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.

Dietary shifts can reduce premature deaths related to particulate matter pollution in China.

Shifting towards more meat-intensive diets may have indirect health consequences through environmental degradation. Here we examine how trends in dietary patterns in China over 1980-2010 have worsened fine particulate matter (PM) pollution, thereby inducing indirect health impacts. We show that changes in dietary composition alone, mainly by driving the rising demands for meat and animal feed, have enhanced ammonia (NH) emissions from Chinese agriculture by 63% and increased annual PM by up to ~10 µg m (~20% of total PM increase) over the period.

A dynamic microsimulation model for epidemics.

A large evidence base demonstrates that the outcomes of COVID-19 and national and local interventions are not distributed equally across different communities. The need to inform policies and mitigation measures aimed at reducing the spread of COVID-19 highlights the need to understand the complex links between our daily activities and COVID-19 transmission that reflect the characteristics of British society. As a result of a partnership between academic and private sector researchers, we introduce a novel data driven modelling framework together with a computationally efficient approach to running complex simulation models of this type.

Area level deprivation and monthly COVID-19 cases: The impact of government policy in England.

This paper aims to understand the relationship between area level deprivation and monthly COVID-19 cases in England in response to government policy throughout 2020. The response variable is monthly reported COVID-19 cases at the Middle Super Output Area (MSOA) level by Public Health England, with Index of Multiple Deprivation (IMD), ethnicity (percentage of the population across 5 ethnicity categories) and the percentage of the population older than 70 years old and time as predictors. A GEE population-averaged panel-data model was employed to model trends in monthly COVID-19 cases with the population of each MSOA included as the exposure variable.