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Prevalence and incidence of work-related musculoskeletal disorders in secondary industries of 21st century Europe: a systematic review and meta-analysis

Abstract

Objective

Over the course of the twenty-first century, work-related musculoskeletal disorders are still persisting among blue collar workers. At present, no epidemiological overview exists. Therefore, a systematic review and meta-analysis was performed on the epidemiology of work-related musculoskeletal disorders (WMSD) within Europe’s secondary industries.

Methods

Five databases were screened, yielding 34 studies for the qualitative analysis and 17 for the quantitative analysis. Twelve subgroups of WMSDs were obtained for the meta-analysis by means of predefined inclusion criteria: back (overall), upper back, lower back, neck, shoulder, neck/shoulder, elbow, wrist/hand, leg (overall), hip, knee, and ankle/feet.

Results

The most prevalent WMSDs were located at the back (overall), shoulder/neck, neck, shoulder, lower back and wrist WMSDs with mean 12-month prevalence values of 60, 54, 51, 50, 47, and 42%, respectively. The food industry was in the majority of subgroups the most prominent researched sector and was frequently associated with high prevalence values of WMSDs. Incidence ratios of upper limb WMSDs ranged between 0.04 and 0.26. Incidence ratios could not be calculated for other anatomical regions due to the lack of sufficient articles.

Conclusion

WMSDs are still highly present among blue collar workers. Relatively high prevalence values and low incidence ratios indicate a limited onset of WMSDs with however long-term complaints.

Peer Review reports

Introduction

Work-related musculoskeletal disorders (WMSDs) are impairments of the musculoskeletal system, primarily caused by the performance of work tasks and the direct environment in which work is carried out [1]. Secondary industries, known for converting raw materials into products for the consumer, comprise several risk factors that contribute to the development of WMSDs [2]. Repetitive movements, awkward postures as well as continuous and excessive use of force might overload the musculoskeletal system, enhancing the risk of developing WMSDs [3]. Furthermore, psychosocial risk factors such as job related stress, lack of support from colleagues or managers, high mental workload and lack of recognition for the work done are supplementary addons in triggering the development of WMSDs in this sector [4].

The consequences of WMSDs impact both social and individual level, and result in an extensive and varied burden of costs [5]. In European industries, work absenteeism is reported in more than 50% of employees affected by WMSDs, which is significantly higher than in workers infected by the influenza virus (10–12%) [1, 6, 7]. Employees suffering from WMSDs are also absent from work for a longer period of time compared to workers with other health problems [1]. Furthermore, WMSDs are responsible for permanent incapacity in 60% of all reported cases [5]. Not surprisingly, the financial costs of WMSDs in Europe are estimated at 240 billion euros, accounting for 2% of the gross domestic product of EU-15 [5]. In addition to the substantial socio-economic impact, the individual employee has to pay a relatively high price as well, with studies reporting a significant decreased quality of life in people suffering from musculoskeletal disorders [8, 9]. Despite these known negative consequences, a clear epidemiological overview of WMSDs in European secondary industries is missing.

Since 2000, strategies to optimize Europe’s industrial activities are constantly explored to ensure recovery from economic crises and to remain a considerable competitor to other continents [10, 11]. Numerous research fields are therefore encouraged to develop strategies for improving overall industrial work. These innovations change the familiar way of industrial work performance (e.g. robots, exoskeletons) and could therefore impact the development of WMSDs due to changed physical and psychosocial demands [12, 13]. However, in order to objectify the impact of these industrial technologies, recent epidemiological data regarding WMSDs in the previous setting, thus without these technological advances, are necessary first.

Due to the increased risk of incurring WMSDs in secondary industries and the detrimental impact of WMSDs in general, as well as the lack of a clear epidemiological overview on WMSDs in secondary industries of twenty-first century Europe, a systematic review and meta-analysis were performed. The aim was to provide an overall insight on the prevalence and incidence of WMSDs in Europe’s secondary industries during the twenty-first century.

Methods

The review and meta-analysis was developed and reported in accordance with the preferred reporting items for systematic reviews and meta-analyses [14].

