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Original Article

Arsenic Exposure from Drinking Water and Birth Weight

Hopenhayn, Claudia*; Ferreccio, Catterina; Browning, Steven R.; Huang, Bin§; Peralta, Cecilia*; Gibb, Herman; Hertz-Picciotto, Irva

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doi: 10.1097/01.ede.0000072104.65240.69
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Inorganic arsenic is a naturally occurring element. At high exposures, it has been associated with skin, lung, and bladder cancers,1–4 vascular diseases,5,6 hypertension7,8 and diabetes.9,10 Limited attention has been directed toward investigating relatively moderate or low exposure levels, particularly effects on reproductive outcomes.

Arsenic crosses the placenta in both animals11 and humans,12 and experimental studies support a role for arsenic as a developmental toxicant. Reproductive studies in an arsenic-contaminated community surrounding a Swedish copper smelter found that women working in the smelter or living nearby gave birth to infants of lower birth weight, and had a higher incidence of spontaneous abortions and congenital malformations.13–16 Although arsenic levels were high, confounding from other chemicals or lifestyle factors could not be excluded. Studies around a copper smelter in Bulgaria found a higher incidence of toxemia in pregnant women17 and lower birth weight in newborns,18 but again, the results were limited by methodologic concerns.

Investigations focused on arsenic exposure from drinking water have reported increased rates of miscarriage,19,20 stillbirths20 and congenital heart disease.21,22 In the county of Antofagasta, Chile, rates of stillbirths and infant mortality increased over a time period coinciding with a substantial increase in the arsenic concentration (from 90 μg/L to 800 μg/L) in the local public drinking water supply.23 A retrospective survey of mothers in Bangladesh compared self-reported pregnancy outcomes in two villages with contrasting arsenic levels in drinking water (mean = 240 μg/L vs. <20 μg/L), and found increases in miscarriages, stillbirths and preterm births in the more highly exposed village.24

This investigation is a prospective cohort study conducted in Chile, with the primary aim to compare reproductive outcomes of pregnant women from two large cities with contrasting arsenic concentrations in their public drinking water: Antofagasta (40 μg/L) and Valparaíso (<1 μg/L). In this paper, we report overall study methods and an analysis of the association between arsenic in drinking water and birth weight of singleton livebirths.


Study Location

Antofagasta and Valparaíso are coastal cities, similar in size (around 250,000 population each) and general sociodemographic characteristics.23 At the start of this study, Antofagasta, which lies in the northern region, had drinking water arsenic concentrations around 40 μg/L, based on our water sampling determinations and on the local water company’s records; in contrast, Valparaíso, located in Central Chile, had arsenic levels below 1 μg/L.

Study Design

Pregnant women, 18 to 45 years of age, living in Antofagasta and Valparaíso were recruited between November 1998 and January 2000 from the Chilean public health care system for maternal and child health. This system employs uniform standards of prenatal health care nationwide. In each study city, prenatal health care routinely takes place in outpatient clinics associated with one main hospital: Hospital Regional in Antofagasta and Hospital Carlos Van Buren in Valparaíso. Trained midwives usually provide prenatal health care, but suspected high-risk pregnancies or women developing complications are referred to specialized physicians at the hospital. For this study, women were enrolled at three local clinics in Antofagasta and two local clinics in Valparaíso.

Eligibility Criteria

We selected potential participants from the list of women attending the study clinics. We approached women during normal prenatal visits between 16 and 35 weeks of gestation. Women were asked to complete an eligibility questionnaire if they were 18 years or older; a resident of Antofagasta or Valparaíso for the last 12 months and planning to continue residing in the same city for the following 12 months; planning to continue their prenatal health care at the clinic where they were recruited and to deliver the baby at the associated hospital; using the city’s public water supply for drinking and cooking purposes; and not working at a mine or smelter.


Most of the eligible women were invited into the study, and were given a clear description of what participation entailed. On busy clinic days, some of the eligible women were missed, but if they were still eligible at their next prenatal care visit, we made attempts to recruit them then. Interested participants signed a consent form. Women who enrolled agreed to be interviewed, to provide a spot urine sample, to authorize the study team to access their medical records on their pregnancy and delivery, and to obtain a small sample of their newborn’s hair. The study protocol was reviewed and approved by the University of Kentucky Institutional Review Board, and by the participating Chilean institutions.

