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. 2015 Jul 16;26(2):158–174. doi: 10.1080/09603123.2015.1061114

Toxic trace elements in maternal and cord blood and social determinants in a Bolivian mining city

Flavia L Barbieri a,b,c,*, Jacques Gardon a,b,d, María Ruiz-Castell a,b,e, Pamela Paco V a,b, Rebecca Muckelbauer c, Corinne Casiot d,f, Rémi Freydier d,f, Jean-Louis Duprey a,g, Chih-Mei Chen c,h, Jacqueline Müller-Nordhorn c, Thomas Keil h,i
PMCID: PMC4733940  PMID: 26179629

Abstract

This study assessed lead, arsenic, and antimony in maternal and cord blood, and associations between maternal concentrations and social determinants in the Bolivian mining city of Oruro using the baseline assessment of the ToxBol/Mine-Niño birth cohort. We recruited 467 pregnant women, collecting venous blood and sociodemographic information as well as placental cord blood at birth. Metallic/semimetallic trace elements were measured using inductively coupled plasma mass spectrometry. Lead medians in maternal and cord blood were significantly correlated (Spearman coefficient = 0.59; p < 0.001; 19.35 and 13.50 μg/L, respectively). Arsenic concentrations were above detection limit (3.30 μg/L) in 17.9 % of maternal and 34.6 % of cord blood samples. They were not associated (Fischer’s p = 0.72). Antimony medians in maternal and cord blood were weakly correlated (Spearman coefficient = 0.15; p < 0.03; 9.00 and 8.62 μg/L, respectively). Higher concentrations of toxic elements in maternal blood were associated with maternal smoking, low educational level, and partner involved in mining.

Keywords: environmental exposure, metallic trace elements, maternal exposure, prenatal exposure, risk factors

Introduction

Mining is an important activity in the Andean regions. Mining activities may contaminate the environment with metallic and semimetallic elements and other pollutants, as shown in studies from Colombia (Cordy et al. 2011), Ecuador (Guimaraes et al. 2011), Peru (van Geen et al. 2012; Yacoub et al. 2012), Chile (Oyarzun et al. 2006), and Bolivia (Hudson-Edwards et al. 2001; Cooke et al. 2011). In Bolivia, mining activities have been carried out for over 3000 years, before Incan times (Abbott & Wolfe 2003; Cooke et al. 2011). Oruro has been one of the most important mining cities for over 400 years, particularly for the exploitation of silver and tin and, later, also gold, zinc, and lead (Torres 1890; Bouton 1998; Banks et al. 2002).

The area is naturally rich in multiple metallic and semimetallic elements, including gold, silver, tin, copper, zinc, antimony, lead, cadmium, and arsenic (Keutsch & de Brodtkorb 2008; Bundschuh et al. 2012). The mining waste may contain a combination of several potentially toxic elements depending on the exploited material and the technique employed. Some of the central districts of Oruro are located in close vicinity to mining pits or smelters, sometimes even on top of abandoned mining waste.

Located in the Bolivian plateau at ~ 3700 m of altitude above sea level, Oruro is cold and arid, with long dry seasons and strong winds, which contribute to making it a dusty city (SENAMHI http://www.senamhi.gob.bo/sismet/index.php). Besides, many roads in Oruro are unpaved and most mining tailings and waste piles remain exposed subject to wind erosion and leaching processes. This constitutes a source of pollution, especially when these materials are used as bulk-fill in improvised construction projects (Yáñez et al. 2002).

Previous studies have shown both outdoor and indoor environmental contamination with a combination of several metallic and semimetallic trace elements in Oruro, such as lead, arsenic, antimony, cadmium, and others (Banks et al. 2002; Fonturbel et al. 2011; Goix et al. 2011). Children from the mining and metallurgical districts seem particularly at risk, with higher concentrations of toxic elements in hair, mainly lead, arsenic, antimony, and cadmium (Barbieri et al. 2011; Barbieri et al. 2014).

Several of these metallic and semimetallic trace elements are associated with toxic health effects. Lead in particular is often associated with cardiovascular risk (Gump et al. 2011), impaired fertility (Buck Louis et al. 2012), nephrotoxicity (Cabral et al. 2012), and neurotoxicity (Liu et al. 2013), the latter also associated with arsenic, mercury, and antimony. Arsenic is also associated with genotoxicity (Niedzwiecki et al. 2013). Children are the most vulnerable population as these elements often affect early human development (Al-Saleh et al. 2011; Cace et al. 2011; Hawkesworth et al. 2013; Al-Saleh et al. 2014). Toxic trace elements, especially lead, often impair neurological development (Liu et al. 2013; Parajuli et al. 2013), intellectual performance (Bellinger et al. 1992; Riojas-Rodríguez et al. 2010; Mazumdar et al. 2011; Yorifuji et al. 2011), and behavior (Needleman 1982; Min et al. 2007; Surkan et al. 2007) in children. In particular, prenatal exposure can be harmful to the early development (Yorifuji et al. 2011; Xie et al. 2013; Al-Saleh et al. 2014).

Our objective was to assess maternal and neonatal exposure to lead, arsenic, and antimony in the city of Oruro, Bolivia, as well as to determine the associations between social determinants and maternal exposure to toxic trace elements.

Methods

Setting and study design

The city of Oruro is placed in the central Bolivian highland. It counted approximately 220,000 inhabitants in 2009 (INE).

The ToxBol project was conceived as a multidisciplinary research project to study the origin of polymetallic contamination in Oruro and its impact on the environment, health, and society (Goix et al. 2011; Moya et al. 2011; Ruiz-Castell et al. 2012; Goix et al. 2013; Ruiz-Castell et al. 2013). The health component of the ToxBol project was the “Mine-Niño” pregnancy cohort, which took place between 2007 and 2010. For the present study, we analyzed cross-sectional data from the Mine-Niño cohort: the baseline assessment of the mothers during pregnancy and their newborns at birth.

Ethics statement

The National Bioethics Committee of Bolivia evaluated and approved each component of the Mine-Niño cohort prior to the recruitment of the participants (Appendix 1: Letter of Acceptance). Participating women signed a written informed consent after receiving written information and detailed explanations about the project. Parents or legal guardians signed the informed consent form on behalf of underage participants (under 18 years of age according to Bolivian law). A member of the team read and explained the text to adult illiterate women. Members of our local staff translated the text to either Quechua or Aymara for non-Spanish speaking women. Personal data was protected according to international recommendations regarding anonymity and privacy.

Study population

We worked in cooperation with two health care centers: the “Barrios Mineros” Hospital (HBM) is a basic facility with a small maternity ward, located in a mining district. It admits women mostly from that neighborhood, although not necessarily miners’ families. It provides modest mother–infant primary health care services, free of charge, and financed by the State. The “10 de Febrero” Clinic (CNS) is part of the Bolivian statutory health insurance provided to registered workers. It admits women from all over the city as long as they, their parents, or husbands are insured.