Search strategy

The PubMed, Web of Science, ScienceDirect, Cochrane library and Scopus databases, were searched for eligible articles. The final search of the databases was performed on the eight of March 2021. Three authors (RG, BT, JG) developed the search strategy in accordance with the PECO framework [15] that comprised key-words related to prevalence and incidence numbers (epidemiology OR incidence OR prevalence), industrial work (industry OR industrial worker OR industrial work OR industrial workplace OR industrial task) and musculoskeletal disorders (musculoskeletal pain OR occupational injury OR injury OR musculoskeletal disorder OR musculoskeletal disease OR musculoskeletal complaint OR musculoskeletal pain OR cumulative trauma disorders). No filters were added with the exception of publication year set from 2000 to 2021 to only include articles that researched WMSDs in the twenty-first century. In addition, reference lists of studies included in this review were screened for relevant articles not provided by the initial search strategy. Detailed descriptions for each database are displayed in supplementary figure A.

Eligibility criteria

Studies were included if they (i) provided prevalence or incidence data, defined in accordance with the definitions of incidence and prevalence provided by the Centers for Disease Control and Prevention (CDC) [16], (ii) focused on manual work in secondary industry (manufacturing), (iii) included countries of the European Union (EU-28), (iv) reported WMSDs that corresponded to an anatomical region (e.g. neck, back, hip, etc.), (v) adopted an observational study design (cross-sectional, cohort, or health surveys), (vi) used validated or non-validated questionnaires and (vii) were published in peer-reviewed journals. Studies were excluded when they (i) did not provide an overall number of WMSDs in secondary industry, (ii) did not make a clear distinction in manual or administrative workers for reported WMSD data, (iii) failed to clearly differentiate between industry sector, (iv) presented prevalence/incidence numbers based on claims or hospital records and (v) were published before the year 2000.

Study selection

Databases were searched by one author (RG) and articles were imported in the Rayyan web application for duplicate removal and screening [17]. First, articles were screened on title and abstract by one author according to above mentioned and predefined eligibility criteria. Next, remaining articles were screened on full text by two independent researchers (RG and BT). Disagreements were solved through discussion. If consensus could not be accomplished, a third researcher (JG) would take part in the process to make inclusion by majority possible. When full texts could not be found or data of interest for this meta-analysis was missing, authors would be contacted through e-mail to request full text or data.

Data extraction and risk of bias

One author (RG) extracted the following data of included full texts to answer the research question: study design, type of industry, period of measurement, response rate, demographic characteristics of included participants (age and gender), tools for examination of WMSDs, examiner (e.g. self-report or occupational physician), type of WMSDs (e.g. neck pain or shoulder pain), prevalence and incidence data per WMSD. Occupational physicians were selected as representative for the examiner variable when options between different healthcare professionals were given. Furthermore, self-reporting prevalence data took precedence over physical examination data when studies reported both. Results will be subdivided in a Prevalence section containing a qualitative and quantitative analysis, and an Incidence section limited to a quantitative analysis.

Risk of bias was assessed in accordance with Hoy et al. [18]. They used a risk of bias tool specifically developed for prevalence studies. Risk of bias was verified through ten questions that can be answered with “high risk” or “low risk”. Questions one to four assess the selection and nonresponse bias (external validity), five to nine the measurement bias and ten the bias related to the analysis. When articles provided insufficient information to answer a question a “high risk”- score was assigned to that item. Risk of bias was analyzed by two independent researchers (RG and JG). Consensus was established through discussion.

