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Table 9 Risk of bias assessment

From: Bridging the knowledge gap! Health outcomes in informal e-waste workers

Study

Internal validity – bias

Internal validity – confounder

Perfor-mance bias

Detection bias

Attrition bias

Reporting bias

Selection bias

Igharo et al. [2018]

high

low

low

high

low

low

high

Armah et al. [2019]

low

low

low

high

low

low

high

Eguchi et al. [2015]

low

low

low

low

low

low

high

Zheng et al. [2017]

low

low

low

low

low

low

high

Nti et al. [2020]

low

high

low

low

low

low

high

Yuan et al. [2008]

low

low

low

low

low

low

high

Wang, H et al. [2010]

low

low

low

low

low

low

high

Kuntawee et al. [2020]

low

low

low

low

low

low

low

Wang, Y et al. [2018]

low

low

low

low

low

low

high

Yohannessen et al. [2019]

low

low

low

low

low

low

high

Fischer et al. [2020]

low

low

low

low

low

low

high

Burns et al. [2016]

low

low

low

low

low

low

high

Feldt et al. [2014]

low

low

low

low

low

low

high

Igharo et al. [2020]

high

high

low

low

low

low

high

Zhao et al. [2021]

low

low

low

low

low

low

high

Carlson et al. [2021]

low

low

low

low

low

low

high

Neitzel et al. [2020]

low

low

low

low

low

low

high

Acquah et al. [2021]

low

high

low

high

low

low

high

Eguchi et al. [2014]

low

low

low

low

low

low

high

Mishra [2019]

n.a.

n.a.

n.a.

low

low

low

n.a.

Ohajinwa et al. [2017]

low

low

low

high

low

low

high

Seith et al. [2019]

low

low

low

low

low

low

high

Adusei et al. [2020]

low

low

low

low

low

low

high

Burns et al. [2019]

low

low

low

low

low

low

high

Decharat [2018]

high

low

low

low

low

low

high

Acquah et al. [2021]

low

high

low

high

low

low

high

high risk count

3

4

0

5

0

0

24

share of high risk

12%

15%

0

19%

0

0

92%