INTRODUCTION

The global rise in electronic waste (e-waste) has contributed to the release of harmful substances. Inadequate disposal and recycling practices pose serious risks to environmental and human health [1]. Heavy metals (HMs) such as lead (Pb) and cadmium (Cd), stemming from e-waste recycling and disassembly processes, have been accumulating in the air, soil, water, sediment, plants, animals, and dust for decades [2,3]. Workers engaged in e-waste dismantling, as well as vulnerable populations such as children, pregnant women, and community-dwelling adults (CDAs) living near e-waste recycling facilities, face heightened health risks [4–7].

E-waste workers (EWWs) are exposed to a variety of occupational hazards, including physical, chemical, ergonomic, and psychosocial risks [8]. These hazards can lead to a range of health issues such as blurred vision, skin rashes or itching, numbness in the hands and feet, headaches, and coughing [9]. These individuals are also prone to sleep disturbances, including insomnia and poor sleep quality (PSQ), which are associated with further complications like headaches [10] and adverse psychological outcomes, including anxiety and depression [11]. Research has documented the prevalence of insomnia among EWWs in Thailand [12], and in Hong Kong, individuals with potential exposure to neurotoxins from e-waste have exhibited neurological changes related to sleep and circadian rhythms [13]. Additionally, individuals with mood disorders have been found to have average blood Pb levels as high as 40 μg/dL [14], and a study of 35 108 Korean adults linked higher serum Cd levels with short sleep duration (less than 6 hours per night) [15]. In one study, even after an e-waste dismantling facility had been closed for 2 years, residents living near the site displayed elevated blood Pb concentrations [16]. Exposure to HMs significantly impacts the health of both EWWs and members of the surrounding community.

Thailand’s e-waste situation has been impacted by global waste management dynamics, highlighting deficiencies in the country’s e-waste management infrastructure and regulatory framework. These shortcomings have prompted environmental and health concerns [17]. The Pollution Control Department of the Ministry of Natural Resources and Environment of Thailand identifies the Ban-Kok subdistrict of the Kuengnai district in Ubon Ratchathani Province as one of 5 communities where most residents are engaged in disassembling electrical appliances. The hypothesis of this study is that EWWs and CDAs exhibit differences in HM exposure, as well as in physical and mental health symptoms. Furthermore, the study seeks to determine which factors are associated with these symptoms and whether PSQ is linked to study outcomes. Consequently, we aimed to compare the health effects of HM exposure between EWWs and CDAs in Northeastern Thailand, investigate factors associated with physical and mental health symptoms, and examine the role of PSQ as a contributing factor to these health outcomes.

METHODS

Study Design and Participants

A cross-sectional study was conducted from September 2023 to October 2023 in the Ban-Kok subdistrict of the Kuengnai district, Ubon Ratchathani Province, in Northeastern Thailand. This e-waste site was selected as the study area because it is one of the 5 communities in Thailand where most individuals are employed in the disassembly of electrical appliances. The study employed a purposive sampling method to explore whether CDAs experience similar health effects from HM exposure as EWWs, despite not being directly involved in e-waste dismantling. The study population included 164 EWWs who were involved in the disassembly of electrical appliances at the Ban-Kok site, as well as 164 non-EWWs (that is, CDAs) who worked in and around the dismantling site but were not engaged in e-waste–related tasks. The inclusion criteria for EWWs required at least 1 year of experience in the occupation. For CDAs, participants needed to have resided in the study area for more than a year. Both groups had to be 18 years of age or older. The exclusion criteria ruled out participants with a preexisting clinical diagnosis of a mental disorder, as identified through records from the Ban-Kok Health Promoting Hospital, or those who had relocated out of the area. Data were collected through face-to-face interviews conducted by trained interviewers, who were village health volunteers.

Questionnaire and Urine Sample Collection

The questionnaire was divided into 4 sections. The first section gathered various characteristics of the participants, including sex, age, whether a child under 5 years old lived in the house, education level, body mass index (BMI), smoking status, alcohol consumption, whether the participant engaged in exercise, monthly income, the frequency of house-cleaning, the frequency of wiping surfaces with a wet cloth, and distance from the e-waste facility. The second section inquired about physical health symptoms experienced in the past month. It featured 21 self-reported symptoms categorized by 7 organs or systems: respiratory, digestive, eye irritation, central nervous system (CNS), musculoskeletal, cardiovascular, and skin. The third section assessed mental health using the Thai version of the Hospital Anxiety and Depression Scale (Thai HADS). This 14-item instrument is divided into 2 subscales, with 7 items each for anxiety (HADS-A) and depression (HADS-D). Each item is rated on a 4-point Likert scale ranging from 0 to 3 [18]. A subscale score exceeding 7 was considered indicative of anxiety or depression [19]. The fourth section assessed PSQ using the Thai version of the Pittsburgh Sleep Quality Index (Thai-PSQI) [20]. Urine samples were collected from participants at the end of their last work shift of the week. Each participant provided approximately 50 mL of urine in a polypropylene sample vessel. The samples were stored at 20°C and transported to the Bangkok Occupational Medicine Center, an authorized laboratory for HM analysis.