Quantitative synthesis

In order to perform a meta-analysis, studies were included if they reported (i) sufficient demographic information regarding sample size and (ii) data for specific anatomical locations i.e. neck, shoulder, elbow, wrist/hand, back or leg WMSDs. Twelve subgroups of WMSDs were formulated: neck, shoulder, shoulder/neck, elbow, wrist/hand, upper back, lower back, back (for studies that did not make a distinction between upper or lower back), hip, knee, ankle/feet, leg (for studies that did not make a distinction between hip, knee or ankle/feet). Pooling of data occurred in several stages. First, overall prevalence percentages or incidence ratios were calculated for studies reporting data related to subgroups of the investigated sample size i.e. age, gender, skill level (e.g. unskilled versus skilled), workload (e.g. low or high) or type of manual workers (e.g. welder, metal worker, other manual workers). Overall prevalence percentages were calculated using following formula where “p” corresponds to prevalence, “n” to sample size and “ntot” to the sum of all sample sizes: [(p1 + … + px) (n1 + … + nx)]/ntot. Overall incidence ratios were calculated using following formula where “i” corresponds to incidence cases, “n” to sample size and “y” to the persons-years at risk: (i1 + … + ix)/ [(n1 + … + nx) (y1 + … + yx)]. In order to minimize heterogeneity, no overall prevalence percentages or incidence ratios were calculated for studies that included prevalence periods (e.g. 12-month or 7-day prevalence). Second, standard errors were calculated for each prevalence rate or incidence ratio using following formula with “p” corresponding to either prevalence rate or incidence ratio and “n” to the sample size: sqrt [p (1-p) / n)]. All calculations were performed in Microsoft Excel (version 2002). Third, mean prevalence and incidence values with associated 95% confidence intervals and heterogeneity (I2 statistics) per WMSD-subgroup were calculated through the random-effects model of the R software program (version R-4.0.2). I2 statistics displays the variation across included studies that is due to heterogeneity rather than chance [19].

Results

Study collection

A total of 4371 articles were retrieved. After removing duplicates, 3509 articles were subsequently screened by title and abstract. The remaining 88 articles were evaluated on full text as well as six additional articles, obtained from consulting the references of included articles. A total of 35 studies were included in the qualitative analysis of which 24 authors [20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43] discussed prevalence of WMSDs, seven authors [44,45,46,47,48,49,50] researched incidence of WMSDs and four authors [51,52,53,54] reported both incidence and prevalence rates. Figure 1 displays the results of the screening process in more detail. Due to the limited amount of incidence reports, a meta-analysis could only be performed for studies that reported prevalence data and complied with the predetermined eligibility criteria for quantitative analysis (n = 17).

Fig. 1
figure1

Screening process

Risk of bias assessment

Risk of bias assessment resulted in 12 low risk scored studies [20, 22, 25, 31, 32, 34, 39,40,41,42, 53, 54], 20 studies with moderate risk scores [21, 23, 24, 26, 28,29,30, 33, 35,36,37,38, 43, 45,46,47,48,49, 51, 52] and 3 studies that received high risk scores [27, 44, 50].

Prevalence

Qualitative analysis

The majority of studies was published between 2000 and 2010 [20, 22,23,24,25, 27, 28, 30,31,32,33, 36,37,38,39,40, 51, 52, 54] and most included articles were cross-sectionally designed [20,21,22, 24,25,26, 29, 31, 32, 34,35,36,37,38,39,40,41,42,43, 51, 53]. Thirteen countries and seven different industries were obtained. Further, the majority of studies reported response rates greater than 50% [20,21,22,23, 25, 27, 30,31,32,33,34, 37, 38, 40,41,42, 53, 54] and selected a sample size that ranged between 100 and 500 participants [20, 27, 28, 31,32,33,34, 36,37,38, 40,41,42,43, 52,53,54]. Assessments relying on self-report of the workers were the most prominent tool to evaluate WMSDs [20,21,22,23,24, 30, 31, 35, 37, 38, 40,41,42,43]. The Nordic musculoskeletal questionnaire or a modified version was the most utilized questionnaire to identify WMSDs [22, 26, 29, 31, 34, 35, 37, 39, 42, 43, 51] (Table 1).

Table 1 Qualitative overview of prevalence of WMSDs in secondary industries of Europe

Although the preponderance of studies reported prevalence data over a 12-month period [20,21,22,23,24, 28, 31, 35, 40, 42, 43, 51, 54] or discussed point prevalence data [25, 27, 32, 33, 52, 53], prevalence periods varied for other included articles [26, 30, 37,38,39, 41]. WMSDs were mostly reported in anatomical regions of the neck, shoulder, elbow, hand/wrist, back and legs. Consequently, these anatomical regions are included in the quantitative analysis (3.3.2 Quantitative analysis) and will be discussed in more detail with their corresponding prevalence data (Table 1).

Quantitative analysis

Seventeen of the 28 studies were selected based on aforementioned eligibility criteria [20,21,22,23,24, 26, 27, 30, 31, 33, 35,36,37,38, 40, 42, 43].

