The Prevalence and Associated Factors of Occupational Injury among Workers in Arba Minch Textile Factory, Southern Ethiopia: A Cross Sectional Study

¹College of Medicine and Health Sciences, Arba Minch University P.o,Box. 21, Arba Minch, Ethiopia

²School of Public Health, Addis Ababa University, P.O.Box: 9086, Addis Ababa, Ethiopia

*Corresponding Author:

Gebremichael G
College of Medicine and Health Sciences
Arba Minch University P.o,Box. 21
Arba Minch, Ethiopia
E-mail: kirosalma@gmail.com

Received date: November 2, 2015 Accepted date: December 1, 2015 Published: December 8, 2015

Citation: Gebremichael G, Kumie A, Ajema D (2015) The Prevalence and Associated Factors of Occupational Injury among Workers in Arba Minch Textile Factory, Southern Ethiopia: A Cross Sectional Study. Occup Med Health Aff 3:222. doi: 10.4172/2329-6879.1000222

Copyright: © 2015 Gebremichael G, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Abstract

Background: Occupational accidents and work-related diseases cause over 2.3 million deaths annually; nearly 860,000 people are injured every day globally. There are limited studies that specifically determine the magnitude and associated factors of occupational injury in textile workers done in Ethiopia. Therefore, this study aimed to assess the prevalence and associated factors of occupational injury in Arba Minch Textile Factory workers.

Methods: An institution based cross-sectional study design was implemented from March 3 to March 20, 2015 among selected 433 Arba Minch Textile Factory workers. An Amharic version pre-tested questionnaire and observation checklist were used to collect the data using trained six data collectors and two supervisors. Data was entered into Epi Info version 7 templates, cleaned and analyzed using SPSS Version 21 software. Bivariate logistic regression was used to explore the relation between the dependent and independent variables using crude odds ratio with 95% C.I. Finally, to determine the independent factors associated with occupational injury, multivariate logistic regression model with hierarchical entry of variables was done. Variables with P-value <0.05 in the final model were considered statistically significant.

Results: The one year prevalence of occupational injury was 31.4%. Lower monthly salary was associated with higher odds of injury among the socio-demographic factors [AOR (95%C.I): 3.5(1.7, 7.2)]. Work environment related factors like extra hour duty, health and safety training and workplace supervision had significant association after adjusting all factors [AOR (95%C.I): 2.5 (1.2, 5.4), 0.4 (0.17, 0.97), and 0.36 (0.17, 0.75) respectively]. Among the behavioral factors, Personal Protective Equipment use and job stress showed association with injury significantly.

Conclusion: The significant prevalence and associated factors of occupational injury in the factory workers should alert the factory, governmental and non-governmental organizations working on Occupational Health and Safety.

Keywords

Occupational injury; Prevalence; Factors; Textile factory

Introduction

Occupational injury is any personal injury, disease or death resulting from an occupational accident sustained on worker in connection with the performance of his or her work. An occupational injury may be any kind of wound, and can range from a minor injury, such as a bruise, scrape or cut, to more severe injuries such as shock, concussion, loss of a limb or an eye, fractured bones, suffocation, poisoning or an illness such as cancer resulting from a single accidental exposure to radiation [1].

The International Labour Organization (ILO) estimates that over 2.3 million annual deaths occur due to occupational accidents and work-related diseases, of which over 350,000 are caused by occupational accidents. Consequently, occupational accidents take lives of nearly 1000 people daily respectively. In 2010, there were over 313 million non-fatal occupational accidents (requiring at least four days of absence from work), meaning that nearly 860,000 people are injured every day globally [2,3].

Globally, the ILO estimates that around 4 percent of the world’s gross domestic product (GDP), or about US $2.8 trillion, is lost annually in direct and indirect costs owing to occupational accidents and work-related diseases [4]. According to the U.S. Bureau of Labor Statistics, the rate of nonfatal occupational injury and illness cases was 112 cases per 10,000 full-time workers in 2012, down from 117 in 2011, in which 34 percent of the injuries and illnesses were musculoskeletal [5] Textile industry was the second largest sector with a high percentage of work-related injuries, accounting for 28.7%, following the metal industry and machinery in Turkey [6].