Between 2007 and 2008, we invited healthy women at any stage of pregnancy, who attended their prenatal care at one of the two health care centers. A total of 467 women were initially registered. Exclusion criteria were: living in Oruro less than a year, age below 16 years old, and multiple pregnancies (twins, triplets, etc.).

We obtained venous blood samples from 419 women during pregnancy and cord blood sample from 240 newborns. We recorded the location of each household (or the closest reference point) using a Global Positioning System device (Figure 1).

Figure 1.

Figure 1.

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(Color online) Map of the city of Oruro.

Notes: Location of the two health care centers where the cohort took place, the main mining and metallurgical facilities, and the location of the households of the participating families.

Social and environmental determinants

We applied structured questionnaires in Spanish, Quechua, or Aymara to obtain personal and sociodemographic information. We registered the women’s pregnancy stage in weeks, as stated on her prenatal care chart. When this information was not in the chart, we calculated it using the woman’s recollection of her last menstrual period. We classified pregnancy stage in three categories (first/second/third trimester). We obtained a short health history, with particular interest in smoking habits and the presence of metallic dental fillings. These variables were dichotomous (yes/no).

We classified marital status in three groups (married/cohabiting/single), placing the only two widow women in the “single” group. We categorized the maternal educational level according to the Bolivian educational system in effect (elementary: 8 schooling years; secondary: 12 schooling years; tertiary: more than 12 schooling years).

We registered the main occupation of each woman and that of her husband or partner. When single women lived at the parental home, we registered the occupation of her father. We grouped occupations based on the Classification of Occupations of Bolivia (INE 2009) and ordered them by skill levels according to the National Occupational Classification Matrix 2006 (Canada) (HRSDC 2006).

We asked if anyone in the household, especially the woman or her husband, partner or father, worked in mining, either as miners or other occupations (engineers, cleaning personnel, or others at a mining or metallurgical site). We recorded this information separately as yes/no variables. If a family member worked in mining, we assessed whether or not they brought home their working clothes, tools, or minerals.

Measurement of trace elements in maternal and cord blood

We took a venous blood sample of 5 mL from the pregnant mother during the recruitment interviews using a BD® vacutainer tube free of trace elements. After placental delivery, we took blood samples (25 mL) from the clamped placental cord using the same BD® vacutainer tubes.

We stored the blood samples at 4 °C at the local hospital and transported them in iceboxes to the Environmental Quality Laboratory (LCA) in La Paz, where they were digested, mineralized, and stored at −20 °C. Then, these samples were sent to the Hydrosciences Montpellier Laboratory, France, where they were analyzed using Inductively Coupled Plasma Mass Spectrometry (Q-ICPMS X série II + CCTTM – Thermo Fisher-). The trace elements initially measured were lead, calcium, iron, copper, zinc, arsenic, selenium, rubidium, strontium, cadmium, mercury, antimony, and cesium. The quantification of trace elements concentrations was performed establishing calibration curves by linear regression. Lead concentration values were calculated using bismuth as internal standard, while the other trace elements were determined using indium as internal standard, providing an accurate measure to normalize potential variations of the ICP-MS response. Quality control was performed on every set of 24 digested whole blood samples. It consisted in measuring a calibration blank, all calibration standard solutions, one reference river water sample SLRS-4, two reference materials from SERO AS (Norway) -Seronorm Trace Elements Whole Blood, Level 1 (lowest available concentrations) and Level 2 (elevated concentrations)- two digestion blanks and two subsamples of the pooled blood control sample from this study. The repeated analyses of reference materials allowed accessing the accuracy of the applied analytical procedures. Detection limits were calculated following the International Union of Pure and Applied Chemistry (http://www.iupac.org/) criterion based on three times the standard deviation of 10 consecutive measurements of the blank digestion method solution (3σ) divided by the experimental slope of the calibration graph.

For the present study, we selected the elements with known toxicity and relevance in the mining context that were detectable in the blood samples. The detection limits for these elements were: lead – 3.26 μg/L, arsenic – 3.30 μg/L, and antimony – 0.70 μg/L. Blank values were subtracted from the samples.

Statistical analysis

We used Fisher’s exact test to assess possible differences in social and environmental determinants in the women with and without blood sample taken. In women with blood sample, we also compared the social and environmental determinants by health care center.

We observed the distribution patterns of lead, arsenic, and antimony concentrations in blood and tested normality using the Shapiro-Francia test. In the absence of normality, we used non-parametric tests. The eight newborns with lead below detection limit (3.33 %) were assigned the value of the detection limit (3.26 μg/L). Arsenic was treated as a dichotomous variable, divided in two groups (below/above detection limit), because of the low percentage of concentrations above that level (17.90 % mothers and 34.58 % newborns).

Both in maternal and cord blood, we tested the association between lead and antimony using Spearman rank correlations. We used the Mann-Whitney-Wilcoxon test to test lead/arsenic and antimony/arsenic associations (arsenic as dichotomous variable). To test the associations between maternal blood and cord blood, we used Spearman rank correlations for the continuous variables lead and antimony, and Fisher’s exact test for the dichotomous variable arsenic.

For a secondary analysis, we dichotomized the distributions of toxic elements in maternal blood as “higher/lower,” using as cut-off the 75th percentile for lead and antimony and detection limit for arsenic. We tested the associations between the toxic trace elements in maternal blood and all social determinants using separate logistic regressions for each element. We adjusted for maternal age and health care center. For lead, we added pregnancy trimester as a potential confounder because of its maternal bone release and placental transfer during the second trimester (Moura & Valente 2002; Gulson et al. 2003; Manton et al. 2003a; Ettinger et al. 2007).

For the multivariate analysis, we created an ordinal outcome variable indicating the cumulative high exposure of lead, arsenic, and antimony. This variable was the sum of the toxic elements as dichotomous variable classified as “higher.” We classified this ordinal outcome variable in three categories: “none/one/two or more” because only four samples had three elements classified as “higher.” We included all the social and environmental determinants in an ordinal logistic regression model and adjusted for maternal age and health care center.

We considered statistical significance α error < 0.05 for all our analysis. We used STATA® version 10.0 for all the statistical analyses.

Results

Characteristics of the population

From the 467 recruited women, 48 refused to give a blood sample (47 at the HBM and one at the CNS), four suffered spontaneous abortions or still births and 106 abandoned the project before birth, leaving 357 women who registered a newborn in our study. We could not obtain cord blood samples for 117 newborns. There were no significant differences in age, social, or environmental determinants between the mother–infant pairs with blood samples taken and those without blood samples. Mean age at the start of the study was 25 years (range 16–44 years).

