ArticleEnvironmental epidemiology (Philadelphia, Pa.)2020
Comparing the performance of air pollution models for nitrogen dioxide and ozone in the context of a multilevel epidemiological analysis.
Article in Environmental epidemiology (Philadelphia, Pa.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed, 26 citations in OpenAlex.
- Exposure measurement error in air-pollution epidemiology and its determinants: results from the MELONS study.International journal of epidemiology · 2026Article
- Investigating the Consequences of Measurement Error of Gradually More Sophisticated Long-Term Personal Exposure Models in Assessing Health Effects: The London Study (MELONS).Research report (Health Effects Institute) · 2025Article
- Exposure to ambient air pollution and cognitive function: an analysis of the English Longitudinal Study of Ageing cohort.Environmental health : a global access science source · 2024Article
- Disentangling associations between multiple environmental exposures and all-cause mortality: an analysis of European administrative and traditional cohorts.Frontiers in epidemiology · 2023Article
- Invited Perspective: Impact of Exposure Measurement Error on Effect Estimates-An Important and Neglected Problem in Air Pollution Epidemiology.Environmental health perspectives · 2022Article
- The Impact of Exposure Measurement Error on the Estimated Concentration-Response Relationship between Long-Term Exposure toEnvironmental health perspectives · 2022Article
- Development and Evaluation of Spatio-Temporal Air Pollution Exposure Models and Their Combinations in the Greater London Area, UK.International journal of environmental research and public health · 2022Article
- Assessing the health estimation capacity of air pollution exposure prediction models.Environmental health : a global access science source · 2022Article
- Invited Perspective: TheEnvironmental health perspectives · 2021Article
- Quantifying the short-term effects of air pollution on health in the presence of exposure measurement error: a simulation study of multi-pollutant model results.Environmental health : a global access science source · 2021Article
- Personal exposure to air pollution and respiratory health of COPD patients in London.The European respiratory journal · 2021Article
- Spatio-temporal associations of air pollutant concentrations, GP respiratory consultations and respiratory inhaler prescriptions: a 5-year study of primary care in the borough of Lambeth, South London.Environmental health : a global access science source · 2021Article
- The impact of measurement error in modeled ambient particles exposures on health effect estimates in multilevel analysis: A simulation study.Environmental epidemiology (Philadelphia, Pa.) · 2020Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 2 countries.
Funding
Abstract
Using modeled air pollutant predictions as exposure variables in epidemiological analyses can produce bias in health effect estimation. We used statistical simulation to estimate these biases and compare different air pollution models for London.
methodsOur simulations were based on a sample of 1,000 small geographical areas within London, United Kingdom. "True" pollutant data (daily mean nitrogen dioxide [NO
resultsFor the LUR models, bias estimates ranged from -56% to +7% for short-term exposure and -98% to -68% for long-term exposure and for the dispersion models from -33% to -15% and -52% to +0.5%, respectively. Hybrid1 provided little if any additional benefit, but hybrid2 appeared optimal in terms of bias estimates for short-term (-17% to +11%) and long-term (-28% to +11%) exposure and in preserving coverage probability and statistical power.
conclusionsAlthough exposure error can produce substantial negative bias (i.e., towards the null), combining outputs from different air pollution modeling approaches may reduce bias in health effect estimation leading to improved impact evaluation of abatement policies.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.