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Highlights:
In five years, sawmill workers in El Salto have suffered from one to five accidents.
The main injuries are hitting a body part (57 %), wounds (16 %) and sprains (15 %).
Serious traumatic accidents such as bone fractures and amputations represent 4 %.
The most affected body parts are fingers (35 %) and hands (20 %).
Causes of injuries are material handling (54 %), machines (32 %) and tools (10 %).
Introduction
Risk factors at work vary according to the sector and scale of the companies. Those in the sawmill industry are considered risky because they record a relatively high accident rate (Top, Adanur, & Öz, 2016). Sawmill work has been identified as one of the most dangerous works, even in countries with high occupational safety and health standards (Awosan et al., 2018).
Sawmilling operation involves handling materials and equipment exposing workers to hazards that can affect their occupational safety and health (Onowhakpor, Abusu, Adebayo, Esene, & Okojie, 2017). Among these hazards are injuries resulting from getting stuck or being hit by machinery, falling from a height, lifting heavy objects, repeating unhealthy movements (musculoskeletal injuries), and breathing harmful substances while performing a work activity to meet productivity (Bello & Mijinyawa, 2010). Due to the nature of the activity, there is a high prevalence of hand injuries that can result in serious consequences such as getting hit, wounds, deformities and even amputations (Bamidele, Adebimpe, & Dairo, 2011). All of this is motivated by a piece-rate or performance pay system that encourages fast-paced work against the adoption of good safety practices (Bardomás & Blanco, 2018).
Even though machinery safety principles are covered by international standards and national regulations, moving parts still cause many injuries of different severity (Chinniah, 2015). Rotating and linear movements, inadequate protective equipment, reliability factors, skill, poor safety culture and practices, lack of maintenance, and workplace design are the reasons behind major accidents (Ajayeoba, Raheem, & Adebiyi, 2019; Yadav, Arora, Varadharajan, & Yadav, 2020).
Occupational safety and health standards need procedures for the prevention of injuries and fatalities; the absence or incorrect application of these are related to many preventable machinery accidents (Poisson & Chinniah, 2015). Workers should practice acquired safety-related knowledge, skills, attitudes, and behaviors to prevent accidents, injuries, and damage to personal life and property (Odibo, Nwaogazie, Achalu, & Ugbebor, 2018).
The cost of acquiring, improving and implementing an industrial safety and occupational health prevention plan is less than what a company faces in terms of stopping production, compensation and losing qualified workers (Dorman, 2012; Mitchual, Donkoh, & Bih, 2015). Despite economic and emotional losses related to occupational injuries, in Durango there is no information on the accident rate in the forestry sector, nor specifically, in the sawmill industry in the region of El Salto; moreover, the causes that motivate accidents and the parts of the body that are affected are unknown, which prevents us from knowing the risk factors causing this problem, because recording occupational accidents is not a common practice. Therefore, the objective of this study was to identify the most common injuries, parts of the body affected and agents that lead to accidents among workers in the sawmill industry in the region of El Salto, Durango.
Materials and Methods
Study area
The study area is found in the Sierra Madre Occidental in the Cañones Duranguenses subprovince formed by large-area plateaus, mainly associated with canyons and high mountain ranges with canyons (Comisión Nacional para el Conocimiento y Uso de la Biodiversidad [CONABIO] & Secretaría de Recursos Naturales y Medio Ambiente de Durango [SRNyMA], 2017) in the municipality of Pueblo Nuevo, southwest of the state of Durango, Mexico. The area includes sawmills established in the forest region of El Salto that carry out production process of squared timber, both for private or ejido properties.
