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. Author manuscript; available in PMC: 2021 Apr 1.
Published in final edited form as: Environ Int. 2020 Feb 18;137:105580. doi: 10.1016/j.envint.2020.105580

Birth outcomes associated with maternal exposure to metals from informal electronic waste recycling in Guiyu, China

Stephani S Kim a, Xijin Xu b, Yuling Zhang b, Xiangbin Zheng b, Rongju Liu b, Kim N Dietrich a, Tiina Reponen a, Changchun Xie a, Heidi Sucharew c, Xia Huo d,*, Aimin Chen a,*
PMCID: PMC7257595  NIHMSID: NIHMS1563611  PMID: 32078870

Abstract

Background:

Informal electronic waste (e-waste) recycling is a rapidly growing industry. Informal e-waste recycling creates a mixture of chemicals that can be harmful to humans, especially vulnerable populations like pregnant women and young children.

Objectives:

We aimed to analyze the associations between birth outcomes and living in a community with a history of informal e-waste recycling.

Methods:

The e-waste Recycling Exposure and Community Health (e-REACH) Study enrolled pregnant women in Guiyu, an informal e-waste recycling site (n=314), and an unexposed control site (Haojiang) (n=320) at delivery. We analyzed maternal whole blood samples for lead (Pb), cadmium (Cd), chromium (Cr), and manganese (Mn). We captured data in newborns on birth weight, birth length, head circumference, body mass index (BMI), and Ponderal Index (PI). We compared the birth outcomes between sites after adjustment for covariates, and examined the associations with individual and the mixture of metals.

Results:

The neonates from Guiyu had smaller head circumference (adj β −1.96 cm, 95% CI − 2.39, −1.52), BMI (adj β −0.77 kg/m2, 95% CI − 1.03, −0.51), and PI (adj β −2.01 kg/m3, 95% CI − 2.54, −1.47). Birth weights were lower in Guiyu compared to Haojiang, but the difference was not significant (β − 51, 95% CI −132, 29). Cumulative exposure to metals was related to lower head circumference, BMI, and PI, but not related to birth weight.

Discussion:

We observed slight and statistically significant differences in the head size, BMI, and PI of neonates, but not birth weight, from Guiyu when compared to neonates from Haojiang. Cumulative metal exposure may partially account for the findings.

Keywords: electronic waste, recycling, metals, birth outcomes

1. Introduction

Electronic waste, or e-waste is a broad term that describes discarded consumer products that have an electrical or electronic component. It is currently the fastest growing solid waste stream in the world with the United Nations Environmental Programme (UNEP) and the Basel Action Network (BAN) estimating that the majority of it is either illegally traded or dumped (Hopson and Puckett 2016; UNEP 2007). There are safe and efficient ways to recycle e-waste; however, the majority ends up in developing countries that use informal (or primitive) recycling methods (Hopson and Puckett 2016; Robinson 2009). Informal e-waste recycling consists of sorting and dismantling products by hand, acid baths to obtain precious metals, and burning to get metals or to dispose of unwanted plastic components. These informal recycling methods pose a potential environmental and human health problem because consumer electronic products contain a variety of toxic chemicals, including but not limited to lead (Pb), cadmium (Cd), hexavalent chromium (Cr [VI]), manganese (Mn), mercury (Hg), and brominated flame retardants (e.g., polybrominated diphenyl ethers [PBDEs]) (Song and Li 2014; UNEP 2007). The informal recycling processes, for example, burning, also create additional chemical byproducts, such as polycyclic aromatic hydrocarbons (PAHs) and dioxins. Exposure to these chemicals from informal e-waste recycling could lead to various adverse health outcomes in workers and residents living in proximity to recycling workshops, especially in vulnerable populations like pregnant women and their developing fetuses (Chen et al. 2011; Grant et al. 2013; Heacock et al. 2016).

Prenatal exposure to Pb and Cd has been associated with low birth weight and adverse neurodevelopment in children, along with other adverse pregnancy and birth outcomes (Kim et al. 2013; CC Lin et al. 2013; Nishioka et al. 2014; Sun et al. 2014; Xie et al. 2013). Although Cr(VI) is a known human carcinogen from occupational studies, research on reproductive outcomes from in utero exposure is scarce (Pellerin and Booker 2000). Mn is an essential nutrient for humans, however associations between higher exposure levels and adverse neurodevelopmental outcomes have been observed in children, but as with Cr, the research on high Mn exposure during pregnancy and associated outcomes is limited (CC Lin et al. 2013; Rugless et al. 2014; Vollet et al. 2016). Pb, Cd, Cr, and Mn can pass through the placenta to the fetus, some more readily than others (Chen et al. 2014; Gundacker and Hengstschlager 2012).

