Unveiling the Multifaceted Associations between Work, Psychosocial Risks, and Musculoskeletal Disorders in the Italian workforce

doi:10.21203/rs.3.rs-3241260/v1

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Research Article

Unveiling the Multifaceted Associations between Work, Psychosocial Risks, and Musculoskeletal Disorders in the Italian workforce

https://doi.org/10.21203/rs.3.rs-3241260/v1

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Background

Musculoskeletal diseases (MSDs) pose a significant occupational health challenge and are the leading cause of disability worldwide. These conditions can adversely affect health, quality of life, productivity and result in substantial social and public costs. Psychosocial risk factors have been identified as having a causal link or worsening various MSDs. Given the advancements in technology and digitalization that may lead to new work tasks and organizational changes, MSDs and psychosocial risks have become top priorities for Occupational Safety and Health (OSH) initiatives. This study investigates the association between occupational characteristics, exposure to work-related psychosocial risks, and MSDs in a large cohort of Italian workers.

Methods:

The study draws upon data from a cross-sectional national survey, INSuLa-2, conducted in 2019 by the Italian Workers' Compensation Authority (INAIL). The survey involved 8,000 Italian workers, representing the national workforce population. The data analysis focused on variables related to psychosocial risks and MSDs, encompassing sociodemographic and occupational characteristics, psychosocial risks, and health-related aspects.

Results:

The study population had a mean age of 43.3 ± 11.5 years, with a majority of males (55.5%). The manufacturing/primary industry/mining/utilities sector was the most prevalent occupational sector (25.0%), with blue-collar workers (46.2%) and employees (43.1%) being the most common occupational groups. Participants reported a high prevalence of psychosocial risks, including elevated levels of violence and harassment (43.0%) and job insecurity (44.4%). Among MSDs, back pain was the most prevalent (45.3%), followed by shoulder, neck, and/or upper limb pain (39.4%), and lower limb pain (32.1%). Age, female gender, low educational level, and a BMI higher than 30 were identified as significant risk factors for experiencing MSDs. Occupational sectors with higher risks of MSDs included agriculture, fishing, hunting, healthcare, and social assistance. Furthermore, blue-collar workers were found to have a higher likelihood of experiencing MSDs compared to employees.

Conclusion:

This study provides insights into the association between occupational characteristics, psychosocial risk factors, and MSDs in a substantial cohort of Italian workers. The findings suggest that workers exposed to psychosocial risks in the workplace face an increased risk of MSDs. Consequently, the prevention of MSDs and psychosocial risks should be integrated into workplace health and safety programs to enhance employee health, well-being, and productivity.

Demand-Control Model

Effort and Reward Imbalance

Back Pain

Shoulder

Upper Limb Pain

Lower Limb Pain

Musculoskeletal Diseases (MSDs) have emerged as a significant global concern in the field of occupational health, accounting for over one hundred million Years lived with disability (YLD) in 2019 [1, 2]. In Europe, approximately three out of five workers report suffering from MSDs, which can have detrimental effects on health, quality of life, productivity, and incur substantial social and public costs [3, 4]. The economic burden of work-related MSDs is considerable. Estimates suggest costs as high as $50 billion per year in the United States and up to 2% of the gross domestic product in the European Union [5, 6]. The World Health Organization identifies MSDs as the leading cause of disability worldwide, affecting approximately 1.7 billion people [7]. Common types of MSDs include back pain, muscular pain in the shoulders, neck, upper and lower limb pain. Notably, low back pain remains the primary cause of disability globally [8]. The aging population and changes in the future of work are expected to exacerbate the burden of MSDs, making it a priority in Occupational Safety and Health (OSH) prevention efforts. To effectively address this issue, it is crucial to identify factors and hazards that contribute to or worsen MSDs and develop suitable strategies and interventions for workplaces [9, 10].

While physical work factors like biomechanical strain and ergonomic discomfort play a significant role in MSDs, the biopsychosocial model highlights the importance of psychological, social, and environmental factors in the occurrence of MSDs [13, 14]. Studies have linked psychosocial risk factors, including excessive workload, time pressure, and lack of support, to various types of MSDs, particularly in conjunction with physical risks [15, 20]. The combined effect of biomechanical and psychosocial factors has a stronger impact on MSDs than other occupational and individual characteristics such as gender, age, and education [19].

Psychosocial risk factors can also indirectly affect MSDs through their influence on stress and depression [15, 19]. The ecological model of MSDs proposes that work organization and environmental factors can cause psychological strain, directly impacting physical health [14]. Karasek and Siegrist proposed models that explore the psychological factors contributing to stress and subsequent impacts on MSDs [25, 26]. They highlight the role of high psychological demand, lack of control, low social support in producing tension and occupational stress, and the negative impact of effort-reward imbalance on health.

Effectively managing and preventing MSDs requires adopting a comprehensive approach that considers multiple factors, including individual, occupational, and psychosocial aspects within the workplace [19]. However, few studies have explored the relationships between psychosocial risk factors and MSDs using stratified models to account for modification effects [20]. More research is needed to investigate the association between physical and mental health aspects, psychosocial risks, and MSDs in different workplaces [10].