Data Collection

In-person interviews were conducted by trained midwives. They included questions on sociodemographic characteristics, lifestyle and dietary habits, vitamin and fluid consumption, medical and previous pregnancy history, self-perceived stress, employment history, and paternal exposures. Data abstracted from prenatal records included information on weight gain, blood pressure, nutritional evaluation, blood and urine tests, medication intake, and ultrasound results. Data abstracted from the hospital records included infant birth weight and Apgar score, type of delivery and birth complications.

Exposure Assessment

Water sampling took place in June-July of 1998 during pilot fieldwork. Tap water samples were obtained on two different days from a convenience sample of three households per city. Samples were kept frozen at −20°C on site, transported in dry ice to the University of Washington, and analyzed for arsenic by continuous flow/hydride generation/atomic fluorescence detection.25 Mean inorganic arsenic concentrations were 42 μg/L (range = 32.9–52.7) in Antofagasta and <1 μg/L (0.5–1.1) in Valparaíso (quantitation limit = 0.5 μg/L). To confirm the concentrations in Antofagasta throughout the study period, we obtained data from the local water company, which indicated that, for over 700 yearly arsenic determinations at the distribution point, the average arsenic levels were 40, 31 and 30 μg/L in 1998, 1999 and 2000, respectively. Multielement analysis was also performed on water samples from each city to detect potential exposures from other chemicals.

We assigned maternal arsenic exposure from water using two methods. Given the contrast among the two cities’ water arsenic levels, and the relatively uniform exposure from a single water supply company in each city, we used maternal city of residence as a dichotomous indicator of arsenic exposure. We also derived an individual exposure index based on estimated mean arsenic concentrations and self-reported fluid consumption. For the birth weight analysis, we focus on city of residence as the primary exposure variable, based on the following considerations: first, accurate and representative measures of fluid intake are difficult to obtain from a one-time recall; furthermore, patterns of fluid intake may vary over the course of pregnancy, and women were interviewed across a range of gestational ages (16–35 weeks). Second, the accuracy of reported intake may vary across individuals. Third, although the arsenic water levels in Antofagasta remained fairly constant, on average they did vary within the range of approximately 30–40 μg/L during the study (approximately 25–30% variation range). Therefore, interindividual water consumption variability was likely to be swamped by recall errors, unmeasured variability across pregnancy and variations in water arsenic concentrations. Conversely, the contrast between the two cities was clear at all times. We concluded that the city variable provided better discrimination of putative arsenic exposure effects. Nevertheless, for the sake of completeness we also present the summary results using the individual exposure index in the analysis.

Following the interview, participants provided a urine sample which was placed on ice until transferred to a −20°C freezer later the same day. Ultimately these samples were transported in dry ice to the University of Kentucky for storage and subsequently sent to the laboratory for analysis. For this paper, we present measurements of urinary arsenic for a small subgroup of participants as biologic confirmation of exposure consistent with each city’s arsenic water levels. Urinary analyses were performed by batch hydride generation atomic absorption detection at the University of Washington.26

Potential Covariates

The potential risk factors for reduced birth weight included mother’s age, education and income, smoking, alcohol consumption, stress, parity, adequacy of prenatal health care and maternal weight gain.

Maternal ethnicity was classified as indigenous if women reported being of Rapanui, Aimara, or Mapuche descent.27 Maternal education was coded as basic (0–8 years), middle (9–12 years), or technical or university (>12 years). Total monthly household income was converted from pesos to U.S. dollars.

Maternal body mass index (BMI), calculated as weight (in kilograms) at first prenatal health care visit divided by height (in meters) squared, was categorized in tertiles. Maternal pregnancy weight gain was estimated as the difference in weight between the first and the last prenatal visit. We used the number of blocks walked per day as a measure of physical activity, because daily walking was common in this population, but other regular exercising was not. Women reported the number of cigarettes smoked per day before and during pregnancy, all types of beverage consumption during pregnancy, and alcohol consumption before and during pregnancy. Daily intake of fats, carbohydrates, and proteins was derived from a food frequency questionnaire on the most commonly consumed fruits, vegetables, meats, cereals, and dairy products during pregnancy.28 We also asked for intake of vitamins or supplements during pregnancy.

