Short-term exposure to air pollution and hospital admission after COVID-19 in Catalonia: the COVAIR-CAT study.

BACKGROUND: A growing body of evidence has reported positive associations between long-term exposure to air pollution and poor COVID-19 outcomes. Inconsistent findings have been reported for short-term air pollution, mostly from ecological study designs. Using individual-level data, we studied the association between short-term variation in air pollutants [nitrogen dioxide (NO2), particulate matter with a diameter of <2.5 µm (PM2.5) and a diameter of <10 µm (PM10) and ozone (O3)] and hospital admission among individuals diagnosed with COVID-19. METHODS: The COVAIR-CAT (Air pollution in relation to COVID-19 morbidity and mortality: a large population-based cohort study in Catalonia, Spain) cohort is a large population-based cohort in Catalonia, Spain including 240 902 individuals diagnosed with COVID-19 in the primary care system from 1 March until 31 December 2020. Our outcome was hospitalization within 30 days of COVID-19 diagnosis. We used individual residential address to assign daily air-pollution exposure, estimated using machine-learning methods for spatiotemporal prediction. For each pandemic wave, we fitted Cox proportional-hazards models accounting for non-linear-distributed lagged exposure over the previous 7 days. RESULTS: Results differed considerably by pandemic wave. During the second wave, an interquartile-range increase in cumulative weekly exposure to air pollution (lag0_7) was associated with a 12% increase (95% CI: 4% to 20%) in COVID-19 hospitalizations for NO2, 8% (95% CI: 1% to 16%) for PM2.5 and 9% (95% CI: 3% to 15%) for PM10. We observed consistent positive associations for same-day (lag0) exposure, whereas lag-specific associations beyond lag0 were generally not statistically significant. CONCLUSIONS: Our study suggests positive associations between NO2, PM2.5 and PM10 and hospitalization risk among individuals diagnosed with COVID-19 during the second wave. Cumulative hazard ratios were largely driven by exposure on the same day as hospitalization.

Who is more vulnerable to effects of long-term exposure to air pollution on COVID-19 hospitalisation?

OBJECTIVE: Factors that shape individuals' vulnerability to the effects of air pollution on COVID-19 severity remain poorly understood. We evaluated whether the association between long-term exposure to ambient NO2, PM2.5, and PM10 and COVID-19 hospitalisation differs by age, sex, individual income, area-level socioeconomic status, arterial hypertension, diabetes mellitus, and chronic obstructive pulmonary disease. METHODS: We analysed a population-based cohort of 4,639,184 adults in Catalonia, Spain, during 2020. We fitted Cox proportional hazard models adjusted for several potential confounding factors and evaluated the interaction effect between vulnerability indicators and the 2019 annual average of NO2, PM2.5, and PM10. We evaluated interaction on both additive and multiplicative scales. RESULTS: Overall, the association was additive between air pollution and the vulnerable groups. Air pollution and vulnerability indicators had a synergistic (greater than additive) effect for males and individuals with low income or living in the most deprived neighbourhoods. The Relative Excess Risk due to Interaction (RERI) was 0.21, 95 % CI, 0.15 to 0.27 for NO2 and 0.16, 95 % CI, 0.11 to 0.22 for PM2.5 for males; 0.13, 95 % CI, 0.09 to 0.18 for NO2 and 0.10, 95 % CI, 0.05 to 0.14 for PM2.5 for lower individual income and 0.17, 95 % CI, 0.12 to 0.22 for NO2 and 0.09, 95 % CI, 0.05 to 0.14 for PM2.5 for lower area-level socioeconomic status. Results for PM10 were similar to PM2.5. Results on multiplicative scale were inconsistent. CONCLUSIONS: Long-term exposure to air pollution had a larger synergistic effect on COVID-19 hospitalisation for males and those with lower individual- and area-level socioeconomic status.

Effect of different heat wave timing on cardiovascular and respiratory mortality in France.

