Birth Cohorts in Highly Contaminated Sites: A Tool for Monitoring the Relationships Between Environmental Pollutants and Children's Health

Exposure profiles in pregnant women from a birth cohort in a highly contaminated area of southern Italy

Protecting the health of pregnant women from environmental stressors is crucial for reducing the burden of non-communicable diseases. In industrially contaminated sites, this action is particularly challenging due to the heterogeneous pollutant mixtures in environmental matrices. The aim of this study was to evaluate distribution patterns of mercury, hexachlorobenzene and polychlorobiphenyls in the serum of 161 pregnant women recruited in the framework of the Neonatal Environment and Health Outcomes (NEHO) cohort and living both inside and outside the National Priority Contaminated Site (NPCS) of Priolo. Food macro-categories were determined, and serum levels of contaminants were used to perform k-means cluster analysis and identify the role of food in pollutant transfer from the environment. Two groups of mothers with high and low measured pollutant levels were distinguished. Concentrations in mothers in the high-exposure cluster were at least twofold for all the evaluated pollutants (p < 0.0001) and included mothers living inside and outside NPCS, with a predominance of individuals from the NPCS (p = 0.045). Fish consumption was higher in the high-exposure cluster (p = 0.019). These findings suggest a link between contamination of environmental matrices such as sediment with maternal exposure, through the intake of local food. Such consideration appears poorly investigated in the context of contaminated sites.

The Influence of Sociodemographic Factors, Lifestyle, and Risk Perception on Dietary Patterns in Pregnant Women Living in Highly Contaminated Areas: Data from the NEHO Birth Cohort

During pregnancy, maternal nutrition and lifestyle play a critical role in influencing fetal development and newborn health outcomes. The aim of this study is to investigate the factors influencing the adherence to dietary patterns in pregnant women living in highly contaminated areas, and whether women with higher environmental risk perception manifest different nutritional behaviors during pregnancy. Food consumption data on 816 pregnant women from the Neonatal Environment and Health Outcomes (NEHO) residential birth cohort were analyzed. Dietary patterns were computed by principal component analysis. A multinomial logistic regression was also applied to identify sociodemographic, lifestyle, and pregnancy-related determinants of adherence to dietary patterns during pregnancy. Three patterns of food consumption—explaining 24.9% of the total variance—were identified as “prudent”, “high energy”, and “vegetarian” patterns. Results suggest that food choices during pregnancy follow a social gradient and align with other health behaviors during pregnancy: older, better educated, and physically active women with higher risk perception are more likely to follow healthier dietary patterns. Knowledge about what is eaten can contribute to dietary choices. Interventions to improve the prenatal nutrition knowledge of pregnant women are needed, especially concerning younger mothers and those with lower educational levels.

Analysis of Environmental and Social Significant Factors Affecting the Flow of Maternal Patients in Jilin, China

Background

With the implementation of China's Two-child policy, the number of pregnant women has been increasing year by year in recent years. However, the pregnancy success rate of pregnant women is declining year by year, and it is almost necessary for all the elderly mothers to do pregnancy protection.

Objective

The purpose of this study is to analyze the social and environmental factors that affect the patient flow of pregnant women in Jilin area of China, and further utilize the favorable factors to avoid the negative effects of adverse factors, so as to improve the pregnancy success rate and eugenics level.

Methods

Monthly patient flow data from 2018 to 2020 were collected in the obstetrics department of the First Hospital of Jilin University. The decompose function in R software was used to decompose the time series data, and the seasonal and trend change rules of the data were obtained; the significant factors influencing patient flow were analyzed by using Poisson regression model, and the prediction model was verified by using assumptions, such as the normal distribution of residuals and the constant difference of residuals.

Results

Temperature in environmental factors (P = 4.00E−08) had a significant impact on the flow of obstetric patient. The flow of patients was also significantly affected by the busy farming (P = 0.0013), entrance (P = 3.51E−10) and festivals (P = 0.00299). The patient flow was accompanied by random flow, but also showed trend change and seasonal change. The trend of change has been increasing year by year. The seasonal variation rule is that the flow of patients presents a trough in February every year, and reaches the peak in July.

Conclusion

In this article, Poisson regression model is used to obtain the social and environmental significant factors of obstetric patient flow. According to the significant factors, we should give full play to significant factors to further improve the level of eugenics. By using time series decomposition model, we can obtain the rising trend and seasonal trend of patient flow, and then provide the management with decision support, which is conducive to providing pregnant women with higher level of medical services and more comfortable medical experience.