12-month prevalence

Twelve studies [20,21,22,23,24, 26, 31, 35, 37, 40, 42, 43] were divided in 12 subgroups of WMSDs: neck, shoulder, shoulder/neck, elbow, wrist/hand, upper back, lower back, back (for studies that did not make a distinction between upper or lower back), hip, knee, ankle/feet, leg (for studies that did not make a distinction between hip, knee or ankle/feet). The heterogeneity analysis illustrated a considerable heterogeneity for the majority of subgroups (neck, shoulder, shoulder/neck, wrist/hand, back, upper back, lower back, leg and knee) with ranges between 76 and 96%. A substantial heterogeneity was obtained for the hip subgroup (I 2 = 63%) and both the elbow and ankle/feet subgroups displayed a heterogeneity of non-importance (I2 = 5% and I2 = 36%, respectively) [55]. Due to the lack of sufficient articles, additional subgroup analyses could not be performed to further investigate these high heterogeneity values. Pooled sample sizes of 9540, 7464, 1097, 2984, 4507, 5405, 788, 4922, 1023, 5710, 643 and 788 blue-collar secondary industry workers were obtained for back, shoulder/neck, neck, shoulder, lower back, wrist/hand, knee, leg, upper back, elbow, ankle/feet and hip subgroups, respectively. A distinct distribution of WMSDs over time could not be identified, however the food industry was the industry generally most represented in all subgroups. On average, industrial workers had the most WMSDS in the back (back: mean 60%; SD 13%, lower back: mean 47%; SD 20%), shoulder (shoulder: mean 50%; SD 18%, shoulder/neck: mean 54%; SD 27%) and neck regions (mean 51%; SD 15%). Hip complaints were the least prevalent (mean 11%; SD 7%) (Table 2).

Table 2 Overall prevalence of WMSDs per anatomical region

A mean of 60% (range 38–72%; SD 13%) back WMSDs was reported in secondary industries (Fig. 2). The food industry was the industry most represented in this analysis [20, 24, 35, 40]. Lower back WMSDs obtained a mean prevalence of 47% (range 24–71%; SD 20%) and were researched in the food, transport, shipyard, production, textile, manufacturing and steel industry (Figure B supplementary material). With the exception of the production industry, where Andersen et al. [23] reported both baseline and follow-up prevalence data, all industries were represented equally [21,22,23, 31, 37, 42, 43]. Upper back WMSDs were prevalent in 22% (range 6–37%; SD 12) of the investigated pooled sample size with food [37], transport [31], textile [21], manufacturing [42] and steel [43] industries included in the subgroup analysis (Figure C supplementary material).

Fig. 2
figure2

Meta-analytic overview of prevalence of back WMSDs

Shoulder/neck WMSDs obtained a mean prevalence of 54% (range 19–83%; SD 27%) and were reported in the food [20, 24, 40], shipyard [22] and production [23] industry (Fig. 3). Isolated neck WMSDs were prevalent in 51% (range 32–69%, SD 15%) of the secondary industrial workers (Figure D supplementary material) and isolated shoulder WMSDs in 50% (range 23–78%; SD 18%) of the workers researched (Figure E supplementary material). In both subgroups, the food industry was the industry sector most represented [35, 37, 40] and also the transport [31], textile [21], manufacturing [42] and steel [43] industry were included in the analysis.

Fig. 3
figure3

Meta-analytic overview of prevalence of shoulder/neck WMSDs. (a) = baseline; (b) = 24-month follow-up

Wrist/hand WMSDs, with a mean prevalence of 42% (range 14–64%; SD 16%) were investigated in the food [20, 24, 37, 40], transport [31], shipyard [22] and textile [21] industry (Fig. 4). Further, a prevalence of 29% (range 16–56%; SD 16%) was obtained for overall leg WMSDs in food [20, 24], production [23] and steel industries [43] (Fig. 5). Considering studies that differentiate between hip, knee and ankle/feet WMSDs, mean prevalence values were obtained of 11% (range 8–22; SD 7%), 33% (range 21–46%; SD 11%) and 17% (range 13–24%; SD 6%), respectively (Figure F, G, H supplementary material).