Methods
According to SEMARNAT (Secretaría de Medio Ambiente y Recursos Naturales, 2014), in the "Centro-Occidente" forest supply basin of Durango, 20 main jobs are identified in a typical sawmill in the region, consisting of an average workforce of 26 people. Based on this information, the jobs were categorized according to their function in the following segments: a) machinery and equipment operators (sawyer and bandsaw, wood cutter, and wood-mizer operator); b) assistant operator (sawyer assistant, assistant operator for bandsaw and wood-mizer, cleaning assistant and c) general assistants (to help lift logs, turn wood, gather sawdust, to help piling and lifting wood).
A structured survey was used to create a database on the incidence of occupational accidents, which was applied to workers by direct interviews in sawmills. The survey was based on the document “Recording and notification of occupational accidents and diseases: A code of practice” of the International Labor Organization (ILO, 1996). This survey included four categories: a) personal information of the worker (age, schooling, occupational safety training, years of work experience and years of service); b) place of accident at the company; c) body part affected by injury or accident: upper extremity segments (shoulder, neck, head, forearm, arm, wrist and hand), back segment, abdomen, hip and waist, and lower extremity segments (legs, knee, ankle, foot and toes); d) identification of the agent (material and causal) and mechanism that caused the injury or accident.
Sampling
A random sampling was applied to all sawmills in the region of El Salto, Durango, where the target population is represented by the workers. On this matter, 48 sawmills, 13 pallet and wooden packing box factories, 19 wooden packing box factories and three sawn timber processing industries (SEMARNAT, 2014) are recorded in El Salto sub-basin; however, the number of workers in these companies is unknown. When the size of the target population is unknown, the sample size is estimated with the following formula (Badii, Castillo, & Guillen, 2008):
where,
n = population sample size
z = desired degree of confidence (95 %, equivalent to 1.96)
p = probability of success or expected proportion (0.05)
q = probability of failure 1- p (0.95)
e = acceptable limit of sampling error (3.0 %).
The result of the formula was a sample of 203 workers; however, it was expanded to 300 surveys, of which 100 were targeted to each of the three job categories (operators, assistant operator and general assistant), distributed in 26 sawmills and pallet factories that had an injury in the last five years. Surveys were applied from September to November 2020.
Statistical analysis
With the survey data, summary tables were prepared using descriptive statistics (mean, mode, standard deviation, frequency and proportion to summarize the variables age and years of work experience), as well as cross or contingency tables with non-parametric inferential statistics (Chi-square [Ӽ2] tests of association and independence to test the degree of relationship between two categorical variables) (Janicak, 2007). For this, both the asymptotic method and Fisher's exact test were used as long as more than 20 % of the expected frequencies had values lower than 5 (Sharpe, 2015). Significance level was set at 5 %. The job category (operators, assistant operators and assistant helpers) was related to safety aspects, risks and accidents at work (place of accident, body part affected by injuries or accident, material agent and causal agent). Data were analyzed using the SPSS statistical package version 19 (IBM Corp., 2010).
Results and Discussion
Worker profile in sawmills
The average sawmill worker in the region of El Salto, Durango is 35 years old, has three years of experience in the job, and four and a half years of service in the company (Table 1). As the job position is more hierarchical, the worker's age and years of experience are higher.
Work experience can offer a biased view of skills, since time spend in the same job position, no matter the qualification required for the job, generates a presumption of training that is verifiable in the short term if it is exercised within the company, but difficult to assess outside the company (Aguilar del Castillo, 2016). Also, subcontracting or temporary hiring of workers by sawmill management generates greater labor turnover, making it difficult to a large extent to qualify them (Bardomás & Blanco, 2018). The above explains that found in this study where not only greater work experience is considered to perform a labor function, but it is also associated with greater age of the worker in positions of greater responsibility. Low work experience recorded is perhaps due to higher labor turnover in sawmills in the region of El Salto, Durango.
