Abstract
Context:
Respiratory exposure to welding fumes directly or indirectly, in the long run, can lead to systematic effects among welders.
Aims:
This study aimed to investigate respiratory symptoms and pulmonary function parameters among welders working in the automotive industry.
Material and Methods:
This cross-sectional study was performed among 2304 workers from two groups in the manufacturing and administrative staff (as exposure and control groups). Pulmonary function parameters and respiratory symptoms were collected through periodic spirometry examinations and a standard respiratory symptoms questionnaire. Exposure to welding fumes was obtained from the annual measurement data based on the NIOSH7300 method. Data were analyzed using SPSS software version 22 and linear and multiple regression statistical tests.
Results:
The mean age of the subjects in the case and control groups were 37.5 and 38.5 years, respectively. All the apparent symptoms of respiratory diseases (cough, sputum) in the welder’s group were more than in the control group. Also, there was a significant difference between the respiratory symptoms in the two groups (P < 0.001). The mean value of all spirometric parameters (FVC, FEV1, FEV1/FVC, FEF25-75, PEF) in the case group (welders) was significantly lower than the control group.
Conclusion:
The results of this study revealed that the variables of age and work experience effectively reduce all spirometric parameters of welders. Also, regarding the effect of metal vapors, a significant relationship has been seen between Cu in welding fume and FEF25-75 and FEV1 spirometric parameters.
Keywords: Lung disease, metal fume, occupational exposure, respiratory symptoms, spirometry
INRODUCTION
Despite the tremendous technological development in recent decades, welding is still used in industry, and more than one percent of each country’s workforce is employed in the welding sector.[1] The welding process involves various hazards, including physical factors (noise, heat, and vibration), chemical factors (fumes, vapors, gases, rays (UV, IR)), etc.[2] Respiratory exposure to welding fumes directly or indirectly, in the long run, can lead to systematic effects among welders. The results of welding fume on the spirometric evaluation of welders are different and depend on various factors such as working environment, amount and duration of exposure, type of welding, ventilation status of the workplace, and other confounding factors such as smoking.[3] In recent decades, researchers have studied the effects of metal fumes on welder diseases such as asthma, chronic lung disease, lung cancer, and decreased respiratory parameters.[4] In 2017, the IARC (International Agency for Research on Cancer) updated a related study on welding fumes, and this compound was classified as a carcinogen for humans under Group 1.[5] Various studies have presented the effect of welders’ exposure to welding fumes and reductions in the spirometric parameters of FVC, FEV1, FEV1/FVC.[6] Studies among welders in the automotive industry have revealed that they suffer from chronic respiratory symptoms or symptoms associated with lung problems; in other words, half of the welders eventually experienced at least three systematic symptoms.[7] Various studies have been reported in this field regarding the prevalence of complications and respiratory disorders of workers exposed to welding fumes in the automotive industry.[8] Studies have also emphasized the effect of age and work experience on lung problems.[9] Spirometry is an inexpensive diagnostic technique available for measuring respiratory parameters. Spirometry is a clinical and occupational disease-detecting technique for lung diseases like bronchitis, emphysema, and asthma.[10] Therefore, in this study, the influential variables and their effect on the lung function of welders were investigated using spirometry results, completing a respiratory symptoms questionnaire, and measuring the amount of welding metal vapor concentration.
MATERIAL AND METHODS
Site description and study participants
The present study was a cross-sectional study performed among automotive industry employees in the spring (3 months) and summer (3 months) of 2020. Inclusion criteria included welders with at least one year of experience in the welding hall (case group) and administrative staff without exposure to welding metal vapors and harmful chemical agents in their work environment (control group). All welders in the welding hall (1200 workers) as the exposed group and all administrative staff (1200 workers) as the control group were included in the study. Exclusion criteria included people with a history of respiratory disease, any specific medical condition, a welder in the past, or their current second job. After considering the exclusion criteria and matching the groups as much as possible, the study population of the case and control groups decreased to 1152 people.