Laboratory Testing

Measurement of creatinine levels

Creatinine levels in the urine were quantified using the Jaffe reaction. In this method, creatinine reacts with picric acid in an alkaline solution to produce an orange color. Following a 15-minute incubation period at room temperature, the absorbance was measured at 520 nm to determine the creatinine concentration, which was reported in grams per liter (g/L).

Urinary analysis of HMs

The metal concentrations in the urine samples were adjusted for urinary creatinine concentrations. HMs in the urine samples were analyzed using a graphite furnace atomic absorption spectrophotometer (Spectra AA220Z; Varian Medical Systems, Palo Alto, CA, USA), following calibration with standard solutions. The concentration of each HM was reported in micrograms per liter (μg/L). The limit of quantitation (LOQ) was determined for each HM by calculating the standard deviation of the concentration in the blank sample. The LOQs for Pb and Cd in urine were established at 3 μg/L and 0.5 μg/L, respectively.

Statistical Analysis

Data were analyzed using Stata version 15 (StataCorp, College Station, TX, USA). Descriptive statistics were employed to summarize the demographic characteristics of the participants. Categorical variables were compared using the chi-square and Fisher exact tests. Due to the non-normal distribution of the variables, the Mann-Whitney U-test was utilized to compare urinary HM levels between participants. Logistic regression analysis was performed to identify factors associated with symptoms, with results reported as crude odds ratios (cORs), adjusted odds ratios (aORs), and their respective 95% confidence intervals (CIs). In the initial multivariable analysis model, candidate variables included factors with a p-value ≤0.2 in the univariate analysis, as well as key covariates such as participant sex and age. The final model was established through a backward elimination process. Initially, all variables with a p-value ≤0.2 were included; subsequently, they were removed in an iterative manner until only those with statistical significance (p≤0.05) remained. Noteworthy variables, such as exposure to Pb, were kept in the final model for skin disorders to investigate potential associations. Similarly, exposure to Cd was retained in the final model for depression, in line with previous research indicating a link with depression.

Ethics Statement

This research was approved by the Ethical Review Committee for Human Research at the Faculty of Public Health, Chiang Mai University (ET024/2566).

RESULTS

Participant Demographics

The study included 328 participants: 164 EWWs and 164 CDAs. Table 1 presents their characteristics. Among the EWWs, 75 were male (representing 55.56% of all male participants), while of the CDAs, 104 were female (53.89% of the female participants). Significant age differences were noted between the groups: 84 EWWs were under 50 years old, representing 63.16% of that age group, while 115 CDAs were 50 years old or older (58.97% of that demographic). Additionally, of the participants with a monthly income of >5000 baht, most were EWWs (123 EWWs [73.65%] vs. 44 CDAs [26.35%]). Conversely, more CDAs exercised regularly than EWWs, at 136 CDAs (55.74% of those reporting regular exercise) and 108 EWWs (44.26%). CDAs also tended to live farther from e-waste shops, with 115 living more than 400 meters away (57.84% of the participants in this distance subgroup) compared to 113 EWWs (42.16%). Moreover, more CDAs had a PSQI score above 5 compared with EWWs (80 CDAs [59.26% of those in this score subgroup] vs. 55 EWWs [40.74%]). Regarding mental health, CDAs exhibited higher levels of depression and anxiety, as indicated by HADS scores greater than 7, compared to EWWs. Specifically, 21 CDAs exhibited depression—representing 75.00% of all participants with depression—compared to only 7 EWWs (25.00%), while 26 CDAs displayed anxiety (65.00% of participants with this condition) compared to 14 EWWs (35.00%). Other characteristics showed no significant differences between the groups.

Urinary Heavy Metal Levels in Participants

The median urinary Pb level for all participants was 17.15 μg/g creatinine. Specifically, the level for EWWs was 17.20 μg/g creatinine, while that for CDAs was 12.40 μg/g creatinine. The median Cd level across all participants was 0.60 μg/g creatinine, with EWWs and CDAs displaying levels of 0.60 μg/g creatinine and 0.65 μg/g creatinine, respectively. No statistically significant difference was observed in the median levels of Pb or Cd between the 2 groups. However, a significantly higher percentage of EWWs had urinary Pb levels above the LOQ (≥3 μg/L) compared to CDAs. In contrast, the percentage of participants with urinary Cd levels above the LOQ (≥0.5 μg/L) did not differ significantly between groups (Table 2).