Gestational age (GA) at birth was estimated using an algorithm which uses three GA estimates as previously described:29 (1) gestational age from the mother’s self-reported last menstrual period (LMP-GA); (2) gestational age from the ultrasound routinely performed early in pregnancy (US-GA); and (3) the gestational age assigned by the physician from the clinical examination at birth (CL-GA). We used the LMP-GA as the standard unless this value was missing, exceeded 42 weeks, or was more than 2 weeks different from US-GA or CL-GA. The final GA determination (GA-INDEX) was based on LMP-GA for 92% of women, US-GA for 4%, and CL-GA for 4%.

Maternal stress was measured by two scales. The Everyday Stressors Index30,31 is a 20-item instrument designed to assess common problems faced daily by mothers of young children. This instrument was translated for the specific target population. The internal consistency (Cronbach’s α = 0.77) was comparable to that found in other studies.30,31 Construct validity for the scale has been supported in previous research.32,33 We also used a Major Life Stress index derived from six items selected from Dohrenwend’s PERI Life Events scale, which includes major life stressors (such as losing a job, or death of a family member) that may have occurred within the last 6 months.34

Prenatal health care utilization was based on the Adequacy of Prenatal Health Care Utilization Index.35 Prenatal health care was classified as “inadequate” if there were fewer than five prenatal visits or if the first visit occurred during the third trimester, “intermediate” if there were 5–8 visits with the first occurring in the first or second trimester, and “adequate” if there were 8 or more visits with the first visit in the first or second trimester.

Statistical Analysis

We examined differences in mean birth weight using bivariate analysis for the exposures and covariates potentially related to the exposure of interest. We used analysis of covariance to estimate the effect of city of residence on infant birth weight, while adjusting for confounding variables. Seven variables (city, infant sex, parity, prenatal health care index, maternal height, gestational age, and gestational age squared) were forced into the model. Subsequently, we considered the following maternal variables: age, BMI, education, ethnicity, household income, walking, years of residence, alcohol consumption, caffeinated beverage consumption, cigarette smoking, and stress. A forward selection approach using the change-in-estimate method was employed;36 we retained covariates if their inclusion resulted in at least a 15% change in the coefficient (adjusted birth weight difference) pertaining to the primary exposure variable (city). All covariates in the final model were coded as categorical variables (to avoid assumptions of linearity), except for maternal height, which was treated as a continuous variable and gestational age, which was modeled as both linear and quadratic.37,38 We present the adjusted birth weight differences and their 95% confidence limits for the best main-effects model based on a consideration of confounding and model fit.

Although the focus of this paper is on birth weight after adjustment for gestational age, we recognize the importance of gestational age beyond its adjustment as a covariate. For this reason, preterm delivery and related outcomes will be the topic of a subsequent paper. However, because small babies born early are at highest risk, we briefly present the results of the final multivariable birth weight model for two separate groups: gestational age less than or equal to 38 weeks and gestational age greater than 38 weeks.


We included in the study 886 women who met the initial study eligibility criteria: 442 from Antofagasta and 444 from Valparaíso. For the birth weight analysis, we excluded 32 women (3.6%) who were lost to follow-up (primarily because they delivered at other hospitals), 5 women who gave birth to twins, and 5 women who did not deliver a liveborn (4 stillbirths, 1 miscarriage). This left a total of 844 mother-infant pairs: 424 from Antofagasta and 420 from Valparaíso.

Maternal Characteristics

These Chilean women were primarily (92%) of nonindigenous ethnicity (Table 1). The majority were married or cohabitating, more than 75% of the women had twelve or fewer years of education, and most were of low socioeconomic status. Over 1/2 of the women were multiparous, and approximately 70% initiated prenatal health care in the first trimester. Maternal weight gain averaged 11.1 kg (24.4 lbs). Although we did not have prepregnancy weight, our figures on pregnancy weight gain appear to be a good estimate because the results were similar when we restricted the analysis to the women who started prenatal health care at less than 13 weeks gestational age, before much weight would have been gained. Average duration of residence in each city was over 15 years, and 90% had lived in the same city for at least 5 years (data not shown).

