Epidemiological Investigation of Occupational Accidents of Insured Salaried Employees in Region of Thrace, Greece ()
1. Introduction and Literature Review
According to the International Labour Office (ILO), an occupational accident is “an unexpected and unplanned occurrence, including acts of violence, arising out of or in connection with work, which results in one or more workers incurring a personal injury, disease, or death. A case of occupational injury is the case of one worker incurring an occupational injury as a result of one occupational accident” [1]. This may result in temporary or permanent incapacity for work—defined as the worker’s “inability to perform the normal duties of work in the job or post occupied at the time of the OA or can be fatal (as a result of occupational accidents and where death occurred within one year of the day of the accident)” [1].
Occupational accidents represent a significant public health concern, with global estimates indicating 313 million non-fatal injuries and over 350,000 deaths annually. The associated economic losses are estimated at approximately 4% of global GDP, primarily due to absenteeism, temporary or permanent disability, increased insurance premiums, and reduced productivity [2].
Heinrich’s accident triangle [3], introduced in 1931, established the “1:29:300” ratio, suggesting that for every serious injury, there are 29 minor injuries and 300 incidents with no visible damage. Bird [4] [5], extended this in 1969 with the “1:10:30:600” ratio, adding a property damage category. These models laid the foundation for industrial accident prevention and modern occupational health and safety (OHS) strategies. Although more recently criticized for limited applicability to highly specialized modern industries, the models emphasize that minor incidents indicate systemic hazards and their prevention can reduce severe accidents.
Hill and Trist (1953) studied the accident and absenteeism patterns of a stable organization over a four-year period in order to delineate the role of personal relationships in job effectiveness and satisfaction. They proposed that OAs causal factors are not only the physical risks and hazards of the workplace and the personal behavioral characteristics of the workers, but also the result of maladjustment of the workers to work social context [6].
More recent studies focus on predictive models for occupational accidents and the complex interplay of etiological factors.
For example, Gordon et al. (2005), highlighted the importance of robust data collection systems capable of analyzing factors such as human errors, psychosocial influences, and workplace conditions. According to their developed instrument, the action error which cause the accident, is a result of a “situation awareness reduction” by several human/psychosocial factors, which in turn are related to work environmental factors, procedures and conditions [7].
According to Lundberg et al. (2009), the use of epidemiological models helps to evaluate the factors causing OAs. In most of them, four factors’ domains are under consideration, namely, human, technology, organization, and information [8].
In the subway construction industry, Zhou et al. (2014) revealed 15 accident chains using network theory, identifying soil collapse, struck-by injuries, explosions, and machine collapse as key risks in 60% of cases. These findings suggest topological parameter analysis as valuable preventive tool [9].
In construction, inadequate risk management contributed to 84% of cases in one study of 100 incidents, which also identified problems related to worker/work-team behavior (70%), shortcomings of equipment or personal protective measures (56%), workplace conditions (49%) and suitability and condition of materials (27%). Skilled workers were involved in 60 cases, and unskilled in 27 [10].
This is related to the same sector report conducted by Hale et al. (2012) [11], with the aim of understanding the underlying factors of fatal accidents. Using the Human Factors Analysis and Classification System (HFACS) to classify the accident-related factors in a sample of 26 cases which involving 28 deaths, and a sample of 50 non-fatal cases as a control group, they found four main underlying factors on workplace level, namely (in descending order) “Risk planning, assessment and control”, “Hardware & workplace ergonomics/usability/hazards”, “Participation, motivation & conflict resolution”, and “Competence, suitability”, while in delivery system level “Hardware design, purchase & installation” factor predominates, followed by the “Planning & risk assessment” factor. Finally, at the corporate level, “leadership/top-management” and “contracting strategy” appeared as the main factors.
Epidemiological studies have highlighted regional patterns, such as Brunei Darussalam’s rising occupational accident trend between 2014 and 2018. Males (98%), migrant (86%), and 30 - 39 years old (42.5%) workers, in the construction industry (56.4%) were most affected. “Struck by object” (37.7%) was the commonest cause and “upper limb” (43.9%) was the commonest body part involved [12].
A review [13] of Malaysian epidemiological studies on Oas (2000-2013) noted a higher incidence of fatal accidents among older (+60 years) men, Indian nationality, in transportation and agriculture sectors. “Transport and lifting equipment” (53%) and working environment (22%) were the main material agents, with (53%) and working environment in 22%, with “falling from height (28%)” and “being struck by moving objects (17%)” as primary causes, resulted to fractures and unspecified wounds. In non-fatal OAs, men, aged 40 - 49 years, Indian nationality, agriculture and wood-product manufacturing sectors were more in-risk. The commonest injuries were the unspecified wounds (55%), and superficial injuries (10%), caused by “struck by moving objects (33%)”, “falling from height (19%)”, and “trapped between objects (17%)”, and the material agents most involved were “working environment (41%)”, and “handling of machines (20%)”.