Health Symptoms Reported in the Month Preceding the Interview

The most frequently affected human body system was musculoskeletal, with musculoskeletal disorders (MSDs) reported by 127 participants (38.70%). Of these, 108 (85.04%) were EWWs and 19 (14.96%) were CDAs. Muscle pain was the most prevalent symptom, affecting 123 participants (37.50%), with 104 (63.40%) of the total EWWs and 19 (11.60%) of the CDAs experiencing this issue. Similarly, the CNS was affected in 120 participants (36.60%), with 96 (80.00%) being EWWs and 24 (20.00%) CDAs. Headache was the most common CNS symptom, reported by 95 participants (29.00%), including 84 EWWs (51.20%) and 11 CDAs (6.70%). Additionally, hand-and-foot numbness was reported by 67 participants (20.40%), with 52 EWWs (31.70%) and 15 CDAs (9.10%) affected. Skin disorders were reported by 102 participants (31.10%), of whom 90 (88.24%) were EWWs and 12 (11.76%) were CDAs. Increased sweating was the most prevalent skin-related symptom, affecting 69 participants (21.00%); this included 62 EWWs (37.80%) and 7 CDAs (4.30%). Skin rash was reported by 61 participants (18.60%), consisting of 56 EWWs (34.10%) and 5 CDAs (3.00%) (Table 3).

Potential Factors Associated With Disorders of Human Body Systems

The results of the initial model, with associated factors, are shown in Supplemental Materials 1–3. Multivariable logistic regression analysis revealed several statistically significant factors for each system studied. For MSDs, these factors included EWW status (aOR, 13.26; 95% CI, 7.05 to 24.95), exposure to Pb at levels ≥3 μg/L (aOR, 3.73; 95% CI, 0.90 to 15.54), and exposure to Cd at levels ≥0.5 μg/L (aOR, 1.71; 95% CI, 0.95 to 3.10) (Supplemental Materials 1). Regarding CNS symptoms, significant factors included EWW status (aOR, 12.09; 95% CI, 6.28 to 23.26), female sex (aOR, 1.77; 95% CI, 1.02 to 3.07), using a wet cloth to wipe surfaces more than twice a week (aOR, 0.48; 95% CI, 0.24 to 0.95), and a PSQI score greater than 5 (aOR, 3.14; 95% CI, 1.73 to 5.71) (Supplemental Materials 2). For skin disorders, significant factors included EWW status (aOR, 14.46; 95% CI, 7.01 to 29.79) and a BMI greater than 25 kg/m² (aOR, 2.06; 95% CI, 1.13 to 3.77) (Supplemental Materials 3).

In the final model, variables that were not significant or notable were excluded from the multivariable logistic regression analysis, with the results presented in Table 4. For MSDs, EWW status (aOR, 14.74; 95% CI, 8.04 to 27.00), exposure to Pb at levels ≥3 μg/L (aOR, 4.11; 95% CI, 1.04 to 16.22), and exposure to Cd at levels ≥0.5 μg/L (aOR, 1.80; 95% CI, 1.01 to 3.19) were highly significant. However, female sex (aOR, 1.04; 95% CI, 0.60 to 1.81) and an age of 50 years or older (aOR, 1.09; 95% CI, 0.62 to 1.91) did not show significant differences but were retained in the final model as notable variables. For CNS symptoms, EWW status (aOR, 12.90; 95% CI, 6.87 to 24.21), female sex (aOR, 1.76; 95% CI, 1.02 to 3.03), using a wet cloth to wipe surfaces more than twice a week (aOR, 0.49; 95% CI, 0.25 to 0.96), and a PSQI score greater than 5 (aOR, 3.21; 95% CI, 1.77 to 5.83) were highly significant. Age of 50 years or older (aOR, 1.13; 95% CI, 0.65 to 1.99) did not show a significant difference, yet was included in the final model. Finally, regarding skin disorders, EWW status (aOR, 14.97; 95% CI, 7.53 to 29.76) and a BMI greater than 25 kg/m² (aOR, 2.03; 95% CI, 1.13 to 3.65) were highly significant. However, female sex (aOR, 0.80; 95% CI, 0.46 to 1.41), an age of 50 years or older (aOR, 0.81; 95% CI, 0.46 to 1.43), and exposure to Pb at levels ≥3 μg/L (aOR, 1.97; 95% CI, 0.65 to 5.93) did not show significant differences but were retained in the final model.

