Impact of different exposure models and spatial resolution on the long-term effects of air pollution

Renzi, Matteo; Barbero, Daniela; Ancona, Carla; Angelini, Paola; Stefania, Argentini; Baldacci, Sandra; Bisceglia, Lucia; Bonafede, Michela; Bonomo, Sergio; Bonvicini, Laura; Broccoli, Serena; Basso, Giuseppe; Bucci, Simone; Calori, G.; Carlino, Giuseppe; Cernigliaro, Achille; Antônio, Carlos Roberto; Annamaria, Colacci; Francesca, de' Donato; Fasola, Salvatore; Finardi, S.; Forastiére, Francesco; Galassi, Claudia; Gariazzo, Claudio; Paolo, Giorgi Rossi; Grutta, Stefania La; Licitra, Gaetano; Maio, Sara; Marinaccio, Alessandro; Michelozzi, Paola; Migliore, Enrica; Moro, Ali; Ottone, Marta; Parmagnani, Federica; Pepe, Nicola; Radice, Paola; Ranzi, Andrea; Renzi, Matteo; Scondotto, Salvatore; Scortichini, Matteo; Silibello, Camillo; Sozzi, Roberto; Stafoggia, Massimo; Tinarelli, G.; Francesco, Uboldi; Viegi, Giovanni

doi:10.1016/j.envres.2020.110351

Cited by 18 publications

(11 citation statements)

References 41 publications

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“…Although both models were of similar fine-scale spatial resolution, we generally found somewhat stronger associations with lowest compared with highest resolution models (100 x 100 m for hybrid LUR and 25 x 25 m for RIO-IFDM, respectively). These findings agree with those recently obtained by Gariazzo et al (2021) for associations between both coarse PM or NO2 and non-accidental, respiratory disease and cardiovascular disease mortality.…”

Section: Discussionsupporting

confidence: 93%

“…To our knowledge, only four other studies systematically compared potential heterogeneity in effect estimates using different exposure assessment methods when evaluating the association between long-term exposure to ambient air pollution and various mortality outcomes using cohort data (Yap et al 2012;Jerrett et al, 2016;Klompmaker et al 2020;Gariazzo et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

“…Part of this heterogeneity in air pollution epidemiological studies might be explained by methodological differences in exposure assessment method, study design or statistical data analysis approach, or by study-specific contextual differences. So far there is little evidence on how air pollution exposure assessment method affects mortality risk estimates (Yap et al, 2012;Jerrett et al, 2016;Klompmaker et al 2020;Samoli et al, 2020;Butland et al, 2020;Gariazzo et al, 2021). Multicenter studies provide a great opportunity to investigate some of this heterogeneity.…”

Section: Highlightsmentioning

confidence: 99%

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Variability in the association between long-term exposure to ambient air pollution and mortality by exposure assessment method and covariate adjustment: A census-based country-wide cohort study

Bauwelinck

Chen

Hoogh

et al. 2022

Science of The Total Environment

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Section: Discussionsupporting

confidence: 93%

Section: Discussionmentioning

confidence: 99%

Section: Highlightsmentioning

confidence: 99%

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Variability in the association between long-term exposure to ambient air pollution and mortality by exposure assessment method and covariate adjustment: A census-based country-wide cohort study

Bauwelinck

Chen

Hoogh

et al. 2022

Science of The Total Environment

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“…Indeed, the resolution of 1 km may still disregard local-scale phenomena, such as the low dispersion conditions determined by the city's structure (e.g., street canyons and larger surface roughness caused by the presence of buildings, etc. ), which might cause accumulation of pollutants [18,30]. In our study, we found that the effect of PM 10 (2013-2015) at the resolution of 200 m was somewhat larger than the effect of the same pollutant at the resolution of 1 km, while the effect of PM 2.5 was similar for both resolutions.…”

Section: Acute Effects Of Pm On Cvd Hospitalizationsmentioning

confidence: 41%

Short-Term Effects of Air Pollution on Cardiovascular Hospitalizations in the Pisan Longitudinal Study

