Abstract
Better understanding the risk factors that exacerbate Covid-19 symptoms and lead to worse health outcomes is vitally important in the public health fight against the virus. One such risk factor that is currently under investigation is air pollution concentrations, with some studies finding statistically significant effects while other studies have found no consistent associations. The aim of this paper is to add to this global evidence base on the potential association between air pollution concentrations and Covid-19 hospitalisations and deaths, by presenting the first study on this topic at the small-area scale in Scotland, United Kingdom. Our study is one of the most comprehensive to date in terms of its temporal coverage, as it includes all hospitalisations and deaths in Scotland between 1
s
t
March 2020 and 31
s
t
July 2021. We quantify the effects of air pollution on Covid-19 outcomes using a small-area spatial ecological study design, with inference using Bayesian hierarchical models that allow for the residual spatial correlation present in the data. A key advantage of our study is its extensive sensitivity analyses, which examines the robustness of the results to our modelling assumptions. We find clear evidence that PM2.5 concentrations are associated with hospital admissions, with a 1
μ
gm
−
3
increase in concentrations being associated with between a 7.4% and a 9.3% increase in hospitalisations. In addition, we find some evidence that PM2.5 concentrations are associated with deaths, with a 1
μ
gm
−
3
increase in concentrations being associated with between a 2.9% and a 10.3% increase in deaths.
| Original language | English |
|---|---|
| Article number | 100523 |
| Number of pages | 10 |
| Journal | Spatial and Spatio-temporal Epidemiology |
| Volume | 42 |
| Early online date | 8 Jun 2022 |
| DOIs | |
| Publication status | Published - 1 Aug 2022 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
-
SDG 3 Good Health and Well-being
Keywords
- air pollution
- Bayesian inference
- Covid-19 hospitalisations and deaths
- spatial modelling
- public health
- epidemiology
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