Next Article in Journal
Phytoremediation of Soil Contaminated by Organochlorine Pesticides and Toxic Trace Elements: Prospects and Limitations of Paulownia tomentosa
Next Article in Special Issue
Intrauterine and Extrauterine Environmental PM2.5 Exposure Is Associated with Overweight/Obesity (O/O) in Children Aged 6 to 59 Months from Lima, Peru: A Case-Control Study
Previous Article in Journal
Ce3+/Ce4+-Doped ZrO2/CuO Nanocomposite for Enhanced Photocatalytic Degradation of Methylene Blue under Visible Light
Previous Article in Special Issue
Health Outcomes in Children Associated with Prenatal and Early-Life Exposures to Air Pollution: A Narrative Review
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Toxics
Volume 10
Issue 8
10.3390/toxics10080464
toxics-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Prenatal Exposure to Mercury, Manganese, and Lead and Adverse Birth Outcomes in Suriname: A Population-Based Birth Cohort Study

by
Vinoj H. Sewberath Misser
1,*,
Ashna D. Hindori-Mohangoo
2,3,
Arti Shankar
2,
Jeffrey K. Wickliffe
4,
Maureen Y. Lichtveld
5 and
Dennis R. A. Mans
1
1
Department of Pharmacology, Faculty of Medical Sciences, Anton de Kom University of Suriname, Kernkampweg 5-7, Paramaribo, Suriname
2
Department of Environmental Health Sciences, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA 70112, USA
3
Foundation for Perinatal Interventions and Research in Suriname (Perisur), Paramaribo, Suriname
4
Department of Environmental Health Sciences, School of Public Health, University of Alabama at Birmingham, Birmingham, AL 35294, USA
5
School of Public Health, University of Pittsburgh, Pittsburgh, PA 15260, USA
*
Author to whom correspondence should be addressed.
Toxics 2022, 10(8), 464; https://doi.org/10.3390/toxics10080464
Submission received: 29 June 2022 / Revised: 4 August 2022 / Accepted: 6 August 2022 / Published: 11 August 2022
(This article belongs to the Special Issue Prenatal Exposure to Environmental Pollutants: Effects on Fetal Development, Birth Outcomes and Children’s Health)
Versions Notes

Abstract

:
Globally, adverse birth outcomes are increasingly linked to prenatal exposure to environmental contaminants, such as mercury, manganese, and lead. This study aims to assess an association between prenatal exposure to mercury, manganese, and lead and the occurrence of adverse birth outcomes in 380 pregnant women in Suriname. The numbers of stillbirths, preterm births, low birth weights, and low Apgar scores were determined, as well as blood levels of mercury, manganese, lead, and relevant covariates. Descriptive statistics were calculated using frequency distributions. The associations between mercury, manganese, and lead blood levels, on the one hand, and adverse birth outcomes, on the other hand, were explored using contingency tables, tested with the χ2-test (Fisher’s exact test), and expressed with a p value. Multivariate logistic regression models were computed to explore independent associations and expressed as (adjusted) odds ratios (aOR) with 95% confidence intervals (CI). The findings of this study indicate no statistically significant relationship between blood mercury, manganese, or lead levels and stillbirth, preterm birth, low birth weight, and low Apgar score. However, the covariate diabetes mellitus (aOR 5.58, 95% CI (1.38–22.53)) was independently associated with preterm birth and the covariate hypertension (aOR 2.72, 95% CI (1.081–6.86)) with low birth weight. Nevertheless, the observed high proportions of pregnant women with blood levels of mercury, manganese, and lead above the reference levels values of public health concern warrants environmental health research on risk factors for adverse birth outcomes to develop public health policy interventions to protect pregnant Surinamese women and their newborns from potential long-term effects.

1. Introduction

Globally, stillbirth, preterm birth, low birth weight, and low Apgar score are among the most common adverse birth outcomes. Each year, roughly 15 million babies are born prematurely [1], about 2 million die before or during delivery [2], approximately 20 million have a low birth weight [3], and a low Apgar score has been related to the death of approximately 4 million babies worldwide [4]. Adverse birth outcomes and their consequences represent a critical public health issue in low- and middle-income countries, in particular, where resources for adequate healthcare are, in general, insufficient [3,5]. For instance, from 2000 to 2019, 98% of stillbirths were registered in low- and middle-income countries [2]; in 2014, more than 60% of preterm births were in South Asia and sub-Saharan Africa [6,7]; and in 2013, nearly 22 million newborns with a low birth weight—or 16% of all babies born globally—were found in Asia and Africa [8].
Several factors have been associated with adverse birth outcomes. Some of these include socio-demographic characteristics of the mother, such as age at the birth of the child, marital status, education, occupation, household income, and nutritional status [9,10,11], and her reproductive and obstetric characteristics, such as age of sexual debut, parity, and history of perinatal death [12,13]. The accessibility and availability of maternal health services and the quality of antenatal care also play a role in the occurrence of adverse birth outcomes [13,14,15]. Other major causes of adverse birth outcomes are exposure to environmental pollutants during pregnancy, such as pesticides and herbicides, polycyclic aromatic hydrocarbons, particulate matter, and toxic heavy metals [16,17,18]. These exposures have also been linked to long-term neurodevelopmental deficits in young children [19,20].
The Republic of Suriname is a South American country that borders the Atlantic Ocean to the north and is located between French Guiana and Guyana. Suriname’s major means of subsistence are gold mining and agriculture, in addition to crude oil drilling, fisheries, forestry, and ecotourism [21]. Together, these economic activities have resulted in a gross domestic income in 2020 of USD 2.88 billion and an average per-capita income in that year of about USD 4900 [22]. This positions Suriname on the World Bank’s list of upper–middle income economies [22].
Regrettably, these developments have been achieved at the expense of severe environmental pollution, massive land degradation, and extensive deforestation [23]. The use of considerable amounts of mercury to amalgamate the gold produced in artisanal gold mining activities [24] has led to the poisoning of streams, rivers, and lakes as well as species of fish for human consumption [25,26]. Consequently, unacceptably high mercury levels (particularly in the form of methylmercury) have been detected in various preferred fish species that are high on the food chain, such as the anyumara or wolf fish Hoplias aimara [27,28]. The need to produce increasing amounts of field crops, fruits, and vegetables has resulted in the unregulated and illegal use of banned agricultural pesticides and herbicides that contain, among others, manganese and lead. Manganese is one of the active ingredients of the ethylene bis-dithiocarbamate fungicide mancozeb [29] that is widely used in the large-scale cultivation of bananas and rice [30,31]. Lead is present in glyphosate-based herbicides and has been detected in agricultural products, such as cassava [32,33]. This compound has even been detected in drinking water from the national distribution system [34].
When these heavy metals enter the food chain and are ingested by pregnant women, they increase the risk of adverse birth outcomes. Prenatal exposure to mercury has been associated with reduced placental and fetal growth [35,36], while methylmercury compounds have been related to neurological disorders of the child [35,37]. Manganese exposure during pregnancy predisposes newborns to a lower birth weight [38,39,40], and higher maternal lead concentrations increase the risk of preterm birth [36,41] and overweight or obesity of the child [42].
The hospital-based maternal mortality rate in Suriname over the past thirty years was 127 per 100,000 [43], the stillbirth rate was 16 per 1000 births, and the preterm birth and low birth weight rates were 14% and 15%, respectively [44]. Whether these adverse birth outcomes are attributable to overexposure to mercury, manganese, or lead during pregnancy is not known. Hence, this study aims to assess an association between prenatal exposure to mercury, manganese, and lead and the occurrence of adverse birth outcomes in Suriname. For this purpose, the current study was embedded in the Caribbean Consortium for Research in Environmental and Occupational Health (CCREOH)’s prospective cohort study, in which the blood levels from a sub-cohort of 400 pregnant women from three geographic regions of Suriname were analyzed [45].
The influence of several factors that were not the focus of the current study but may have an impact on adverse birth outcomes have also been assessed. These covariates included medical conditions, such as diabetes mellitus [46,47], hypertension [48], and anemia [44,49,50], as well as maternal age older than 35 years [51,52], multiparity [53,54], alcohol use until at least 3 months before pregnancy [55,56], smoking until at least 3 months before pregnancy [57,58], and the use of prescription medicines such as analgesics [59]. Insight into the exposure patterns to the heavy metals will help design interventions and spark future research on environmental and health policies to ensure the safety of pregnant women and their newborns in Suriname.

2. Subjects and Methods

2.1. Study Design and Setting

In this study, the possible association of prenatal exposure to mercury, manganese, and lead, on the one hand, and adverse birth outcomes in pregnant women in Suriname, on the other hand, was analyzed. As mentioned before, for the current study, the data analyzed were from the Caribbean Consortium for Research in Environmental and Occupational Health (CCREOH)’s cohort of 1200 pregnant women in 2017. The number of 1200 participants was calculated based on a multiple linear regression model using a coefficient of determination of 0.10 and an R2 differential of 0.02 to have 80% power at a 0.05 level of significance, taking into account the potential loss of participants [45]. In this cohort, the pregnant women were enrolled in a longitudinal epidemiological study that evaluated the effects of environmental contaminants on birth outcomes and the neurological development of children born in Suriname [45].
The current study is based on a subset of 400 participants whose blood metal levels were available and have been analyzed. The CCREOH study has been approved by the Institutional Review Board of the Ministry of Health of Suriname (protocol number VG 023-14) and the Institutional Review Board of Tulane University, New Orleans, LA, USA (protocol number 83 093). Each participant entered the study voluntarily, gave informed consent after the purpose of the study was thoroughly explained, and agreed to donate venous blood.

