Nonfatal Occupational Injuries among Artisanal and Small-scale Gold Mining Workers in Oddo Shakiso District, Guji Zone of Oromia Regional State, Southern Ethiopia

Nonfatal Occupational Injuries among Artisanal and Small-scale Gold Mining Workers in Oddo Shakiso District, Guji Zone of Oromia Regional State, Southern Ethiopia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Nonfatal Occupational Injuries among Artisanal and Small-scale Gold Mining Workers in Oddo Shakiso District, Guji Zone of Oromia Regional State, Southern Ethiopia Fentayehu Abebil, Yifokire Tefera, Worku Tefera, Abera Kumie, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1555497/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Artisanal and Small-scale Gold Mining is widely practiced in developing countries. Injuries are among the public health concerns in the mining sector. This study aimed to assess the prevalence of nonfatal occupational injuries and associated factors among workers in Artisanal and Small-scale Gold Mining in Oddo Shakiso District, Oromia, Ethiopia. Methods: Cross sectional study design was employed from April to June 2020. A total of 403 participants was selected with simple random sampling technique. Structured questionnaire was utilized for the data collection. Descriptive statistics were used to characterize the information and the logistic regression was applied to test the association. Predictor variables with p value <0.05 with Odds ratio of 95% CI in multivariate analysis were considered as associated factors. Results: A total of 403 participants were interviewed with a response rate of 95.5%. The prevalence of nonfatal occupational injury was 25.1% in the past 12 months. About one third of the injuries, 32 (31.7%), were on the upper extremity and feet, 18 (17.8%). Symptoms of mercury toxicity [AOR: 2 .39, 95% CI (1.27-4.52)], 1-4 years work experience [AOR: 4.50, 95% CI (1.57-12.9)], full work shift [AOR: 6.06, 95% CI (1.97-18.7)], and job in task of mining activities (AOR: 4.83, 95% CI (1.48-15.7)) were associated with the injury. Conclusion: High prevalence of injuries was found. Work-related factors were found significantly associated with the occurrence of injury. Therefore, both workers and responsible organization should apply intervention focus on the improvement of working condition and safety practice to minimize the injury accident. Background Mining is the activity , occupation and industry concerned with the extraction of minerals [1]. Huge numbers of workers are involved in mining [2]. Artisanal small scale mining (ASM) is mining activity, including gold mining by an individual or a small group, more than 70% globally are an informal work sector, limited use of mechanical tools and labour intensive work nature [3, 4]. ASM has grown in recent years as mineral prices have risen and earning a living from agriculture and other rural activities has become more challenging [4]. There are approximately 40 million ASM miners across 80 countries globally and more 9 million ASM operators in 23 African countries, including Ethiopia [3, 4]. Artisanal small-scale gold mining (ASGM) is one of ASM with an estimate contribution of 20% of the world’s gold product [4]. Despite these significant contributions to major global mineral supply chains, ASGM has many public health challenges. Occupational injuries are the problem among workers in this mining sector [3]. When compared to large-scale miners, small-scale miners are 6–7 times more likely to be experienced occupational injury [5]. Due to the informal sector nature of this mining, the absence of safety laws, law enforcement, training, functional infrastructure, and equipment are some of the most widely reported factors have contributed to an increase in injuries among small-scale miners in most countries [3, 4, 6]. Previous studies reported the common cause of injury, among ASGM workers were machinery/tools, falling, explosion, fire, collapse of mining sites and rock falling [7-10]. Occupational injuries are major health concerns in ASGM occupational setting, especially in Africa [7, 11-13].The prevalence of nonfatal occupational injury among ASGM workers varied from 235-455 in Ghana [7-10] to 447 in Kenya [12] among 1000 workers. Studies also showed underground work, personal protective equipment, education, alternative source of income, shift hours, drug usage, gender and experience as associated factors [9, 12, 13]. Gold mining has a long history in Ethiopia, was started by the 1930’s in Oddo Shakiso district [14]. The mining sector was taken as a priority area for the achievement of growth and transformation plan II (2016-2020) in the country [15]. The country has huge potential of ASGM and more than 450,000 miners directly involved in ASGM job [4]. However, ASGM job is among the most hazardous jobs because of the absence of basic infrastructures and rudimentary tools used in the area [3, 4, 16]. Information about injury among ASGM workers is not adequately addressed in the country [17]. This study aimed to assess the prevalence of injuries and the associated factors among ASGM workers. MATERIALS AND METHODS Study Design and Setting A cross sectional study design was employed from April to June 2020 in Oddo Shakiso District artisanal and small-scale gold mining sites, Guji Zone, Oromia Regional State, Ethiopia. Oddo Shakiso District is 139km far away from Negele (the capital of Guji Zone) and 490 km from south of Addis Ababa, the capital city of Ethiopia. The district is organized by 22 rural kebeles (lowest administrative hierarchies), out of which 11 (50%) have ASGM cooperatives. The total population of the district was 132,017 of which 66, 537 were males and 65,480 females. About 14% of total population practiced ASGM [14]. Source Population. All ASGM workers in the three selected kebeles were included in the study with the criteria of an aged ≥ 18 years old, residing and worked within the district at least a year. Those miners who were absent at the time of data collections were excluded from the study. Sample size determination and sampling procedure There were no similar studies in Ethiopia among ASGM workers. Hence, 50% of the injury prevalence was used to estimate the sample size of the study. A sample size of 422 was calculated using a single population proportion formula with the asssumption of 50 % prevalence, 5% margin of error, 95% confidence level and 10% added for the non response rate. Three kebeles were selected with lottery mothod from 11. A total of about 47 ASGM worker cooperatives with an average of 35 members,a total of 1645 workers were reported in the selected Kebeles [18]. The sample size was proportionally allocated to ASGM worker cooperatives. Study participants (n) were selected by simple random sampling method from each mining site of ASGM worker cooperatives. Data collection Injury was the dependent variable, whereas sociodemographic characteristics (age, gender, religion, marital status, education level, family size, average monthly income), nighttime sleeping disorder, symptom of mercury toxicity, and work-related characteristic (work experience, work shift, tasks, jobs other than mining activities, occupational health and safety training and the use of personal protective equipment) were the independent variables of the study. Pretested structured questionnaire was used for data collection. The tool was developed based on previously publishsed occupational injury related literatures [7, 10, 11, 19-21]. The data collection questionnaire was prepared in English and translated to Afan Oromo (the local language) and back translation was done by the language experts to ensure consistency. The injury types were classified based on the international statistical classification of diseases and related health problems [22]. Three diploma holding health professionals handled the interview data collection, a laboratory technician was participated in urine sample collection and a supervisor contributed in data collection follow up. Three days training was provided for the data collectors about the study objective, the study tools, approach, and ethical issues. A pretest was done among 5% of the sample size in the unselected kebeles. After interviews were conducted, about 20 ml urine samples were collected in clean plastic bottles for protein analysis. All urine containers were properly coded using the unique number of participants. Safety measured were applied in