Table 1. Labor profile of the forest worker in the sawmill industry of El Salto, Durango.
| Variable | Total (n) | Mean | Mode | Standard Deviation | Minimum | Maximum |
|---|---|---|---|---|---|---|
| Age (years) | 300 | 34.84 | 32 | 10.92 | 17 | 63 |
| Job experience (years) | 3.12 | 2 | 3.45 | 0.08 | 20 | |
| Years of service | 4.57 | 2 | 4.43 | 0.08 | 27 | |
| Machinery and equipment operators | ||||||
| Age (years) | 100 | 40.59 | 48 | 9.56 | 18 | 63 |
| Job experience (years) | 4.35 | 3 | 4.22 | 0.17 | 20 | |
| Years of service | 6.73 | 3 | 5.55 | 0.25 | 27 | |
| Assistant operator | ||||||
| Age (years) | 100 | 33.07 | 29 | 11.28 | 17 | 63 |
| Job experience (years) | 2.66 | 2 | 2.98 | 0.08 | 15 | |
| Years of service | 3.74 | 2 | 3.48 | 0.25 | 16 | |
| General assistant | ||||||
| Age (years) | 100 | 30.86 | 18 | 9.43 | 18 | 60 |
| Job experience (years) | 2.35 | 2 | 2.63 | 0.08 | 12 | |
| Years of service | 3.26 | 1 | 3.03 | 0.08 | 15 | |
An important segment of forest workers in sawmills have secondary schooling (56 %) and a smaller proportion have primary (27 %) and high school education (14 %); although there is a relationship between the worker's schooling and the position occupied (P < 0.05), the relationship between both categories does not indicate a higher job hierarchy with higher schooling, which means that the prevailing criterion for occupying a job position is the combination of work experience with age (Table 2).
Table 2. Forestry worker schooling per job category in the sawmill industry in El Salto, Durango.
| Schooling | Job category (n = 300) | P* | ||
|---|---|---|---|---|
| Operator (n, %) | Assistant operator (n, %) | General assistant (n, %) | ||
| Primary | 28 (9.3) | 32 (10.7) | 22 (7.3) | 0.027* |
| Secondary | 58 (19.3) | 44 (14.7) | 67 (22.3) | |
| High school | 11 (3.7) | 22 (7.3) | 10 (3.3) | |
| No schooling | 3 (1.0) | 2 (0.7) | 1 (0.3) | |
* Fisher's exact test, significant (P < 0.05).
Pimenta Parente, Scherer, Zimmermann, and Fonseca (2009) mention that the higher the number of years studied, the better the performance of people in different neuropsychological tasks that require thinking, perception and comprehension. Schettino et al. (2020) point out that the lower the schooling, the more difficult it is to perceive labor problems in the workplace and to adapt to technological innovations, therefore schooling is an important socioeconomic variable for cognitive development of workers. In this study, 2 % of workers represent the no schooling segment (Table 2), suggesting that most workers understand, comprehend and interpret instructions in work activity and are aware of dangers and safety associated with their work (Mitchual, Donkoh, & Bih, 2015a).
Safety training and occupational hazards
Although only 32 % of the workers received training on safety at work, 71 % reported that they know how to act in an accident at work; 56 % consider their work area to be safe. The relationship between job category and perception of safety (P < 0.05) stands out, where the higher the hierarchy, the higher the perception of safety; in addition, it is interesting to note that the higher the job position, the more interesting the tendency of the worker to be trained and to act in an accident at work (Table 3). This can be explained because workers with more years of service have a higher hierarchy and have probably witnessed more work-related accidents, so their behavior should be better when facing this type of incidents.
Table 3. Safety training of workers according to job position in the sawmill industry of El Salto, Durango.
| Survey item | Job category (n = 300) | P* | ||
|---|---|---|---|---|
| Operator (n, %) | Assistant operator (n, %) | General assistant (n, %) | ||
| Have you received safety training at work? | ||||
| Yes | 37 (12.3) | 34 (11.3) | 26 (32.3) | 0.228 |
| No | 63 (21.0) | 66 (22.0) | 74 (24.7) | |
| Do you know how to act in case of an accident at work? | ||||
| Yes | 80 (26.7) | 69 (23.0) | 65 (21.7) | 0.052 |
| No | 20 (6.7) | 31 (10.3) | 35 (11.7) | |
| Is your work area safe? | ||||
| Yes | 54 (18.0) | 66 (22.0) | 48 (16.0) | 0.033* |
| No | 46 (15.3) | 34 (11.3) | 52 (17.3) | |
*Chi2 test, significant (P < 0.05).