Women from the HBM were on average younger, less often married, had lower educational level and social status, and were in earlier stages of pregnancy than the women from CNS (Table 1).

Table 1. Characteristics of the included women with blood sample taken, stratified, and compared by health care center.

N (%)
  CNS a HBM b TOTAL P value c
Maternal age        
15–25 years old 51 (37.0 %) 163 (58.0 %) 214 (51.1 %) < 0.001
25–35 years old 70 (50.7 %) 98 (34.9 %) 168 (40.1 %)  
35–45 years old 17 (12.3 %) 20 (7.1 %) 37 (8.8 %)  
Pregnancy trimester        
First 0 (0 %) 83 (29.8 %) 83 (20.0 %) < 0.001
Second 3 (2.2 %) 182 (65.2 %) 185 (44.6 %)  
Third 133 (97.8 %) 14 (5.0 %) 147 (35.4 %)  
Smoking        
No 118 (85.5 %) 239 (85.1 %) 357 (85.2 %) 0.514
Yes 20 (14.5 %) 42 (15.0 %) 62 (14.8 %)  
Dental amalgams        
No 54 (39.7 %) 137 (57.8 %) 191 (51.2 %) 0.001
Yes 82 (60.3 %) 100 (42.2 %) 182 (48.8 %)  
Marital status        
Married 87 (63.0 %) 95 (33.8 %) 182 (43.4 %) < 0.001
Cohabiting 31 (22.5 %) 141 (50.2 %) 172 (41.1 %)  
Single 20 (14.5 %) 45 (16.0 %) 65 (15.5 %)  
Maternal schooling d        
Tertiary 87 (64.4 %) 93 (33.7 %) 180 (43.8 %) < 0.001
Secondary 38 (28.2 %) 134 (48.6 %) 172 (41.9 %)  
Elementary 10 (7.4 %) 49 (17.8 %) 59 (14.4 %)  
Maternal occupation e        
Professional 39 (28.9 %) 11 (3.9 %) 50 (12.1 %) < 0.001
Technical 36 (26. 7 %) 55 (19.6 %) 91 (21.9 %)  
Administrative 7 (5.2 %) 32 (11.4 %) 39 (9.4 %)  
Basic/manual 53 (39.3 %) 182 (65.0 %) 235 (56.6 %)  
Paternal occupation f        
Professional 36 (27.3 %) 23 (8.4 %) 59 (14.5 %) < 0.001
Technical 24 (18.2 %) 49 (17.3 %) 73 (17.9 %)  
Administrative 23 (17.4 %) 85 (30.9 %) 108 (26.5 %)  
Basic/manual 49 (37.1 %) 118 (42.9 %) 167 (41.0 %)  
Father works in mining f        
No 119 (89.5 %) 237 (85.9 %) 356 (87.0 %) 0.196
Yes 14 (10.5 %) 39 (14.1 %) 53 (13.0 %)  
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a

CNS: Caja Nacional de Salud (statutory health insurance).

b

HBM: Hospital Barrios Mineros (mining district hospital).

c

Fisher’s exact text to test for differences between health care centers.

d

Bolivian educational system (elementary: 8 schooling years; secondary: 12 schooling years; tertiary: more than 12 schooling years).

e

Occupations were categorized based on the Classification of Occupations of Bolivia (INE 2009) and adapted by skill levels according to the National Occupational Classification Matrix (HRSDC 2006).

f

Refers to the husband or partner or father of the interviewed woman. Otherwise, the data from the woman’s father was collected, provided that the woman lived at her father’s household.

Toxic trace elements in maternal and cord blood

Table 2 displays the concentrations of lead, arsenic, and antimony in maternal blood and cord blood. Arsenic was above detection limit (3.30 μg/L) in 18 % of maternal blood samples and 35 % of cord blood samples. Eight cord blood samples (3.33 %) were below detection limit for lead. They were assigned the detection limit value (3.26 μg/L).

Table 2. Lead, arsenic, and antimony concentrations in maternal blood and cord blood (μg/L). Associations between maternal blood and cord blood concentrations.

  N Geometric mean (SD a ) Range Median P5–P95 b Spearman coefficient p value
Lead              
Maternal blood 419 26.53 (48.71) 4.38–801.60 19.35 7.78–51.14    
Cord blood 240 22.57 (57.44) 3.26–707.15 13.50 3.83–42.96 0.59 < 0.01
Antimony              
Maternal blood 418 13.55 (12.89) 2.48–111.41 9.00 4.59–37.79    
Cord blood 240 20.18 (37.76) 3.69–369.18 8.62 4.32–71.66 0.15 0.03
Arsenic c              
Maternal blood 419 3.81 (1.66) <DL–14.33 <DL <DL–7.22    
Cord blood 240 4.25 (3.11) <DL–44.77 <DL <DL–6.48 – 0.72 d
Arsenic (above DL only) c
Maternal blood 75 6.14 (2.98) 3.32–14.33 4.87 3.37–13.02    
Cord blood 83 6.06 (4.81) 3.32–44.77 5.06 3.54–12.11 – –
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a

SD = Standard Deviation.

b

Percentiles 5th and 95th of the distribution.

c

DL = Detection Limit (DL = 3.30 μg/L).

d

Fisher’s exact test using arsenic as dichotomous variable: 0 = below detection limit/1 = above detection limit.

In maternal blood, lead, arsenic, and antimony were not associated with each other. In cord blood, lead and antimony were inversely associated with arsenic, with higher concentrations of lead and antimony in samples below detection limit of arsenic (Mann-Whitney-Wilcoxon p = 0.056 for lead, p = 0.022 for antimony).

We could match 223 mother–infant pairs with both samples taken. Lead in maternal blood and cord blood showed a stronger correlation (Spearman ρ coefficient = 0.59; p < 0.001) than antimony (Spearman ρ coefficient = 0.15; p < 0.027) (Table 2, Figure 2). We found 13 mother–newborn pairs with both samples above detection limit. No association was found between maternal and neonatal arsenic as dichotomous variable (Fisher’s exact test – p = 0.72).

Figure 2.

Figure 2.

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Associations between the concentrations of lead and antimony in maternal blood during pregnancy and cord blood at birth (μg/L) using logarithmic scales.

N = 223 mother-newborn pairs.

Social and environmental determinants and maternal exposure

Women with lower educational level and women whose husband/partner/father was involved in mining activities were significantly more likely to be in the higher exposure category for all three toxic elements adjusted for maternal age and health care center (and for pregnancy trimester for lead). Smoking was associated with high exposure for lead and antimony, but not arsenic. Women with lower social status were more likely to be in the higher exposure category of lead and arsenic (Table 3).