the collection process of the samples to avoid contamination. Immediately after the sample collection, the urine protein was proteinuria was assessed semi-quantitatively using the dipstick [21]. Data collection activities were carried out with close follow-up by the principal investigator and the supervisor. Operational definitions Injury : an incident respondents’ self report as cause to miss work for at least a day excluding the day of injury accident [20, 22, 23]. Artisanal and small-scale gold mining : gold mining conducted by small enterprises/worker cooperative with limited capital investment and production [24]. Excavation : extracting gold from the soil by digging underground wells [24] Panning : uses water to separate heavy gold particles from other lighter particles within a medium sized pan [25] Amalgamation : mixing liquid mercury with the ore to separate the gold from the other minerals or ore [24]. Smoke cigarette : a worker who was a regular cigarette smoker at the time of the study. Drink alcohol : drinking alcohol at least weekly [26] Sleeping problem at night : self reporting of loss sleeping at night Symptoms of mercury toxicity : worker whose proteinuria (protein in urine) found ≥ 0.3g/L by urine dipstick test) [27] and has at least one of the following mercury toxicity indicators: excessive salivation, tremor at work, sleep problems at night, gingival bluish coloration, ataxia of gait and leg (heel- shin) ataxia [28]. Data Analysis Completeness and consistency of data were checked before data entry. Data was entered to Epi Info version 7.2. Cleaning and analysis were done using SPSS software version 20. Descriptive statistics were used to characterize the data. The association between the variables were assessed by bivariate and multivariate analysis. Binary logistic regression analysis was performed to select candidate variables for multivariable logistic regression analysis. Those explanatory variables having P value <0.25 in bivariate analysis were used as a cutoff point to reduce the cofounder effect in the multivariable analysis [29]. Finally, variables with P < 0.05 at 95% confidence level in the multivariate analysis were considered as statistically significant. Model fitness and multi co-linearity assumptions were checked. Hosmer–Lemeshow goodness model fitting was X 2 =9.2 with a degree of freedom of 8 and a significance equal to 0.33. The multi co-linearity result revealed a variance inflation factor 0.4. RESULTS Socio-demographic characteristics A total of 403 participants were interviewed with a response rate of 95.5%. In this study, 317 (79.0%) of those interviewed were males. The mean (±SD) age of the miners was 30.3 (±7.0) with a range of 18-50 years, and the majority of the miners (73.0%) had primary school education. Monthly average income varied from 1,000 to 10,000 with a mean (±SD) of 4775.0 (±1764.0) in Ethiopian Birr (ETB) (Table 1) . Table1:Socio- demographic characteristics of artisanal and small-scale gold mining workers in Oddo Shakiso woreda, Ethiopia, 2020. Variable n=403 Frequency Percent Sex Male 317 79.0 Female 86 21.0 Age 18- 29 195 48.4 30-39 161 40.0 40-50 47 11.6 Marital status Single 193 48.0 Married 178 44.0 Divorced 32 8.0 Religion Protestant 189 47.0 Orthodox 119 30.0 Muslim 54 13.0 * Others 41 10.0 Educational status Informal education 86 21.3 Primary school 292 72.5 Secondary school and above 25 6.2 Family size ≤5 302 75.0 >5 101 25.0 Average monthly income (ETB) 1000- 3500 122 30.0 3501- 5000 141 35.0 5001- 6000 68 17.0 6001- 10,000 72 18.0 * Religion included ‘catholic and wake fena’ Work- Related and personal Characteristics The average year (±SD) of work experience was 9 (±6) with the range of 1-30. The majority of participants, 261 (64.8%), worked both in the morning and afternoon work shift. More than half of the respondents, 221 (54.8%), engaged in excavation and panning job. Out of all miners, 56 (13.9%) had other jobs in addition to mining. All respondents did not take any training about occupational health and safety in any time and nearly all (98.5%) did not use personal protective equipment. Out of the total respondents, 128 (31.8%) drink alcohol and 89 (22.1%) smoke cigarette. Regarding sleeping problem, 47 (10.7%) participants were reporting sleeping problem at night. A total of 72 (17.9%) participants had the symptoms of mercury toxicity during the study period (Table 2). Table 2: Work related characteristics among artisanal and small-scale gold mining workers in Oddo Shakiso woreda, Ethiopia, 2020. Variable n=403 Frequency Percent Experience (in years) 1-4 100 24.8 5-10 194 48.1 >10 109 27.1 Work shift Half shift 53 13.2 Full shift 350 86.8 Tasks (jobs) Panning and amalgamation 43 10.7 Excavation and amalgamation 47 11.7 Excavation, panning and amalgamation 313 77.7 Jobs other than mining Yes 56 13.9 No 347 86.1 Prevalence of non-fatal occupational injuries The prevalence of nonfatal occupational injury was 25.1% (CI: 21.3%- 29.0%) in the past 12 months. About one third of the injuries, 32 (31.7%), were encountered by the upper extremity and foot injury was the second in prevalence affeted body part, 18 (17.8%). Among the injured participants, 46 (45.5%), got a first aid/ medical service and 55 (54.5%) was treated by traditional methods. Abrasion was the leading type of injury with 31 (30.7%), followed by lacerations by 19 (18.8%). Collapse of mining pits, 31 (30.7%) and falling, 29 (28.7%) were the leading mechanism of injury. More than one third, 79 (78.2%) of the injuries lets the workers absent from work for more than 5 days (Table 3) . Table 3: Characteristics of nonfatal occupational injuries among respondents in artisanal and small-scale gold mining workers in Oddo Shakiso, Ethiopia, 2020 Variable Frequency Percent Nonfatal injury (n= 403) Yes 101 25.1 No 302 74.9 Types of injury (n=101) Abrasion 31 30.7 Lacerations 19 18.8 Punctured wounds 15 14.9 Fracture 10 9.9 Spinal cord injury 9 8.9 Dislocation 6 5.9 Neurogenic shock 4 4 Others 7 6.9 Injury frequency (n=101) Once 85 84.2 Twice or more times 16 15.8 Injured body parts (n=101) Upper extremity 32 31.7 Feet 18 17.8 Lower extremity 13 12.9 Head 13 12.9 Back 12 11.9 Face 7 6.9 Others 6 5.9 Cause of injury (n=101) Collapse of mining pits 31 30.7 Falling 29 28.7 Hit by objects 13 12.9 Assault 10 9.9 Improper use of tools 6 5.9 Fires and explosion 4 4.0 Drowning 3 3.0 Others 5 4.9 Factors associated with non-fatal occupational injury Each variable was analyzed using bivariate logistic regression. Variables include: age group, marital status, drinking alcohol, symptoms of mercury toxicity, monthly income, work experience task/job, work shift and having job other than mining were candidates to multivariate logistic regression analysis. In the multivariate logistic regression analysis, symptoms of mercury toxicity[AOR: 2.39, 95% CI (1.27-4.52)], 1-4 years work experience [AOR: 4.50, 95% CI (1.57-12.9)],involved intwo work shifts [AOR: 6.06, 95% CI (1.97-18.7)], and engaged in all task in minnig activities [AOR: 4.83, 95% CI (1.48-15.7)] were significantly associated with injury at p value < 0.05 (Table 4) . Table 4: Logistic regression analysis result of non fatal occupational injury and associated factors among artisanal and small-scale gold mining workers in Oddo Shakiso, Ethiopia, 2020 Variables Injury Bivariate analysis Multivariate analysis Yes No COR (95% CI) AOR (95% CI) Age in years <30 57 138 1.54 (098-2.42) 1.10 (0.54-2.24) ≥30 44 164 1 1 Marital status Single 57 136 1 1 Married 38 140 0.65 (0.40-1.04) 1.13 (0.56-2.26) Divorced 6 26 0.55 (0.23-1.40) 0.73 (0.24-2.24) Drink alcohol Yes 40 88 1.60 (1.00-2.55) 0.64 (0.38-1.09) No 61 214 1 1 Symptoms of mercury toxicity Yes 24 48 1.65 (0.95-2.87) 2.39 (1.27-4.52) * No 77 254 1 1 Average level of income (in ETB) 1000- 3500 35 87 1.61 (0.91-2.85) 1.29 (0.63-2.61) 3501- 5000 38 103 1.48 (0.85-2.58) 1.20 (0.65-2.21) >5000 28 112 1 1 Work experience 1-4 35 65 2.26 (1.20-4.23) * 4.50 (1.57-12.9) * 5-10 45 149 1.78 (1.05-3.03) * 1.46 (0.71-3.03) >10 21 88 1 1 Work shift + Half shift 4 49 1 1 Full shift 97 253 4.50 (1.65-13.36) * 6.06 (1.97-18.7) * Task (job) Panning and amalgamation only 4 39 1 Excavation and amalgamation only 9 38 2.31 (0.66-8.14) 2.90 (0.71-11.8) Panning, excavation an amalgamation 88 225 3.81 (1.32-11.0) * 4.83 (1.48-15.7) * Having job other than mining Yes 8 48 1 No 93 254 2.20 (1.00-4.81) 1.75 (0.77-3.98) * Statistically significant at P-value <0.05; + Half shift includes work in either morning or afternoon; Full shift includes work in the morning and afternoon or work in the afternoon and evening DISCUSSION The prevalence of nonfatal occupational injury was 25.1% (95% CI: 21.3-29.0%) in the past 12 months. This finding is in line with similar studies in Ghana (23.5%, 25%and 28.9%) [8-10] and higher than the Zimbabwe (15.8%) [19] but lower than other studies reported in Kenya (44.7%) [12] and the Democratic Republic of the Congo (72.2%) [30].The reason for the disparity between the current and the previous studies might be the type of mining activities [30], variation in working population characterisitcs (interms of alcohol,drug use, safety culture etc) [12, 13, 30]. The other explanation could be the use of a definition "at least one day off from work following an injury" the similar finding from Ghana [9] and in this investigation might have lowered the prevalence of injury as compared with other studies defination with out considering the day after the accident [12, 30]. A definition of work place injury can have a big impact on the prevalence estimate [31]. The most common injury type in the current study was an abrasion followed by lacerations. It is in line with other study [8]. Other similar studies reported laceration were reported as the most common injury type [7, 10, 13]. The disparity between these studies might be due to the difference machines or the local tools used for mining, access to safety awareness and personal protective equipment utilization among mining workers. Among the injured body part, this study reported the upper extremity as the most affected among the participants. This is consistent with findings in studies on AGSM in Kenya [12] , Ghana [10] and the Democratic Republic of the Congo [30]. The explanation the of findings might be due to the rudimentary mining methods, poor processing and the handling activities with hand in these sectors, that might lead to the upper extremities inury [9, 12, 32]. The most frequently occurring injuries on this report was due to collapse of the mine pits and falls. It is in line the similar study in Ghana [11]. It might be possible that one risk factor for injury among AGSM personnel is the reality of underground work or digging without safety operation [9, 33] . Various predicted factors might increase the likelihood of injury occurrence among different studies. The current study identified symptoms of mercury toxicity, less year’s work experience, involved intwo work shifts and engaged in various tasks of mining such as panning, excavation and amalgamation job were significantly associated with injury. The study participants with having mercury toxicity symptoms had more than two times of likelihood of injury accident [(AOR: 2.39, 95% CI (1.27-4.52)] compared with workers who had no such symptoms. AGSM workers used mercury metal to extract gold from ore through amalgamation. The amalgam is heated to evaporate the mercury and isolate the gold so that workers might expose to evaporate forming through inhalation [34]. High mercury exposure causes central nervous system abnormalities, which can lead to exhaustion, cognitive impairment [35, 36] and effects on muscle that lead to muscle weakness [37]. These health problems might contribute to the occurrence of injury. The current study identified work related conditions such as year of experience in gold mining, work shift and task associated with occupational injury. Workers who had 1-4 years experience reported more than four odds of injury [AOR: 4.50, 95% CI (1.57-12.9)] than the more experienced workers` group. Other similar studies reported support this finding with the less work experience as predictor of occupational injury [10, 33, 38, 39]. Because; workers in this sector used rudimentary mining devices and poor processing [9, 12, 32]; and previous work experience might be increasing the skill to perform the job without injury accident [38, 40]. The other reason might be almost all participants in the current study did not use personal protective equipment and had no history of training about occupational health and safety to prevent injury. So that, unskilled workers might be more frequently prone to injuries due to this work condition than the experienced group [41]. Workers involved in full work shift had an injury likelihood more than six times [AOR: 6.06, 95% CI (1.97-18.7)] higher than the half shift workers in the current study. The full shift miners engaged in work for extended hour in a day. The disparity in injury accidents could be attributable to a risk factor for occupational injuries, which is the long work hour each day [13, 39] . Also, all participants in the curent study practiced gold separation from ore through amalgamation. But workers engaged both panninig and excavation job had more than four odds of injury [AOR: 4.83, 95% CI (1.48-15.7)] compared with paninig workers.The reason for this finding could be that workers who have several jobs are more exhausted and prone to making mistakes, hence working numerous jobs is linked to an increased risk of injury [42] . Strength and Limitations High participation rate, apply a standard case definition of injury and standard assessment instruments were the strength of this study. However, the findings were based on self report of respondents and the injury case was not validated clinically. In addition, ASGMs were informal sectors where temporary employment is a common practice, severely injured workers may not return to their job during the data collection period or forever, hence the true prevalence estimate might be higher. Conclusion and Recommendations In general, injury prevalence was substantially higher among ASGM workers. Symptoms of mercury toxicity, less years of work experience, involved intwo work shifts and engaged all tasks were significantly associated with injury. Findings suggest that the improvement in working condition and safety practice to reduce occupational injuries. Recommendation emphasized on eliminating the use of mercury in gold extraction, training in occupational safety and health, specially on the use of safe work tools and the means to create a safe working environment. Declarations Acknowledgements The authors would like to acknowledge Addis Ababa University College of Health Sciences School of Public Health and NORHED Project for the financial support in conducting this research activity. Our deepest gratitude also goes to Oddo Shakiso mining office and health office. We have also heartfelt acknowledgement to the study participants, the data collectors for their respective contributions. Authors’ Contributions FA was involved in the write-up of the research proposal, supervision of the data collection, data entry, data cleaning, data analysis, and writing of the manuscript. YT, WT, AB, HM and GK were involved in the write-up of the research proposal, the data analysis and writing the manuscript. Funding This research was funded by the Norwegian program for capacity Building in Higher Education and research for development (NORHED) for data collection. However, the funding body had no role in data analysis, interpretation of data and writing the manuscript. Availability of data and materials The datasets generated and/or analyzed during the current study are not publicly available due to study participant privacy/consent agreements but are available from the corresponding author on reasonable request. Ethical Consideration The research was ethically approved by Addis Ababa University Ethical Review Board. Permission was given from Ministry of Mine, Oromia Mine Bureau, Oddo Shakiso District Mining Office and from respective kebele leaders. The participants were asked whether they are volunteers or not to participate in the study. In the data collection process, the interview was conducted only from fully volunteer study participants. Each volunteer participant had an equal chance of being interviewed; there was full right to ask any question, refuse or terminate from participation. Data was coded and kept secret to ensure its confidentiality. Consent for publication N/A Conflicts of Interest The authors have declared that they had no conflicts of interest. 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American Journal of Public Health 2014. 104 (1). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1555497","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":572515038,"identity":"17fe251c-2768-4540-b4e6-8102dbc25877","order_by":0,"name":"Fentayehu Abebil","email":"","orcid":"","institution":"Ministry of Mines and Petroleum","correspondingAuthor":false,"prefix":"","firstName":"Fentayehu","middleName":"","lastName":"Abebil","suffix":""},{"id":572515039,"identity":"f8190595-87fb-403e-993b-021a4df01a58","order_by":1,"name":"Yifokire Tefera","email":"","orcid":"","institution":"Addis Ababa University","correspondingAuthor":false,"prefix":"","firstName":"Yifokire","middleName":"","lastName":"Tefera","suffix":""},{"id":572515040,"identity":"4549c928-488d-4d08-9354-54d6dd505835","order_by":2,"name":"Worku Tefera","email":"","orcid":"","institution":"Addis Ababa University","correspondingAuthor":false,"prefix":"","firstName":"Worku","middleName":"","lastName":"Tefera","suffix":""},{"id":572515041,"identity":"c22efc5a-f2a7-4f73-a06f-91faebee654a","order_by":3,"name":"Abera Kumie","email":"","orcid":"","institution":"Addis Ababa University","correspondingAuthor":false,"prefix":"","firstName":"Abera","middleName":"","lastName":"Kumie","suffix":""},{"id":572515042,"identity":"aca23bac-36df-4659-be5d-762e5a0833c7","order_by":4,"name":"Hailemichael Mulugeta","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIiWNgGAWjYBACAwbGxgMQ5uEDBh+AFBs7YS0NEC2MxxIKZ4C0MBPUwsAA0cJ8RuEzD5hBQIs5++GGw7w5dvkGx84wbrb5tU2ej5mB8cPHHNxaLHsSgVq2JVtuOHP2sHFu323DNmYGZsmZ2/A47ABYC7OBwY1zaca5PbcZgVrYmHnxaTn/EKSl3sDg/hvz35Y9t+0Ja7kBtuWwgcGBMwbGDD9uJxKh5WHDwbnbjhtIHjiWYNjbcDu5jZmxGb9fzqc/fPB2W7UB3wFgVP74c9t2fnvzwQ8f8WiBA4UDQIKxDcRkbCBCPRDIg9X9IU7xKBgFo2AUjCwAAKfvXgfBJpvGAAAAAElFTkSuQmCC","orcid":"","institution":"Debre Berhan University","correspondingAuthor":true,"prefix":"","firstName":"Hailemichael","middleName":"","lastName":"Mulugeta","suffix":""},{"id":572515043,"identity":"2190d028-b910-4a8f-ba7f-4f4a6c8d3752","order_by":5,"name":"Genanew Kassie","email":"","orcid":"","institution":"Kotebe Metropolitan University","correspondingAuthor":false,"prefix":"","firstName":"Genanew","middleName":"","lastName":"Kassie","suffix":""}],"badges":[],"createdAt":"2022-04-13 18:59:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1555497/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1555497/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100365550,"identity":"82832a82-47e1-4ee4-b986-56fcfa832953","added_by":"auto","created_at":"2026-01-16 07:55:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1169917,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1555497/v1/67e12067-2299-4601-bb0c-a2dd68f2da4a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Nonfatal Occupational Injuries among Artisanal and Small-scale Gold Mining Workers in Oddo Shakiso District, Guji Zone of Oromia Regional State, Southern Ethiopia","fulltext":[{"header":"Background","content":"\u003cp\u003e\u003cem\u003eMining\u003c/em\u003e is the \u003cem\u003eactivity\u003c/em\u003e, \u003cem\u003eoccupation\u003c/em\u003e and industry concerned with the \u003cem\u003eextraction\u003c/em\u003e of minerals [1].\u0026nbsp;Huge numbers of workers are involved in mining\u0026nbsp;[2]. Artisanal small scale mining (ASM) is mining activity, including gold mining by an individual or a small group, more than 70% globally are an informal work sector, limited use of mechanical tools and labour intensive work nature\u0026nbsp;[3, 4]. ASM has grown in recent years as mineral prices have risen and earning a living from agriculture and other rural activities has become more challenging\u0026nbsp;[4]. \u0026nbsp;There are approximately 40 million ASM miners across 80 countries globally and more 9 million ASM operators in 23 African countries, including Ethiopia\u0026nbsp;[3, 4].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eArtisanal small-scale gold mining (ASGM) is one of ASM with an estimate contribution of 20% of the world’s gold product [4]. Despite these significant contributions to major global mineral supply chains,\u0026nbsp;ASGM has many public health challenges. Occupational injuries are \u0026nbsp;the problem among workers in this mining sector [3].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhen compared to large-scale miners, small-scale miners are 6–7 times more likely to be experienced occupational injury [5]. Due to the informal sector nature of this mining, the absence of safety laws, law enforcement, training, functional infrastructure, and equipment are some of the most widely reported factors have contributed to an increase in injuries among small-scale miners in most countries [3, 4, 6]. Previous studies reported the common cause of injury, among ASGM workers were machinery/tools, falling, explosion, fire, collapse of mining sites and rock falling [7-10].\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Occupational injuries are major health concerns in ASGM occupational setting, especially in Africa [7, 11-13].The prevalence of nonfatal occupational injury among ASGM workers varied from 235-455 in Ghana [7-10] to 447 in Kenya [12] among 1000 workers. \u0026nbsp;Studies also showed underground work, personal protective equipment, education, alternative source of income, shift hours, drug usage, gender and experience as associated factors [9, 12, 13].\u003c/p\u003e\n\u003cp\u003eGold mining has a long history in Ethiopia, was started by the 1930’s in Oddo Shakiso district [14]. The mining sector was taken as a priority area for the achievement of growth and transformation plan II (2016-2020) in the country [15]. The country has huge potential of ASGM and more than 450,000 miners directly \u0026nbsp; involved in ASGM job [4]. However, ASGM job is among the most hazardous jobs because of the absence of basic infrastructures and rudimentary tools used in the area [3, 4, 16]. Information about injury among ASGM workers is not adequately addressed in the country [17]. This study aimed to assess the prevalence of injuries and the associated factors among ASGM workers.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS ","content":"\u003cp\u003eStudy Design and Setting\u003c/p\u003e\n\u003cp\u003eA cross sectional study design was employed from April to June 2020 in Oddo Shakiso District artisanal and small-scale gold mining sites, Guji Zone, Oromia Regional State, Ethiopia. Oddo Shakiso District is 139km far away from Negele (the capital of Guji Zone) and 490 km from south of Addis Ababa, the capital city of Ethiopia. The district is organized by 22 rural kebeles (lowest administrative hierarchies), out of which 11 (50%) have ASGM cooperatives. The total population of the district was 132,017 of which 66, 537 were males and 65,480 females. About 14% of total population practiced ASGM [14].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSource Population.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll ASGM workers in the three selected kebeles were included in the study with the criteria of an aged ≥ 18 years old, residing and worked within the district at least a year. Those miners who were absent at the time of data collections were excluded from the study.\u003c/p\u003e\n\u003ch2\u003eSample size determination and sampling procedure\u003c/h2\u003e\n\u003cp\u003eThere were no similar studies in Ethiopia among ASGM workers. Hence, 50% of the injury prevalence was used to estimate the sample size of the study. A sample size of 422 was calculated using a single population proportion formula with the asssumption of 50 % prevalence, 5% margin of error, 95% confidence level and 10% added for \u0026nbsp;the non response rate.\u003c/p\u003e\n\u003cp\u003eThree kebeles were selected with lottery mothod from 11. A total of about 47 ASGM worker cooperatives with an average of 35 members,a total of 1645 workers were reported in the selected Kebeles [18].\u0026nbsp;The sample size was proportionally allocated to ASGM worker cooperatives. Study participants (n) were selected by simple random sampling method from each mining site of ASGM worker cooperatives.\u003c/p\u003e\n\u003ch2\u003eData collection\u003c/h2\u003e\n\u003cp\u003eInjury was the dependent variable, whereas sociodemographic characteristics (age, gender, religion, marital status, education level, family size, average monthly income), nighttime sleeping disorder,\u0026nbsp;symptom of mercury toxicity, and work-related characteristic (work experience, work shift, tasks, jobs other than mining activities, occupational health and safety training and the use of personal protective equipment) were the independent variables of the study.