BACKGROUND: Previous epidemiological evidence suggests that the impact of heat waves on mortality may change over time within the summer season. The consideration of heat wave timing could help to optimize the implementation of heat alert systems. We explored the effect of the timing of extreme heat events on mortality risk during the summer season in France. METHODS: Summertime daily mortality data for 21 French cities from 2000 to 2015 were obtained from the French National Institute of Health and Medical Research. Heat waves were defined according to the official definition of Météo France. The order of heat wave over time, from June to August, was assessed. We also used ambient temperature and considered different summer periods. To quantify mortality risk (for cardiovascular and respiratory causes) for the first and second or later heat waves, quasi-Poisson models were performed. We used distributed lag non-linear models to estimate whether the non-linear exposure-response associations between temperature and mortality differ across different summer periods. RESULTS: Compared with non-heat wave days, the second and later heat waves of the summer season were associated with a higher relative risk (RR) for cardiovascular and respiratory mortality (RR, 95%CI: 1.38, 1.23-1.53; RR, 95%CI: 1.74, 1.45-2.08, respectively) as compared to first heat wave (RR, 95%CI: 1.30, 1.17-1.45, RR, 95%CI: 1.56, 1.33-1.83, respectively). Small increase from the median temperature was associated to an increased risk in mortality in the first stage of the summer (from June to mid-July), while only more extreme temperatures were harmful later in the summer. After the exclusion from the analysis of the August 2003 heat-wave, only results for earlier heat waves episodes and first-period exposures were confirmed. CONCLUSIONS: The timing of extreme temperatures modulates heat-related risks in France. Such information could be used to update local heat action plans to optimize health benefits.

Long-term exposure to air pollution and severe COVID-19 in Catalonia: a population-based cohort study.

The association between long-term exposure to ambient air pollutants and severe COVID-19 is uncertain. We followed 4,660,502 adults from the general population in 2020 in Catalonia, Spain. Cox proportional models were fit to evaluate the association between annual averages of PM2.5, NO2, BC, and O3 at each participant's residential address and severe COVID-19. Higher exposure to PM2.5, NO2, and BC was associated with an increased risk of COVID-19 hospitalization, ICU admission, death, and hospital length of stay. An increase of 3.2 µg/m3 of PM2.5 was associated with a 19% (95% CI, 16-21) increase in hospitalizations. An increase of 16.1 µg/m3 of NO2 was associated with a 42% (95% CI, 30-55) increase in ICU admissions. An increase of 0.7 µg/m3 of BC was associated with a 6% (95% CI, 0-13) increase in deaths. O3 was positively associated with severe outcomes when adjusted by NO2. Our study contributes robust evidence that long-term exposure to air pollutants is associated with severe COVID-19.

The Role of Ozone as a Mediator of the Relationship Between Heat Waves and Mortality in 15 French Urban Areas.

Heat and tropospheric ozone have acute impacts on rates of premature death. Warm temperatures affect the photochemical processes in ozone formation, suggesting ozone as a mediator of the acute health effect of heat on mortality. We assembled a summertime daily time-series data set of 15 French urban areas during 2000-2015 to decompose the acute total effect of heat waves on mortality into natural direct and indirect effects using a regression-based product method under the potential outcomes framework. For each area, we estimated the effect of heat waves on mortality using a quasi-Poisson model with adjustment for covariates such as lagged nitrogen dioxide concentration, and we modeled ozone with a linear regression of heat waves and the same set of covariates. We pooled estimates across areas using random-effects models. We also provide R software code (R Foundation for Statistical Computing, Vienna, Austria) with which to reproduce or replicate our analysis. Most areas demonstrated evidence of mediation by ozone, with the pooled relative risks for natural indirect effects being 1.03 (95% confidence interval (CI): 1.02, 1.05), 1.03 (95% CI: 1.01, 1.04), and 1.04 (95% CI: 1.00, 1.07) for nonaccidental, cardiovascular, and respiratory mortality, respectively. We found evidence of a mediation effect by ozone in the association between heat waves and mortality in France which varied by geographic location and cause of mortality.

On the supra-linearity of the relationship between air pollution, mortality and hospital admission in 18 French cities.

PURPOSE: Understanding the relationship between an environmental determinant and a given health outcome is key to inform public health policies. The short-term mortality and morbidity responses to outdoor air pollutants are traditionally assessed as a log-linear relationship, but few studies suggest a possible deviation from linearity. This paper investigates the shape of the relationship between ozone, NO2 and fine particulate matter (PM10 and PM2.5), mortality and hospital admissions in 18 French cities between 2000 and 2017. METHOD: A multi-centric time series design, using quasi-Poisson generalized additive models, was used. Four approaches were compared to model concentration-response curves (log-linear, piecewise-linear with a priori defined breakpoints, piecewise-linear with no a priori breakpoint and cubic spline). RESULTS: All the models indicated evidence of supra-linearity between PM10, PM2.5, NO2, mortality and hospital admissions. For instance, with a log-linear model, a 10 µg/m3 increase in PM2.5 was associated with a 0.4% [95% CI 0.2; 0.7] increase in non-accidental mortality. When using a piecewise model with a priori set breakpoint at 10 µg/m3, the mortality increase was 3.8% [4.4; 6.3] below 10 µg/m3, and 0.3% [0; 0.6] above. Non-significant impacts of ozone were found for concentrations below 90 µg/m3 to 120 µg/m3, with some variability in the identified threshold across the heath indicator studied. CONCLUSION: The supra-linearity of the relationship between PM10, PM2.5, NO2, mortality and hospital admissions supports the need to further reduce air pollution concentrations well below regulatory values.