Sub-Saharan Africa appears to have the greatest rate per worker of occupational injuries followed by Asia (excluding China and India) [7]. According to the Central Statistical Agency (CSA) reports, in Ethiopia, the textile industry accounts for 1.7%-2.13% of the major industries and 8%-12.1% of the persons engaged in work [8,9]. There are particularly serious data limitations in the area of work-related diseases and occupational accidents, especially in developing countries. This is due to factors including long latency of many diseases before the symptoms are detected and the weakness in the national capacity for identification, diagnosis and compensation of occupational diseases [2].

The textiles sector poses many hazards that can cause injury to workers, from transport in the workplace, exposure to excessive noise and harmful substances, dangerous large work equipment and plant, risk of slips from a wet working environment, manual handling and working with unsafe machinery, to risks of fire and explosions [10]. In Ethiopia, traditional weavers had poor working conditions and environment, and weavers and owners of the small-scale enterprises were not aware of the benefits of improving working conditions, occupational safety and occupational health [11]. A higher prevalence (36.9%) of occupational injury was observed among textile factory workers in Northern Ethiopia [12].

Occupational injuries result from interplay of complex various risk factors. The leading causes of work-related injuries globally include exposure to physical, mechanical and chemical hazards, and unsafe practices. Besides, socio-demographic characteristic of workers, work arrangements, environmental, psychosocial factors, and social conditions are other potential factors associated with occupational injuries [13-17]. Geographical differences in work injuries also suggest that wider determinants of work injuries are functioning across workplaces at a geographic level [13].

Although some studies are done on the factors like sociodemographic, work environment and behavioral, affecting occupational injuries among textile workers in few factories in Ethiopia [16,18], information on occupational health and safety in textile industry is still minimal. Furthermore, there are limited studies that specifically determine the magnitude and associated factors of the problem in textile workers. Therefore, the main aim of this study was to assess the prevalence and associated factors of occupational injury in Arba Minch Textile workers.

Methods

Study design and setting

An institution based cross-sectional study design was implemented in Arba Minch Textile Factory, which is located 450 km south West of Addis Ababa and about 275 km far from Awassa, in Arba Minch town, the capital of Gamo Gofa zone. Arba Minch Textile Factory is established in 1991 with 1500 workers, during the data collection period it is operating with about 581 workers. There is an insurance mechanism for workers that may be injured during work in the factory. The factory owns a clinic which is functional for 24 hours a day inside its compound. The factory uses cotton to manufacture towels and bed sheets for export. The factory production process flow includes: the cotton passes the blowing and feeding processes in carding section. Then proceed to drawing, roving, ring frames, and open end (winding). Finally, it is warped, sized, and checked for quality through brushing and folding in the weaving department.

Sample

The production workers involved in weaving, spinning, finishing and engineering works in Arba Minch Textile Factory were the study populations. Permanent workers in the factory who worked for at least one year in the factory were included, whereas, workers involved in office and managerial activities were excluded.

The total sample size was calculated for each specific objective by using Epi Info version 7 and considering different parameters, and the largest sample size was taken. Considering 95% confidence level, 5% margin of error and 36.7% proportion of occupational injury [12], and adding 10% non-response a sample size of 394 was calculated. Finally, since the total eligible number of workers was not much different from the calculated sample size (394), all eligible workers (441) were included.

Data collection procedures

The study used a standard structured questionnaire adapted from occupational injury statistics and different relevant literature on occupational injury [14-21] with required modifications based on the research objectives. The questionnaire was prepared in English and translated by language experts to the local language (Amharic) and then back translated to English by another person to insure its consistency. It had socio-demographic, work environment related factors, and workers behavioral factors sections. It had both openended questions and questions with options. Pretest was conducted prior to the actual data collection on a Small and Micro Enterprise making cultural cloths in Arba Minch town. Interview was conducted at the workplace by the data collectors.

