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Several studies have already explored individual and environmental risk factors for COVID-19 morality, however most study populations consisted of the overall population and mainly from China or the US. Our study focused on COVID-19 mortality in the elderly in seven European cities. Long-term exposure to air pollution was estimated through annual pollutant concentrations at the residential address averaged over the last two years of the study period between February and May 2020. We focused on the main outdoor air pollutants PM10, PM2.5, NO2 and O3. Short-term variations in air pollutants and weather parameters (e.g. temperature, UV, relative humidity) were also examined through a 20-day period before the confirmed PCR diagnostic of COVID-19. Individual risk factors such as smoking status, sex, body mass index (BMI), ischemic heart disease, diabetes, hypertension, chronic renal failure, history of cancer, COPD, and lung fibrosis, were taken into account. We found positive associations for diabetes and COVID-19 mortality (OR 2.2 CI 95% :1.1, 4.4). Using a multivariate logistic regression model adjusted for all patient characteristics and city, we fail to reject the null hypothesis of no association between COVID-19 mortality and long-term and short-term increase in PM2.5, PM10, NO2 and O3. Our study suffers from the fact that patient profiles strongly differ between high-polluted and less-polluted cities. Strong differences in COVID-19 mortalities were observed between cities, which could be due to differences in COVID-19 management and treatment, such as accessibility to reanimation and intensive units between cities. Overall, our study highlights the need to improve estimation of individual exposure to air pollution. Indeed, even with the high-efficiency modelisation systems used in our study, we were unable to estimate the effect of air pollution within each city, because variations in air pollution exposure were too small. Individual markers of air pollution exposure such as recently demonstrated with urinary black carbon or passive individual samplers, would be most suitable for future explorations. Concerning weather parameters, although previous studies concluded that increase in temperature and UV index could decrease COVID-19 morality, our data did not allow us to reject the null hypotheses.

Because the elderly account for 80% of deaths from COVID-19 and they may be more vulnerable to air pollution, in this retrospective study we aimed to explore individual and environmental risk factors for COVID-19 mortality in the geriatric departments of seven European University hospitals, between T. Bourdrel, L. Zabrocki et al.

Local environmental organizations and media have recently expressed concerns over air pollution induced by maritime traffic and its potential adverse health effects on the population of Mediterranean port cities. We explore this issue with unique high-frequency data from Marseille, France’s largest port for cruise ships, over the 2008- 2018 period. Using a new pair-matching algorithm designed for time series data, we create hypothetical randomized experiments and estimate the variation in air pollutant concentrations caused by a short-term increase in cruise vessel traffic. We carry out a randomization-based approach to compute 95% Fisherian intervals (FI) for constant treatment effects consistent with the matched data and the hypothetical intervention. At the hourly level, cruise vessels’ arrivals increase concentrations of nitrogen dioxide (NO2) by 4.7 μg/m³ (95% FI: [1.4, 8.0]), of sulfur dioxide (SO2) by 1.2 μg/m³ (95% FI: [-0.1, 2.5]), and of particulate matter (PM10) by 4.6 μg/m³ (95% FI: [0.9, 8.3]). At the daily level, cruise traffic increases concentrations of NO2 by 1.2 μg/m³ (95% FI: [-0.5, 3.0]) and of PM10 by 1.3 μg/m³ (95% FI: [-0.3, 3.0]). Our results suggest that well-designed hypothetical randomized experiments provide a principled approach to better understand the negative externalities of maritime traffic.

A growing literature in economics and epidemiology has exploited changes in wind patterns as a source of exogenous variation to better measure the acute health effects of air pollution. Since the distribution of wind components is not randomly distributed over time and related to other weather parameters, multivariate regression models are used to adjust for these confounding factors. However, this type of analysis relies on its ability to correctly adjust for all confounding factors and extrapolate to units without empirical counterfactuals. As an alternative to current practices and to gauge the extent of these issues, we propose to implement a causal inference pipeline to embed this type of observational study within an hypothetical randomized experiment. We illustrate this approach using daily data from Paris, France, over the 2008–2018 period. Using the Neyman–Rubin potential outcomes framework, we first define the treatment of interest as the effect of North-East winds on particulate matter concentrations compared to the effects of other wind directions. We then implement a matching algorithm to approximate a pairwise randomized experiment. It adjusts nonparametrically for observed confounders while avoiding model extrapolation by discarding treated days without similar control days. We find that the effective sample size for which treated and control units are comparable is surprisingly small. It is however reassuring that results on the matched sample are consistent with a standard regression analysis of the initial data. We finally carry out a quantitative bias analysis to check whether our results could be altered by an unmeasured confounder: estimated effects seem robust to a relatively large hidden bias. Our causal inference pipeline is a principled approach to improve the design of air pollution studies based on wind patterns.

Changes in wind patterns can substantially alter the air pollution level of a city. However, it is challenging to estimate a causal effect from observed data. Since wind patterns are not randomly distributed over time and are related to other weather parameters influencing air pollution, researchers must adjust for these confounding factors. As an alternative to current practices, we implement a causal inference pipeline to embed an observational study within an hypothetical randomized experiment. We illustrate this new approach for air pollution studies using 4018 daily observations from Paris, France, over the 2008-2018 period. Using the Neyman-Rubin potential outcomes framework, we first define treatment of interest as the comparison of air pollutant concentrations when winds are blowing from the North-East (824 units) with concentrations when wind come from other directions (3,194 units). We then implement a matching algorithm to approximate a pair randomized experiment and find 119 matched pairs. By selecting units that are comparable in regards to various confounders, matching allows us to adjust nonparametrically for observed confounders while avoiding model extrapolation to treated days without similar control days. Once the balance of treated and control groups was deemed satisfactory, we estimate the average differences in air pollutant concentrations and their sampling variability using Neymanian inference, a mode of inference specifically designed for randomized experiments. We find that North-East winds increase PM10 concentrations by 4.8 μg/m³ (95% CI: 2.6, 6.9). As in any observational studies, an unobserved confounder could bias our results. We therefore carry out a quantitative bias analysis which reveals that an unobserved variable 2 times more common among treated units could make our data compatible with small negative effects up to very large effects (95% CI: -2.3, 10). Our causal inference approach highlights the importance of checking covariates balance and bias from unmeasured confounders in air pollution studies.

Local environmental organizations and media have recently expressed concerns over air pollution induced by maritime traffic and its potential adverse health effects on the population of Mediterranean port cities. We explore this issue with unique highfrequency data from Marseille, France's largest port for cruise ships, over the 2008-2018 period. Using a new pair-matching algorithm designed for time series data, we create hypothetical randomized experiments and estimate the variation in air pollutant concentrations caused by a short-term increase in cruise vessel traffic. We carry out a randomization-based approach to compute 95% Fisherian intervals (FI) for constant treatment effects consistent with the matched data and the hypothetical intervention. At the hourly level, cruise vessels' arrivals increase concentrations of nitrogen dioxide (NO 2 ) by 4.7 µg/m 3 (95% FI: [1.4, 8.0]), of sulfur dioxide (SO 2 ) by 1.2 µg/m 3 (95% FI: [-0.1, 2.5]), and of particulate matter (PM 10 ) by 4.6 µg/m 3 (95% FI: [0.9, 8.3]). At the daily level, cruise traffic increases concentrations of NO 2 by 1.2 µg/m 3 (95% FI: [-0.5, 3.0]) and of PM 10 by 1.3 µg/m 3 (95% FI: [-0.3, 3.0]). Our results suggest that well-designed hypothetical randomized experiments provide a principled approach to better understand the negative externalities of maritime traffic.