Monitoring vs. modeled exposure data in time-series studies of ambient air pollution and acute health outcomes

Ebelt, Stefanie; D’Souza, Rohan; Yu, Haofei; Scovronick, Noah; Moss, Shannon; Chang, Howard H.

doi:10.1038/s41370-022-00446-5

Cited by 9 publications

(7 citation statements)

References 45 publications

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“…Potential measurement errors may be present, as exposure was based on city level averages and did not differentiate among heterogeneity in populations living in non-urban areas, air pollutant concentrations, or potential interactive effects, nor did the estimate deal with personal exposure. Previous studies have, however, found that assessing exposure at city level correlates strongly with temporal variation in personal exposure, thus utilising city level exposure assessment is a valid approach for time series studies focusing on short term exposure effects 4142. Lastly, this study did not consider the impact of different PM 2.5 compositions, as well as the interaction between temperature and other air pollutants (ie, nitrogen oxides and sulphur dioxide), which could influence the PM 2.5 -O 3 interaction.…”

Section: Discussionmentioning

confidence: 99%

Interactive effects of ambient fine particulate matter and ozone on daily mortality in 372 cities: two stage time series analysis

Liu,

Chen,

Sera

et al. 2023

BMJ

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Objective To investigate potential interactive effects of fine particulate matter (PM 2.5 ) and ozone (O 3 ) on daily mortality at global level. Design Two stage time series analysis. Setting 372 cities across 19 countries and regions. Population Daily counts of deaths from all causes, cardiovascular disease, and respiratory disease. Main outcome measure Daily mortality data during 1994-2020. Stratified analyses by co-pollutant exposures and synergy index (>1 denotes the combined effect of pollutants is greater than individual effects) were applied to explore the interaction between PM 2.5 and O 3 in association with mortality. Results During the study period across the 372 cities, 19.3 million deaths were attributable to all causes, 5.3 million to cardiovascular disease, and 1.9 million to respiratory disease. The risk of total mortality for a 10 μg/m 3 increment in PM 2.5 (lag 0-1 days) ranged from 0.47% (95% confidence interval 0.26% to 0.67%) to 1.25% (1.02% to 1.48%) from the lowest to highest fourths of O 3 concentration; and for a 10 μg/m 3 increase in O 3 ranged from 0.04% (−0.09% to 0.16%) to 0.29% (0.18% to 0.39%) from the lowest to highest fourths of PM 2.5 concentration, with significant differences between strata (P for interaction <0.001). A significant synergistic interaction was also identified between PM 2.5 and O 3 for total mortality, with a synergy index of 1.93 (95% confidence interval 1.47 to 3.34). Subgroup analyses showed that interactions between PM 2.5 and O 3 on all three mortality endpoints were more prominent in high latitude regions and during cold seasons. Conclusion The findings of this study suggest a synergistic effect of PM 2.5 and O 3 on total, cardiovascular, and respiratory mortality, indicating the benefit of coordinated control strategies for both pollutants.

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Section: Discussionmentioning

confidence: 99%

Interactive effects of ambient fine particulate matter and ozone on daily mortality in 372 cities: two stage time series analysis

Liu,

Chen,

Sera

et al. 2023

BMJ

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“…The relatively poor performance of both models compared to the smoke validation data (overall, Di RMSD = 29.3 μg/m 3 and R 2 = 0.07; Reid RMSD = 27.8 μg/m 3 and R 2 = 0.15) indicates that relying on estimates from machine learning models of air pollution concentrations to analyze the health impacts of wildfire smoke could introduce substantial exposure measurement error. Of course, only using monitoring data would likely result either in a much smaller sample size for health analyses (if one only considered people living in immediate vicinity of a monitor) or more exposure measurement error than the modeled estimates, , especially for areas without a monitor close by.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of Model-Based PM_2.5 Estimates for Exposure Assessment during Wildfire Smoke Episodes in the Western U.S.

Considine

Hao

deSouza

et al. 2023

Environ. Sci. Technol.

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Investigating the health impacts of wildfire smoke requires data on people’s exposure to fine particulate matter (PM2.5) across space and time. In recent years, it has become common to use machine learning models to fill gaps in monitoring data. However, it remains unclear how well these models are able to capture spikes in PM2.5 during and across wildfire events. Here, we evaluate the accuracy of two sets of high-coverage and high-resolution machine learning-derived PM2.5 data sets created by Di et al. and Reid et al. In general, the Reid estimates are more accurate than the Di estimates when compared to independent validation data from mobile smoke monitors deployed by the US Forest Service. However, both models tend to severely under-predict PM2.5 on high-pollution days. Our findings complement other recent studies calling for increased air pollution monitoring in the western US and support the inclusion of wildfire-specific monitoring observations and predictor variables in model-based estimates of PM2.5. Lastly, we call for more rigorous error quantification of machine-learning derived exposure data sets, with special attention to extreme events.