Comparison of various heat waves definitions and the burden of heat-related mortality in France: Implications for existing early warning systems.

INTRODUCTION: In France, a heat warning system (HWS) has been implemented almost two decades ago and rely on some official heat wave (HW) definitions. However, no study has compared the burden associated with a large set of alternative HW definitions to the official definitions. Such comparison could be particularly helpful to identify HW conditions for which effective HWS would minimize the health burden across various geographical contexts and possibly update thresholds to trigger HWS. The aim of this study is to identify (and rank) definitions that drive the highest health burden in terms of mortality to inform future HWS across multiple cities in France. METHODS: Based on weather data for 16 French cities, we compared the two official definitions used in France to: i) the Excess Heat Factor (EHF) used in Australia, and ii) 18 alternative hypothetical HW definitions based on various combinations of temperature metrics, intensity, and duration. Propensity score matching and Poisson regressions were used to estimate the effect of each HW exposure on non-accidental mortality for the May-September period from 2000 to 2015. RESULTS: The associations between HW and mortality differed greatly depending on the definition. The greatest burden of heat was 1,055 (95% confidence interval "CI": [856; 1,302]) deaths per summer and was obtained with the EHF. The EHF identified HW with 2.46 (95% CI: [1.92; 3.58]) or 8.18 (95% CI: [6.63; 10.61]) times the global burden at the national level obtained with the climatological indicator of the French national weather service and the HW indicator of the French national HWS, respectively and was the most impactful definition pattern for both temperate oceanic and Mediterranean climate types. CONCLUSION: Identifying the set of extreme heat conditions that drive the highest health burden in a given geographical context is particularly helpful when designing or updating heat early warning systems.

Improving the design stage of air pollution studies based on wind patterns.

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.

Spatial variation in the joint effect of extreme heat events and ozone on respiratory hospitalizations in California.

Extreme heat and ozone are co-occurring exposures that independently and synergistically increase the risk of respiratory disease. To our knowledge, no joint warning systems consider both risks; understanding their interactive effect can warrant use of comprehensive warning systems to reduce their burden. We examined heterogeneity in joint effects (on the additive scale) between heat and ozone at small geographical scales. A within-community matched design with a Bayesian hierarchical model was applied to study this association at the zip code level. Spatially varying relative risks due to interaction (RERI) were quantified to consider joint effects. Determinants of the spatial variability of effects were assessed using a random effects metaregression to consider the role of demographic/neighborhood characteristics that are known effect modifiers. A total of 817,354 unscheduled respiratory hospitalizations occurred in California from 2004 to 2013 in the May to September period. RERIs revealed no additive interaction when considering overall joint effects. However, when considering the zip code level, certain areas observed strong joint effects. A lower median income, higher percentage of unemployed residents, and exposure to other air pollutants within a zip code drove stronger joint effects; a higher percentage of commuters who walk/bicycle, a marker for neighborhood wealth, showed decreased effects. Results indicate the importance of going beyond average measures to consider spatial variation in the health burden of these exposures and predictors of joint effects. This information can be used to inform early warning systems that consider both heat and ozone to protect populations from these deleterious effects in identified areas.

The effects of an air quality alert program on premature mortality: A difference-in-differences evaluation in the region of Paris.