Guiyu, a town of 150,000 residents in southeast China, has been recycling e-waste since the 1980s, with approximately 75% of the households involved in the workshops (CS Wong et al. 2007a; MH Wong et al. 2007). The former agricultural town found better financial security with e-waste and workshops have employed an additional 100,000 migrant workers from other regions of China (CS Wong et al. 2007a; CS Wong et al. 2007b; MH Wong et al. 2007). The widespread recycling of e-waste in Guiyu for decades has created a town contaminated with high concentrations of Pb, Cd, PAHs, PBDEs, and other chemicals in the air, soil, dust, and water (Wang et al. 2005; CS Wong et al. 2007a; CS Wong et al. 2007b; Yu et al. 2006). Residents of Guiyu were also found to have high concentrations of metals in their bodies, specifically the children. Several studies out of Guiyu observed elevated blood Pb (BPb) in children (Huo et al. 2007). Additional assessments have observed elevated blood Cd in neonates and preschool-aged children, along with elevated concentrations of Pb and Cr (Li et al. 2008; Li et al. 2011; S Lin et al. 2013; Zheng et al. 2008). In nearly all the studies, children provided blood to measure postnatal and childhood exposure. However, including the mother’s exposure at delivery would give a better profile of the prenatal exposures for potential reproductive and developmental toxicity. The potential impact on birth outcomes from environmental exposures to these chemicals remains to be characterized in communities recycling e-waste with informal facilities and methods.

Over the course of 2011–2013, the e-Waste Recycling Exposure and Community Health (e-REACH) Study collected biological samples from pregnant women and their neonates, along with environmental samples from air, soil, and road dust. The results from the analysis of metal concentrations in the environmental and biological samples have been published (Kim et al. 2018; Yekeen et al. 2016; Zheng et al. 2016). A previous subset analysis of urinary cadmium and birth outcomes suggested adverse impact in female neonates (Zhang et al. 2018). This paper will focus on birth outcomes and their associations with the maternal blood concentrations of four metals in the full dataset, investigating both individual and mixture of chemicals.

2. Methods

The e-REACH Study enrolled pregnant women from Guiyu (n=314) and Haojiang (n=320), an e-waste unexposed site. Both sites are in Shantou, Guangdong Province in southeast China. Haojiang (a district of Shantou city) is located approximately 25 miles from Guiyu and has no history of informal e-waste recycling. We collaborated with a local hospital from each site. The women were eligible to participate if they were 18 years or older with a singleton pregnancy, had lived in their respective town for the duration of their pregnancy, and consented to participate in the study. Women were excluded if they had a multiple pregnancy, used assistive reproductive technology to become pregnant, had a history of psychiatric or thyroid disorders, or lived outside of their respective town for a cumulative of three months or more during their pregnancy. This study was approved by both Institutional Review Boards at Shantou University Medical Center and the University of Cincinnati College of Medicine.

The study nurses consented and enrolled pregnant women when they arrived at the hospital for delivery. The women answered a questionnaire that captured demographic information, medical history, family history, reproductive history, and environmental exposure to informal e-waste recycling at enrollment. The study nurses used an electronic neonate-weighing scale to measure birth weight, a calibrated length board for birth length, and a measuring tape for head circumference (Zhang et al. 2018). They also completed additional questions on the health of mother and neonate, including pregnancy and delivery complications, using medical records. The study nurses collected maternal blood while the women were at the hospital for delivery. The biological specimens were stored at −20°C at the Laboratory of Environmental Medicine and Developmental Toxicology at Shantou University Medical College until assay. Subsequently, they were analyzed for Pb, Cd, Cr, and Mn concentrations using graphite furnace atomic absorption spectrometry (GFAAS) in the lab, as previously described (Kim et al. 2018).

2.1. Statistical analysis

Gestational age was calculated based on last menstrual period (LMP) and the date of delivery. Newborn body mass index (BMI) and Ponderal Index (PI) were calculated using the recorded birth weight and birth length. The mean and standard deviation (SD) were calculated for gestational age (weeks), birth weight (g), birth length (cm), head circumference (cm), BMI (kg/m2), and PI (kg/m3). We used multiple linear regression models to calculate unadjusted and adjusted differences (β) and 95% confidence intervals (CI) of birth outcomes between Guiyu and Haojiang. Models were adjusted for maternal age, maternal education, maternal occupation, maternal pre-pregnancy BMI, gravidity, environmental tobacco smoke (ETS), and neonate sex.

We calculated odds ratios (OR) for small-for-gestational-age (SGA) and preterm birth in association with site, Guiyu and Haojiang, using multiple logistic regression models adjusted for the same covariates described above. We defined SGA separately based on sex- and gestational-age-specific birth weight less than the 10th percentile of a Chinese fetal growth curve since Chinese neonates are approximately 100 g smaller than US-born neonates (Mi et al. 2002). Preterm birth was defined as birth before 37 weeks’ gestation.