Potential Factors Associated with Mental Health

Our study incorporated monthly income into the mental health model, with the findings presented in Table 5. Multivariable logistic regression analysis identified several statistically significant factors associated with depression: CDA status (aOR, 3.61; 95% CI, 1.16 to 11.27), an education level higher than primary school (aOR, 0.29; 95% CI, 0.10 to 0.88), cleaning the house more than 3 times per week (aOR, 2.96; 95% CI, 1.14 to 7.71), a PSQI score greater than 5 (aOR, 4.80; 95% CI, 1.76 to 13.14), and exposure to Cd at levels ≥0.5 μg/L (aOR, 3.76; 95% CI, 1.15 to 12.35).

In the final model for depression and anxiety, several factors were identified as highly significant for depression. These included CDA status (aOR, 5.45; 95% CI, 1.96 to 15.18), an education level higher than primary school (aOR, 0.31; 95% CI, 0.11 to 0.89), smoking (aOR, 3.96; 95% CI, 1.05 to 14.92), cleaning the house more than 3 times per week (aOR, 3.60; 95% CI, 1.49 to 8.71), and a PSQI score greater than 5 (aOR, 5.34; 95% CI, 1.98 to 14.44). Female sex (aOR, 0.91; 95% CI, 0.34 to 2.45), an age of 50 years or older (aOR, 0.41; 95% CI, 0.15 to 1.11), and exposure to Cd at levels ≥0.5 μg/L (aOR, 2.87; 95% CI, 0.96 to 8.57) were not significantly associated with depression but were retained in the final model. For anxiety, a PSQI score greater than 5 (aOR, 2.07; 95% CI, 1.03 to 4.15) was highly significant. CDA status (aOR, 1.64; 95% CI, 0.80 to 3.36), female sex (aOR, 1.28; 95% CI, 0.64 to 2.56), and an age of 50 years or older (aOR, 1.47; 95% CI, 0.68 to 3.18) were not significantly associated with anxiety yet remained in the final model.

DISCUSSION

This study examines the health impacts of HM exposure on EWWs and non-EWW counterparts (CDAs), identifies factors associated with physical and mental health symptoms, and explores PSQ as a potential contributor to these health outcomes. The findings indicated that working in environments contaminated with HMs can adversely impact the musculoskeletal system, whereas PSQ affects the CNS in these workers. Additionally, depression and anxiety were observed to be common among CDAs.

Our study revealed no significant differences in the urinary levels of Pb and Cd between EWWs and CDAs. Moreover, the concentrations of both metals were within the standard limits established in Thailand. Nevertheless, relative to CDAs, EWWs exhibited a higher rate of exposure to Pb above the LOQ. Similarly, EWWs in Buriram Province, Thailand, have been reported to face higher Pb exposure than other groups [21]. Prior research has also identified the presence of Cd on the skin and elevated levels of Pb in the blood of EWWs, surpassing those found in non-EWW residents of the study area [22].

In the month preceding the interview, participants most frequently reported symptoms associated with MSDs, including muscle pain and weakness. These were followed by CNS issues (headaches, numbness in the hands and feet, seizures, and speech difficulties), and skin disorders like rashes, increased sweating, skin damage, and warts or skin tags. Muscle pain emerged as the most frequently experienced symptom. Similarly, EWWs engaged in informal recycling activities at Agbogbloshie in Ghana have reported a high prevalence and intensity of MSD symptoms, particularly affecting the lower back, shoulders, and knees [23]. These workers, including collectors, dismantlers, and burners, often engage in prolonged activities such as walking, sitting, and standing for 5 or more days a week, with some tasks that involve lifting and carrying loads [24]. Moreover, EWWs commonly experience headaches, with those directly involved in disassembly activities exhibiting significantly more symptoms than other workers [12,25]. A systematic review of health symptoms among EWWs revealed varying prevalences of skin conditions depending on the recycling activities performed, with skin disorders affecting between 87.50% and 100% of workers [26]. A study in Thailand found that EWWs presenting with rashes or itching were statistically more likely to be male, under 40 years old, and have less than a secondary school education [9]. In Accra, Ghana, 83.30% of participants reported experiencing excessive sweating before, during, and after handling and recycling e-waste [25].

