Health impact assessment for air pollution in the presence of regional variation in effect sizes: The implications of using different meta-analytic approaches.

The estimated health effects of air pollution vary between studies, and this variation is caused by factors associated with the study location, hereafter termed regional heterogeneity. This heterogeneity raises a methodological question as to which studies should be used to estimate risks in a specific region in a health impact assessment. Should one use all studies across the world, or only those in the region of interest? The current study provides novel insight into this question in two ways. Firstly, it presents an up-to-date analysis examining the magnitude of continent-level regional heterogeneity in the short-term health effects of air pollution, using a database of studies collected by Orellano et al. (2020). Secondly, it provides in-depth simulation analyses examining whether existing meta-analyses are likely to be underpowered to identify statistically significant regional heterogeneity, as well as evaluating which meta-analytic technique is best for estimating region-specific estimates. The techniques considered include global and continent-specific (sub-group) random effects meta-analysis and meta-regression, with omnibus statistical tests used to quantify regional heterogeneity. We find statistically significant regional heterogeneity for 4 of the 8 pollutant-outcome pairs considered, comprising NO2, O3 and PM2.5 with all-cause mortality, and PM2.5 with cardiovascular mortality. From the simulation analysis statistically significant regional heterogeneity is more likely to be identified as the number of studies increases (between 3 and 30 in each region were considered), between region heterogeneity increases and within region heterogeneity decreases. Finally, while a sub-group analysis using Cochran's Q test has a higher median power (0.71) than a test based on the moderators' coefficients from meta-regression (0.59) to identify regional heterogeneity, it also has an inflated type-1 error leading to more false positives (median errors of 0.15 compared to 0.09).

Using epidemiology to estimate the impact and burden of exposure to air pollutants.

This paper focuses on the use of results of epidemiological studies to quantify the effects on health, particularly on mortality, of long-term exposure to air pollutants. It introduces health impact assessment methods, used to predict the benefits that can be expected from implementation of interventions to reduce emissions of pollutants. It also explains the estimation of annual mortality burdens attributable to current levels of pollution. Burden estimates are intended to meet the need to communicate the size of the effect of air pollution on public health to policy makers and others. The implications, for the interpretation of the estimates, of the assumptions and approximations underlying the methods are discussed. The paper starts with quantification based on results obtained from studies of the association of mortality risk with long-term average concentrations of particulate air pollution. It then tackles the additional methodological considerations that need to be addressed when also considering the mortality effects of other pollutants such as nitrogen dioxide (NO2). Finally, approaches that could be used to integrate morbidity and mortality endpoints in the same assessment are touched upon. This article is part of a discussion meeting issue 'Air quality, past present and future'.