Clustering of Environmental Parameters and the Risk of Acute Ischaemic Stroke.

Acute ischaemic stroke (AIS) risk on days with similar environmental profiles remains unknown. We investigated the association between clusters of days with similar environmental parameters and AIS incidence in Singapore. We grouped calendar days from 2010 to 2015 with similar rainfall, temperature, wind speed, and Pollutant Standards Index (PSI) using k-means clustering. Three distinct clusters were formed 'Cluster 1' containing high wind speed, 'Cluster 2' having high rainfall, and 'Cluster 3' having high temperatures and PSI. We aggregated the number of AIS episodes over the same period with the clusters and analysed their association using a conditional Poisson regression in a time-stratified case-crossover design. Comparing the three clusters, Cluster 3 had the highest AIS occurrence (IRR 1.09; 95% confidence interval (CI) 1.05-1.13), with no significant difference between Clusters 1 and 2. Subgroup analyses in Cluster 3 showed that AIS risk was amplified in the elderly (≥65 years old), non-smokers, and those without a history of ischaemic heart disease/atrial fibrillation/vascular heart disease/peripheral vascular disease. In conclusion, we found that AIS incidence may be higher on days with higher temperatures and PSI. These findings have important public health implications for AIS prevention and health services delivery during at-risk days, such as during the seasonal transboundary haze.

Clustering of Environmental Parameters and the Risk of Acute Myocardial Infarction.

The association between days with similar environmental parameters and cardiovascular events is unknown. We investigate the association between clusters of environmental parameters and acute myocardial infarction (AMI) risk in Singapore. Using k-means clustering and conditional Poisson models, we grouped calendar days from 2010 to 2015 based on rainfall, temperature, wind speed and the Pollutant Standards Index (PSI) and compared the incidence rate ratios (IRR) of AMI across the clusters using a time-stratified case-crossover design. Three distinct clusters were formed with Cluster 1 having high wind speed, Cluster 2 high rainfall, and Cluster 3 high temperature and PSI. Compared to Cluster 1, Cluster 3 had a higher AMI incidence with IRR 1.04 (95% confidence interval 1.01-1.07), but no significant difference was found between Cluster 1 and Cluster 2. Subgroup analyses showed that increased AMI incidence was significant only among those with age ≥65, male, non-smokers, non-ST elevation AMI (NSTEMI), history of hyperlipidemia and no history of ischemic heart disease, diabetes or hypertension. In conclusion, we found that AMI incidence, especially NSTEMI, is likely to be higher on days with high temperature and PSI. These findings have public health implications for AMI prevention and emergency health services delivery during the seasonal Southeast Asian transboundary haze.