Demographic and Prenatal Characteristics of Women in Two Cities in Chile

Women in Antofagasta were more likely to be married or cohabitating, to be multiparous, and to have higher income and adequate prenatal health care, but less likely to have at least 9 years of education (Table 1).

In Table 2 we compare maternal behaviors by city of residence. Fewer than 15% of the women reported alcohol consumption during pregnancy, but more than 90% consumed caffeinated beverages. Twelve percent reported smoking during their pregnancy, although only nine women smoked more than five cigarettes a day. Before pregnancy, the smoking rates were substantially higher at more than 50% in both towns. Slightly over one half of the women reported taking iron supplements during pregnancy; more women in Antofagasta took calcium, but more women in Valparaíso took folic acid.

Selected Lifestyle and Dietary Characteristics for the Women Residing in Two Cities in Chile

Biologic Exposure Assessment

Total urinary inorganic arsenic, measured in spot urine samples of 19 women per city, averaged 54.3 μg/L (SD = 33.8) in Antofagasta, and 5.3 μg/L (SD = 3.3) in Valparaíso, confirming the contrast in exposure between the two cities. The relation between arsenic levels in drinking water and urinary arsenic levels was very consistent with that found in other population studies.39

Birth Weight Analysis

Table 3 shows the results of the bivariate analysis for mean birth weight by city, as well as by other maternal and infant characteristics. Overall mean birth weight was 3396 g (SD = 507) and mean gestational age at birth was 39.2 weeks (SD = 1.5). Results are consistent with established findings regarding effects on birth weight for infant sex and gestational age, and maternal parity, age, BMI, height, and prenatal health care. In the unadjusted analysis, birth weight did not differ by city (3 g difference).

Distribution of Birth Weight (grams) and Unadjusted Differences by Selected Maternal and Infant Characteristics for Cohort of Women

Final multivariable analyses based on 813 mother-infant pairs with complete data for all 9 covariates (96% of total) are presented in Table 4. Residence in Antofagasta was associated with a reduction in birth weight of −57 g (95% CI: −122 to 9). The difference between the crude and adjusted birth weight analysis was due primarily to four major confounders: adequacy of prenatal health care, parity, BMI and income (in that order). When these were removed from the multivariable model the effect of city dropped to −6 g.

Multivariable Model Results for Birthweight Differences According to Selected Maternal and Infant Characteristics and Location

The analysis using individual arsenic exposure instead of the “city” term resulted in an estimate of –0.26 g birth weight per μg of arsenic (95% CI = −0.85, 0.31), which at the mean Antofagasta water intake of 2.3 L (almost 100 μg arsenic/day) would represent −26 g (95% CI −85, 31).

Table 5 presents the birth weight results for the same model as Table 4, divided in two gestational age periods. The effect of city (as measure of exposure) is almost twice as strong among the earlier deliveries (GA ≤ 38 weeks vs. >38 weeks). However, the interaction of gestational age and birth weight in the multivariable model was not statistically significant (P = 0.74).

Multivariable Model Results for the Birthweight Differences, by Gestational Age Group, Adjusted for Variables as Shown in Table 4


Although previous studies have suggested a negative effect of arsenic on birth weight,16,19 this is the first prospective study and the first to adjust for a wide range of confounders. We found a decrease in the adjusted mean birth weight (−57 g) of singleton infants born to women living in Antofagasta, compared with Valparaíso, after adjusting for known birth weight risk factors. This difference may be causally related to arsenic exposure from Antofagasta’s drinking water (30–40 μg/L) compared with arsenic levels in Valparaíso drinking water (<1 μg/L). The contrast in exposure was confirmed by similar differences in the urinary arsenic levels of study participants from the two cities.

The magnitude of the adjusted birth weight difference is similar to findings from other environmental factors. Two meta-analyses of environmental tobacco smoke exposure during pregnancy found a decrease in birth weight of −31 to −41 g.40,41 Low benzene exposures42 and occupational exposure to polychlorinated biphenyls43 both showed decreases of –58 g.