Fasola

Maio

Baldacci

et al. 2021

IJERPH

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Air pollution effects on cardiovascular hospitalizations in small urban/suburban areas have been scantly investigated. Such effects were assessed among the participants in the analytical epidemiological survey carried out in Pisa and Cascina, Tuscany, Italy (2009–2011). Cardiovascular hospitalizations from 1585 subjects were followed up (2011–2015). Daily mean pollutant concentrations were estimated through random forests at 1 km (particulate matter: PM10, 2011–2015; PM2.5, 2013–2015) and 200 m (PM10, PM2.5, NO2, O3, 2013–2015) resolutions. Exposure effects were estimated using the case-crossover design and conditional logistic regression (odds ratio—OR—and 95% confidence interval—CI—for 10 μg/m3 increase; lag 0–6). During the period 2011–2015 (137 hospitalizations), a significant effect at lag 0 was observed for PM10 (OR = 1.137, CI: 1.023–1.264) at 1 km resolution. During the period 2013–2015 (69 hospitalizations), significant effects at lag 0 were observed for PM10 (OR = 1.268, CI: 1.085–1.483) and PM2.5 (OR = 1.273, CI: 1.053–1.540) at 1 km resolution, as well as for PM10 (OR = 1.365, CI: 1.103–1.690), PM2.5 (OR = 1.264, CI: 1.006–1.589) and NO2 (OR = 1.477, CI: 1.058–2.061) at 200 m resolution; significant effects were observed up to lag 2. Larger ORs were observed in males and in subjects reporting pre-existent cardiovascular/respiratory diseases. Combining analytical and routine epidemiological data with high-resolution pollutant estimates provides new insights on acute cardiovascular effects in the general population and in potentially susceptible subgroups living in small urban/suburban areas.

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“…Wang et al [ 35 ] compared exposure estimates and associations for NO 2 and various PM sizes based on predictions from a land-use regression and a dispersion model, and found that health effect estimates did not differ significantly. More recently, Gariazzo et al [ 36 ] investigated the effects of long-term exposure to PM and NO 2 using four exposure models in a large administrative cohort, and found consistent health effect estimates across the different exposure assessment models. Our work expands on this body of literature and assesses variability in acute CVD effect estimates from a time-series analysis using different existing exposure models.…”

Section: Discussionmentioning

confidence: 99%

Short-term PM2.5 and cardiovascular admissions in NY State: assessing sensitivity to exposure model choice

Si-liang

et al. 2021

Environ Health

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Background Air pollution health studies have been increasingly using prediction models for exposure assessment even in areas without monitoring stations. To date, most studies have assumed that a single exposure model is correct, but estimated effects may be sensitive to the choice of exposure model. Methods We obtained county-level daily cardiovascular (CVD) admissions from the New York (NY) Statewide Planning and Resources Cooperative System (SPARCS) and four sets of fine particulate matter (PM2.5) spatio-temporal predictions (2002–2012). We employed overdispersed Poisson models to investigate the relationship between daily PM2.5 and CVD, adjusting for potential confounders, separately for each state-wide PM2.5 dataset. Results For all PM2.5 datasets, we observed positive associations between PM2.5 and CVD. Across the modeled exposure estimates, effect estimates ranged from 0.23% (95%CI: -0.06, 0.53%) to 0.88% (95%CI: 0.68, 1.08%) per 10 µg/m3 increase in daily PM2.5. We observed the highest estimates using monitored concentrations 0.96% (95%CI: 0.62, 1.30%) for the subset of counties where these data were available. Conclusions Effect estimates varied by a factor of almost four across methods to model exposures, likely due to varying degrees of exposure measurement error. Nonetheless, we observed a consistently harmful association between PM2.5 and CVD admissions, regardless of model choice.

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Impact of different exposure models and spatial resolution on the long-term effects of air pollution

Cited by 18 publications

References 41 publications

Variability in the association between long-term exposure to ambient air pollution and mortality by exposure assessment method and covariate adjustment: A census-based country-wide cohort study

Variability in the association between long-term exposure to ambient air pollution and mortality by exposure assessment method and covariate adjustment: A census-based country-wide cohort study

Short-Term Effects of Air Pollution on Cardiovascular Hospitalizations in the Pisan Longitudinal Study

Short-term PM2.5 and cardiovascular admissions in NY State: assessing sensitivity to exposure model choice

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