Exposure Index (MJEI). To test the correspondence between the exposures measured as percentage exposed within each occupation (group-based exposure) and the individual reported exposures we use Spearman`s Rho, the results from a rank correlation analysis is presented in Table 7.
Table 7
Rang correlation between exposures at the individual level and the occupational level – Spearman`s Rho
| | Men | Women |
| Composite Mechanical Job Exposure Index | .642 (.000) | .626 (.000) |
| Single exposures | | |
| Heavy lifting (>20 kg) | .468 (.000) | .382 (.000) |
| Hands above shoulder height | .424 (.000) | .296 (.000) |
| Neck flexion | .202 (.000) | .193 (.000) |
| Heavy physical work | .394 (.000) | .380 (.000) |
| Squatting/kneeling | .465 (.000) | .403 (.000) |
| Forward bending | .283 (.000) | .284 (.000) |
| Awkward lifting | .349 (.000) | .357 (.000) |
| Standing/walking | .600 (.000) | .637 (.000) |
The rank correlation between the Composite Mechanical Job Exposure Index based on the individual reported exposures and the group-based exposures is .642 for men and .626 for women (see Table 7). Thus, the correlation between the index based individual reported exposures and the group-based exposures is strong for both genders. For each of the eight exposures the correlation between the individual reported exposure and the group-based exposure is weak for “neck flexion” and “forward bending”. Whereas the correlation is moderate for “heavy lifting”, “hands above head”, “Heavy physical work”, “Squatting/kneeling”, “Awkward lifting” and strong for “Standing/walking”. When comparing the sensitivity measures with the correlations it shows that those exposures with a low sensitivity, “work with neck flexion” and “forward bending” for both genders and “hands above shoulder” for women, also have a weaker correlation. Nevertheless, an overall correlation of .642 for men and .626 for women demonstrates that the Composite Occupational Mechanical Job Exposure Index (MJEI), based on five Norwegian nationwide Survey of Living Conditions on work environment, is strongly correlated with the overall mechanical job exposures experienced at the individual level.
Low-back pain, long-term sick leave, disability benefits and mortality
To test the predictive validity of the Composite Mechanical Job Exposure Index individual reported low-back pain and long-term sick leave is used as outcome variables in the analysis based on the five surveys. Individual reported low-back pain is measured as a dummy-variable: “Have you during the last month been bothered by lower back pain?” “Very or quite bothered” = 1, “a little or not at all bothered” = 0. Individual reported sick leave is also measured as a dummy-variable: “Have you during the last 12 months had continuous sick leave for more than 14 days?” “Yes”=1, “No”= 0.
Furthermore, the predictive validity of the Composite Mechanical Job Exposure Index is tested merging the index to register data using receipt of disability benefit in the period 2008 to 2017, the number of long-term sick leave periods between 2008 and 2015 and mortality between 2008 and 2017 as outcome variables. “Disability” and “mortality” are both measured as dummy variables: “disabled during 2008 to 2017” = 1, “not disabled during 2008 to 2017” = 0 and “dead during 2008 to 2015” = 1, “not dead during 2008 to 2017” = 0. “Long-term sick leave periods” is measured as a continues variable and sums up the number of sick leave periods exceeding 16 days between 2008 and 2015.
Predictive validity of the Composite Mechanical Job Exposure Index
As shown in figure 1, for both men and women, the unadjusted occupational MJEI estimate is not significantly lower than the individual MJEI estimates (unadjusted and adjusted), thus the occupational MJEI shows a reproduceable likelihood for lower-back pain for men. When adjusting for level of education and age, the reproduceable likelihood for lower-back pain is significantly lower for men, but still significant.
Figure 2 shows the likelihood of reporting a long-term sick leave among men and women, according to the occupational MJEI and the individual MJEI. The occupational MJEI shows a reproduceable likelihood for long-term sick leave for both men and women, and the adjusted occupational MJEI estimate does not significantly differ from the individual estimates.
When investigating the association between the occupational MJEI and disability 2008-2017, the occupational MJEI does not predict a higher likelihood for disability among men when adjusting for age and level of education (Figure 3). For women the occupational MJEI predicts a higher likelihood for disability during 2008 to 2017 both before and after adjusting for age and level of education.
As shown in figure 4, the occupational MJEI predicts higher mortality among men both before and after adjusting for age and level of education. For women the occupational MJEI predicts higher mortality after adjusting for age and level of education.
The occupational MJEI predicts a significantly higher probability of having ten or more long-term sick leave periods during 2008 to 2015 for both men and women, before and after adjusting for age and level of education. As shown in figure 5, the predicted likelihood is almost twice as high for women compared to men.