AI Article Synopsis
- Research indicates that environmental factors, including the built and natural surroundings, may influence maternal health during pregnancy, particularly in relation to gestational diabetes and hypertension.
- A study involving 61,640 women in Rhode Island analyzed how elements like air pollution, green spaces, and proximity to water sources impacted the likelihood of gestational diabetes (GDM), gestational hypertension, and preeclampsia.
- Findings show that higher levels of fine particulate matter and living near major roads increased the risk of GDM, while proximity to coastlines and recreational facilities appeared to lower the risks, highlighting the complex relationship between environment and pregnancy health.
Article Abstract
Background: Mounting evidence suggests that the natural and built environment can affect human health, but relatively few studies have considered links between features of the residential natural and built environment other than air pollution and complications of pregnancy.
Objectives: To quantify the impact of features of the maternal residential natural and built environments on risk of gestational diabetes mellitus (GDM), gestational hypertension and preeclampsia among 61,640 women who delivered at a single hospital in Rhode Island between 2002 and 2012.
Methods: We estimated residential levels of ambient fine particulate matter (PM) and black carbon (BC) using spatiotemporal models, neighborhood green space using remote sensing and proximity to recreational facilities, and neighborhood blue space using distance to coastal and fresh water. We used logistic regression to separately estimate the association between each feature and GDM, gestational hypertension, and preeclampsia, adjusting for individual and neighborhood markers of socioeconomic status.
Results: GDM, gestational hypertension, and preeclampsia were diagnosed in 8.0%, 5.0%, and 3.6% of women, respectively. We found 2nd trimester PM (OR = 1.08, 95% CI: 1.00, 1.15 per interquartile range increase in PM) and living close to a major roadway (1.09, 95% CI: 1.00, 1.19) were associated with higher odds of GDM, while living <1 km from the coast was associated with lower odds of GDM (0.87, 95% CI: 0.78, 0.96). Living <500 m from a recreational facility was associated with lower odds of gestational hypertension (0.89, 95% CI: 0.80, 0.99). None of these features were associated with odds of preeclampsia. Results were qualitatively similar in mutually-adjusted models and sensitivity analyses.
Conclusions: In this small coastal US state, risk of GDM was positively associated with PM and proximity to busy roadways, and negatively associated with proximity to blue space, highlighting the importance of the natural and built environment to maternal health.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.scitotenv.2018.07.237 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
A novel hybrid model by integrating TCN with TVFEMD and permutation entropy for monthly non-stationary runoff prediction.
Sci Rep
December 2024
College of Water Resources Science and Engineering, Taiyuan University of Technology, Taiyuan, 030024, China.
Accurate prediction of runoff is of great significance for rational planning and management of regional water resources. However, runoff presents non-stationary characteristics that make it impossible for a single model to fully capture its intrinsic characteristics. Enhancing its precision poses a significant challenge within the area of water resources management research.
View Article and Find Full Text PDFThe proximity ligation-based Hi-C and derivative methods are the mainstream tools to study genome-wide chromatin interactions. These methods often fragment the genome using enzymes functionally irrelevant to the interactions per se, restraining the efficiency in identifying structural features and the underlying regulatory elements. Here we present Footprint-C, which yields high-resolution chromatin contact maps built upon intact and genuine footprints protected by transcription factor (TF) binding.
View Article and Find Full Text PDFHumans have a long-standing relationship with the natural world, particularly in how they engage with plants-referred to as people-plant relationships. While plants naturally live outdoors, people have been including them inside built environments for centuries. Although the benefits of indoor plants are well documented in research, there is limited exploration of individuals' subjective relationships with their indoor plants.
View Article and Find Full Text PDFA new risk assessment model of venous thromboembolism by considering fuzzy population.
BMC Med Inform Decis Mak
December 2024
Department of Respiration, Peking Union Medical College Hospital, No.1, Shuaifuyuan, Dongcheng District, Beijing, 100730, China.
Background: Inpatients with high risk of venous thromboembolism (VTE) usually face serious threats to their health and economic conditions. Many studies using machine learning (ML) models to predict VTE risk overlook the impact of class-imbalance problem due to the low incidence rate of VTE, resulting in inferior and unstable model performance, which hinders their ability to replace the Padua model, a widely used linear weighted model in clinic. Our study aims to develop a new VTE risk assessment model suitable for Chinese medical inpatients.
View Article and Find Full Text PDFLeveraging anatomical constraints with uncertainty for pneumothorax segmentation.
Health Care Sci
December 2024
Centre for Quantitative Medicine, Duke-NUS Medical School Singapore.
Background: Pneumothorax is a medical emergency caused by the abnormal accumulation of air in the pleural space-the potential space between the lungs and chest wall. On 2D chest radiographs, pneumothorax occurs within the thoracic cavity and outside of the mediastinum, and we refer to this area as "lung + space." While deep learning (DL) has increasingly been utilized to segment pneumothorax lesions in chest radiographs, many existing DL models employ an end-to-end approach.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!