Estimating the environmental burden of disease resulting from exposure to chemicals in European countries - potentials and challenges revealed in selected case studies.

Background: The increased use of chemicals leads to a continuous deposition of chemicals in the environment and to a continuous increase in exposure of the global and the European population. Comprehensive burden of disease analyses are however still missing for many countries.

Methods: Using the World Health Organization's Environmental Burden of Disease (EBD) approach and combining data from the European Human Biomonitoring (HBM) dashboard with disease and population data, we estimated the comprehensive attributable burden (AB) for the year 2021, in the best-case quantified by disability-adjusted life years (DALY).

The effects of climatic and non-climatic factors on malaria mortality at different spatial scales in western Kenya, 2008-2019.

Background: Malaria mortality is influenced by several factors including climatic and environmental factors, interventions, socioeconomic status (SES) and access to health systems. Here, we investigated the joint effects of climatic and non-climatic factors on under-five malaria mortality at different spatial scales using data from a Health and Demographic Surveillance System (HDSS) in western Kenya.

Methods: We fitted Bayesian spatiotemporal (zero-inflated) negative binomial models to monthly mortality data aggregated at the village scale and over the catchment areas of the health facilities within the HDSS, between 2008 and 2019.

Malaria, climate variability, and interventions: modelling transmission dynamics.

Assessment of the relative impact of climate change on malaria dynamics is a complex problem. Climate is a well-known factor that plays a crucial role in driving malaria outbreaks in epidemic transmission areas. However, its influence in endemic environments with intensive malaria control interventions is not fully understood, mainly due to the scarcity of high-quality, long-term malaria data.

The relative effect of climate variability on malaria incidence after scale-up of interventions in western Kenya: A time-series analysis of monthly incidence data from 2008 to 2019.

Background: Despite considerable progress made over the past 20 years in reducing the global burden of malaria, the disease remains a major public health problem and there is concern that climate change might expand suitable areas for transmission. This study investigated the relative effect of climate variability on malaria incidence after scale-up of interventions in western Kenya.

Methods: Bayesian negative binomial models were fitted to monthly malaria incidence data, extracted from records of patients with febrile illnesses visiting the Lwak Mission Hospital between 2008 and 2019.

Long-term air pollution exposure and COVID-19 case-severity: An analysis of individual-level data from Switzerland.

Several studies are pointing out that exposure to elevated air pollutants could contribute to increased COVID-19 mortality. However, literature on the associations between air pollution exposure and COVID-19 severe morbidity is rather sparse. In addition, the majority of the studies used an ecological study design and were applied in regions with rather high air pollution levels.

Substantial Reduction in Particulate Matter Air Pollution across Europe during 2006-2019: A Spatiotemporal Modeling Analysis.

Air pollution poses the largest environmental health risk in Europe. Particulate matter (PM) concentrations are the most harmful pollutants representing the main air quality indicator in the Sustainable Development Goals (SDGs). The air quality surveillance in Europe is based on a monitoring network that is too coarse for a comprehensive evaluation of the air pollution burden.

Bayesian geostatistical modelling of high-resolution NO exposure in Europe combining data from monitors, satellites and chemical transport models.

Bayesian geostatistical regression (GR) models estimate air pollution exposure at high spatial resolution, quantify the prediction uncertainty and provide probabilistic inference on the exceedance of air quality thresholds. However, due to high computational burden, previous GR models have provided gridded ambient nitrogen dioxide (NO) concentrations at smaller areas of investigation. Here, we applied these models to estimate yearly averaged NO concentrations at 1 km spatial resolution across 44 European countries, integrating information from in situ monitoring stations, satellites and chemical transport model (CTM) simulations.

Bayesian geostatistical modelling of PM and PM surface level concentrations in Europe using high-resolution satellite-derived products.

Air quality monitoring across Europe is mainly based on in situ ground stations, which are too sparse to accurately assess the exposure effects of air pollution for the entire continent. The demand for precise predictive models that estimate gridded geophysical parameters of ambient air at high spatial resolution has rapidly grown. Here, we investigate the potential of satellite-derived products to improve particulate matter (PM) estimates.