An observational checklist for inspecting hazards in working environment, adapted from various studies, was also used. The checklist contained identification of general hazards in the working environment, presence of health and safety regulations, personnel and trainings, as well as first aid equipment in the working sections. Six trained data collectors with BSc in public health officer background, and two supervisors qualified with masters of public health was recruited to collect data and supervise the data collection process.

Data analysis procedures

Data was coded, stored in a proper area and kept confidential. Then it was entered into a prepared Epi info template. The data was exported to SPSS version 21 for cleaning and analysis. The data was cleaned by running simple frequency and cross tabulation to check for completeness and consistency, and sorting to identify outliers.

For specific objective one, descriptive statistical methods such as frequencies, percentages, proportion with 95% C.I, and mean and standard deviation were used to summarize the socio-demographic, work environment and behavioral characteristics of the workers.

For specific objective two, cross tabulation and bivariate logistic regression was used to explore the relation between occupational injury and the different independent variables using crude odds ratio (COR) with 95% C.I.

Finally, to determine the independent factors associated with occupational injury, multivariable logistic regression model with hierarchical entry of variables was done. To limit the number of variables and unstable estimates in the subsequent models, only variables with P-value <0.3 in the bivariate analysis were taken to the regression model. In the first model, the effect of socio-demographic factors on occupational injury was assessed. In the second model, work environment related factors were included, and their effect was examined in the presence of socio-demographic factors. Finally, behavioral factors were added to explore their effect in the presence of socio-demographic and work environment related factors, in the final model.

Model fitting was checked using log likelihood, and Hosmer- Lemeshow goodness of fit test. Finally, variables with P <0.05 in the multivariate analysis were considered significant, and presented by Adjusted Odds Ratio (AOR) with 95% C.I.

Data quality assurance

The quality of the data was maintained before, during and after the data collection. Before the data collection designing/adapting structured standard questionnaire, two days training of data collectors and supervisors about the objective, questionnaire, methodology and ethical issues of the study, and pre-testing of the questionnaire was undertaken. During the data collection period, the collected data was checked for completeness and consistencies by the supervisors and the investigator through close follow up. Missed variable/s during the first visit was filled by re-interviewing the study participants.

After the data collection, the collected data was rechecked for its completeness and consistency by the supervisors and principal investigator. Then, it was entered into Epi Info Version 7, then 5% of the data set was double entered to check the accuracy of the entered data.

Ethical consideration

An ethical clearance and official letter was obtained from the Research and Ethical Committee of School of Public Health, Addis Ababa University. Permission was asked from the Arba Minch Textile Factory. Informed verbal consent was obtained from each participant after a necessary explanation about the purpose, benefits and risks of the study and also their right on decision of participating in the study. The study participants were clearly informed that there is no any direct financial benefit and risk, but the study findings would be used to design and implement prevention control strategies in the textile factories in the future, and occupational health and safety education will be given accordingly. Workers found injured during the data collection were advised to visit health institution earlier. Finally, anonymity and confidentiality was secured by omitting participant`s name in the questionnaire and during interview.

Results

A total of 433 study participants, out of the 441 workers were involved in this study, yielding a 98% response rate.

Socio-demographic characteristics

Majority of the participants were males (57.7%), married (67.7%), with secondary educational status (56.6%) and permanently employed (84.5%). The mean ± SD age of the respondents was 37.48 ± 9.1 with 22 and 65 the minimum and maximum years respectively. The mean monthly salary of the workers was ETB 1431, and most of the workers (78.8%) had work experience of six years and above (Table 1).

Table 1: Socio-demographic Characteristics of Participants Arba Minch Textile Factory, Ethiopia, March, 2015.

Characteristics of occupational injury

The one year occupational injury prevalence was 136 (31.4 percent: 95% C.I; 27, 35.8), and the two weeks prevalence was 39 (9%: 95% C.I; 6.3, 11.7).

Twenty nine percent of the injured workers sustained injury more than once, yielding a median number of injuries one times. Hand was the most affected body part 54 (39.7%) with laceration 75 (55.1%) and machinery 76 (56%) the major type and cause of injury respectively (Table 2).