Table 3. Logistic regression between individual elements (lead, antimony, and arsenic) in maternal blood and the covariates included in the analysis.

  Odds ratio (95 % Confidence interval)
N (%) Lead a , c Antimony a , d Arsenic b , d
Smoking        
No e 357 (85.2)      
Yes 62 (14.8) 2.14 (1.19–3.85) 1.86 (1.04–3.34) 0.80 (0.38–1.68)
Dental amalgams        
No e 191 (51.21)      
Yes 182 (48.79) 0.58 (0.36–0.95) 0.87 (0.53–1.44) 1.01 (0.55–1.86)
Marital status        
Married e 182 (43.44)      
Cohabiting 172 (41.05) 1.15 (0.67–1.97) 0.76 (0.45–1.30) 0.77 (0.42–1.40)
Single 65 (15.51) 1.66 (0.83–3.30) 1.05 (0.53––2.09) 0.83 (0.38–1.82)
Maternal schooling f        
Tertiary e 180 (43.8)      
Secondary 172 (41.85) 1.33 (0.78–2.26) 1.01 (0.60–1.69) 1.68 (0.91–3.10)
Elementary 59 (14.36) 2.51 (1.27–4.98) 2.00 (1.04–3.83) 2.86 (1.36–6.03)
Maternal occupation g        
Professional e 50 (12.05)      
Technical 91 (21.93) 0.60 (0.25–1.41) 0.76 (0.30–1.92) 1.02 (0.29–3.57)
Administrative 39 (9.4) 0.48 (0.15–1.50) 1.27 (0.45–3.60) 1.89 (0.49–7.24)
Basic/manual 235 (56.63) 1.45 (0.69–3.03) 1.37 (0.60–3.11) 1.93 (0.62–6.03)
Paternal h occupation g        
Professional e 59 (14.5)      
Technical 73 (17.94) 1.10 (0.45–2.95) 0.79 (0.32–1.94) 1.26 (0.35–4.51)
Administrative 108 (26.54) 1.73 (0.77–2.43) 1.36 (0.62–3.01) 3.18 (1.02–9.88)
Basic/manual 167 (41.03) 1.87 (0.88–4.40) 1.39 (0.66–2.93) 2.71 (0.89–8.20)
Father h works in mining        
No e 356 (87.04)      
Yes 53 (12.96) 2.00 (1.06–3.75) 1.82 (0.98–3.39) 2.04 (1.05–3.97)
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a

Dichotomous transformation for Pb and Sb: 0 = first three quartiles of the distribution/1 = last quartile of the distribution.

b

Dichotomous transformation for As: 0 = below detection limit/1 = above detection limit (DL = 3.30 μg/L).

c

Adjusted for maternal age and pregnancy trimester.

d

Adjusted for maternal age and health care center.

e

Reference category.

f

Bolivian educational system (elementary: 8 schooling years; secondary: 12 schooling years; tertiary: more than 12 schooling years).

g

Occupations were categorized based on the Classification of Occupations of Bolivia (INE 2009) and adapted by skill levels according to the National Occupational Classification Matrix 2006 (HRSDC 2006).

h

Refers to the husband or partner of the interviewed woman. Otherwise, the data from the woman’s father was collected, provided that the woman lived at her father’s household.

About 53 % of the women in this study were in the higher exposure group of at least one toxic element and approximately 13 % of them were in the higher exposure group of at least two of them simultaneously.

The adjusted ordinal logistic regression showed that women with more toxic elements in the higher exposure group were more likely in their second trimester of pregnancy (OR = 2.28; p = 0.007), smoked (OR = 1.77; p = 0.05), were single (OR = 2.19; p = 0.03), and had husbands/partners/fathers involved in mining activities (OR = 2.16; p = 0.037) (Table 4).

Table 4. Multivariate ordinal logistic regression between the sum of toxic trace elements in maternal blood considered higher exposurea and the covariates included in the analysis, adjusted by health care center and maternal age.

  N (%) Odds ratio Confidence interval 95 % p value
Pregnancy trimester        
First b 83 (20)      
Second 185 (44.58) 2.28 (1.25–4.14) 0.007
Third 147 (35.42) 1.81 (0.53–6.26) 0.346
Smoking        
No b 357 (85.2)      
Yes 62 (14.8) 1.77 (1.00–3.15) 0.050
Dental amalgams        
No b 191 (51.21)      
Yes 182 (48.79) 0.93 (0.59–1.45) 0.745
Marital status        
Married b 182 (43.44)      
Cohabiting 172 (41.05) 1.12 (0.67–1.90) 0.663
Single 65 (15.51) 2.19 (1.08–4.44) 0.030
Maternal schooling c        
Tertiary b 180 (43.8)      
Secondary 172 (41.85) 0.86 (0.46–1.59) 0.632
Elementary 59 (14.36) 1.26 (0.55–2.88) 0.592
Maternal occupation d        
Professional b 50 (12.05)      
Technical 91 (21.93) 0.54 (0.24–1.20) 0.131
Administrative 39 (9.4) 0.48 (0.16–1.43) 0.189
Basic/manual 235 (56.63) 1.31 (0.57–3.01) 0.520
Paternal e occupation d        
Professional b 59 (14.5)      
Technical 73 (17.94) 1.18 (0.24–2.66) 0.683
Administrative 108 (26.54) 2.22 (0.99–4.95) 0.052
Basic/manual 167 (41.03) 1.52 (0.71–3.26) 0.284
Father e works in mining        
No b 356 (87.04)      
Yes 53 (12.96) 2.16 (1.05–4.44) 0.037
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a

We dichotomized as “higher/lower” the trace elements distributions in maternal blood using as cut-off the 75th percentile for lead and antimony and detection limit for arsenic. The “mixed exposure” variable was the sum of the elements in the “higher” group.

b

Reference category for each covariate.

c

Bolivian educational system (elementary: 8 schooling years; secondary: 12 schooling years; tertiary: more than 12 schooling years).

d

Occupations were categorized based on the Classification of Occupations of Bolivia (INE 2009) and adapted by skill levels according to the National Occupational Classification Matrix 2006 (HRSDC 2006).

e

Refers to the husband or partner of the interviewed woman. Otherwise, the data from the woman’s father was collected, provided that the woman lived at her father’s household.

Discussion

Main findings

Our study showed different degrees of exposure to toxic trace elements for both pregnant women and their newborn children in the Bolivian mining town of Oruro. Blood lead concentrations were relatively low, but antimony concentrations were elevated. Even though arsenic was detectable in only a third of cord blood samples and a fifth of maternal blood samples, the levels detected were also elevated.