2.2. Collection of Maternal Socio-Demographic and Dietary Data

Enrollment of the participants and collection of maternal socio-demographic and dietary data was conducted by trained recruiters through face-to-face-interviews using questionnaires on encrypted iPads, as described in the CCREOH’s study protocol [46].The following maternal socio-demographics were included this study: age at delivery (16–19 years, 20–34 years, or 35 years and older, according to the reproductive age categories of the World Health Organization [60]); region of residence (urban-coastal, rural-coastal, or rural-interior region of Suriname as distinguished by the Surinamese General Bureau of Statistics [61]); monthly household income ≤ USD 75 (SRD 1500) or >USD 75 (SRD 1500); educational level (none or primary education or secondary or tertiary education); and food consumption.

2.3. Collection of Data on Birth Outcomes

Data on birth outcomes of the study participants were retrieved from their birth and medical records at the hospitals and primary healthcare clinics where the delivery took place. Stillbirth was defined as a fetus born with no signs of life from 22+ completed weeks of gestation or a birthweight of 500+ g [62,63,64,65]; preterm birth was defined as a birth between 22 + 0 and 36 + 6 weeks of gestation [66]; a low birthweight was defined as a weight at birth of less than 2.500 g regardless of the gestational age [67]; and a low Apgar score was defined as a score of less than seven at 5 min after birth [68].

2.4. Determination of Blood Concentrations of Mercury, Manganese, and Lead

Whole blood samples from the 400 study participants were collected during the first and second trimesters of their pregnancy using trace element vacutainers with potassium EDTA. The blood samples were frozen and shipped to the Wisconsin State Laboratory of Hygiene (WSLH) at the University of Wisconsin–Madison (Madison, WI, USA). The Trace Element Research Group of that institution determined the concentrations of mercury, manganese, and lead in the samples using sector field inductively coupled plasma mass spectrometry.
The detection limits for mercury, manganese, and lead were 0.05 µg/L, 0.07 µg/L, and 0.06 µg/L, respectively. Blood levels of mercury were categorized as <3.5 µg/L and ≥3.5 µg/L, using 3.5 µg/L as the reference level [26,69,70]. Those for manganese were categorized as <13.0 µg/L or ≥13.0 µg/L [71]. Using the reference level 3.5 µg/dL, the blood lead levels of were categorized as <3.5 µg/dL and ≥3.5 µg/dL [72]. The reference level cutoff points are referred to as public health action levels.

2.5. Covariates Included in the Study

The covariates considered relevant to the current study included elevated levels of blood glucose (≥6.1 mmol/L, obtained after fasting) [73], raised blood pressure (>120/80 mm Hg) [74], and low hemoglobin levels (anemia) (<6.8 mmol/L) [75], as well as maternal age 35 years and over, multiparity (at least 1 previous live birth), alcohol use until at least 3 months before pregnancy, smoking until at least 3 months before pregnancy, and the use of prescription medicines (at least one medicine prescribed). These data were retrieved from the medical records (based on laboratory results) of the participants and from information obtained by interviewing them.

2.6. Statistical Analyses

Descriptive statistics were calculated using frequency distributions. The potential associations between prenatal exposure to mercury, manganese, and lead, on the one hand, and adverse birth outcomes, on the other hand, were explored using contingency tables, tested with the χ2-test, and expressed with a p value. Fisher’s exact test was performed to adjust for sensitivity. Logistic regression models were computed to explore crude and independent associations of the categorized blood levels of the heavy metals and the covariates with the adverse birth outcomes and expressed as crude and adjusted odds ratios (OR) with 95% confidence intervals (CI). Only variables that were statistically significantly associated at the bivariate level at p < 0.10 were included in the multivariate models. In the crude models, each variable was tested individually against the relevant birth outcome. In the adjusted models, the variables were evaluated together against the relevant birth outcome to establish associations between the variables. All analyses were conducted using IBM “Statistical Package for the Social Sciences” (SPSS) version 25.

3. Results

3.1. Sociodemographic Characteristics

Due to missing data on adverse birth outcomes, 20 participants were excluded from the analyses. The socioeconomic characteristics of the remaining 380 women are presented in Table 1. Their average age was 28.5 ± 6.3 years. The majority (74.7%) were 20–34 years old, 15.3% were 35 years or older, and 10.0% were aged between 16 and 19 years. About two-thirds of the women resided in Suriname’s urban–coastal region, 22.4% in the country’s rural–coastal region, and 11.6% in the rural–interior part. Around 30% had an income of less than USD 75 (SRD 1500), and about 18% had received no or only primary education. Together with rice, leafy vegetables, and bread, fish was among the most consumed items, being part of the diets of more than 95% of the participants.

3.2. Adverse Birth Outcomes

A total of 74 of the 380 evaluable women had experienced an adverse birth outcome (Table 1). The most frequent adverse birth outcomes were preterm birth, which occurred in 50 cases (13.3%), and low birthweight, which was seen in 43 cases (11.6%). Stillbirth and low Apgar score were registered in 17 and 19 cases, respectively, corresponding to 4.5% and 5.2%, respectively, of the 380 pregnancies (Table 1).

3.3. Blood Concentrations of Mercury, Manganese, and Lead

As shown in Table 1, more than one-third of the 380 women (154 or 40.5%) had blood mercury levels ≥ 3.5 µg/L. However, elevated blood levels of manganese (≥13.0 µg/L) were seen in about a 1.5 times higher number, namely in 243 women or 63.9% (Table 1). On the other hand, 81 or 21.3% of women had blood lead levels above the reference level of 3.5 µg/dL (Table 1). Thus, in the period covered by the study, elevated blood levels of mercury and manganese were seen in about two of five and three of five pregnant women, respectively, while high blood lead levels were seen in roughly one of five of the women.

3.4. Covariates

Assessment of the covariates considered relevant to the current study (Table 1), showed that 15 of the 380 participants (10.9%) were diabetics, 55 (32.7%) suffered from hypertension, and more than two-thirds (n = 203; 84.6%)) had anemia. In addition, 58 (15.3%) were 35 years or older, more than two-thirds (n = 265; 70.1%) were multiparous, more than half (n = 173; 57.8%) had used alcohol until at least 3 months before their pregnancy, a minority (n = 18; 4.8%) had smoked until at least 3 months before their pregnancy, and more than half (n = 224; 58.9%) had used prescription medicines (for instance, medicines for blood and blood-forming organs, including anti-anemic drugs, as well as medicines for alimentary tract and metabolism, including drugs for acid-related disorders). When compared to the frequencies of women with heavy metal blood levels that were too high, these frequencies were sufficiently high to suggest that these covariates might impact adverse birth outcomes.