The validity of our final multivariable model is supported by the substantial portion of the birth weight variability accounted for (R2 = 0.31) and by the estimated magnitude and direction of established risk factors for birth weight, such as gestational age, parity, infant sex, BMI and adequacy of prenatal health care. The need to control for these factors in our study is evident, because the crude analysis showed no effect, whereas the adjusted analysis found a −57 g difference between cities. Adequacy of prenatal health care, parity, BMI, and income were the major confounders (in that order). Three of these variables—prenatal health care, parity, and income—were all higher in Antofagasta, and were all positively associated with birth weight, indicating that the unadjusted analyses would be confounded, with a bias toward a higher birth weight in Antofagasta. This explains why in the unadjusted analyses, the cities did not differ.

We attempted to assess the birth weight effect on premature and full-term infants, but the number of preterm infants in our sample was small. We did find that the association between arsenic and birth weight is almost twice as large in infants delivered at 38 weeks or less compared with those born later, although this interaction was not statistically significant. Still, this hypothesis warrants further investigation in a larger sample to allow evaluation of differences based on the usual classification of prematurity (<37 weeks).

The association between cigarette smoking and birth weight was weak (−20 g) compared with other studies, probably because smokers smoked very few cigarettes per day (mean <3). It is also possible that misclassification of smoking due to under-reporting could have attenuated the association. Smoking before pregnancy was much higher but the dose was still low (65% of smokers smoked <5 cigarettes/d).

Additional strengths of this investigation include high participation rates and low loss-to-follow-up rates (3.6%). Also there was a high degree of data completeness (with 96% of the final study sample included in the multivariable model). Because all women were recruited from the Chilean public health system, which is used primarily by persons of low socioeconomic status, some degree of homogeneity was achieved, although income still played a role in the multivariable model. The impact of prenatal health care may also have reflected socioeconomic differences.

The results using individual arsenic exposure indicated that at the mean Antofagasta water consumption of 2.3 L, a decrease of –26 in birth weight might be expected. However, the dichotomized classification of exposure based on city of residence was considered the most appropriate because the clear contrast in the water levels between the cities and the likelihood that misclassification of arsenic intake based on individual fluid consumption calculations (for reasons explained in Methods) would overshadow the limited variation in exposures within cities. Furthermore, the contrast in arsenic water concentration was confirmed by a large difference in the mean urinary arsenic measurements among a sample of participants.

Potential confounding from other unmeasured risk factors, particularly other environmental exposures, merits consideration. Although we did not control for other contaminants, the multielement analysis of water samples from both cities did not indicate exposure levels of concern for cadmium, manganese or chromium. Given suggestive evidence that chlorination by-products could reduce birth weight,44,45 we also measured total trihalomethanes. We did not find substantial concentrations or substantial differences between the two cities (data not shown). Finally, we measured lead concentrations in cord blood samples from 30 participants on each city and found similar low concentrations in both (mean 3.2 μg/dl in Valparaiso and 3.4 μg/dl in Antofagasta).

There is no obvious biologic mechanism by which arsenic could affect birth weight. Arsenic has vascular effects, and it is possible that some type of placental abnormality or decreased blood flow could affect fetal growth.

Although there has been much research on effects of environmental arsenic, most have been in highly exposed populations and few have included reproductive outcomes. Our findings suggest the possibility that arsenic reduces intrauterine grow that moderate exposure levels. The stronger association in those of lower gestational age underscores the potential for adverse consequences of lowered birth weight. However, the clinical importance of these results, or their implications for other possible effects on the fetus, remain to be determined.


We are grateful for the contributions of many colleagues, collaborators, study staff and assistants; in particular, we thank Alex Bingcang, Jose Centeno, Andrés Gomez-Caminero, Derek Moore, Juan Carlos Ramírez-Suarez, and Michelle Stump for help with data collection and data entry, and collection of biologic samples. In Chile, Alex Arroyo for providing the data from the Antofagasta water company; Aliro Bolados, Javier Egaña, Rosa García, Silvana Hakim, Angélica Olguín, Claudia Benavides, Jessica Bravo, midwives at Hospital Regional in Antofagasta and Hospital Van Buren in Valparaíso, staff at the study clinics of Consultorio Centro-Sur, Corvalis, Consultorio Norte, Plaza Justicia, and Quebrada Verde; and Michael Bates and Mary Lou Biggs for helpful comments on the manuscript.


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arsenic; drinking water; reproductive effects; environmental exposure; birth weight

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