Table 2: The affected body parts, Type and Causes of Injury of
Participants, Arba Minch Textile Factory, Ethiopia, March, 2015.

Regarding to the reasons of injury, absence of Personal Protective Equipment (PPE) 84 (61.8%) was the commonest reason given followed by improper hand working instruments 19 (14%), absence of safety education 14 (10.3%), disorder of normal operation 11 (8.1%), and misuse of PPE 8 (5.9%) respectively.

Significant amount 29 (21.3%) of the injuries occurred in Monday, Saturday 23 (16.9%), Tuesday 20 (14.7%), Friday 19 (14%), Sunday 15 (11%) and the rest 30 (22%) did not remember the exact day of injury. Regarding the time of injury, fifty one (37.5%) happened in the afternoon, while 35 (25.7%), 23 (17%), and 9 (6.6%) injuries occurred during morning, night, and evening respectively. Whereas, 18 (13.2%) injured respondents did not remember the actual time of injury.

Thirty five (25.7%) injured respondents were hospitalized for 619 days resulting in an average of a month working days lost per worker. Likewise, a total of 2,124 working days were lost due to injuries yielding an average of 16 days lost per worker per year, as confirmed from the factory`s report of sick leaves.

Work environment and behavioral characteristics

Almost all of workers 428 (98.8%) spent more than four hours per day in their work, moreover, 395 (91.2%) and 38(8.8%) respondents work for 48 hours or less, and more than 48 hours per week respectively. Two hundred and ninety six (68.4%) participants reported that there was no regular workplace supervision, but the other 137 (31.6%) had weekly 88 (20.3%), fortnightly 14 (3.2%), monthly and more 35 (8.1%) supervision. Two hundred and sixty (60%) workers did not involve in manual handling activities, whereas, the other 173 (40%) involved in manual handling activities.

Behavioral characteristics

Three hundred and thirty four (77%) workers encountered sleeping disturbance. Working in evening shift 239 (71.6%) was the major reason of the sleeping disturbance followed by working for more than 8 hours without shifting 42 (12.6%). Respecting to PPE use, 344 (79.4%) participants did not use PPE at all, where by lack of protective equipment 287 (83.4%) was the major reason of not using PPE in the factory workers (Table 3).

Table 3: Reasons of Sleeping Disturbance and Not Using PPE of the Participants, Arba Minch Textile Factory, Ethiopia, March, 2015.

Work environment observation

Inspection of the selected work environments of Arba Minch Textile Factory had identified variant occupational health and safety findings such as excessive dust, dust, and temperature. The observation also revealed that training needs in connection with new employment, equipment or other changes had not been taken into account. Besides, first aid equipment, warning signs and safety rules were available in none of the working sections.

Factors Associated with Occupational Injury

Bivariate analysis

Socio-demographic factors: The bivariate logistic analysis of sociodemographic characteristics with occupational injury revealed that only age category is significantly associated, while monthly salary was marginally significant. Sex, marital status, educational level, employment condition, and work experience did not show significant association with occupational injury (Table 4).

Table 4: Bivariate analysis of Socio-demographic Factors of Participants Associated with Occupational Injury, Arba Minch Textile Factory, Ethiopia, March, 2015 (n=433).

Work Environment Related Factors: Out of the selected work environment related factors, working hours, extra hour duty, health and safety training, workplace supervision, and manual handling of materials, had significantly associated with occupational injury. However, working department and working shift did not show significant association (Table 5).

Table 5: Bivariate analysis of work environment related factors of participants associated with occupational injury, Arba Minch Textile Factory, Ethiopia, March, 2015 (n=433).

Behavioral Factors: Khat chewing, cigarette smoking, PPE use and job satisfaction did not show significant association, while, alcohol consumption, sleeping disturbance problem, harassment/disagreement with managers/colleagues, and job stress showed significant association, with injury (Table 6).

Table 6: Bivariate analysis of behavioral factors of participants associated with occupational injury, Arba Minch Textile Factory, Ethiopia, March, 2015 (n=433).

