ArticleStatistics in medicine2018
Multivariate space-time modelling of multiple air pollutants and their health effects accounting for exposure uncertainty.
Article in Statistics in medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review of Joint Spatial and Spatiotemporal Models in Health Research.International journal of environmental research and public health · 2023Pooled it
- Quantifying the effects of air pollution on respiratory ill health treated in primary care when the locations of the populations at risk are partially unknown.Statistical methods in medical research · 2026Article
- A scalable two-stage Bayesian approach accounting for exposure measurement error in environmental epidemiology.Biostatistics (Oxford, England) · 2025Article
- A scalar-on-quantile-function approach for estimating short-term health effects of environmental exposures.Biometrics · 2024Article
- A two-stage latent factor regression method to model the common and unique effects of multiple highly correlated exposure variables.Journal of applied statistics · 2024Article
- A Bayesian framework for incorporating exposure uncertainty into health analyses with application to air pollution and stillbirth.Biostatistics (Oxford, England) · 2023Article
- Quantifying the impact of air pollution on Covid-19 hospitalisation and death rates in Scotland.Spatial and spatio-temporal epidemiology · 2022Article
- Pedestrian exposure to black carbon and PMJournal of exposure science & environmental epidemiology · 2022Article
- Long-term exposure to air pollution and COVID-19 incidence: A multi-country study.Spatial and spatio-temporal epidemiology · 2021Article
- Population-weighted exposure to air pollution and COVID-19 incidence in Germany.Spatial statistics · 2021Article
- Application of Bayesian Additive Regression Trees for Estimating Daily Concentrations of PMAtmosphere · 2020Article
- Accounting for measurement error to assess the effect of air pollution on omic signals.PloS one · 2020Article
- Multivariate Air Pollution Prediction Modeling with partial Missingness.Environmetrics · 2019Article
- A hierarchical modelling approach to assess multi pollutant effects in time-series studies.PloS one · 2019Article
- Multivariate space-time modelling of multiple air pollutants and their health effects accounting for exposure uncertainty.Statistics in medicine · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The long-term health effects of air pollution are often estimated using a spatio-temporal ecological areal unit study, but this design leads to the following statistical challenges: (1) how to estimate spatially representative pollution concentrations for each areal unit; (2) how to allow for the uncertainty in these estimated concentrations when estimating their health effects; and (3) how to simultaneously estimate the joint effects of multiple correlated pollutants. This article proposes a novel 2-stage Bayesian hierarchical model for addressing these 3 challenges, with inference based on Markov chain Monte Carlo simulation. The first stage is a multivariate spatio-temporal fusion model for predicting areal level average concentrations of multiple pollutants from both monitored and modelled pollution data. The second stage is a spatio-temporal model for estimating the health impact of multiple correlated pollutants simultaneously, which accounts for the uncertainty in the estimated pollution concentrations. The novel methodology is motivated by a new study of the impact of both particulate matter and nitrogen dioxide concentrations on respiratory hospital admissions in Scotland between 2007 and 2011, and the results suggest that both pollutants exhibit substantial and independent health effects.
Indexed as
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.