Assessment of respiratory symptoms
In the first phase, the standard respiratory symptoms questionnaire was completed, including demographic information, previous employment history, smoking habits, history of respiratory diseases, and questions related to the symptoms of respiratory disorders (cough, sputum, wheezing).[11]
Pulmonary function tests (PFTs)
In this research, a MIRLABIII spirometer equipped with a printer was used. Various parameters such as FVC (Forced Vital Capacity), FEV1 (Forced Expiratory Volume in the first second), FEV1/FVC (Forced Expiratory Volume in the first second/Forced Vital Volume Capacity), FEF25-75 (Forced Expiratory Flow at 25-75% of the pulmonary volume), PEF (Peak Expiratory Flow) were evaluated. According to the American Chest Specialists Association standards, a trained technician assessed pulmonary capacity using a calibrated portable spirometer at the factory.[12] The spirometer was calibrated according to the relevant instructions. All participants were asked to avoid smoking 2 hours before spirometry. Also, the necessary training was provided to make the participants more familiar with spirometry testing and its maneuvers. Before the spirometry test, each person sat for 5 minutes and was asked to stand in a normal and comfortable position and place a special clip on their nose. At least three acceptable tests were performed for each of the subjects, and the highest values of vital lung capacity (FVC, FEV1, FEV1/FVC, FEF25-75, PEF) were selected.
Personal exposure monitoring
The required information for welding metal vapors was collected through annual measurement of harmful chemical factors in the work environment, during which welding metal vapors (Cu, Fe, Pb) were obtained in the workstations according to the NIOSH7300 procedure. The pollutant sampling interface was a PVC filter with a diameter of 37 mm and a pore size of 5 micrometers. After placing the filter in a 37 mm three-piece retaining cassette and using an SKC sampling pump (standard model) with a flow rate of 1.5 liters per minute for 2 hours, the filters were weighted with a digital scale. Then, all samples were compared with the standard level of occupational exposure limits (OEL) in terms of mg.m3, which for metal fume vapors such as Fe, Cu, and Pb are 5, 0.2, and 0.05 mg.m3, respectively.[13]
Statistics analysis
Data analysis was performed by SPSS version 22 software. Descriptive statistics were initially reported to calculate the mean, standard deviation, and frequency. The Kolmogorov-Smirnov test was used to check the data normality. A paired t-test was used to compare the mean of variables between the two groups in data analysis. The relationship between spirometric parameters with independent variables (age, BMI, work experience, exposure to Fe, Cu, and Pb in welding fume) was analyzed using linear and multiple regression models. In all tests, a significant relationship was calculated from 0.001.
RESULTS
This study was performed among welders in the operational unit (1200 workers) and the control group (1200 workers). Considering the inclusion and exclusion criteria, 1152 welders and 1152 administrative staff were examined. All 2304 participants were male. The mean and standard deviation of the age of the subjects in the case and control groups were 37.5 ± 5.09 and 38.5 ± 7.18 years, respectively. The average concentration of welding fumes in the operational unit, including the measurement of Cu, Pb, and Fe metal vapors, were 0.107, 0.010, and 1.378 mg.m3, respectively. The results of the subjects’ demographic data are presented in Table 1.
Table 1.
Demographic characteristics of the exposed and unexposed groups
| Parameter | Category | (n=1152) No (%) | |
|---|---|---|---|
|
| |||
| Unexposed group | Exposed group | ||
| Age (year) | 30< | 114 (9.9) | 121 (10.5) |
| 30-39 | 607 (52.7) | 596 (51.7) | |
| 40-49 | 327 (28.4) | 415 (36) | |
| <50 | 104 (9) | 20 (1.7) | |
| Work experience (year) | 5-10 | 589 (51.1) | 486 (42.2) |
| 10-15 | 331 (28.7) | 454 (39.4) | |
| 15-20 | 147 (12.8) | 163 (14.1) | |
| 20-25 | 49 (4.3) | 37 (3.2) | |
| 25-30 | 36 (3.1) | 12 (1) | |
| Marital Status | Single | 253 (22) | 161 (14) |
| Married | 899 (78) | 991 (86) | |
| Employment Type | Official | 82 (7.1) | 118 (10.2) |
| Contractual | 1070 (92.9) | 1034 (89.8) | |
| Smoking | Smoker | 171 (14.8) | 270 (23.4) |
| Non-smoker | 981 (85.2) | 882 (76.6) | |
| Education level | High school | - | 14 (1.2) |
| diploma | 57 (4.9) | 1093 (94.9) | |
| Associate Degree | 142 (12.3) | 30 (2.6) | |
| Bachelor | 723 (62.8) | 15 (1.3) | |
| Master | 219 (19) | - | |
| BodyMass Index (kg.m2) | Lightweight (BMI= <18.5) | 9 (0.8) | 13 (1.1) |
| Normal (BMI=18.5-24.9) | 425 (36.9) | 406 (35.2) | |
| Overweight (BMI = 25-29.9) | 603 (52.3) | 599 (52) | |
| Grade 1 obesity (BMI =30-34.9) | 86 (7.5) | 114 (9.9) | |
| Grade 2 obesity (BMI =35-39.9) | 5 (0.4) | 9 (0.8) | |
Table 2 presents the frequency of respiratory symptoms in the study groups. As shown in Table 2, cough and sputum in welders are significantly higher than in the control group (administrative staff). Also, respiratory symptoms of cough and wheezing with 24.6% (283 workers) and 2.2% (25 workers) are the most and most minor problems in respiratory symptoms, respectively [Table 2].