We found a good correlation between lead concentrations in maternal and cord blood, whereas for antimony concentrations, the correlation was weak. It was not possible to properly assess associations between maternal and cord blood arsenic because of the limited statistical power. Independent risk factors for higher concentrations of toxic elements were maternal smoking, lower maternal educational level, and a close family member involved in mining activities.

Maternal and neonatal exposure

With regard to lead, 5 % of the maternal and cord blood samples had levels above the current recommended level of 50 μg/L suggested by the Center for Disease Control and Prevention (CDC 2013). In other Andean mining or metallurgical sites, lead concentrations in maternal and neonatal blood were higher compared to the city of Oruro (Conklin et al. 2008; Pebe et al. 2008; Fonturbel et al. 2011; van Geen et al. 2012). The median levels of blood lead from our study were comparable to those in non-exposed populations (CDC 2005; Goullé et al. 2005; Plusquellec et al. 2007; Xie et al. 2013). The significant correlation between maternal and cord blood suggests placental transfer of lead (Manton et al. 2003b; Lin et al. 2010; Al-Saleh et al. 2011), with potential harmful effects on fetal development (Yorifuji et al. 2011).

The detection limit for arsenic in our study was rather high (3.3 μg/L). Therefore, we could not assess how many samples were above the reference level (1 μg/L) recommended by the United States Agency for Toxic Substances and Disease Registry (ATSDR). The population above detection limit had arsenic concentrations at least threefold higher than these recommended levels (ATSDR 2007).

In Oruro, the median concentrations of antimony in both maternal and cord blood were comparable to high occupational exposure (ATSDR 1992; Kentner et al. 1995). Previous studies in Oruro suggested that household dust (Fonturbel et al. 2011), soil and airborne particles (Goix et al. 2011) were contaminated with antimony. Most studies and official recommendations for antimony exposure refer to the occupational context (ATSDR 1992). Very few studies have addressed antimony exposure from environmental sources and the obtained blood concentrations tended to be low (Bazzi et al. 2005; Goullé et al. 2005).

Factors involved in the exposure of the population to toxic trace elements

A factor explaining the variability of exposure could be the bioavailability of the metallic and semimetallic elements in the compounds where they are found (Glorennec 2006). The bioavailability of lead in some mining wastes may be lower than lead paint or lead vapors (Freeman et al. 1994). Studies have shown that blood concentrations of trace elements, particularly of lead in children, were lower than expected in populations exposed to mining wastes (Malcoe et al. 2002), even though the environment was strongly contaminated with these toxic elements (Zota et al. 2011). This could be a factor explaining the relatively low concentrations of lead found in our population as well as the toxic trace elements that were undetectable in our samples, such as mercury or cadmium.

In Oruro, it is complicated to identify more and less exposed areas. Some districts are located in clear contact with the mining centers or built on top of abandoned mining wastes, but also small furnaces and smelters are scattered all over the city (Figure 1). The mining districts are generally impoverished areas, where most streets are unnamed, unnumbered, and unpaved, and most households are built in precarious conditions. Thus, these houses are more likely to be contaminated by air particles and dust from the mining wastes and tailings, especially during the long dry/windy seasons. In combination with low educational level and the resulting lack of knowledge about prevention and general precautions, the differences observed in maternal blood of our population seem to point to socioeconomic inequalities as a possible risk factor for higher exposure to toxic trace elements, especially lead. This was also observed in other populations (Berglund et al. 2011; Liu et al. 2012).

Potential limitations

A potential limitation of our study was the assessment method to determine arsenic exposure. The biomarker most commonly used to monitor short-term arsenic and antimony exposure is urine (ATSDR 1992; ATSDR 2007; Sundar & Chakravarty 2010), whereas nails are typically used to determine chronic exposure (ATSDR 2007). In the case of antimony, blood concentrations were also correlated to occupational exposure (Kentner et al. 1995), which is why we do not consider that the biomarker of choice affected our study results.

The population included in our study is representative for the city of Oruro and other mining cities in Bolivian Andean regions but not of other Bolivian regions. Due to the high percentage of study participants without a blood sample, the generalizability of these results should be interpreted with caution. However, we found no significant differences between participants with and without a blood sample with regard to the risk factors: maternal smoking, educational level, and paternal mining.

Conclusions

Pregnant women and their unborn children in the Bolivian town of Oruro are exposed to various toxic trace elements, such as lead, arsenic, and antimony. Twelve percent of our cohort children were born with at least two of these toxic elements above recommendation limits. As exposure is expected to increase with age, these infants are at high risk of developing negative health effects. In order to protect children from potential harmful effects, preventive measures should focus on families with lower educational level, smoking mothers, and family members involved in mining activities.

Appendix 1.

Appendix 1.

Funding Statement

This work was supported by the ANR (Agence Nationale de la Recherche), an agency dependent of the French government. Grant: 39SEST2006 – ToxBol. The ANR did not participate in any aspect or stage of the planning, execution, or aftermath of the project.

Disclosure statement

There are no competing interests for the present study, financial or otherwise.