3.5. Associations between Prenatal Exposure to Heavy Metals or Covariates and Adverse Birth Outcomes

The results from bivariate associations between the blood levels of the heavy metals or the covariates, on the one hand, and the occurrence of adverse effects, on the other hand, are shown in Table 2. No statistically significant associations were found between the blood levels of mercury or manganese and the occurrence of stillbirth, preterm birth, low birth weight, or low Apgar scores (p values > 0.10). Maternal age 35 years and over, underlying anemia, multiparity, and smoking were also not statistically significantly associated with any of the adverse birth outcomes (p values > 0.10).
On the other hand, blood concentrations of lead ≥3.5 µg/dL were statistically significantly associated with the occurrence of preterm birth (p value of 0.065), though not with that of stillbirth, low birth weight, and low Apgar score (p values of 0.220, 0.333, and 0.266, respectively). The presences of diabetes mellitus, alcohol consumption, and the use of prescription medicines were also statistically significantly associated with preterm birth (p values of 0.028, 0.034 and 0.006, respectively). The same held true for alcohol consumption, as well as underlying hypertension and the occurrence of low birth weight (p values of 0.015, 0.015, respectively). Together, these observations suggest that high lead blood concentrations, as well as the presence of diabetes mellitus, alcohol consumption until at least 3 months before pregnancy, and the use of prescription medicines may represent independent variables for the occurrence of preterm birth, and that underlying hypertension and, the use of alcohol until at least 3 months before pregnancy may represent independent variables for the occurrence of low birth weight.
Following up on these findings, multivariate logistic regression models were conducted to determine whether blood lead levels ≥ 3.5 µg/dL underlying diabetes mellitus or hypertension, pre-pregnancy alcohol use, and prescribed medicine use were independent predictors of preterm birth or low birth weight. As shown in Table 3, in the crude model, women with blood lead levels ≥ 3.5 µg/dL had a 1.88 (95% CI 0.98–3.60) higher risk of having a preterm birth when compared to those with blood lead levels < 3.5 µg/dL. Furthermore, the odds of having a preterm birth were 4.29 (95% CI 1.26–14.62) times higher in diabetic women than non-diabetic women, 2.53 (95% CI 1.28–5.00) times higher in women who had prescribed medicines than in those who had not, and 0.50 (95% CI 0.27–0.93) times lower in those who had used alcohol at least 3 months before pregnancy than in those who had not (Table 3).
Further, women with an elevated blood pressure had 3.16 (95% CI 1.28–7.76) times greater odds of giving birth to a baby with a low birth weight than women with a normal tension, while pre-pregnancy alcohol use was associated with a decreased risk of 0.43 (95% CI 0.22–0.85) when compared to the abstinence of alcohol (Table 4). Thus, the ORs found in the crude models indicate that each of the individual variables (diabetes mellitus, hypertension, prescription medicine use, and alcohol intake at least 3 months before pregnancy) were predictive factors for preterm birth or low birth weight.
Next, the influence of underlying diabetes mellitus, hypertension, prescribed medicine, or alcohol intake on the occurrence of preterm birth or low birth weight was explored in the adjusted models. In these analyses, the aOR for an association between blood lead levels ≥ 3.5 µg/dL and preterm birth decreased from 1.88 (95% CI 0.98–3.60) to 1.38 (95% CI 0.40–4.79), and the association between prescription medicine use, as well as pre-pregnancy alcohol use, and preterm birth changed from 2.53 (95% CI 1.28–5.00) to 2.1 (95% CI 0.43–10.16) and 0.50 (95% CI 0.27–0.93) to 0.36 (95% CI 0.11–1.145), respectively (Table 3). The OR of a preterm birth for women with diabetes mellitus increased from 4.29 (95% CI 1.26–14.62) to 5.58 (95% CI 1.38–22.83) after adjustment (Table 3). Thus, as expected, diabetes mellitus can be considered an independent risk factor for preterm birth. Furthermore, women with high blood pressure had 2.72 (95% CI 1.081–6.86) times higher odds of having a low-birth-weight newborn (Table 4). On the other hand, the OR of 0.43 (95% CI 0.22–0.85) changed to 0.47 (95% CI 0.18–1.23) for low-birth-weight babies when using alcohol at least 3 months before pregnancy (Table 4).
Together, these findings suggest that there was no association between prenatal exposure to mercury, manganese, or lead and adverse birth outcomes, but that the covariates of high blood glucose levels and preterm birth, and high blood pressures and low birth weight, showed to be independently related to the adverse birth outcomes.

4. Discussion

In this study, the association of blood levels of mercury, manganese, and lead, on the one hand, and the occurrence of adverse birth outcomes, including still birth, preterm birth, low birth weight, and low Apgar score, on the other hand, was assessed in 380 pregnant Surinamese women. Covariates, such as underlying hypertension, diabetes mellitus, and anemia, as well as multiparity, age over 35 years, alcohol use until at least 3 months before pregnancy, smoking until at least 3 months before pregnancy, and the use of prescription medicines, such as anti-anemic drugs, were also included in the analyses.
The women enrolled in the study were part of a larger group of 1200 women who had given birth and in whom the effects of environmental contaminants on birth outcomes and neurological development had been studied [45]. Their average age of 28.5 ± 6.3 years and the distribution of the age groups (about three-quarters aged between 20 and 34 years), regions of residence (about two-thirds living in Suriname’s urban–coastal region), monthly household income (USD 75 or SRD 1.500 for almost two-thirds), education levels (secondary or tertiary education for more than 80%), and food consumption (bread, rice, fish, and leafy vegetables were the most commonly consumed items by at least 80%) represented a good cross-section of the pregnant Surinamese woman [76,77,78,79].
The frequency of adverse birth outcomes in the current sub-cohort (19.5%) and the frequency distribution of the birth outcomes (13.3% preterm births, 11.6% low birth weight, 4.5% stillbirth, and 4.2% low Apgar score) were also roughly within the same range as those previously mentioned for Suriname [44,80] and for fourteen other lower–middle-income countries in several parts of the world [81].
While investigating this group of pregnant Surinamese women, no statistically significant associations were found between blood levels of mercury, manganese, or lead and the occurrence of stillbirth, preterm birth, low birth weight, or low Apgar score. This was unexpected since blood concentrations of the heavy metals were above public health action levels in 40.5%, 63.9%, and 21.3%of the women, respectively. As mentioned before, these compounds are present in various (banned) agricultural pesticides that are, nevertheless, abundantly used in Suriname [82]. Residues of these substances have been detected in tap water [34] and foods that are among the most consumed by pregnant Surinamese women, such as leafy vegetables [83] and fish [26]. The elevated blood mercury levels also coincided with those found in the hair and blood samples of women and children from interior villages in Suriname where too high mercury levels have been detected in regularly consumed fish species [28]. Moreover, the blood lead levels above the reference may be attributed to the consumption of agricultural products, such as cassava [32,33,84], and the drinking of contaminated tap water [34], in addition to the ingestion of lead-contaminated (herbal) food supplements [85], exposure to lead-based paints, fuels, and mosquito coils [86,87], and/or the application of lead-containing cosmetics [88]. Whether and which of these causes might be the reason for the levels of these heavy metals in the pregnant Surinamese women is not clear and needs further investigation. This is particularly necessary when considering the relatively high frequency of preterm births found in the current study (12.5%) as well as in a previous study that was also conducted in Suriname [44].
Nevertheless, that the high number of blood levels above the reference levels of these compounds was apparently not associated with adverse birth outcomes in the current study may be tentatively explained by the relatively small number of participants included in the study. The study was initially designed and powered for 1200 participants based on the effect size for the longitudinal study with this number of women. However, as mentioned before, at the time of the current study, blood specimens’ data were only available for 400 women. Consequently, the effect size was smaller than the effect size for which the study was originally powered. Obviously, the resulting lower power is a limitation of the current study. The remaining blood specimen data are anticipated to be available in the near future.
In addition, the high frequency of women with blood metal levels above public health action reference levels may not lead to immediate adverse health effects in pregnant women and newborns, but rather manifest as neurodevelopmental disorders in children many years after birth [89,90,91], childhood overweight or obesity [42], impaired liver or kidney functions [92], lower bone density [92,93] or cardiovascular disorders at a more advanced age [94,95,96].
The association of each of the studied covariates in the current study with adverse birth outcomes has been well documented [55,58,97,98,99,100,101,102]. In accordance with these data, we found statistically significant risks for the occurrence of preterm birth and low birth weight in women suffering from hypertension or diabetes mellitus. Why no association was found between other risk factors for adverse birth effects, such as underlying anemia in the mother, maternal age over 35 years, multiparity, and pre-pregnancy smoking [58,102], with any of the negative outcomes is not clear but may due to the limited sample size [103].
There is also no explanation at hand for the apparent risk reduction in preterm birth in women who had used alcohol before their pregnancy and who were at the same time suffering from diabetes mellitus or had used prescription medicines, such as anti-anemic drugs. However, some studies have shown an independent relationship of diabetes mellitus and preterm birth in pregnant women [104,105], which emphasizes the importance of screening pre-pregnancy and during pregnancy for diabetes mellitus as a public heath intervention [106].
The apparent attenuation of the risk of preterm birth in women who had been exposed to higher lead concentrations and had also used prescription medicines may tentatively be attributed to interactions of both substances and lead to changes in their pharmacological fate and modification of the risk for this adverse birth outcome. This may hold true for, for instance, iron supplements, which are often prescribed for anemia in pregnant Surinamese women [107]. This can be explained by the fact that several heavy metals, including lead, share the same transporter proteins and receptors with iron, [108,109] causing anemia [92,110], and thus modifying the risk of preterm birth [111]. Obviously, both this supposition and that mentioned in the preceding paragraph must be verified in future studies.
In summary, the results of the current study suggest that there were no associations between prenatal exposure to mercury, manganese, or lead and adverse birth outcomes. However, the small sample size of the study decreases its statistical power, warranting caution when trying to extrapolate these findings to the general population. For the same reason, the absence of an association between the high mercury, manganese, or lead blood levels and adverse birth outcomes must be considered with care. The same holds true for the confounding effects of underlying diabetes mellitus and/or hypertension, as well as the use of prescription medicines and alcohol use.
Nevertheless, our data warrant more careful assessments of pregnant women for risk factors for adverse birth outcomes, such as prenatal exposure to hazardous environmental compounds; underlying (chronic) diseases, such as diabetes mellitus; the use of certain prescription medicines; and pre-pregnancy alcohol use, not only separately but also when these risks are present in various combinations. As shown by the current data, these practices will likely help improve environmental and health policies to ensure the safety of (pregnant) women and their newborns in Suriname. Future studies should include an increased number of pregnant women and specifically enroll women with underlying anemia, diabetes mellitus, and/or hypertension, as well women using prescription medicines, such as anti-anemic drugs in order to eliminate type II errors.

5. Conclusions

The findings of this study suggest that there were no statistically significant relationships between mercury, manganese, and lead blood levels above reference (high) values and adverse birth outcomes, including stillbirths, preterm birth, low birth weight, and low Apgar score. These findings and the alarming frequency of blood levels of mercury, manganese, and lead above the reference levels, indicate an urgent need for environmental health research and public health policy interventions to evaluate and protect pregnant Surinamese women more meticulously for potential risk factors for adverse birth outcomes and the effects in the long term for mothers and their newborns.