Table 2.
prevalence of respiratory symptoms in exposed and unexposed groups
| Symptoms | Unexposed group No (%) | Exposed group No (%) | P |
|---|---|---|---|
| Cough | 138 (12) | 283 (24.6) | * <0.001 |
| Sputum | 35 (3) | 234 (20.3) | * <0.001 |
| Cough and sputum | 29 (2.5) | 188 (16.3) | * <0.001 |
| Wheezing | 11 (1) | 25 (2.2) | * 0.019 |
*Chi-square or Fisher s exact test. *%Predicted pulmonary function (comparison of PFT in all subjects (exposed vs non-exposed)). ** Two-independent sample t-test
Table 3 compared pulmonary function parameters in the exposed and unexposed groups (control). Findings showed that these values are lower in the exposed group (welders) than in the control group (administrative staff). Furthermore, there was a significant difference for all parameters among the case and control groups [Table 3]. Besides, a significant correlation was reported between independent variables (age, BMI, work-experience, smoking, fumeFe, Cu, Pb) and the dependent variable. Multiple regression analysis results showed a significant relationship between spirometric parameters (FVC, FEV1, FEV1/FVC, FEF25-75, PEF) and the variables of age and work experience. BMI had a significant linear relationship with FVC, FEV1, FEV1/FVC, and FEF25-75 parameters. Smoking also affected only FVC, FEV1, and FEV1/FVC parameters. Regarding the effect of metal vapors, only Cu fume showed a significant relationship to spirometric parameters (FEV1 and FEF25-75) and reduced these parameters. All variables were entered into the model for multiple regression analysis as oscillating variables and analyzed by the stepwise method accordingly; age variables were 0.299 and 0.297 times the effective reduction in FEV1 and PEF. The Cu fume had a -0.133 and -0.162 decrease in FEV1 and FEF25-75, respectively. Also, smoking decreased the FEV1 by 0.117, but the other variables’ effect was insignificant [Tables 4 and 5].
Table 3.
Results of spirometric parameters in exposed and unexposed groups
| * Variables (Pred%) | (Mean±SD) | P | |
|---|---|---|---|
|
| |||
| Unexposed group (n=1152) | Exposed group (n=1152) | ||
| FVC (Pred %) | 104.9±0.439 | 101.2±0.465 | ** <0.001 |
| FEV1 (Pred %) | 102.2±0.438 | 96.4±0.434 | ** <0.001 |
| FEV1/FVC (Pred %) | 100.3±0.229 | 98.3±0.231 | ** <0.001 |
| FEF25-75 (Pred %) | 90.8±0.64 | 86.5±0.649 | ** <0.001 |
| PEF (Pred %) | 104±0.535 | 98.6±0.385 | ** <0.001 |
Table 4.
