The Influence of Selected Meteorological Factors on the Prevalence and Course of Stroke

Forecasting extremely high ischemic stroke incidence using meteorological time serie

MOTIVATION

The association between weather conditions and stroke incidence has been a subject of interest for several years, yet the findings from various studies remain inconsistent. Additionally, predictive modelling in this context has been infrequent. This study explores the relationship of extremely high ischaemic stroke incidence and meteorological factors within the Slovak population. Furthermore, it aims to construct forecasting models of extremely high number of strokes.

METHODS

Over a five-year period, a total of 52,036 cases of ischemic stroke were documented. Days exhibiting a notable surge in ischemic stroke occurrences (surpassing the 90th percentile of historical records) were identified as extreme cases. These cases were then scrutinized alongside daily meteorological parameters spanning from 2015 to 2019. To create forecasts for the occurrence of these extreme cases one day in advance, three distinct methods were employed: Logistic regression, Random Forest for Time Series, and Croston's method.

RESULTS

For each of the analyzed stroke centers, the cross-correlations between instances of extremely high stroke numbers and meteorological factors yielded negligible results. Predictive performance achieved by forecasts generated through multivariate logistic regression and Random Forest for time series analysis, which incorporated meteorological data, was on par with that of Croston's method. Notably, Croston's method relies solely on the stroke time series data. All three forecasting methods exhibited limited predictive accuracy.

CONCLUSIONS

The task of predicting days characterized by an exceptionally high number of strokes proved to be challenging across all three explored methods. The inclusion of meteorological parameters did not yield substantive improvements in forecasting accuracy.

Subarachnoid Haemorrhage Incidence Pattern Analysis with Circular Statistics

Knowledge about biological rhythms of diseases may not only help in understanding the pathophysiology of diseases but can also help health service policy makers and emergency department directors to allocate resources efficiently. Aneurysmal subarachnoid haemorrhage (SAH) has high rates of morbidity and mortality. The incidence of SAH has been attributed to patient-related factors such as characteristics of aneurysms, smoking, and hypertension. There are studies showing that the incidence of aneurysmal SAH appears to behave in periodic fashions over long time periods. However, there are inconsistencies in the literature regarding the impact of chronobiological factors such as circadian, seasonal, and lunar cycle factors on the occurrence of SAH. In this study, we focused on the analysis of a temporal pattern of SAH (infradian rhythms) with a novel approach using circular statistical methods. We aimed to see whether there is a circular pattern for the occurrence of SAH at all and if so, whether it can be related to known temporal patterns based on available literature. Our study did not support the notion that aneurysmal subarachnoid haemorrhages occur on any specific day in a cycle with specific lengths up to 365 days including specific weekdays, full moon, equinoxes, and solstices. Hence, we found no relationship between SAH incidence and timing. Study in larger populations using similar circular statistical methods is suggested.

The impact of meteorological parameters on the number of applications to the emergency department with acute urticaria: A retrospective study

The study aimed to investigate the relationship between the patients who applied to the emergency department with acute urticarial and meteorological factors and determine the associated weather conditions. The relationship between acute urticaria patients who applied to the emergency department of a training and research hospital in a city with high altitude and continental climate characteristics in the eastern part of Turkey between January 2019 and December 2019 and meteorological data was evaluated retrospectively. The necessary data for the weather were obtained from the data of the Erzurum Meteorology Directorate, and the patient data were obtained from the hospital information management system and patient files. The meteorological data of the patients at the date of admission and the characteristics of the patients at that date were compared. The study identified 691 patients who applied to the emergency department diagnosed with urticaria in 2019. According to the seasons, it was observed that the patients applied most frequently in the summer months [n = 239; 34.6%]. In the univariable regression model, which was created by taking the values of weather events as a reference on the days when the urticaria presentation was not observed (Group I), it was determined that every 1-hour increase in the sunshine hour increased the probability of urticaria by 7.4% (p = 0.018). When the meteorological parameters on the days without urticaria (Group I) and the days with urticaria presentation (Group II) were compared, the sunshine hours were 7.9 (4.0-10.6) hours in Group II and 6.65 (3.3-8.85) hours in Group I (p = 0.001). Regarding relative humidity, higher humidity rates were observed in Group I compared to Group II (p = 0.009). In terms of mean temperature, daily maximum, and minimum temperature, higher temperature rates were detected in Group II (p<0.001). A relationship was determined between urticaria attacks and relative humidity and daily maximum and minimum temperature in patients who applied to the emergency department with acute urticaria.

The Impact of Weather and Mode of Transport on Outcomes of Patients With Acute Ischemic Stroke Undergoing Mechanical Thrombectomy

BACKGROUND

Efficient transfer to mechanical thrombectomy (MT)-capable centers is essential for patients with stroke. Weather may influence stroke risk, transportation, and outcomes.

OBJECTIVE

To investigate how weather affects stroke patient transfer and outcomes after MT.