Epidemiology of Occupational Accidents and Reporting
Procedures in Greece
In Greece, fatal accidents in the construction sector accounted for 40.7%of the 189 incidents reported between 2010 and 2013 [14]. According to e-Unified Social Security Fund (e-USSF) statistics using NACE Rev. 2 and ISCO 2008 classifications, occupational accidents from 2017 to 2020 were recorded as follows (See Appendix):
2017 [15]: 5.143 work-related accidents took place. Of the 4.749 OAs for which data was collected, 50 were fatal (1.1%). 43.3% had occurred in the 30 - 44 age group, and men (71.3%) and Greek nationality (89.1%) predominated. Most accidents occurred in companies employing up to 49 workers (53.8%), in “Wholesale and Retail Trade” (25.0%) and “Manufacturing” industries (19.8%), and the occupational categories “Plant & Machine Operators” (20.9%) and “Service—sales workers” (20.4%).
2018 [16]: 5.493 work-related accidents took place, increased by 6.8% compared to 2017. Of the 5.058 with recorded data,50 were fatal (0.99%). Demographics with the highest prevalence were Greek nationality (89.2%), men (69.8%) and 30 - 44 age group (42.7%) Companies employing up to 49workers (54%), “Wholesale and Retail Trade” (25.7%) and “Manufacturing” Industries (19.2%), “Plant & Machine Operators” (20.9%) and “Service—sales workers” (20.5%) were the most affected.
2019 [17]: 5.712 work-related accidents took place, increased by 4% compared to 2018. Of the 4.895 with recorded data, 53 were fatal (1%). Demographics with the highest prevalence were Greek nationality (89.8%), men (69.8%) and 35 - 49 age group (43.2%). Companies employing up to 49 workers (55.3%), “Wholesale and Retail Trade” (26.8%) and “Manufacturing” Industries (18%), “Plant & Machine Operators” (22%) and “Service—sales workers” (19.2%) were the most affected.
2020 [18]: 4378 work-related accidents took place. Of the 3657 with recorded data, 44 were fatal (1%). Total accidents decreased by 23.4% compared to 2019, due to restricted economic activity because of Covid-19 pandemic. Demographics with the highest prevalence were Greek nationality (91.2%), men (70.7%) and 35-49 age group (43.7%). Companies employing up to 49 workers (56%), “Wholesale and Retail Trade” (29.7%) and “Manufacturing” Industries (20.3%), “Plant & Machine Operators” and “Service—sales workers” (both with 20.5%) were the most affected.
According to Greek Legislation (Presidential Decree [PD] 17/961, as codified by Law No. 3850/20102, Articles 8 (par. 2) and 43 (par. 2) respectively), OAs must be reported by the employer to the competent authorities (local department of the Labour Inspectorate, local Police authorities, and the victim’s Social Security Organization’s services) within 24 hours. Cases of late reporting and/or deliberate concealment are investigated by the competent department of the Labour Inspectorate. The employer is subject to sanctions provided by Article 16 of PD 17/96 and Articles 71 & 72 of L. 3850/2010 when fails to report the work accident to the competent department of the Labor Inspectorate.
Since the establishment of the Labor Inspectorate’s integrated information system (IIS) (ministerial decision 34331/Δ9.8920/20163, the employer can report OAs electronically (Article 2A(f)). Additionally, following the social security reform and digital transformation under Law 4670/20204 and Articles 14 and 15 (§ 2), the Joint Ministerial Decision No. 49876/14967 (14-12-2020)5 and the General Document 549811/22-11-2022 of the e-USSF6, employees can report OAs electronically to the e-USSF digital platform, provided that an electronic opinion of incapacity for work due to the accident has been issued by the competent health authorities.
In Greece, Oas are recorded by:
The department of the Labour Inspectorate records notifications of OAs collected from its central and regional services, according to Article 43 (par. 2) of Law 3850/2010.
The Unified Social Security Fund records all incidents of accidents resulting in more than three (3) days’ absence from work. This recording is harmonized with Phase III of Eurostat’s European Statistics on Accidents at Work (ESAW) project methodology, which describes the place of the accident and provides information about the victim and the time of the accident.
The Hellenic Statistical Authority has been collecting data on OAs since 1998, as part of the Public Health and Occupational Health and Safety sectors. The reference period is defined by the year in which the accident occurred.
The aim of this study was to record data on occupational and compensable accidents involving the insured workforce in the area of Alexandroupolis (capital of Evros Prefecture) from 2017 to 2023, focusing on the provision of safety measures and education in Occupational Health and Safety (OHS). Additionally, it aimed to identify demographic, social, and occupational factors that correlate with OAs and/or contribute to their severity.