Measuring Risk Perception in Pregnant Women in Heavily Polluted Areas: A New Methodological Approach from the NEHO Birth Cohort

Risk perception (RP) evaluation during pregnancy and its relationship with lifestyles are considered useful tools for understanding communities living in high-risk areas and preventing dangerous exposure. It is well known that exposure to pollutants and less-healthy lifestyles may result in increased disease occurrence during life. Our work investigated environmental RP through ad hoc questionnaires administered to 611 mothers within the NEHO birth cohort, recruited in three heavily contaminated areas of Southern Italy. Four different RP indices, an exploratory factorial analysis (EFA), and a latent class analysis were evaluated from questionnaires. The highest values of risk perception index were observed in the Milazzo site (0.64 ± 0.16) and the lowest in the Crotone site (0.5 ± 0.18). EFA revealed four latent factors, including different items describing environmental pollution, and subjects were classified into four latent classes with different RP indices. Significant RP profiles were different among the sites (p < 0.001). Our results did not demonstrate any association between RP and lifestyles during pregnancy. Improving healthy lifestyle behaviours, particularly in polluted areas, would generate co-benefits by preventing further risk factors. As remediation interventions can take a long time, it needs to improve healthy lifestyles in residents until remediation is completed.

Human Biomonitoring of Environmental and Occupational Exposures by GC-MS and Gas Sensor Systems: A Systematic Review

Environmental chemicals and contaminants coming from multiple external sources enter the human body, determining a potential risk for human health. Human biomonitoring (HBM), measuring the concentrations of biomarkers in human specimens, has become an emerging approach for assessing population-wide exposure to hazardous chemicals and health risk through large-scale studies in many countries. However, systematic mapping of HBM studies, including their characteristics, targeted hazardous pollutants, analytical techniques, and sample population (general population and occupationally exposed workers), has not been done so far. We conducted a systematic review of the literature related to airborne hazardous pollutants in biofluids to answer the following questions: Which main chemicals have been included in the literature, which bodily fluids have been used, and what are the main findings? Following PRISMA protocol, we summarized the publications published up to 4 February 2021 of studies based on two methods: gas-chromatography/mass spectrometry (GC/MS) and electronic noses (e-noses). We screened 2606 records and 117 publications were included in the analysis, the most based on GC/MS analysis. The selected HBM studies include measurements of biomarkers in different bodily fluids, such as blood, urine, breast milk, and human semen as well as exhaled air. The papers cover numerous airborne hazardous pollutants that we grouped in chemical classes; a lot of hazardous and noxious compounds, mainly persistent organic pollutants (POPs) and volatile organic compounds (VOCs), have been detected in biological fluids at alarming levels. The scenario that emerged from this survey demonstrates the importance of HBM in human exposure to hazardous pollutants and the need to use it as valid tool in health surveillance. This systematic review represents a starting point for researchers who focus on the world of pollutant biomonitoring in the human body and gives them important insights into how to improve the methods based on GC/MS. Moreover, it makes a first overview of the use of gas sensor array and e-noses in HBM studies.

The Neonatal Environment and Health Outcomes (NEHO) Birth Cohort Study: Behavioral and Socioeconomic Characteristics and Drop-Out Rate from a Longitudinal Birth Cohort in Three Industrially Contaminated Sites in Southern Italy

Pregnant women living in industrially contaminated sites (ICSs) are exposed to environmental contaminants through different pathways, and thus children’s health may be affected by pollutants. We created the Neonatal Environment and Health Outcomes (NEHO) longitudinal birth cohort in three ICSs in the Mediterranean area of southern Italy, collecting comprehensive information on personal data and lifestyles by questionnaire. Through multiple correspondence analysis, we identified possible clusters of enrolled women, and a neural network classifier analysis (NNCA) was performed to identify variables capable of predicting the attrition rate of the study. NEHO recruited 845 mother–child pairs over two years. The mothers’ mean age was 31.1 ± 5.2 SD years. We found significant differences in socioeconomic status (SES) among the three evaluated ICS, and an overall 11.1% prevalence of mothers who actively smoked during pregnancy. Active smoking during pregnancy was strongly associated with the lowest socioeconomic level (p < 0.0001). By means of the NNCA, we found that smoking during pregnancy and the lowest education level characterized the cluster with the highest attrition rate (p < 0.001). Our results demonstrate that reason for public health concern still exists regarding smoking during pregnancy and that SES influences both lifestyles, producing negative pregnancy outcomes and a higher survey attrition rate.