Fig. 4
figure4

Meta-analytic overview of prevalence of wrist/hand WMSDs. (a) = wrist; (b) = fingers

Fig. 5
figure5

Meta-analytic overview of prevalence of leg WMSDs. (a) = baseline; (b) = 24-month follow-up

Food [20, 24, 37], transport [31], production [23], textile [21] and steel [43] industries were all affected by elbow WMSDs with a prevalence mean of 21% (range 15–30%; SD 4%) (Fig. 6).

Fig. 6
figure6

Meta-analytic overview of prevalence of elbow WMSDs. (a) = baseline; (b) = 24-month follow-up

3-month prevalence

Sundstrup et al. [41] reported 3-month prevalence values of 60, 52, 48, 40% for the shoulder, wrist/hand, neck and elbow, respectively. No further analysis was performed due to the lack of sufficient articles.

1-month prevalence

Two studies reported a 1-month prevalence value for shoulder WMSDs with a calculated mean prevalence of 22% (range 11–27%; SD 8%) [30, 38].

7-day prevalence

WMSDs during a 7-day period were researched in three studies [26, 36, 37]. Prevalence values of 47, 44, 26, 18, 16 and 15% for shoulder, neck, wrist/hand, upper back, hip and ankle feet WMSDs, respectively, were solely researched in one study [37]. Overall back WMSDs with a prevalence of 26% were reported by [26] and neck/shoulder WMSDs with a prevalence of 52% were discussed by Nordander et al. [36]. Lower back, elbow and knee WMSDs were both researched in two studies and mean prevalence values of 34% (range 29–39%; SD 7%), 29% (range 9–47%; SD 27%) and 27% (range 15–38%; SD 16%), respectively, were obtained [36, 37].

Point-prevalence

De Zwart et al. [27] investigated different secondary industry sectors of which mean point-prevalence values of 16% (range 11–20%; SD 3%), 11% (range 7–15%; SD 3%), 5% (range 4–6%; SD 0%) and 5% (range 3–6%; SD 1%) were obtained for shoulder, neck, wrist/hand and elbow WMSDs, respectively. Kaergaard and Andersen [33] also investigated point-prevalence, however they focused on the textile industry and shoulder/neck WMSDs where a prevalence of 78% was reported.

Incidence

Qualitative analysis

The majority of studies were published between 2000 and 2010 [44,45,46,47,48,49, 51, 52, 54]. Four countries and ten different industries were obtained. Similar to prevalence data, cohort and cross-sectional study designs were preferred in incidence research with nine [44,45,46,47,48,49,50, 52, 54] and two studies [51, 53], respectively. Further, the majority of studies reported a response rate greater than 50% [44, 46,47,48, 53, 54] and included sample sizes between 100 and 500 participants which is in line with included prevalence studies [48,49,50, 52,53,54]. Further, evaluation of WMSDs was primarily performed through a combination of reviewing obtained questionnaire data and conducting physical examinations [48, 51, 52, 54]. All studies reported the employment of a health professional to identify WMSDs [44,45,46,47,48,49, 51, 52, 54] (Table 3).

Table 3 Qualitative overview of incidence of WMSDs in secondary industries of Europe

Upper limb and neck/back disorders were the most commonly studied WMSDs with 11 [44,45,46,47,48,49,50,51,52,53,54] and 6 [44,45,46,47, 49, 54] studies, respectively, reporting incidence data. A further discrimination in upper limb WMSDs was made by five studies [47, 51,52,53,54] with carpal tunnel syndrome being the most frequently reported upper limb disorder [51,52,53,54]. Leclerc et al. [52] and Roquelaure et al. [54] both documented epicondylitis lateralis and wrist tendinitis as upper limb WMSDs. In addition, three studies reported incidence data regarding lower limb disorders without further anatomical details [44,45,46]. Overall, incidence ratios were reported or calculated for six of the eleven studies [48,49,50, 52,53,54]. Calculations could not be performed for other studies due to the lack of sample size descriptions. Overall, incidence ratios of upper limb WMSDs ranged from 0.04 to 0.26 [48,49,50]. A maximum ratio of 0.14 was reported for carpal tunnel syndrome [53], minimum ratios of 0.12 were stated by Leclerc et al. [52] and Roquelaure et al. [54]. Two authors documented incidence ratios regarding lateral epicondylitis and wrist tendinitis of 0.13 and 0.06, respectively, and 0.02 and 0.03, respectively [52, 54]. Häkkänen et al. [49] stated an overall incidence ratio of 0.19 for back WMSDs and 0.14 for neck/shoulder disorders (Table 3).