Author Contributions

Conceptualization: V.H.S.M. and D.R.A.M.; methodology and analysis: V.H.S.M., A.S., A.D.H.-M., D.R.A.M. and J.K.W.; writing: V.H.S.M. and D.R.A.M.; writing—review and editing: D.R.A.M., A.D.H.-M., J.K.W., A.S. and M.Y.L.; funding acquisition: J.K.W. and M.Y.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Fogarty International Center of the National Institutes of Health (NIH) grant numbers U01TW010087 and U2RTW0 0104. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Institutional Review Board Statement

This study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of the Ministry of Health of Suriname (protocol number VG 023–14) and the Institutional Review Board of Tulane University, New Orleans (LA), USA (protocol number 83 093).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

References

  1. Howson, C.P.; Kinney, M.V.; McDougall, L.; Lawn, J.E. Born Too Soon: Preterm Birth Matters. Reprod. Health 2013, 10, S1. [Google Scholar] [CrossRef] [Green Version]
  2. Hug, L.; You, D.; Blencowe, H.; Mishra, A.; Wang, Z.; Fix, M.J.; Wakefield, J.; Moran, A.C.; Gaigbe-Togbe, V.; Suzuki, E.; et al. Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Lancet 2021, 398, 772–785. [Google Scholar] [CrossRef]
  3. Blencowe, H.; Krasevec, J.; de Onis, M.; Black, R.E.; An, X.; Stevens, G.A.; Borghi, E.; Hayashi, C.; Estevez, D.; Cegolon, L.; et al. National, Regional, and Worldwide Estimates of Low Birthweight in 2015, with Trends from 2000: A Systematic Analysis. Lancet Glob. Health 2019, 7, e849–e860. [Google Scholar] [CrossRef] [Green Version]
  4. Aslam, H.M.; Saleem, S.; Afzal, R.; Iqbal, U.; Saleem, S.M.; Shaikh, M.W.A.; Shahid, N. Risk Factors of Birth Asphyxia. Ital. J. Pediatr. 2014, 40, 94. [Google Scholar] [CrossRef] [Green Version]
  5. Liu, L.; Oza, S.; Hogan, D.; Perin, J.; Rudan, I.; Lawn, J.E.; Cousens, S.; Mathers, C.; Black, R.E. Global, Regional, and National Causes of Child Mortality in 2000-13, with Projections to Inform Post-2015 Priorities: An Updated Systematic Analysis. Lancet 2015, 385, 430–440. [Google Scholar] [CrossRef]
  6. Blencowe, H.; Cousens, S.; Oestergaard, M.Z.; Chou, D.; Moller, A.B.; Narwal, R.; Adler, A.; Vera Garcia, C.; Rohde, S.; Say, L.; et al. National, Regional, and Worldwide Estimates of Preterm Birth Rates in the Year 2010 with Time Trends since 1990 for Selected Countries: A Systematic Analysis and Implications. Lancet 2012, 379, 2162–2172. [Google Scholar] [CrossRef] [Green Version]
  7. Chawanpaiboon, S.; Vogel, J.P.; Moller, A.B.; Lumbiganon, P.; Petzold, M.; Hogan, D.; Landoulsi, S.; Jampathong, N.; Kongwattanakul, K.; Laopaiboon, M.; et al. Global, Regional, and National Estimates of Levels of Preterm Birth in 2014: A Systematic Review and Modelling Analysis. Lancet Glob. Health 2019, 7, e37–e46. [Google Scholar] [CrossRef] [Green Version]
  8. UNICEF; WHO; World Bank Group; United Nations. Levels and Trends in Child Mortality 2020; UNICEF: New York, NY, USA, 2020; ISBN 978-92-806-5147-8.
  9. Godah, M.W.; Beydoun, Z.; Abdul-Khalek, R.A.; Safieddine, B.; Khamis, A.M.; Abdulrahim, S. Maternal Education and Low Birth Weight in Low- and Middle-Income Countries: Systematic Review and Meta-Analysis. Matern. Child Health J. 2021, 25, 1305–1315. [Google Scholar] [CrossRef]
  10. Guarnizo-Herreño, C.C.; Torres, G.; Buitrago, G. Socioeconomic Inequalities in Birth Outcomes: An 11-Year Analysis in Colombia. PLoS ONE 2021, 16, e0255150. [Google Scholar] [CrossRef]
  11. Vidiella-Martin, J.; Been, J.V.; Van Doorslaer, E.; García-Gómez, P.; Van Ourti, T. Association Between Income and Perinatal Mortality in the Netherlands Across Gestational Age. JAMA Netw. Open 2021, 4, e2132124. [Google Scholar] [CrossRef]
  12. Sebayang, S.K.; Dibley, M.J.; Kelly, P.J.; Shankar, A.V.; Shankar, A.H. Determinants of Low Birthweight, Small-for-Gestational-Age and Preterm Birth in Lombok, Indonesia: Analyses of the Birthweight Cohort of the SUMMIT Trial. Trop. Med. Int. Health 2012, 17, 938–950. [Google Scholar] [CrossRef]
  13. Adane, A.A.; Ayele, T.A.; Ararsa, L.G.; Bitew, B.D.; Zeleke, B.M. Adverse Birth Outcomes among Deliveries at Gondar University Hospital, Northwest Ethiopia. BMC Pregnancy Childbirth 2014, 14, 90. [Google Scholar] [CrossRef] [Green Version]
  14. Gebremedhin, M.; Ambaw, F.; Admassu, E.; Berhane, H. Maternal Associated Factors of Low Birth Weight: A Hospital Based Cross-Sectional Mixed Study in Tigray, Northern Ethiopia. BMC Pregnancy Childbirth 2015, 15, 222. [Google Scholar] [CrossRef] [Green Version]
  15. Zhu, L.; Zhang, Y.; Liu, Y.; Zhang, R.; Wu, Y.; Huang, Y.; Liu, F.; Li, M.; Sun, S.; Xing, L.; et al. Maternal and Live-Birth Outcomes of Pregnancies Following Assisted Reproductive Technology: A Retrospective Cohort Study. Sci. Rep. 2016, 6, 35141. [Google Scholar] [CrossRef]
  16. Jaacks, L.M.; Diao, N.; Calafat, A.M.; Ospina, M.; Mazumdar, M.; Ibne Hasan, M.O.S.; Wright, R.; Quamruzzaman, Q.; Christiani, D.C. Association of Prenatal Pesticide Exposures with Adverse Pregnancy Outcomes and Stunting in Rural Bangladesh. Environ. Int. 2019, 133, 105243. [Google Scholar] [CrossRef]
  17. Yang, L.; Shang, L.; Wang, S.; Yang, W.; Huang, L.; Qi, C.; Gurcan, A.; Yang, Z.; Chung, M.C. The Association between Prenatal Exposure to Polycyclic Aromatic Hydrocarbons and Birth Weight: A Meta-Analysis. PLoS ONE 2020, 15, e0236708. [Google Scholar] [CrossRef]
  18. Shih, Y.H.; Chen, H.Y.; Christensen, K.; Handler, A.; Turyk, M.E.; Argos, M. Prenatal Exposure to Multiple Metals and Birth Outcomes: An Observational Study within the National Children’s Study Cohort. Environ. Int. 2021, 147, 106373. [Google Scholar] [CrossRef]
  19. Eskenazi, B.; Kogut, K.; Huen, K.; Harley, K.G.; Bouchard, M.; Bradman, A.; Boyd-Barr, D.; Johnson, C.; Holland, N. Organophosphate Pesticide Exposure, PON1, and Neurodevelopment in School-Age Children from the CHAMACOS Study. Environ. Res. 2014, 134, 149–157. [Google Scholar] [CrossRef] [Green Version]
  20. Furlong, M.A.; Engel, S.M.; Barr, D.B.; Wolff, M.S. Prenatal Exposure to Organophosphate Pesticides and Reciprocal Social Behavior in Childhood. Environ. Int. 2014, 70, 125–131. [Google Scholar] [CrossRef] [Green Version]
  21. General Bureau of Statistics. 9th Environment Statistics Publication 2015-2019; General Bureau of Statistics: Paramaribo, Suriname, 2020. [Google Scholar]
  22. The World Bank Suriname|Data. Available online: https://data.worldbank.org/country/suriname?view=chart (accessed on 23 February 2022).
  23. Strategic Environmental Advice; Tropenbos International Suriname. Report of the Strategic Environmental and Social Assessment (SESA) Accompanying the Development of the National REDD+ Strategy of the Republic of Suriname; REDD+ Program Management Unit: Paramaribo, Suriname, 2017. [Google Scholar]
  24. Heemskerk, M.; Negulic, E.; Duijves, C. Reducing the Use and Release of Mercury by Artisanal and Small- Scale Gold Miners in Suriname; Social Solutions: Paramaribo, Suriname, 2016. [Google Scholar]