Using the natural log-transformed values for Pb, Cd, Cr, and Mn concentration in maternal blood as independent variables, we calculated unadjusted and adjusted differences and 95% CI for birth weight, head circumference, BMI, and Ponderal Index using multiple linear regression models with all the metals mutually adjusted and the same covariates described above. We mutually adjusted the metals in the model because they were low to moderately correlated (Kim et al. 2018). We calculated the unadjusted and adjusted ORs and 95% CI for SGA by metal concentrations. We repeated the metal and birth outcome analyses after stratification by site to observe if the associations were similar while the exposure levels may differ.

To assess both individual and mixture of metals in relation to each of the birth weight, head circumference, BMI, and Ponderal Index at birth, we used Bayesian Kernel Machine Regression (BKMR) without assumption of linearity of the associations. BKMR models flexible function of four metal concentrations while adjusting for the same covariates described above (Bobb et al. 2015). We converted the natural log-transformed metal concentrations and continuous covariates to standard normal distribution. We estimated: a) outcome change for an increase of a single metal from the 25th to the 75th percentile (interquartile range, IQR) while other three metals were held at either the 25th, 50th or 75th percentile; b) bivariate exposure-response function of two metals if the second metal was fixed at the 25th, 50th, or 75th percentile; c) cumulative impact of all four metals at a particular percentile from the 25th to the 75th percentile at an interval of 5th compared with a reference while all four metals were at the 50th percentile.

For sensitivity analyses, we stratified by the sex of the neonate to examine if the associations differ in boys and girls. We investigated whether a subset of neonates whose parents were involved in e-waste recycling had different birth outcomes from the other neonates in Guiyu. Additionally, we examined if migrant status would affect the birth outcomes in the study. All statistical analyses were performed using SAS 9.4 (SAS Institute Inc.) and R 3.6.1 “bkmr” package (R Development Core Team).

3. Results

Table 1 describes the geometric mean (GM) and range of metal concentrations in maternal blood by demographic characteristics and by site, Guiyu and Haojiang. Mothers in Guiyu were younger, had lower education, and higher gravidity. We observed higher concentrations of Pb, Cd, and Cr in maternal blood in Guiyu, but blood Mn concentrations were higher in the control site. Overall, blood Pb and Cd concentrations increased with age in both sites. Blood Pb and blood Cr concentrations increased with higher gravidity in Guiyu but remained steady in Haojiang. Nearly half the women in both Guiyu (52.9%) and Haojiang (47.2%) were exposed to ETS during pregnancy, however, blood Cd concentrations did not differ between those who were exposed to ETS and those who were not in both sites. Only three women reported active smoking in this study.

Table 1.

Geometric mean (GM) and range of maternal blood metal concentrations by demographic characteristics in Guiyu and Haojiang