Discussion

The goal of this meta-analysis was to provide insight on the prevalence and incidence of WMSDs in Europe’s secondary industries. Results showed that back (overall), shoulder/neck, neck, shoulder, lower back and wrist WMSDs were the most prevalent with mean values of 60% (range 38–72%), 54% (range 18–83%), 51% (range 32–69%), 50% (range 23–78%), 47% (range 24–71%) and 42% (range 14–64%), respectively. Incidence ratios of upper limb disorders were the most common reported and ranged from 0.04 to 0.26 [48,49,50]. The food industry was the most prominent researched sector for the prevalence of back (overall), elbow, leg (overall), shoulder, neck, shoulder/neck, and wrist/hand WMSDs subgroups. This specific industry type was frequently associated with high prevalence values of WMSDs [20, 24, 35, 37, 40]. The food sector is often characterised by working in a cold environment, which could form an additional risk factor for developing WMSDs [20, 24, 40].

Back WMSDs

Overall back WMSDs obtained the highest prevalence values of all subgroups (60%). This finding is corroborated by the European Agency for Safety and Health at Work (EU-OSHA) with a 12-month prevalence value of 55% reported in industrial workers (i.e. plant and machine operators, assemblers), indicating the significant susceptibility of this population for developing back WMSDs [1]. Further, lower back WMSDs were more frequently reported (47%) compared to upper back WMSDs (22%). This corresponds to values reported in the systematic review of Briggs et al. [56] where a 12-month prevalence range between 3 to 55% and medians around 30% were obtained for upper back WMSDs in most occupational groups. This difference between prevalence of lower and upper back WMSDs may explain the growing interest in lower back preventive measures since this anatomical location is clearly more prone to develop WMSDs.

It is assumed that the presence of biomechanical risk factors (e.g. lifting heavy loads or performing repetitive task) and psychosocial risk factors (e.g. low job control or level of support from colleagues) increases the development of WMSDs [2, 57]. This correlation was found in the majority of studies [20, 35, 37, 42]. Therefore, to effectively prevent this type of WMSDs, implementation of robotic devices e.g. exoskeletons or collaborative robots could offer great potential by reducing biomechanical loads. Further, studies implementing the Nordic musculoskeletal questionnaire (NMQ) reported higher prevalence values in the lower and upper back subgroup [22, 26, 31, 35, 37, 42, 43]. It is possible that this examining method provided a more thorough self-evaluation than non-validated questionnaires. However, this was not the case for the overall back WMSDs subgroup. Since other factors could have influenced these results (e.g. characteristics of sample size, presence of risk factors, etc.) and high heterogeneity was obtained for all back WMSDs subgroups, it is recommended to interpret these observations with caution.

Regarding incidence of back WMSDs, Häkkänen et al. [49] reported incidence ratios of 0.19 for males (indicating that over the course of 1 year 19 out of 100 persons reported new back WMSDs) and 0.24 for females working in the transport industry. This indicates a relative limited development of new back WMSDs between 1987 and 1990. A European report of 2005 described relatively similar 1-year incidence ratios of low back WMSDs that ranged between 0.12 and 0.29 for Austria and 0.28 for the Czech Republic [58]. These relatively low incidence values and high prevalence values indicate a limited onset of back WMSDs with long-term complaints indicating the need for effective prevention strategies. Nevertheless, studies researching incidence of (lower) back WMSDs in secondary industries of twenty-first century Europe are scarce and results should be interpreted with caution.