2. Materials and Methods
Study type, population, and sample selection criteria
This study employed a retrospective observational design. The study population comprised workers insured by the Unified Social Security Fund in the region of Thrace. Sample selection criteria included:
Workers insured by the Unified Social Security Fund
Age: 18 - 64 years
Occupational and non-occupational compensable accidents resulting in at least 3 days of absence from work. Accident cases were classified as occupational if they occurred at work, on the way to work, or when leaving work, and as non-occupational otherwise
Time period: 2017-2023
2.1. Data Collection Methods
Data for all compensable accidents meeting the sample selection criteria were collected from accident records from the emergency department (ED) of the University General Hospital of Alexandroupolis.
After compiling data from all eligible accidents, a personal telephone interview was conducted with all accident victims (or their relatives in the case of fatal accidents) to collect personal and qualitative data. The response rate for this phase was 73.9%.
2.2. Study Variables and Statistical Analysis Methods
The demographic and personal variables were “age”, “gender”, “nationality (Greek/non-Greek)”, “level of education”, smoking habits, height, and body weight (to calculate Body Mass Index—BMI). The occupational variables included “occupation”, “employment status”, “sector of the employer’s economic activity”, “number of employees at local unit”, “length of service with the employer” and “safety measures and education in Occupational Health & Safety (OHS)”. The accident-related variables included “place (occupational/non-occupational)”, “mode” and “type” of injury, “affected body part”, “material agent for injury” (all as classified by ILO), “hospitalization”, “means of transport to healthcare”, “motor-vehicular accidents”, “medication (due to the accident)”, “accident outcome”, “duration of absence from work” and “personal perception of the underlying cause of the accident”. The variables “age”, “BMI”, “number of employees”, “length of service” and “duration of absence” were recorded both as absolute numbers and as ordered categories.
Statistical calculations and analysis were performed using SPSS version 29 (SPSS Inc., Chicago, IL, USA). Descriptive statistics included frequency and percentage. For statistical hypothesis testing, normality test, Kruskal-Wallis’ test, Chi-square test (with Monte Carlo Exact Test to estimate the exact significance level) and binary and ordinal logistic regression were applied. The level of statistical significance was set at a p-value ≤ 0.05.
3. Results
In the study population, 489 accidents were recorded from 2017 to 2023, of which 385 (78,73%) occurred at work, or on the way to/from work. There was fluctuation in the number of cases reported from year to year as shown in Table 1 and Figure 1.
Table 1. Accidents of insured workers aged 18 - 64 years, in Alexandroupolis, 2017-2023.
Year |
Occupational
Accidents |
Non-Occupational Accidents |
Total |
2017 |
57 |
8 |
65 |
2018 |
42 |
12 |
54 |
2019 |
50 |
19 |
69 |
2020 |
55 |
5 |
60 |
2021 |
68 |
17 |
85 |
2022 |
55 |
29 |
84 |
2023 |
58 |
14 |
72 |
Total |
385 |
104 |
489 |
Figure 1. Accidents of insured workers aged 18 - 64 years, in Alexandroupolis, 2017-2023.
Most of the reported cases involved early-to-late middle-aged workers (mean age 43 years old), men (63% of the total sample and 66.5% of the OAs only), and individuals with mainly primary education, with more than 95% of them being Greek (Table 2).
Table 2. Demographics of the studied population.
|
Total (n = 489) |
only OAs (n = 385) |
Gender [n (%)] |
(n = 486) |
(n = 382) |
Men |
306 (63%) |
254 (66.5%) |
Women |
180 (37%) |
128 (33.5%) |
Age-groups [n (%)] |
(n = 468) |
(n = 369) |
18 - 24 |
32 (6.8%) |
26 (7%) |
25 - 34 |
105 (22.4%) |
80 (21.7%) |
35 - 44 |
115 (24.6%) |
83 (22.5%) |
45 - 54 |
136 (29.1%) |
111 (30.1%) |
55 - 64 |
80 (17.1%) |
69 (18.7%) |
Level of education [n (%)] |
(n = 482) |
(n = 381) |
Primary |
225 (46.7%) |
197 (51.7%) |
Secondary |
174 (36.1%) |
125 (32.8%) |
Post-secondary non-tertiary |
37 (7.7%) |
28 (7.4%) |
Tertiary |
46 (9.5%) |
31 (8.1%) |
Nationality [n (%)] |
(n = 486) |
(n = 385) |
Greek |
468 (96.3%) |
368 (95.6%) |
Non-Greek |
18 (3.7%) |
17 (4.4%) |
Abbreviations: OA—Occupational Accidents. Note: all the % frequencies were calculated according to the valid cases, which are presented on top of each column.