Univariate simple and multiple linear regression model examining the association between spirometric parameters (FVC, FEV1, FEV1/FVC) and independent variables
| Variables | FEV1/FVC | FEV1 | FVC | |||
|---|---|---|---|---|---|---|
|
|
|
|
||||
| Multiple linear regression (CI95%) β P | Simple Linear regression (CI95%) β P | Multiple linear regression (CI95%) β P | Simple Linear regression (CI95%) β P | Multiple linear regression (CI95%) β P | Simple Linear regression (CI95%) β P | |
| Age | 0.110 (-0.074, 0.331) 0.212 | 0.139 (0.124, 0.226) 0.001* | 0.299 (0.286, 1.004) 0.001* | 0.266 (0.547, 0.737) 0.001* | -0.080 (-0.642, 0.241) 0.372 | 0.096 (0.138, 0.339) 0.001* |
| BMI | -0.101 (-0.241, -0.041) 0.074 | -0.058 (-0.241, -0.041) 0.006 | -0.078 (-0.846, 0.134) 0.151 | -0.091 (-0.618, -0.237) 0.001* | -0.083 (-1.050, 0.154) 0.144 | -0.058 (-0.475, -0.083) 0.005* |
| Work experience | 0.073 (-0.169, 0.413) 0.410 | 0.103 (0.107, 0.247) 0.001* | 0.078 (-0.286, 0.983) 0.151 | 0.190 (0.492, 0.758) 0.001* | 0.097 (-0.286, 0.983) 0.280 | 0.058 (0.057, -0.332) 0.006* |
| Smoking | -0.035 (-3.449, -1.769) 0.527 | 0.069 (0.573, 2.207) 0.001* | 0.117 (0.544, 9.777) 0.029* | 0.046 (0.181, 3.308) 0.029* | 0.081 (-1.535, 9.841) 0.152 | 0.059 (0.703, 3.907) 0.005* |
| Fe Fume | -0.039 (-0.917, 0.450) 0.502 | -0.082 (-1.136, 0.265) 0.143 | 0.006 (-1.141, 1.271) 0.911 | -0.038 (-1.618, 0.784) 0.495 | 0.074 (-0.543, 2.438) 0.212 | 0.069 (-0.519, 2.277) 0.217 |
| Pb Fume | -0.095 (-5.728, 0.505) 0.100 | 0.049 (-13.637, 35.825) 0.378 | -0.027 (-54.620, -32.572) 0.619 | -0.046 (-64.630, 26.323) 0.410 | 0.000 (-53.817, 53.599) 0.997 | 0.017 (-45.032, 61.662) 0.764 |
| Cu Fume | 0.058 (-11.535, 37.742) 0.296 | -0.015 (-7.668, 5.918) 0.090 | -0.133 (-12.234, -1.204) 0.017* | -0.015 (-10.885, 0.122) 0.055* | -0.015 (-7.668, 5.918) 0.800 | 0.019 (-5.310, 7.598) 0.217 |
*P less than 0.001 is considered as significant, CI = Confidence interval
Table 5.
Univariate simple and multiple linear regression model examining the association between spirometric parameters (FEF25-75, PEF) and independent variables
| Variables | PEF | FEF 25-75 | ||
|---|---|---|---|---|
|
|
|
|||
| Multiple linear regression (CI95%) β P | Simple Linear regression (CI95%) β P | Multiple linear regression (CI95%) β P | Simple Linear regression (CI95%) β P | |
| Age | 0.297 (0.336, 1.214) 0.001* | 0.247 (0.598, 0.826) 0.001* | 0.134 (-0.127, 0.995) 0.129 | 0.142 (0.358, 0.643) 0.001* |
| BMI | 0.058 (-0.273, 0.925) 0.286 | 0.036 (-0.027, 0.431) 0.083 | -0.109 (-1.518, 0.115) 0.053* | -0.085 (-0.528, 0.024) 0.001* |
| Work experience | 0.013 (-0.584, 0.679) 0.882 | 0.165 (0.489, 0.805) 0.001* | 0.021 (-0.706, 0.905) 0.808 | 0.108 (0.323, 1.740) 0.001* |
| Smoking | 0.043 (-3.362, 7.953) 0.425 | 0.034 (-0.298, 3.443) 0.099 | 0.070 (-2.592, 11.851) 0.208 | 0.100 (-2.830, 1.740) 0/640 |
| Fe Fume | 0.032 (-1.910, 1.055) 0.571 | -0.039 (-1.975, 0.938) 0.484 | 0.057 (-0.948, 2.836) 0.327 | 0.003 (-1.758, 1.862) 0.955 |
| Pb Fume | -0.013 (-60.095, 46.753) 0.806 | -0.045 (-78.013, 32.421) 0.417 | 0.020 (-55.823, 80.564) 0.721 | 0.020 (-56.297, 80.990) 0.724 |
| Cu Fume | -0.033 (-8.788, 4.727) 0.555 | -0.031 (-8.638, 4.781) 0.572 | -0.162 (-20.981, -3.730) 0.005* | -0.128 (-18.015, -1.476) 0.021* |
*P less than 0.001 is considered as significant, CI=Confidence interval
DISCUSSION
Because of welders’ exposure to various harmful factors, including welding fumes,[1] The present study investigated the changes in spirometric parameters and respiratory symptoms of workers in the operational unit exposed to welding fumes compared with employees of non-exposed office units. The incidence of respiratory symptoms in exposed individuals (welders) was significantly higher than in controls (administrative staff). Respiratory symptoms (cough, sputum) were highest among welders, respectively, compared to the control group (administrative staff).
One of the variables studied in the present study was BMI, which revealed that BMI frequency with a range of 25-29.9 in the two groups of welders and administrative staff was 52% and 52.3%, respectively.