METHODS

We retrospectively collected data for patients with stroke transferred from spoke to our hub hospital to undergo MT between 2017 and 2021. We examined associations between weather, transportation, and patient outcomes.

RESULTS

We included 543 patients with a mean age of 71.7 years. The median National Institutes of Health Stroke Score increased from 14 to 15 after transportation. The median modified Rankin Scale was 4 at discharge and 90 days, and 3 at the final follow-up (mean 91.7 days). Higher daily temperatures were associated with good outcome, whereas daily drizzle was associated with poor outcome. More patients were transferred by air when visibility was better, and by ground during heavier precipitation, higher humidity, rain, mist, and daily drizzle, fog, and thunder . Patient outcomes were not associated with transportation mode. Among the independent predictors of good outcome, none was a weather variable. Lower hourly relative humidity ( P = .003) and longer road distance ( P < .001) were independent predictors of using air transportation, among others.

CONCLUSION

During transportation, higher temperature was associated with good outcome, whereas daily drizzle was associated with poor outcome after MT. Although weather was associated with transportation mode, no differences in outcomes were found between transportation modes. Further studies are needed to modify transfer protocols, especially during cold and rainy days, and potentially improve outcomes.

Association Between the Incidence of Hospitalizations for Acute Cardiovascular Events, Weather, and Air Pollution

Background

The incidence of hospitalizations for cardiovascular events has been associated with specific weather conditions and air pollution. A comprehensive model including the interactions between various environmental factors remains to be developed.

Objectives

The purpose of this study was to develop a comprehensive model of the association between weather patterns and the incidence of cardiovascular events and use this model to forecast near-term spatiotemporal risk.

Methods

We present a spatiotemporal analysis of the association between atmospheric data and the incidence rate of hospital admissions related to heart failure (922,132 episodes), myocardial infarction (521,988 episodes), and ischemic stroke (263,529 episodes) in ∼24 million people in Canada between 2007 and 2017. Our hierarchical Bayesian model captured the spatiotemporal distribution of hospitalizations and identified weather and air pollution-related factors that could partially explain fluctuations in incidence.

Results

Models that included weather and air pollution variables outperformed models without those covariates for most event types. Our results suggest that environmental factors may interact in complex ways on human physiology. The impact of environmental factors was magnified with increasing age. The weather and air pollution variables included in our models were predictive of the future incidence of heart failure, myocardial infarction, and ischemic strokes.

Conclusions

The increasing importance of environmental factors on cardiovascular events with increasing age raises the need for the development of educational materials for older patients to recognize environmental conditions where exacerbations are more likely. This model could be the basis of a forecasting system used for local, short-term clinical resource planning based on the anticipated incidence of events.

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.

Need for considering urban climate change factors on stroke, neurodegenerative diseases, and mood disorders studies

The adverse health impacts of climate change have been well documented. It is increasingly apparent that the impacts are disproportionately higher in urban populations, especially underserved communities. Studies have linked urbanization and air pollution with health impacts, but the exacerbating role of urban heat islands (UHI) in the context of neurodegenerative diseases has not been well addressed. The complex interplay between climate change, local urban air pollution, urbanization, and a rising population in cities has led to the byproduct of increased heat stress in urban areas. Some urban neighborhoods with poor infrastructure can have excessive heat even after sunset, increasing internal body temperature and leading to hyperthermic conditions. Such conditions can put individuals at higher risk of stroke by creating a persistent neuroinflammatory state, including, in some instances, Alzheimer’s Disease (AD) phenotypes. Components of the AD phenotype, such as amyloid beta plaques, can disrupt long-term potentiation (LTP) and long-term depression (LTD), which can negatively alter the mesolimbic function and thus contribute to the pathogenesis of mood disorders. Furthermore, although a link has not previously been established between heat and Parkinson’s Disease (PD), it can be postulated that neuroinflammation and cell death can contribute to mitochondrial dysfunction and thus lead to Lewy Body formation, which is a hallmark of PD. Such postulations are currently being presented in the emerging field of ‘neurourbanism’. This study highlights that: (i) the impact of urban climate, air pollution and urbanization on the pathogenesis of neurodegenerative diseases and mood disorders is an area that needs further investigation; (ii) urban climate- health studies need to consider the heterogeneity in the urban environment and the impact it has on the UHI. In that, a clear need exists to go beyond the use of airport-based representative climate data to a consideration of more spatially explicit, high-resolution environmental datasets for such health studies, especially as they pertain to the development of locally-relevant climate adaptive health solutions. Recent advances in the development of super-resolution (downscaled climate) datasets using computational tools such as convolution neural networks (CNNs) and other machine learning approaches, as well as the emergence of urban field labs that generate spatially explicit temperature and other environmental datasets across different city neighborhoods, will continue to become important. Future climate – health studies need to develop strategies to benefit from such urban climate datasets that can aid the creation of localized, effective public health assessments and solutions.