Eight fatal accidents were registered, all of which were occupational, as they happened in work (n = 3), and while traveling to (n = 1) or from (n = 4) work. Of the non-fatal accidents (n = 481), over 22,937 days of work absencewere caused (with 4 missing cases for this variable). In terms of the “sector of economic activity”, most accidents occurred in “Manufacturing Industries” (28.3% of the OAs and 28.6% of the total sample),”Other activities” (22.7% & 23.9%, respectively), and “Wholesale and Retail Trade” (19.5% & 19.7%, respectively). Elementary occupations were the most at risk (27% of OAs and 26.5% of the total), followed by service and sales workers (20.5% & 22.2%) and clerical support workers (18.4% & 19.1% respectively). The descriptive statistics for the occupational variables are presented in Table 3. Two occupational variables “Number of employees at local unit” and “Length of service with the employer”- and one accident-related variable, “Material Agent” (which is the cause that led to injury), are presented in a separate table, because they are particularly relevant for OAs (Table 5). Personal habits and characteristics (namely smoking and BMI) did not differ significantly between the OAs and non-OAs groups or the total sample, especially considering the high percentage of missing data (non-response). The BMI data was missing for over 28% in the OAs group, the total sample, and 26% in the non-OAs group (data not shown).
Table 3. Absolute and relative frequencies of occupational variables.
|
Total (n = 489) |
only OAs (n = 385) |
Occupation [n (%)] |
(n = 487) |
(n = 385) |
Professionals |
37 (7.6%) |
21 (5.5%) |
Technicians and associate professionals |
8 (1.6%) |
6 (1.6%) |
Clerical support workers |
93 (19.1%) |
71 (18.4%) |
Service and sales workers |
108 (22.2%) |
79 (20.5%) |
Craft and related trades workers |
55 (11.3%) |
51 (13.2%) |
Plant and machine operators, and assemblers |
56 (11.5%) |
52 (13.5%) |
Elementary occupations |
129 (26.5%) |
104 (27%) |
Unknown |
1 (0.2%) |
1 (0.3%) |
Employment status ([n (%)] |
(n = 489) |
(n = 385) |
Salaried employees |
446 (91.2%) |
342 (88.8%) |
Self-employed |
40 (8.2%) |
40 (10.4%) |
Trainee/Apprentice |
1(0.2%) |
1 (0.3%) |
Other |
2 (0.4%) |
2 (0.5%) |
Sector of economic activity [n (%)] |
(n = 476) |
(n = 375) |
Manufacturing/Production |
136 (28.6%) |
106 (28.3%) |
Construction |
44 (9.2%) |
38 (10.1%) |
Wholesale & Retail Trade, Repair of motor vehicles and motorcycles |
94 (19.7%) |
73 (19.5%) |
Transportation & Storage |
22 (4.6%) |
19 (5.1%) |
Accommodation & Food Service Activities |
66 (13.9%) |
54 (14.4%) |
Other |
114 (23.9%) |
85 (22.7%) |
Safety measures and education in OHS [n (%)] |
(n = 477) |
(n = 376) |
No |
344 (72.1%) |
266 (70.7%) |
Minimal/Inadequate |
63 (13.2%) |
56 (14.9%) |
Yes |
70 (14.7%) |
54 (14.4%) |
Additional Abbreviations: OHS, Occupational Health and Safety.
Motor-vehicular accidents accounted for 15.7% of OAs and the 13.4% of the total. The two most prevalent causes of accidents (the “mode of injury”) were “falls from the same height” (48.8% of OAs and 52.8% of the total) and “collisions with immobile objects and falling against or being struck by moving objects” (22.9% & 21.9% respectively). The most frequent type of injury (74.8% of OAs and 77.9% of the total) was “bone fractures”, followed by “dislocations, sprains and strains” (14.8% & 13.5% respectively). Regarding the injured body part, most accidents affected the upper and lower extremities with relative frequencies ranging from 34.4% to 42.3%. The predominant material agent that caused the injury in OAs was “other material agents” (76.1%), followed by “transport vehicles and lifting equipment” (9.9%). In 96.7% of all accidents and 96.6% of OAs, first aid was administered directly after the accident at the hospital’s ED. 17.6% & 19% of the workers who sustained accidents and OAs respectively, were hospitalized after first aid was administered. However, only 1% received continuing medication due to the accident after being discharged from the ED or Hospital. The main means of transport for injured individuals to a first aid unit or hospital was by private car and ambulance accounting for 75.2% and 23.8% for OAs, and 77% and 22.2% for the total, respectively. Continuing physical symptoms, disabilities and/or incapacity were reported in 23% of OAs and 21% of all accidents. The mean duration of work absence due to the accident was 48 days. Most accidents took place in small and medium-sized enterprises; in fact, 66.2% occurred in enterprises employing up to 49 employees (Table 4 & Table 5).