References

  1. Abbott MB, Wolfe AP. Intensive pre-incan metallurgy recorded by lake sediments from the Bolivian Andes. Science. 2003;301:1893–1895. doi: 10.1126/science.1087806. [DOI] [PubMed] [Google Scholar]
  2. Al-Saleh I, Shinwari N, Mashhour A, Mohamed GE, Rabah A. Heavy metals (lead, cadmium and mercury) in maternal, cord blood and placenta of healthy women. Int J Hyg Environ Health. 2011;214:79–101. doi: 10.1016/j.ijheh.2010.10.001. [DOI] [PubMed] [Google Scholar]
  3. Al-Saleh I, Shinwari N, Mashhour A, Rabah A. Birth outcome measures and maternal exposure to heavy metals (lead, cadmium and mercury) in Saudi Arabian population. Int J Hyg Environ Health. 2014;217:205–218. doi: 10.1016/j.ijheh.2013.04.009. [DOI] [PubMed] [Google Scholar]
  4. [ATSDR] Agency for Toxic Substances and Disease Registry . Atlanta (GA): U.S. Department of Health and Human Services, Public Health Service; 1992. Toxicological profile for antimony and compounds. [PubMed] [Google Scholar]
  5. [ATSDR] Agency for Toxic Substances and Disease Registry . Atlanta (GA): U.S. Department of Health and Human Services, Public Health Service; 2007. Toxicological profile for arsenic. [PubMed] [Google Scholar]
  6. Banks D, Holden W, Aguilar E, Mendez C, Koller D, Andia Z, Rodriguez J, Saether OM, Torrico A, Veneros R, Flores J. Contaminant source characterization of the San Jose Mine, Oruro. Bolivia Mine Water Hydrogeol Geochem. 2002;198:215–239. [Google Scholar]
  7. Barbieri FL, Cournil A, Souza Sarkis JE, Bénéfice E, Gardon J. Hair trace elements concentration to describe polymetallic mining waste exposure in Bolivian Altiplano. Biol Trace Elem Res. 2011;139:10–23. doi: 10.1007/s12011-010-8641-1. [DOI] [PubMed] [Google Scholar]
  8. Barbieri E, Fontúrbel FE, Herbas C, Barbieri FL, Gardon J. Indoor metallic pollution and children exposure in a mining city. Sci Total Environ. 2014;487:13–19. doi: 10.1016/j.scitotenv.2014.03.136. [DOI] [PubMed] [Google Scholar]
  9. Bazzi A, Nriagu JO, Inhorn MC, Linder AM. Determination of antimony in human blood with inductively coupled plasma-mass spectrometry. J Environ Monit. 2005;7:1251–1254. doi: 10.1039/b510088g. [DOI] [PubMed] [Google Scholar]
  10. Bellinger DC, Stiles KM, Needleman HL. Low-level lead exposure, intelligence and academic achievement: a long-term follow-up study. Pediatrics. 1992;90:855–861. [PubMed] [Google Scholar]
  11. Berglund M, Lindberg AL, Rahman M, Yunus M, Grandér M, Lönnerdal B, Vahter M. Gender and age differences in mixed metal exposure and urinary excretion. Environ Res. 2011;111:1271–1279. doi: 10.1016/j.envres.2011.09.002. [DOI] [PubMed] [Google Scholar]
  12. Bouton L, Karmokolias Y, Elkan W, Miller RR, Fitchett DA, Jaspersen FZ. The private sector and development: five case studies, results on the ground. Washington (DC): The World Bank; 1998. http://documents.worldbank.org/curated/en/1998/09/1684353/private-sector-development-five-case-studies [Google Scholar]
  13. Buck Louis GM, Sundaram R, Schisterman EF, Sweeney AM, Lynch CD, Gore-Langton RE, Chen Z, Kim S, Caldwell KL, Barr DB. Heavy metals and couple fecundity, the LIFE Study. Chemosphere. 2012;87:1201–1207. doi: 10.1016/j.chemosphere.2012.01.017. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Bundschuh J, Litter MI, Parvez F, Román-Ross G, Nicolli HB, Jean JS, Liu CW, López D, Armienta MA, Guilherme LRG, et al. One century of arsenic exposure in Latin America: a review of history and occurrence from 14 countries. Sci Total Environ. 2012;429:2–35. doi: 10.1016/j.scitotenv.2011.06.024. [DOI] [PubMed] [Google Scholar]
  15. Cabral M, Dieme D, Verdin A, Garcon G, Fall M, Bouhsina S, Dewaele D, Cazier F, Tall-Dia A, Diouf A, Shirali P. Low-level environmental exposure to lead and renal adverse effects: a cross-sectional study in the population of children bordering the Mbeubeuss landfill near Dakar, Senegal. Human Exp Toxicol. 2012;31:1280–1291. doi: 10.1177/0960327112446815. [DOI] [PubMed] [Google Scholar]
  16. Cace IB, Milardovic A, Prpic I, Krajina R, Petrovic O, Vukelic P, Spiric Z, Horvat M, Mazej D, Snoj J. Relationship between the prenatal exposure to low-level of mercury and the size of a newborn’s cerebellum. Med Hypotheses. 2011;76:514–516. doi: 10.1016/j.mehy.2010.12.005. [DOI] [PubMed] [Google Scholar]
  17. [CDC] Centers for Disease Control and Prevention Blood lead levels – United States, 1999-2002. 2005 (1545-861X (Electronic) 0149-2195 (linking)) [Google Scholar]
  18. [CDC] Centers for Disease Control and Prevention Response to advisory committee on childhood lead poisoning prevention recommendations in “low level lead exposure harms children: a renewed call of primary prevention”. 2013 http://www.cdc.gov/nceh/lead/ACCLPP/acclpp_main.htm [Google Scholar]
  19. Conklin L, Sanchez CA, Neri A, Staley P, Blumenthal W, Jarrett JM, LePrell R, Durant J, Suarez-Soto RJ. Atlanta, GA: Centers for Disease Control and Prevention; 2008. Exposiciones a metales pesados en niños y mujeres en edad fértil en tres comunidades mineras Cerro de Pasco, Perú [Exposure to heavy metals in children and bearing age women in three mining communitites of Cerro de Pasco, Peru]http://www.diresapasco.gob.pe/portal/sama/Peru%20Report%20FINAL%20Spanish_maintext.pdf Spanish. [Google Scholar]
  20. Cooke CA, Balcom PH, Kerfoot C, Abbott MB, Wolfe AP. Pre-colombian mercury pollution associated with the smelting of argentiferous ores in the Bolivian Andes. Ambio. 2011;40:18–25. doi: 10.1007/s13280-010-0086-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Cordy P, Veiga MM, Salih I, Al-Saadi S, Console S, Garcia O, Mesa LA, Velásquez-López PC, Roeser M. Mercury contamination from artisanal gold mining in Antioquia, Colombia: the world’s highest per capita mercury pollution. Sci Total Environ. 2011;410–411:154–160. doi: 10.1016/j.scitotenv.2011.09.006. [DOI] [PubMed] [Google Scholar]
  22. Ettinger AS, Hu H, Hernández-Ávila M. Dietary calcium supplementation to lower blood lead levels in pregnancy and lactation. J Nutr Biochem. 2007;18:172–178. doi: 10.1016/j.jnutbio.2006.12.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Fonturbel FE, Barbieri E, Herbas C, Barbieri FL, Gardon J. Indoor metallic pollution related to mining activity in the Bolivian Altiplano. Environ pollut. 2011;159:2870–2875. doi: 10.1016/j.envpol.2011.04.039. [DOI] [PubMed] [Google Scholar]