Kwame, Kusi, and Lawer (2014) indicate that workers in the sawmill industry usually acquire their skills and experience over the years, but do not have the safety perspective to carry out their work. In this sense, Pucci, Nión, and Ciapessoni (2013) mention that the notion of risk is a social construct that brings into play a multiplicity of interests and representations; damage assessment depends on what is represented as acceptable danger and risk thresholds. On the other hand, risk management refers to the management of uncertainty through a process of organizational learning, given that workers who do not have established models of behavior to follow, must construct mechanisms and attitudes on the fly to face these situations. This reasoning explains the fact that workers who have no training on safety at work say they know how to act or react in an accident at work.
Accidents causing injuries to workers
In the last five years, 95 % of the sawmill workers in the region of El Salto, Durango have experienced one to five occupational accidents that resulted in injuries of varying severity; of these, 33 % required medical leave with work incapacity from two days to four months. The relationship between job position and worker incapacity is significant (P < 0.05); the higher the hierarchy, the higher the number of medical leaves due to incapacity, which implies that injuries to machine operators are more serious than those suffered by helpers (Table 4).
Alcántara de Cerqueira and de Freitas (2013) believe that the ability to work is the result of a dynamic process between the individual's resources in relation to his or her work, undergoing changes due to various factors, including age. In this sense, chronological aging is considered one of the determining factors of functional aging; therefore, the older the age group, the greater the possibility of losing the ability to work and suffering an accident.
Regarding the place where accidents occurred, most of the correspond to production areas (96 %), where the interaction people-materials-machinery is present and the greatest possibility of finding cutting, rolling, transmission mechanisms, high noise and dust levels due to the proximity of saws (Silva-Lugo et al., 2020).
Table 4. Occupational accident rate of workers per job position in the sawmill industry of El Salto, Durango.
| Survey item | Job category (n = 300) | P* | ||
|---|---|---|---|---|
| Operator (n, %) | Assistant operator (n, %) | General assistant (n, %) | ||
| How many injury accidents have you had in the last five years? | ||||
| One | 34 (11.3) | 45 (15.0) | 52 (17.3) | 0.095 |
| Two | 33 (11.0) | 30 (10.0) | 20 (6.7) | |
| Three | 15 (5.0) | 13 (4.3) | 20 (6.7) | |
| Four | 7 (2.3) | 6 (2.0) | 3 (1.0) | |
| Five | 4 (1.3) | 3 (1.0) | 0 (0.0) | |
| More than five | 7 (2.3) | 3 (1.0) | 4 (1.3) | |
| Were you unable to work because of the injury? | ||||
| Yes | 45 (15.0) | 31 (10.3) | 23 (7.7) | 0.004* |
| No | 55 (18.3) | 69 (23.0) | 77 (25.7) | |
| Place of accident | ||||
| Production areas | 96 (32.0) | 96 (32.0) | 96 (32.0) | 1.000 |
| Warehouses or storerooms | 3 (1.0) | 2 (0.7) | 3 (1.0) | |
| Corridors | 1 (0.3) | 2 (0.7) | 1 (0.3) | |
* Ӽ2 test, significant (P < 0.05).
Bello and Mijinyawa (2010) mention that, in sawmills in southwestern Nigeria, 90 % of accidents with injuries occurred in yards and production areas. On the other hand, in sawmills in Kenya, Ogoti-Mong’are, Mburu, and Kiiyukia (2017) reported that 86 % of accidents and injuries also occurred in the same areas. In both cases, injuries were because of logs rolling into the sawmill, during sawmilling, equipment maintenance, and lumber stacking. This is consistent with the results of this study regarding the site with the highest occurrence of injuries reported by forestry workers.
