The 26 included articles consisted of 23 cross-sectional studies [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24], 1 scoping review [34], 1 nested case-control study [33], 1 longitudinal cohort study [18] and comprised a publication period from 2008 to 2021. Geographically, the included studies have been conducted in the following regions:

Africa: Nigeria [19, 32, 34, 36], Ghana [18, 20,21,22,23,24,25,26, 30, 31, 34],

Asia: Vietnam [28, 29], China [38, 39, 41,42,43], Thailand [27, 33, 35, 37], India [34],

South America: Chile [40].

We often found overlaps of health outcomes across categories. Therefore, we assigned each study to the category most likely to apply and the outcomes of the study to the relevant section. We grouped the studies into 7 categories depending on the mainly investigated organ system/health effect to provide a better overview of the results.

A total of 9 cross-sectional studies on the potential effects of exposure to e-waste and hormonal health were identified (Table 2). In 2014 and 2015 Eguchi et al. analysed concentrations of thyroid hormones (THs) in serum samples from e-waste workers in Vietnam and found FT3, TT3 [28, 29] and TT4 concentrations [29] to be significantly lower than the samples from the control group living at a rural site [28, 29]. The multiple linear regression showed a significant association between specific circulating TH levels and organic contaminants (OC) [43].

Wang, H. et al. [38] included a third group of residents with environmental exposure, but without direct exposure through work in the informal e-waste sector, additionally to the e-waste worker and the control group of completely unexposed individuals. Significantly lower serum T3, fT3 and fT4 levels in e-waste workers and residents were found compared to the control group (p < 0.001) [38].

Yuan et al. reported significantly higher median level of serum TSH in e-waste workers [41]. A stepwise multivariate logistic regression analysis proofed previous exposure to e-waste and gender to be independent statistically significant predictors of serum TSH levels [41].

Focusing on lipid metabolism, Igharo et al. [32] investigated the lipid profile and atherogenic indices of e-waste workers in Nigeria. In comparison to the control group, the results of the lipid profile showed a significant increase in both total cholesterol and LDL cholesterol among e-waste workers. Notably, Atherogenic coefficient (AC), Castelli’s Risk index I and II (CRI-I and CRI-II) were significantly increased in e-waste workers [32].

They also examined serum samples from male e-waste workers for different fertility hormones. These hormones such as LH, FSH, Testosterone, Prolactin, Progesterone and Oestrogen were significantly lower in the serum of e-waste workers when compared to the control group while Inhibin was significantly elevated [19].

The male reproductive health of male e-waste workers was also analysed by Wang, Y et al. [39] and found to be negatively affected in terms of sperm quality. Sperm volume, number and motility were inversely proportional to the duration of handling e-waste and significantly lower in the e-waste workers than in the control group. Wang et al. identified exposure time, total polychlorinated biphenyls (PCBs), malondialdehyde (MDA) and Pb as predominant risk factors for semen quality [39].

Two studies focused especially on respiratory health in e-waste workers [18, 33] (Table 3). In their longitudinal cohort study, Nti et al. measured the effects of particulate matter exposure on the lung function of 207 study participants using spirometry in Ghana. The regression analysis showed a significant change only in the PM10, PM2.5–10 fraction and the lung function parameter FEF25–75 [18]. Kuntawee and colleagues conducted a nested case-control study with asthmatic and non-asthmatic people from an e-waste recycling site and a control area. They couldn’t associate personal characteristics and occupational factors to asthma, but ‘years of work’, showed a statistically significant association to a higher likelihood of asthma [33].

In several studies conducted in India, Ghana and Thailand difficulties in breathing [34], as well as cough (also with sputum) [30, 34, 37], chest pain and other respiratory problems were significantly more frequently reported in e-waste workers than in controls [34].

In Ghana, no significant changes in serum creatinine and eGFR were detected in a cross-sectional study at an e-waste recycling site between e-waste workers and control group [30]. (Table 4).

Neitzel et al. [35] performed blood tests with a focus on renal markers in e-waste workers in Thailand, where differing GFR values didn’t prove to be gender-specific significant, but they were found to be significantly correlated with lower lead and cadmium blood levels in females (Table 4). A regression analysis of GFR and lead exposure showed a significant positive correlation among informal e-waste workers [35].

Concerning cardiovascular symptoms, abnormal heart beating was noted throughout various studies, [25, 34, 37, 40] (Tables 5, 8). chest pain was reported significantly more (25.3%) in e-waste worker than within the control group [30] (Tables 4, 5, 8). Adusei et al. measured hypertension across activity spaces in e-waste workers (without control group), which was most common in collectors (17.1%), followed by burners (9.1%) and dismantlers (7.7%) [22]. High blood pressure was also diagnosed among workers in other studies [25, 34] (Tables 7, 8). Diabetes, hypertension, shortness of breath and other cardiac symptoms showed no significant differences between e-waste workers and a control group in the study of Fisher et al. [31] (Table 8).

A personal noise measurement over a 24-hour period and a hearing assessment was aimed to assess the hearing capacity of e-waste workers in Agbogbloshie, Ghana. The presence of a noise notch was positive for both ears for 32%, for the right/left ear only in 20%/18% and negative for 40% of the examined EWW (Table 6) [26]. Self-reported hearing difficulties were recorded in 2 studies at 26% [25, 26] and data on self-reported exposure to noise at work varied between 84.5% [26], 87% [24] and 95.9% [25] (Table 5). Burns et al. note that 24.6% of the e-waste workers experienced tinnitus very often and 3.5% of EWW were diagnosed with hearing loss. Difficulties in hearing were furthermore self-reported by 26.3% in a study conducted in Ghana [34] (Table 8).

Acquah et al. investigated musculoskeletal disorder symptoms among EWW in Agbogbloshie using the Cornell Musculoskeletal Discomfort Questionnaire (CMDQ). 90% of the e-waste workers reported heavy load handling, as well as 79% daily lifting, long walking (53%) and carrying (77%) [20] (Table 7).

Aquah and colleagues [21] calculated a pain score considering the body regions with statistically significant differences between e-waste workers and a control group for the lower and upper extremity as well as for the whole body. Comparing discomfort and pain prevalence for e-waste workers across body regions, discomfort prevalence was highest in the lower back area [21, 34]. For knees, lower legs, and upper arms, chi-square tests revealed statistically significant differences in discomfort prevalence by job category, with the highest discomfort prevalence primarily among collectors [20]. (Tables 7, 8).

General body pain was identified as a major health problem within Mishra’s research [34]. In Ghanaian e-waste workers pain scores for upper extremities were significantly higher [20], as well as back pain (including neck) and work-related injuries compared to the control group [31] (Table 8). That also showed the high injury prevalence in a study carried out in Nigeria on three e-waste sites. 89% of the e-waste workers had been injured at some time and 38% in the last 1–2 weeks [36] while 7% were hospitalized [24]. With 96% [34], 59.5% [36] and 65% [24] cuts were the most common type of injury as also burns [34], while hand, or fingers were the most frequently injured body part with 73% [36] and 46% [24]. 40% of EWW in Ghana reported occupational accidents [34]. The job category as a risk factor associated with injuries occurring within 6 months was confirmed with statistical significance [36]. Adusei et al. also investigated the prevalence of skin conditions i