Multivariate logistic regression

After the bivariate logistic regression was done for each variable, to limit the number of variables and unstable estimates in the subsequent models, only variables with P-value <0.3 were taken to a multivariate regression model hierarchically. In the first model, the effect of sociodemographic factors on occupational injury was assessed. In the second model, work environment related factors were included, and their effect was seen in the presence of socio-demographic factors. Finally, behavioral factors were added to explore their effect in the presence of socio-demographic and work environment related factors, in the final model.

In the final model, some socio-demographic factors (monthly salary), work environment related factors (extra hours duty, health and safety training, and regular workplace supervision and manual handling of very heavy objects), and behavioral factors (PPE use and job stress) showed significant association with occupational injury (Table 7).

Table 7: The Adjusted Effect of Selected Socio-demographic, Work Environment Related and Behavioral Factors of Participants Associated with Occupational Injury, Arba Minch Textile Factory, Ethiopia, March, 2015 (n=433).

From the socio-demographic factors, salary becomes significant in the second and final models. Workers earning monthly salary of ETB 1431 and less were 3.5 times more likely to be injured compared to those earning ETB 1432 and more, in both the second and final models[AOR (95% C.I): 3.5 (1.7,7.2)] (Table 8).

Table 8: The Relative Effect of Selected Socio-demographic, Work Environment Related and Behavioral Factors of Participants Associated with Occupational Injury, Arba Minch Textile Factory, Ethiopia, March, 2015: Condensed Model (n=433).

The odds of having injury in workers with extra hour duty was 4 times more after adjusting all factors [AOR (95% C.I): 4.1 (1.7,9.8)]. Having health and safety training, and regular workplace supervision were associated with 60% and 64% times decreased odds of injury, respectively [AOR (95% C.I): 0.4 (0.17,0.97), 0.36 (0.17,0.75)]. Furthermore, manual handling of very heavy objects was significantly associated with injury [AOR (95% C.I): 12.4 (5.4, 28)]. On the other hand, participants working in day shift had only marginally significant (P-value=0.05) higher odds of injury compared to those working at rotating and irregular shifts [AOR (95% C.I): 26 (1.04, 658)].

Among the behavioral factors, PPE use and job stress showed association with injury significantly. Workers who use PPE had 2.5 times lower probability of injury than those who do not use [AOR (95%C.I): 0.4(0.15, 0.9)]. Moreover, the odds of injury among job stressed was 2.4 more than among the non-stressed [AOR (95% C.I): 2.4 (1.1, 5.4)]. Sleeping disturbance and alcohol consumption lost statistical significance in the final model.

Discussion

In this study, the one year occupational injury prevalence was 31.4 percent [95% C.I: (27, 35.8)], and the two weeks prevalence was 9% [95% C.I: (6.3, 11.7)]. The annual prevalence is comparable with studies done at Turkey, Gonder, and Addis Ababa factories workers [6,12,15,22]. But, is lower relative to studies done at Egypt, and different factories in Ethiopia [17,19,23,24], unlike the two week prevalence which is comparable. The disparity in the annual prevalence may be due to smaller sample size, different study area, definitions and factory settings.

Hand was the most affected body part 54 (39.7%) with laceration 75 (55.1%) and machinery 76 (56%) the major type and cause of injury respectively, in this study. This was consistent with studies conducted in Egypt, Tendaho, Addis Ababa, Kombolcha, and other areas [12,17,19,23-25]. This finding may be attributed to the low availability and utilization of Personal Protective Equipment, absence of regular training regarding safety and machine safeguards. Besides, great concern may not be given by both the workers and the managers, assuming lacerations are minor injuries.

This study found out that workers paid below the mean had about 3.5 times more likelihood of being injured compared to those earning higher salary. This is supported by studies conducted in Thailand and Oromia, Ethiopia [14,25], and may be explained as higher payment is related with high experience, and higher educational level, although this factors were not significant in this study. Likewise, workers with lower experience and educational status are usually placed in working sections involving manual contact with machines and others. On the contrary, there was no significant difference in occupational injury by monthly salary, according to various literatures conducted in Ethiopian factories [15,16,18,19].