N (%) N (%) Blood Lead (μg/dL) Blood Cadmium (μg/L) Blood Chromium (μg/L) Blood Manganese (μg/L)
Characteristics Guiyu Haojiang Guiyu Haojiang Guiyu Haojiang Guiyu Haojiang Guiyu Haojiang
Age Group
 18–24 years 119 (37.9) 58 (18.1) 6.3 (2.5–21) 3.5 (1.2–7.1) 1.6 (0.3–4.1) 1.2 (0.1–7.0) 14 (2.4–141) 9.8 (5.7–175) 25.5 (9–46.4) 30.5 (12–131)
 25–29 years 116 (37.0) 147 (45.9) 6.8 (1.9–26) 3.8 (0.9–16) 1.7 (0.3–4.8) 1.3 (0.1–4.4) 13.6 (3.1–49) 8.7 (4.4–114) 26.4 (8.4–320) 28 (4.4–171)
 >30 years 79 (25.2) 115 (36.0) 6.9 (2.5–27) 4.0 (1.7–11) 1.9 (0.3–4.7) 1.5 (0.1–4.3) 13.7 (4.5–189) 8.8 (4.8–45) 26.0 (13–64) 28.2 (8.1–78)
Education
 < Elementary 96 (30.6) 61 (19.1) 7.1 (2.9–27) 3.9 (1.7–8.0) 1.7 (0.3–4.7) 1.4 (0.1–4.1) 12.9 (2.4–189) 9.4 (4.4–44) 26.7 (10–63) 28.7 (4.4–131)
 Junior high 181 (57.6) 158 (49.4) 6.4 (1.9–26) 3.8 (0.9–15) 1.7 (0.3–4.8) 1.3 (0.1–4.4) 14.4 (3.1–141) 9.1 (4.8–175) 25.7 (8.4–321) 29.7 (8.1–171)
 > High school 21 (6.7) 89 (27.8) 6.9 (4.1–20) 3.7 (0.9–16) 1.9 (1.0–3.1) 1.3 (0.1–4.3) 12.9 (3.9–88) 8.3 (5.0–78) 24.9 (11–49) 26.5 (8.2–66)
Occupation
 Farmer 26 (8.3) 47 (14.7) 6.7 (4.0–21) 4.2 (0.9–11) 1.5 (0.3–3.7) 1.4 (0.1–4.1) 13.7 (4.5–33) 9.1 (5.5–44) 28.9 (10–64) 29.0 (14–55)
 Industrial Work 71 (22.6) 55 (17.2) 7.3 (2.9–27) 4.1 (2.2–15) 1.7 (0.7–4.7) > 1.5 (0.5–3.9) 13.6 (3.7–189) 8.8 (5.0–66) 25.2 (12–46) 30.3 (8.1–171)
Managerial/Professional 29 (9.2) 31 (9.7) 6.7 (4.0–20) 3.2 (0.9–5.9) 1.9 (0.9–4.5) 1.5 (0.5–4.3) 14.6 (3.9–88) 8.9 (5.2–31) 25.8 (12–49) 24.6 (8.2–45)
 Unemployed 118 (37.6) 115 (35.9) 6.4 (1.9–20) 3.7 (1.2–16) 1.7 (0.3–4.8) 1.2 (0.1–3.9) 14.8 (3.1–141) 9.2 (4.8–175) 26 (8.4–321) 28.5 (4.4–131)
 Other 30 (9.6) 42 (13.1) 6.7 (3.2–21) 3.8 (2.0–11) 1.7 (0.9–3.0) 1.5 (0.4–3.5) 14.0 (2.4–62) 8.6 (4.4–73) 25.4 (9.0–37) 29.8 (15–67)
Gravidity
 0 pregnancies 64 (20.4) 143 (44.7) 6.1 (2.5–23) 3.8 (0.9–11) 1.6 (0.3–4.1) 1.4 (0.1–7.0) 13.1 (3.5–88) 9.0 (4.4–175) 25 (9–50.4) 27.5 (4.4–131)
 1 pregnancy 105 (33.4) 132 (41.3) 6.8 (1.9–21) 3.8 (0.9–16) 1.7 (0.5–4.5) 1.2 (0.1–4.4) 13.8 (2.4–141) 8.9 (4.8–114) 25.7 (11–44) 29.2 (8.2–171)
 >2 pregnancies 142 (45.2) 44 (13.8) 6.9 (2.5–27) 3.9 (1.7–7.0) 1.8 (0.3–4.8) 1.5 (0.7–4.1) 14.2 (3.1–189) 8.7 (5.8–24) 26.7 (8.4–321) 30.1 (8.1–78)
Environmental tobacco smoke
 No 110 (35.0) 136 (42.5) 7.2 (3.1–26) 3.8 (0.9–16) 1.7 (0.3–4.7) 1.5 (0.1–7.0) 12.8 (2.4–104) 9.0 (5.3–66) 25.0 (11–64) 28.7 (8.2–111)
 Yes 166 (52.9) 151 (47.2) 6.2 (1.9–14) 3.8 (0.9–15) 1.7 (0.3–4.8) 1.2 (0.1–4.4) 14.1 (3.1–141) 8.7 (4.4–114) 25.7 (8.4–44) 29.2 (4.4–171)
Baby Sex
 Male 174 (55.4) 168 (52.5) 6.7 (1.9–23) 3.8 (0.9–15) 1.7 (0.3–4.8) 1.3 (0.1–4.4) 13.5 (2.4–141) 8.8 (4.8–175) 25.2 (8.4–49) 28.6 (8.1–131)
 Female 140 (44.6) 152 (47.5) 6.6 (2.5–27) 3.8 (0.9–16) 1.7 (0.3–4.1) 1.4 (0.1–7.0) 14.1 (3.9–189) 9.0 (4.4–114) 26.9 (11–321) 28.4 (4.4–171)
Total 314 (100) 320 (100) 6.7 (1.9–27) 3.8 (0.9–16) 1.7 (0.3–4.8) 1.4 (0.1–7.0) 13.8 (2.4–189) 8.9 (4.4–175) 25.9 (8.4–320) 28.5 (4.4–171)
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There were slight and statistically significant differences in gestational age, birth length, head circumference, newborn BMI, and Ponderal Index between Guiyu and Haojiang (Table 2). Gestational age was slightly longer in Guiyu compared to Haojiang with an adjusted β of 0.44 week (95% CI 0.21, 0.66), similar to the unadjusted estimate 0.42 week (95% CI 0.23, 0.60). Birth length was also greater in Guiyu with an adjusted β of 1.17 cm (95% CI 0.83, 1.52). We observed lower birth weight in Guiyu than in Haojiang, along with smaller head circumference, however only the difference in head circumference was statistically significant after adjusting for covariates (adjusted β [95% CI] −1.96 cm [−2.39, −1.52]). If additionally adjust for gestational age in the model for birth weight, the difference of birth weight between the two sites was −89 g (95% CI −172, −7). We observed lower MI (adjusted β [95% CI] −0.77 kg/m2 [−1.03, −0.51]) and Ponderal Index (adjusted β [95% CI] −2.01 kg/m3 [−2.54, −1.47]) in Guiyu compared to Haojiang.