Upper limb and neck WMSDs

The neck, shoulder, and neck/shoulder subgroup obtained the highest prevalence values of upper limb WMSDs with 51, 50 and 54%, respectively. These findings are in line with the work of Buckle and Devereux [59]. No clear trends in prevalence data could be observed in studies reporting high psychosocial stress or the presence of biomechanical risk factors. Although the exact reason for this observation remains speculative, it would not be surprising to find the cause in the known high heterogeneity. Also no discrepancies were found between studies that obtained prevalence data through validated questionnaires, non-validated questionnaires or expert assessment. An exception was the neck subgroup where the NMQ tends to result in higher prevalence values. It is possible that the thoroughness of this questionnaire led to this observation. However, since only one study in this subgroup refrained from using the NMQ [21], under-representation of other questionnaires could also have attributed to finding this trend. The need for effective prevention strategies is again highlighted in these subgroups. Implementation of new robotic devices to optimise employee’s ergonomics and therefore reduce risks for developing WMSDs form an extremely promising strategy to combat WMSDs in the future [12, 13].

Incidence of upper limb WMSDs was researched the most. Between 1987 and 1990, incidence ratios of 0.13 were reported (indicating that over the course of 1 year 13 out of 100 persons reported new upper limb WMSDs) [49]. Later from 1996 to 1997 and from 1997 to 2000, incidence ratios of 0.26 and 0.8 were obtained, respectively [48]. These results carefully suggest that the occurrence of new upper limb WMSDs decreases over time in secondary industries. However, some caution when interpreting these results is advised since no other studies were included to support these statements. Monaco et al. [50] reported an incidence ratio of 0.04 between 2012 and 2015, implying a further decline of upper limb WMSDs. This is contradicted by data provided by EU-OSHA stating that a significant reduction in upper limb WMSDs incidence data is still absent over the beginning course of the twenty-first century [58]. Lower incidence ratios are consistently reported compared to their prevalence values, indicating that - although the onset of new WMSDs is relatively limited - employees experience long-term chronic disorders, leading high prevalence numbers. This hypothesis is strengthened by the finding of Gold et al. [60] who states that employees affected by upper limb WMSDs were still experiencing the same disorder at least 1 year later.

More specific examples of chronic WMSDs were research by Leclerc et al. [52] who reported incidence ratios of 0.12, 0.13 and 0.06 for carpal tunnel syndrome, lateral epicondylitis and wrist tendinopathy, respectively. In accordance with Leclerc et al. [52], other included studies found similar incidence ratios of 0.14 and 0.12 for carpal tunnel syndrome, 0.02 for lateral epicondylitis and 0.03 for wrist tendinopathy [53, 54]. Notably, incidence values for these specific WMSDs tend to have higher consensus compared to other WMSDs. Although not similar in all variables, both Leclerc et al. [52] and Roquelaure et al. [54] investigated WMSDs during the same period (between 1996 and 1997) and worked with occupational physicians to obtain incidence data. In addition, carpal tunnel syndrome and lateral epicondylitis are two well-defined diagnoses compared to vague WMSD complaints in a nondelineated anatomical area. This clearly emphasizes the need for more precise definitions of WMSDs as well as standardized study procedures. In contrast to upper limb incidence data, there is a noticeable lack of information regarding neck WMSDs of industrial workers of Europe.

Lower limb WMSDs

Lower limb WMSDs were less prevalent than back or upper limb WMSDs with 29, 11, 33, 17% obtained for the leg, hip, knee, ankle/feet WMSDs subgroups, respectively. EU-OSHA reported a similar trend with a 12-month prevalence of 29 and 30% lower limb WMSDs in 2010 and 2015, respectively [58]. It is possible that there are fewer risk factors present in an industrial setting for developing lower limb WMSDs compared to risk factors for upper limb WMSDs [2]. However, it is noteworthy that epidemiological research or risk assessments regarding lower limbs is generally under-represented which could mediate this discrepancy. Some contradicting trends in prevalence of WMSDs were found in lower limb subgroups regarding the examination method and the presence of biomechanical and/or psychosocial risk factors. Since prevalence values of WMSDs are not only influenced by abovementioned factors, it is plausible that the high heterogeneity, contributed to this absence of homogenous trends in included subgroups.

Three studies reported incidence rates of lower limb WMSDs, however overall incidence ratios could not be calculated due to the lack of sample size information [44,45,46]. Chen et al. [44] reported incidence ratios of 0.0121 (indicating that over the course of 1 year 1 out of 100 persons reported new lower limb WMSDs), 0.000049 and 0.0317 in the food, chemical and metallic industry, respectively. An incidence ratio of 0.000028 was obtained by cherry et al. [45] in the metallic industry. Substantially less information regarding lower limb incidence values is available in European reports and scientific literature to support this research. However, the trend of relatively low incidence values and higher prevalence values again indicate that recurrence of lower limb WMSDs is common among industrial workers. Insufficient time to heal or rehabilitation could contribute to these long-term disorders. Strategies that temporarily relieve the employee from hard labour and effective rehabilitation could benefit the healing process and limit recurrence of WMSDs.