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“…However, the patients in our study were usually taken to the nearest hospital for timely treatment, and the resultant non-differential misclassification could only cause Berkson bias, which would not influence the mean estimates of the associations but could lead to an inflation of confidence intervals. 36 , 76 …”

Section: Discussionmentioning

confidence: 99%

“…Since the database did not include the location of symptom onset (complete address) for nearly three-quarters (73.2%) of the study population, we used concentrations of air pollutants measured at the nearest monitoring station to the reporting hospital to represent exposure for all participants, a proxy widely used in epidemiological studies of air pollution. 29 , 36 The median distance between the included hospitals and the nearest monitors was 4.4 km (range 0.04–49.9 km).…”

Section: Methodsmentioning

confidence: 99%

Hourly air pollution exposure and the onset of symptomatic arrhythmia: an individual-level case–crossover study in 322 Chinese cities

Xue

Xiang

et al. 2023

CMAJ

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Background: Few studies have explored the relationship between air pollution and arrhythmia onset at the hourly level. We aimed to examine the association of exposure to air pollution with the onset of acute symptomatic arrhythmia at an hourly level. Methods: We conducted a nationwide, time-stratified, case–crossover study in China between 2015 and 2021. We obtained hourly information on the onset of symptomatic arrhythmia (including atrial fibrillation, atrial flutter, atrial and ventricular premature beats and supraventricular tachycardia) from the Chinese Cardiovascular Association Database — Chest Pain Center (including 2025 certified hospitals in 322 cities). We obtained data on hourly concentrations of 6 air pollutants from the nearest monitors, including fine particles (PM 2.5 ), coarse particles (PM 2.5–10 ), nitrogen dioxide (NO 2 ), sulfur dioxide (SO 2 ), carbon monoxide (CO) and ozone. For each patient, we matched the case period to 3 or 4 control periods during the same hour, day of week, month and year. We used conditional logistic regression models to analyze the data. Results: We included a total of 190 115 patients with acute onset of symptomatic arrhythmia. Air pollution was associated with increased risk of onset of symptomatic arrhythmia within the first few hours of exposure; this risk attenuated substantially after 24 hours. An interquartile range increase in PM 2.5 , NO 2 , SO 2 and CO in the first 24 hours after exposure (i.e., lag period 0–24 h) was associated with significantly higher odds of atrial fibrillation (1.7%–3.4%), atrial flutter (8.1%–11.4%) and supraventricular tachycardia (3.4%–8.9%). Exposure to PM 2.5–10 was associated with significantly higher odds of atrial flutter (8.7%) and supraventricular tachycardia (5.4%), and exposure to ozone was associated with higher odds of supraventricular tachycardia (3.4%). The exposure–response relationships were approximately linear, without discernible concentration thresholds. Interpretation: Exposure to air pollution was associated with the onset of symptomatic arrhythmia shortly after exposure. This finding highlights the importance of further reducing air pollution and taking prompt protective measures for susceptible populations during periods of elevated levels of air pollutants.

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Monitoring vs. modeled exposure data in time-series studies of ambient air pollution and acute health outcomes

Cited by 9 publications

References 45 publications

Interactive effects of ambient fine particulate matter and ozone on daily mortality in 372 cities: two stage time series analysis

Interactive effects of ambient fine particulate matter and ozone on daily mortality in 372 cities: two stage time series analysis

Evaluation of Model-Based PM_2.5 Estimates for Exposure Assessment during Wildfire Smoke Episodes in the Western U.S.

Hourly air pollution exposure and the onset of symptomatic arrhythmia: an individual-level case–crossover study in 322 Chinese cities

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Monitoring vs. modeled exposure data in time-series studies of ambient air pollution and acute health outcomes

Cited by 9 publications

References 45 publications

Interactive effects of ambient fine particulate matter and ozone on daily mortality in 372 cities: two stage time series analysis

Interactive effects of ambient fine particulate matter and ozone on daily mortality in 372 cities: two stage time series analysis

Evaluation of Model-Based PM2.5 Estimates for Exposure Assessment during Wildfire Smoke Episodes in the Western U.S.

Hourly air pollution exposure and the onset of symptomatic arrhythmia: an individual-level case–crossover study in 322 Chinese cities

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Evaluation of Model-Based PM_2.5 Estimates for Exposure Assessment during Wildfire Smoke Episodes in the Western U.S.