Table 2.

Mean, standard deviation, unadjusted and adjusted differences, unadjusted and adjusted odds ratios (OR) and 95% confidence intervals (CI) of birth outcome measures in Guiyu and Haojiang

Birth outcomes Guiyu (Mean ± SD) n = 314 Haojiang (Mean ± SD) n = 320 Unadjusted Differences P (95% CI) Adjusted Differencesa P (95% CI)
Gestational age (weeks) 39.85 ± 0.83 39.43 ± 1.40 0.42 (0.23, 0.60) 0.44 (0.21, 0.66)
Birth weight (g) 3226 ± 424 3263 ± 425 −37 (−104, 29) −51 (−132, 29)
Birth length (cm) 51.65 ± 2.35 50.35 ± 1.02 1.30 (1.01, 1.58) 1.17 (0.83, 1.52)
Head circumference (cm) 33.64 ± 2.88 35.37 ± 1.73 −1.74 (−2.11, −1.36) −1.96 (−2.39, −1.52)
Body mass index (kg/m2) 12.08 ± 1.29 12.86 ± 1.42 −0.78 (−0.99, −0.56) −0.77 (−1.03, −0.51)
Ponderal index (kg/m3) 23.45 ± 2.82 25.52 ± 2.70 −2.08 (−2.51, −1.64) −2.01 (−2.54, −1.47)
n (%) n (%) Unadjusted OR (95% CI) Adjusteda OR (95% CI)
Small for gestational age 29 (9.2) 21 (6.6) 1.54 (0.86, 2.76) 1.17 (0.57, 2.40)
Preterm birthb 23 (7.3) 10 (3.1) 2.45 (1.15, 5.24) 1.67 (0.66, 4.23)
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a

Adjusted for maternal age, maternal education, maternal occupation, maternal BMI, gravidity, environmental tobacco smoke, and baby sex

b

Defined as <37 weeks gestation

The percentage of SGA neonates was greater in Guiyu compared with Haojiang (9.2% vs. 6.6%, respectively) (Table 2). Both unadjusted and adjusted ORs for SGA were not statistically significant. Guiyu had a higher percentage of preterm birth compared to Haojiang (7.3% vs. 3.0%, respectively) with an unadjusted OR of 2.45 (95% CI 1.15, 5.24), though after adjusting for covariates the association was no longer statistically significant (OR 1.67, 95% CI 0.66, 4.23).

The associations between metal concentrations measured in maternal blood and birth weight, head circumference, BMI, and Ponderal Index are shown in Table 3. Blood Pb was associated with smaller head circumference, and lower Ponderal Index. Blood Cd was associated with lower birth weight, BMI and Ponderal Index although only the association for BMI was statistically significant. Blood Cr was associated with lower head circumference but the association was not statistically significant after covariates adjustment. Blood Mn was generally associated with high growth of the baby, albeit non-significantly. In the stratified analysis by site, only blood Pb was associated with higher birth weight in Guiyu, while all other associations were not statistically significant.

Table 3.

Differences in birth weight, head circumference, baby’s body mass index (BMI), and Ponderai index (PI) and 95% confidence intervals (CI) by metal concentrations in maternal blood