This study aimed to examine the associations between demographic variables, occupational characteristics, exposure to work-related psychosocial risks, health status, and MSDs in a large Italian cohort of workers based on data from the national survey on health and safety at work [28]. The study will focus on musculoskeletal pain in the back, lower limbs, and shoulders, neck, and/or upper limbs as outcomes of MSDs. The Effort/Rewards Imbalance (ERI) model [26], which considers the balance between work effort and rewards, will serve as the basis for investigating psychosocial risks. Additionally, the study will explore other psychosocial factors, including workload, control, support, job insecurity, harassment, and violence at work by colleagues or external customers/users, to better understand their associations with MSDs. Sociodemographic and occupational characteristics, as well as physical and mental health conditions, will be integrated with psychosocial risks in the multivariate model to gain a comprehensive understanding of the associations with MSDs.

Participants and procedures

The study participants are a cohort of Italian workers, which answered the second wave of a cross-sectional national survey conducted in 2019 by the Italian Workers’ Compensation Authority (INAIL) to investigate workers’ perceptions of health and safety at work. This survey – named INSuLa- 2 [28] - counts a sample of 8,000 Italian workers representative of the national workforce population aged from 16 to 64 years, excluding self-employed, military and civil protection personnel. INSuLa survey allowed for a comprehensive investigation of various variables on a highly representative sample of the national workforce in terms of demographics (gender, age, occupational position, educational level) and organizational characteristics (size, sectors) [29]. A quota sampling method was applied to identify a tailored sample that was in proportion to some characteristics of the general workforce population. Starting from the national workforce population provided by the Labour Force Survey of the Italian Institute for Statistics, quotas were identified reflecting the population percentages in relation to the geographical area, gender and age, type of contract, occupational level and occupational sector. The survey was conducted between March - May 2019 and data were collected through structured interviews, using the Computer-assisted telephone interviewing (CATI) method, by trained interviewers. A random procedure was applied to contact eligible persons via mobile telephone and landline (50.9% of the total through mobile). Participants were selected proportionally based on the sampling strategy to reach the expected proportions. To ensure the maximum representativeness, the respondents contacted on landlines were selected within the family based on screening criteria for verifying eligibility conditions. Using the "birthday rule”, the interview was done with the family member who had completed most recent years.

The standardized questionnaire used was based on the first wave of the survey [31] and an updated literature review and benchmarking analysis of the most prominent recent European surveys in the field. It includes several questions aiming to investigate the main aspects linked to health and safety at work in terms of working conditions, risk exposure and perceptions, health status and outcomes, risk management and prevention, and OSH policies. This study includes a selection of variables linked to psychosocial risks and MSDs.

Measures

Sociodemographic and occupational characteristics

Participants’ sociodemographic information included gender, age (16–24 yy; 25–34 yy; 35-44yy; 45–54 yy; 55–64 yy), educational level (lower and upper secondary school, graduate and post-graduate), marital status (single; married; divorced; widowed), and body mass index as health status information (underweight; normal; overweight; obese).

Occupational characteristics collected were occupational sector (using the Italian Ateco code 2007, the economic activity classification code derived from Nace Rev. 2), the occupational position (top manager; middle manager; white collar; blue collar; other type of employment), and the number of hours worked a week (1–34 hours/week; 35–40 hours/week; 41–48 hours/week; 49–54 hours/week; >=55h hours/week).

Psychosocial risks

Psychosocial risks are collected through fourteen statements describing aspects of the work organization, job design, and the social context at work measured with a 5-step Likert scale (1 = disagree and 5 = completely agree). In particular, ten statements were from the validated short version of the Effort reward imbalance, frequently used in large-scale epidemiological studies to measure the balance between the effort required by the work and the rewards received in terms of recognition or awards. The effort is defined as the commitment required to carry out work, it includes pressures experienced, interruptions and interference and commitment required. The reward is the perceived recognition for work that encompasses various aspects, from the recognition and respect by superiors and colleagues to the economic rewards and expected results, including perceived job security. In line with Pikhart and colleagues [32] the effort-reward ratio was computed for every respondent and then, this ratio was converted into a binary variable (balance = < 1; imbalance = > 1).

For an in-depth view of the aspects of organization and management of work that may constitute psychosocial risks, four further statements were included relating to control, support from the supervisor and colleagues, presence of violent behaviour and / or harassment by colleagues or by external customers / users, and job insecurity. These were adapted by items from the Management Standard Indicator tool [33, 34] and were already collected in the first wave of INSuLa [31]. Scores were categorized using the distribution criteria by calculating quartiles, and four categories were created corresponding to Low, Moderate, High, Very high, for each variable.

Health-related aspects

Several self-report health measures were included in this study. First, we included a validated measure of the General health [3] largely used in European surveys on working conditions, asking “How is your health in general?” (5 = very good; 4 = good; 3 = fair; 2 = bad; 1 = very bad). We also examined eventual periods of work impediments due to the state of health [35], asking whether respondents have been hampered in their daily activities in any way by any longstanding illness, or disability, infirmity or mental health problem (1 = Yes, a lot; 2 = Yes, to some extent; 3 = No). As a measure of mental health, we included “Depressive symptoms” through the 5 items Well-being index developed by the World Health Organization, one of the most widely used measure assessing subjective psychological well-being [36]. This index has showed its strong potential as a screening tool for depression in several studies and its applicability across different study fields [35]. Five statements describe moods and workers reported how they are closest to their feelings over the last two weeks using the following answer scale: 0 - All of the time; 1 - Most of the time; 2 -More than half of the time; 3 - Less than half of the time; 4 -Some of the time; 5 - At no time. The sum of scores was multiplied by 4 and a score from 0 to 100 is obtained, where 0 is the best wellbeing and 100 is the worst wellbeing. We adopted cut-off scores [35] to identify depressive and major depressive symptoms among respondents. A cut-off score of ≥ 50 on the WHO-5 was used to define depression symptoms. Moreover, a restrictive threshold of ≥ 72 allowed to identify major depression symptoms among respondents. According to the cut-offs we created a three level variables (absence of depressive symptoms; depressive symptoms; major depressive symptoms).