  25. Ouboter, P.; Landburg, G.; Hawkins, W.; Taylor, R.; Lichtveld, M.Y.; Harville, E.W.; Zijlmans, C.; MacDonald-Ottevanger, S.; Drury, S.; Wickliffe, J. Mercury Levels in Women and Children from Suriname. ISEE Conf. Abstr. 2018, 2018, isesisee.2018.P02.2340. [Google Scholar] [CrossRef]
  26. Wickliffe, J.K.; Lichtveld, M.Y.; Zijlmans, C.W.; MacDonald-Ottevanger, S.; Shafer, M.; Dahman, C.; Harville, E.W.; Drury, S.; Landburg, G.; Ouboter, P.; et al. Exposure to Total and Methylmercury among Pregnant Women in Suriname: Sources and Public Health Implications. J. Expo. Sci. Environ. Epidemiol. 2021, 31, 117–125. [Google Scholar] [CrossRef]
  27. Ouboter, P.E.; Landburg, G.A.; Quik, J.H.M.; Mol, J.H.A.; Van Der Lugt, F. Mercury Levels in Pristine and Gold Mining Impacted Aquatic Ecosystems of Suriname, South America. Ambio 2012, 41, 873. [Google Scholar] [CrossRef]
  28. Ouboter, P.; Landburg, G.; Satnarain, G.; Starke, S.; Nanden, I.; Simon-Friedt, B.; Hawkins, W.; Taylor, R.; Lichtveld, M.; Harville, E.; et al. Mercury Levels in Women and Children from Interior Villages in Suriname, South America. Int. J. Environ. Res. Public Health 2018, 15, 1007. [Google Scholar] [CrossRef] [Green Version]
  29. Gullino, M.L.; Tinivella, F.; Garibaldi, A.; Kemmitt, G.M.; Bacci, L.; Sheppard, B. Mancozeb: Past, Present, and Future. Plant Dis. 2010, 94, 1076–1087. [Google Scholar] [CrossRef] [Green Version]
  30. Abdoel Wahid, F.; Wickliffe, J.; Wilson, M.; Van Sauers, A.; Bond, N.; Hawkins, W.; Mans, D.; Lichtveld, M. Presence of Pesticide Residues on Produce Cultivated in Suriname. Environ. Monit. Assess. 2017, 189, 303. [Google Scholar] [CrossRef]
  31. Ministry of Agriculture Animal Husbandry and Fisheries Mancozeb Import Statistics; Ministry of Agriculture Animal Husbandry and Fisheries: Paramaribo, Suriname, 2019.
  32. Rimbaud, D.; Restrepo, M.; Louison, A.; Boukhari, R.; Ardillon, V.; Carles, G.; Lambert, V.; Jolivet, A. Blood Lead Levels and Risk Factors for Lead Exposure among Pregnant Women in Western French Guiana: The Role of Manioc Consumption. J. Toxicol. Environ. Health A 2017, 80, 382–393. [Google Scholar] [CrossRef] [Green Version]
  33. Ohiagu, F.O.; Lele, K.C.; Chikezie, P.C.; Verla, A.W.; Enyoh, C.E. Bioaccumulation and Health Risk Assessment of Heavy Metals in Musa Paradisiaca, Zea Mays, Cucumeropsis Manii and Manihot Esculenta Cultivated in Onne, Rivers State, Nigeria. Environ. Health Toxicol. 2020, 35, e2020011. [Google Scholar] [CrossRef]
  34. Suriname Water Company Resultaten SWM Waterkwaliteit Na de Zuivering 2019; Suriname Water Company: Paramaribo, Suriname, 2019.
  35. Murcia, M.; Ballester, F.; Enning, A.M.; Iñiguez, C.; Valvi, D.; Basterrechea, M.; Rebagliato, M.; Vioque, J.; Maruri, M.; Tardon, A.; et al. Prenatal Mercury Exposure and Birth Outcomes. Environ. Res. 2016, 151, 11–20. [Google Scholar] [CrossRef]
  36. Ashrap, P.; Watkins, D.J.; Mukherjee, B.; Boss, J.; Richards, M.J.; Rosario, Z.; Vélez-Vega, C.M.; Alshawabkeh, A.; Cordero, J.F.; Meeker, J.D. Maternal Blood Metal and Metalloid Concentrations in Association with Birth Outcomes in Northern Puerto Rico. Environ. Int. 2020, 138, 105606. [Google Scholar] [CrossRef]
  37. Debes, F.; Budtz-Jørgensen, E.; Weihe, P.; White, R.F.; Grandjean, P. Impact of Prenatal Methylmercury Exposure on Neurobehavioral Function at Age 14 Years. Neurotoxicol. Teratol. 2006, 28, 536–547. [Google Scholar] [CrossRef] [Green Version]
  38. Zota, A.R.; Ettinger, A.S.; Bouchard, M.; Amarasiriwardena, C.J.; Schwartz, J.; Hu, H.; Wright, R.O. Maternal Blood Manganese Levels and Infant Birth Weight. Epidemiology 2009, 20, 367–373. [Google Scholar] [CrossRef] [Green Version]
  39. Ashley-Martin, J.; Dodds, L.; Arbuckle, T.E.; Ettinger, A.S.; Shapiro, G.D.; Fisher, M.; Monnier, P.; Morisset, A.S.; Fraser, W.D.; Bouchard, M.F. Maternal and Cord Blood Manganese (Mn) Levels and Birth Weight: The MIREC Birth Cohort Study. Int. J. Hyg. Environ. Health 2018, 221, 876–882. [Google Scholar] [CrossRef]
  40. Eum, J.-H.; Cheong, H.-K.; Ha, E.-H.; Ha, M.; Kim, Y.; Hong, Y.-C.; Park, H.; Chang, N. Maternal Blood Manganese Level and Birth Weight: A MOCEH Birth Cohort Study. Environ. Health 2014, 13, 31. [Google Scholar] [CrossRef] [Green Version]
  41. Perkins, M.; Wright, R.O.; Amarasiriwardena, C.J.; Jayawardene, I.; Rifas-Shiman, S.L.; Oken, E. Very Low Maternal Lead Level in Pregnancy and Birth Outcomes in an Eastern Massachusetts Population. Ann. Epidemiol. 2014, 24, 915–919. [Google Scholar] [CrossRef] [Green Version]
  42. Wang, G.; DiBari, J.; Bind, E.; Steffens, A.M.; Mukherjee, J.; Azuine, R.E.; Singh, G.K.; Hong, X.; Ji, Y.; Ji, H.; et al. Association Between Maternal Exposure to Lead, Maternal Folate Status, and Intergenerational Risk of Childhood Overweight and Obesity. JAMA Netw. Open 2019, 2, e1912343. [Google Scholar] [CrossRef]
  43. Kodan, L.R.; Verschueren, K.J.C.; Paidin, R.; Paidin, R.; Browne, J.L.; Bloemenkamp, K.W.M.; Rijken, M.J. Trends in Maternal Mortality in Suriname: 3 Confidential Enquiries in 3 Decades. AJOG Glob. Rep. 2021, 1, 100004. [Google Scholar] [CrossRef]
  44. Verschueren, K.J.C.C.; Prüst, Z.D.; Paidin, R.R.; Kodan, L.R.; Bloemenkamp, K.W.M.M.; Rijken, M.J.; Browne, J.L. Childbirth Outcomes and Ethnic Disparities in Suriname: A Nationwide Registry-Based Study in a Middle-Income Country. Reprod. Health 2020, 17, 62. [Google Scholar] [CrossRef]
  45. Zijlmans, W.; Wickliffe, J.; Hindori-Mohangoo, A.; MacDonald-Ottevanger, S.; Ouboter, P.; Landburg, G.; Codrington, J.; Roosblad, J.; Baldewsingh, G.; Ramjatan, R.; et al. Caribbean Consortium for Research in Environmental and Occupational Health (CCREOH) Cohort Study: Influences of Complex Environmental Exposures on Maternal and Child Health in Suriname. BMJ Open 2020, 10, e034702. [Google Scholar] [CrossRef]
  46. Belay, D.M.; Bayih, W.A.; Alemu, A.Y.; Sinshaw, A.E.; Mekonen, D.K.; Ayele, A.S.; Belayneh, W.H.; Tegared, H.A.; Birihane, B.M. Adverse Birth Outcome and Associated Factors among Diabetic Pregnant Women in Ethiopia: Systematic Review and Meta-Analysis. PLoS ONE 2020, 15, e0241811. [Google Scholar] [CrossRef]
  47. Bidhendi Yarandi, R.; Vaismoradi, M.; Panahi, M.H.; Gåre Kymre, I.; Behboudi-Gandevani, S. Mild Gestational Diabetes and Adverse Pregnancy Outcome: A Systemic Review and Meta-Analysis. Front. Med. 2021, 8, 993. [Google Scholar] [CrossRef]
  48. Prüst, Z.D.; Kodan, L.R.; van den Akker, T.; Bloemenkamp, K.W.M.; Rijken, M.J.; Verschueren, K.J.C. The Burden of Severe Hypertensive Disorders of Pregnancy on Perinatal Outcomes: A Nationwide Case-Control Study in Suriname. AJOG Glob. Rep. 2021, 1, 100027. [Google Scholar] [CrossRef]
  49. Lin, L.; Wei, Y.; Zhu, W.; Wang, C.; Su, R.; Feng, H.; Yang, H. Prevalence, Risk Factors and Associated Adverse Pregnancy Outcomes of Anaemia in Chinese Pregnant Women: A Multicentre Retrospective Study. BMC Pregnancy Childbirth 2018, 18, 111. [Google Scholar] [CrossRef] [Green Version]