Birth Weight (g) Head Circumference (cm) BMI (kg/m2) PI (kg/m3)
β (95% CI) β (95% CI) β (95% CI) β (95% CI)
Unadjusted Adjusteda Unadjusted Adjusteda Unadjusted Adjusteda Unadjusted Adjusteda
Both sites combined
79 60 −0.77 −0.75 −0.16 −0.14 −0.73 −0.62
  lnPb (μg/dL) (6,151) (−15, 135) (−1.19, −0.34) (−1.17, −0.32) (−0.40, 0.08) (−0.39, 0.11) (−1.23, −0.24) (−1.13, −0.11)
−44 −56 −0.20 −0.14 −0.17 −0.21 −0.34 −0.40
  lnCd (μg/L) (−104, 16) (−116, 5) (−0.56, 0.15) (−0.48, 0.21) (−0.37, 0.03) (−0.41, −0.01) (−0.75, 0.08) (−0.82, 0.01)
−7 3 −0.40 −0.28 −0.15 −0.07 −0.39 −0.19
  lnCr fog/L) (−63, 50) (−54, 60) (−0.73, −0.07) (−0.60, 0.05) (−0.33, 0.04) (−0.26, 0.12) (−0.78, −0.01) (−0.58, 0.20)
54 51 0.25 0.26 0.16 0.14 0.25 0.21
  lnMn (μg/L) (−34, 141) (−36, 139) (−0.26, 0.76) (−0.24, 0.75) (−0.13, 0.45) (−0.14, 0.43) (−0.35, 0.85) (−0.39, 0.81)
Guiyu only
164 125 0.29 0.36 0.40 0.29 0.59 0.39
  lnPb (μg/dL) (52, 276) (12, 238) (−0.48, 1.06) (−0.35, 1.07) (0.06, 0.75) (−0.06, 0.64) (−0.17, 1.34) (−0.38, 1.17)
3 −47 −0.12 0.12 −0.00 −0.07 −0.02 −0.05
  lnCd (μg/L) (−106, 112) (−158, 65) (−0.88, 0.63) (−0.58, 0.82) (−0.34, 0.33) (−0.42, 0.28) (−0.76, 0.72) (−0.81, 0.72)
−4 −8 −0.28 −0.22 −0.00 0.01 0.01 0.06
  lnCr fog/L) (−76, 68) (−80, 63) (−0.78, 0.22) (−0.67, 0.24) (−0.23, 0.22) (−0.21, 0.23) (−0.48, 0.49) (−0.43, 0.55)
91 69 0.12 0.49 −0.10 −0.18 −0.63 −0.78
  lnMn (μg/L) (−41, 222) (−62, 200) (−0.79, 1.02) (−0.33, 1.31) (−0.51, 0.31) (−0.59, 0.23) (−1.52, 0.25) (−1.68, 0.12)
Haojiang only
89 66 −0.07 −0.12 0.25 0.20 0.37 0.31
  lnPb (μg/dL) (−41, 218) (−69, 201) (−0.60, 0.46) (−0.67, 0.43) (−0.18, 0.68) (−0.25, 0.65) (−0.46, 1.19) (−0.55, 1.16)
−57 −55 −0.14 −0.15 −0.17 −0.15 −0.30 −0.24
  lnCd (μg/L) (−131, 16) (−133, 22) (−0.45, 0.16) (−0.47, 0.16) (−0.42, 0.07) (−0.40, 0.11) (−0.77, 0.17) (−0.73, 0.25)
31 31 0.18 0.14 0.06 0.04 0.08 0.03
  lnCr fog/L) (−67, 130) (−70, 133) (−0.23, 0.58) (−0.27, 0.56) (−0.27, 0.39) (−0.29, 0.38) (−0.54, 0.71) (−0.61, 0.67)
−6 15 −0.08 −0.08 0.12 0.22 0.34 0.56
  lnMn (μg/L) (−127, 115) (−112, 142) (−0.57, 0.42) (−0.60, 0.43) (−0.29, 0.52) (−0.20, 0.64) (−0.43, 1.11) (−0.25, 1.36)
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a

Adjusted for maternal age, maternal education, maternal occupation, maternal BMI, gravidity, environmental tobacco smoke, and baby sex Bold: p<0 05

We observed an increased OR for SGA by maternal blood Cd concentrations and a lower OR for SGA by maternal blood Mn (Table 4). In the stratified analysis, blood Cd was also associated an increased OR for SGA in Haojiang.

Table 4.

Unadjusted and adjusted odds ratios (OR) and 95% Confidence Intervals (CI) for small for gestational age (SGA) by metal exposure

Metals Odds Ratio (95% CI)
Unadjusted Adjusteda
Both sites combined
  lnPb (μg/dL) 0.87 (0.46, 1.66) 0.69 (0.33, 1.46)
  lnCd (μg/L) 1.77 (0.90, 3.48) 2.10 (0.997, 4.43)
  lnCr (μg/L) 1.45 (0.95, 2.22) 1.36 (0.85, 2.18)
  lnMn (μg/L) 0.34 (0.16, 0.71) 0.34 (0.16, 0.72)
Guiyu only
  lnPb (μg/dL) 1.13 (0.45, 2.85) 1.12 (0.38, 3.31)
  lnCd (μg/L) 1.21 (0.46, 3.21) 1.48 (0.52, 4.23)
  lnCr (μg/L) 1.50 (0.88, 2.58) 1.47 (0.80, 2.70)
  lnMn (μg/L) 0.27 (0.09, 0.76) 0.26 (0.09, 0.80)
Haojiang only
  lnPb (μg/dL) 0.38 (0.11, 1.34) 0.37 (0.09, 1.56)
  lnCd (μg/L) 2.61 (0.96, 7.15) 3.47 (1.04, 11.53)
  lnCr (μg/L) 1.20 (0.49, 2.95) 1.03 (0.38, 2.77)
  lnMn (μg/L) 0.43 (0.14, 1.38) 0.33 (0.10, 1.08)
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a

Adjusted for maternal age, maternal education, maternal occupation, maternal BMI, gravidity, environmental tobacco smoke, and baby sex Bold: p<0.05