Types and effects of worker injuries
In sawmills, workers suffer injuries mainly caused by hits, contusions or crushing (56.7 %) from handling logs and sawn timber; open wounds (15.7 %) caused by contact with sharp or rotating edges; and sprains, strains and muscle tears (14.7 %) from false movements. Serious traumatic accidents such as fractures, amputations and eye loss represent 3.6 %.
The job category is related to the type of injury suffered (P < 0.05); the frequency of hits and crushing is higher among helpers, because they handle materials more often than equipment operators; while the incidence of open wounds is higher in operators because of their proximity to saws and, consequently, they have a higher risk of amputation. Fingers (35 %) and hands (20.3 %) are the most affected body parts (Table 5).
Bamidele et al. (2011) found that, in sawmills in southwestern Nigeria, approximately one-third of injuries occur on the hands of workers who ever operate a cutting machine. Kwame et al. (2014) reported that the most common accidents in sawmills in Tamale Metropolis, Ghana, included cutting injuries (36.67 %), fractures (21.67 %), and sprains (8.33 %). In the present study, hand injuries accounted for 20 %; however, considering only finger injuries, the incidence is 35 % with a higher prevalence in equipment operators, which is significantly consistent with that found in Nigerian and Ghanaian sawmills.
Table 5. Type of injury per job position in the sawmill industry of El Salto, Durango.
| Survey item | Job category (n = 300) | P* | ||
|---|---|---|---|---|
| Operator (n, %) | Assistant operator (n, %) | General assistant (n, %) | ||
| Type of injury | ||||
| Hit, contusion or crushing | 41 (13.7) | 61 (20.3) | 68 (22.7) | 0.003* |
| Open wound | 25 (8.3) | 13 (4.3) | 9 (3.0) | |
| Muscle strain, sprain or tear | 16 (5.3) | 11 (3.7) | 17 (5.7) | |
| Multiple injuries | 8 (2.7) | 10 (3.3) | 4 (1.3) | |
| Fracture | 4 (1.3) | 3 (1.0) | 0 (0.0) | |
| Dislocation | 3 (1.0) | 1 (0.3) | 2 (0.7) | |
| Amputation | 3 (1.0) | 0 (0.0) | 0 (0.0) | |
| Eye loss | 0 (0.0) | 1 (0.3) | 0 (0.0) | |
| Body part affected | ||||
| Fingers | 42 (14.0) | 32 (10.7) | 31 (10.3) | 0.43 |
| Hand | 15 (5.0) | 24 (8.0) | 22 (7.3) | |
| Foot | 7 (2.3) | 10 (3.3) | 15 (10.7) | |
| Back | 4 (1.3) | 7 (2.3) | 7 (2.3) | |
| Arm | 7 (2.3) | 3 (1.0) | 3 (1.0) | |
| Shoulder | 7 (2.3) | 3 (1.0) | 3 (1.0) | |
| Leg | 5 (1.7) | 4 (1.3) | 4 (1.3) | |
| Toes | 4 (1.3) | 1 (0.3) | 5 (1.7) | |
| Forearm | 4 (1.3) | 3 (1.0) | 1 (0.3) | |
| Wrist | 2 (0.7) | 4 (1.3) | 1 (0.3) | |
| Ankle | 1 (0.3) | 3 (1.0) | 3 (1.0) | |
| Knee | 0 (0.0) | 3 (1.0) | 2 (0.7) | |
| Head | 1 (0.3) | 0 (0.0) | 2 (1.3) | |
| Hip | 1 (0.3) | 1 (0.3) | 1 (0.3) | |
| Abdomen | 0 (0.0) | 1 (0.3) | 0 (0.0) | |
| Eyes | 0 (0.0) | 1 (0.3) | 1 (0.3) | |
*Ӽ2 test, significant (P < 0.05).