Extra hour duty, health and safety training, regular workplace supervision, and manual handling of very heavy objects were significant work environment related determinants of occupational injuries in this survey. On the other hand working shift was only marginally significant (P-value=0.05) work environment related factor.

Studies conducted in several factories across the world, showed that various work environment related factors bear on workers towards occupational injuries in different extents. Studies done at different parts of Ethiopia, Japan and Thailand reported that hours worked per week significantly affect the likelihood of injury [14-17,19,26]. But, in this study there is no significant difference in injury by hours worked per week, which may be attributed due to the small number of workers passing more than 48 hours per week, in this study. This finding is supported by another similar literature done at Northern Ethiopia by Aderaw, et al. [1].

On the other hand, participants working in day shift had only marginally significant (P-value=0.05) higher odds of injury compared to those working with rotating and irregular shifts. This finding is not in line with various studies conducted, where working shift was not significant associated factor with occupational injury. This may be attributed because of the majority (63%) of the injuries in this study occurred during day time.

A strong association exists between health and safety training, and work-related injuries, as agreed upon by many scholars [17-20]. Consistent with these literatures, this study revealed that having health and safety training is statistically related with significant decrease in injury. Health and safety training refreshes and motivates workers about occupational health, safety, standards and practices, which in turn may be associated with decreased injuries.

Regular workplace supervision came about lowering occupational injury by about 3 times odds, in this study, independently. This finding is similar with a study done by Tadesse and Kumie [15]. On the other hand, other scholars disclosed that regular workplace supervision had no significant association with occupational injury [17,18,20]. This could possibly explained by regular workplace supervisions which could inform workers about health and safety, and help easily identify workplace hazards.

Evidences from literatures dictating the work environment determinants of occupational injury evinced, involving in activities requiring manual handling exposes workers to injury considerably [13,16]. Comparably, this survey indicated that manual handling of very heavy objects was significantly associated with prominent injury presence.

Among the behavioral factors of the study participants, Personal Protective Equipment use and job stress appeared with significant association with injury. Having harassment/disagreement with managers or colleagues in the work place and sleeping disturbance lost significance in the final model.

Workers who use Personal Protective Equipment had 2.5 times lower odds of injury than those workers who do not use. This is substantiated with the findings of studies done before in different areas [19,23,27]. Furthermore, job stress is significantly related with elevated work related injury in this survey. In line with this finding, several scholars from Ethiopia and other nations had strongly agreed upon in their articles [13,18,20,23].

Conclusions

The one year occupational injury prevalence in the factory was high. The study showed that the independent factors associated with occupational injury were, monthly salary, extra hour duty, health and safety training, regular workplace supervision, Personal Protective Equipment use and job stress. Based on the findings from this study, provision of sustainable and proper health and safety training for workers, regular and continuous workplace supervisions, and standard quality personal protective equipment for all workers with strict follow up of proper utilization, should be encouraged. This study was not without limitations. The one year injury prevalence may be under or over-estimated due to recall bias. Social desirability bias might be also introduced, although much effort is done to minimize it.

Competing Interests

The authors declare that they have no competing interests.

Authors’ Contributions

GG was the principal investigator of the study leading from the conception, design and supervising data collection process to the final analysis and preparation of the manuscript. AK participated in the design of the study, reviewing and criticizing the whole document especially on the method and analysis part. DA participated in reviewing the document and provided critical comments. All authors read and approved the final manuscript.

Acknowledgments

We are grateful to the School of Public Health of Addis Ababa University and the Norwegian Programme for Capacity Development in Higher Education and Research for Development (NORHED) for funding this study. We extend our heartfelt thanks to the study participants, supervisors, and data collectors, where, without their participation this will not be realized. The members of Arba Minch Textile Factory Share Company, especially Ato Anwar Sadik, Head of Human Resource Administration, are deeply acknowledged for their willingness and collaboration.

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