We illustrate the findings of BKMR models for a mixture of four metals using Ponderal Index. We observed no interaction of metals for Ponderal Index in the bivariate exposure-response function as the dose-response curves of the first metal were largely parallel while the second metal was fixed at the 25th, 50th, or 75th percentile (Figure 1). When the four metal concentrations co-increase from the 25th to the 75th percentile, the Ponderal Index deceases monotonously in the cumulative risk estimation (Figure 2). The estimates of Ponderal Index by an IQR increase of a single metal while the other three metals were held at the 25th, 50th, or 75th percentile were similar to each other (Supplemental Figure S1); and the overall magnitude and direction of associations were similar to the Table 3 results. Supplemental Figures provide additional information on BKMR models of birth weight (S2-S4), head circumference (S5-S7), and BMI (S8-S10), in the order of single metal, bivariate exposure-response function, and cumulative impact. We also did not observe interactions between metals for these outcomes. We did not observe cumulative impact of the four metals on birth weight, but we found cumulative impact of these metals on head circumference and BMI.

Figure 1.

Figure 1.

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Bivariate exposure-response function showing a single metal (expos1) while the second metal was fixed at the 25th, 50th, or 75th percentile for the Ponderal Index outcome

Figure 2.

Figure 2.

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Cumulative impact of all 4 predictors at a particular percentile (from the 25th to the 75th at an interval of 5th) compared with a reference while all predictors were at the 50th percentile for the Ponderal Index outcome

In a sensitivity analysis, males and females had similar associations when compared Guiyu to Haojiang birth outcomes, although the regression estimates may slightly differ (Supplemental Table S1). In the sex-stratified analysis by metal exposures, the adjusted regression estimates were similar except for the associations between blood Mn and birth weight and BMI, in which we observed positive associations in males but null association in females (Supplemental Table S2). The comparison between 87 neonates with parental involvement in informal e-waste recycling and 227 neonates without such parental involvement did not have statistically significant findings, although neonates with parental recycling involvement tended to have a slightly higher gestational age and birth length (Supplemental Table S3). A total of 83 women in Guiyu (26%) and 51 women in Haojiang (16%) reported themselves as domestic migrants, however, their birth outcomes were not markedly different from the local residents and additional adjustment for migrant status in the between-site comparisons did not yield different findings (data not shown).

4. Discussion

We observed that neonates from Guiyu had slightly decreased BMI and Ponderal Index compared to Haojiang neonates. Although the difference in birth weight was not statistically significant, Guiyu neonates were slightly smaller than those in Haojiang. Head circumference was decreased in Guiyu compared to neonates from Haojiang. Despite the lower birth weight and head circumference, neonates in Guiyu had a slightly increased birth length. The increased birth length and decreased head circumference could be associated with the higher prevalence of vaginal deliveries in Guiyu (99.7%) compared to only 45% in Haojiang. Haojiang is located across the bay from a larger city, Shantou, and therefore has access to more advanced health care. The delivery method could lead to slight changes in the neonate’s size, depending on the tools used to extract it from the mother. The about half-week longer mean gestational age of neonates in Guiyu is also likely associated with the availability to schedule caesarian sections in Haojiang, whereas in Guiyu they wait for the mother to go into labor.

We observed inverse associations between blood Pb concentrations and head circumference and Ponderal Index, between blood Cd concentrations and BMI. The associations between blood Cd concentrations and birth weight and Ponderal Index were inverse and close to statistical significance. Blood Cd was also related to a higher risk of SGA. We did not find interactions between the four metals for birth outcomes, but we observed a cumulative impact of the metals on head circumference, BMI, and Ponderal Index, despite blood Mn having a positive association with the outcomes. The results suggest additive impact of lead, cadmium, and chromium on these three outcomes, but not on birth weight as we found no cumulative impact.

Higher maternal concentrations of blood Pb, Cd, and lower maternal concentrations of Mn during pregnancy have been associated with lower birth weight (Tian et al. 2009; Xie et al. 2013; Zota et al. 2009). Our findings suggest that high blood Pb, Cd, and Cr concentrations, as well as low Mn concentrations, may reduce fetal growth in this study. Prior studies of a mixture of metals showed diverse results, but generally pointed to impaired fetal growth by Pb and Cd exposure and growth promotion by Mn (Cassidy-Bushrow et al. 2019; Freire et al. 2019; Luo et al. 2017; Wai et al. 2017). We observed decreased head circumference, BMI, and PI in Guiyu compared with Haojiang. The cumulative impact of higher blood Pb, Cd, Cr, and lower Mn concentrations in Guiyu may partially explain the findings despite that the individual metals may not all have statistically significant associations with these outcomes.