The final model of our findings suggests that the accumulation of musculoskeletal pain symptoms among EWWs is linked to environmental exposure levels of Pb and Cd. A previous study demonstrated that chronic exposure to Pb can exacerbate discomfort in the musculoskeletal system. These effects are linked to pain and discomfort in the lower back, upper back, and ankle/foot regions [27]. Additionally, the Pb content in bones tends to increase with age and occupational exposure [28]. In turn, exposure to Cd may cause neurological damage or tissue/muscle injury, which can lead to chronic musculoskeletal pain [29]. Furthermore, environmental exposure to Cd in ambient air has been associated with kidney disease and bone weakening over time [30]. Our research focused on symptoms observed in the month prior to the interview and did not consider the number of years the EWWs had worked with e-waste. Consequently, future studies should investigate the relationship between symptoms and the duration of EWW exposure. Additionally, PSQ was found to be associated with CNS symptoms. Prior research has indicated that PSQ can significantly impact neurobehavioral functions. A study conducted in Hong Kong found that EWWs with PSQ were more likely to experience neurobehavioral changes than those without PSQ [13].

Our findings indicate that a BMI greater than 25 kg/m² is associated with skin disorders. This observation aligns with previous research demonstrating a causal relationship between higher BMI and an increased risk of inflammatory skin conditions such as psoriasis and eczema, which frequently involve skin irritation [31]. Moreover, obesity appears to exacerbate various inflammatory skin conditions due to adverse changes in skin physiology, endocrine imbalances, metabolic deviations, altered circulation, and disturbances in the skin microbiome [32]. Additionally, exposure to Pb has been identified as a contributing factor to conditions that impact skin disorders. Specifically, it can lead to decreased skin hydration and elasticity, even in skin that appears normal, suggesting that Pb has detrimental effects on the skin’s biophysical properties [33].

Our investigation revealed that CDAs exhibited higher depression scores than EWWs, particularly among males, those under 50 years of age, and those with a primary school education or less. CDAs may clean their living spaces frequently in response to concerns about their environment. These conditions could contribute to increased rates of smoking and PSQ, potentially resulting in depression. Furthermore, a significantly lower percentage of CDAs than EWWs reported monthly incomes exceeding 5000 baht, which may also have contributed to the higher prevalence of depression within this group. Regarding anxiety, for female participants and those aged 50 years and older, CDAs exhibited higher scores than EWWs. This difference may be attributed to PSQ, as we have been unable to determine the specific issues affecting these individuals. Further research, including in-depth interviews, is necessary to identify the factors contributing to mental health issues among CDAs.

The limitations of this study include its cross-sectional design, which constrained our ability to establish causal relationships between risk factors and outcomes, such as the potential inverse association between the duration of exposure to HMs and symptoms. Additionally, the reliance on self-reported data for symptoms, PSQ, and mental health information may have introduced underestimations and recall bias. Furthermore, this study was conducted at a single site in Northeastern Thailand, potentially limiting the generalizability of the findings to other regions. Finally, the study sample was drawn from a specific subpopulation, which is not representative of the general population due to the selection process known as Berkson bias. This type of selection bias can lead to misleading conclusions about the strength or existence of relationships between variables. Despite these limitations, the study explores the effects of HM exposure on physical and mental health, with a focus on sleep quality. It offers a comprehensive assessment of the health effects on populations involved in disassembling electrical appliances in residential areas.

In conclusion, our findings revealed that EWWs face a higher risk of MSDs, CNS symptoms, and skin disorders compared to CDAs. In contrast, CDAs experience a greater risk of depression. Environmental exposure to Pb and Cd can adversely impact musculoskeletal health. Furthermore, PSQ contributes to CNS symptoms and depression. Health-related sectors should implement monitoring and prevention strategies to address these adverse health outcomes among EWWs and CDAs. Future research could focus on preventive protection factors to reduce environmental exposure among EWWs. A qualitative study is warranted to clarify the relationship between sleep quality and depression among CDAs.

Supplemental Materials

Notes

Conflict of Interest

The authors have no conflicts of interest associated with the material presented in this paper.

Funding

None.

Acknowledgements

The authors extend their gratitude to all participants of this study, to the Faculty of Public Health at Chiang Mai University for providing resources and materials, and to the village health volunteers in the Ban-Kok subdistrict for their support with local surveillance data.

Author Contributions

Conceptualization: Sangwijit C, Ong-artborirak P, Siewchaisakul P. Data curation: Sangwijit C. Formal analysis: Sangwijit C, Siewchaisakul P. Funding acquisition: None. Methodology: Sangwijit C, Ong-artborirak P. Project administration: Ong-artborirak P, Siewchaisakul P. Visualization: Siewchaisakul P. Writing – original draft: Sangwijit C, Ong-artborirak P. Writing – review & editing: Naksen W, Kallawicha K, Siewchaisakul P.

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