\u003c/p\u003e\n\u003cp\u003ePretested structured questionnaire was used for data collection. The tool was developed based on previously publishsed occupational injury related literatures [7, 10, 11, 19-21]. The data collection questionnaire was prepared in English and translated to Afan Oromo (the local language) and back translation was done by the language experts to ensure consistency. The injury types were classified based on the international statistical classification of diseases and related health problems [22].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThree diploma holding health professionals handled the interview data collection, a laboratory technician was participated in urine sample collection and a supervisor contributed in data collection follow up. Three days training was provided for the data collectors about the study objective, the study tools, approach, and ethical issues. A pretest was done among 5% of the sample size in the unselected kebeles. After interviews were conducted,\u0026nbsp;about 20 ml urine samples were collected in clean plastic bottles for protein analysis. All urine containers were properly coded using the unique number of participants. Safety measured were applied in the collection process of the samples to avoid contamination. Immediately after the sample collection, the urine protein was proteinuria was assessed semi-quantitatively using the dipstick [21].\u0026nbsp;\u0026nbsp;Data collection activities were carried out with close follow-up by the principal investigator and the supervisor.\u003c/p\u003e\n\u003ch2\u003eOperational definitions\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eInjury\u003c/em\u003e\u003c/strong\u003e: an incident respondents’ self report as cause to miss work for at least a day excluding the day of injury accident [20, 22, 23].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eArtisanal and small-scale gold mining\u003c/em\u003e\u003c/strong\u003e: gold mining conducted by small enterprises/worker cooperative with limited capital investment and production [24].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eExcavation\u003c/em\u003e\u003c/strong\u003e: extracting gold from the soil by digging underground wells [24]\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e\u003cem\u003ePanning\u003c/em\u003e: \u0026nbsp;uses water to separate heavy gold particles from other lighter particles within a medium sized pan [25]\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAmalgamation\u003c/em\u003e\u003c/strong\u003e: mixing liquid mercury with the ore to separate the gold from the other minerals or ore [24].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSmoke cigarette\u003c/em\u003e\u003c/strong\u003e:\u0026nbsp;\u0026nbsp;a worker who was a regular cigarette smoker at the time of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDrink alcohol\u003c/em\u003e\u003c/strong\u003e:\u0026nbsp;drinking alcohol at least weekly [26]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSleeping problem at night\u003c/em\u003e\u003c/strong\u003e: self reporting of loss sleeping at night\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSymptoms of mercury toxicity\u003c/em\u003e\u003c/strong\u003e: worker whose\u0026nbsp;proteinuria\u0026nbsp;(protein in urine)\u0026nbsp;\u0026nbsp;found \u0026nbsp;≥ 0.3g/L by urine dipstick test) [27] and has at least one of the following mercury toxicity indicators:\u0026nbsp;excessive salivation, tremor at work, sleep problems at night, gingival bluish coloration, ataxia of gait and \u0026nbsp;leg (heel- shin) ataxia\u0026nbsp;[28].\u003c/p\u003e\n\u003ch2\u003eData Analysis\u003c/h2\u003e\n\u003cp\u003eCompleteness and consistency of data were checked before data entry. Data was entered to Epi Info version 7.2. Cleaning and analysis were done using SPSS software version 20. Descriptive statistics were used to characterize the data. The association between the variables were assessed by bivariate and multivariate analysis. Binary logistic regression analysis was performed to select candidate variables for multivariable logistic regression analysis. Those explanatory variables having P value \u0026lt;0.25 in bivariate analysis were used as a cutoff point to\u0026nbsp;reduce the\u0026nbsp;cofounder\u0026nbsp;effect in the\u0026nbsp;multivariable analysis [29]. Finally, variables with P \u0026lt; 0.05 at 95% confidence level in the multivariate analysis were considered as statistically significant.\u0026nbsp;Model fitness and multi co-linearity assumptions were checked. Hosmer–Lemeshow goodness model fitting was X\u003csup\u003e2\u003c/sup\u003e=9.2 with a degree of freedom of 8 and a significance equal to 0.33. The multi co-linearity result revealed a variance inflation factor \u0026lt;3 and tolerances \u0026gt;0.4.\u0026nbsp;\u003c/p\u003e"},{"header":"RESULTS","content":"\u003ch2\u003eSocio-demographic characteristics\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eA total of 403 participants were interviewed with a response rate of 95.5%. In this study, 317 (79.0%) of those interviewed were males. The mean (±SD) age of the miners was 30.3 (±7.0) with a range of 18-50 years, and\u0026nbsp;the majority of the miners (73.0%)\u0026nbsp;had primary school education. Monthly average income varied from 1,000 to 10,000 with a mean (±SD) of 4775.0 (±1764.0) in Ethiopian Birr (ETB) \u003cstrong\u003e(Table 1)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eTable1:Socio- demographic characteristics of artisanal and small-scale gold mining workers in Oddo Shakiso woreda, Ethiopia, 2020.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"649\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003en=403\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e79.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;18- 29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e48.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;30-39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e40.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;40-50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Single\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e48.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e44.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Divorced\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eReligion\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Protestant\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e47.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Orthodox\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Muslim\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003csup\u003e\u0026nbsp; \u0026nbsp;*\u003c/sup\u003e\u003c/strong\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducational status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Informal education\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Primary school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e72.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Secondary school and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFamily size\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;≤5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e75.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;\u0026gt;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage monthly income (ETB)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;1000- 3500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;3501- 5000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e35.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;5001- 6000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;6001- 10,000 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003e*\u003c/sup\u003e\u003c/strong\u003e\u003cem\u003eReligion included ‘catholic and wake fena’\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWork- Related and personal Characteristics\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe average year (±SD) of work experience was 9 (±6) with the range of 1-30. The majority of participants, 261 (64.8%), worked both in the morning and afternoon work shift. More than half of the respondents, 221 (54.8%), engaged in excavation and panning job. Out of all miners, 56 (13.9%) had other jobs in addition to mining. All respondents did not take any training about occupational health and safety in any time and nearly all (98.5%) did not use personal protective equipment.