  50. Rahman, M.A.; Khan, M.N.; Rahman, M.M. Maternal Anaemia and Risk of Adverse Obstetric and Neonatal Outcomes in South Asian Countries: A Systematic Review and Meta-Analysis. Public Health Pract. 2020, 1, 100021. [Google Scholar] [CrossRef]
  51. Frick, A.P. Advanced Maternal Age and Adverse Pregnancy Outcomes. Best Pract. Res. Clin. Obstet. Gynaecol. 2021, 70, 92–100. [Google Scholar] [CrossRef]
  52. Londero, A.P.; Rossetti, E.; Pittini, C.; Cagnacci, A.; Driul, L. Maternal Age and the Risk of Adverse Pregnancy Outcomes: A Retrospective Cohort Study. BMC Pregnancy Childbirth 2019, 19, 261. [Google Scholar] [CrossRef]
  53. Hinkle, S.N.; Albert, P.S.; Mendola, P.; Sjaarda, L.A.; Yeung, E.; Boghossian, N.S.; Laughon, S.K. The Association between Parity and Birthweight in a Longitudinal Consecutive Pregnancy Cohort. Paediatr. Perinat. Epidemiol. 2014, 28, 106–115. [Google Scholar] [CrossRef] [Green Version]
  54. Schimmel, M.S.; Bromiker, R.; Hammerman, C.; Chertman, L.; Ioscovich, A.; Granovsky-Grisaru, S.; Samueloff, A.; Elstein, D. The Effects of Maternal Age and Parity on Maternal and Neonatal Outcome. Arch. Gynecol. Obstet. 2015, 291, 793–798. [Google Scholar] [CrossRef]
  55. McDonald, B.W.; Watson, P.E. Maternal Alcohol Intakes before and during Pregnancy: Impact on the Mother and Infant Outcome to 18 Months. Nord. Alkohol. Nark. 2020, 37, 153–171. [Google Scholar] [CrossRef] [Green Version]
  56. Nykjaer, C.; Alwan, N.A.; Greenwood, D.C.; Simpson, N.A.B.; Hay, A.W.M.; White, K.L.M.; Cade, J.E. Maternal Alcohol Intake Prior to and during Pregnancy and Risk of Adverse Birth Outcomes: Evidence from a British Cohort. J. Epidemiol. Community Health 2014, 68, 542–549. [Google Scholar] [CrossRef] [Green Version]
  57. Regan, A.K.; Bombard, J.M.; O’Hegarty, M.M.; Smith, R.A.; Tong, V.T. Adverse Birth Outcomes Associated With Prepregnancy and Prenatal Electronic Cigarette Use. Obstet. Gynecol. 2021, 138, 85–94. [Google Scholar] [CrossRef]
  58. Wojtyla, C.; Wojtyla-Buciora, P.; Ciebiera, M.; Orzechowski, S.; Wojtyla, A. The Effect of Active and Passive Maternal Smoking before and during Pregnancy on Neonatal Weight at Birth. Arch. Med. Sci. 2021, 17, 352–360. [Google Scholar] [CrossRef]
  59. Sujan, A.C.; Rickert, M.E.; Quinn, P.D.; Ludema, C.; Wiggs, K.K.; Larsson, H.; Lichtenstein, P.; Almqvist, C.; Öberg, A.S.; D’Onofrio, B.M. A Population-Based Study of Concurrent Prescriptions of Opioid Analgesic and Selective Serotonin Reuptake Inhibitor Medications during Pregnancy and Risk for Adverse Birth Outcomes. Paediatr. Perinat. Epidemiol. 2021, 35, 184–193. [Google Scholar] [CrossRef]
  60. World Health Organization Reproductive Health Indicators Reproductive Health and Research Guidelines for Their Generation, Interpretation and Analysis for Global Monitoring; World Health Organization: Geneva, Switzerland, 2006; ISBN 92 4 156315 X.
  61. General Bureau of Statistics. Statistisch Jaarboek 2018-2019 Suriname; General Bureau of Statistics: Paramaribo, Suriname, 2020. [Google Scholar]
  62. Smith, L.K.; Hindori-Mohangoo, A.D.; Delnord, M.; Durox, M.; Szamotulska, K.; Macfarlane, A.; Alexander, S.; Barros, H.; Gissler, M.; Blondel, B.; et al. Quantifying the Burden of Stillbirths before 28 Weeks of Completed Gestational Age in High-Income Countries: A Population-Based Study of 19 European Countries. Lancet 2018, 392, 1639–1646. [Google Scholar] [CrossRef]
  63. Da Silva, F.T.; Gonik, B.; McMillan, M.; Keech, C.; Dellicour, S.; Bhange, S.; Tila, M.; Harper, D.M.; Woods, C.; Kawai, A.T.; et al. Stillbirth: Case Definition and Guidelines for Data Collection, Analysis, and Presentation of Maternal Immunization Safety Data. Vaccine 2016, 34, 6057–6068. [Google Scholar] [CrossRef] [Green Version]
  64. Gissler, M.; Durox, M.; Smith, L.; Blondel, B.; Broeders, L.; Hindori-Mohangoo, A.; Kearns, K.; Kolarova, R.; Loghi, M.; Rodin, U.; et al. Clarity and Consistency in Stillbirth Reporting in Europe: Why Is It so Hard to Get This Right? Eur. J. Public Health 2022, 32, 200–206. [Google Scholar] [CrossRef]
  65. PeriNed Perinatale Zorg in Nederland 2017; Perined: Utrecht, The Netherlands, 2019.
  66. World Health Organization Preterm Birth. Available online: https://www.who.int/news-room/fact-sheets/detail/preterm-birth (accessed on 23 February 2022).
  67. World Health Organization 2018 Global Reference List of 100 Core Health Indicators (plus Health-Related SDGs). World Health Organ. 2018, 1, 123–127.
  68. American College of Obstetrics and Gynecology The Apgar Score|ACOG. Available online: https://www.acog.org/clinical/clinical-guidance/committee-opinion/articles/2015/10/the-apgar-score (accessed on 23 February 2022).
  69. Mahaffey, K.R.; Clickner, R.P.; Bodurow, C.C. Blood Organic Mercury and Dietary Mercury Intake: National Health and Nutrition Examination Survey, 1999 and 2000. Environ. Health Perspect. 2004, 112, 562–570. [Google Scholar]
  70. National Research Council. Toxicological Effects of Methylmercury; National Academies Press: Washington, DC, USA, 2000; ISBN 978-0-309-07140-6. [Google Scholar]
  71. Lucchini, R.G.; Aschner, M.; Kim, Y.; Šarić, M. Manganese. In Handbook on the Toxicology of Metals; Elsevier: Amsterdam, The Netherlands, 2015; Volume 1, pp. 975–1011. ISBN 9780123973399. [Google Scholar]
  72. CDC (National Center for Environmental Health) Blood Lead Reference Value | Lead | CDC. Available online: https://www.cdc.gov/nceh/lead/data/blood-lead-reference-value.htm (accessed on 12 April 2022).
  73. World Health Organization. Definition and Diagnosis of Diabetes Mellitus and Intermediate Hyperglycemia: Report of a WHO/IDF Consultation. 1.Diabetes; World Health Organization: Geneva, Switzerland, 2006; ISBN 9789241594936.
  74. World Health Organization. Clinical Guidelines for the Management of Hypertension; World Health Organization: Geneva, Switzerland, 2005; ISBN 978-92-9021-407-6.
  75. World Health Organization. Haemoglobin Concentrations for the Diagnosis of Anaemia and Assessment of Severity; World Health Organization: Geneva, Switzerland, 2011.
  76. The Global Nutrition Report Global Nutrition Report|Country Nutrition Profiles—Global Nutrition Report. Available online: https://globalnutritionreport.org/resources/nutrition-profiles/latin-america-and-caribbean/south-america/suriname/ (accessed on 1 February 2022).
  77. Knoema Demographics—Suriname—Knoema.Com. Available online: https://knoema.com/atlas/Suriname/topics/Demographics#Fertility (accessed on 24 February 2022).
  78. General Bureau of Statistics. Demografische Statistieken 2015–2018; General Bureau of Statistics: Paramaribo, Suriname, 2019. [Google Scholar]
  79. The World Bank Literacy Rate, Adult Female (% of Females Ages 15 and above) - Suriname | Data. Available online: https://data.worldbank.org/indicator/SE.ADT.LITR.FE.ZS?end=2018&locations=SR&start=2018&view=bar (accessed on 24 February 2022).
