Many epidemiological studies examining long-term health effects of exposure to air pollutants have characterized exposure by the outdoor air concentrations at sites that may be distant to subjects' residences at different points in time. The temporal and spatial mobility of subjects and the spatial scale of exposure assessment could thus lead to misclassification in the cumulative exposure estimation. This paper attempts to fill the gap regarding cumulative exposure assessment to air pollution at a fine spatial scale in epidemiological studies investigating long-term health effects. We propose a conceptual framework showing how major difficulties in cumulative long-term exposure assessment could be surmounted. We then illustrate this conceptual model on the case of exposure to NO₂ following two steps: (i) retrospective reconstitution of NO₂ concentrations at a fine spatial scale; and (ii) a novel approach to assigning the time-relevant exposure estimates at the census block level, using all available data on residential mobility throughout a 10- to 20-year period prior to that for which the health events are to be detected. Our conceptual framework is both flexible and convenient for the needs of different epidemiological study designs.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4808982PMC
http://dx.doi.org/10.3390/ijerph13030319DOI Listing

Publication Analysis

Top Keywords

spatial scale
16
conceptual framework
12
cumulative exposure
12
fine spatial
12
exposure assessment
12
exposure
9
exposure air
8
air pollution
8
pollution fine
8
scale epidemiological
8

Similar Publications

: About 65 million people worldwide are affected by epilepsy, with temporal lobe epilepsy being the most common type resistant to drugs and often requiring surgical treatment. Although open surgical approaches, such as temporal lobectomy, have been the method of choice for decades, minimally invasive MRgLITT has demonstrated promising results. However, it remains unknown whether patients who underwent one of these two approaches would show better performance on vestibulo-spatial tasks.

View Article and Find Full Text PDF

Traffic flow prediction is a pivotal element in Intelligent Transportation Systems (ITSs) that provides significant opportunities for real-world applications. Capturing complex and dynamic spatio-temporal patterns within traffic data remains a significant challenge for traffic flow prediction. Different approaches to effectively modeling complex spatio-temporal correlations within traffic data have been proposed.

View Article and Find Full Text PDF

Diagnosis of Autism Spectrum Disorder (ASD) by Dynamic Functional Connectivity Using GNN-LSTM.

Sensors (Basel)

December 2024

College of Information Science and Engineering, Hunan Normal University, Changsha 410081, China.

Early detection of autism spectrum disorder (ASD) is particularly important given its insidious qualities and the high cost of the diagnostic process. Currently, static functional connectivity studies have achieved significant results in the field of ASD detection. However, with the deepening of clinical research, more and more evidence suggests that dynamic functional connectivity analysis can more comprehensively reveal the complex and variable characteristics of brain networks and their underlying mechanisms, thus providing more solid scientific support for computer-aided diagnosis of ASD.

View Article and Find Full Text PDF

Interacting hand reconstruction presents significant opportunities in various applications. However, it currently faces challenges such as the difficulty in distinguishing the features of both hands, misalignment of hand meshes with input images, and modeling the complex spatial relationships between interacting hands. In this paper, we propose a multilevel feature fusion interactive network for hand reconstruction (HandFI).

View Article and Find Full Text PDF

Arbitrary-oriented ship detection has become challenging due to problems of high resolution, poor imaging clarity, and large size differences between targets in remote sensing images. Most of the existing ship detection methods are difficult to use simultaneously to meet the requirements of high accuracy and speed. Therefore, we designed a lightweight and efficient multi-scale feature dilated neck module in the YOLO11 network to achieve the high-precision detection of arbitrary-oriented ships in remote sensing images.

View Article and Find Full Text PDF

Want 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!