BACKGROUND: Daily exposure to air pollution has been shown to increase cardiovascular and respiratory mortality. While increases in short-term exposure to air pollutants at any daily concentrations has been shown to be associated to adverse health outcomes, days with extreme levels, also known as air pollution peaks based on specific thresholds, have been used to implement air quality alerts in various cities across the globe. OBJECTIVES: We aimed at evaluating the potential effects of the Air Quality Alerts (AQA) system on different causes of premature mortality in Paris, France. METHODS: Air quality alerts (AQA) based on particulate matter (PM10) levels and related interventions were implemented in the region of Paris in 2008 and were revised to be more stringent in 2011. In this study, we applied a difference-in-differences (DID) approach coupled with propensity-score matching (PSM) to daily mortality data for the period 2000 to 2015 to evaluate the effects of the Paris AQA program on different causes of premature mortality for the entire population and for adults > 75 years old. RESULTS: Overall, results did not show evidence of a reduction in mortality of the PM10 AQA program when first implemented in 2008 with initial thresholds (80 µg/m3); DID estimates were slightly above 1 for cardiovascular and respiratory mortality. However, when evaluating the drastic reduction in revised thresholds in 2011 (50 µg/m3) to trigger interventions, we identified a reduction in cardiovascular (DID = 0.84, 95% CI: 0.755 to 0.930) mortality, but no change in respiratory mortality was detected (DID = 0.97, 95% CI: 0.796, 1.191). DISCUSSION: Our study suggests that AQA may not have health benefits for the population when thresholds are set at high daily PM10 levels. Given that such policies are implemented in many other metropolitan areas across the globe, evaluating the effectiveness of AQA is important to provide public authorities and researchers a rationale for defining specific thresholds and extending the scope of these policies to lower air pollution levels.

Association of Pneumococcal Conjugate Vaccine Coverage With Pneumococcal Meningitis: An Analysis of French Administrative Areas, 2001-2016.

Geographic variations of invasive pneumococcal disease incidence and serotype distributions were observed after pneumococcal conjugate vaccine introduction at regional levels and among French administrative areas. The variations could be related to regional vaccine coverage (VC) variations that might have direct consequences for vaccination-policy impact on invasive pneumococcal disease, particularly pneumococcal meningitis (PM) incidence. We assessed vaccine impact from 2001 to 2016 in France by estimating the contribution of regional VC differences to variations of annual local PM incidence. Using a mixed-effect Poisson model, we showed that, despite some variations of VC among administrative areas, vaccine impact on vaccine-serotype PM was homogeneously confirmed among administrative areas. Compared with the prevaccine era, the cumulative VC impact on vaccine serotypes led, in 2016, to PM reductions ranging among regions from 87% (25th percentile) to 91% (75th percentile) for 7-valent pneumococcal conjugate vaccine serotypes and from 58% to 63% for the 6 additional 13-valent pneumococcal conjugate vaccine serotypes. Nonvaccine-serotype PM increases from the prevaccine era ranged among areas from 98% to 127%. By taking into account the cumulative impact of growing VC and VC differences, our analyses confirmed high vaccine impact on vaccine-serotype PM case rates and suggest that VC variations cannot explain PM administrative area differences.

Impact of pneumococcal conjugate vaccines on pneumococcal meningitis cases in France between 2001 and 2014: a time series analysis.

BACKGROUND: Pneumococcal meningitis (PM) is a major invasive pneumococcal disease. Two pneumococcal conjugate vaccines (PCVs) have been introduced in France: PCV7 was recommended in 2003 and replaced in 2010 by PCV13, which has six additional serotypes. The impact of introducing those vaccines on the evolution of PM case numbers and serotype distributions in France from 2001 to 2014 is assessed herein. METHODS: Data on 5166 Streptococcus pneumoniae strains isolated from cerebrospinal fluid between 2001 and 2014 in the 22 regions of France were obtained from the National Reference Center for Pneumococci. The effects of the different vaccination campaigns were estimated using time series analyses through autoregressive moving-average models with exogenous variables ("flu-like" syndromes incidence) and intervention functions. Intervention functions used 11 dummy variables representing each post vaccine epidemiological period. The evolution of serotype distributions was assessed for the entire population and the two most exposed age groups (<5 and > 64 years old). RESULTS: For the first time since PCV7 introduction in 2003, total PM cases decreased significantly after starting PCV13 use: -7.1 (95% CI, -10.85 to -3.35) cases per month during 2013-2014, and was confirmed in children < 5 years old (-3.5; 95% CI, -4.81 to -2.13) and adults > 64 years old (-2.0; 95% CI, -3.36 to -0.57). During 2012-2014, different non-vaccine serotypes emerged: 12F, 24F in the entire population and children, 6C in the elderly; serotypes 3 and 19F persisted in the entire population. CONCLUSIONS: Unlike other European countries, the total PM cases in France declined only after introduction of PCV13. This suggests that vaccine pressure alone does not explain pneumococcal epidemiological changes and that other factors could play a role. Serotype distribution had changed substantially compared to the pre-vaccine era, as in other European countries, but very differently from the US. A highly reactive surveillance system is thus necessary not only to monitor evolutions due to vaccine pressure and to verify the local serotypic appropriateness of new higher-valent pneumococcal vaccines, but also to recognise and prevent unexpected changes due to other internal or external factors.