Search

BackgroundInadequate or excessive intake of micronutrients in pregnancy has potential to negatively impact maternal/offspring health outcomes.ObjectiveThe aim was to compare risks of inadequate or excessive micronutrient intake in diverse females with singleton pregnancies by strata of maternal age, race/ethnicity, education, and prepregnancy BMI.MethodsFifteen observational cohorts in the US Environmental influences on Child Health Outcomes (ECHO) Consortium assessed participant dietary intake with 24-h dietary recalls (n = 1910) or food-frequency questionnaires (n = 7891) from 1999-2019. We compared the distributions of usual intake of 19 micronutrients from food alone (15 cohorts; n = 9801) and food plus dietary supplements (10 cohorts with supplement data; n = 7082) to estimate the proportion with usual daily intakes below their age-specific daily Estimated Average Requirement (EAR), above their Adequate Intake (AI), and above their Tolerable Upper Intake Level (UL), overall and within sociodemographic and anthropometric subgroups.ResultsRisk of inadequate intake from food alone ranged from 0% to 87%, depending on the micronutrient and assessment methodology. When dietary supplements were included, some women were below the EAR for vitamin D (20-38%), vitamin E (17-22%), and magnesium (39-41%); some women were above the AI for vitamin K (63-75%), choline (7%), and potassium (37-53%); and some were above the UL for folic acid (32-51%), iron (39-40%), and zinc (19-20%). Highest risks for inadequate intakes were observed among participants with age 14-18 y (6 nutrients), non-White race or Hispanic ethnicity (10 nutrients), less than a high school education (9 nutrients), or obesity (9 nutrients).ConclusionsImproved diet quality is needed for most pregnant females. Even with dietary supplement use, >20% of participants were at risk of inadequate intake of ≥1 micronutrients, especially in some population subgroups. Pregnancy may be a window of opportunity to address disparities in micronutrient intake that could contribute to intergenerational health inequalities.

BACKGROUND: Preterm birth (PTB), defined as birth before 37 wk gestation, is associated with hypertension, diabetes, inadequate prenatal care, unemployment or poverty, and metal exposure. Indigenous individuals are more likely to have maternal risk factors associated with PTB compared with other populations in the United States; however, the role of environmental metals on PTB among pregnant Indigenous women remains uncertain. Previous research identified associations between PTB and individual metals, but there is limited investigation on metal mixtures and this birth outcome. OBJECTIVES: We used a mixtures analysis framework to investigate the association between metal mixtures and PTB among pregnant Indigenous women from the Navajo Birth Cohort Study (NBCS). METHODS: Maternal urine and blood samples were collected at the time of study enrollment and analyzed for metals by inductively coupled plasma dynamic reaction cell mass spectrometry. Bayesian Profile Regression was used to identify subgroups (clusters) of individuals with similar patterns of coexposure and to model association with PTB. RESULTS: Results indicated six subgroups of maternal participants with distinct exposure profiles, including one group with low exposure to all metals and one group with total arsenic, cadmium, lead, and uranium concentrations exceeding representative concentrations calculated from the National Health and Nutrition Examination Survey (NHANES). Compared with the reference group (i.e., the lowest exposure subgroup), the subgroup with the highest overall exposure had a relative risk of PTB of 2.9 times (95% credible interval: 1.1, 6.1). Exposures in this subgroup were also higher overall than NHANES median values for women 14-45 years of age. DISCUSSION: Given the wide range of exposures and elevated PTB risk for the most exposed subgroups in a relatively small study, follow-up investigation is recommended to evaluate associations between metal mixture profiles and other birth outcomes and to test hypothesized mechanisms of action for PTB and oxidative stress caused by environmental metals. https://doi.org/10.1289/EHP10361, Introduction There are >160,000 abandoned hardrock mines in the western United States. (1) An estimated 600,000 tribal community members live within 10 km of these abandoned mines, increasing the potential [...]