In this study, the mean of all spirometric parameters in welders was significantly lower than in the control group (administrative staff). Between spirometric evaluation, age, and work experience variables, BMI and smoking are among the most important variables affecting FVC, FEV1, and FEV1/FVC. The findings of this study, similar to other reported studies, have shown that coughing and sputum are among the most apparent symptoms in exposed groups.[14,15]
In this study, cough and sputum were prominent respiratory symptoms in welders. The study of Ithnin et al.[16] also revealed a significant difference between the symptoms of cough and sputum in welders and administrative staff, which is one of the obvious respiratory symptoms of the individual, in a study conducted by Oghuvwu et al. among welders in Nigeria, frequency of wheezing, respiratory symptoms (44.4%), cough (22.2%), and shortness of breath (11.1%) was among significant respiratory symptoms and in welders was more than control groups. Compared to the above study, wheezing was the lowest incidence of respiratory symptoms.[15]
In another study performed by G.E. Erhabor et al. on the lung health status of welders, 29.55% of welders had respiratory symptoms of cough with sputum. Therefore, it can be said that respiratory symptoms of cough and sputum are among the most apparent respiratory symptoms in the exposed group (welders), which is consistent with the findings of the present study.[17] In the study by Ahmad et al.[10] on the respiratory status of welders, respiratory symptoms such as chest tightness, wheezing, and shortness of breath were reported to be higher in the exposed group than in the control group. However, wheezing is the lowest percentage of obvious respiratory symptoms in the present study. The results of various studies indicated that the incidence of asthma in overweight people is higher than in normal-weight people.[18]
The study of Sun et al. on the relationship between BMI and decreased FEV1 revealed a significant relationship between BMI and reduced FEV1. In the above research, BMI in the exposed groups was higher. A meaningful decrease was seen in all spirometric parameters in exposed individuals [Table 3], so BMI should be considered as one of the causes and parameters in the onset of respiratory symptoms.[19] In this study, the mean of all spirometric parameters (FVC, FEV1, FEV1/FVC, FEF25-75, PEF) was lower in welders than in controls and was in line with the results of G.E. Erhabor et al. research.[17] Also, the study of Fasanmi K.T. on 103 welders and 99 members of the control group in Nigeria demonstrated a decrease in the spirometric evaluation of FVC, FEV1, and FEV1/FVC in welders compared to controls.[20] Thetkatuck A, in the study of metal fumes of a smelting plant in Thailand on 399 welders, observed no significant relationship between the parameters of FVC, FEV1, and the concentration of fumes. At the same time, the concentration of welding fume contaminants was within the allowable range. There was also a 2.5% decrease in the FEV1 parameter and a 2% decrease in the FVC parameter. Related to the above study, although the concentration of welding metal vapors was in the allowed range, reductions in all spirometric parameters of welders were seen.[14] This is consistent with the present study results that all indicators of pulmonary function were significantly related to age and work experience. In the study performed by Oghuvwu et al. on the symptoms and parameters of spirometric evaluation of welders, there was no significant relationship between spirometric parameters with age, work experience, and smoking, and was not consistent with the present study results.[15] Therefore, it can be stated that the effect of exposure of welders to welding metal vapors causes adverse effects, especially in pulmonary parameters, which increase with age and work experience, in another similar study by Alotaibi. In some cases, a high incidence of chronic bronchitis was observed among welders due to welding fume on the lungs.[21] However, in the cross-sectional study, the relationship between welding metal vapors and spirometric evaluation cannot be stated indeed; in the present study, a relatively large sample size was studied to investigate the effects of respiratory exposure to welding metal vapors, and at the same time, respiratory symptoms and respiratory function parameters were evaluated to examine the impact of exposure to welding metal vapors more accurately. Furthermore, it should be noted that to study the reduction of respiratory parameters of welders as accurately as possible, it should be done periodically and reviewed in different years.
CONCLUSION
Based on the findings of this study and comparison with the results of previous studies, welding is one of the professions that can have respiratory problems for employees. Therefore, this profession was classified as an occupation with respiratory complications. All measures, including a thorough medical examination using spirometry and a detailed respiratory symptoms questionnaire in periodic and initial tests, should be implemented to prevent this issue. All employees should be aware of the side effects of welding fumes and the need for proper use of personal protective equipment to express more definitively the impact of welding fume on respiratory parameters.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
Acknowledgement
This result was from a part of the master’s thesis of the first author, Elham Saadiani, with the ethics code of IR.QUMS.REC.1398.224, which has been done with the support of Qazvin University of Medical Sciences. Therefore, the authors express their special thanks to the Qazvin University of Medical Sciences.
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