  80. Prüst, Z.D.; Verschueren, K.J.C.; Bhikha-Kori, G.A.A.; Kodan, L.R.; Bloemenkamp, K.W.M.; Browne, J.L.; Rijken, M.J. Investigation of Stillbirth Causes in Suriname: Application of the WHO ICD-PM Tool to National-Level Hospital Data. Glob. Health Action 2020, 13, 1794105. [Google Scholar] [CrossRef]
  81. McElroy, S.; Ilango, S.; Dimitrova, A.; Gershunov, A.; Benmarhnia, T. Extreme Heat, Preterm Birth, and Stillbirth: A Global Analysis across 14 Lower-Middle Income Countries. Environ. Int. 2022, 158, 106902. [Google Scholar] [CrossRef]
  82. Abdoel Wahid, F.Z.; Wickliffe, J.; Wilson, M.; Hawkins, W.B.; Van Sauers, A.M.; Lichtveld, M.Y. Dietary Exposure of Pregnant Women in Suriname to Pesticides in Produce. Ann. Glob. Health 2017, 83, 8. [Google Scholar] [CrossRef] [Green Version]
  83. Abdoel Wahid, F.; Hindori-Mohangoo, A.; Wickliffe, J.; Wilson, M.; Zijlmans, C.; Lichtveld, M.Y. Dietary Exposure to Pesticides in Tannia in Pregnant Surinamese Women. ISEE Conf. Abstr. 2018, 2018, isesisee.2018.P01.2380. [Google Scholar] [CrossRef]
  84. Carneiro, M.F.H.; de B. Evangelista, F.S.; Barbosa, F. Manioc Flour Consumption as a Risk Factor for Lead Poisoning in the Brazilian Amazon. J. Toxicol. Environ. Health Part A 2013, 76, 206–216. [Google Scholar] [CrossRef]
  85. CDC (National Center for Environmental Health) Sources of Lead|Lead|CDC. Available online: https://www.cdc.gov/nceh/lead/prevention/sources.htm (accessed on 2 February 2022).
  86. Kasumba, J.; Hettick, B.; French, A.; Wickliffe, J.K.; Lichtveld, M.Y.; Hawkins, W.B.; van Sauers-Muller, A.; Klein, D. Analysis of Pesticides and Toxic Heavy Metals Contained in Mosquito Coils. Bull. Environ. Contam. Toxicol. 2016, 97, 614–618. [Google Scholar] [CrossRef]
  87. Hassoon, H.A. Determination of Lead Levels in Fuel Used for Vehicles in Baghdad City. Iraqi J. Sci. 2019, 60, 2629–2635. [Google Scholar] [CrossRef]
  88. U.S Food and Drug Administration Lead in Cosmetics|FDA. Available online: https://www.fda.gov/cosmetics/potential-contaminants-cosmetics/lead-cosmetics (accessed on 3 February 2022).
  89. Liu, W.; Xin, Y.; Li, Q.; Shang, Y.; Ping, Z.; Min, J.; Cahill, C.M.; Rogers, J.T.; Wang, F. Biomarkers of Environmental Manganese Exposure and Associations with Childhood Neurodevelopment: A Systematic Review and Meta-Analysis. Environ. Health 2020, 19, 104. [Google Scholar] [CrossRef]
  90. Lozano, M.; Murcia, M.; Soler-Blasco, R.; González, L.; Iriarte, G.; Rebagliato, M.; Lopez-Espinosa, M.J.; Esplugues, A.; Ballester, F.; Llop, S. Exposure to Mercury among 9-Year-Old Children and Neurobehavioural Function. Environ. Int. 2021, 146, 106173. [Google Scholar] [CrossRef]
  91. Patel, N.B.; Xu, Y.; McCandless, L.C.; Chen, A.; Yolton, K.; Braun, J.; Jones, R.L.; Dietrich, K.N.; Lanphear, B.P. Very Low-Level Prenatal Mercury Exposure and Behaviors in Children: The HOME Study. Environ. Health 2019, 18, 4. [Google Scholar] [CrossRef] [Green Version]
  92. Balali-Mood, M.; Naseri, K.; Tahergorabi, Z.; Khazdair, M.R.; Sadeghi, M. Toxic Mechanisms of Five Heavy Metals: Mercury, Lead, Chromium, Cadmium, and Arsenic. Front. Pharmacol. 2021, 12, 643972. [Google Scholar] [CrossRef]
  93. Lu, J.; Lan, J.; Li, X.; Zhu, Z. Blood Lead and Cadmium Levels Are Negatively Associated with Bone Mineral Density in Young Female Adults. Arch. Public Health 2021, 79, 116. [Google Scholar] [CrossRef]
  94. Wu, C.; Woo, J.G.; Zhang, N. Association between Urinary Manganese and Blood Pressure: Results from National Health and Nutrition Examination Survey (NHANES), 2011-2014. PLoS ONE 2017, 12, e0188145. [Google Scholar] [CrossRef]
  95. Genchi, G.; Sinicropi, M.; Carocci, A.; Lauria, G.; Catalano, A. Mercury Exposure and Heart Diseases. Int. J. Environ. Res. Public Health 2017, 14, 74. [Google Scholar] [CrossRef] [Green Version]
  96. Liu, S.; Tsui, M.T.-K.; Lee, E.; Fowler, J.; Jia, Z. Uptake, Efflux, and Toxicity of Inorganic and Methyl Mercury in the Endothelial Cells (EA.Hy926). Sci. Rep. 2020, 10, 9023. [Google Scholar] [CrossRef]
  97. Euro-Peristat Project. Core Indicators of the Health and Care of Pregnant Women and Babies in Europe in 2015; Euro Peristat: Paris, France, 2018. [Google Scholar]
  98. Berhe, A.K.; Ilesanmi, A.O.; Aimakhu, C.O.; Mulugeta, A. Effect of Pregnancy Induced Hypertension on Adverse Perinatal Outcomes in Tigray Regional State, Ethiopia: A Prospective Cohort Study. BMC Pregnancy Childbirth 2019, 20, 7. [Google Scholar] [CrossRef] [Green Version]
  99. Crump, C.; Sundquist, J.; Sundquist, K. Preterm Birth and Risk of Type 1 and Type 2 Diabetes: A National Cohort Study. Diabetologia 2020, 63, 508–518. [Google Scholar] [CrossRef] [Green Version]
  100. Wu, H.; Zhao, M.; Liang, Y.; Liu, F.; Xi, B. Maternal Age at Birth and Neonatal Mortality: Associations from 67 Low-Income and Middle-Income Countries. Paediatr. Perinat. Epidemiol. 2021, 35, 318–327. [Google Scholar] [CrossRef]
  101. Liu, Y.; Li, N.; An, H.; Li, Z.; Zhang, L.; Li, H.; Zhang, Y.; Ye, R. Impact of Gestational Hypertension and Preeclampsia on Low Birthweight and Small-for-Gestational-Age Infants in China: A Large Prospective Cohort Study. J. Clin. Hypertens. 2021, 23, 835–842. [Google Scholar] [CrossRef]
  102. Oh, S.S.; Park, S.; You, Y.-A.; Jee, Y.; Ansari, A.; Kim, S.M.; Lee, G.; Kim, Y.J. Prenatal Exposure to Alcohol, Tobacco, and Coffee: Associated Congenital Complications and Adverse Birth Outcomes. Int. J. Environ. Res. Public Health 2021, 18, 3140. [Google Scholar] [CrossRef]
  103. Serdar, C.C.; Cihan, M.; Yücel, D.; Serdar, M.A. Sample Size, Power and Effect Size Revisited: Simplified and Practical Approaches in Pre-Clinical, Clinical and Laboratory Studies. Biochem. Med. 2021, 31, 27–53. [Google Scholar] [CrossRef]
  104. Zhao, D.; Yuan, S.; Ma, Y.; An, Y.X.; Yang, Y.X.; Yang, J.K. Associations of Maternal Hyperglycemia in the Second and Third Trimesters of Pregnancy with Prematurity. Medicine 2020, 99, e19663. [Google Scholar] [CrossRef]
  105. Kapustin, R.V.; Alekseyenkova, E.N.; Arzhanova, O.N.; Petyaeva, A.V.; Atayeva, M.G.; Yusenko, S.R. Preterm Birth in Women with Diabetes Mellitus. J. Obstet. Womens Dis. 2020, 69, 17–26. [Google Scholar]
  106. Geurtsen, M.L.; van Soest, E.E.L.; Voerman, E.; Steegers, E.A.P.; Jaddoe, V.W.V.; Gaillard, R. High Maternal Early-Pregnancy Blood Glucose Levels Are Associated with Altered Fetal Growth and Increased Risk of Adverse Birth Outcomes. Diabetologia 2019, 62, 1880. [Google Scholar] [CrossRef] [Green Version]
  107. Sewberath Misser, V.H.; Shankar, A.; Hindori-Mohangoo, A.; Wickliffe, J.; Lichtveld, M.; Mans, D.R.A. Prevalence and Safety of Prescription Medicine Use during Pregnancy in the Republic of Suriname in the Year 2017: A Pharmacoepidemiological Analysis. Adv. Pharmacoepidemiol. Drug Saf. 2021, 10, 249. [Google Scholar]
  108. Loréal, O.; Cavey, T.; Bardou-Jacquet, E.; Guggenbuhl, P.; Ropert, M.; Brissot, P. Iron, Hepcidin, and the Metal Connection. Front. Pharmacol. 2014, 5, 128. [Google Scholar] [CrossRef]