This study has several limitations. Guiyu is in rural China, and though we trained the staff and provided instrumentation, the participating hospital had limited experience in academic research. Measurement errors in gestational age and anthropometrics cannot be ruled out but are likely to be unbiased and random by individual metal exposure levels. LMP is a reliable method for estimating gestational age for large epidemiologic research studies, but an ultrasound is a more accurate method for estimating gestational age in a cohort of moderate size. Although, most of the women reporting receiving an ultrasound early in pregnancy (73.4%), only four percent of the medical records stated using an early ultrasound to determine gestational age compared to 90.7% reporting use of LMP. These were two separate questions on the questionnaire with the second question completed by the nurse using medical records. Birth weights were rounded to 50 grams in many Guiyu newborns due to the much familiar and less precise use of ‘jin’(500 grams) and ‘liang’ (50 grams) for neonate weight in rural China. There could be measurement errors for birth length and head circumference after delivery. In addition, at the time of the study, the local government closed several e-waste recycling workshops because of the widespread contamination, creating tension and suspicion within Guiyu. Some participants may have intentionally skipped the questions related to e-waste or have chosen not to participate, which could lead to selection bias. We included women who lived in the respective town during pregnancy (<3 months outside of their town) to examine the role of exposure during pregnancy on birth outcomes. The observed association may be a result of cumulative exposures before the current pregnancy as well as during the pregnancy because metals can accumulate in the body and be released during pregnancy (e.g., Pb in bones and Cd in soft tissues). In addition, women who did not live in the exposed town during pregnancy were excluded even if they were exposed before pregnancy. We may have missed an opportunity to study pre-pregnancy exposure and consequent birth outcomes. The more aggressive obstetric invention for delivery, i.e., via cesarean section, and higher educational attainment in the control site Haojiang may suggest differences in health care and nutrition status in the two sites. We did not collect nutrition data from the pregnant women. Although we conducted stratified analysis by site, the sample size was halved and exposure levels not entirely overlapping. We did not observe significantly more adverse birth outcomes if parents were involved in informal e-waste recycling in Guiyu, which could be explained by a small number of neonates (n=87) with that exposure and a wide-spread exposure scenario in Guiyu as the recycling activities were performed by workshops in the open environment scattered around the town.

Despite these limitations, this unique study captured pregnant women living in a mixture exposure environment during pregnancy. To the best of our knowledge, it is one of the first studies to collect biological samples maternal-infant pairs living in an informal e-waste recycling community to quantify the association between metal exposures and birth outcomes. We were able to measure metal exposures in maternal blood, enabling us to analyze the associations with exposure site as well as individual and a mixture of metal exposure. We were able to estimate interaction and cumulative impact of 4 metals on birth outcomes. We also adjusted for various sociodemographic factors in the data analysis to account for potential confounding. As smoking among pregnant women is extremely rare in rural China, we did not encounter the problem of residual confounding by maternal active smoking, while cigarettes contain both lead and cadmium and maybe other metals.

5. Conclusion

In conclusion, mothers in Guiyu had higher concentrations of Pb, Cd, and Cr, and we observed smaller head circumference, BMI, and PI in the Guiyu neonates compared to neonates from the control site. The mixtures model suggested a cumulative impact of the four metals and head circumference and BMI, but not birth weight.

This paper only focuses on one community in China; however, it is concerning that ewaste is expected to grow in volume by 5–10% annually (Sthiannopkao and Wong 2013). Guiyu is not the only site in China with a long history of informal e-waste recycling. The residents of Taizhou, in Zhejiang Province in East China, have also been recycling e-waste using primitive methods and researchers have observed elevated levels of heavy metals, PBDEs, PAHs, dioxins, phthalates, and other chemicals not only in the environment, but also in the residents (Chan et al. 2007; Han et al. 2009; Liu et al. 2009; Tang et al. 2010a; Tang et al. 2010b). The exposure profiles of each recycling site depend on the types of e-waste and the recycling methods. It is important to recognize that the fetal exposure to these chemicals, in Guiyu and other informal ewaste recycling communities around the world, continue beyond birth and postnatal exposures are also associated with adverse health effects. Future studies should focus on interventions that will limit exposure to the environment and humans while maintaining or increasing economic gains from recycling. In the meantime, it is recommended that women do not work in e-waste recycling during pregnancy and while breastfeeding. Efforts should be taken to ensure vulnerable populations are not occupationally exposed to informal e-waste recycling processes and to minimize environmental exposures.

Supplementary Material

1

Highlights.

  • ◾

    E-waste is a growing problem around the world

  • ◾

    Neonates born in an e-waste recycling community in China are exposed to heavy metals

  • ◾

    These neonates had smaller head circumference, BMI, and Ponderal Index

Acknowledgements:

Funding for this study was provided by National Institutes of Health/ National Institute of Environmental Health Sciences (NIEHS) grants RC4ES019755, T32ES010957, P30ES006096 and the Project of International Cooperation and Innovation Platform in Guangdong Universities (2013gjhz0007). The authors also extend thanks to all the participants in the study.

Footnotes

Conflict of Interests

The authors declare no conflicts of interest.

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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