\u0026nbsp;Out of the total respondents, 128 (31.8%) drink alcohol and 89 (22.1%) smoke cigarette.\u0026nbsp;Regarding sleeping problem, 47 (10.7%) participants were reporting sleeping problem at night. A total of 72 (17.9%) participants had the symptoms of mercury toxicity during the study period\u0026nbsp;\u003cstrong\u003e(Table 2).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 2: Work related characteristics among artisanal and small-scale gold mining workers in Oddo Shakiso woreda, Ethiopia, 2020.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"625\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003en=403\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eExperience (in years)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;1-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e24.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;5-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e48.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;\u0026gt;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWork shift\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Half shift\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e13.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Full shift\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e86.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTasks (jobs)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Panning and amalgamation\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e10.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Excavation and amalgamation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e11.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Excavation, panning and amalgamation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e77.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eJobs other than mining\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;No\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e86.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003ePrevalence of non-fatal occupational injuries\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe prevalence of nonfatal occupational injury was 25.1% (CI: 21.3%- 29.0%) in the past 12 months. About one third of the injuries, 32 (31.7%), were encountered by the upper extremity and foot injury was the second in prevalence affeted body part, 18 (17.8%). Among the injured participants, 46 (45.5%), got a first aid/ medical service and 55 (54.5%) was treated by traditional methods. Abrasion was the leading type of injury with 31 (30.7%), followed by lacerations by 19 (18.8%). Collapse of mining pits, 31 (30.7%) and falling, 29 (28.7%) were the leading mechanism of injury. More than one third, 79 (78.2%) of the injuries lets the workers absent from work for more than 5 days \u003cstrong\u003e(Table 3)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eTable 3: Characteristics of nonfatal occupational injuries among respondents in artisanal and small-scale gold mining workers in Oddo Shakiso, Ethiopia, 2020\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"631\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNonfatal injury (n=\u003c/strong\u003e\u003cstrong\u003e403)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Yes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;No\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e74.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTypes of injury (n=101)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Abrasion\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Lacerations\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Punctured wounds\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Fracture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Spinal cord injury\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Dislocation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Neurogenic shock \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Others\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eInjury frequency (n=101)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Once\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e84.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Twice or more times\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eInjured body parts (n=101)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Upper extremity\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e31.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Feet\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Lower extremity\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Head\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Back\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Face\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Others\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCause of injury (n=101)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCollapse of mining pits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFalling\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHit by objects\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAssault\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eImproper use of tools\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFires and explosion\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDrowning\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOthers\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch1\u003eFactors associated with non-fatal occupational injury\u003c/h1\u003e\n\u003cp\u003eEach variable was analyzed using bivariate logistic regression. Variables include:\u0026nbsp;age group, marital status, drinking alcohol, symptoms of mercury toxicity, monthly income, work experience task/job, work shift and having job other than mining were candidates to\u0026nbsp;multivariate logistic regression\u0026nbsp;analysis. In the multivariate logistic regression analysis,\u0026nbsp;symptoms of mercury toxicity[AOR: 2.39, 95% CI (1.27-4.52)], 1-4 years work experience [AOR:\u0026nbsp;4.50, 95% CI (1.57-12.9)],involved intwo work shifts [AOR: 6.06, 95% CI (1.97-18.7)], and engaged in all task in minnig activities [AOR:\u0026nbsp;4.83, 95% CI (1.48-15.7)] were significantly associated with injury at p value \u0026lt; 0.05 \u003cstrong\u003e(Table 4)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp id=\"_Toc67896455\"\u003eTable 4: Logistic regression analysis result of non fatal occupational injury and associated factors among artisanal and small-scale gold mining workers in Oddo Shakiso, Ethiopia, 2020\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"631\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Injury\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBivariate analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMultivariate analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Yes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;No\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge in years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;\u0026lt;30\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.54 (098-2.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.10 (0.54-2.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; ≥30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Single\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.65 (0.40-1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.13 (0.56-2.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Divorced\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.55 (0.23-1.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.73 (0.24-2.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrink alcohol\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.60 (1.00-2.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.64 (0.38-1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSymptoms of mercury toxicity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.65 (0.95-2.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.39 (1.27-4.52) *\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage level of income\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(in ETB)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;1000- 3500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.61 (0.91-2.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.29 (0.63-2.