Table 4. Absolute and relative frequencies of accident-related variables.
|
Total (n = 489) |
only OAs
(n = 385) |
Mode of injury [n (%)] |
(n = 489) |
(n = 385) |
Fall of person from a height |
41 (8.4%) |
36 (9.4%) |
Falling of person—on the same level |
258 (52.8%) |
188 (48.8%) |
Slipping, collapse and being struck, struck by falling objects |
23 (4.7%) |
20 (5.2%) |
Collision with immobile objects and falling against or being struck by moving objects |
107 (21.9%) |
88 (22.9%) |
Trapping, being crushed—inside or between objects |
19 (3.9%) |
18 (4.7%) |
Physical strain/over-exertion |
7 (1.4%) |
5 (1.3%) |
Exposure to or contact with hazardous substances, electric current, extreme temperatures or radiation |
7 (1.4%) |
6 (1.6%) |
Other types of injury not included in this list |
27 (5.5%) |
24 (6.2%) |
Type of injury [n (%)] |
(n = 489) |
(n = 385) |
Wounds and superficial injuries |
13 (2.7%) |
13 (3.4%) |
Bone fractures |
381 (77.9%) |
288 (74.8%) |
Dislocations, sprains and strains |
66 (13.5%) |
57 (14.8%) |
Traumatic amputations |
9 (1.8%) |
8 (2.1%) |
Concussion and internal injuries |
11 (2.2%) |
11(2.9%) |
Burns, scalds and frostbites |
4 (0.8%) |
3 (0.8%) |
Other |
5 (1%) |
5 (1.2%) |
Body site injured [n (%)] |
(n = 489) |
(n = 385) |
Whole body and multiple sites |
20 (4.1%) |
17 (4.4%) |
Lower extremities |
207 (42.3%) |
153 (39.8%) |
Upper extremities |
168 (34.4%) |
136 (35.3%) |
Torso and organs |
52 (10.6%) |
42 (10.9%) |
Back |
14 (2.9%) |
9 (2.3%) |
Neck |
3 (0.6%) |
3 (0.8%) |
Head |
25 (5.1%) |
25 (6.5%) |
Motor/Vehicular accident [n (%)] |
(n = 485) |
(n = 381) |
No |
420 (86.6%) |
321 (84.3%) |
Yes |
65 (13.4%) |
60 (15.7%) |
Means of transport to HC [n (%)] |
(n = 487) |
(n = 382) |
Ambulance |
108 (22.2%) |
91 (23.8%) |
Private car |
375 (77%) |
288 (75.2%) |
Taxi |
4 (0.8%) |
4 (1%) |
Place of first aid provision [n (%)] |
(n = 489) |
(n = 385) |
Hospital ED |
473(96.7%) |
372 (96.6%) |
Primary Health Care Unit |
16 (3.3%) |
13 (3.4%) |
Hospitalization [n (%)] |
(n = 488) |
(n = 384) |
No |
402 (82.4%) |
311 (81%) |
Yes |
86 (17.6%) |
73 (19%) |
Medication due to the accident [n (%)] |
(n = 481) |
(n = 380) |
No |
468 (97.3%) |
368 (96.8%) |
Yes |
5 (1%) |
4 (1.1%) |
Death |
8 (1.7%) |
8 (2.1%) |
Duration of absence from work (days) [n (%)] |
(n = 477) |
(n = 374) |
≤3 |
12 (2.5%) |
11 (2.9%) |
4 - 6 |
50 (10.5%) |
43 (11.5%) |
7 - 13 |
30 (6.3%) |
26 (7%) |
14 - 20 |
35 (7.3%) |
27 (7.2%) |
21 - 30 |
72 (15.1%) |
55 (14.7%) |
31 - 60 |
157 (32.9%) |
119 (31.8%) |
61 - 90 |
63 (13.2%) |
47 (12.6%) |
91 - 180 |
48 (10.1%) |
37 (9.9%) |
>180 |
10 (2.1%) |
9 (2.4%) |
Accident outcome [n (%)] |
(n = 481) |
(n = 379) |
No consequences |
372 (77.3%) |
284 (74.9%) |
Related Health problems/Disability/Incapacity |
101 (21%) |
87 (23%) |
Death |
8 (1.7%) |
8 (2.1%) |
Additional Abbreviations: ED—Emergency Department.
Table 5. Size of enterprise (number of employees at local unit), length of service with the employer and material agent of contact/mode of injury (only occupational accidents).