Musculoskeletal diseases

Variables measuring the occurrence of MSDs come from a focus on health conditions. Participants reported to have had or not some diseases and/or chronic conditions linked to three main areas of the musculoskeletal apparatus, namely: 1. Back, 2. Lower limbs (hips, legs, knees, feet, etc.), and 3. Shoulders, neck and/or upper limbs. Questions refer to the last 12 months frame time and provide a binary answer (yes/no), including the possibility of reporting “don’t know”. This study includes diseases in Back, Lower limbs and Shoulders, Neck and/or upper limbs in the last 12 months as outcomes related to MSDs.

Statistical analysis

Descriptive statistics were used to obtain frequencies and percentages. Univariate logistic regression analysis was used to determine the association among sociodemographic characteristics, occupational characteristics, psychosocial risks, health-related aspects, and each of the three MSDs (namely Back, Lower limb and Shoulder, neck and/or upper limb pain) treated as dichotomic outcomes.

Independent variables with a p-value < 0.10 from the univariate logistic regression analysis, together with age, gender, education level, nutritional status and marital status, were included in the multivariate logistic regression analyses. A manual backward stepwise approach was used to select variables to be included in the final models. The final models were then determined by including the independent variables with a p-value < 0.05.

Missing data were handled with a list-wise deletion approach and only participants with complete information for all exposures and outcomes were included in the final analyses. The STATA V.13.1 statistical package was used for all analyses.

Sample characteristics overview

Table 1 gives an overview of the characteristics of the study population. The sample mean age is 43.3 +-11.5 years (range 16–64 years); a higher proportion are male (55.5%); 51.7% attends high schools; 63.0% is married. The prevalence of overweight and obesity is respectively 26.0% and 5.8%.

Table 1

Characteristics of the study population
Variables	Frequencies
Variables	N	%
Sociodemographic characteristics
Age
16–24	461	5,8
25–34	1.558	19,5
35–44	2.231	27,9
45–54	2.374	29,7
55–64	1.376	17,2
Gender
Male	4.436	55,5
Female	3.564	44,6
Educational level
Lower secondary	2.030	25,4
Upper secondary	4.138	51,7
Graduate and post graduate	1.787	22,3
Do not answer	45	0,6
Nutritional status
Underweight	188	2,4
Normal	3.695	46,2
Overweight	2.083	26,0
Obese	460	5,8
Do not answer	1.574	19,7
Marital Status
Single	2.462	30,8
Married	5.043	63,0
Divorced	349	4,4
Widowed	146	1,8
Occupational characteristics
Occupational sector
Agriculture, fishing, and hunting	240	3,0
Manufacturing/Primary industry/Mining/Utilities	1.997	25,0
Construction	433	5,4
Wholesale and retail trade/Automotive and motorcycle repair	1.031	12,9
Accommodation and food services	574	7,2
Transportation and warehousing	471	5,9
Information and communication	231	2,9
Professional, financial and business services	976	12,2
Healthcare and social assistance	331	4,1
Education services	685	8,6
Public administration, social security	725	9,1
Other public and personal services	306	3,8
Occupational position
Top and middle manager	672	8,4
Employees	3.450	43,1
Blue collar	3.698	46,2
Other type of employment	180	2,3
Working hours
1–34 hours/week	2.023	25,3
35–40 hours/week	4.772	59,7
41–48 hours/week	633	7,9
49–54 hours/week	395	4,9
>=55h hours/week	177	2,2
Psychosocial risks
Control
Low	1.752	21,9
Moderate	2.426	30,3
High	2.691	33,6
Very high	1.131	14,1
Support
Low	3.679	46,0
Moderate	1.239	15,5
High	1.657	20,7
Very high	1.425	17,8
Violence
Low	3.080	38,5
Moderate	1.477	18,5
High	1.913	23,9
Very high	1.530	19,1
Job insecurity
Low	2.710	33,9
Moderate	1.689	21,1
High	2.389	29,9
Very high	1.212	15,2
Effort/reward
Balance	5.959	74,5
Imbalance	2.041	25,5
Hindered at work due to health problems
No	7.450	93,1
Yes for a certain period	403	5,0
Yes for a long period	121	1,5
Do not answer	26	0,3
Health-related aspects
Health status
Poor	233	2,9
Fair	1.866	23,3
Good	4.256	53,2
Very good	1.642	20,5
Do not answer	3	0,0
Depression
Not depressed	71,83	71,8
Depression	1.520	19,0
Major depression	734	9,2

With regard to occupational characteristics, most representative occupational sector is the Manufacturing/Primary industry/Mining/Utilities sector (25.0%), followed by Wholesale and retail trade/Automotive and motorcycle repair (12.9%) and Professional, financial and business services (12.2%). Higher proportions of participants are either blue collars (46.2%) or employees (43.1%); the majority (59.7%) works between 35 and 40 hours per week.