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;3501- 5000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.48 (0.85-2.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.20 (0.65-2.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;\u0026gt;5000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWork experience\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; 1-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.26 (1.20-4.23) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4.50 (1.57-12.9)\u0026nbsp;*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; 5-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.78 (1.05-3.03) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.46 (0.71-3.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026gt;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWork shift +\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Half shift\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Full shift\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.50 (1.65-13.36) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.06 (1.97-18.7) *\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTask (job)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Panning and amalgamation only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Excavation and amalgamation \u0026nbsp; only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.31 (0.66-8.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.90 (0.71-11.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Panning, excavation an amalgamation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3.81 (1.32-11.0) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4.83 (1.48-15.7)\u0026nbsp;*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHaving job other than mining\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Yes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;No\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.20 (1.00-4.81)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.75 (0.77-3.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003e*\u003c/sup\u003e\u003c/strong\u003e\u003cem\u003eStatistically significant at P-value \u0026lt;0.05; + Half shift includes work in either morning or afternoon; Full shift includes work in the morning and afternoon or work in the afternoon and evening\u003c/em\u003e\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe prevalence of nonfatal occupational injury was\u0026nbsp;25.1% (95% CI: 21.3-29.0%) in the past 12 months. This finding is in line with similar studies in Ghana (23.5%, 25%and 28.9%) [8-10] and higher than \u0026nbsp;the Zimbabwe (15.8%) [19] but lower than other studies reported in\u0026nbsp;Kenya (44.7%)\u0026nbsp;[12]\u0026nbsp;and the Democratic Republic of the Congo\u0026nbsp;(72.2%)\u0026nbsp;[30].The reason\u0026nbsp;for the disparity between the current and the previous studies might be the\u0026nbsp;type of mining activities \u0026nbsp;[30], variation in working population characterisitcs (interms of alcohol,drug use,\u0026nbsp;safety culture etc) [12, 13, 30]. The other explanation could be the use of a definition \"at least one day off from work following an injury\" \u0026nbsp; the similar finding from Ghana\u0026nbsp;[9]\u0026nbsp;and in this investigation might have lowered the prevalence of injury as compared with other studies defination with out considering the day after the accident\u0026nbsp;[12, 30]. A definition of work place injury can have a big impact on the prevalence estimate\u0026nbsp;[31].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe most common injury type in the current study was an abrasion followed by lacerations. It is in line with other study [8]. Other similar studies reported laceration were reported as the most common injury type [7, 10, 13].\u0026nbsp;The disparity between these studies might be due to the difference machines or the local tools used for mining, access to safety awareness and personal protective equipment utilization among mining workers.\u0026nbsp;Among the injured body part, this study reported the upper extremity as the most affected among the participants.\u0026nbsp;This is consistent with findings in studies on AGSM in Kenya\u0026nbsp;[12]\u0026nbsp;, Ghana\u0026nbsp;[10]\u0026nbsp;and \u0026nbsp;the\u0026nbsp;Democratic Republic of the Congo\u0026nbsp;[30]. The explanation the of findings might be due to the rudimentary\u0026nbsp;mining methods,\u0026nbsp;poor\u0026nbsp;processing and\u0026nbsp;the handling activities with hand in these sectors, that might lead to the upper extremities inury\u0026nbsp;[9, 12, 32].\u0026nbsp;The most frequently occurring injuries on this report was due to collapse of the mine pits and falls. It is in line the similar study in Ghana\u0026nbsp;[11]. It might be possible that one risk factor for injury among AGSM personnel is the reality of underground work or digging without safety operation\u0026nbsp;[9, 33]\u0026nbsp;.\u003c/p\u003e\n\u003cp\u003eVarious predicted factors might increase the likelihood of injury occurrence among different studies.\u0026nbsp;The current study identified symptoms of mercury toxicity, less year’s work experience, involved intwo work shifts and engaged in various tasks of mining such as panning, excavation and amalgamation job were significantly associated with injury.\u0026nbsp;The study participants with\u0026nbsp;having mercury toxicity symptoms had more than two times of likelihood of injury accident [(AOR: 2.39, 95% CI (1.27-4.52)]\u0026nbsp;compared with workers who had no such symptoms. AGSM\u0026nbsp;workers used mercury metal to extract gold from ore through amalgamation. The amalgam is heated to evaporate the mercury and isolate the gold so that workers might expose to evaporate forming through inhalation [34].\u0026nbsp;High mercury exposure causes central nervous system abnormalities, which can lead to exhaustion, cognitive impairment [35, 36]\u0026nbsp;and\u0026nbsp;effects on\u0026nbsp;muscle that lead to muscle weakness\u0026nbsp;[37]. These health problems might contribute to the occurrence of injury.\u003c/p\u003e\n\u003cp\u003eThe current study identified work related conditions such as year of experience in gold mining, work shift and task associated with occupational injury.\u0026nbsp;Workers who had 1-4 years experience reported more than four odds of injury [AOR: 4.50, 95% CI (1.57-12.9)] than the more experienced workers` group.\u0026nbsp;Other similar studies reported support this finding with the less work experience as predictor of occupational injury\u0026nbsp;[10, 33, 38, 39].\u0026nbsp;Because; \u0026nbsp;workers in this sector used rudimentary\u0026nbsp;mining devices and\u0026nbsp;poor\u0026nbsp;processing\u0026nbsp;[9, 12, 32];\u0026nbsp;and previous work experience might be increasing the skill to perform the job without injury accident\u0026nbsp;[38, 40].\u0026nbsp;The other reason might be almost all participants in the current study did not use personal protective equipment and had no history of\u0026nbsp;training about occupational health and safety to prevent injury.\u0026nbsp;So that,\u0026nbsp;unskilled workers\u0026nbsp;might be more frequently prone to injuries due to this work condition than the experienced group\u0026nbsp;[41].\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWorkers involved in full work shift had an injury likelihood more than six times [AOR: 6.06, 95% CI (1.97-18.7)] higher than the half shift workers in the current study. The full shift miners engaged in work for extended hour in a day. The disparity in injury accidents could be attributable to a risk factor for occupational injuries, which is the long work hour each day\u0026nbsp;\u003cstrong\u003e[13, 39]\u003c/strong\u003e. Also, all participants in the curent study practiced gold separation from ore through amalgamation. But workers engaged both panninig and excavation job had more than four odds of injury [AOR: 4.83, 95% CI (1.48-15.7)] compared with paninig workers.The reason for this finding could be that workers who have several jobs are more exhausted and prone to making mistakes, hence working numerous jobs is linked to an increased risk of injury\u0026nbsp;\u003cstrong\u003e[42]\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStrength and Limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHigh participation rate, apply a standard case definition of injury and standard assessment instruments were the strength of this study. However, the findings were based on self report of respondents and the injury case was not validated clinically. In addition, ASGMs were informal sectors where temporary employment is a common practice, severely injured workers may not return to their job during the data collection period or forever, hence the true prevalence estimate might be higher.