Number of employees at local unit [n (%)] |
(n = 364) |
1 - 3 |
50 (13.8%) |
4 - 9 |
68 (18.7%) |
10 - 19 |
53 (14.6%) |
20 - 49 |
70 (19.2%) |
50 - 99 |
49 (13.5%) |
100 - 249 |
27 (7.4%) |
250 - 499 |
18 (4.9%) |
500 - 999 |
14 (3.8%) |
>1000 |
15 (4.1%) |
Length of service with the employer (months) [n (%)] |
(n = 281) |
<12 |
90 (32%) |
12 - 36 |
65 (23.1%) |
37 - 60 |
45 (16%) |
61 - 120 |
35 (12.5%) |
121 - 180 |
13 (4.6%) |
181 - 240 |
16 (5.7%) |
>240 |
17 (6.1%) |
Material Agent of contact/mode of injury [n (%)] |
(n = 385) |
Machinery |
25 (6.5%) |
Transport vehicles and lifting equipment |
38 (9.9%) |
Other equipment |
16 (4.2%) |
Materials, substances and radiation |
5 (1.3%) |
Working environment |
4 (1%) |
Other material agents |
293 (76.1%) |
Other material agents not classified in this list |
4 (1%) |
Considering the 8 fatal OAs, the predominant gender and age -range were men (87.5%, n = 7), individuals aged 45 - 54 years (57.1%). The occupation categories most affected were “service and sales workers” and “elementary occupations,” each accounting for 37.5%, while “plant and machine operators and assemblers” accounted for the remaining 25%. In the “Manufacturing/Production” sector, 37.5% of fatal accidents occurred, followed by 25% in both the “Construction” sector and “Wholesale & Retail Trade, Repair of Motor Vehicles and Motorcycles” sector. The remaining 12.5% occurred in the “Transportation & Storage” sector. Half of these fatal accidents were motor/vehicular accidents, and in 62.5% of the cases, the mode of injury was “collision with immobile objects and falling against or being struck by moving objects”. The predominant material agent of contact and mode of injury was “other material agents” (75%). All fatal accidents involved concussions and internal injuries, with 87.5% affecting the head and 12.5% affecting the head and multiple body parts.
Considering the victims’ perception of the underlying cause of the accident, which was recorded during the phone interviews, “carelessness” was mentioned by 47.3% of the OAs group and 83% of the non-OAs group. “Working conditions” was the second most mentioned cause in the OAs group (27.7%) and third in the non-OAs group (7%). “Human error/third-party fault” was mentioned by 17.3% of OAs, ranking third in OAs and second in non-OAs (10%). Finally, three causes were mentioned only by the OAs group: “Lack of preventive/protective measures” (3.5%), “Damage/malfunctions” (2.9%), and “Other causes” (1.3%) (Figure 2).
Figure 2. Victims’ perception on the underlying cause of the accident.
Inferential Statistical Analysis
The inferential statistical analysis, conducted to test the research hypothesis, demonstrated several correlations (Table 6).
First, the “place of the accident” variable, which represents the probability of an OA, correlates with Gender—men having twice the risk for OA compared to
Table 6. Statistically significant correlations’ summary.
Variable (1) |
Variable (2) |
Test method |
OAs sample |
Total sample |
n |
p-value |
n |
p-value |
Place of accident (OA/non-OA) |
Gender |
Chi-square with Monte Carlo exact test |
|
|
486 |
<0.05 |
Age (years)* |
Mann-Whitney U |
|
|
471 |
<0.05 |
Occupation |
Chi-square with Monte Carlo exact test |
|
|
487 |
<0.001 |
Accident outcome |
Chi-square with Monte Carlo exact test |
|
|
481 |
<0.05 |
Days of absence from work* |
Type of injury |
Kruskal-Wallis |
374 |
<0.001 |
477 |
<0.001 |
Body site injured |
Kruskal-Wallis |
374 |
<0.001 |
477 |
<0.001 |
Accident outcome |
Kruskal-Wallis |
368 |
<0.001 |
|
|
Age (years)* |
Mode of injury |
Kruskal-Wallis |
372 |
<0.001 |
471 |
<0.001 |
Number of employees at local unit* |
Material Agent of contact/mode of injury |
Kruskal-Wallis |
364 |
<0.05 |
460 |
<0.05 |
Safety measures and education in OHS |
Kruskal-Wallis |
357 |
<0.001 |
452 |
<0.001 |
Mode of injury |
Gender |
Chi-square with Monte Carlo exact test |
382 |
<0.001 |
|
|
Level of education |
Chi-square with Monte Carlo exact test |
382 |
<0.05 |
|
|
Occupation |
Chi-square with Monte Carlo exact test |
385 |
<0.05 |
|
|
Sector of economic activity |
Chi-square with Monte Carlo exact test |
375 |
<0.05 |
|
|
Age (range) |
Chi-square with Monte Carlo exact test |
369 |
<0.001 |
|
|
Accident outcome |
Chi-square with Monte Carlo exact test |
379 |
<0.05 |
|
|
Material agent |
Gender |
Chi-square with Monte Carlo exact test |
382 |
<0.001 |
|
|
Occupation |
Chi-square with Monte Carlo exact test |
385 |
<0.05 |
|
|
Sector of economic activity |
Chi-square with Monte Carlo exact test |
375 |
<0.05 |
|
|
Safety measures and education in OHS |
Chi-square with Monte Carlo exact test |
376 |
<0.05 |
|
|
Type of injury |
Gender |
Chi-square with Monte Carlo exact test |
382 |
<0.05 |
|
|
Sector of economic activity |
Chi-square with Monte Carlo exact test |
375 |
<0.001 |
|
|
Duration of absence from work |
Chi-square with Monte Carlo exact test |
374 |
<0.001 |
|
|
Accident outcome |
Chi-square with Monte Carlo exact test |
379 |
<0.001 |
|
|
Safety measures and education in OHS |
Chi-square with Monte Carlo exact test |
376 |
<0.05 |
|
|
Body site injured |
Level of education |
Chi-square with Monte Carlo exact test |
381 |
<0.05 |
|
|
Sector of economic activity |
Chi-square with Monte Carlo exact test |
375 |
<0.05 |
|
|
Duration of absence from work |
Chi-square with Monte Carlo exact test |
374 |
<0.001 |
|
|
Accident outcome |
Chi-square with Monte Carlo exact test |
379 |
<0.001 |
|
|
Sector of economic activity |
Number of employees at local unit |
Chi-square with Monte Carlo exact test |