As for psychosocial risks, 21.9% of respondents reports low levels of job autonomy; larger percentages report lack of support from either managers or colleagues (46.0%), high levels of violence and harassment at work (43.0%) and high levels of job insecurity (44.4%); 25.5% shows imbalance between personal efforts and rewards; 6.5% reports to have felt hindered at work due to health problems.

As for health-related aspects, most of participants (73.7%) reports to be in good or very good health; 19.0% is screened as having depression while 9.2% receives a diagnosis of major depression.

The 12-month-period prevalence of MSDs in any body part of the study population is 58.5%. Back pain is found to be the most prevalent (45.3%) MSDs during the past 12 months, followed by Shoulders, neck and/or upper limb pain (39.4%) and Lower limb pain (32.1%). As supplementary materials we show the prevalence of the three types of MSDs, respectively, according to participants’ epidemiological features as well as the results of the univariate analyses.

The results from multivariable logistic regression models for Back, Lower limb and Shoulder, neck and/or upper limb pain are presented in Tables 2, 3 and 4, respectively.

Table 2

Multivariate logistic regression analysis assessing factors associated with the occurrence of back pain in a representative sample of 8,000 Italian workers
Variables	OR	95% CI		p-value
Sociodemographic characteristics
Age
16–24	ref	ref	ref	0.0000
25–34	1.80	1.36	2.38
35–44	2.08	1.56	2.77
45–54	2.42	1.80	3.26
55–64	2.68	1.96	3.67
Gender
Male	ref	ref	ref	0.0000
Female	1.31	1.15	1.48	0.0000
Educational level
Lower secondary	1.54	1.28	1.87	0.0000
Upper secondary	1.13	0.98	1.32
Graduate and post graduate	ref	ref	ref
Nutritional status
Underweight	0.88	0.64	1.22	0.0209
Normal	ref	ref	ref
Overweight	1.16	1.03	1.31
Obese	1.28	1.04	1.59
Marital Status
Single	ref	ref	ref	0.0247
Married	1.04	0.91	1.19
Divorced	0.72	0.55	0.95
Widowed	0.79	0.52	1.22
Occupational characteristics
Occupational sector
Agriculture, fishing, and hunting	2.34	1.52	3.59	0.0000
Manufacturing/Primary industry/Mining/Utilities	1.20	0.88	1.62
Construction	1.43	1.00	2.06
Wholesale and retail trade/Automotive and motorcycle repair	1.20	0.88	1.64
Accommodation and food services	1.43	1.02	2.02
Transportation and warehousing	1.13	0.79	1.62
Information and communication	1.32	0.87	2.02
Professional, financial and business services	1.11	0.81	1.52
Healthcare and social assistance	2.17	1.47	3.20
Education services (both private and public)	1.23	0.87	1.73
Public administration, social security	1.27	0.91	1.76
Other public and personal services	ref	ref	ref
Occupational position
Top and middle manager	ref	ref	ref	0.0000
Employees	1.27	1.03	1.56
Blue collar	1.56	1.24	1.97
Other type of employment	0.75	0.49	1.15
Working hours
1–34 hours/week	1.12	0.97	1.28	0.0000
35–40 hours/week	ref	ref	ref
41–48 hours/week	1.53	1.26	1.87
49–54 hours/week	1.52	1.19	1.94
>=55h hours/week	1.85	1.27	2.68
Psychosocial risks
Violence
Low	ref	ref	ref	0.0000
Moderate	1.41	1.21	1.63
High	1.39	1.21	1.60
Very high	1.59	1.36	1.87
Effort/reward
Balance	ref	ref	ref	0.0000
Imbalance	1.55	1.36	1.76	0.0000
Health-related aspects
Health status
Poor	1.81	1.29	2.54	0.0000
Fair	2.03	1.77	2.32
Good	ref	ref	ref
Very good	0.62	0.54	0.72
Hampered in daily activities due to health problems
No	ref	ref	ref	0.0215
Yes for a certain period	1.37	1.08	1.75	0.0215
Yes for a long period	1.28	0.83	1.96
Depressive symptoms
No	ref	ref	ref	0.0000
Yes	1.44	1.25	1.65
Major depressive symptoms	1.53	1.26	1.86