\u003c/p\u003e"},{"header":"Conclusion and Recommendations","content":"\u003cp\u003eIn general, injury prevalence was substantially higher among ASGM workers. Symptoms of mercury toxicity, less years of work experience, involved intwo work shifts and engaged all tasks were significantly associated with injury. Findings suggest that the improvement in working condition and safety practice to reduce occupational injuries. Recommendation emphasized on eliminating the use of mercury in gold extraction, training in occupational safety and health, specially on the use of safe work tools and the means to create a safe working environment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to acknowledge Addis Ababa University College of Health Sciences School of Public Health and NORHED Project for the financial support in conducting this research activity. Our deepest gratitude also goes to Oddo Shakiso mining office and health office. We have also heartfelt acknowledgement to the study participants, the data collectors for their respective contributions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ Contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFA was involved in the write-up of the research proposal, supervision of the data collection, data entry, data cleaning, data analysis, and writing of the manuscript. YT, WT, AB, HM and GK were involved in the write-up of the research proposal, the data analysis and writing the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the Norwegian program for capacity Building in Higher Education and research for development (NORHED) for data collection. However, the funding body had no\u003c/p\u003e\n\u003cp\u003erole in data analysis, interpretation of data and writing the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are not publicly available due to study participant privacy/consent agreements but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003ch2\u003eEthical Consideration\u003c/h2\u003e\n\u003cp\u003eThe research was ethically approved by Addis Ababa University Ethical Review Board. Permission was given from Ministry of Mine, Oromia Mine Bureau, Oddo Shakiso District Mining Office and from respective kebele leaders. The participants were asked whether they are volunteers or not to participate in the study. In the data collection process, the interview was conducted only from fully volunteer study participants. Each volunteer participant had an equal chance of being interviewed; there was full right to ask any question, refuse or terminate from participation. Data was coded and kept secret to ensure its confidentiality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eN/A\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have declared that they had no conflicts of interest. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eMinistry of Mines and Petroleum, Addis Ababa, Ethiopia,\u0026nbsp;\u003csup\u003e2\u0026nbsp;\u003c/sup\u003eSchool of Public Health, College of Health Sciences, Addis Ababa University, Addis Ababa, Ethiopia,\u003csup\u003e3\u003c/sup\u003e College of Health Sciences, Debre Berhan University, Debre Berhan, Ethiopia,\u0026nbsp;\u003csup\u003e4\u0026nbsp;\u003c/sup\u003eColleges of Medical and Health Sciences, Kotebe Metropolitan University, Addis Ababa, Ethiopia.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eVictoria, R.N., \u003cem\u003eMining and the Law in Africa: Exploring the social and environmental impacts\u003c/em\u003e. 2019, UK: Palgrave Pivot.\u003c/li\u003e\n\u003cli\u003eDonoghue, A.M., \u003cem\u003eOccupational health hazards in mining: an overview.\u003c/em\u003e Occupational Medicine, 2004. \u003cstrong\u003e54\u003c/strong\u003e(5): p. 283-289.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization, \u003cem\u003eArtisanal and small-scale gold mining and health\u003c/em\u003e. 2016: Geneva, Switzerland.\u003c/li\u003e\n\u003cli\u003eThe International Institute for Sustainable Development, \u003cem\u003eGlobal Trends in Artisanal and Small-Scale Mining (ASM): A review of key numbers and issues\u003c/em\u003e. 2018: Ottowa, Canada.\u003c/li\u003e\n\u003cli\u003eInternational Labor Organization, \u003cem\u003eSocial and labour issues in small scale mines: Report for discussion at the tripartite meeting on social and labour issues in small-scale mines\u003c/em\u003e. 1999, International Labor Organization: Geneva, Switzerland.\u003c/li\u003e\n\u003cli\u003eInternational Labour Organization, \u003cem\u003eILO Introductory Report: Global Trends and Challenges on Occupational Safety and Health\u003c/em\u003e. 2011: Geneva, Switzerland.\u003c/li\u003e\n\u003cli\u003eE.Kyeremateng, A. and E.C. 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Stenberg, \u003cem\u003eAssessment of Workers\u0026rsquo; Knowledge and Views of Occupational Health Hazards of Gold Mining in Obuasi Municipality, Ghana.\u003c/em\u003e International Journal of Occupational Safety and Health, 2020.\u003cstrong\u003e 1 \u003c/strong\u003e(2020): p. 38 - 52.\u003c/li\u003e\n\u003cli\u003eMarie, L., et al., \u003cem\u003eUnexpected events: Learning opportunities or injury risks for apprentices in low-skilled jobs? A pilot study.\u003c/em\u003e Safety Science, 2016. \u003cstrong\u003e86\u003c/strong\u003e: p. 1-9.\u003c/li\u003e\n\u003cli\u003eHelen, R.M.-W., et al., \u003cem\u003eWork in Multiple Jobs and the Risk of Injury in the US Working Population.\u003c/em\u003e American Journal of Public Health 2014. \u003cstrong\u003e104\u003c/strong\u003e(1).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-1555497/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1555497/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Background: Artisanal and Small-scale Gold Mining is widely practiced in developing countries. Injuries are among the public health concerns in the mining sector. This study aimed to assess the prevalence of nonfatal occupational injuries and associated factors among workers in Artisanal and Small-scale Gold Mining in Oddo Shakiso District, Oromia, Ethiopia.\nMethods: Cross sectional study design was employed from April to June 2020. A total of 403 participants was selected with simple random sampling technique. Structured questionnaire was utilized for the data collection. Descriptive statistics were used to characterize the information and the logistic regression was applied to test the association. Predictor variables with p value \u003c0.05 with Odds ratio of 95% CI in multivariate analysis were considered as associated factors.\nResults: A total of 403 participants were interviewed with a response rate of 95.5%. The prevalence of nonfatal occupational injury was 25.1% in the past 12 months. About one third of the injuries, 32 (31.7%), were on the upper extremity and feet, 18 (17.8%). Symptoms of mercury toxicity [AOR: 2 .39, 95% CI (1.27-4.52)], 1-4 years work experience [AOR: 4.50, 95% CI (1.57-12.9)], full work shift [AOR: 6.06, 95% CI (1.97-18.7)], and job in task of mining activities (AOR: 4.83, 95% CI (1.48-15.7)) were associated with the injury.\nConclusion: High prevalence of injuries was found. Work-related factors were found significantly associated with the occurrence of injury. Therefore, both workers and responsible organization should apply intervention focus on the improvement of working condition and safety practice to minimize the injury accident.","manuscriptTitle":"Nonfatal Occupational Injuries among Artisanal and Small-scale Gold Mining Workers in Oddo Shakiso District, Guji Zone of Oromia Regional State, Southern Ethiopia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-13 07:17:04","doi":"10.21203/rs.3.rs-1555497/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9532f7e5-806b-4fbd-a908-de69f03f83d2","owner":[],"postedDate":"January 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-13T07:17:04+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-13 07:17:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1555497","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1555497","identity":"rs-1555497","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}