The agents leading to accidents were material handling (53.7 %), followed by machinery and equipment operation (32 %), tool handling (10.3 %) and work environment (4 %). The mechanisms that caused accidents and injuries correspond mainly to getting stuck by moving objects (30 %), getting hit by moving objects (23.3 %), falling objects (14.3 %) and false movements (13.7 %) (Table 5). There is a relationship between jobs and the causal agent and mechanism of the injury (P < 0.05) indicating that injuries depend on the degree of contact that the worker has with a causal agent in his/her place of work, and both the mechanism that led to the accident and the type of injury will be manifested to that degree (Table 6).
Table 6. Agent and causal mechanism of injury per place of work in the sawmill industry of El Salto, Durango.
| Survey item | Job category (n = 300) | P* | ||
|---|---|---|---|---|
| Operator (n, %) | Assistant operator (n, %) | General assistant (n, %) | ||
| Causal agent | ||||
| Materials | 43 (14.3) | 55 (18.3) | 63 (21.0) | 0.000* |
| Machines and equipment | 48 (16.0) | 28 (9.3) | 20 (6.7) | |
| Tools, implements or instruments | 8 (2.7) | 9 (3.0) | 14 (4.7) | |
| Work environment | 1 (0.3) | 8 (2.7) | 3 (1.0) | |
| Mechanism of accident | ||||
| Getting stuck by moving objects | 24 (8.0) | 33 (11.0) | 33 (11.0) | 0.006* |
| Getting hit by moving objects | 34 (11.3) | 19 (6.3) | 17 (5.7) | |
| Falling objects | 11 (3.7) | 15 (5.0) | 17 (5.7) | |
| False movements | 22 (7.3) | 9 (3.0) | 10 (3.3) | |
| Stepping on objects | 2 (0.7) | 10 (3.3) | 9 (3.0) | |
| Physical efforts when lifting objects | 2 (0.7) | 4 (1.3) | 4 (1.3) | |
| Getting hit by immobile objects | 0 (0.0) | 5 (1.7) | 3 (1.0) | |
| Collapsing | 2 (0.7) | 2 (0.7) | 4 (1.3) | |
| Physical efforts when pushing or pulling objects | 3 (1.0) | 0 (0.0) | 2 (0.7) | |
| Falling from heights | 0 (0.0) | 3 (1.0) | 1 (0.3) | |
Jones and Kumar (2004) analyzed a database on injuries to workers in the sawmill industry in Alberta, Canada, for the period 1997-2002, and found that the greatest number of injuries resulted from getting hit by stationary objects and falling objects (30.4 %), bodily overexertion when lifting or pushing objects (27.9 %) and getting stuck by fixed and moving objects (16.4 %). The differences in the mechanism that led to injuries in this study with the reference studies may be due to the higher technological level of Canadian sawmills, where possibly aspects of protection of moving parts and equipment maintenance lead to a drastic reduction in accidents due to getting stuck by moving objects. Finally, Alcántara de Cerqueira and de Freitas (2013) mention that most sawmill workers focus their fears on two high-risk factors as generators of injury mechanisms: band saw breakage and getting hit by logs or sawn timber.
Conclusions
The forestry worker in sawmills of El Salto, Durango is a mature person with little work experience and secondary schooling; he has suffered one to five accidents in the last five years and has no occupational safety training. Fingers are the most affected by injuries produced by hits and wounds when handling materials; operating machinery, equipment, and tools that cause to get stuck; and getting hit by moving objects. Machine operators are the most likely to be injured by cutting injuries, while helpers are exposed to be hit and crushed. An injury depends on the degree of contact the worker has with a causal agent; to that extent both the mechanism that led to the accident and the type of injury are manifested. Training workers in aspects that promote personal and work area safety is necessary to reduce the risks of accidents associated with the functions of each job.