Table 3

Multivariate logistic regression analysis assessing factors associated with the occurrence of lower limb pain in a representative sample of 8,000 Italian workers
Variables	OR	95% CI		p-value
Sociodemographic characteristics
Age
16–24	ref	ref	ref	0.0000
25–34	1.47	1.08	2.02
35–44	1.40	1.01	1.93
45–54	1.79	1.28	2.48
55–64	2.20	1.56	3.11
Gender
Male	ref	ref	ref	0.0000
Female	1.39	1.22	1.59	0.0000
Educational level
Lower secondary	1.34	1.09	1.65	0.0081
Upper secondary	1.09	0.93	1.29
Graduate and post graduate	ref	ref	ref
Nutritional status
Underweight	1.01	0.71	1.43	0.0081
Normal	ref	ref	ref
Overweight	1.17	1.03	1.33
Obese	1.40	1.13	1.75
Marital Status
Single	ref	ref	ref	0.8327
Married	1.07	0.92	1.24
Divorced	1.10	0.83	1.46
Widowed	1.04	0.67	1.62
Occupational characteristics
Occupational sector
Agriculture, fishing, and hunting	2.85	1.84	4.42	0.0000
Manufacturing/Primary industry/Mining/Utilities	1.12	0.81	1.55
Construction	1.48	1.00	2.19
Wholesale and retail trade/Automotive and motorcycle repair	1.31	0.93	1.84
Accommodation and food services	1.38	0.95	1.99
Transportation and warehousing	1.00	0.68	1.48
Information and communication	0.92	0.57	1.50
Professional, financial and business services	1.01	0.71	1.42
Healthcare and social assistance	1.94	1.29	2.90
Education services (both private and public)	1.42	0.98	2.06
Public administration, social security	1.17	0.81	1.67
Other public and personal services	ref	ref	ref
Occupational position
Top and middle manager	ref	ref	ref	0.0000
Employees	1.51	1.20	1.90
Blue collar	2.08	1.62	2.68
Other type of employment	1.10	0.69	1.77
Working hours
1–34 hours/week	0.97	0.84	1.13	0.0000
35–40 hours/week	ref	ref	ref
41–48 hours/week	1.25	1.01	1.54
49–54 hours/week	1.01	0.78	1.32
>=55h hours/week	3.06	2.10	4.46
Psychosocial risks
Violence
Low	ref	ref	ref	0.0000
Moderate	1.26	1.08	1.48
High	1.41	1.21	1.64
Very high	1.44	1.22	1.71
Effort/reward
Balance	ref	ref	ref	0.0017
Imbalance	1.25	1.09	1.43	0.0017
Health-related aspects
Health status
Poor	4.04	2.87	5.69	0.0000
Fair	2.20	1.93	2.52
Good	ref	ref	ref
Very good	0.50	0.42	0.59
Hampered in daily activities due to health problems
No	ref	ref	ref	0.0088
Yes for a certain period	1.47	1.15	1.88	0.0088
Yes for a long period	0.99	0.64	1.52
Depressive symptoms
No	ref	ref	ref	0.0000
Yes	1.49	1.29	1.71
Major depressive symptoms	1.34	1.10	1.63

Table 4

Multivariate logistic regression analysis assessing factors associated with the occurrence of shoulder, neck and/or upper limb pain in a representative sample of 8,000 Italian workers
Variables	OR	95% CI		p-value
Sociodemographic characteristics
Age
16–24	ref	ref	ref	0.0007
25–34	1.44	1.07	1.94
35–44	1.65	1.22	2.23
45–54	1.89	1.39	2.59
55–64	1.80	1.30	2.50
Gender
Male	ref	ref	ref	0.0000
Female	1.52	1.34	1.73	0.0000
Educational level
Lower secondary	1.25	1.03	1.52	0.0129
Upper secondary	1.01	0.87	1.18
Graduate and post graduate	ref	ref	ref
Nutritional status
Underweight	0.87	0.62	1.22	0.0281
Normal	ref	ref	ref
Overweight	1.16	1.03	1.32
Obese	1.25	1.01	1.55
Marital Status
Single	ref	ref	ref
Married	1.12	0.98	1.29	0.2717
Divorced	0.95	0.73	1.26
Widowed	1.18	0.77	1.82
Occupational characteristics
Occupational sector
Agriculture, fishing, and hunting	1.81	1.18	2.76	0.0007
Manufacturing/Primary industry/Mining/Utilities	0.98	0.72	1.34
Construction	1.16	0.80	1.68
Wholesale and retail trade/Automotive and motorcycle repair	0.98	0.71	1.35
Accommodation and food services	0.90	0.63	1.28
Transportation and warehousing	0.92	0.64	1.32
Information and communication	1.20	0.78	1.85
Professional, financial and business services	0.84	0.61	1.16
Healthcare and social assistance	1.49	1.01	2.19
Education services (both private and public)	0.89	0.63	1.26
Public administration, social security	1.00	0.71	1.40
Other public and personal services	ref	ref	ref
Occupational position
Top and middle manager	ref	ref	ref	0.0000
Employees	1.24	1.00	1.53
Blue collar	1.49	1.18	1.89
Other type of employment	0.62	0.39	0.98
Working hours
1–34 hours/week	1.21	1.05	1.39	0.0006
35–40 hours/week	ref	ref	ref
41–48 hours/week	1.25	1.02	1.53
49–54 hours/week	1.18	0.92	1.51
>=55h hours/week	1.88	1.30	2.73
Psychosocial risks
Violence
Low	ref	ref	ref	0.0000
Moderate	1.42	1.22	1.65
High	1.31	1.14	1.52
Very high	1.39	1.18	1.63
Effort/reward
Balance	ref	ref	ref	0.0000
Imbalance	1.59	1.39	1.82	0.0000
Health-related aspects
Health status
Poor	2.36	1.69	3.30	0.0000
Fair	2.26	1.98	2.59
Good	ref	ref	ref
Very good	0.50	0.43	0.59
Hampered in daily activities due to health problems
No	ref	ref	ref	0.0007
Yes for a certain period	1.56	1.22	1.99	0.0007
Yes for a long period	1.36	0.89	2.09
Depressive symptoms
No	ref	ref	ref	0.0000
Yes	1.59	1.39	1.83
Major depressive symptoms	1.17	0.96	1.41

Sociodemographic characteristics and MSDs

All sociodemographic variables excepting marital status are found to be strongly associated with MSDs. The probability of experiencing all MSDs increases with age and is maximum in the age ranging from 55 to 64 years with regard to Back and Lower limb pain (respectively, OR: 2.68, 95% CI: 1.96–3.67 and OR: 2.20, 95% CI: 1.56–3.11) and in the 45–54 age class with regard to Shoulder, neck and/or upper limb pain (OR: 1.89, 95% CI: 1.39–2.59) compared to those in the youngest group. Females have significantly higher odds of experiencing all MSDs than males, especially Shoulder, neck and/or upper limb pain (respectively, OR: 1.31, 95% CI: 1.15–1.48; OR: 1.39, 95% CI: 1.22–1.59; OR: 1.52, 95% CI: 1.34–1.73). Those with lower secondary school are significantly more likely to experience either Back, Lower limb or Shoulder, neck and/or upper limb pains compared to those with the highest education level (respectively, OR: 1.54, 95% CI: 1.28–1.87; OR: 1.34, 95% CI: 1.09–1.65; OR: 1.25, 95% CI: 1.03–1.52). The likelihood of experiencing MSDs is also higher in participants with BMI≥30 compared to those with normal weight (OR ranging from 1.16 to 1.40, with p < 0.05 for all three MSDs). In addition, the chance of having Back pain is found to be lower in those reporting to be divorced and widowed compared to those without a partner (respectively, OR: 0.72, 95%CI: 0.55–0–95 and OR: 0.79, 95%CI: 052–1.22).

Occupational characteristics and MSDs

All occupational aspects investigated in this study are found to be strongly associated with MSDs. Those working in Agriculture, fishing, and hunting and in Healthcare and social assistance are most likely to experience either Back (respectively, OR: 2.34, 95%CI:1.52–3.59 and OR: 2.17, 95%CI: 1.47–2.20), Lower limb (OR: 2.85, 95%CI: 1.84–4.42 and OR:1.94, 95%CI: 1.29–2.90) or Shoulder, neck and/or upper limb pain (OR: 1.81, 95%CI: 1.18–2.76 and OR: 1.49, 95%CI: 1.01–2.19). Blue collar workers have an increased likelihood of experiencing all three MSDs compared to those holding top and middle management positions (respectively, OR: 1.56, 95%CI: 1.24–1.79; OR:2.08, 95%CI: 1.62–2.68; OR:1.49, 95%CI: 1.18–1.89). Finally, long working hours are found to be significantly associated with higher odds of experiencing all three MSDs compared to standard working hours, and this is particularly evident for those working > = 55h hours/week (respectively, OR: 1.85, 95%CI: 1.27–2.68; OR: 3.06, 95%CI: 2.10–4.46; OR: 1.88, 95%CI: 1.30–2.73).

Psychosocial risks and MSDs

Among psychosocial risks, violence or harassment at work and effort-reward imbalance are significantly associated with MSDs. Specifically, those reporting moderate to very high levels of violence and harassment at work are more likely to experience either Back, Lower limb or Shoulder, neck and/or upper limb pain compared to those reporting lower levels (OR for very high levels of violence are respectively: 1.59, 95%CI: 1.36–1.87; 1.44, 95%CI: 1.22–1.71; 1.38, 95%CI: 1.18–1.63). The likelihood of experiencing MSDs is also higher in those reporting imbalance between efforts and rewards compared to those with balance among these two factors (respectively, OR: 1.55, 95%CI: 1.36–1.76; OR: 1.25, 95%CI: 1.09–1.43; OR: 1.59, 95%CI: 1.39–1.82).

Other health related aspects and MSDs

Those reporting worse health status are significantly more likely to experience MSDs compared with those in good health (respectively, OR: 1.81, 95% CI: 1.29 2.54; OR: 4.04, 95% CI: 2.87–5.69; OR: 2.36, 95% CI: 1.69–3.30). Also, those reporting to have felt hindered at work for a time due to health problems are more likely to experience all three MSDs compared to those who did not feel hindered at all (respectively, OR: 1.37, 95% CI: 1.08–1.75; OR: 1.47, 95% CI: 1.15–1.88; OR: 1.56, 95% CI: 1.22–1.99). Finally, those reporting depression symptoms had greater odds of experiencing either Back (OR: 1.44, 95%CI: 1.25–1.65), Lower limb (OR: 1.49, 95%CI: 1.29–1.71) or Shoulder, neck and/or upper limb pain (OR: 1.59, 95%CI: 1.39–1.83) compared to those not reporting symptoms. The likelihood of experiencing either Back or Lower limb pain was also greater in those reporting major depression symptoms (respectively: OR 1.53, 95%CI: 1.26–1.86; OR: 1.34, 95%CI: 1.10–1.63).

This study explores the associations among various factors, including demographic variables, occupational characteristics, psychosocial risk factors, and health status, with MSDs in a large Italian cohort of workers representing the national workforce. The findings suggest that workers experiencing psychosocial risks at work face a higher risk of MSDs. The study underscores the importance of considering both physical and psychosocial factors in preventing and managing MSDs in the workplace. According to the biopsychosocial model, MSDs are triggered by a wide range of work-related factors and are a significant issue in workplace prevention and management. They represent one of the most common work-related disorders with consequences such as disability, workday loss, and early retirement [1].

While some evidence from previous studies exists [10, 16, 20], a debate is ongoing on the relationships between workplace factors and MSDs to gain a comprehensive and complementary view of how different work-related risk factors affect the development and progression of various MSDs and to establish organizational strategies and actions with a prevention approach. Therefore, a comprehensive analysis of individual, occupational, and psychosocial risk factors associated with MSDs is needed to drive integrated actions at policy and organizational levels. Although previous studies have considered different factors related to MSDs, few studies have adopted a multiple-factor modelling approach to address the main individual, occupational, and work aspects that may have a combined significant association with such diseases [10, 20]. Our findings confirm the association between sociodemographic factors (gender, age, educational level, occupational position) and MSDs. Specifically, women, older people, lower educational level, and blue-collar workers showed a higher risk of musculoskeletal pain.

The study reveals a higher risk for females in developing MSDs than males, particularly related to shoulder, neck, and upper limb pain. These findings support existing literature that emphasizes a greater prevalence and impact of these disorders in females [38, 39], who often experience more severe conditions than males [40]. This discrepancy is primarily attributed to biological factors. However, the biopsychosocial model of MSD emergence suggests that other factors related to gender diversity may contribute to these disorders. Among these factors, workplace psychosocial risks play a significant role [15, 17, 19]. These risks can interact with physical stressors, increasing the risk of MSDs. An important finding is the link between workplace violence or harassment and MSDs. Women often face a higher risk of involvement in such incidents [41], leading to psychological stress, anxiety, and fear that impact musculoskeletal health. Stress and anxiety can cause muscular tension, increasing pain and discomfort in the shoulders, neck, and upper limbs. Additionally, women often bear more family responsibilities, affecting their work quality. Balancing personal and work life is crucial for overall health and contributes to MSD development. Increased conflict between family and work responsibilities heightens stress, making women more susceptible to MSDs. Women frequently hold jobs exposed to combined physical and psychosocial risks, such as roles involving customer, pupil, or patient interactions, or in sectors like healthcare, education, or public administration [17]. Our study also confirms that age is a significant individual risk factor for MSDs. This is an important priority in OSH due to the changing age profile in employment, with a higher prevalence of older adults in the workforce due to increased life expectancy and longer working age [4]. Age increases the risk of MSDs for biological reasons, and potential concurrent health problems in aging workers may contribute to a decreased likelihood of coping well. However, it is essential to recognize that physical and psychosocial risks associated with MSDs change differently for each age group, and the impact on the increase of MSDs risk depends more on work demands than age [42]. Previous studies have shown that manual work is more associated with the risk of developing MSDs in older workers and early retirement due to disability. However, the likelihood of developing diseases depends more on work demands than age [43].

Educational level and marital status were associated with Back, Lower limb, or Shoulder, neck, and/or upper limb pains. Specifically, those with lower education levels and those reporting being divorced or widowed showed a higher likelihood of experiencing MSDs than those without a partner. Education appears to have emerged as a protective factor for MSDs, possibly due to the association between higher levels of education and working positions. However, most of these sociodemographic factors correspond to non-modifiable aspects of an individual. Although recovery interventions can be implemented for those groups identified at higher risk, we are interested in identifying aspects that can be managed to eliminate the source of risk or protect exposed workers with a prevention approach, such as addressing psychosocial risks.

Regarding psychosocial risks, our study confirms a higher association with MSDs in cases of effort/reward imbalance [44] and shows a link with a moderate or very high risk of being the victim of violent behaviour and harassment at work. During the pandemic, violence and harassment at work have become growing risks in the workplace. They are important psychosocial risk factors affecting individual health and well-being, which are among the priorities for the future of work [22, 45]. Surprisingly, our multivariate models did not show any significant association between more traditional psychosocial aspects, such as control, support, job insecurity, and reported MSDs, as found in previous studies [20]. Nevertheless, according to Buruck and colleagues [20], other work factors may play a role in reducing the impact of stressors on health beyond traditional models of stress at work, particularly those that are becoming increasingly important in the future of work. Moreover, our findings indicate that those reporting major depressive symptoms have a higher likelihood of experiencing either Back or Lower limb pain, highlighting the comorbidity between MSDs and depressive symptoms that could account for the chronicity of the diseases over time, leading to an increase in early retirement from work [46]. This further reinforces the need to include psychosocial aspects in studying MSDs.

