Impact of weather and pollution on the rate of cerebrovascular events in a large metropolitan area

Exploring the Effects of Local Air Pollution on Popliteal Artery Aneurysms

Objectives: A growing body of evidence highlights the effects of air pollution on chronic and acute cardiovascular diseases, such as associations between PM10 and several cardiovascular events. However, evidence of the impact of fine air pollutants on the development and progression of peripheral arterial aneurysms is not available. Methods: Data were obtained from the multicenter PAA outcome registry POPART and the German Environment Agency. Means of the mean daily concentration of PM10, PM2.5, NO2, and O3 concentrations were calculated for 2, 10, and 3650 days prior to surgery for each patient. Additionally, weighted ten-year averages were analyzed. Correlation was assessed by calculating Pearson correlation coefficients, and regression analyses were conducted as multiple linear or multiple logistic regression, depending on the dependent variable. Results: For 1193 patients from the POPART registry, paired air pollution data were available. Most patients were male (95.6%) and received open surgical repair (89.9%). On a regional level, the arithmetic means of the daily means of PM10 between 2000 and 2022 were neither associated with average diameters nor runoff vessels. Negative correlations for mean PAA diameter and mean NO2, as well as a positive correlation with mean O3, were found; however, they were not statistically significant. On patient level, no evidence for an association of mean PM10 exposure over ten years prior to inclusion in the registry and PAA diameter or the number of runoff vessels was found. Weighted PM10, NO2, and O3 exposure over ten years also did not result in significant associations with aneurysm diameter or runoff vessels. Short-term air pollutant concentrations were not associated with symptomatic PAAs or with perioperative complications. Conclusions: We found no indication that long-term air pollutant concentrations are associated with PAA size or severity, neither on a regional nor individual level. Additionally, short-term air pollution showed no association with clinical presentation or treatment outcomes.

Residential Nitrogen Dioxide Exposure and Cause-Specific Cerebrovascular Mortality: An Individual-Level, Case-Crossover Study

BACKGROUND

Existing studies have already shown a connection between nitrogen dioxide (NO2) exposure and cerebrovascular mortality. However, the differential effects of NO2 on cerebrovascular disease and its subtypes remain unclear and require further exploration.

METHODS

Daily stroke mortality data between 2013 and 2021 in Shanghai, China were collected. Residential daily air pollution data for each decedent were predicted from a satellite model. An individual-level, time-stratified, case-crossover design was applied to examine the relationship between NO2 exposure and cerebrovascular mortality. A combination of conditional logistic regression and distributed lag models with a maximum lag of 7 days was used for data analysis.

RESULTS

A total of 219,147 cases of cerebrovascular mortality were recorded. Among them, the proportion of sequelae of cerebrovascular disease, hemorrhagic stroke and ischemic stroke was 50.7%, 17.1% and 27.5%, respectively. The monotonic increases in mortality risks of cerebrovascular diseases, sequelae of cerebrovascular disease and ischemic stroke were observed, without any discernible thresholds. Each 10 μg/m3 increase in NO2 concentration was associated with increments of 3.62% [95% confidence interval (CI): 2.56%, 4.69%] for total cerebrovascular mortality, 4.29% (95% CI: 2.81%, 5.80%) for sequelae of cerebrovascular disease mortality and 4.30% (95% CI: 2.30%, 6.33%) for ischemic stroke mortality. No positive associations between NO2 exposure and hemorrhagic stroke mortality were observed. A greater risk of NO2 was observed in the warm season, in patients with less than 9 years of education and in those with single marital status. The effects of NO2 were robust to mutual adjustment of co-pollutants.

CONCLUSIONS

Short-term exposures to NO2 may increase the risk of cerebrovascular mortality, specifically for ischemic stroke and sequelae of cerebrovascular disease.

Impact of desert dust storms, PM10 levels and daily temperature on mortality and emergency department visits due to stroke

Objective

It is known that the inhalation of air pollutants adversely affects human health. These air pollutants originated from natural sources such as desert storms or human activities including traffic, power generating, domestic heating, etc. This study aimed to investigate the impacts of desert dust storms, particulate matter ≤10 μm (PM10) and daily maximum temperature (MT) on mortality and emergency department (ED) visits due to stroke in the city of Gaziantep, Southeast Turkey.

Method

The data on mortality and ED visits due to stroke were retrospectively recruited from January 1, 2009, to March 31, 2014, in Gaziantep City Centre.

Results

PM10 levels did not affect ED visits or mortality due to stroke; however, MT increased both ED visits [adjusted odds ratio (OR) = 1.002, 95% confidence interval (CI) = 1.001-1.003] and mortality (OR = 1.006, 95% CI = 0.997-1.014) due to stroke in women. The presence of desert storms increased ED visits due to stroke in the total population (OR = 1.219, 95% CI = 1.199-1.240), and all subgroups. It was observed that desert dust storms did not have an increasing effect on mortality.

Conclusion

Our findings suggest that MT and desert dust storms can induce morbidity and mortality due to stroke.

Cluster analysis of weather and pollution features and its role in predicting acute cardiac or cerebrovascular events

BACKGROUND

Despite mounting evidence, the impact of the interplay between weather and pollution features on the risk of acute cardiac and cerebrovascular events has not been entirely appraised. The aim of this study was to perform a comprehensive cluster analysis of weather and pollution features in a large metropolitan area, and their association with acute cardiac and cerebrovascular events.

METHODS

Anonymized data on acute myocardial infarction (AMI) and acute cerebrovascular events were obtained from 3 tertiary care centers from a single large metropolitan area. Weather and pollution data were obtained averaging measurements from several city measurement stations managed by the competent regional agency for enviromental protection, and from the Metereological Center of Italian Military Aviation. Unsupervised machine learning was performed with hierarchical clustering to identify specific days with distinct weather and pollution features. Clusters were then compared for rates of acute cardiac and cerebrovascular events with Poisson models.

RESULTS

As expected, significant pairwise correlations were found between weather and pollution features. Building upon these correlations, hierarchical clustering, from a total of 1169 days, generated 4 separate clusters: mostly winter days with low temperatures and high ozone concentrations (cluster 1, N.=60, 5.1%), days with moderately high temperatures and low pollutants concentrations (cluster 2, N.=419, 35.8%), mostly summer and spring days with high temperatures and high ozone concentrations (cluster 3, N.=673, 57.6%), and mostly winter days with low temperatures and low ozone concentrations (cluster 4, N.=17, 1.5%). Overall cluster-wise comparisons showed significant differences in adverse cardiac and cerebrovascular events (P<0.001), as well as in cerebrovascular events (P<0.001) and strokes (P=0.001). Between-cluster comparisons showed that cluster 1 was associated with an increased risk of any event, cerebrovascular events, and strokes in comparison to cluster 2, cluster 3 and cluster 4 (all P<0.05), as well as AMI in comparison to cluster 3 (P=0.047). In addition, cluster 2 was associated with a higher risk of strokes in comparison to cluster 4 (P=0.030). Analysis adjusting for season confirmed the increased risk of any event, cerebrovascular events and strokes for cluster 1 and cluster 2.

CONCLUSIONS

Unsupervised machine learning can be leveraged to identify specific days with a unique clustering of adverse weather and pollution features which are associated with an increased risk of acute cardiovascular events, especially cerebrovascular events. These findings may improve collective and individual risk prediction and prevention.