  109. Hegazy, A.A.; Zaher, M.M.; Abd El-Hafez, M.A.; Morsy, A.A.; Saleh, R.A. Relation between Anemia and Blood Levels of Lead, Copper, Zinc and Iron among Children. BMC Res. Notes 2010, 3, 133. [Google Scholar] [CrossRef] [Green Version]
  110. Słota, M.; Wąsik, M.; Stołtny, T.; Machoń-Grecka, A.; Kasperczyk, S. Effects of Environmental and Occupational Lead Toxicity and Its Association with Iron Metabolism. Toxicol. Appl. Pharmacol. 2022, 434, 115794. [Google Scholar] [CrossRef]
  111. Kim, K.N.; Lee, M.R.; Lim, Y.H.; Hong, Y.C. Blood Lead Levels, Iron Metabolism Gene Polymorphisms and Homocysteine: A Gene-Environment Interaction Study. Occup. Environ. Med. 2017, 74, 899–904. [Google Scholar] [CrossRef]
Table
Table 1. General characteristics of the study population (N = 380).
Table 1. General characteristics of the study population (N = 380).
Number *Proportion
Total number of participants380100.0%
Age categories
16–19 years3810.0%
20–34 years28474.7%
35+ years5815.3%
Region of residence
Urban–coastal25166.1%
Rural–coastal8522.4%
Rural–interior4411.6%
Household income
<USD 7511231.2%
≥USD 7524768.8%
Education level
No or primary education 6617.5%
Secondary or tertiary education31282.5%
Parity
Nulliparous11329.9%
Multiparous26570.1%
Food consumption
Fish (yes)36497.1%
Rice (yes)36296.3%
Cassava (yes)10327.4%
Plantains (yes)9324.7%
Potatoes (yes)13736.4%
Bread (yes)29898.9%
Leafy vegetables (yes)37399.5%
Blood levels of heavy metals
Mercury
<3.5 µg/L 22659.5%
≥3.5 µg/L15440.5%
Manganese
<13.0 µg/L 13736.1%
≥13.0 µg/L 24363.9%
Lead
<3.5 µg/dL 29978.9%
≥3.5 µg/dL8121.3%
Adverse birth outcomes
Adverse birth outcomes7419.5%
Stillbirths174.5%
Gestational age at birth < 37.0 weeks5013.3%
Birth weight < 2500 g4311.6%
Apgar scores < 7 at 5 min195.2%
Covariates
Blood glucose level ≥ 6.1 mmol/L (elevated)1510.9%
Blood pressure > 120/80 mm Hg (elevated and high)5532.7%
Anemia < 6.8 mmol/L (severe, moderate, and mild)20384.6%
Prescription medicine use (yes)22458.9%
Alcohol use 3 months before pregnancy (yes) 17357.8%
Smoking 3 months before pregnancy (yes)184.8%
* Numbers may not add up to 380 due to missing values: preterm birth (n = 2); low birth weight (n = 5); low Apgar score (n = 12); household income (n = 21); educational level (n = 2); food consumption: fish (n = 5); rice, cassava, plantains, potatoes, bread, leafy vegetables (n = 4); blood glucose levels (n = 242); blood pressure levels (n = 212); anemia (n = 140); parity (n = 2); alcohol use 3 months before pregnancy (n = 18); smoking 3 months before pregnancy (n = 4).
Table
Table 2. Association of blood metal levels and covariates with each of the adverse birth outcomes.
Table 2. Association of blood metal levels and covariates with each of the adverse birth outcomes.
VariablesLive Birth StatusPreterm Birth < 37.0 WeeksBirth Weight < 2500 gApgar Score < 7 at 5 min
Number of Womenχ2- Test ResultNumber of Womenχ2- Test ResultNumber of Womenχ2- Test ResultNumber of Womenχ2- Test Result
StillbirthLive Birth<37 Weeks≥37 Weeks<2500 g≥2500 gYesNo
Mercury
 <3.5 µg/L72192.472,
p = 0.134		321930.254,
p = 0.648		311942.270
p = 0.144		92111.284,
p = 0.337
 ≥3.5 µg/L10144191341313710138
Manganese
 <13.0 µg/L91282.402,
p = 0.195		131222.684,
p = 0.117		131200,764,
p = 0.408		91251.038,
p = 0.333
 ≥13.0 µg/L8235382053121110224
Lead
 <3.5 µg/dL112882.073,
p = 0.220		352633.682,
p = 0.065		322620.974,
p = 0.333		132751.140,
p = 0.266
 ≥3.5 µg/dL67516641269674
Age categories
 16-19 years1370.375,
p = 0.918		5330.096,
p = 1.000		7311.938,
p = 0.358		2340.015,
p = 1.000
 20-34 years13271392443025014261
 35+ years355750750354
Blood glucose levels
 <6.1 mmol/L71161.281,
p = 0.254		141086.139,
p = 0.028		161060.001,
p = 1.000		71131.696,
p = 0.214
 ≥6.1 mmol/L21359213211
Blood pressure levels
 ≤120/80 mm Hg51082.542,
p = 0.180		14981.764,
p = 0.246		101026.719,
p = 0.015		61033.146.
p = 0.117
 >120/80 mm Hg64911431342744
Anemia
 No anemia ≥ 6.8 mmol/L2350.000,
p = 1.000		3332.178,
p = 0.224		2341.913,
p = 0.273		2320.027
p = 1.000
 Anemia < 6.8 mmol/L11192371652817413183
Previous live births
 Nulliparous51080.002,
p = 1.000		101033.062,
p = 0.100		13990.006,
p = 1.000		61030.031,
p = 0.803
 Multiparous12253412223123013244
Prescription medicine use
 Yes112130.244,
p = 0.802		391847.443,
p = 0.006		291950.790,
p = 0.416		132020.824,
p = 0.476
 No615012143151366147
Alcohol use 3 months before pregnancy
 Yes51682.415,
p = 0.141		171554.969,
p = 0.034		131586.114,
p = 0.015		61612.097,
p = 0.164
 No12177341543015613170
Smoking 3 months before pregnancy
 Yes0180.895,
p = 1.000		1171.049,
p = 0.487		0182.546,
p = 0.146		1170.004,
p = 1.000
 No17341503064430918328
Fisher’s exact test, p < 0.10 is used as cut-off for inclusion in (multivariate) logistic regression models.
Table
Table 3. Crude and adjusted odds ratios with 95% confidence interval for preterm birth.
Table 3. Crude and adjusted odds ratios with 95% confidence interval for preterm birth.
Logistic Regression Model Analysis for Preterm Birth
Crude ModelAdjusted Model
VariableOdds Ratio (95% CI)p ValueOdds Ratio (95% CI)p Value
Blood lead level
<3.5 µg/dLreference reference
≥3.5 µg/dL1.88 (0.98–3.60)0.0581.38 (0.40–4.79)0.615
Blood glucose level
<6.1 mmol/Lreference reference
≥6.1 mmol/L4.29 (1.26–14.62)0.0205.58 (1.38–22.53)0.016
Prescribed medication
Noreference reference
Yes2.53 (1.28–5.00)0.0082.1 (0.43–10.16)0.358
Alcohol use 3 months before pregnancy
Noreference
Yes0.50 (0.27–0.93)0.0280.36 (0.11–1.145)0.083
Table
Table 4. Crude and adjusted odds ratio with 95% confidence interval (CI) for low birth weight.
Table 4. Crude and adjusted odds ratio with 95% confidence interval (CI) for low birth weight.
Logistic Regression Model Analysis for Low Birth Weight
Crude ModelAdjusted Model
VariableOdds Ratio (95% CI)p ValueOdds Ratio (95% CI)p Value
Blood pressure levels
≤120/80 mm Hgreference reference
>120/80 mm Hg3.16 (1.28–7.76)0.0122.72 (1.081–6.86)0.034
Alcohol use 3 months before pregnancy
Noreference reference
Yes0.43 (0.22–0.85)0.0150.47 (0.18–1.23)0.124
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Sewberath Misser, V.H.; Hindori-Mohangoo, A.D.; Shankar, A.; Wickliffe, J.K.; Lichtveld, M.Y.; Mans, D.R.A. Prenatal Exposure to Mercury, Manganese, and Lead and Adverse Birth Outcomes in Suriname: A Population-Based Birth Cohort Study. Toxics 2022, 10, 464. https://doi.org/10.3390/toxics10080464

AMA Style

Sewberath Misser VH, Hindori-Mohangoo AD, Shankar A, Wickliffe JK, Lichtveld MY, Mans DRA. Prenatal Exposure to Mercury, Manganese, and Lead and Adverse Birth Outcomes in Suriname: A Population-Based Birth Cohort Study. Toxics. 2022; 10(8):464. https://doi.org/10.3390/toxics10080464

Chicago/Turabian Style

Sewberath Misser, Vinoj H., Ashna D. Hindori-Mohangoo, Arti Shankar, Jeffrey K. Wickliffe, Maureen Y. Lichtveld, and Dennis R. A. Mans. 2022. "Prenatal Exposure to Mercury, Manganese, and Lead and Adverse Birth Outcomes in Suriname: A Population-Based Birth Cohort Study" Toxics 10, no. 8: 464. https://doi.org/10.3390/toxics10080464

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop