Modelling spatio‐temporal variation in exposure to particulate matter: a two‐stage approach
Bibliographic record
Abstract
Abstract Studies investigating associations between air pollution exposure and health outcomes benefit from the estimation of exposures at the individual level, but explicit consideration of the spatio‐temporal variation in exposure is relatively new in air pollution epidemiology. We address the problem of estimating spatially and temporally varying particulate matter concentrations (black smoke = BS = PM4) using data routinely collected from 20 monitoring stations in Newcastle‐upon‐Tyne between 1961 and 1992. We propose a two‐stage strategy for modelling BS levels. In the first stage, we use a dynamic linear model to describe the long‐term trend and seasonal variation in area‐wide average BS levels. In the second stage, we account for the spatio‐temporal variation between monitors around the area‐wide average in a linear model that incorporates a range of spatio‐temporal covariates available throughout the study area, and test for evidence of residual spatio‐temporal correlation. We then use the model to assign time‐aggregated predictions of BS exposure, with associated prediction variances, to each singleton pregnancy that occurred in the study area during this period, guided by dates of conception and birth and mothers' residential locations. In work to be reported separately, these exposure estimates will be used to investigate relationships between maternal exposure to BS during pregnancy and a range of birth outcomes. Our analysis demonstrates how suitable covariates can be used to explain residual spatio‐temporal variation in individual‐level exposure, thereby reducing the need to model the residual spatio‐temporal correlation explicitly. Copyright © 2007 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".