Limitations and future research

It is evident that with the high obtained heterogeneity in the majority of subgroups, the included studies did not utilise uniformity in methodology and reporting strategies. Often, lack of information or discrepancies regarding demographic characteristics of included sample sizes, performed tasks, examining methods and examining periods challenged pooling and interpretation of obtained results. This resulted for the majority of subgroups in a limited number of included studies and considerable heterogeneity that could not be further investigated and challenges the interpretation of obtained results. In order to improve uniformity and generalisation of results, a criteria document for physical examination and standardized questionnaires (e.g. the NMQ) could be used. Further, assessment of risk of bias was performed with the tool as described by Hoy et al. [18] which was the most applicable for included studies. Kane and Shamliyan [61] previously argued that uniformity of this assessment tool is challenged by unclearly defined constructs. Therefore, guidelines for each item of the assessment tool were subjectively expanded and adjusted to this research’s specific subject through discussion between authors. This self-interpreted variant of the initial definitions provided by Hoy et al. [18] could have influenced the risk assessment scores. Finally, only a limited number of studies reporting incidence values could be included due to scarcity in literature. This inhibited profound quantitative calculations of incidence ratios and highlights the need for more qualitative incidence research. Nevertheless, besides the high variability, the obtained high prevalence values, especially in food industries, indicate the need for effective rehabilitation and prevention strategies tailored to the unique characteristics of each industry type.

Conclusion

This review and meta-analysis aimed to provide an epidemiological overview of WMSDs in in twenty-first century Europe’s secondary industries. Back (overall), shoulder/neck, neck, shoulder, lower back and wrist WMSDs were the most prevalent with mean values of 60% (range 38–72%), 54% (range 18–83%), 51% (range 32–69%),50% (range 23–78%), 47% (range 24–71%) and 42 (range 14–64%), respectively. Upper limb disorders were the most investigated WMSD in incidence studies and obtained incidence ratios from 0.04 to 0.26. Data regarding lower limb and back WMSDs were scarce and incidence ratios could not be calculated. Although the onset of WMSDs in general appears to be limited, high prevalence values indicate long-term complaints. These results should be interpreted with caution due the high heterogeneity in the majority of subgroups. However, this highlights the need for future research in the epidemiology of WMSDs as well as the effectiveness of new prevention strategies.

Availability of data and materials

All data generated or analysed during this study are included in this manuscript.

Abbreviations

WMSDs:

Work-related Musculoskeletal Disorders

CDC:

Centers for Disease Control and Prevention

EU-OSHA:

European Agency for Safety & Health at Work

NMQ:

Nordic Musculoskeletal questionnaire

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Acknowledgements

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Funding

This work was supported by Horizon 2020; the SOPHIA-project (grant Agreement No. 871237).

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All authors revised the manuscript critically for important intellectual content, approved the final version of the manuscript to be published, and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. Renée Govaerts, Bruno Tassignon and Jo Ghillebert made substantial contributions to the conception or design and the work, the acquisition, analysis, and interpretation of data for the work, performed the literature search and also drafted and/or revised the work. Romain Meeussen, Bram Vanderborght and Kevin De Pauw made substantial contributions to the conception and design of the work, and interpretation of data. Ben Serrien, Toon Ampe, Sander De Bock and Ilias El Makrini made substantial contributions to the analysis, and interpretation of data for the work.

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Correspondence to Kevin De Pauw.

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Govaerts, R., Tassignon, B., Ghillebert, J. et al. Prevalence and incidence of work-related musculoskeletal disorders in secondary industries of 21st century Europe: a systematic review and meta-analysis. BMC Musculoskelet Disord 22, 751 (2021). https://doi.org/10.1186/s12891-021-04615-9

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Keywords

  • Epidemiology
  • Occupational health
  • Ergonomics
  • Musculoskeletal system
  • Injury
  • Prevention