The multifactorial nature of MSDs and the associations found in our data with sociodemographic, occupational, and work-related factors, as well as health status, underscore the need for diverse and context-sensitive approaches to managing MSDs risk at work. Integrated risk assessment systems that consider both physical and psychosocial hazards are essential in defining the most effective prevention strategies. Although psychosocial risks have a negative impact on MSDs, they also offer potential solutions as they represent modifiable aspects of work and work organization that can be effectively managed. Additionally, diversity-sensitive risk assessment and management interventions can better cater to the specific needs and individual characteristics of workers, such as women and older workers. To achieve this, organizations should adopt an integrated model based on a participative approach to identify and address all potential hazards, including psychosocial ones, and to implement customized interventions [11]. Participation is crucial in this approach as it ensures the effectiveness of the assessment and management strategy, fostering reliable risk assessment. All stakeholders, including those not directly involved but who influence the jobs involved in the interventions, need to be engaged for interventions to succeed. Therefore, better integration of roles among employers, employees, line managers, and organizational and local OHS professionals is essential.

Embracing the total worker health approach [47], health promotion and training are essential components of this multidisciplinary and integrated approach, integrating public health interventions with occupational health interventions. This approach fosters a link between workers' health, work organization, and the work environment in terms of protection, prevention, and promotion. Our findings underscore the need for approaches and strategies that combine occupational safety and health prevention with health promotion actions. National policies and strategies reflecting this approach are taking shape, with initiatives to adopt healthy lifestyles alongside traditional workplace risk reduction efforts. However, more action is needed at the policy level to drive progress in this direction. Developing effective policies and strategies also relies on the availability of consistent data and analytics over time. Data are essential to monitor MSD prevalence over time, identify critical issues, and provide evidence for designing and delivering interventions tailored to different worker groups and their specific needs. This data-driven approach will support the establishment of a learning health system where feedback loops inform OSH specialists, policymakers, researchers, and enterprises in designing or redesigning effective interventions to tackle MSDs at work.

Limitations and future perspectives

Some limitations should be considered when interpreting the results of this study. Firstly, while the sample size represents a large and representative portion of the Italian workforce, the cross-sectional design of the study precludes establishing causal relationships. Future longitudinal studies would be necessary to explore causality further. Secondly, the study relied on self-reported measures for psychosocial risks, MSDs, and health outcomes, which may introduce recall and social desirability biases. To enhance the validity of MSDs diagnosis and assessment, it would be preferable to include interviews, medical examinations, and self-report questionnaires. Thirdly, the study did not encompass self-employed workers, military or civil protection personnel, potentially limiting the generalizability of the findings to these groups. Fourthly, the study could not demonstrate presumed interaction effects, such as those related to autonomy and professional position, on the biomechanical or psychosocial pathways. However, clear direct effects were observed in the model. To explore these interactions, future research may need to delve into specific areas of the work field and focus on particular factors, rather than employing an integrated model like the one presented here. Finally, it is important to note that the study was conducted before the COVID-19 pandemic, which has likely significantly impacted workers' perceptions of psychosocial risks and MSDs. The pandemic introduced new work practices, such as smart working, with benefits and challenges. Aspects such as increased reliance on technology and potential feelings of isolation have not been investigated in this study and warrant further exploration in the context of the COVID-19 pandemic.

Our findings underscore the significant importance of MSDs, presenting a challenging and complex health issue that requires the collaboration of occupational medicine and public health professionals. A multidisciplinary approach is crucial to comprehensively assess and manage all potential determinants of MSDs. This involves integrating the management and evaluation of biomechanical, ergonomic, and medical aspects with psychosocial and organizational factors. Moreover, promoting effective worker involvement in the development and implementation of interventions and fostering collaboration among various stakeholders (such as employers, employees, occupational physicians, general practitioners, prevention experts, policymakers, and researchers) is essential to establish a shared understanding and representation of MSDs and workers' activities. Additionally, establishing a monitoring system to track changes over time is crucial, along with adopting a more inclusive risk assessment approach that addresses the specific needs of vulnerable groups. This approach will support the development of a sustainable, holistic prevention policy, driving systems and services towards high-value care that meets the needs of individuals affected by MSDs.

Funding

INSuLa 2 is the second wave of a survey project supported and developed by INAIL in its triennial research plan. This survey was financed and developed by INAIL for contributing to research in occupational health and safety and to investigate the workers' perceptions of the health and safety at work at the national level. In this study, Inail provided data to the authors for developing secondary analysis for research aims.

Authors’ Contributions

CDT contributed to conception of this study and the study design. She took care of the theoretical framework and literature review by writing the Introduction and the Discussion, GA took care to the Methods and the Results and to the definition of the data analytical strategy. FR contributed to the literature review and the interpretation of Results and Discussion. SI contributed to the conception of this study and interpretation of data, and he took care of critically revising the work. All authors read and approved the manuscript.

Ethics approval and consent to participate

We analysed secondary data provided by the INSuLa survey, developed in 2019 by the INAIL (Italian Workers’ Compensation Authority) in the triennial research plan 2019–2021. INSuLa was developed on the basis of a project commissioned by the Italian Ministry of Health. The survey was commissioned and approved by the Ministry of Health’s Scientific Committee and ratified by INAIL’s Scientific Committee, which approved the study and procedures of data collection. All procedures in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Declaration of Helsinki and its amendments, or comparable ethical standards. Verbal informed consent was obtained from all participants, since the interviews were conducted by telephone. The verbal informed consent is allowed in surveys in accordance with the procedure of the Computer Assistance Telephone Interview.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Acknowledgements

Not applicable.

Availability of data and materials

Not applicable.

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Unveiling the Multifaceted Associations between Work, Psychosocial Risks, and Musculoskeletal Disorders in the Italian workforce

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