354 |
<0.001 |
|
|
Safety measures and education in OHS |
Chi-square with Monte Carlo exact test |
366 |
<0.001 |
|
|
women (OR = 1.94, CI 95%, p < 0.05)—Age (p < 0.05), Occupation (p < 0.001) and Accident outcome (p < 0.05). The median age in the OA category was 4 years higher than in the non-OA category (44 versus 40), while 48.8% of the OAs involved the age-range 45 - 64 years, compared to 35% in the non-Oas group. Innon-OAs, the predominant age-range was 35 - 44 years (31%). Regarding occupation, the main differences were observed in the “Craft and related trades workers” and “Plant and machine operators, and assemblers” which made up 13.2% & 13.5% of OAs respectively, compared to 4% in both occupation categories among non-OAs. On the other hand, the “professionals” category constituted 16% of non-OAs but only 5.5% of OAs. Continuing accident-related health problems and disabilities/incapacity were reported in23% of OAs, compared to 14% in non-OAs, while, as previously mentioned, all fatal accidents were occupational (2.1% of OAs and 1.7% of the Total).
As continuing variables, the Duration of absence of work(in days) correlates with the “Type of injury” (p < 0.001), “Body site injured” (p < 0.001), and “Accident outcome” (p < 0.001), “Age (in years)” correlates with “Mode of injury” (p < 0.001), and “Number (absolute) of employees at the local unit” correlates with “Material Agent of contact/mode of injury” (p < 0.05) and “Safety measures and education in OHS” (p < 0.001).
By implementing the Chi-square test with the Monte-Carlo exact test for valid estimation, interesting correlations were identified in the OA sample between key accident-related variables (namely “mode” and “type of injury”, “body site injured” and “material agent” causing the injury) and demographic, occupational and accident-related outcome variables (such as “duration of absence” and “accident outcome”).Additionally, the same statistical methods confirmed expected correlations between “sector of employer’s economic activity” and “number of employees at local unit”, as well as between “safety measures and education in OHS”.
Table 6 summarizes all the aforementioned results.
Ordinal and binary logistic regressions were performed with “duration of absence” (in its ordinal form) and “accident outcome” (for the non-fatal cases only) as dependent variables, respectively. These variables were used as measures of the accident’s severity, and the regression aimed to examine whether selected occupational variables (such as “sector of economic activity”, “occupation”, and “length of service with the employer”), accident-related variables (such as “mode of injury” and “material agent”), and age had predictive value. The analysis did not result in a valid predictive model for either of the two severity measures.
4. Discussion
The results are largely in agreement with official national data on OAs. According to the e-USSF’s ESAW Reports for Greece, for the years 2017 to 2021 (the most recent year with available data), 58% - 59% of the OAs occurred at the age-range 35 - 54 years old each year, with a mean rate of 30% for each one of the 35 - 44 and 45 - 54 years old age categories. In this study, the corresponding relative frequency for the 35 - 54 age range was 52.6% for the total time period.
The findings of our study showed that the number of occupational accidents in the region of Thrace ranged from 42 (2018) to 68 (2021) annually. Taking the average population from the active labor force, according to the 2021 national census in the regional unit under consideration, we can estimate a prevalence rate between 0.9 and 1.5 per 1000 workers annually (with a mean prevalence of 1.4 per 1000 workers over the seven-year period from 2017-2023). The national prevalence rate per 1000 workers was 2.4 for the years 2017 and 2018, 2.3 for 2019 and 2 for 2020. As noted in the Eurostat report for 2021: “low incidence rates for non-fatal accidents may reflect an under-reporting problem linked to: poorly-established reporting systems, little financial incentive for victims to report, non-binding legal obligations for the employers” [19] among other factors.
A high rate of occupational accidents was observed among male workers (66.5%), as men are more likely to be employed in labor, intensive and high risk industries such as construction and manufacturing. In fact, most occupational accidents occurred in the manufacturing sector (28.3%), while in analytical national ESAW reports the corresponding sector was “Wholesale & Retail Trade, Repair of motor vehicles and motorcycles” (25%, 25.7% & 26.8% for the years 2017, 2018 & 2019 respectively). In this study, the latter sector accounted for 19.5%, ranking third, while the “other activities” sector ranked second (22.7%). The primary cause of occupational accidents was “falling from the same level” (48.8%). In the national reports falls in general accounted for nearly30% (28.3% - 30.6% for the 2017-2019 time period). The main mode of injury was “Slipping, collapse and being struck, struck by falling objects” in 2017 (39%) and “Collision with immobile objects and falling against or being struck by moving objects” in 2018 and 2019 (38.3% and 37.9% respectively). Upper and lower extremities were the primary body parts injured, and fractures were the most common type of injury, in both this study and in the national reports. The mean duration of absence from work of 1.5 months, in both this study and the national reports confirms the significant productivity losses caused by OAs.