Contaminated unregulated drinking water in rural communities is a continuing health issue, leading many resource users to unknowingly consume water with elevated levels of harmful chemicals. Though geographic information systems (GIS) and Internet technology have been particularly useful for water resource management, to date, there exist few studies that specifically address the development of Internet-based GIS applications to increase user access to unregulated drinking water source information. Informed by an existing user centered development framework for Internet mapping, we created an Internet GIS that visualized groundwater contamination on the Navajo Nation in the southwest United States. We employed a usability focus group, expert review of content and pilot test of a prototype GIS application to gather empirical evidence on effectiveness for informing users of a significant water quality issue. Results suggested that the designed and deployed GIS application was appropriate for the target audience of environmental nongovernmental organization (ENGO) and institution of higher education (IHE) professionals. Usability testing and expert review confirmed the importance of these evaluation measures to ensure a high quality GIS prior to deployment. Use of existing Internet mapping guidelines was found to be insufficient for creating a refined GIS interface appropriate for the target audience. Additionally, we demonstrated that an existing user centered design and evaluation framework could be applied successfully to visualize water quality for unregulated groundwater wells in the rural southwest United States. Suggestions for continued research in the use of Internet GIS to inform rural residents about drinking water quality are provided. © 2014 Elsevier Ltd.

Urban Green Space (UGS) is vital for improving the public health and sustainability of cities. Vector data on UGS such as open data from governments and OpenStreetMap are available for retrieval by interested users, but the availability of UGS data is still limited on global and temporal scales. This study develops the UGS Extractor, a web-based application for the automatic extraction of UGS given user inputs of Area of Interest and Date of Interest. To accommodate various types of green spaces, such as parks or lawns, the application additionally allows users to set parameters for the minimum size of each UGS and the Minimum Urban Neighbor Density, enabling customization of what qualifies as UGS. The UGS Extractor implements a methodological framework that applies object-based image processing, edge detection and extraction, and image neighborhood analysis on the near real-time 10m Dynamic World collection of Land Use/Land Cover images. The application's utility was demonstrated through two case studies. In the first, the UGS Extractor accurately mapped major parks when compared to open data sources in New Orleans, USA. In the second, the UGS Extractor demonstrated significant increases in the total area of UGS from 2015 to 2023 in Songdo, South Korea, which consequently improved green space accessibility. These results underscore the UGS Extractor's utility in extracting specific types of UGS and analyzing their temporal trends. This user-friendly application overall offers higher spatial resolution compared to publicly available satellite-based methods while facilitating temporal studies not possible with vector datasets. [ABSTRACT FROM AUTHOR]

Personal exposure studies suffer from uncertainty issues, largely stemming from individual behaviour uncertainties. Built on spatial-temporal exposure analysis and methods, this study proposed a novel approach to spatial-temporal modelling that incorporated behaviour classifications taking into account uncertainties, to estimate individual livestock exposure potential. The new approach was applied in a community-based research project with a Tribal community in the southwest United States to address questions on potential livestock exposure to abandoned uranium mines (AUMs). The study aimed to 1) classify Global Positioning System (GPS) data from livestock into three behaviour subgroups – grazing, travelling or resting; 2) calculate the daily cumulative exposure potential for livestock; 3) assess the performance of the computational method with and without behaviour classifications. Using Lotek Litetrack GPS collars, we collected data at a 20-min-interval for two flocks of sheep and goats during the spring and summer of 2019. Analysis and modelling of GPS data demonstrated no significant difference in individual cumulative exposure potential within each flock when animal behaviours with probability/uncertainties were considered. However, when daily cumulative exposure potential was calculated without consideration of animal behaviour or probability/uncertainties, significant differences among animals within a herd were observed, which does not match animal grazing behaviours reported by livestock owners. These results suggest that the proposed method including behaviour subgroups with probability/uncertainties more closely resembled the observed grazing behaviours. Results from the research may be used for future intervention and policy-making on remediation efforts in communities where grazing livestock may encounter environmental contaminants. [ABSTRACT FROM AUTHOR]