  24. Freeman GB, Johnson JD, Liao SC, Feder PI, Davis AO, Ruby MV, Schoof RA, Chaney RL, Bergstrom PD. Absolute bioavailability of lead acetate and mining waste lead in rats. Toxicology. 1994;91:151–163. doi: 10.1016/0300-483X(94)90141-4. [DOI] [PubMed] [Google Scholar]
  25. Glorennec P. Analysis and reduction of the uncertainty of the assessment of children’s lead exposure around an old mine. Environ Res. 2006;100:150–158. doi: 10.1016/j.envres.2005.03.007. [DOI] [PubMed] [Google Scholar]
  26. Goix S, Point D, Oliva P, Polve M, Duprey JL, Mazurek H, Guislain L, Huayta C, Barbieri FL, Gardon J. Influence of source distribution and geochemical composition of aerosols on children exposure in the large polymetallic mining region of the Bolivian Altiplano. Sci Total Environ. 2011;412–413:170–184. doi: 10.1016/j.scitotenv.2011.09.065. [DOI] [PubMed] [Google Scholar]
  27. Goix S, Resongles E, Point D, Oliva P, Duprey JL, de la Galvez E, Ugarte L, Huayta C, Prunier J, Zouiten C, Gardon J. Transplantation of epiphytic bioaccumulators (Tillandsia capillaris) for high spatial resolution biomonitoring of trace elements and point sources deconvolution in a complex mining/smelting urban context. Atmos Environ. 2013;80:330–341. doi: 10.1016/j.atmosenv.2013.08.011. [DOI] [Google Scholar]
  28. Goullé JP, Mahieu L, Castermant J, Neveu N, Bonneau L, Lainé G, Bouige D, Lacroix C. Metal and metalloid multi-elementary ICP-MS validation in whole blood, plasma, urine and hair. Reference values. Forensic Sci Int. 2005;153:39–44. doi: 10.1016/j.forsciint.2005.04.020. [DOI] [PubMed] [Google Scholar]
  29. Guimaraes JRD, Betancourt O, Miranda MR, Barriga R, Cueva E, Betancourt S. Long-range effect of cyanide on mercury methylation in a gold mining area in southern Ecuador. Sci Total Environ. 2011;409:5026–5033. doi: 10.1016/j.scitotenv.2011.08.021. [DOI] [PubMed] [Google Scholar]
  30. Gulson BL, Mizon KJ, Korsch MJ, Palmer JM, Donnelly JB. Mobilization of lead from human bone tissue during pregnancy and lactation – a summary of long-term research. Sci Total Environ. 2003;303:79–104. doi: 10.1016/S0048-9697(02)00355-8. [DOI] [PubMed] [Google Scholar]
  31. Gump BB, MacKenzie JA, Bendinskas K, Morgan R, Dumas AK, Palmer CD, Parsons PJ. Low-level Pb and cardiovascular responses to acute stress in children: the role of cardiac autonomic regulation. Neurotoxicol Teratol. 2011;33:212–219. doi: 10.1016/j.ntt.2010.10.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Hawkesworth S, Wagatsuma Y, Kippler M, Fulford AJC, Arifeen SE, Persson LA, Moore SE, Vahter M. Early exposure to toxic metals has a limited effect on blood pressure or kidney function in later childhood, rural Bangladesh. Int J Epidemiol. 2013;42:176–185. doi: 10.1093/ije/dys215. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. [HRSDC] Human Resources and Skills Development Canada – Government of Canada National occupational classification. 2006 http://www30.hrsdc.gc.ca/NOC/English/NOC/2006/AboutNOC.aspx [Google Scholar]
  34. Hudson-Edwards KA, Macklin MG, Miller JR, Lechler PJ. Sources, distribution and storage of heavy metals in the Río Pilcomayo, Bolivia. J Geochem Explor. 2001;72:229–250. doi: 10.1016/S0375-6742(01)00164-9. [DOI] [Google Scholar]
  35. [INE] Instituto Nacional de Estadística de Bolivia [National Institute of Bolivian Statistics]: sistema Nacional de Información Estadística de Bolivia. Spanish http://www.ine.gob.bo/indice/visualizador.aspx?ah=PC2031004.HTM [Google Scholar]
  36. [INE] Instituto Nacional de Estadística de Bolivia Clasificación de Ocupaciones de Bolivia – COB 2009. 2009 http://www.ine.gob.bo/pdf/COB2009/COB2009.pdf [Google Scholar]
  37. [IUPAC] International Union of Pure and Applied Chemistry . 2nd. Oxford: Blackwell Scientific Publications; 1997. Compendium of chemical terminology.http://www.iupac.org/ (the “Gold Book”). McNaught AD and Wilkinson A, compilers. [Google Scholar]
  38. Kentner M, Leinemann M, Schaller KH, Weltle D, Lehnert G. External and internal antimony exposure in starter battery production. Int Arch Occup Environ Health. 1995;67:119–123. doi: 10.1007/BF00572235. [DOI] [PubMed] [Google Scholar]
  39. Keutsch F, Brodtkorb MK. Metalliferous paragenesis of the San José mine, Oruro, Bolivia. J S Am Earth Sci. 2008;25:485–491. doi: 10.1016/j.jsames.2007.12.003. [DOI] [Google Scholar]
  40. Lin CM, Doyle P, Wang DL, Hwang YH, Chen PC. The role of essential metals in the placental transfer of lead from mother to child. Reprod Toxicol. 2010;29:443–446. doi: 10.1016/j.reprotox.2010.03.004. [DOI] [PubMed] [Google Scholar]
  41. Liu J, Ai Y, McCauley L, Pinto-Martin J, Yan C, Shen X, Needleman H. Blood lead levels and associated sociodemographic factors among preschool children in the South Eastern region of China. Paediatric Perinatal Epidemiol. 2012;26:61–69. doi: 10.1111/ppe.2012.26.issue-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Liu KS, Hao JH, Zeng Y, Dai FC, Gu PQ. Neurotoxicity and biomarkers of lead exposure: a review. Chung-kuo i hsueh k’o hsueh tsa chih [Chin Med Sci J] 2013;28:178–188. doi: 10.1016/s1001-9294(13)60045-0. [DOI] [PubMed] [Google Scholar]
  43. Malcoe LH, Lynch RA, Kegler MC, Skaggs VJ. Lead sources, behaviors, and socioeconomic factors in relation to blood lead of native American and white children: a community-based assessment of a former mining area. Environ Health Perspect. 2002;110:221–231. doi: 10.1289/ehp.02110s2221. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Manton WI, Angle CR, Stanek KL, Kuntzelman D, Reese YR, Kuehnemann TJ. Release of lead from bone in pregnancy and lactation. Environ Res. 2003a;92:139–151. doi: 10.1016/S0013-9351(03)00020-3. [DOI] [PubMed] [Google Scholar]
  45. Manton WI, Angle CR, Stanek KL, Kuntzelman D, Reese YR, Kuehnemann TJ. Release of lead from bone in pregnancy and lactation. Environ Res. 2003b;92:139–151. doi: 10.1016/S0013-9351(03)00020-3. [DOI] [PubMed] [Google Scholar]
  46. Mazumdar M, Bellinger DC, Gregas M, Abanilla K, Bacic J, Needleman HL. Low-level environmental lead exposure in childhood and adult intellectual function: a follow-up study. Environ Health. 2011;10:24. doi: 10.1186/1476-069X-10-24. http://www.ehjournal.net/content/10/March/2011 [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Min JY, Min KB, Cho SI, Kim R, Sakong J, Paek D. Neurobehavioral function in children with low blood lead concentrations. NeuroToxicology. 2007;28:421–425. doi: 10.1016/j.neuro.2006.03.007. [DOI] [PubMed] [Google Scholar]
  48. Moura M, Gonçalves Valente JG. Blood lead levels during pregnancy in women living in Rio de Janeiro, Brazil. Sci Total Environ. 2002;299:123–129. doi: 10.1016/S0048-9697(02)00255-3. [DOI] [PubMed] [Google Scholar]
  49. Moya N, Hughes RM, Domínguez E, Gibon FM, Goitia E, Oberdorff T. Macroinvertebrate-based multimetric predictive models for evaluating the human impact on biotic condition of Bolivian streams. Ecol Indic. 2011;11:840–847. doi: 10.1016/j.ecolind.2010.10.012. [DOI] [Google Scholar]
  50. Needleman HL. The neuropsychiatric implications of low level exposure to lead. Psychol Med. 1982;12:461–463. doi: 10.1017/S0033291700055550. [DOI] [PubMed] [Google Scholar]
  51. Niedzwiecki MM, Hall MN, Liu X, Oka J, Harper KN, Slavkovich V, Ilievski V, Levy D, van Geen A, Mey JL. A dose–response study of arsenic exposure and global methylation of peripheral blood mononuclear cell DNA in Bangladeshi adults. Environ Health Perspect. 2013;121:1306–1312. doi: 10.1097/01.ede.0000391815.42977.9b. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Oyarzun R, Guevara S, Oyarzún J, Lillo J, Maturana H, Higueras P. Environ Geochem Health. 2006;The as-contaminated Elqui river basin: a long lasting perspective (1975–1995) covering the initiation and development of Au-Cu-As mining in the high Andes of northern Chile;28:431–443. doi: 10.1007/s10653-006-9045-1. [DOI] [PubMed] [Google Scholar]
  53. Parajuli RP, Fujiwara T, Umezaki M, Watanabe C. Association of cord blood levels of lead, arsenic, and zinc with neurodevelopmental indicators in newborns: a birth cohort study in Chitwan Valley, Nepal. Environ Res. 2013;121:45–51. doi: 10.1016/j.envres.2012.10.010. [DOI] [PubMed] [Google Scholar]
  54. Pebe G, Villa H, Escate L, Cervantes G. Blood lead levels in newborns from La Oroya, 2004–2005. Rev Peru Med Exp Salud Publica. 2008;25:355–359. [Google Scholar]
  55. Plusquellec P, Muckle G, Dewailly E, Ayotte P, Jacobson S, Jacobson J. The relation of low-level prenatal lead exposure to behavioral indicators of attention in Inuit infants in Arctic Quebec. Neurotoxicol Teratol. 2007;29:527–537. doi: 10.1016/j.ntt.2007.07.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Riojas-Rodríguez H, Solís-Vivanco R, Schilmann A, Montes S, Rodríguez S, Ríos C, Rodríguez-Agudelo Y. Intellectual function in mexican children living in a mining area and environmentally exposed to manganese. Environ Health Perspect. 2010;118:1465–1470. doi: 10.1289/ehp.0901229. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Ruiz-Castell M, Carsin AE, Barbieri FL, Paco P, Gardon J, Sunyer J. Child patterns of growth delay and cognitive development in a Bolivian mining city. Am J Human Biol. 2013;25:94–100. doi: 10.1002/ajhb.22346. [DOI] [PubMed] [Google Scholar]
  58. Ruiz-Castell M, Paco P, Barbieri FL, Duprey JL, Forns J, Carsin AE, Freydier R, Casiot C, Sunyer J, Gardon J. Child neurodevelopment in a Bolivian mining city. Environ Res. 2012;112:147–154. doi: 10.1016/j.envres.2011.12.001. [DOI] [PubMed] [Google Scholar]
  59. [SENAMHI] Servicio Nacional de Meteorología e Hidrología [National Service of Meteorology and Hydrology] Ministerio de Medio Ambiente y Agua [Ministry of Environment and Water - Government of Bolivia] http://www.senamhi.gob.bo/sismet/index.php Spanish. [Google Scholar]
  60. Sundar S, Chakravarty J. Antimony toxicity. Int J Environ Res Pub Health. 2010;7:4267–4277. doi: 10.3390/ijerph7124267. [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Surkan P, Zhang A, Trachtenberg F, Daniel D, Mckinlay S, Bellinger D. Neuropsychological function in children with blood lead levels <10 μg/dL. Epidemiology. 2007;18:S55–S56. doi: 10.1016/j.neuro.2007.07.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Torres R. El mineral de Oruro [Mineral in Oruro]. Anales del Instituto de Ingenieros de Chile. 1890;10:448–455. Spanish. [Google Scholar]
  63. van Geen A, Bravo C, Gil V, Sherpa S, Jack D. Lead exposure from soil in Peruvian mining towns: a national assessment supported by two contrasting examples. Bull World Health Org. 2012;90:878–886. doi: 10.2471/BLT.12.106419. [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Xie X, Ding G, Cui C, Chen L, Gao Y, Zhou Y, Shi R, Tian Y. The effects of low-level prenatal lead exposure on birth outcomes. Environ Pollut. 2013;175:30–34. doi: 10.1016/j.envpol.2012.12.013. [DOI] [PubMed] [Google Scholar]
  65. Yacoub C, Perez-Foguet A, Miralles N. Trace metal content of sediments close to mine sites in the Andean region. Sci World J. 2012;2012:732519. doi: 10.1100/2012/732519. [DOI] [PMC free article] [PubMed] [Google Scholar]
  66. Yáñez L, Ortiz D, Calderón J, Batres L, Carrizales L, Mejía J, Martínez L, García-Nieto E, Díaz-Barriga F. Overview of human health and chemical mixtures: problems facing developing countries. Environ Health Perspect. 2002;110:901–909. doi: 10.1289/ehp.02110s6901. [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Yorifuji T, Debes F, Weihe P, Grandjean P. Prenatal exposure to lead and cognitive deficit in 7-and 14-year-old children in the presence of concomitant exposure to similar molar concentration of methylmercury. Neurotoxicol Teratol. 2011;33:205–211. doi: 10.1016/j.ntt.2010.09.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. Zota AR, Schaider LA, Ettinger AS, Wright RO, Shine JP, Spengler JD. Metal sources and exposures in the homes of young children living near a mining-impacted Superfund site. J Expo Sci Environ Epidemiol. 2011;21:495–505. doi: 10.1038/jes.2011.21. [DOI] [PMC free article] [PubMed] [Google Scholar]

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