An important observation is that higher frequency and most serious accidents were recorded in the year 2021. This could be attributed to changes in the way workers submit accident reports. Until 21/01/2021, occupational accidents were reported to the social security agency by a handwritten accident report filed by the employee with the written consent of the employer, or by the employer themselves. This often resulted in accidents going unreported when there was disagreement with the employer.
As of 22/01/2021, the electronic submission of workers’ accident declarations via the platform of the Unified Social Security Fund came into effect following a joint ministerial decision.
According to Article 2: “The employee’s treating physician fills in the electronic-prescription application of the ‘e-Government Center for Social Security S.A (IDIKA S.A)’ the certificate of incapacity for work, which includes indicative data such as: the date of issue, the physician’s identification data, the insured’s ID information, the insured’s Social Security Number, details of the illness according to the ICD-10 coding, and the proposed days of sick leave. In the event of an accident, the doctor enters free text descriptive data including: the date and time of the accident, whether it was a traffic accident, place of the accident, and the date of report submission.”
Another noteworthy issue is the nearly complete lack of safety measures and training in OHS (85.6% overall), which makes it impossible to draw conclusions regarding their correlation with the frequency and severity of OAs. However, the results confirmed the inadequacy of safety measures and training in small and medium-sized enterprises, which constitute the majority in Greece. The fact that almost half (47.3%) of OA victims, when interviewed by phone, cited “carelessness/inattention” as the main cause of the accident—considering it their own responsibility—relates both to ignorance of OHS rules and to low reporting rates of OAs. However, based on the existing perceptions, “working conditions” were considered a contributing cause by nearly one-third of the injured workers (27.7%). Given that “third-party fault/human error” is one of the primary factors targeted by occupational accident prevention strategies, our results suggest that at least 45% of OAs could be prevented.
5. Limitations
This study has several limitations. First, to ensure the analysis complied with the statistical classifications of the ILO and the European Commission for the Statistics of Accidents at Work (and to make the results comparable), our data exhibited large dispersion and variability across many variables. This resulted in the inability to develop a predictive model. However, this issue is common in similar surveys, and the use of qualitative data, rather than quantitative, is often suggested. Second, because the study population was drawn from the ED, the cases encompassed a variety of workplaces from different sectors, making it unfeasible to investigate workplace-specific factors that contribute to accidents.
6. Conclusion
Occupational accidents can have a disastrous impact on employees, business operations, and the productivity of the national economy. Significant efforts are needed in their prevention through the implementation of safe practices, provision of proper training and equipment, and fostering a culture of safety awareness. Thorough and systematic examination and improvement of working conditions must be a priority, regardless of the size of the enterprise.
7. Study Ethics and Approvals
Throughout the conduct of this study, data collection and processing were in compliance with the General Personal Data Regulation (GPDR). Participation in the phone interviews was voluntary, and participants gave informed consent for the use of their data. The study was approved by the Scientific Council of the University General Hospital of Alexandroupolis, with protocol number 1332 on 12-01-2023.
Conflicts of Interest
The authors declare no conflicts of interest.
NOTES
1Presidential Decree 17/96 (Government Gazette 11/A [18.1.1996]) on “Measures to Improve the Safety and Health of Workers at Work in Compliance with Directives 89/391/EEC and 91/383/EEC”.
2Law 3850/2010 (Government Gazette 84/A [2.6.2010]) on “Ratification of the Code of Laws on the Health and Safety of Workers”.
3Ministerial Decision 34331/Δ9.8920/2016 (Government Gazette 2458/Β [10.8.2016]) on the “Simplification of Labor Inspectorate Body (LAB) Procedures through the Integrated Information System of the LAB (IIS-LAB)”.
4Law 4670/2020 (Government Gazette 43/A [28.2.2020]) on “Social Security Reform and Digital Transformation of the Unified Social Security Fund (e-USSF) and Other Provisions”.
5Joint Ministerial Decision 49876/14967/2020 (Government Gazette 5497/B [14.12.2020]) on “Definition of the Electronic Procedure for the Granting of Sickness—Accident Benefit by e-USSF”.
6E-Unified Social Security Fund: General Document 549811/22-11-2022 “Inclusion in the Electronic Application for the Granting of Sickness Benefits a) of Insured Persons of IKA-ETAM of Special Categories and Insured Persons of TAYTEKO and b) of the Electronic Submission of the Accident Declaration of Insured Persons of IKA-ETAM, TAXI and OAEE of e-EFKA.